# document

id: 61f16a0268dfc11c2ef6c35d9a6c26d8
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 12973
content:
" Further details on data sources and processing are available in the chapter data table \r\n(Table 5.SM.6)."


id: 713f25eca7dccf6ad4b037897a3a9972
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 15456
content:
"Takahashi et al., 2014; Lauvset et al., 2015; Wakita et al., 2017; Merlivat \r\net al., 2018). Nevertheless, the high agreement of pH decline among \r\nthese available time-series studies leads to high confidence in the \r\ntrend of acidification in these zones. In the Arctic Ocean, a temporally \r\nlimited time series of carbonate chemistry measurements prevents \r\ndrawing robust conclusions on ocean acidification trends. However, \r\nthe carbonate saturation state (O) is generally low, and observational \r\nstudies show with robust evidence that the recent extensive melting \r\nof sea ice leading to enhanced air-sea CO2 exchange, large freshwater \r\ninputs, together with river discharge and glacial drainage, as well \r\nas the degradation of terrestrial organic matter in seawater, result \r\nin the decline of  O  of aragonite to undersaturation (Bates et al., \r\n2009; Chierici and  Fransson, 2009; Yamamoto-Kawai et al., 2009; \r\nAzetsu-Scott et al., 2010; Robbins et al., 2013; Fransson et al"


id: 3399b32578604ea248aa3b1b657909d7
created_at: 2023-03-16 14:02:00
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", 2015; \r\nSemiletov et al., 2016; Anderson et al., 2017; Qi et al., 2017; Beaupre\u0002Laperriere et al., 2020; Y. Zhang et al., 2020; SROCC Section 3.2.1.2.4, \r\nIPCC, 2019b). The low saturation state of aragonite (O about 1) has \r\nalso been observed in surface waters of the Antarctic coastal zone \r\nassociated with freshwater input from glaciers (Mattsdotter Bjork \r\net al., 2014) and upwelling of deep water (Hauri et al., 2015) as well \r\nas along eastern boundary upwelling systems (Feely et al., 2016)."


id: 649bd842d8a19e198f448de75c12f0d9
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 13400
content:
"A significant increase in acidification resulting from net metabolic CO2\r\nrelease coupled with ocean circulation changes has been shown with \r\nhigh confidence in large swathes of intermediate waters in the Pacific \r\nand Atlantic oceans (Dore et al., 2009; Byrne et al., 2010; Rios et al., \r\n2015; Chu et al., 2016; Carter et al., 2017; Lauvset et al., 2020). For \r\nexample, ocean circulation contributes a pH change of -0.013 +- 0.013 \r\nto the overall observed change of -0.029 +- 0.014 for 1993-2013 at \r\ndepths around 1000 m at 30degS-40degS in the South Atlantic ocean (Rios \r\net al., 2015). Long-term repeated observations in the North Pacific show \r\na decline in dissolved oxygen (-4.0 mmol kg-1 per decade at maximum) \r\nbeing sustained in the intermediate water since the 1980s (Takatani \r\net al., 2012; Sasano et al., 2015). The amplification of acidification \r\nassociated with the weakening ventilation is thought to have been \r\noccurring persistently"


id: 5c59da90c6ae52f6fbe7e9698d124b9d
created_at: 2023-03-16 14:02:00
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vector_id: 12041
content:
" In contrast, for the North Pacific subtropical \r\nmode water, large decadal variability in pH and aragonite saturation \r\nstate with amplitudes of about 0.02 and about 0.1, respectively, are \r\nsuperimposed on secular declining trends due to anthropogenic CO2\r\ninvasion (Oka et al., 2019). This is associated with the variability in \r\nventilation due to the approximately 50% variation in the formation \r\nvolume of the mode water that is forced remotely by the Pacific \r\nDecadal Oscillation (Qiu et al., 2013; Oka et al., 2015)."


id: e393268de47222c20a4c2ca7a347a614
created_at: 2023-03-16 14:02:00
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"In oxygen-depleted waters, microbial processes (denitrification and \r\nanammox, i.e., anaerobic ammonium oxidation; Kuypers et al., 2005; \r\nCodispoti, 2007; Gruber and Galloway, 2008) remove fixed nitrogen, \r\nand when upwelled waters reach the photic zone, primary production \r\nbecomes nitrogen-limited (Tyrrell and Lucas, 2002). However, in \r\nother oceanic regions, increased water-column stratification due \r\nto warming may reduce the amount of N2O reaching the surface \r\nand thereby decrease N2O flux to the atmosphere. Landolfi et al. \r\n(2017) suggest that, by 2100, under the RCP8.5 scenario, total N2O \r\nproduction in the ocean may decline by 5% and N2O emissions be \r\nreduced by 24% relative to the pre-industrial era due to decreased \r\norganic matter export and anthropogenic-driven changes in ocean \r\ncirculation and atmospheric N2O concentrations"


id: 5047a2ae9b2c0a505f7d7b0a27e6573e
created_at: 2023-03-16 14:02:00
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content:
" Projected oxygen \r\nloss in the ocean is thought to result in an ocean-climate feedback \r\nthrough changes in the natural emissions of GHGs (low confidence)."


id: f83ae904a65f67521e7a74f58c370693
created_at: 2023-03-16 14:02:00
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vector_id: 36566
content:
"Projections with CMIP5 ESMs, reported in AR5 (Section  6.4.4) \r\nand SROCC (Section  5.2.2.3; IPCC, 2019b), showed changes in \r\nglobal mean surface ocean pH from 1870-1899 to 2080-2099 of \r\n-0.14 +- 0.001 (inter-model standard deviation) under RCP2.6 and \r\n-0.38 +-  0.005 under RCP8.5 with pronounced regional variability \r\n(Bopp et al., 2013; Hurd et al., 2018). They also projected faster pH \r\ndeclines in mode waters below seasonal mixed layers (Resplandy \r\net al., 2013; Watanabe and Kawamiya, 2017) as has been observed in \r\nthe Atlantic (Salt et al., 2015) and in the Pacific (Carter et al., 2019), \r\nbecause of the net CO2 release by respiration and lowering CO2\r\nbuffering capacity of seawater. In these CO2 concentration-driven \r\nsimulations, the level of acidification in the surface ocean is primarily \r\ndetermined by atmospheric CO2 concentration and regional seawater \r\ncarbonate chemistry, thereby providing consistent projections across \r\nmodels"


id: ec1caca09d88ecdcda1a0dfd7fc50d71
created_at: 2023-03-16 14:02:00
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vector_id: 34646
content:
" New projections with CMIP6 ESMs show greater surface pH \r\ndecline of -0.16 +- 0.002 under the SSP1-2.6 and -0.44 +- 0.005 under \r\nSSP5-8.5 from 1870-1899 to 2080-2099 (Section 4.3.2.5 and Cross\u0002Chapter Box 5.3; Kwiatkowski et al., 2020). The greater pH declines \r\nin CMIP6 are primarily a  consequence of higher atmospheric CO2\r\nconcentrations in SSPs than their CMIP5-RCP analogues (Kwiatkowski \r\net al., 2020). Ocean acidification is also projected to occur with high \r\nconfidence in the Abyssal Bottom Waters in regions such as the \r\nnorthern North Atlantic and the Southern Ocean (Sulpis et al., 2019), \r\nwith the rates of global mean pH decline of -0.018 +- 0.001 under \r\nSSP1-2.6 and -0.030 +-  0.002 under SSP5-8.5 from 1870-1899 to \r\n2080-2099 in CMIP6 (Kwiatkowski et al., 2020)."


id: 7667fd8478ef7addb07b3909026555d1
created_at: 2023-03-16 14:02:00
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"The areas with relatively rapid oxygen decrease include OMZs in the \r\ntropical oceans, where oxygen content has been decreasing at \r\na rate of 0.9-3.4 umol kg-1 per decade in the thermocline for the \r\npast five decades (Stramma et al., 2008). Low oxygen, low pH and \r\nshallow aragonite saturation horizons in the OMZs of the eastern \r\nboundary upwelling regions co-occur, affecting ecosystem structure \r\n(Chavez et al., 2008) and function in the water column, including the \r\npresently unbalanced nitrogen cycle (Paulmier and Ruiz-Pino, 2009). \r\nThe coupling between upwelling, productivity, and oxygen depletion \r\nfeeds back to biological productivity and the role of these regions as \r\nsinks or sources of climate active gases. When OMZ waters upwell and \r\nimpinge on the euphotic zone, they release significant quantities of \r\nGHGs, including N2O (0.81-1.35 TgN yr-1), CH4 (0.27-0.38 TgCH4 yr-1), \r\nand CO2 (yet to be quantified) to the atmosphere, exacerbating \r\nglobal warming (Paulmier et al"


id: 832484091ef9fcefbb93a7007d6da044
created_at: 2023-03-16 14:02:00
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", 2008; Naqvi et al., 2010; Kock \r\net al., 2012; Arevalo-Martinez et al., 2015; Babbin et al., 2015; \r\nFarias et al., 2015). Modelling projections suggest a global decrease \r\nof 4-12% in oceanic N2O emissions (from 3.71-4.03  TgN  yr-1 to \r\n3.54-3.56  TgN  yr-1) from 2005 to 2100 under RCP8.5, despite \r\na  tendency to increased N2O production in the OMZs, associated \r\nprimarily with denitrification (Martinez-Rey et al., 2015). It is difficult \r\nto single out the contribution of nitrification and denitrification, \r\nwhich can occur simultaneously. A rigorous separation of these two \r\nprocesses would require more mechanistic parametrization, which \r\nhas been hindered by the still large conceptual and parametric "


id: 3aae044f115f2847cb95af24c2295ca1
created_at: 2023-03-16 14:02:00
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content:
"[H+] and -16 +-  7% for pH from 1990-1999 to 2090-2099 under \r\nRCP8.5 in CMIP5 (Kwiatkowski and Orr, 2018).\r\nThe signal of ocean acidification in surface ocean is large and is \r\nprojected to emerge beyond the range of natural variability within \r\nthe time scale of a decade in all ocean basins (Schlunegger et al., \r\n2019). There is high agreement among modelling studies that the \r\nlargest pH decline and large-scale undersaturation of aragonite \r\nin surface seawater start to occur first in polar oceans (Orr et al., \r\n2005; Steinacher et al., 2009; Hurd et al., 2018; Jiang et al., 2019). \r\nUnder SSP5-8.5, the largest surface pH decline, exceeding 0.45 \r\nbetween 1995-2014 and 2080-2099, occurs in the Arctic Ocean \r\n(Kwiatkowski et al., 2020). The freshwater input from sea ice melt \r\nis an additional factor leading to a  faster decline of aragonite \r\nsaturation level than expected from the anthropogenic CO2 uptake \r\n(Yamamoto et al., 2012)"


id: 2b6cd0cadbd143a5a65ecaeea1694570
created_at: 2023-03-16 14:02:00
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" The increase in riverine and glacial \r\ndischarges that provide terrigenous carbon, nutrients and alkalinity \r\nas well as freshwater are the other factors modifying the rate of \r\nacidification in the Arctic Ocean. However, their impacts have been \r\nprojected in a limited number of studies with extensive knowledge \r\ngaps and model simplifications leading to low confidence in \r\ntheir impacts (Terhaar et al., 2019; Hopwood et al., 2020). In the \r\nSouthern Ocean, the aragonite undersaturation starts in the 2030s \r\nin RCP8.5, and the area that experiences aragonite undersaturation \r\nfor at least one month per year by 2100 is projected to be more \r\nthan 95%. Under RCP2.6, short periods (less than one month) of \r\naragonite undersaturation are expected to be found in less than \r\n2% of the area during this century (Sasse et al., 2015; Hauri et al., \r\n2016; Negrete-Garcia et al., 2019)"


id: f32ccb9c53dd4d5373dd0ed3976b22e6
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" These long term projections are \r\nmodified at interannual time scales by large-scale climate modes \r\n(Rios et al., 2015) such as the ENSO and the Southern Annular \r\nMode (Conrad and Lovenduski, 2015). In other regions, acidification \r\ntrends are influenced by a range of processes such as changes in \r\nocean circulation, temperature, salinity, carbon cycling, and the \r\nstructure of the marine ecosystem. As, at present, models do not \r\nresolve fine-scale variability of these processes, current projections \r\ndo not fully capture the changes that the marine environment will \r\nexperience in the future (Takeshita et al., 2015; Turi et al., 2016)."


id: 5bddf76113f2d94b4781e084bc08729d
created_at: 2023-03-16 14:02:00
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content:
"Observations and data products including models (Astor et al., 2013; \r\nBakker et al., 2016; Kosugi et al., 2016; Vargas et al., 2016; Laruelle \r\net al., 2017, 2018; Orselli et al., 2018; Roobaert et al., 2019; Cai et al., \r\n2020; H. Sun et al., 2020) confirm the strong spatial and temporal \r\nvariability in the coastal ocean surface carbonate chemistry and sea\u0002air CO2 fluxes (high agreement, robust evidence). The anthropogenic \r\nCO2-induced acidification is either mitigated or enhanced through \r\nbiological processes; primary production removes dissolved CO2\r\nfrom the surface, and respiration adds CO2 and consumes oxygen \r\nin the subsurface layers. The relative intensity of these processes is \r\ncontrolled by natural or anthropogenic eutrophication"


id: 2f94ce038c57b9dbdb111f5565cb2608
created_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 4908
content:
" Other drivers \r\nof variability include biological community composition, freshwater \r\ninput from rivers or melting ice, sea ice cover and calcium carbonate \r\nprecipitation/dissolution dynamics, coastal upwelling and regional \r\ncirculation, and seasonal surface cooling (Fransson et al., 2015, \r\n2017; Feely et al., 2018; Roobaert et al., 2019; Cai et al., 2020; Hauri \r\net al., 2020; Monteiro et al., 2020b; H. Sun et al., 2020). Near-shore \r\nsurface waters are often supersaturated with CO2, regardless of the \r\nlatitude, especially in highly populated areas receiving substantial \r\namounts of domestic and industrial sewage (Chen and Borges, 2009). \r\nNevertheless, thermal or haline-stratified eutrophic coastal areas \r\nmay act as net atmospheric CO2 sinks (Chou et al., 2013; Cotovicz \r\nJr. et al., 2015). Continental shelves, excluding near-shore areas, \r\nact as CO2 sinks at a  rate of 0.2 +-  0.02  PgC yr-1 (Laruelle et al., \r\n2014; Roobaert et al., 2019), considering ice-free areas only"


id: fdb1cb4be7a0e36fedf1d80bf47bca99
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 37432
content:
" Under \r\nincreasing atmospheric CO2 and eutrophication, such ecosystems \r\nwould be more vulnerable to ecological and seawater chemistry \r\nchanges, impacting the local economy."


id: caf8ee6c62e3b7136c736d4d1e86f62e
created_at: 2023-03-16 14:02:00
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vector_id: 29498
content:
"Observations and models predict an expansion and intensification \r\nof low-pH deep water intrusions for the north-east Pacific coastal \r\nupwelling area (high agreement, robust evidence) (Hauri et al., 2013; \r\nFeely et al., 2016; Cai et al., 2020). Areas such as the California Current \r\nSystem are naturally exposed to intrusions of low-pH, high pCO2sea\r\ndeep waters from remineralization processes and anthropogenic CO2\r\nintrusion (Feely et al., 2008, 2010, 2018; Chan et al., 2019; Lilly et al., \r\n2019; Cai et al., 2020).The eastern Pacific coastal upwelling displays \r\nseasonality in subsurface aragonite undersaturation as a consequence \r\nof the interplay between anthropogenic CO2, respiration and \r\nintrusion  of upwelling waters (Feely et al., 2008, 2010, 2016, 2018; \r\nHauri et al., 2013; Vargas et al., 2016; Chan et al., 2019; Lilly et al., \r\n2019)"


id: dd2680aafecd7862b95068020b17f822
created_at: 2023-03-16 14:02:00
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vector_id: 32291
content:
" The  coastal south-east Pacific upwelling combined with low\u0002pH, low-alkalinity, organic matter-rich river inputs display extreme \r\ntemporal variability in surface seawater pCO2 and low aragonite \r\nsaturation (Vargas et al., 2016; Osma et al., 2020)."


id: 2e1faccb888603a5cfea757e0715d703
created_at: 2023-03-16 14:02:00
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"Since SROCC (Bindoff et al., 2019), there is further evidence that \r\nanthropogenic eutrophication via continental runoff and atmospheric \r\nnutrient deposition, and ocean warming are very likely the main \r\ndrivers of deoxygenation in coastal areas (Levin and Breitburg, \r\n2015; Levin et al., 2015; Royer et al., 2016; Breitburg et al., 2018; \r\nCocquempot et al.,  2019; Fagundes et al., 2020; Limburg et al., \r\n2020). Increasing intensity and frequency of wind-driven upwelling \r\nis responsible for longer and more intense coastal hypoxia, fuelled \r\nby organic matter degradation from primary production (medium \r\nto high agreement, medium evidence) (Rabalais et al., 2010; \r\nBakun  et al.,  2015; Varela et al., 2015; Fennel and Testa, 2019; \r\nLimburg et al., 2020). Locally, submarine groundwater discharge may \r\nenhance the eutrophication state (low agreement, limited evidence, \r\nLuijendijk et al., 2020). Since AR5 (Ciais et al., 2013) and SROCC \r\n(Bindoff et al"


id: 5e754f8f93427cfa0922813f5a15dbf3
created_at: 2023-03-16 14:02:00
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", 2019) new observations and model studies confirm the \r\ntrends in increasing coastal hypoxia caused by eutrophication, ocean \r\nwarming and changes in circulation (Claret et al., 2018; Dussin et al., \r\n2019; Limburg et al., 2020), as well as the ubiquitous impacts on \r\nmarine organisms and fisheries (AR6 WGII Chapter 3; Carstensen and \r\nConley, 2019; Fennel and Testa, 2019; Osma et al., 2020). Following \r\nopen ocean deoxygenation trends since the 1950s, more than \r\n700 coastal regions are being reported as hypoxic (dissolved oxygen \r\nconcentration <2  mg O2 L-1) (Limburg et al., 2020). Additionally, \r\ndeoxygenation or increasing severe hypoxic periods in coastal areas \r\nmay enhance the sea-to-air fluxes of N2O and CH4 especially through \r\nmicrobial-mediated processes in the water column-sediment \r\ninterface (medium agreement) (Middelburg and Levin, 2009; Naqvi \r\net al., 2010; Farias et al., 2015; Limburg et al., 2020)."


id: 1a0315f86f530104f8036fb2ac95ae06
created_at: 2023-03-16 14:02:00
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"Temperate, non-upwelling coastal areas along the north-west \r\nAtlantic display a trend of decreasing seawater pH, mainly attributed \r\nto the combined effects of eutrophication and decreasing seawater \r\nbuffering capacity (high agreement, robust evidence). Observations \r\nshow an increasing north to south gradient of aragonite saturation \r\nstate (Sutton et al., 2016; Fennel et al., 2019; Cai et al., 2020). Low \r\nalkalinity and total inorganic carbon concentration, combined with \r\nan ocean signal of acidification, diminishes the buffering capacity \r\nalong the decreasing salinity gradient from the ocean to the coast. \r\nModels suggest that, in this area, the aragonite saturation is \r\nseasonally controlled by nutrient availability and primary production, \r\nsupporting the finding that eutrophication is the main driver for \r\nexacerbating acidification (Cai et al., 2017, 2020)"


id: 1e3359b8fbccb141bcc65c060b64a96f
created_at: 2023-03-16 14:02:00
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" The coastal Gulf of \r\nMexico is facing a parallel increase in bottom water acidification and \r\ndeoxygenation off the Mississippi Delta driven by eutrophication (Cai \r\net al., 2011; Laurent et al., 2017; Fennel et al., 2019)."


id: 6357306e0f093dc16a1ec3622e2682cb
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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"The AR5 (WGI, Box  6.3) and SRCCL (IPCC, 2019a) concluded with \r\nhigh confidence that rising atmospheric CO2 increases leaf-level \r\nphotosynthesis. This effect is represented in all ESMs. New studies since \r\nAR5 add evidence that the leaf-level CO2 fertilization is modulated \r\nby acclimation of photosynthesis to long-term CO2 exposure, \r\ngrowth temperature, seasonal drought, and nutrient availability, \r\nbut these effects are not yet routinely represented in ESMs (Smith \r\nand Dukes, 2013; Baig et al., 2015; Kelly et al., 2016; Drake et al., \r\n2017; Jiang et al., 2020a). Cross-Chapter Box 5.1 assesses multiple \r\nlines of evidence, which suggest that the ratio of plant CO2 uptake \r\nto water loss - plant water-use efficiency (WUE) - increases in near \r\nproportionality to atmospheric CO2"


id: 8822d6d798cbdfbee03c0cdafe408365
created_at: 2023-03-16 14:02:00
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" Despite advances in the regional \r\ncoverage of field experiments, observations of the consequences of \r\nCO2 fertilization at ecosystem level are still scarce, in particular from \r\noutside the temperate zone (Song et al., 2019). New syntheses since \r\nAR5 corroborate that the effect of elevated CO2 on plant growth and \r\necosystem carbon storage is generally positive (high confidence), \r\nbut is modulated by temperature, water and nutrient availability \r\n(Reich et al., 2014; Obermeier et al., 2017; Penuelas et al., 2017; \r\nHovenden et al., 2019; Song et al., 2019). Plant carbon allocation, \r\nchanges in plant community composition, disturbance, and natural \r\nplant mortality are important processes affecting the magnitude of the \r\nresponse, but are currently poorly represented in models (De Kauwe \r\net al., 2014; Friend et al., 2014; Reich et al., 2018; A.P. Walker et al., \r\n2019; K. Yu et al., 2019), and thus contribute strongly to uncertainty \r\nin ESM projections (Arora et al., 2020)."


id: 16f25dd7abe75ddf0c2e2b5f6ab43bbc
created_at: 2023-03-16 14:02:00
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"Field studies with elevated CO2 have demonstrated that the initial \r\nstimulation of above-ground growth may decline if insufficient \r\nnutrients such as nitrogen or phosphorus are available (Finzi et al., \r\n2007; Norby et al., 2010; Hungate et al., 2013; Reich and Hobbie, \r\n2013; Talhelm et al., 2014; Terrer et al., 2018). Model-data syntheses \r\nhave demonstrated that capturing the observed long-term effect \r\nof elevated CO2 depends on the ability of models to predict the \r\neffect of vegetation on soil biogeochemistry (Zaehle et al., 2014; \r\nKoven et al., 2015b; Medlyn et al., 2015; Walker et al., 2015). Meta\u0002analyses of CO2 manipulation experiments point to increased soil \r\nmicrobial activity and accelerated turnover of soil organic matter \r\n(van Groenigen et al., 2017) as a result of increased below-ground \r\ncarbon allocation by plants (Song et al"


id: 94b288abe5bf37f69f30744c9d9fde7e
created_at: 2023-03-16 14:02:00
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vector_id: 20903
content:
", 2019), and increased root \r\nexudation or mycorrhizal activity due to enhanced plant nutrient \r\nrequirements under elevated CO2 (Drake et al., 2011; Terrer et al., \r\n2016; Meier et al., 2017). These effects are not considered in most \r\nESMs. One global model that attempts to represent these processes \r\nsuggests that elevated CO2-related carbon accumulation is reduced \r\nin soils but increased in vegetation relative to more conventional \r\nmodels (Sulman et al., 2019)."


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created_at: 2023-03-16 14:02:00
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vector_id: 26612
content:
"Our understanding of the effects of phosphorus limitation is less \r\ndeveloped than for nitrogen, but a  growing body of literature \r\nsuggests that it is just as important, particularly in regions with \r\nhighly weathered soils (Wang et al., 2018; Terrer et al., 2019; Du \r\net al., 2020). CO2 experiments collectively show that soil phosphorus \r\nis an important constraint on the CO2 fertilization effect on plant \r\nbiomass (Terrer et al., 2019; Jiang et al., 2020a). For example, despite \r\nincreases in photosynthesis after four years of CO2 exposure, a free\u0002air CO2 enrichment experiment in a phosphorus-limited mature forest \r\necosystem did not find an increase in biomass production (Jiang \r\net al., 2020b). The lack of free-air CO2 enrichment experiments in \r\nphosphorus-limited tropical forests limits our understanding of the \r\nrole of phosphorus availability in constraining the CO2 fertilization \r\neffect globally (Norby et al., 2016; Fleischer et al., 2019)"


id: 9fa33f44adad01608de78d444c36c0c5
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 22660
content:
" Models \r\naccounting for the effects of phosphorus availability, in addition to \r\nnitrogen, generally show an even stronger reduction of the response \r\nof ecosystem carbon storage to elevated CO2 (Goll et al., 2012; Zhang \r\net al., 2014; X. Yang et al., 2019). Insufficient data and uncertainties \r\nin the process formulation cause large uncertainty in the magnitude \r\nof this effect (Medlyn et al., 2016; Fleischer et al., 2019)."


id: 366ca685ba34fe55489a8f7e61bcffce
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5954
content:
"As a  major advance since AR5 (WGI, Section  6.4.2), six out of \r\n11  models in the C4MIP-CMIP6 ensemble account for nitrogen \r\ncycle dynamics over land (Table  5.4). On average, these models \r\nexhibit a  25-30% lower CO2 fertilization effect on land carbon \r\nstorage, compared to models that do not account for nitrogen cycle \r\ndynamics (Figure 5.29 and Table 5.5). The only model in the C4MIP\u0002CMIP6 ensemble that explicitly represents the effect of P availability \r\non  plant growth suggests the lowest carbon storage response \r\nto increasing CO2 (Arora et al., 2020). The lower CO2 effect due to \r\ndecreased nutrient availability is generally consistent with analyses \r\nof the implicit nutrient limitation in CMIP5 simulations (Wieder \r\net al., 2015; Zaehle et al., 2015) and independent assessments by \r\nstand-alone land models (Zaehle et al., 2010; Warlind et al., 2014; \r\nZhang et al., 2014; Goll et al., 2017; Meyerholt et al., 2020)"


id: eb0a69e78b59b4c25e640deede484834
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 34483
content:
" The \r\nsimulated effects are generally consistent with expectations based \r\non independent observations (Walker et al., 2021). However, the \r\nmagnitude of nutrient feedbacks in these models is poorly constrained \r\nby observations, owing to the limited geographic distribution of \r\navailable observations and the uncertain scaling of results obtained \r\nfrom manipulation experiments to transient system dynamics (Song \r\net al., 2019; Wieder et al., 2019; Meyerholt et al., 2020)."


id: 2794b959c941541a4fef033d7ad93670
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 3633
content:
"The projected weakening of ocean carbon uptake is driven by \r\na  combination of decreasing carbonate buffering capacity and \r\nwarming, which are positive feedbacks under weak to no mitigation \r\nscenarios (SSP4 and 5). In high mitigation scenarios (SSP1-2.6), \r\nweakening ocean carbon uptake is driven by decreasing emissions \r\n(Cross-Chapter Box  5.3). The detailed understanding of carbonate \r\nchemistry in seawater that has accumulated over more than half \r\na  century (e.g.,  Revelle and Suess, 1957; Egleston et al., 2010), \r\nprovides high confidence that the excess CO2 dissolved in seawater \r\nleads to a non-linear reduction of the CO2 buffering capacity, that \r\nis smaller dissolved inorganic carbon (DIC) increase with respect \r\nto pCO2 increase along with the increase in cumulative ocean CO2\r\nuptake. Recent studies (Katavouta et al., 2018; Jiang et al., 2019; Arora \r\net al., 2020; Rodgers et al"


id: 5936ed49d3fc7e16c9fa557e4b6c9f59
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 11529
content:
", 2020) suggest with medium confidence\r\nthat the decrease in the ocean CO2 uptake ratio to anthropogenic \r\nCO2 emissions, under low to no mitigation scenarios over the \r\n21st century, is predominantly attributable to the ocean carbon\u0002concentration feedback through the reduction of the seawater CO2\r\nbuffering capacity, but with contributions from physical drivers such \r\nas warming and wind stress (medium confidence) and biological \r\ndrivers (low confidence) (Sections 5.2.1.3.3 and 5.4.4)."


id: e2da79c0b0949cdef1e460289f016c5
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 33188
content:
"Climate change also drives changes to vegetation composition \r\nand ecosystem carbon storage through other disturbances such \r\nas forest dieback that lead to biome shifts in tropical forests (Cox \r\net al., 2004; Jones et al., 2009; Brando et al., 2014; Le Page et al., \r\n2017; Zemp et al., 2017), and temperate and boreal regions (Joos \r\net al., 2001; Lucht et al., 2006; Scheffer et al., 2012; Lasslop et al., \r\n2016). The AR5 assessed that large-scale loss of tropical forests due \r\nto climate change is unlikely (WGI, Section 6.4.9). Newer ecosystem \r\nmodelling approaches that include a  greater degree of ecosystem \r\nheterogeneity and diversity show a reduced sensitivity of such forest \r\ndieback-type changes (Levine et al., 2016; Sakschewski et al., 2016), \r\nsupporting the AR5 assessment (Section 5.4.9)"


id: a587fda86a8731e375f9fe44138fb7c9
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 23614
content:
" Beyond such biome \r\nshifts, observations of tropical forests also show that increasing tree \r\nmortality rates within tropical forests may reduce carbon turnover \r\ntimes and storage (Brienen et al., 2015), that increased tree mortality \r\nrates in tropical forests and elsewhere are expected with increased \r\ntemperatures and vapour pressure deficit (Cross-Chapter Box  5.1; "


id: db14fa6183c118fe59f0c2e5e366f9ae
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 31979
content:
"In nutrient-limited ecosystems, prolonged soil warming can induce \r\na  fertilization effect through increased decomposition, which \r\nincreases nutrient availability and thereby vegetation productivity \r\n(Melillo  et al., 2011). Models that include this process tend to \r\nshow  a  weaker carbon-climate feedback than those that do not \r\n(Thornton et al., 2009; Zaehle et al., 2010; Warlind et al., 2014; \r\nMeyerholt et al., 2020). In CMIP6, six out of 11 ESMs include \r\na representation of the nitrogen cycle, and the mean of those models \r\npredicts a weaker carbon-climate feedback than the overall ensemble \r\nmean (Arora et al., 2020; Section  5.4.8). These models only partly \r\naccount for the interactions of nutrient effects with other processes, \r\nsuch as shifts of vegetation zones under climate changes (Sakaguchi \r\net al., 2016) leading to either changes in species composition or \r\nchanges in plant tissue nutrient to carbon ratios (Thomas et al., 2015; \r\nAchat et al., 2016; Du et al"


id: 371e4ce77c9bc7260bb39458bba6e0a4
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 6057
content:
"CMIP6 ESMs predict losses of soil carbon with warming, which are \r\nlarger than climate-driven vegetation carbon losses (Arora et al., \r\n2020). As in CMIP5 (Todd-Brown et al., 2013), there is also a large \r\nCMIP6 ensemble spread in climate-driven soil carbon changes, \r\npartially driven by a large spread in the current soil carbon stocks \r\npredicted by the models. In CMIP5 ESMs, much of the soil carbon \r\nlosses with warming can be traced to decreased carbon inputs, \r\nwith a  weaker contribution from changing soil carbon lifetimes \r\ndue to faster decomposition rates (Koven et al., 2015b), which may \r\nbe an artefact of the lack of permafrost carbon (Box 5.1)"


id: 940610de984a7e4abd9813762e5ecf0
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 20796
content:
" Isotopic \r\nconstraints suggest that CMIP5 ESMs systematically overestimated \r\nthe transient sensitivity of soil 14C responses to atmospheric 14C changes, implying that the models respond too quickly to \r\nchanges in either inputs or turnover times, and that therefore the \r\nsoil contribution to all feedbacks may be weaker than currently \r\nprojected (He et al., 2016). Using natural gradients of soil carbon \r\nturnover as a constraint on long-term responses to warming suggests \r\nthat both CMIP5 and CMIP6 ESMs may systematically underestimate \r\nthe temperature sensitivity at high latitudes, and may overestimate \r\nthe temperature sensitivity in the tropics (Koven et al., 2017; Wieder "


id: 9c4089852ad803898504a5e28ad75342
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 22136
content:
"Despite accumulating evidence of increased carbon losses, it is difficult to scale up site- and ecosystem-level measurements to assess \r\nthe net carbon balance over the entire permafrost region, due to the high spatial heterogeneity, the strong seasonal cycles, and the \r\ndifficulty in monitoring these regions consistently across the year. The Special Report on Ocean and the Cryosphere in a Changing \r\nClimte (SROCC) assessed with high confidence that ecosystems in the permafrost region act as carbon sinks during the summer \r\ngrowing season, and that wintertime carbon losses are significant, consistent with a multi-decadal small increase in CO2 emissions \r\nduring early winter at Barrow, Alaska (Sweeney et al., 2016; Webb et al., 2016; Meredith et al., 2019). These findings have been further \r\nstrengthened by recent comprehensive synthesis of in-situ wintertime flux observations that show large carbon losses during the non\u0002growing season (Natali et al., 2019)"


id: e927e0b6ff320e10b25b78d9a5b338fe
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 34199
content:
" Increased autumn and winter respiration are a key large-scale fingerprint of top-down permafrost \r\nthaw predicted by ecosystem models (Parazoo et al., 2018). However, the length of these wintertime observational records is too short \r\nto unequivocally determine whether winter carbon losses are higher now than they used to be. One study inferred a multi-year net CO2\r\nsource for the tundra in Alaska (Commane et al., 2017), which is equivalent to 0.3 PgC yr-1 when scaled up to the northern permafrost \r\nregion (low confidence) (Meredith et al., 2019)."


id: 4c51f80b34c45d886d0f4ee2d3a26bf6
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 9435
content:
"In addition to CO2, CH4 emissions from the northern permafrost region contribute to the global methane budget, but evidence as to \r\nwhether these emissions have increased from thawing permafrost is mixed. The SROCC assigned low confidence to the degree of \r\nrecent additional CH4 emissions from diverse sources throughout the permafrost region. These include observed regional lake area \r\nchange, which suggest a 1.6-5 Tg CH4 yr-1 increase over the last 50 years (Walter Anthony et al., 2016), ice-capped geological sources \r\n(Walter Anthony et al., 2012; Kohnert et al., 2017), and shallow Arctic Ocean shelves. The shallow subsea emissions are particularly \r\nuncertain due to the wide range of estimates (3 Tg CH4 yr-1 (Thornton et al., 2016b) to 17 Tg CH4 yr-1 (Shakhova et al"


id: 683676da6d91bc9724e2fb03aaf6df3f
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 13979
content:
", 2014)), and \r\nthe lack of a baseline with which to infer any changes; however, the upper half of this range in flux estimates is inconsistent with the \r\natmospheric inversions constrained by the pan-Arctic CH4 concentration measurements (Berchet et al., 2016)."


id: 1be57ecfb12553edd736517f48955923
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 1853
content:
"Radiogenic and stable isotopic measurements on CH4 trapped in Antarctic ice support the view that CH4 emissions from fossil carbon \r\nreservoirs, including permafrost and methane hydrates, remained small in response to the deglacial warming. Mass-balance calculations \r\nreveal that geological CH4 emissions have not exceeded 19 Tg yr-1, highlighting that the deglacial increase in CH4 emissions was \r\npredominantly related to contemporary CH4 emissions from tropical wetlands and seasonally inundated floodplains (Bock et al., 2017; \r\nPetrenko et al., 2017; Dyonisius et al., 2020). Isotopic constraints on CO2 losses from permafrost with warming after the Last Glacial \r\nMaximum (LGM) are weaker than for CH4"


id: 362ce9feb87ea3b19fc4ade0f2a07d51
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5926
content:
" While the biosphere as a whole held less carbon during the LGM than the pre-industrial, \r\nthat change in stocks was smaller than the change in plant productivity, and so carbon losses at high latitudes may have been offset \r\nby increased tropical productivity in response to warming during the Last Deglacial Transition (LDT; Ciais et al., 2012). There is also \r\npaleoclimate evidence for processes that mitigate carbon losses with warming on longer time scales, such as longer-term carbon \r\naccumulation in lake deposits following thermokarst thaw (Walter Anthony et al., 2014), and long-term accumulation of carbon in \r\npermafrost soils following LDT carbon loss (Lindgren et al., 2018), particularly in peatlands which accumulated carbon at a slow but \r\npersistent rate in warm paleoclimates (Treat et al., 2019)."


id: 99e4f611ceeb0811814d11a7dfce68fc
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
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vector_id: 21740
content:
"The processes that govern permafrost carbon loss are grouped into gradual and abrupt mechanisms. Gradual processes include the \r\ndeepening of the seasonally thawed active layer into perennially frozen permafrost layers and lengthening of the thawed season \r\nwithin the active layer, which increases the amount of organic carbon that is thawed and the duration of thaw. Abrupt thaw processes \r\ninclude ice-wedge polygon degradation, hillslope collapse, thermokarst lake expansion and draining, all of which are processes largely \r\noccurring in regions with very high soil carbon content (Olefeldt et al., 2016a, b). Abrupt thaw processes can contribute up to half of \r\nthe total net greenhouse gas release from permafrost loss, the rest attributed to gradual thaw (Schneider von Deimling et al., 2015; \r\nTuretsky et al., 2020). Increased fire frequency and severity (Hu et al"


id: ca60cfec94d9099b4f3218a7458604b3
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 29404
content:
", 2010) also contributes to abrupt emissions and the removal of \r\nthe insulating cover which leads to an acceleration of permafrost thaw (Genet et al., 2013). Ecological feedbacks can both mitigate \r\nand amplify carbon losses: nutrient release from increased organic matter decomposition can drive vegetation growth that partially \r\noffsets soil carbon losses (Salmon et al., 2016), but also lead to biophysical feedbacks that further amplify warming (Myers-Smith \r\net al., 2011)."


id: 33460ab5b7994072372009cd1dfc6c4e
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5483
content:
"Ocean warming reduces the vertical supply of nutrients to the \r\nupper ocean due to increasing stratification (Section 9.2.1.4) but may \r\nalso act to alleviate seasonal light limitation. The projected effect is \r\nto decrease PP at low latitudes and increase PP at high latitudes \r\n(Kwiatkowski et al., 2020). Future changes to dust deposition due to \r\ndesertification (Mahowald et al., 2017), alterations to the nitrogen \r\ncycle (Section 5.3.3.2; SROCC, Section 5.2.3.1.2), and reducing sea \r\nice cover (Ardyna and Arrigo, 2020) all have the potential to alter \r\nPP regionally. Higher ocean temperatures tend to result in higher \r\nmetabolic rates, although respiration may increase more rapidly \r\nthan PP (Boscolo-Galazzo et al., 2018; Brewer, 2019; Cavan et al., \r\n2019)"


id: b93c5ff624a1bffab3804aafbf5fdf53
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 26678
content:
" Ocean warming and reduced PP are expected to result in \r\nlower zooplankton abundance, and the expansion of oxygen \r\nminimum zones (OMZs) may reduce the ability of zooplankton to \r\nremineralize POC, thus increasing the efficiency of the BCP and \r\nforming a negative climate feedback (Cavan et al., 2017). Increased \r\nmicrobial respiration due to warming may result in greater quantities \r\nof organic carbon transferred into the dissolved organic carbon \r\npool (Jiao et al., 2014; Legendre et al., 2015; Roshan and DeVries, \r\n2017) which, while increasing the residence time of carbon in the \r\nocean, would ultimately reduce the sedimentary burial, and hence \r\nsequestration on geologic time scales (Olivarez Lyle and Lyle, 2006)."


id: 9e908cfed81f2e2721d01a206e9e97e8
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 22485
content:
"Most models project that smaller phytoplankton are favoured in \r\nfuture ocean conditions (medium confidence; Cabre et al., 2015; Fu \r\net al., 2016; Flombaum et al., 2020) driven by warming water and/\r\nor changing nutrient availability, which would alter the magnitude \r\nand efficiency of the BCP by altering the sinking speed, respiration \r\nrate and aggregation/fragmentation of sinking particles. There is low \r\nconfidence in the sign of the resulting feedback: regions in which \r\nsmall phytoplankton dominate may have a  more efficient pump, \r\nalthough the total amount of organic carbon reaching the sea floor \r\nis lower (Herndl and Reinthaler, 2013; Bach et al., 2016; Richardson, \r\n2019). Alternatively, an increase in small phytoplankton could result \r\nin a less efficient pump, due either to a greater fraction of PP being \r\nprocessed through the upper ocean microbial loop (Jiao et al., 2014) \r\nor generation of slower sinking particles (Guidi et al., 2009; Leung \r\net al., 2021)"


id: 3fd7a5153d0485789ebd25c97a6e6e7f
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 7428
content:
" Variable phytoplankton stoichiometry is predicted to \r\nincrease the amount of carbon stored via the BCP relative to the \r\namount of PP, so that fixed stoichiometry models (as in CMIP5) may \r\nunderestimate cumulative ocean carbon uptake to 2100 by 0.5-3.5% \r\n(2-15 PgC; RCP8.5 scenario; Kwiatkowski et al., 2020). Other climate \r\neffects such as deoxygenation or ocean acidification could also result \r\nin alterations to the magnitude and efficiency of the BCP (Krumhardt \r\net al., 2019; Raven et al., 2021; Taucher et al., 2021)."


id: 18488a69ef08d086a635cbf4734b22b7
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 1309
content:
"Since AR5 (Section  6.3.2.5.6), progress has been made in \r\nunderstanding the biological drivers of ocean carbon uptake in both \r\ncoupled climate models and observations (SROCC, Section 5.2.2.6). \r\nHere we focus on potential feedbacks between biological processes \r\nand climate. In CMIP5 models, the direction of modelled PP in response \r\nto increased atmospheric CO2 concentration and climate warming \r\nwas unclear (Taucher and Oschlies, 2011; Laufkotter et al., 2015). \r\nThis remains the case in the CMIP6 models; inter-model uncertainty \r\nhas increased in CMIP6 models, compared to CMIP5. The projected \r\nglobal multi-model mean change in PP in 13 models run under the \r\nSSP5-8.5 scenario is -3 +- 9% (2080-2099 mean values relative to \r\n1870-1899 +- the inter-model standard deviation; Kwiatkowski et al., \r\n2020). Under the low-emissions, high-mitigation scenario SSP1-2.6, \r\nthe global change in PP is -0.56  +-  4%"


id: 94570eb057344180f754dba4ae4b8589
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 20847
content:
" Observations in the \r\ncontemporary period provide little direct constraint on the modelled \r\nresponses of PP to climate change, partly due to insufficiently long \r\nrecords (Henson et al., 2016). However, there is some indication of an \r\nemergent constraint on changes in tropical PP based on interannual \r\nvariability derived from remote sensing (Section 5.4.6; Kwiatkowski \r\net al., 2017)."


id: 6cb739e9545ee7882b8538c7319872b3
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 14718
content:
"This distinction between the relatively high fidelity with which \r\nthe ocean carbon sink is simulated, and the much wider range of \r\nsimulations of the land carbon sink, is also evident in the zonal \r\ndistribution of the sinks (Figure  5.24). We compare the ESM \r\nsimulations to estimates from three atmospheric inversion models: \r\nCopernicus Atmosphere Monitoring Service (CAMS; Chevallier \r\net al., 2005), Carbon Tracker 2017 (Peters et al., 2007) and Model for \r\nInterdisciplinary Research on Climate Atmospheric Transport Model \r\n(MIROC-ATM4; Saeki and Patra, 2017). The ocean carbon cycle \r\ncomponents of CMIP6 ESMs are able to simulate the tropical CO2 \r\nsource and mid-latitude CO2 sink, with relatively small model spread \r\n(Figure 5.24a). The CMIP6 ensemble (red wedge) simulates a larger \r\nocean carbon sink at 50degN and a weaker sink in the Southern Ocean, \r\nthan the inversion estimate, but with some evidence of a reduction in \r\nthese residual errors compared to CMIP5 (blue wedge)"


id: ed9cca3a6c9febb874bb7692aedf2b27
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 34849
content:
" The spread in \r\ninversion fluxes arises primarily from differences in the atmospheric \r\nCO2 measurement networks and from transport model uncertainties."


id: c4f9df9441aec68a58990e724cb05d6f
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 28564
content:
"Figure 5.27 | Maps of carbon-concentration and carbon-climate feedback \r\nterms, as well as net carbon changes under the idealized 1% per year \r\ncarbon dioxide (CO2) scenario, as evaluated from CMIP6 Earth system \r\nmodels (ESMs). Shown are the model means from nine CMIP6 ESMs. Uncertainty \r\nis represented using the simple approach (see Cross-Chapter Box Atlas.1 for more \r\ninformation): No overlay indicates regions with high model agreement, where >=80% \r\nof models agree with the ensemble mean on the sign of change; diagonal lines \r\nindicate regions with low model agreement, where <80% of models agree with the \r\nensemble mean on the sign of change. Also shown are zonal-mean latitude profiles \r\nof land (green) and ocean (blue) feedbacks"


id: dee15381204412a73976e615f75c0fea
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 32563
content:
" On the land, the zonal mean feedback for \r\nthe mean of the ensemble of models that include nitrogen is shown as dashed lines, \r\nand for carbon-only models as dash-dotted lines, and the carbon-climate feedback \r\nfrom one permafrost-carbon enabled ESM is shown as a dotted line. Carbon changes \r\nare calculated as the difference between carbon stocks at different times on land and \r\nfor the ocean as the time integral of atmosphere-ocean CO2 flux anomalies relative \r\nto the pre-industrial. The denominator for gamma here is the global mean surface \r\nair temperature. Further details on data sources and processing are available in the \r\nchapter data table (Table 5.SM.6)."


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vector_id: 9773
content:
"Land biosphere models show high agreement that long-term \r\nwarming will increase N2O release from terrestrial ecosystems (Xu\u0002Ri et al., 2012; B.D.  Stocker et al., 2013; Zaehle, 2013; Tian et al., \r\n2019). A positive land N2O climate feedback is consistent with paleo\u0002evidence based on reconstructed and modelled emissions during \r\nthe last deglacial period (Schilt et al., 2014; H. Fischer et al., 2019; \r\nJoos et al., 2020). The response of terrestrial N2O emissions to \r\natmospheric CO2 increase and associated warming is dependent on \r\nnitrogen availability (van Groenigen et al., 2011; Butterbach-Bahl \r\net al., 2013; Tian et al., 2019). Model-based estimates do not account \r\nfor the potentially strong emissions increases in boreal and  arctic \r\necosystems associated with future warming and permafrost thaw \r\n(Elberling et al., 2010; Voigt et al., 2017). There is medium confidence \r\nthat the land N2O climate feedback is positive, but low confidence in \r\nthe magnitude (0.02 +- 0"


id: be2612b936da6e3a07d6330c507ee764
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 27473
content:
"The CH4 feedbacks may arise from changing wetland emissions \r\n(including rice farming) and from sources that are expected to \r\ngrow under climate change (e.g., related to permafrost thaw, fires, \r\nand freshwater bodies). CH4 emissions from wetlands and landfills \r\ngenerally increase with warming due to enhanced decomposition \r\nwith higher temperatures, thereby potentially providing a  positive \r\nCH4 feedback on climate (Dean et al., 2018). The contribution of \r\nwetlands to interannual variability of atmospheric CH4 is shaped by \r\nthe different impacts of temperature and precipitation anomalies \r\non wetland emissions (e.g., during El Nino episodes) and therefore \r\nthe relationship between climate anomalies and the wetland \r\ncontribution to the CH4 growth rate is complex (Pison et al., 2013; \r\nNisbet et al., 2016; X.  Zhang et al., 2020)"


id: 702f4a8c69aa6ad6e285bef65d7b862c
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 15265
content:
" As assessed by SROCC \r\n(IPCC, 2019b), there is high agreement across model simulations \r\nthat wetlands CH4 emissions will increase in the 21st century, but \r\nlow agreement in the magnitude of the change (Denisov et al., 2013; \r\nShindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017; Koffi \r\net al., 2020). Climate change increases wetland emissions (Gedney \r\net al., 2004, 2019; Volodin, 2008; Ringeval et al., 2011; Denisov et al., \r\n2013; Shindell et al., 2013) and gives rise to an estimated wetland \r\nCH4-climate feedback of 0.03 +- 0.01 W m-2 degC-1 (mean +- 1 standard \r\ndeviation; limited evidence, high agreement) (Arneth et al., 2010; \r\nShindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017). The \r\neffect of rising CO2 on productivity, and therefore on the substrate \r\nfor methanogenesis, can further increase the projected increase in \r\nwetland CH4 emissions (Ringeval et al., 2011; Melton et al., 2013)"


id: de37c90ed07fe84ca98cde2d84a066f1
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 32451
content:
" \r\nModel projections accounting for the combined effects of CO2 and \r\nclimate change suggest a potentially larger climate feedback (0.01-\r\n0.16 W m-2 degC-1) (limited evidence, low agreement) (Gedney et al., \r\n2019; Thornhill et al., 2021). Methane release from wetlands depends \r\non the nutrient availability for methanogenic and methanotrophic \r\nmicroorganisms that can further modify this feedback (Stepanenko \r\net al., 2016; Donis et al., 2017; Beaulieu et al., 2019). Methane \r\nemissions from thermokarst ponds and wetlands resulting from "


id: c0f4b3bf2d905f5126d89d54b338912
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
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vector_id: 27932
content:
"Figure 5.29 | Estimates of the biogeochemical climate feedback parameter (a). The parameter a (W m-2 degC-1) for a feedback variable x is defined as ax = dN\r\ndx\r\ndx\r\ndT where dN\r\ndx is the change in top-of-atmosphere energy balance in response to a change in x induced by a change in surface temperature (T), as in Section 7.4.1.1. (a) Synthesis of \r\nbiogeochemical feedbacks from panels (b) and (c). Orange (blue) bars correspond to positive (negative) feedbacks increasing (decreasing) radiative forcing at the top of the \r\natmosphere. Bars denote the mean and the error bar represents the 5-95% range of the estimates; (b) carbon-cycle feedbacks as estimated by coupled carbon-cycle climate \r\nmodels in the CMIP5 (Arora et al., 2013) and CMIP6 (Arora et al"


id: 3bbde0ed3ca303ab16cd4d005ce4f041
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 6756
content:
", 2020) ensembles, where dots represent single model estimates, and filled (open) circles are those estimates \r\nwhich do (not) include the representation of a terrestrial nitrogen cycle; (c) Estimates of other biogeochemical feedback mechanisms based on various modelling studies. Dots \r\nrepresent single estimates, and coloured bars denote the mean of these estimates with no weighting being made regarding the likelihood of any single estimate, and error bars \r\nthe 5-95% range derived from these estimates. Results in panel (c) have been compiled from (a) Section 5.4.3.2 (Eliseev et al., 2014a; Harrison et al., 2018); (b) Section 5.4.3.3 \r\n(Schneider von Deimling et al., 2012; Burke et al., 2013, 2017b; Koven et al., 2015a, c; MacDougall and Knutti, 2016b; Gasser et al., 2018; Kleinen and Brovkin, 2018), where \r\nthe estimates from Burke et al., 2013 have been constrained as assessed in their study (c) Section 5.4.7 (Schneider von Deimling et al., 2012, 2015; Koven et al"


id: d72f5d99d6aeb005289c31495e4ffece
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 31369
content:
", 2015c; Turetsky \r\net al., 2020); (d) Section 5.4.7 (Arneth et al., 2010; Denisov et al., 2013; Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017); (f) Section 5.4.7 (Xu-Ri et al., 2012; \r\nB.D. Stocker et al., 2013; Zaehle, 2013; Tian et al., 2019); (g) Section 5.4.7 (Martinez-Rey et al., 2015; Landolfi et al., 2017; Battaglia and Joos, 2018b). (h) Section 6.3, Table 6.9 \r\nmean and the 5-95% range the assessed feedback parameter. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6)."


id: a6976875ff79cb278355c130d4de5191
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 23735
content:
"Other feedback contributions, such as the non-CO2 biogeochemical \r\nfeedback, can be converted into a  carbon-equivalent feedback \r\nterm (g; Section  5.4.5.5, 7.6) by reverse application of the linear \r\nfeedback approximation (Gregory et al., 2009). The contributions \r\nof non-CO2 biogeochemical feedbacks combine to a  linear \r\nfeedback term of 30 +- 27 PgCeq degC-1 (1 standard deviation range, \r\n111 +- 98 Gt CO2-eq degC-1), including a feedback term of -11 [-18 to \r\n-5]  PgCeq degC-1 (5-95% range, -40 [-62 to -18] Gt  CO2-eq degC-1) \r\nfrom natural CH4 and N2O sources.  The biogeochemical feedback \r\nfrom permafrost thaw leads to a combined linear feedback term of \r\n-21 +- 12 PgCeq degC-1 (1 standard deviation range -77 +- 44 Gt CO2-\r\neq  degC-1). For the integration of these feedbacks in the assessment \r\nof the remaining carbon budget (Section  5.5.2), two individual \r\nnon-CO2 feedbacks (tropospheric ozone, and methane lifetime) are \r\ncaptured in the AR6-calibrated emulators (Box 7.1)"


id: 35bf93f872a964837be1f6526d9087d7
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5868
content:
" Excluding those \r\ntwo contributions, the resulting combined linear feedback term for \r\napplication in Section 5.5.2 is assessed at a reduction of 7 +- 27 PgCeq \r\ndegC-1 (1 standard deviation range, -26 +- 97 PgCeq degC-1). For the same \r\nreasons as for the feedback terms expressed in W m-2 degC-1 (see above), \r\nthere is overall low confidence in the magnitude of these feedbacks."


id: 10228f0b9b04040f6094ce90ce6935f5
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 27
content:
"The climate feedback parameter for CO2 (-1.13 +- 0.45 W m-2 degC-1, mean \r\nand 1  standard-deviation range) is dominated by the contribution \r\nof the CO2-induced increase of ocean and land carbon \r\nstorage (-1.46  +-  0.41  W m-2 degC-1, corresponding to a  bL+O of \r\n1.66 +- 0.31 PgC ppm-1), with smaller contributions from the carbon \r\ncycle's response to climate (0.24 +- 0.17 W m-2 degC-1, corresponding \r\nto gL+O of -50 +-  34  PgC degC-1), and emissions from permafrost \r\nthaw (0.09  [0.02  to 0.20] W  m-2 degC-1, corresponding to g  of \r\n-18 [3 to 41] PgC degC-1, mean and 5-95% range) (Figure 5.29a). This \r\nestimate does not include an estimate of the fire-related CO2 feedback \r\n(range: 0.01-0.06 W m-2 degC-1), as only limited evidence was available \r\nto inform its assessment. The sum (mean and 5-95th  percentile \r\nrange) of feedbacks from natural emissions of CH4 including \r\npermafrost thaw, and N2O (0.05 [0.02 to 0"


id: 2ac2d8918ddb7411e2f3a4a1edd7579a
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 4155
content:
"09]  W  m-2  degC-1), and \r\nfeedbacks from aerosol and atmospheric chemistry (-0.20 [-0.41 to \r\n0.01] W m-2 degC-1) leads to an estimate of the non-CO2 biogeochemical \r\nfeedback parameter of -0.15 [-0.36 to +0.06] W m-2 degC-1. There is \r\nlow confidence in the estimate of the non-CO2 biogeochemical \r\nfeedbacks, due to the large range in the estimates of a  for some \r\nindividual feedbacks (Figure 5.29c), which can be attributed to the \r\ndiversity in how models account for these feedbacks, limited process\u0002level understanding, and the existence of known feedbacks where \r\nthere is insufficient evidence to assess the feedback strength."


id: 88a0ed03247de420b0e65381ae523fd7
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 19024
content:
"Published examples of abrupt biogeochemical changes in models \r\ninclude tropical rain forest dieback (Cox et al., 2004; Jones et al., \r\n2009; Brando et al., 2014; Le Page et al., 2017; Zemp et al., 2017), \r\nand temperate and boreal forest dieback (Joos et al., 2001; Lucht \r\net al., 2006; Scheffer et al., 2012; Lasslop et al., 2016; Section 5.4.3). \r\nSuch transitions may be related to: (i) large-scale changes in mean \r\nclimate conditions crossing particular climate thresholds (Joos et al., \r\n2001; Cox et al., 2004; Lucht et al., 2006; Hirota et al., 2011; Scheffer \r\net al., 2012; Le Page et al., 2017; Zemp et al., 2017); (ii) temperature \r\nand precipitation extremes (Staver et al., 2011; Higgins and Scheiter, \r\n2012; Scheffer et al., 2012; Pavlov, 2015; Zemp et al., 2017); or \r\n(iii) possible enhancement and intermittency in fire activity (Staver \r\net al., 2011; Higgins and Scheiter, 2012; Lasslop et al., 2016; Brando \r\net al., 2020)"


id: d96f81e9617f2f4099ac53f2c5af6cba
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 31719
content:
" Simulated changes in forest cover are a combination \r\nof the effects of CO2 on photosynthesis and water-use efficiency \r\n(Section 5.4.1), and the effects of climate change on photosynthesis, \r\nrespiration and disturbance (Section  5.4.3). In ESMs, direct CO2 \r\neffects tend to enhance forest growth, but the impacts of climate \r\nchange vary between being predominantly negative in the tropics \r\nand predominantly positive in the boreal zone (Figure 5.27)."


id: 32176cae9eb858fa793cdd1f8fb4184d
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5289
content:
"There is large uncertainty in release of GHGs from permafrost in the \r\n21st century. The largest of these estimates implies tens to hundreds \r\nof gigatons of carbon released in the form of CO2 (Box 5.1) and CH4 \r\nemissions up to 100 TgCH4 yr-1 (Box 5.1). A carbon dioxide release of \r\nsuch magnitude would lead to an increase in the CO2 accumulation \r\nrate in the atmosphere of <=1 ppm yr-1. These emissions develop at \r\na multi-decadal time scale. Assuming a CH4 lifetime in the atmosphere \r\nof the order of 10 years and the associated feedback parameter of \r\n1.34 +- 0.04 (Section 6.2.2.1), this would increase the atmospheric CH4\r\ncontent by about 500 ppb over the century, corresponding to a rate of \r\n<=10 ppb yr-1. Irrespective of its origin, additional CH4 accumulation of \r\nsuch a magnitude is not expected to modify the temperature response \r\nto anthropogenic emissions by more than a few tenths of a degC (Gedney \r\net al., 2004; Eliseev et al., 2008; Denisov et al., 2013)"


id: 8ea0e80855226e1f35f5edef967e9785
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 19965
content:
"Most ESMs project continuing carbon accumulation in tropical forests \r\nas a result of direct CO2 effects overwhelming the negative effects of \r\nclimate change (Huntingford et al., 2013; Drijfhout et al., 2015; Boulton \r\net al., 2017). In the real world, forests may be less vulnerable to climate \r\nchanges than those modelled in ESMs because of the greater plant \r\ntrait diversity, which confers additional resilience (Reyer et al., 2015; \r\nLevine et al., 2016; Sakschewski et al., 2016), and because of possible \r\nacclimation of vegetation to warming (Good et al., 2011, 2013; Lloret \r\net al., 2012; Mercado et al., 2018). On the contrary, forests may be more \r\nvulnerable in the real world due to indirect climate change effects such \r\nas insect outbreaks and diseases not considered here (Section 5.4.3.2) \r\nor model limitations in representing the effects disturbances such as \r\nwildfire and droughts"


id: d9a306bd2e1f20b7c7b0785218aa8c47
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 31752
content:
" In general, forests are most vulnerable when \r\nclimate change is combined with increased rates of direct deforestation \r\n(Nobre et al., 2016; Le Page et al., 2017)."


id: ba970b6004ebaa5b6e12623a55fd24aa
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 26892
content:
"The total global clathrate reservoir is estimated to contain 1500-\r\n2000  PgC (Archer et al., 2009; Ruppel and Kessler, 2017), held \r\npredominantly in ocean sediments, with only an estimated 20 PgC \r\nin and under permafrost (Ruppel, 2015). The present-day CH4\r\nrelease from shelf clathrates is <10 TgCH4 yr-1 (Kretschmer et al., \r\n2015; Saunois et al., 2020). Despite polar amplification (Chapter 7), \r\nsubstantial releases from the permafrost-embedded subsea \r\nclathrates is very unlikely (Minshull et al., 2016; Malakhova and \r\nEliseev, 2017, 2020). This is consistent with an overall small release \r\nof CH4 from the shelf clathrates during the last deglacial transition, \r\ndespite large reorganizations in climate state (Bock et al., 2017; \r\nPetrenko et al., 2017; Dyonisius et al., 2020)"


id: 7b003fa5c6ac5d0e5adef6f6a8055373
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 16944
content:
" The long time scales \r\nassociated with clathrate destabilization makes it unlikely that CH4\r\nrelease from the ocean to the atmosphere will deviate markedly from \r\nthe present-day value through the 21st century (Hunter et al., 2013), \r\ncorresponding to no more than additional 20 ppb of atmospheric CH4\r\n(i.e., <0.2 ppb  yr-1). Another possible source of CH4 is gas clathrates \r\nin deeper terrestrial permafrost and below it (Buldovicz et al., 2018; \r\nChuvilin et al., 2018), which may have caused recent craters in the \r\nnorth of Russia (Arzhanov et al., 2016, 2020; Arzhanov and Mokhov, \r\n2017; Kizyakov et al., 2017, 2018). Land clathrates are formed at \r\ndepths greater than 200  m  (Ruppel and Kessler, 2017; Malakhova "


id: 179d80a514dfc7a36f37efd6f63d7142
created_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 1208
content:
"feedbacks weaken after emissions peak at 2100, which together drives \r\nthe land to become a net carbon source after 2100 (Tokarska et al., \r\n2016). The difference in both timing and magnitude of this transition \r\nacross the ensemble, leads to an assessment of medium confidence in \r\nthe likelihood and low confidence in the timing and strength, of the \r\nland transitioning from a net sink to a net source under such a scenario. \r\nBased on high agreement across all available models, we assess with \r\nhigh confidence that the ocean sink strength would weaken but not \r\nreverse under a long-term high emissions scenario. In the SSP5-3.4-\r\novershoot scenario, both the terrestrial and ocean reservoirs act \r\nas transient carbon sources during the overshoot period, when net \r\nanthropogenic CO2 emissions are negative and CO2 concentrations \r\nare falling, and then revert to near-zero (land) or weak sink (ocean) \r\nfluxes after stabilization of atmospheric CO2. The SSP1-2"


id: 6dbd6ca65701e1da17924ee7ee559df
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 14874
content:
"6 scenario, \r\ncharacterized by lower peak CO2 concentrations, a smaller overshoot, \r\nand much less carbon loss from land-use change, shows instead \r\na  relaxation towards a  neutral biosphere on land, and a  sustained \r\nweak sink in the ocean (see also Section 5.6.2.2.1.2)."


id: 9ad7cfb94c2e76216f518d2df276368d
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 21895
content:
"The AR5 assessed with very high confidence that the carbon cycle in \r\nthe ocean and on land will continue to respond to climate change and \r\nrising atmospheric CO2 concentrations created during the 21st century \r\n(WGI, Chapter  6, Executive Summary). Since AR5, experiments with \r\nthe Community Earth System Model version 1  (CESM1) under the \r\nRCP8.5 extension scenario to 2300, suggest that both land and ocean \r\ncarbon-climate feedbacks strengthen in time, land and ocean carbon\u0002concentration feedbacks weaken, and the relative importance of ocean \r\nsinks versus land sinks increases (Randerson et al., 2015). Under high \r\nemissions scenarios, this relative strengthening of land carbon-climate \r\nfeedbacks leads the terrestrial biosphere to shift from sink to source at \r\nsome point after 2100 in all of the CMIP5 ESMs and CMIP5-era Earth \r\nsystem models of intermediate complexity (EMICs) (Tokarska et al., \r\n2016)"


id: 3270b146169127f3938960144b3d6d70
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5348
content:
" The strengthening of land and ocean carbon-climate feedbacks \r\nprojected beyond 2100 under high emissions scenarios offsets the \r\ndeclining climate sensitivity to incremental increases of CO2, leading \r\nto a net strengthening of carbon cycle feedbacks, as measured by the \r\ngain parameter, from one century to the next (Randerson et al., 2015)."


id: 1b4cd387ffbc1def4bd5da00da235b8b
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 1757
content:
"Longer-term memory residing in the ocean enables predictability \r\nof the ocean carbon sink (McKinley et al., 2017; Li and Ilyina, 2018). \r\nThe predictive horizon of the globally integrated air-sea CO2 fluxes \r\nhas been assessed in perfect-model frameworks that are based on an \r\nidealized ensemble of simulations in which each ensemble member \r\nserves as a verification, while no observations are assessed. Perfect\u0002model studies provide an estimate of the upper range of potential \r\npredictability for the integrated air-sea CO2 fluxes of about two years \r\nglobally and up to a decade in some regions (Seferian et al., 2018a; \r\nSpring and Ilyina, 2020). Evidence is also emerging for predictive skill \r\nof the global air-sea CO2 fluxes of up to six years based on prediction \r\nsystems initialized with observed physical climate states (Ilyina et al"


id: 80db81385b20bba02dd0935a9eb1ab75
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 17798
content:
", \r\n2021), with a potential for even longer-term regional predictability \r\nin some regions, including the North Atlantic and subpolar Southern \r\nOcean (H. Li et al., 2016; Lovenduski et al., 2019a)."


id: 1d1504f1c7a851d2766ec903ce522ec2
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 2018
content:
"Science at the time of AR5 established a  near-linear relationship \r\nbetween cumulative emissions of CO2 and the resulting global \r\nwarming (Allen et al., 2009; Matthews et al., 2009; Meinshausen \r\net al., 2009; Zickfeld et al., 2009; T.F. Stocker et al., 2013). The amount \r\nof global warming per unit of cumulated carbon dioxide emissions \r\nis called the transient climate response to cumulative CO2 emissions \r\n(TCRE). This TCRE relationship is now used to estimate the \r\namount of CO2 emissions that would be consistent with limiting \r\nglobal warming to specific levels (Allen et al., 2009; Matthews \r\net al., 2009, 2012;  Meinshausen et al., 2009; Zickfeld et al., 2009; \r\nM.  Collins  et al.,  2013; T.F.  Stocker et al., 2013; Knutti and Rogelj, \r\n2015; Rogelj et al., 2016, 2019; Goodwin et al., 2018)"


id: fbb632d22f49419c6a7d8ba4847e8c6a
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 37103
content:
" The remainder \r\nof CO2 emissions that would be in line with limiting global warming \r\nto a specific temperature level (while accounting for all other factors \r\naffecting global warming) can be estimated with the help of the TCRE \r\nand is referred to as the remaining carbon budget (Rogelj et al., 2019; \r\nMatthews et al., 2020). Section 5.5.1 first assesses the TCRE as one \r\nof the core concepts underlying the notion of a  remaining carbon \r\nbudget, and Section 5.5.2 then integrates this with other contributing \r\nfactors from across this assessment to provide a  consolidated \r\nassessment following the approach of the Special Report on Global \r\nWarming of 1.5degC (SR1.5) (Rogelj et al., 2018b). The historical carbon \r\nbudget of CO2 already emitted is assessed in Section 5.2.1.5."


id: b786504925bb4f8fe785a2d59cc75297
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vector_id: 26400
content:
"Models suggest that predictability of the air-sea CO2 flux is related \r\nto predictability of ocean biogeochemical state variables such as \r\ndissolved inorganic carbon (DIC) and total alkalinity (TA; Lovenduski \r\net al., 2019a), as well as the mixed layer depth (H.  Li et al., 2016). \r\nTemperature variations largely control shorter-term predictability of \r\nthe ocean carbon sink, while longer-term predictability is related to \r\nnon-thermal drivers such as ocean circulation and biology (Li et al., \r\n2019). Although there is a substantial spatial heterogeneity, initialized \r\npredictions suggest stronger multi-year variations of the air-sea CO2\r\nflux and generally tend to outperform uninitialized simulations on the \r\nglobal scale (Li et al., 2019). The predictive skill of air-sea CO2 flux \r\nshows a  consistent spatial pattern in different models, despite the \r\nwide range of techniques used to assimilate observational information \r\n(Regnier et al., 2013)"


id: 233609f4236ec91ad5cb605023c8b132
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 2987
content:
" ESM-based prediction systems also demonstrate \r\npredictability of other marine biogeochemical properties such as net \r\nprimary production (Seferian et al., 2014; Yeager et al., 2018; Park et al., \r\n2019) and seawater pH (Brady et al., 2020)."


id: 181c15d7ea8af7f6800d6520c9dc5f27
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 1285
content:
"that the cumulative fraction of carbon taken up by the terrestrial \r\nbiosphere is constant, and that the climate feedback parameter and \r\nocean heat uptake efficacy do not change in time, both studies agree \r\nthat pathway independence is sensitive to the rate of CO2 emissions, \r\nsuch that pathway independence is expected to break down at both \r\nvery high and very low absolute CO2 emissions rates (MacDougall, \r\n2017; Seshadri, 2017). Note that, in pathways with strongly \r\ndeclining emissions, the cumulative sink fraction by the combined \r\nterrestrial biosphere and ocean is expected to increase (Figure 5.25). \r\nThe studies also agree that no similar relationship analogous to TCRE \r\ncan be expected for short-lived non-CO2 forcers, for which the annual \r\nemissions are a  closer proxy for the implied warming (M.  Collins \r\net al., 2013; Sections 6.4, 7.6)"


id: 452b25c2353ad37e6ec413e1448947f6
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 8308
content:
" MacDougall (2017) suggests that two \r\nadditional constraints are required to create pathway independence: \r\nfirst, the transport of heat and carbon into the deep ocean should be \r\ngoverned by processes with similar time scales; and second, the ratio \r\nof the net change in the atmospheric carbon pool to the net change \r\nin the ocean carbon pool should be close to the ratio of the enhanced \r\nlongwave radiation to space (i.e.,  the radiative response of the \r\nsurface) to ocean heat uptake. If these ratios are identical, then TCRE \r\nwould be completely path independent (MacDougall, 2017). If the \r\nratios are close but not identical, TCRE would be only approximately \r\npath independent over a wide range of cumulative emissions (Cross\u0002Chapter Box 5.3; MacDougall, 2017)."


id: 635db14cd3ed0de05ee1eea10ab08ebe
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 13208
content:
"There is increased confidence in the near-constancy of TCRE because of \r\nthe variety of methods that have been used to examine this relationship: \r\nsensitivity studies with Earth system models of intermediate complexity \r\n(EMICs; Herrington and Zickfeld, 2014; Ehlert et al., 2017); theory\u0002based equations used to examine ESM and EMIC output (Goodwin \r\net al., 2015; R.G. Williams et al., 2016, 2017b); and simple analytical \r\nmodels that capture aspects of the TCRE relationship (MacDougall and \r\nFriedlingstein, 2015). All studies agree that the near-constancy of the \r\nTCRE arises from compensation between the diminishing sensitivity \r\nof radiative forcing to CO2 at higher atmospheric concentration and \r\nthe diminishing ability of the ocean to take up heat and carbon at \r\nhigher cumulative emissions (Allen et al., 2009; Matthews et al., 2009; \r\nFrolicher and Paynter, 2015; Goodwin et al., 2015; Gregory et al., 2015; \r\nMacDougall and Friedlingstein, 2015; MacDougall, 2016; Tokarska \r\net al"


id: 58b33b62fac43ef2bc0ec293bdcf107e
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 11449
content:
"The role of the ocean in setting the coupled climate-carbon cycle response is threefold. First, the ocean and land carbon sinks together \r\nset the airborne fraction (AF) of CO2 in the atmosphere, which sets the radiative forcing that drives the additional heat in the atmosphere, \r\nmost of which is taken up by the ocean (Sections 7.2 and 9.2; Katavouta et al., 2019; Williams et al., 2019). the land carbon sink does \r\nnot appear to play an important role in determining the linearity and path-independence of TCRE (Section 5.5.1.1; Goodwin et al., 2015; \r\nMacDougall and Friedlingstein, 2015; Ehlert et al., 2017). Second, the ocean sets the thermal response through ocean heat uptake \r\n(Section 9.2; Frolicher et al., 2015; Bronselaer and Zanna, 2020). Third, there is a feedback within the ocean carbon-heat nexus as ocean \r\nwarming, particularly under low or no mitigation scenarios, weakens the ocean sink of CO2, which influences the AF, and hence the \r\nradiative forcing (Box 7"


id: f0c181cfa02dcabab745de2bc0a616a2
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 35371
content:
"1; Williams et al., 2019). The near-linear relationship between cumulative CO2 emissions and global warming \r\n(TCRE) is thought to arise, to a large extent, from the compensation between the decreasing ability of the ocean to take up heat and \r\nCO2 at higher cumulative CO2 emissions, pointing to similar processes that determine ocean uptake of heat and carbon (Section 5.5.1.1; \r\nGoodwin et al., 2015; MacDougall and Friedlingstein, 2015; Williams et al., 2016; Zickfeld et al., 2016; Ehlert et al., 2017)."


id: 15ffd73398f468ceda572b54d3a8c974
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 984
content:
"Processes that drive the ocean carbon-heat nexus and its change\r\nThe air-sea flux of heat and all gases across the ocean interface is driven by a common set of complex and turbulent diffusion and \r\nmixing processes that are difficult to observe (Sections 5.2.1.3 and 9.2.1.2; Wanninkhof et al., 2009; Wanninkhof, 2014; Cronin et al., \r\n2019; Watson et al., 2020). These processes are typically simplified into widely verified expressions that link the flux to wind stress, the \r\nsolubility and the gradient across the air-sea interface (medium confidence). Because the ocean has a higher heat capacity than the \r\natmosphere (the heat capacity of the upper 100 m of the ocean is about 30 times larger than the heat capacity of the atmosphere), \r\nthe partitioning of heat between the atmosphere and the ocean is primarily influenced by the temperature differences between air\r\nand seawater"


id: 2d6233326f9c576669b6e6d59a125124
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 4581
content:
" Similarly, the unique seawater carbonate buffering capacity enables CO2 to be stored in the ocean as dissolved salts, \r\nrather than just as dissolved gas; this increases the capacity of seawater to store CO2 by two orders of magnitude beyond the solubility \r\nof CO2 gas and approximates the partitioning ratio of heat between the atmosphere and the ocean (Section 9.2.2.1; Zeebe and Wolf\u0002Gladrow, 2009; Bronselaer and Zanna, 2020). The role of the biological carbon pump in influencing the ocean sink of anthropogenic \r\ncarbon into the ocean interior is assessed to be minimal during the historical period, but this may change, particularly in regional \r\ncontexts, by 2100 (medium confidence) (Laufkotter et al., 2015; Kwiatkowski et al., 2020). Its role is important in the natural or pre\u0002industrial carbon cycle (medium confidence) (Henson et al., 2016)."


id: e9d9ccd283b829806dacd963483b2780
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 34287
content:
"Under climate change, the buffering capacity of the ocean decreases (increasing Revelle Factor), which reflects a decreasing capacity \r\nfor the ocean to take up additional anthropogenic CO2 and store it in the dissolved inorganic carbon reservoir (Egleston et al., 2010). \r\nIn contrast to CO2, there is no physical limitation that would reduce the ability of surface ocean temperature to equilibrate with the \r\natmospheric temperature. However, both carbon and heat fluxes depend on air-sea heat fluxes that in turn depend on gradients of \r\ncharacteristics at the air-sea interface. These gradients at the air-sea interface respond to ocean dynamics, such as the volume of \r\nthe surface mixed-layer that equilibrates with the atmosphere, and ocean circulation that can flush the surface layer with water \r\nmasses that have not equilibrated with the atmosphere for a long time"


id: cf2f8e18c6a4dea94aaa44f2c5ef4d5c
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 30133
content:
" Limited recent evidence suggests that the effect of small-scale \r\ndynamics absent in climate and Earth system models might be locally important (Bachman and Klocker, 2020). In summary, changes \r\nin heat and carbon uptake by the ocean rely on a combination of unique chemical and shared physical processes, any of which have \r\nthe potential to disrupt the coherence of heat and CO2 change in the ocean."


id: 5273a4b278619656ed13288768fed1f8
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 10429
content:
"The role of the large-scale circulation in shaping these fluxes is to: (i) flush the ocean surface layer with deep waters that are relatively \r\ncold and with weak or no anthropogenic CO2 and heat content because they have been isolated from the atmosphere for centuries; \r\nand (ii) transport the anthropogenic CO2 and heat at depth, away from the atmosphere (Frolicher et al., 2015; Marshall et al., 2015; \r\nArmour et al., 2016). For instance, in the Southern Ocean, upwelled water masses take up a large amount of anthropogenic CO2\r\nand heat (Cross-Chapter Box  5.3, Figure  1), which are then exported northward by the circulation to be stored at depth in the \r\nSouthern Hemisphere subtropical gyres (Cross-Chapter Box 5.3, Figure 1; Figure 9.7)"


id: 6fd77716263483646f8422c1bfc8bb58
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 15219
content:
" In the North Atlantic, the signature of the Atlantic \r\nmeridional overturning circulation (AMOC) is also clearly visible, with large amounts of heat and carbon being stored beneath the \r\nNorth Atlantic subtropical gyre at 1 km depth (Cross-Chapter Box 5.3, Figure 1). In summary, the net air-sea fluxes of anthropogenic \r\nCO2 and heat depend on large-scale circulation, which is associated with upper ocean stratification, mixed-layer depth, and water\u0002mass formation, transport and mixing (Sections 9.1-9.3)."


id: cff4bd5c58bf360b94e751eab9b30745
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 30243
content:
"gpp\r\nFuture projections of the ocean carbon-heat nexus in the second half of the 21st century, particularly those under weak or no \r\nmitigation scenarios, are characterized by the strengthening of the two largest positive feedbacks: weakening surface ocean CO2 \r\nbuffering capacity (increasing Revelle Factor) and warming that further reduces CO2 solubility and strengthens ocean stratification, \r\nwhich reduces exchange between the ocean surface and interior (Jiang et al., 2019; Bronselaer and Zanna, 2020). These are offset \r\nby a growing but scenario-dependent negative feedback from increasing carbon and heat air-sea fluxes towards the ocean, due to \r\nincreased atmospheric temperature and CO2 concentrations (Talley et al., 2016; Jiang et al., 2019; McKinley et al., 2020). The Southern \r\nOcean in particular is one of the regions where the projected feedback can be largest and where inter-model differences are strongest \r\n(Roy et al., 2011; Frolicher et al., 2015; Hewitt et al"


id: d9b88f4a74cbc1943deb4d2f82d099ab
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 31770
content:
", 2016; Mongwe et al., 2018). These projected trends in ocean carbonate chemistry \r\n(Section 5.4.2), together with surface ocean warming (Section 9.2.1.1), explain the slow down and long-term reduction of the ocean \r\nsink for anthropogenic CO2 even as emissions continue to rise beyond 2050 under weak-to-no-mitigation scenarios (Figures 2.7.1 \r\nand 5.25, and Technical Summary TS Box 7). Projected change in the North Atlantic and Southern Ocean overturning circulation \r\nalso impact air-sea fluxes of heat and carbon. The very likely decline in AMOC in the 21st century for all shared socio-economic \r\npathways (SSP) scenarios (Section 9.2.3.1) tends to reduce heat and carbon uptake, resulting in a positive feedback. In contrast, in \r\nthe Southern Ocean, the future 21st century projected increase in upper ocean overturning circulation (low confidence) - due to \r\nincreasing wind forcing projected for all scenarios, except those with large mitigation (SSP1-2"


id: f353d88f60e49977ba2bae33d2bec26f
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 35792
content:
"6) - produces a negative feedback, with \r\nincreasing heat and carbon uptake and storage despite the increasing stratification and outgassing of natural CO2 in the upwelling \r\nzone (Sections 9.2.3.2 and 5.2.1.3)."


id: 11407757c71e65f8e4a4c47000b507c6
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 190
content:
"In summary, a combination of unique chemical properties of seawater carbonate combined with shared physical ocean processes \r\nexplain the coherence and scaling in the uptake and storage of both CO2 and heat in the ocean, which is the basis for the carbon-\r\nheat nexus (high confidence). In this way, the processes of the ocean carbon-heat nexus help understand the quasi-linear and path \r\nindependence of properties of TCRE, which forms the basis for the zero emissions commitment (ZEC; Section 5.5) (medium confidence). \r\nFuture projections under low or no mitigation indicate with high confidence that carbon chemistry and warming will strengthen the \r\npositive feedback to climate change by reducing ocean carbon uptake, and medium confidence that ocean circulation may partially \r\ncompensate that positive feedback by slightly increasing anthropogenic carbon storage"


id: ee5f50de901486c245e6cce625b4e160
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 34973
content:
" Increasing ocean warming and stratification \r\nmay decrease exchanges between the surface and subsurface ocean, which could reduce the path independence of TCRE, though this \r\neffect can be partially counterbalanced regionally by increasing circulation associated with increasing winds (low confidence)."


id: 3a9dfa43edcb7d970479dd654f8a4659
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 6587
content:
"The AR5 indicated that the concept of a constant ratio of cumulative \r\nemissions of CO2 to temperature was applicable to scenarios with \r\nincreasing cumulative CO2 emissions up to 2000  PgC (M.  Collins \r\net al., 2013). Recent analyses added confidence to this insight \r\n(Herrington and Zickfeld, 2014; Steinacher and Joos, 2016) and \r\nshowed some evidence of a potentially larger window of constant \r\nTCRE (Leduc et al., 2015; Tokarska et al., 2016). Using an analytical \r\napproach, MacDougall and Friedlingstein (2015) quantified \r\na  window of constant TCRE -  defined as the range in cumulative \r\nemissions over which the TCRE remains within 95% of its maximum \r\nvalue - as between 360 to 1560 PgC. However, models with a more \r\nsophisticated ocean representation suggest that TCRE could also \r\nremain constant for considerably larger quantities of cumulative \r\nemissions, up to at least 3000 PgC (Leduc et al., 2015; Tokarska et al., \r\n2016)"


id: 73103c8e0d8483e3bf1489b11dc4730e
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 15717
content:
" Beyond this upper limit, studies are inconclusive, with some \r\nsuggesting that TCRE will decrease (Leduc et al., 2015) and others \r\nindicating that the linearity would hold up to as much as 5000 PgC \r\n(Tokarska et al., 2016)."


id: ff53673c0ebc16411f3323496c9c283b
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 37719
content:
"Global average temperature increase responds over a time scale of \r\nabout 10 years following the emission of a 100 PgC pulse of CO2\r\n(Joos et al., 2013; Ricke and Caldeira, 2014), with larger emission \r\npulses associated with longer time scales and smaller pulses with \r\nshorter ones (Joos et al., 2013; Matthews and Solomon, 2013; \r\nZickfeld and Herrington, 2015). This behaviour is confirmed in other \r\nstudies, including those that calculate the temperature response \r\nto an instantaneous doubling or quadrupling of atmospheric CO2\r\n(Matthews et al., 2009; Gillett et al., 2013; Herrington and Zickfeld, \r\n2014; Leduc et al., 2015; Hajima et al., 2020b). These findings \r\nsuggest that the TCRE is sensitive to the rate of emissions, but \r\nstudies assessing this sensitivity have found diverging results. For \r\nexample, an increase in TCRE and its surrounding uncertainty was \r\nreported for experiments that imply a  gradual decline in annual \r\nCO2 emissions (Tachiiri et al., 2019)"


id: c902c492cb7f394b0f9d229e991197d0
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 29186
content:
" These studies suggest that, in \r\nmost cases, TCRE would be expected to increase in scenarios with \r\ndecreasing annual emissions rates. This increase in TCRE for annual \r\nCO2 emissions declining towards zero can be the result of the zero \r\nemissions commitment (ZEC) which is the amount of warming \r\nprojected to occur following a complete cessation of emissions (see \r\nSection 4.7.1.1 for its assessment), as well as Earth system processes \r\nthat are unrepresented in current TCRE estimates (Section 5.5.2.2.4) \r\nand other factors. When using TCRE to estimate CO2 emissions \r\nconsistent with a  specific maximum warming level, these factors \r\nhave to be taken into account (see Figure  5.31). Combined with \r\nrecent literature on the ZEC (MacDougall et al., 2020) and emissions "


id: 95f7a8bc05c122f0fad963f4a852a5c7
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 21097
content:
"As cumulative emissions increase, weakening land and ocean carbon \r\nsinks increase the airborne fraction of CO2 emissions (see Figure 5.25), \r\nbut each unit increase in atmospheric CO2 has a smaller effect on \r\nglobal temperature owing to the logarithmic relationship between \r\nCO2 and its radiative forcing (Matthews et al., 2009; Etminan et al., \r\n2016). At high values of cumulative emissions, some models simulate \r\nless warming per unit CO2 emitted, suggesting that the saturation \r\nof CO2 radiative forcing becomes more important than the effect of \r\nweakened carbon sinks (Herrington and Zickfeld, 2014; Leduc et al., \r\n2015). The behaviour of carbon sinks at high emissions levels remains \r\nuncertain, as models used to assess the limits of the TCRE show \r\na large spread in net land carbon balance (Section 5.4.5), and most \r\nestimates did not include the effect of permafrost carbon feedbacks \r\n(Sections 5.5.1.2.3 and 5.4)"


id: 2dcb4e6a2fed4ae90ba7ae0e0bfc25aa
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 4644
content:
" The latter would tend to further increase \r\nthe airborne fraction at high cumulative emissions levels, and could \r\ntherefore extend the window of linearity to higher total amounts of \r\nemissions (MacDougall et al., 2015). Leduc et al. (2016) suggested \r\nfurther that a declining strength of snow and sea ice feedbacks in \r\na  warmer world would also contribute to a  smaller TCRE at high "


id: 583ce2732c36fc979b064069b36854a4
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 11386
content:
"Whether TCRE remains an accurate predictor of CO2-induced \r\nwarming when annual CO2 emissions reach zero and are followed \r\nby net carbon dioxide removal (also referred to as TCRE reversibility) \r\ntherefore hinges on contributions of slow components of the climate \r\nsystem that cause unrealized warming from past CO2 emissions. Such \r\nslow components can arise from either physical climate (i.e., ocean \r\nheat uptake) or carbon cycle (i.e.,  ocean carbon uptake and \r\npermafrost carbon release) processes. The combined effect of these \r\nprocesses determines the magnitude and sign of the ZEC (MacDougall \r\net al., 2020), which in turn impacts TCRE reversibility. As discussed in \r\nSection 4.7.1.1, recent model estimates of the ZEC suggest a range \r\nof +-0.19degC centred on zero (MacDougall et al., 2020). This suggests \r\nlow agreement among models as to the reversibility of the TCRE in \r\nresponse to net-negative CO2 emissions"


id: 5e4c84e08cddfc1c2911de2292d3d40a
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 12366
content:
" Furthermore, most models \r\nused for ZEC assessments to date do not represent permafrost carbon \r\nprocesses, although understanding their contribution is essential to \r\nquantify the TCRE contribution. There is therefore limited evidence\r\nthat quantifies the impact of permafrost carbon feedbacks on the \r\nreversibility of TCRE, leading to low confidence that the TCRE remains \r\nan accurate predictor of temperature changes in scenarios of net\u0002negative CO2 emissions on time scales of more than a half a century."


id: 7c2b4540a90b0167dec360f7d72e7515
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 17137
content:
"There are relatively few studies that have assessed how the TCRE \r\nis expected to change in scenarios of declining emissions followed \r\nby net negative annual CO2 emissions. Conceptually, the literature \r\nsuggests that the small lag of about a decade between CO2 emissions \r\nand temperature change (Ricke and Caldeira, 2014; Zickfeld and \r\nHerrington, 2015) would result in more warming at a given amount \r\nof cumulative emissions in a scenario where that emissions level is \r\nfirst exceeded and then returned to by deploying negative emissions \r\n(referred to as an 'overshoot', as is often the case in scenarios that \r\naim to limit radiative forcing in 2100 to 2.6 or 1.9 W m-2 (Riahi et al., \r\n2017; Rogelj et al., 2018a). Zickfeld et al"


id: 5db8df1e63807929d8bba7bd1a5c898f
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 12271
content:
" (2016) showed this to \r\nhold across a range of scenarios, with positive emissions followed \r\nby negative emissions, whereby the TCRE increased by about 10% \r\nacross the transition from positive to negative emissions as a result \r\nof the thermal and carbon inertia of the deep ocean. However, CMIP6 \r\nresults for the SSP5-3.4-overshoot scenario show diverging trends \r\nacross various ESMs (Figure 5.30). In an idealized CO2-concentration\u0002driven setting, Tachiiri et al. (2019) also reported an increase in \r\nTCRE. Exploring pathways with emissions rates and overshoots closer \r\nto mitigation pathways considered over the 21st century (in this case \r\nup to about 300 PgC), a recent emissions-driven EMIC experiment \r\nshowed pathway independence of TCRE (Tokarska et al., 2019a)"


id: c7af0c793bffce23baf970e4870fbbcc
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 28997
content:
" \r\nFurthermore, also in absence of net negative emissions, warming \r\nwould not necessarily remain perfectly constant on time scales of \r\ncenturies and millennia, but could decrease or increase (Frolicher and \r\nPaynter, 2015; R.G. Williams et al., 2017a; Hajima et al., 2020b). These \r\nadditional changes in global mean temperature increase at various \r\ntime scales are known as the ZEC (C.D. Jones et al., 2019; MacDougall \r\net al., 2020), assessed in Section 4.7.1.1, and have to be integrated \r\nwhen using TCRE to estimate warming or remaining carbon budgets \r\nin overshoot scenarios."


id: 34a195794e14e7807286a19c334c5c6c
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 5686
content:
"Since AR5, new studies have further expanded the evidence base for \r\nestimating the value of TCRE. These studies rely on ESMs or EMICs, \r\nobservational constraints and concepts of attributable warming, \r\nor theoretically derived equations (see Table  5.7 for an overview). \r\nSeveral studies have endeavoured to partition the uncertainty in \r\nthe value of TCRE into constituent sources. For example, TCRE can \r\nbe decomposed into terms of TCR and the airborne fraction of \r\nanthropogenic CO2 emissions over time (Allen et al., 2009; Matthews \r\net al., 2009). These two terms are assessed individually (see Section 5.4 \r\nand Chapter 7, respectively) and allow the integration of evidence \r\nassessed elsewhere in the report into the assessment of TCRE \r\n(Section 5.5.1.4). Further studies use a variety of methods, including \r\nanalysing the outputs from CMIP5 (R.G. Williams et al., 2017b) or \r\nCMIP6 (Arora et al"


id: e110a038688b0d1d14f3ba25a71d2a9
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content:
", 2020; Jones and Friedlingstein, 2020), conducting \r\nperturbed parameter experiments with a single model (MacDougall \r\net al., 2017), Monte-Carlo methods applied to a simple climate model \r\n(Spafford and Macdougall, 2020), or observations and estimates of \r\nthe contribution of CO2 and non-CO2 forcers (Matthews et al., 2021). \r\nAll of the studies agree that uncertainty in climate sensitivity - either "


id: 8045e73a40bf81cb54ebd72b9e5caf21
created_at: 2023-03-16 14:02:00
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vector_id: 17709
content:
"The AR5-assessed (W.J. Collins et al., 2013) TCRE range was based \r\nin part on the ESMs available at the time, which did not include \r\nsome potentially important Earth system feedbacks. Since then, \r\na  number of studies have assessed the importance of permafrost \r\ncarbon feedbacks, in particular on remaining carbon budgets \r\n(MacDougall and Friedlingstein, 2015; MacDougall et al., 2015; \r\nBurke et al., 2017b; Gasser et al., 2018; Lowe and Bernie, 2018), \r\na development highlighted and assessed in the IPCC Special Report \r\non Global Warming of 1.5degC (Rogelj et al., 2018b). MacDougall and \r\nFriedlingstein (2015) reported a TCRE increase of about 15% when \r\nincluding permafrost carbon feedbacks. The overall linearity of the \r\nTCRE during the 21st century was not affected, but they also found \r\nthat permafrost carbon feedbacks caused an increase in TCRE on \r\nmulti-century time scales under declining CO2 emissions rates"


id: d0d44f52fc4bb673636710310a3305f0
created_at: 2023-03-16 14:02:00
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content:
"Studies differ in how they define TCRE, in the methods they use, and \r\ntheir assumptions, such as the assumed climate sensitivity distribution \r\nor the choice of metrics of global temperature change (e.g., GMST \r\nor GSAT, see Table 5.7). This makes TCRE estimates from individual \r\nstudies difficult to compare. The combined assessment of TCRE \r\ntherefore takes advantage of the well-established decomposition \r\nof TCRE in two factors: the TCR and the AF (Section  5.5.1.3). This \r\nprovides a  TCRE assessment range for CO2-induced warming \r\nat the time of doubling CO2 concentrations that builds on the \r\nbroader Working Group 1 assessment. Expert judgement based on \r\nthe airborne fraction range found in CMIP6 models (Arora et al., \r\n2020; Jones and Friedlingstein, 2020) suggest a value of 53% with \r\na 1-sigma range of +-6%, which is double the sigma range based on \r\nthe spread of CMIP6 models only. Combining this range with the \r\nAR6 TCR assessment (Section 7.5, best estimate 1.8degC, 1"


id: 2cc18eb003dfc13a24d655cc0c89886d
created_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 4475
content:
"4degC-2.2degC \r\nlikely and 1.2degC-2.4degC very likely range) results in a 5-95% range of \r\n1.0-2.3degC per 1000 PgC (0.27degC-0.63degC per 1000 GtCO2). Based on \r\nexpert judgement that accounts for the incomplete coverage of all \r\nEarth system components, this results in a consolidated assessment \r\nthat TCRE would fall likely in the range of 1.0-2.3degC per 1000 PgC, \r\nwith a best estimate of 1.65degC per 1000 PgC (0.45degC per 1000 GtCO2). \r\nWarming here reflects the human-induced GSAT increase and assumes \r\na normal distribution. Some studies using observational constraints \r\nsupport a lognormal shape for the TCRE distribution (Spafford and \r\nMacdougall, 2020), but such a distribution is currently not supported \r\nby the combined assessment of TCR and airborne fraction. Finally, \r\nthis assessed TCRE range needs to be considered in combination with \r\nthe ZEC (Section 4.7.1.1) when estimating the CO2-induced warming \r\nof low-emissions scenarios."


id: b304d3cfdc0e6db65b52e88b7e30bcd0
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 25683
content:
"Two approaches were used in AR5 to determine carbon budgets \r\n(M. Collins et al., 2013; T.F. Stocker et al., 2013; Clarke et al., 2014; \r\nIPCC, 2014; Rogelj et al., 2016). Working Group I (WGI) reported \r\nthreshold exceedance budgets (TEB) that correspond to the amount \r\nof cumulative CO2 emissions at the time a  specific temperature \r\nthreshold is exceeded, with a  given probability in a  particular \r\ngreenhouse-gas and aerosol (pre-cursor) emissions scenario \r\n(M. Collins et al., 2013; IPCC, 2013b; T.F. Stocker et al., 2013). WGI \r\nalso reported TEBs for the hypothetical case that only CO2 would be \r\nemitted by human activities (M. Collins et al., 2013; IPCC, 2013b; \r\nT.F. Stocker et al., 2013)"


id: 7f6912ec1c4af2b6f6a0bd5a6c2eed37
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 17599
content:
" The AR5 Working Group III used threshold \r\navoidance budgets (TAB) that correspond to the cumulative CO2\r\nemissions over a  given time period of a  subset of greenhouse\u0002gas and aerosol (precursor) emissions scenarios in which global\u0002mean temperature increase stays below a  specific temperature \r\nthreshold with at least a  given probability (Clarke et al., 2014). \r\nThe AR5 synthesis report used TABs defined until the time of peak \r\nwarming over the 21st century (IPCC, 2014). Drawbacks have been \r\nidentified for TEBs and TABs (Rogelj et al., 2016). TABs provide \r\nan estimate of the cumulative CO2 emissions under pathways "


id: 6a0b1f6344d77da16852583f0ab89c60
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 14291
content:
"Since the publication of AR5 (M.  Collins et al., 2013), several \r\nnew approaches have been proposed that provide a  solution \r\nto the identified limitations of TABs and TEBs. Most of these \r\napproaches indirectly rely on the concept of TCRE (Section 5.5.1), \r\nfor example, because they estimate modelled cumulative CO2\r\nemissions until a  temperature threshold is crossed and use this \r\nbudget to infer insights for pathways that attempt to limit warming \r\nto below this threshold and thus need to follow a different path \r\n(Friedlingstein et al., 2014a; Matthews et al., 2017; Millar et al., \r\n2017b; Goodwin  et al., 2018; Tokarska and Gillett, 2018). In this \r\nreport, the assessment framework of SR1.5 for remaining carbon \r\nbudgets is applied (Rogelj et al., 2018b, 2019)"


id: 12dc391f8547a046e2185c602638df69
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 471
content:
" This framework allows \r\nintegration of multiple lines of evidence to assess the contributions \r\nof five components that together result in a consolidated assessment \r\nof the remaining carbon budget (TCRE, historical human-induced \r\nwarming, non-CO2 warming, the ZEC, and adjustments due to \r\nadditional Earth system feedbacks, see Section  5.5.2.2). It builds \r\non the advances in estimating remaining carbon budgets or \r\nrelated quantities that have been published since AR5 (Rogelj \r\net al., 2015a; Haustein et al., 2017; Matthews et al., 2017, 2021; "


id: 76af8d8ecac40fcfe9e613b87ebee890
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 16252
content:
"that have as a  common characteristic the fact that they do not \r\nexceed a  specific global warming threshold. However, the actual \r\nlevel of maximum warming can vary between pathways, leading \r\nto an unnecessary and poorly constrained spread in TAB estimates \r\n(Rogelj et al., 2016). Therefore, the TAB approach typically does \r\nnot result in accurate projections of the remaining carbon budget. \r\nOne drawback of TEBs is that they provide an estimate of the \r\ncumulative CO2 emissions at the time global warming crosses \r\na given threshold of interest in a specific emissions scenario - for \r\nexample, most of the standard scenarios used in climate change \r\nresearch, such as the Representative Concentration Pathways \r\n(RCPs) or Shared Socio-economic Pathways (SSPs), exceed global \r\nwarming of 1.5degC or 2degC (see Cross-Chapter Box 1.5) (M. Collins \r\net al., 2013; T.F.  Stocker et al., 2013; Friedlingstein et al., 2014a; \r\nMillar et al., 2017b)"


id: 6299991a3e8f19819de1b23e92e782f5
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 13078
content:
" Because of potential variations in non-CO2\r\nwarming at that point in time, or potential lags of about a decade \r\nin CO2 warming (Joos et al., 2013; Ricke and Caldeira, 2014; Rogelj \r\net al., 2015a, 2016, 2018b; Zickfeld and Herrington, 2015) TEBs also \r\ndo not provide a precise estimate of the remaining carbon budget \r\nfor limiting warming to a specific level."


id: a2ea65b67621b32e6b171142079cfc2c
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 23198
content:
"Non-CO2 emissions contribute either cumulatively (N2O, and other \r\nlong-lived climate forcers) or in proportion to their annual emissions \r\n(CH4 and other short-lived climate forcers) to global warming, and \r\nthus also affect estimates of remaining carbon budgets by reducing \r\nthe amount of warming that could still result from CO2 emissions \r\n(Meinshausen et al., 2009; Friedlingstein et al., 2014a; Knutti and \r\nRogelj, 2015; Rogelj et al., 2015a, 2016; R.G. Williams et al., 2016, \r\n2017b; Matthews et al., 2017; Collins et al., 2018; Mengis et al., 2018; \r\nTokarska et al., 2018; Zickfeld et al., 2021). The size of this contribution \r\nhas been estimated both implicitly (Meinshausen et al., 2009; \r\nFriedlingstein et al., 2014a; Rogelj et al., 2016; Matthews et al., 2017; \r\nMengis et al., 2018; Tokarska et al., 2018) and explicitly (Rogelj et al., \r\n2015a, 2018b; Collins et al., 2018; Matthews et al., 2021) by varying \r\nthe assumptions of non-CO2 emissions and associated warming"


id: 234ef0fa84f7643270192418c3002db8
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 3008
content:
" \r\nInternally consistent evolutions of future CO2 and non-CO2 emissions \r\nallow for derivation of non-CO2 warming contributions consistent \r\nwith global CO2 emissions reaching net zero levels, and therewith \r\ncapping maximum future CO2 emissions (Smith and Mizrahi, 2013; \r\nClarke et al., 2014; Huppmann et al., 2018; Rogelj et al., 2018b; \r\nMatthews et al., 2021). Pathways that reflect such development \r\ntypically show a stabilization or decline in non-CO2 radiative forcing \r\nand warming at, and after the time of, global CO2 emissions reaching \r\nnet zero levels, as illustrated in the scenario database underlying \r\nSR1.5 (Huppmann et al., 2018; Rogelj et al., 2018b)."


id: 9254cc1a04d3688818155b4f7aeba8e0
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 20544
content:
"The impact of non-CO2 emissions on remaining carbon budgets \r\nis  assessed with emulators (Meinshausen et al., 2009; Millar \r\net al., 2017b; Gasser et al., 2018; Goodwin et al., 2018; Rogelj et al., \r\n2018b; C.J. Smith et al., 2018; Matthews et al., 2021) that incorporate \r\nsynthesized climate and carbon-cycle knowledge (Cross-Chapter \r\nBox 7.1). The estimated implied non-CO2 warming can subsequently \r\nbe applied to reduce the remaining allowable warming for estimating \r\nthe remaining carbon budget (Figure  5.31; Rogelj et al., 2018b, \r\n2019). Alternative methods estimate the non-CO2 fraction of total \r\nanthropogenic forcing (Matthews et al., 2021), or do not correct for \r\nnon-CO2 warming directly. The latter methods instead consider CO2\r\nand non-CO2 warming together to define a CO2-forcing equivalent \r\ncarbon budget from which eventual non-CO2 contributions expressed \r\nin CO2-forcing-equivalent emissions have to be subtracted to obtain \r\na  remaining carbon budget (Jenkins et al"


id: 942b01ecbeabaa986bf1dbc2d8cd9ef0
created_at: 2023-03-16 14:02:00
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vector_id: 20824
content:
", 2018; Matthews et al., \r\n2020). These studies also use emulators to invert a specified evolution \r\nof non-CO2 forcing to a  corresponding amount of equivalent CO2\r\nemissions (Matthews et al., 2020), or alternatively use empirical \r\nrelationships linking changes in non-CO2 greenhouse gas emissions \r\nto warming (Cain et al., 2019). Methods to express non-CO2 emissions \r\nin CO2 equivalence are assessed in Section 7.6, yet their applicability \r\nand related uncertainties for remaining carbon budgets have not yet \r\nbeen covered in-depth in the literature."


id: 6f948293108b43de13adf891da9de927
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 15185
content:
"Advances in methods to estimate remaining carbon budgets have \r\nshown the importance of applying an estimate of historical warming \r\nto date that is as accurate as possible (Millar et al., 2017b; Tokarska \r\nand Gillett, 2018). This becomes particularly important when \r\nassessing remaining carbon budgets for global warming levels \r\nthat are relatively close to present-day warming, such as a 1.5degC \r\nor 2degC levels (Rogelj et al., 2018b). Also shown to be important is \r\nthe definition of global average temperature by which historical \r\nwarming is estimated (Cross-Chapter Box  2.3; Cowtan and Way, \r\n2014; Allen et al., 2018; Pfleiderer et al., 2018; Richardson et al., \r\n2018; Tokarska et al., 2019b), as is the correct isolation of human\u0002induced global warming (Haustein et al., 2017; Allen et al., 2018) to \r\nremove the effect of internal variability. Based on the assessment \r\nin Section 3.3 (Table 3"


id: f0767a06c7d391e7621906a933349a35
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 35330
content:
"1), here we apply an assessed best-estimate \r\nof historical warming expressed as an increase in GSAT of 1.07degC \r\n(0.8-1.3degC, likely range) between 1850-1900 and 2010-2019. \r\nThis choice implies global coverage and is consistent with AR5 \r\nwhere carbon budgets were reported in GSAT (M.  Collins et al., \r\n2013; T.F. Stocker et al., 2013), SR1.5 where GSAT was the central \r\nmetric for remaining carbon budgets (Rogelj et al., 2018b), and \r\nrecent studies that highlight how GSAT enables an easy translation \r\nwith AR5 (Tokarska et al., 2019b). The  use of other historical "


id: 24a7dd1e4f06b8caf167776603c23c34
created_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 3211
content:
"The combination of the five components assessed in \r\nSections  5.5.2.2.1-5.5.2.2.5 allows for an overall assessment of \r\nthe remaining carbon budget in line with different levels of global \r\naverage warming, as documented in SR1.5 (Rogelj et al., 2018b). The \r\noverall assessment of remaining carbon budgets (Table 5.8) reflects \r\nthe uncertainty in TCRE quantification and provides estimates of the \r\nuncertainties surrounding the contributions of each of the respective \r\nfurther components. A  formal combination of all uncertainties is \r\nnot possible because they are not all independent, or because they \r\nrepresent choices rather than probabilistic uncertainties (Matthews \r\net al., 2021). In light of all uncertainties related to TCRE, non-CO2\r\nforcing and response, the level of non-CO2 mitigation, and historical \r\nwarming, there is a  small probability that the remaining carbon \r\nbudget for limiting warming to 1.5degC since the 1850-1900 period \r\nis effectively zero"


id: 9a2909dff34d8e727e0705ede376d126
created_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 21779
content:
" However, applying best estimate values for all but \r\nuncertainties in Earth system feedbacks and TCRE, the remaining \r\ncarbon budgets in line with the Paris Agreement are generally small \r\nyet not zero (see Table 5.8)."


id: f332731a74b97ad87e5249b46efdf7c3
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 35773
content:
"Use of TCRE for estimating remaining carbon budgets needs to \r\nconsider the zero emissions commitment (ZEC), the potential \r\nadditional warming after a complete cessation of net CO2 emissions. \r\nBased on the ZEC assessment presented in Section 4.7.1.1, the ZEC's \r\ncentral value is taken to be zero with a  likely range of +-0.19degC, \r\nnoting that it might either increase or decrease after half a century. \r\nZEC uncertainty is assessed for a time frame of half a century, as this \r\nmost appropriately reflects the time between stringent mitigation \r\npathways reaching net zero CO2 emissions and the end of the \r\ncentury. For shorter time horizons, a similar central zero value applies, \r\nbut with a smaller range (MacDougall et al., 2020)"


id: 7013b7394ff5602e1a257d8f25a7c217
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 15248
content:
" Experiments that \r\nramped up and down emissions following a bell-shaped trajectory \r\n(MacDougall and Knutti, 2016a) show that when annual CO2\r\nemissions decline to zero at a pace consistent with those currently \r\nassumed in mitigation scenarios (Huppmann et al., 2018; Rogelj \r\net al., 2018b), the ZEC will already be realized to a large degree at the \r\ntime of reaching net zero CO2 emissions (MacDougall et al., 2020)."


id: 3bbc4b2d52ead1eebf2cd4878b040ae7
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 6752
content:
"There is robust evidence supporting the concept of TCRE as well \r\nas high confidence in the range of historical human-induced \r\nwarming. Combined with the assessed uncertainties in the Earth \r\nsystem's response to non-CO2 emissions and less well-established \r\nquantification of some of the effect of non-linear Earth system \r\nfeedbacks, this leads to medium confidence being assigned \r\nto  the assessed remaining carbon budget estimates while noting \r\nthe  identified and assessed uncertainties and potential variations. \r\nThe reported values are applicable to warming and cumulative \r\nemissions over the 21st century. For climate stabilization beyond the \r\n21st century, this confidence would decline to very low confidence\r\ndue to uncertainties in Earth system feedbacks and the ZEC"


id: 5b6fc137bee08ff2c4586cbbfead36a5
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 11886
content:
"  For \r\nestimates of total carbon budgets in line with limiting global \r\nwarming to a specific level, an estimate of historical CO2 emissions \r\nshould be added to the remaining carbon budget values reported \r\nin Table  5.8. Historical CO2 emissions between 1850 and 2019 \r\nhave been estimated at about 655 +-  65  PgC (1-sigma range, or \r\n2390 +- 240 GtCO2, see Table 5.1), while since 1 January 2015, an \r\nadditional 57  PgC (210  GtCO2) has been emitted until the end of \r\n2019 (Friedlingstein et al., 2020)."


id: 3b822a325430f23b7b280ec294eb116a
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 6713
content:
"b\r\n Historical CO2 emissions between 1850 and 2019 have been estimated at about 655 +- 65 PgC (likely range, or 2390 +- 240 GtCO2, see Table 5.1). Note that 57 PgC \r\n(210 GtCO2) have been emitted from the middle of the 2010-2019 reference period (2015) until the end of 2019 (Friedlingstein et al., 2020).\r\nc\r\n TCRE: transient climate response to cumulative CO2 emissions, assessed to fall likely between 1.0-2.3degC EgC-1 with a normal distribution. PgC values are rounded to the \r\nnearest 10; GtCO2 values to the nearest 50. For comparison, assuming a lognormal distribution with a 1.0-2.3degC EgC-1 central 66% range instead of a normal distribution \r\nwould increase remaining carbon budgets at the 17th, 33rd, 50th, 67th, and 83rd percentile with 3%, 10%, 12%, 9%, 2%, respectively. Future non-CO2 contributions in \r\nthese remaining carbon budget estimates are based on the scenarios assessed in the SR1"


id: cccf017b7ae95aa373fdad9ae1494f18
created_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 29761
content:
"5 report and estimated as the median quantile regression of non-CO2 warming since \r\n2010-2019 relative to total additional warming since 2010-2019 at the time scenarios reach net-zero CO2 emissions (Forster et al., 2018; Huppmann et al., 2018; Rogelj et al., \r\n2018b)."


id: f3a0a61d6cd306d7ac3574318828e100
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 35842
content:
"d\r\n Additional Earth system feedbacks are included in the remaining carbon budget estimates as discussed in Section 5.5.2.2.5. The tropospheric ozone and methane lifetime \r\ncontributions are included through the non-CO2 warming projections by the AR6-calibrated Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) \r\nemulator, while the remaining feedbacks are assessed totalling a combined feedback of magnitude 7 +- 27 PgC K-1 (1-sigma range, or 26 +- 97 GtCO2 degC-1).\r\ne\r\n Variations due to different scenario assumptions related to the future evolution of non-CO2 emissions in mitigation scenarios reaching net zero CO2 emissions (Huppmann \r\net al., 2018; Rogelj et al., 2018b) of at least +-0.1degC (spread across scenarios). Combined with a central estimate of TCRE (1.65degC EgC-1) this results in at least +-60 PgC or \r\n+-220 GtCO2. This spread reflects the variation in the underlying scenario ensemble but is not a formal likelihood"


id: 4f0cfe87edc76ea677cbd586ab61367a
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 9879
content:
"The reliance on an ensemble of opportunity (i.e., a serendipitous collection of scenario data from a variety of sources and studies) is \r\navoided by methodologically separating the assessment of future warming contributions of non-CO2 emissions from the spread in \r\ntransient climate response to cumulative CO2 emissions (TCRE; Section 5.5.2; Rogelj et al., 2018b, 2019). This facilitates the explicit \r\nrepresentation of TCRE uncertainty by a formal distribution, in this case a normal distribution with a 1.0-2.3degC 1000 PgC-1 1-sigma \r\nrange (Section  5.5.1.4). The effect of this methodological advance can be estimated from a  direct comparison of the frequency \r\ndistribution of TCRE in CMIP5 models that were used in AR5, and the formal TCRE distribution used in AR6, but is limited in precision. \r\nFor estimates of the remaining carbon budget in line with limiting warming to 1"


id: 11545480a3a98007b175dad69f5132f3
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 208
content:
"5degC or 2degC relative to pre-industrial levels, this \r\nimprovement is estimated to lead to a reduction of budgets of the order of about 100 GtCO2 between AR5 and AR6."


id: 6d888afe93efc16f3bdcc30ed5bc24d8
created_at: 2023-03-16 14:02:01
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content:
"The fourth and fifth methodological advancements are to explicitly account for the zero emissions commitment (ZEC; Section 5.5.2.2.4) \r\nand adjust estimates for Earth system feedbacks that are typically not represented in Earth system models (ESMs; Section 5.5.2.2.5). \r\nThe central estimate of the assessed ZEC used in SR1.5 and AR6 is zero (Section 4.7.1.1). ZEC uncertainties are reported separately \r\n(Table 5.8), and the additional consideration of ZEC therefore does result in a better understanding but not in a net shift of central \r\nestimates of the remaining carbon budget compared to AR5. Furthermore, AR5 did not explicitly account for Earth system feedbacks \r\nnot represented in ESMs. The SR1.5 assessed that they could reduce the remaining carbon budgets by about 100 GtCO2 over centennial \r\ntime scales. This assessment has been updated in AR6, including a wider range of biogeochemical feedbacks and new evidence \r\n(Section 5.5.2.2.5)"


id: e995d21e320cf3963de4203266bc73aa
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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" Some of these feedbacks are captured in the estimation of non-CO2 warming (see below), while the combined \r\neffect of remaining positive and negative feedbacks is assessed to reduce the remaining carbon budget estimates by 7 +- 27 PgC K-1\r\n(1-sigma range, or 26 +- 97 GtCO2 degC-1) compared to AR5."


id: 560e814451504f7d66524b5e9d45428f
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 11030
content:
"For historical warming, SR1.5 used GSAT increase between 1850-1900 and 2006-2015 of 0.97degC as its main starting point, while \r\nalso providing values for other temperature metrics. Remaining carbon budgets were expressed starting from 1 January 2018 by \r\naccounting for historical emissions emitted from 1 January 2011 until the end of 2017. AR6 uses anthropogenic (human-induced) \r\nwarming until the 2010-2019 period, which is assessed at the 0.8-1.3degC range, with a best estimate of 1.07degC (Table 3.1), and \r\nsubsequently accounts for historical emissions from 1  January 2015 until the end of 2019 to express remaining carbon budget \r\nestimates from 1 January 2020 onwards. The human-induced warming between the 1850-1900 and 2006-2015 periods used in SR1.5 \r\nwas assessed by AR6 at 0.97degC (Table 3.1)"


id: de0c291f7dcb9cddf7fb6bee317fd790
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 32434
content:
" In a like-with-like comparison, the combined effect of data and methodological updates \r\nin historical warming estimates thus results in no shift in estimated remaining carbon budgets between SR1.5 and AR6. However, the \r\nemissions of the years passed since SR1.5 reduce the remaining carbon budget by about 85 GtCO2. Note that AR6 also updated its \r\nGSAT assessment for total warming between the 1850-1900 and 2006-2015 periods, reporting 0.94degC of warming. On a like-with\u0002like basis, this would have resulted in slightly larger remaining carbon budgets compared to SR1.5 (Cross-Chapter Box 2.3)."


id: db23304e64caaae6ba5566a1c2d8466c
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 31991
content:
"The non-CO2 contribution to future warming in emissions scenarios (Huppmann et al., 2018) is re-assessed with AR6-calibrated \r\nemulators, in this case MAGICC7 (Cross-Chapter Box 7.1; Meinshausen et al., 2009, 2011a, 2020). The re-assessment of non-CO2\r\nwarming with MAGICC7 results in a relationship that closely matches the average relationship applied in SR1.5 (shown in Section 2.\r\nSM.1.1.2 in Forster et al., 2018), and therefore does not change estimates of the remaining carbon budget relative to SR1.5. The \r\nmedian ZEC assessment remained the same between SR1.5 and AR6, and therefore does not change the median remaining carbon \r\nbudget estimates. Finally, as indicated above, AR6 expanded the assessment of Earth system feedbacks compared to SR1.5 and \r\nincluded it in its central remaining carbon budget estimates. Some feedbacks are accounted for through the non-CO2 warming \r\nestimate (Section 5.5.2.2"


id: 7cf1c8cc8567e2a00ed233bfe5216a64
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 17255
content:
"5), while the remainder combines to reduce the median remaining carbon budget estimates for 1.5degC and \r\n2degC of warming by about 10 to 20 GtCO2, respectively, compared to SR1.5."


id: 7b5b67cd551c00667900a104c566f0d3
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 16996
content:
"This subsection assesses evidence about the response of the global \r\ncarbon cycle to CDR from idealized model simulations which \r\nassume that CO2 is removed from the atmosphere directly and \r\nstored permanently in the geologic reservoir, which is analogous to \r\ndirect air carbon capture with carbon storage (DACCS) (Table 5.9). \r\nThe carbon cycle response to specific land and ocean-based CDR \r\nmethods is assessed in Section  5.6.2.2.2. At the time of AR5 \r\nthere were very few studies about the global carbon cycle response \r\nto CDR. Based on these studies and general understanding of the \r\ncarbon cycle, AR5 WGI Chapter 6 assessed that it is virtually certain\r\nthat deliberate removal of CO2 from the atmosphere will be partially \r\noffset by outgassing of CO2 from the ocean and land carbon sinks. \r\nLow confidence was placed on any quantification of effects"


id: 737a5cdc29e2d3e148931e67e008f98c
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 15790
content:
" Since \r\nAR5 WGI Chapter 6, several studies have investigated the carbon \r\ncycle response to CDR in idealized 'pulse' removal simulations, \r\nwhereby a specified amount of CO2 is removed instantly from the \r\natmosphere, and scenario simulations with CO2 emissions and \r\nremovals following a plausible trajectory. In addition, a dedicated \r\ncarbon dioxide removal model intercomparison project was \r\ninitiated (CDRMIP; Keller et al., 2018b) which includes a  range \r\nof CDR experiments from idealized simulations to simulations of "


id: 96728f5d22665926a0a7fab2e83e55d4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 21189
content:
"then rebounds (Figure  5.32a). This rebound is due to CO2 release \r\nby the terrestrial biosphere and the ocean in response to declining \r\natmospheric CO2 levels (Figure 5.32b,c; M. Collins et al., 2013). For \r\nthe model simulations shown in Figure 5.32, 23 +- 6% (mean +- 1 \r\nstandard deviation) of the 100  PgC removed remains out of the \r\natmosphere 80-100  years after the instantaneous removal. The \r\nremainder is offset by CO2 outgassing from the land (49 +-  12%) \r\nand ocean (29 +- 7%). While the direction of the CO2 flux is robust \r\nacross models, the relative contribution of the outgassing from land \r\nand ocean reservoirs to the atmospheric CO2 rebound after removal \r\nvaries. These results corroborate the high confidence placed by AR5 \r\nWGI (Chapter 6) on the partial compensation of CO2 removal from \r\nthe atmosphere by CO2 outgassing from the land and ocean"


id: b63fa3460e2e1b53049b0d3e79af26dd
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 26201
content:
" Due \r\nto disagreement between models, the magnitude of this outgassing \r\nand the relative contribution of land and ocean fluxes remains \r\nlow confidence."


id: 4034fed5105c8e3593d247e0fe15db36
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 7476
content:
"Since AR5 WGI (Chapter  6), studies with ESMs have explored \r\nthe land and ocean carbon sink response to scenarios with CO2\r\nemissions gradually declining during the 21st century. As CDR \r\nand other mitigation activities are ramped up, CO2 emissions in \r\nthese scenarios reach net zero and, as removals exceed emissions, \r\nbecome net negative. Studies exploring the carbon sink response \r\nto such scenarios (e.g., RCP2.6, SSP1-2.6) show that, when net CO2\r\nemissions are positive but start to decline, uptake of CO2 by the land \r\nand ocean begins to weaken (compare land and ocean CO2 fluxes \r\nin panels a and b of Figure 5.33; Tokarska and Zickfeld, 2015; Jones \r\net al., 2016b). During the first decades after CO2 emissions become \r\nnet negative, both the ocean and land carbon sinks continue to take \r\nup CO2, albeit at a lower rate. For the land carbon sink, the sink-to\u0002source transition occurs decades to a  century after CO2 emissions \r\nbecome net negative (Figure 5.33c)"


id: 373116eb5832d3c365f98d15c1332471
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 6069
content:
" The ocean remains a sink of CO2\r\nfor centuries after emissions become net negative (Figure 5.33c-e; \r\nSection 5.4.9; Figure 5.30). Whether the transition to source occurs \r\nat all, the timing of the transition and the magnitude of the CO2\r\nsource are determined by the magnitude of the removal and the rate \r\nand amount of net CO2 emissions prior to emissions becoming net \r\nnegative (medium confidence) (Tokarska and Zickfeld, 2015; Jones \r\net al., 2016b). For scenarios with large amounts of CO2 removal, such \r\nas SSP5-3.4-overshoot, the land source is larger than for SSP1-2.6 \r\nand the ocean also turns into a source (Section 5.4.10, Figure 5.30). \r\nWhile the qualitative response to scenarios with net-negative \r\nemissions is largely robust across models, the timing of the sink-to\u0002source transition and the magnitude of the CO2 source vary between \r\nmodels, particularly for the land sink"


id: d4e4067b42492e0ceb13898dd661350f
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 31021
content:
" Due to low agreement between \r\nmodels, there is low confidence in the timing of the sink-to-source \r\ntransition and the magnitude of the CO2 source in scenarios with \r\nnet-negative CO2 emissions."


id: 6e8658387a19d3f77fd96c7a95f21953
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 15019
content:
"It is well understood that land and ocean carbon fluxes are sensitive \r\nto the level of atmospheric CO2 and climate change and differ under \r\nvaried future scenarios (Section  5.4). It is therefore important to \r\nestablish to what extent the removal effectiveness of CDR -  here \r\ndefined as the fraction of total CO2 removed that remains out of \r\nthe atmosphere -  is dependent on the scenario in which CDR is \r\napplied. Different metrics have been proposed to quantify the \r\nremoval effectiveness of CDR (Tokarska and Zickfeld, 2015; Jones \r\net al., 2016b; Zickfeld et al., 2016). One is the airborne fraction (AF) \r\nof cumulative CO2 emissions, defined in the same way as for positive \r\nemissions (i.e., as the fraction of total CO2 emissions remaining in the \r\natmosphere), with its use extended to periods of declining and net \r\nnegative CO2 emissions"


id: 7afcb79200575bdd3c432ca1cb1d2e0b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 16940
content:
" This metric, however, has not proven to be \r\nuseful to quantify the removal effectiveness of CDR in simulations \r\nwhere CDR is applied from a trajectory of increasing atmospheric CO2\r\nconcentration. This is because it measures the carbon cycle response \r\nto CDR as well as to the prior atmospheric CO2 trajectory (Tokarska \r\nand Zickfeld, 2015; Jones et al., 2016b). A  more useful metric is \r\nthe perturbation airborne fraction (PAF; Jones et al., 2016b), which \r\nmeasures the AF of the perturbation (in this case the CO2 removal) \r\nrelative to a reference scenario (Tokarska and Zickfeld, 2015; Jones \r\net al., 2016b). The advantage of this metric is that it isolates the \r\nresponse to a CO2 removal from the response to atmospheric CO2\r\nprior to when the removal is applied. A disadvantage is that the PAF \r\ncannot be calculated from a  single model simulation, but instead "


id: 5c3c6fe61da94dd6d43f4e6eeb8eef10
created_at: 2023-03-16 14:02:01
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vector_id: 12028
content:
"In scenario simulations and idealized simulations with instantaneous \r\nCO2 removals applied from an equilibrium state, the removal \r\neffectiveness of CDR is found to be slightly dependent on the rate \r\nand amount of CDR (Tokarska and Zickfeld, 2015; Jones et al., \r\n2016b; Zickfeld et al., 2021), and to be strongly dependent on the \r\nemissions scenario from which CDR is applied (Jones et al., 2016b; \r\nZickfeld et al., 2021). The fraction of CO2 removed remaining out of \r\nthe atmosphere decreases slightly for larger removals and decreases \r\nstrongly when CDR is applied from a lower background atmospheric \r\nCO2 concentration (Figure  5.34), due to state dependencies and \r\nclimate-carbon cycle feedbacks that lead to a  stronger overall \r\nresponse to CO2 removal (Zickfeld et al., 2021)"


id: bd24ee0ffb424763dc234a4fa13043c1
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 27326
content:
" Based on the high \r\nagreement between studies, we assess with medium confidence that \r\nthe removal effectiveness of CDR is only slightly dependent on the \r\nrate and magnitude of removal and is smaller at lower background \r\natmospheric CO2 concentrations. Simulations with Earth system \r\nmodels of intermediate complexity (EMIC) with instantaneous CO2\r\nremoval from different equilibrium initial states suggest that the \r\nsmaller removal effectiveness of CDR at lower background CO2\r\nlevels results in greater cooling per unit CO2 removed (Zickfeld et al., \r\n2021). However, there is low confidence in the robustness of this \r\nresult as climate sensitivity has been shown to exhibit opposite state \r\ndependence in EMICs and ESMs (Section 7.4.3.1)."


id: 4d93342d949b5e9ee98d7e7669b0e759
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 9635
content:
"in model simulations with positive and negative CO2 emission pulses \r\napplied from a pre-industrial climate state. Simulations with seven \r\nCMIP6 ESMs and the UVic Earth System Climate Model (ESCM) of \r\nintermediate complexity suggest that the carbon cycle response is \r\nasymmetric for pulse emissions/removals of +-100 PgC (Figure 5.35). \r\nFor all models, the fraction of CO2 remaining in the atmosphere \r\nafter an emission is larger than the fraction of CO2 remaining out \r\nof the atmosphere after a  removal (by 4  +- 3%; mean +-  standard \r\ndeviation). In other words, an emission of CO2 into the atmosphere \r\nis more effective at raising atmospheric CO2 than an equivalent \r\nCO2 removal is at lowering it. Sensitivity experiments with the UVic \r\nESCM suggest that the asymmetry increases for larger amounts of \r\nemissions/removals and is insensitive to the background atmospheric \r\nCO2 concentration from which the emissions/removals are applied \r\n(Figure  5.35)"


id: 14c9940a2680f0cb1f66382453ab346
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 779
content:
" This asymmetry in the atmospheric CO2 response \r\noriginates from asymmetries in the land and ocean carbon fluxes \r\ndue to non-linearities in the carbon cycle response to CO2 and \r\ntemperature (Section  5.4) (Zickfeld et al., 2021). Given medium \r\nevidence and high agreement, there is medium confidence in the \r\nsign of the asymmetry of the carbon cycle response to positive and \r\nnegative CO2 emissions. The sign of the symmetry of the temperature \r\nresponse differs between models, with three out of seven examined \r\nESMs showing a smaller temperature response to a 100 PgC emission \r\nthan to an equivalent CO2 removal. Therefore, there is low confidence\r\nin the sign of the asymmetry of the temperature response to positive \r\nand negative CO2 emissions."


id: c7a5b15b29799d4d9ddf4b0415bd4bcf
created_at: 2023-03-16 14:02:01
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vector_id: 28992
content:
"y\r\nThe AR5 WGI (Chapter 6) discussed the CDR methods, their implications \r\nand unintended side effects on carbon cycle and climate, including \r\ntheir time scales and potentials. Since then, three IPCC special \r\nreports (SR) have been published. First, SR1.5 Chapter  4  (IPCC, \r\n2018) assessed the potentials and current understanding, including \r\nthe side effects, of bioenergy with carbon capture and storage \r\n(BECCS), afforestation/reforestation, soil carbon sequestration, \r\nbiochar, enhanced weathering, ocean alkalinization, direct air carbon \r\ncapture with storage (DACCS) and ocean fertilization. Second, SRCCL \r\nChapter  6  (IPCC, 2019a) assessed the potentials, co-benefits and \r\ntrade-offs of land-based mitigation options. It assessed with high \r\nconfidence that land-based CDR options do not sequester carbon \r\nindefinitely, except for peatland restoration"


id: 94e38f92f3e391e2cbe800bf97b7b53e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 20935
content:
" Multiple co-benefits \r\nwere identified in the deployment of CDR options, many of them \r\nwith a  potential to make positive contributions to sustainable \r\ndevelopment, enhancement of ecosystem functions and services \r\nand other societal goals. However, their potential was concluded to \r\nbe context specific, and limits were identified in their contribution \r\nto global mitigation, such as competition for land. The third report, \r\nthe Special Report on Ocean and the Cryosphere in a  Changing \r\nClimate (SROCC) Chapter 5 (IPCC, 2019b), assessed the potential of \r\nmarine options for climate change mitigation. It concluded that the \r\nfeasibility of open ocean fertilization and alkalinization approaches \r\nwere negligible, due to their inconclusive influence on ocean carbon \r\nstorage on long time scales, due to the unintended side effects on \r\nmarine ecosystems, and the associated governance challenges"


id: 8f1c18b86b120e713e524a959f252782
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 20054
content:
" The \r\nassessment of blue carbon ecosystems concluded that they could \r\ncontribute only minimally to atmospheric CO2 reduction globally, \r\nbut emphasized that the benefits of protecting and restoring coastal \r\nblue carbon extend beyond climate change mitigation (SROCC \r\nSection 5.5.12)."


id: 920efdcff7c11ae302e84f4b8768fc0c
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 20503
content:
"A range of mechanisms could enhance CO2 sequestration of forest\u0002based methods under future scenarios, including CO2 fertilization, \r\nsoil carbon enrichment due to enhanced litter input, or the \r\nnorthward shift of the tree line in future climate projection (Bathiany \r\net al., 2010; Sonntag et al., 2016; Boysen et al., 2017b; Harper et al., \r\n2018). There is low confidence in the net direction of feedbacks of \r\nafforestation on global mean temperature. The feedbacks are highly \r\nregion dependent. For instance, afforestation at high latitudes \r\nwould decrease albedo and increase local warming, while at low \r\nlatitudes,  the cooling effect of enhanced evapotranspiration could \r\nexceed the warming effect due to albedo decrease (Pearson et al., \r\n2013; W. Zhang et al., 2013; Jia et al., 2019, SRCCL, Section 2.6.1). \r\nBoth afforestation and reforestation affect the hydrological cycle \r\nthrough increased volatile organic compound (VOC) emissions and \r\ncloud albedo (Teuling et al"


id: 9d4bed0c9e8de191edc99bef08e1b9c0
created_at: 2023-03-16 14:02:01
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vector_id: 22275
content:
", 2017; Kalliokoski et al., 2020), enhanced \r\nprecipitation (Ellison et al., 2017) and increased transpiration, with \r\npotential effects on runoff and, especially in dry regions, on water \r\nsupply (Figure 5.36 and Cross-Chapter Box 5.1; Farley et al., 2005; \r\nSmith et al., 2016; Krause et al., 2017; Teuling et al., 2019). Forest\u0002based methods can either raise or lower N2O emissions, depending \r\non tree species, previous land use, soil type and climatic factors \r\n(low confidence) (Figure 5.36 and Supplementary Materials, Table 5.\r\nSM.4; Benanti et al., 2014; Chen et al., 2019; McDaniel et al., 2019). \r\nAfforestation will decrease biodiversity if native species are replaced \r\nby monocultures (high confidence), while there is medium confidence\r\nthat biodiversity is improved when forests are introduced into land \r\nareas with degraded soils or intensive monocultures, or where \r\nnative species are re-introduced into managed land (Figure  5.36, \r\nSupplementary Materials Table 5.SM"


id: 6937d6854df919170e12d3678f12ee98
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 14143
content:
"4; Hua et al., 2016; Williamson \r\nand Bodle, 2016; P. Smith et al., 2018; Holl and Brancalion, 2020)."


id: 2dc711760c22c4b0f3c73dd803c376cc
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 4640
content:
"Peatlands are less extensive than forests, croplands and grazing \r\nlands, yet per unit area, they hold high carbon stocks (Griscom et al., \r\n2017). Peatland restoration relies on back-conversion or building \r\nof high-carbon-density soils through flooding -  that is, rewetting \r\n(Leifeld et al., 2019). High water level and anoxic conditions are \r\nprerequisites for restoring by returning drained and/or degraded \r\npeatlands back to their natural state as CO2 sinks, but restoration \r\nalso results in enhanced CH4 emissions which are similar to or higher \r\nthan the pre-drainage fluxes (high confidence) (Koskinen et al., 2016; \r\nWilson et al., 2016a; Hemes et al., 2019; Renou-Wilson et al., 2019; \r\nHoll et al., 2020). In a  multi-decadal time frame, the reduction in \r\nCO2 emissions from rewetting more than compensates for the \r\ninitial increase in radiative forcing due to enhanced CH4 emissions \r\n(Gunther et al., 2020)"


id: da9905499a747ea86ebee9c30bfda6aa
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 31920
content:
" Rewetting drained peatlands will decrease \r\nN2O emissions (medium confidence) (Wilson et al., 2016b; H.  Liu \r\net al., 2020; Tiemeyer et al., 2020). Restored wetlands and peatlands \r\nact as buffer zones that provide infiltration and nutrient retention \r\nand offer protection to water quality (Daneshvar et al., 2017; Lundin \r\net al., 2017), particularly in nutrient-loaded agricultural catchments. \r\nPeatland restoration can also recover much of the original biodiversity \r\n(medium confidence) (Meli et al., 2014; P. Smith et al., 2018)."


id: 6692f9b8e32ec6ac1e7f511f05fb7448
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 13724
content:
"Soil carbon losses from human agriculture accounted for about \r\n116 PgC in the last 12,000 years (Section 5.2.1.2; Sanderman et al., \r\n2017). With best management practices, two-thirds of these losses \r\nmay be recoverable, setting a  theoretical maximum of 77  PgC \r\nthat can be sequestered in soils. Methods to increase soil carbon \r\ncontent may be applied to the restoration of marginal or degraded \r\nland (Paustian et al., 2016; Smith, 2016), but may also be used in \r\ntraditional agricultural lands. A  simple practice is to increase the \r\ninput of carbon to the soil by selecting appropriate varieties or \r\nspecies with greater root mass (Kell, 2011) or higher yields and net \r\nprimary production (NPP) (Burney et al., 2010). In addition, improved \r\nagricultural practices also increase soil carbon content. These \r\ninclude  using crop  rotation cycles, increasing the amount of crop \r\nresidues, using crop cover to prevent periods of bare soil (Poeplau \r\nand Don, 2015; Griscom et al"


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vector_id: 13051
content:
", 2017), optimizing grazing (Henderson \r\net al., 2015) and residue management (Wilhelm et al., 2004), using \r\nirrigation (Campos et al., 2020), employment of low-tillage or no\u0002tillage (W. Sun et al., 2020), agroforestry, cropland nutrient recycling, \r\nand avoiding grassland conversion (Paustian et al., 2016; Fargione \r\net al., 2018). With medium confidence, methods that seek soil \r\ncarbon sequestration will diminish nitrous oxide (N2O) emissions and \r\nnutrient leaching, and improve soil fertility and biological activity \r\n(Figure  5.36; Tonitto et al., 2006; Fornara et al., 2011; Paustian \r\net al., 2016; Smith et al., 2016; SRCCL, Section 2.6.1.3, IPCC, 2019a). \r\nHowever, if improved soil carbon sequestration practices involve \r\nhigher fertilization rates, N2O emissions would increase (Gu et al., \r\n2017). Some soil carbon sequestration methods, such as cover crops \r\nand crop diversity, can increase biodiversity (medium confidence) \r\n(Paustian et al., 2016; P. Smith et al., 2018)."


id: eee2d4593c39dc0a084c5d1fcff3286e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 35059
content:
"The concept of BECCS rests on the premise that bioenergy production \r\nis carbon neutral - that is, as much CO2 is sequestered when growing \r\nbiomass as feedstock as is released by its combustion. If these \r\nemissions are also captured and stored, the net effect is removal of \r\nCO2 from the atmosphere (Fuss et al., 2018). Sequestration potentials \r\nfrom BECCS depend strongly on the feedstock, climate, and \r\nmanagement practices (Beringer et al., 2011; Kato and Yamagata, \r\n2014; Heck et al., 2016; Smith et al., 2016; Krause et al., 2017). \r\nIf woody bioenergy plants replace marginal land, net carbon uptake \r\nincreases, enriching soil carbon (Don et al., 2012; Heck et al., 2016; \r\nBoysen et al., 2017a, b). However, replacing carbon-rich ecosystems \r\nwith herbaceous bioenergy plants could deplete soil-carbon stocks \r\nand reduce the additional sink capacity of standing forests (Don et al., \r\n2012; Harper et al., 2018)"


id: e987cda2af35a66a6ae9b782b0d93d98
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 34250
content:
" Furthermore, wood-based BECCS may not \r\nbe carbon negative in the first decades, initially emitting more CO2\r\nthan sequestering (Sterman et al., 2018). BECCS has several trade\u0002offs to deal with, including possible threats to water supply and soil \r\nnutrient deficiencies (medium confidence) (SRCCL Chapters 2 and 6, \r\nand Cross-Chapter Box 5.1; Smith et al., 2016; Krause et al., 2017; de \r\nConinck et al., 2018; Heck et al., 2018; Roy et al., 2018). Deployment \r\nof BECCS at the scales envisioned by many 1.5degC-2.0degC mitigation \r\nscenarios could threaten biodiversity and require large land areas, \r\ncompeting with afforestation, reforestation and food security \r\n(Anderson and Peters, 2016; P. Smith et al., 2018). Additional risks \r\nand side effects are related to geologic carbon storage (Fuss et al., \r\n2018; see also Section 5.6.2.2.4)."


id: 55222e456b8036d1eec46670fae23003
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 10853
content:
"Biochar is produced by burning biomass at high temperatures under \r\nanoxic conditions (pyrolysis) and, when added to soils, can increase \r\nsoil carbon stocks and fertility for decades to centuries (Woolf et al., \r\n2010; Lehmann et al., 2015). Biochar application improves many soil \r\nqualities and increases crop yield (medium confidence) (Ye et al., 2020; \r\nSRCCL, Chapter 4.9.5), particularly in already degraded or weathered \r\nsoils (Woolf et al., 2010; Lorenz and Lal, 2014; Jeffery et al., 2016), \r\nincreases soil water holding capacity (medium confidence) (Karhu \r\net al., 2011; Liu et al., 2016; B.M.C. Fischer et al., 2019; Verheijen et al., \r\n2019) and evapotranspiration (low confidence) (B.M.C. Fischer et al., \r\n2019). The use of biochar reduces nutrient losses (low confidence) \r\n(Woolf et al., 2010), enhances fertilizer nitrogen use efficiency and \r\nimproves the bioavailability of phosphorus (Figure  5.36; Clough \r\net al., 2013; Shen et al., 2016; Z. Liu et al., 2017)"


id: f94fb502d2e32dbd7c0209566daed6c9
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 36733
content:
" Biochar addition \r\nmay decrease methane (CH4) emissions in inundated and acid soils \r\nsuch as rice fields (low confidence) (Jeffery et al., 2016; Huang et al., \r\n2019; S. Wang et al., 2019; S. Yang et al., 2019). In non-inundated, \r\nneutral soils, CH4 uptake from the atmosphere is suppressed after \r\nbiochar application (low confidence) (Jeffery et al., 2016), and soil \r\nN2O emissions decline (medium confidence) (Cayuela et al., 2014; \r\nKammann et al., 2017). The potential risks of introducing harmful \r\ncontaminants into the soil environment are not well understood \r\n(Lorenz and Lal, 2014). With low confidence, application of biochar \r\ncan have co-benefits for soil microbial biodiversity (P. Smith et al., \r\n2018), while the potential trade-offs for biodiversity are due to land \r\nrequirements (Tisserant and Cherubini, 2019)."


id: c4bb2f53de9dc436e668c6ede34cf146
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 28524
content:
"Both ocean biological and physical processes drive the CO2 exchange \r\nbetween the ocean and atmosphere. However, the ocean physical \r\nprocesses that remove CO2 from the atmosphere, such as large\u0002scale circulation, cannot be feasibly altered, so ocean CDR methods \r\nfocus on increasing the productivity of ocean ecosystems, and \r\nsubsequent sequestration of carbon (GESAMP, 2019). There has \r\nbeen no change to the assessment of SROCC (SROCC Section 5.5.1): \r\nthere is low confidence that nutrient addition to the open ocean, \r\neither through artificial ocean upwelling or iron fertilization, could \r\ncontribute to climate change mitigation, due to its inconclusive \r\neffect on carbon sequestration and risks of adverse side effects on \r\nmarine ecosystems (Figure 5.36, Table 5.9; Supplementary Materials \r\nText 5.SM.3 and Table 5.SM.4; AR6 WGIII Chapter 12; Gattuso et al., \r\n2018; Boyd and Vivian, 2019; Feng et al., 2020)"


id: 3c389e3d7a792b85827ce681eb24077e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 6839
content:
" In addition, ocean \r\nfertilization is currently prohibited by the London Protocol (Dixon \r\net al., 2014; GESAMP, 2019)."


id: da88de43a489ba06763a494044beb45a
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 31911
content:
"Enhanced weathering (EW) is based on naturally occurring \r\nweathering processes of silicate and carbonate rocks, removing CO2\r\nfrom the atmosphere. Weathering is accelerated by spreading ground \r\nrocks on soils, coasts or oceans. EW increases the alkalinity and pH \r\nof natural waters, helps dampen ocean acidification and increases \r\nocean carbon uptake (Beerling et al., 2018). The dissolution of \r\nminerals stimulates biological productivity of croplands (Hartmann \r\net al., 2013; Beerling et al., 2018), but can also liberate toxic trace \r\nmetals (such as nickel, chromium, copper) into soil or water bodies \r\n(Keller et al., 2018a; Strefler et al., 2018). EW can also contribute \r\nto freshwater salinization as a  result of increased salt inputs and \r\ncation exchange in watersheds, and so adversely affecting drinking \r\nwater quality (low confidence) (Kaushal et al., 2018). With a medium \r\nconfidence, amendment of soils with minerals will have lower N2O \r\nemissions (Kantola et al"


id: 266b4f370970c7fd88dc3fc6b280b55d
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 3456
content:
", 2017; Blanc-Betes et al., 2020) but will not \r\nhave a marked effect on evapotranspiration or albedo (Fuss et al., \r\n2018; de Oliveira Garcia et al., 2020). The mining of minerals can \r\ncause adverse impacts on biodiversity, however, the use of waste \r\nmaterials such as concrete demolition or steel slags for EW can reduce \r\nthe need for mining (Renforth, 2019). The spreading of minerals on \r\nland has a neutral impact on biodiversity (P. Smith et al., 2018)."


id: 95cc95f777359b8f3f13166c2043e783
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 21070
content:
"Coastal wetlands and seagrass meadows store significant amounts \r\nof carbon and are among the most productive ecosystems per unit \r\narea (Griscom et al., 2017, 2020; Ortega et al., 2019; Serrano et al., \r\n2019). These rates could be reduced in the future, since these habitats \r\nare vulnerable to changing conditions, such as temperature, salinity, \r\nsediment supply, storm severity and continued coastal development \r\n(Bindoff et al., 2019; NASEM, 2019). These ecosystems are under \r\nthreat from anthropogenic conversion and degradation and are being \r\nlost at rates between 0.7% and 7% per annum with consequent CO2\r\nemissions (e.g., Atwood et al., 2017; Howard et al., 2017; Hamilton \r\nand Friess, 2018; Sasmito et al., 2019). Although sea level rise might \r\nlead to greater carbon sequestration in coastal wetlands (Rogers \r\net al., 2019), there is high confidence that the frequency and intensity \r\nof marine heatwaves will increase (Cross-Chapter Box 9"


id: da8f5f2f3e43bdbc307b447aa7c3a1f0
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 31913
content:
"Direct air carbon capture with carbon storage (DACCS) is a combination \r\nof two techniques, direct capture of CO\r\n2 from ambient air (DAC) and \r\ncarbon storage. DAC entails contacting the air, capturing the CO\r\n2\r\non a liquid solvent or solid sorbent, and regenerating the solvent or \r\nsorbent. Different DAC methods have been proposed, which differ \r\nby the chemical process used to capture the CO\r\n2 and to recover it \r\nfrom the sorbent or solvent (NASEM, 2019). The captured CO\r\n2 may \r\nbe either stored geologically as a high-pressure gas or sequestered \r\nby a mineral carbonation process. Storage is potentially permanent \r\nin both pressurised gas and mineral form (Fuss et al., 2018). DACCS \r\nhas significant requirements of energy and, (depending on the type \r\nof technology), water and materials (Smith et al., 2016; NASEM, \r\n2019). Compared to other CDR methods, it has a small land footprint \r\n(Smith et al., 2016; NASEM, 2019)"


id: 6a82ba02f7a94a1c1932916d85f89307
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 14374
content:
" Side effects of DACCS include \r\nCO\r\n2-depleted air leaving the air contactor, which could have adverse \r\neffects on crop and ecosystem productivity, and VOC emissions \r\n(NASEM, 2019). Additional risks and side effects are related to the \r\nhigh pressure at which CO\r\n2 is stored in geologic formations (Fuss \r\net al., 2018). DACCS is assessed in detail in WGIII Chapter 12."


id: ce6547f4197232150bc52b67b25302b1
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 30006
content:
"SRM-mediated sunlight changes directly affect the carbon cycle. In \r\nparticular, SAI would reduce the sunlight reaching the Earth's surface, \r\nbut also increase the fraction of sunlight that is diffuse. These changes \r\nin the quantity and quality of the sunlight have opposing effects \r\non the photosynthesis of land plants. On their own, reductions in \r\nphotosynthetically active radiation (PAR) will reduce photosynthesis. \r\nHowever, diffuse light is more effective than direct light in accessing \r\nthe light-limited leaves within plant canopies, leading to the so-called \r\n'diffuse-radiation' fertilization effect (Mercado et al., 2009). The \r\nestimated balance between the negative impacts of reducing PAR \r\nand the positive impacts of increasing diffuse fraction differ between \r\nmodels (Kalidindi et al., 2015; Xia et al., 2016; C.-E. Yang et al., 2020) \r\nand across different ecosystems"


id: 52ad0bc06c84db42d61e4b8478ec87f0
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 10467
content:
" The change in the absolute amount \r\nof direct and diffuse radiation could also depend on the height of \r\nthe additional sulphate aerosol layer in the stratosphere and the \r\nhygroscopic growth of aerosols (Krishnamohan et al., 2019, 2020). "


id: b6530309351e3a72520b1d4f64a77a2e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 26213
content:
"SRM-mediated cooling also affects the terrestrial carbon cycle. \r\nRelative to a high-GHG world without SRM, the simulated responses \r\nof net primary production (NPP) to SRM differ widely between \r\nmodels, such that even the sign of global mean change is uncertain \r\n(Glienke et al., 2015). SRM-induced cooling would decrease NPP at \r\nhigh latitudes by reducing the length of the growing season (Glienke \r\net al., 2015). At low latitudes, the NPP response to SRM-induced \r\ncooling is sensitive to the effect of nitrogen limitation (Glienke et al., \r\n2015; Duan et al., 2020). SRM-induced cooling tends to increase NPP \r\nin models without the nitrogen cycle because of reduced heat stress. \r\nHowever, in models including the nitrogen cycle, this is counteracted \r\nby reductions in NPP because of reductions in nitrogen mineralization \r\nand nitrogen availability (Glienke et al., 2015). SRM-induced \r\nchanges in the hydrological cycle (Section 8.6"


id: ede1f9aabf0db6ea1b8a33b3d2aa9da4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 34896
content:
"3), including changes \r\nin evapotranspiration, precipitation, and soil moisture, also pose \r\nstrong constraints on the vegetation response (Dagon and Schrag, \r\n2019). For the same amount of global mean cooling, different SRM \r\noptions, such as SAI, MCB, and CCT, would have different effects on \r\ngross primary production (GPP) and NPP because of different spatial \r\npatterns of temperature, available sunlight and hydrological cycle \r\nchanges (Section 4.6.3.3) (Duan et al., 2020). Modelling studies show \r\nthat SRM-induced cooling would reduce plant and soil respiration \r\n(Tjiputra et al., 2016; Cao and Jiang, 2017; Muri et al., 2018; \r\nC.-E. Yang et al., 2020). Despite the large uncertainty in modelled \r\nNPP response, existing modelling studies consistently show that SRM "


id: a31874174600d57c766abd654941a988
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 23222
content:
"Modelling studies consistently show that, relative to a  high-CO2 \r\nworld without SRM, SRM-induced cooling (Section  4.6.3.3) would \r\nreduce plant and soil respiration, and also reduce the negative \r\neffects of warming on ocean carbon uptake (Tjiputra et al., 2016; Xia \r\net al., 2016; Cao and Jiang, 2017; Jiang et al., 2018; Muri et al., 2018; \r\nSonntag et al., 2018; Plazzotta et al., 2019; C.-E. Yang et al., 2020). \r\nA multi-model study (Plazzotta et al., 2019) indicates that, relative \r\nto a  high-CO2 concentration world without SRM, stratospheric \r\nsulphur dioxide (SO2) injection increases the allowable CO2 emissions \r\nby enhancing CO2 uptake by both land and ocean (Figure  5.37). \r\nAs a result of enhanced global carbon uptake, SRM would reduce the \r\nburden of atmospheric CO2 (high confidence)"


id: 9b5c821d671c3488b2e84d39516bdbad
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 21990
content:
" However, the amount \r\nof SRM-induced reduction in atmospheric CO2 depends on the future \r\nemissions scenario and modelled oceanic and terrestrial carbon \r\nsinks, which differ widely between models (Tjiputra et al., 2016; Xia \r\net al., 2016; Cao and Jiang, 2017; Muri et al., 2018). Models that \r\ninclude the terrestrial nitrogen cycle usually report a much smaller \r\nreduction of atmospheric CO2 in response to SRM than models \r\nwithout the nitrogen cycle, mainly because nitrogen limitation leads \r\nto a weaker terrestrial carbon sink (Tjiputra et al., 2016; Muri et al., \r\n2018; C.-E. Yang et al., 2020). Large-scale application of SAI is found "


id: 623dfbdf425b0caec9a52af6a6df4ce6
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 13010
content:
"On land, it is mainly the vegetation that captures CO2\r\nfrom the atmosphere through plant photosynthesis, \r\nwhich ultimately accumulates both in vegetation and \r\nsoils. As more CO2 accumulates in the atmosphere, \r\nplant carbon capture increases through the CO2 \r\nfertilization effect in regions where plant growth is not \r\nlimited by, for instance, nutrient availability. Climate \r\nchange affects the processes responsible for the uptake \r\nand release of CO2 on land in multiple ways. Land CO2\r\nuptake is generally increased by longer growing seasons \r\ndue to global warming in cold regions and by nitrogen \r\ndeposition in nitrogen-limited regions. Respiration by \r\nplants and soil organisms, natural disturbances such \r\nas fires, and human activities such as deforestation all \r\nrelease CO2 back into the atmosphere. The combined \r\neffect of climate change on these processes is to weaken \r\nthe future land sink"


id: 8d09961ce0e159283bb9fe83842f6ee5
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 19714
content:
" In particular, extreme temperatures \r\nand droughts as well as permafrost thaw (see FAQ 5.2) \r\ntend to reduce the land sink regionally. In the ocean, "


id: 4bf101d368fd5cd0ca5fef68e37e6ac3
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 9372
content:
"While permafrost processes are complex, they are beginning to be included in models that represent the \r\ninteractions between the climate and the carbon cycle. The projections from these permafrost carbon models show \r\na wide range in the estimated strength of a carbon-climate vicious circle, from both carbon dioxide and methane, \r\nequivalent to 14-175 billion tonnes of carbon dioxide released per 1degC of global warming. By comparison, in \r\n2019, human activities have released about 40 billion tonnes of carbon dioxide into the atmosphere. This has two \r\nimplications. First, the extra warming caused by permafrost thawing is strong enough that it must be considered \r\nwhen estimating the total amount of remaining emissions permitted to stabilize the climate at a given level of \r\nglobal warming (i.e., the remaining carbon budget, see FAQ 5.4)"


id: 2d9368a0c8cf15952b73936aa85d45e7
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 4608
content:
" Second, the models do not identify any one \r\namount of warming at which permafrost thaw becomes a 'tipping point' or threshold in the climate system \r\nthat would lead to a runaway global warming. However, models do project that emissions would continuously \r\nincrease with warming, and that this trend could last for hundreds of years."


id: 2e5559b1524dedfbd7b1ba28bf38015
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 4731
content:
"Permafrost can also be found in other cold places (e.g., mountain ranges), but those places contain much less \r\ncarbon than in the Arctic. For instance, the Tibetan plateau contains about 3% as much carbon as is stored in \r\nthe Arctic. There is also concern about carbon frozen in shallow ocean sediments. These deposits are known as \r\nmethane hydrates or clathrates, which are methane molecules locked within a cage of ice molecules. They formed \r\nas frozen soils that were flooded when sea levels rose after the last ice age. If these hydrates thaw, they may \r\nrelease methane that can bubble up to the surface. The total amount of carbon in permafrost-associated \r\nmethane hydrates is much less than the carbon in permafrost soils. Global warming takes millennia to penetrate \r\ninto the sediments beneath the ocean, which is why these hydrates are still responding to the last deglaciation"


id: 4202bde1f398849edb099812cab3ec1b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 7790
content:
" \r\nAs a result, only a small fraction of the existing hydrates could be destabilised during the coming century. Even \r\nwhen methane is released from hydrates, most of it is expected to be consumed and oxidised into carbon dioxide \r\nin the ocean before reaching the atmosphere. The most complete modelling of these processes to date suggests \r\na release to the atmosphere at a rate of less than 2% of current human-induced methane emissions."


id: 20f5370f1b00c483fe3cf9a0646f78bc
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 2617
content:
"The term negative carbon dioxide (CO2) emissions\r\nrefers to the removal of CO2 from the atmosphere by \r\ndeliberate human activities, in addition to removals \r\nthat occur naturally, and is often used as synonymous \r\nwith carbon dioxide removal. Negative CO2 emissions \r\ncan compensate for the release of CO2 into the \r\natmosphere by human activities. They could be \r\nachieved by strengthening natural CO2 sequestration \r\nprocesses on land (e.g., by planting trees or through \r\nagricultural practices that increase the carbon content \r\nof soils) and/or in the ocean (e.g.,  by restoration of \r\ncoastal ecosystems) or by removing CO2 directly from \r\nthe atmosphere. If CO2 removals are greater than \r\nhuman-caused CO2 emissions globally, emissions are \r\nsaid to be net negative. It should be noted that CO2\r\nremoval technologies are unable, or not yet ready, to \r\nachieve the scale of removal that would be required to \r\ncompensate for current levels of emissions, and most \r\nhave undesired side effects"


id: aa34000b8eff2dc4c8988eda0b2aab3c
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 24315
content:
"If the CO2 concentration in the atmosphere starts to go down, the Earth's climate would respond to this change \r\n(FAQ 5.3, Figure 1). Some parts of the climate system take time to react to a change in CO2 concentration, so \r\na  decline in atmospheric CO2 as a  result of net negative emissions would not lead to immediate reversal of \r\nall climate change trends. Recent studies have shown that global surface temperature starts to decline within \r\na few years following a decline in atmospheric CO2, although the decline would not be detectable for decades \r\ndue to natural climate variability (see FAQ 4.2). Other consequences of human-induced climate change, such as \r\nreduction in permafrost area, would take decades; yet others, such as warming, acidification and oxygen loss \r\nof the deep ocean, would take centuries to reverse following a decline in the atmospheric CO2 concentration"


id: feb9feb2b12a90be322fa53b6c9873cd
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 37620
content:
" \r\nSea level would continue to rise for many centuries to millennia, even if large deliberate CO2 removals were \r\nsuccessfully implemented."


id: 6ab1f90343bf6864bacddbca29d54b82
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 14400
content:
"The concept of a  remaining carbon budget implies that, to stabilize global warming at any particular level, \r\nglobal emissions of CO2 need to be reduced to net zero levels at some point. 'Net zero CO2 emissions' describes \r\na  situation where all the anthropogenic emissions of CO2 are counterbalanced by deliberate anthropogenic \r\nremovals so that, on average, no CO2 is added or removed from the atmosphere by human activities. Atmospheric \r\nCO2 concentrations in such a situation would gradually decline to a long-term stable level as excess CO2 in the \r\natmosphere is taken up by ocean and land sinks (see FAQ 5.1). The concept of a remaining carbon budget also \r\nmeans that, if CO2 emissions reductions are delayed, deeper and faster reductions are needed later to stay within \r\nthe same budget"


id: 216e375170487aeeae165d1e062768e6
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 2685
content:
" If the remaining carbon budget is exceeded, this will result in either higher global warming \r\nor a need to actively remove CO2 from the atmosphere to reduce global temperatures back down to the desired \r\nlevel (see FAQ 5.3)."


id: 6a4d7db691bd51bd91db8f81fd4dff2d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 14324
content:
"Estimating the size of remaining carbon budgets depends on a set of choices. These choices include: (1) the global \r\nwarming level that is chosen as a limit (for example, 1.5degC or 2degC relative to pre-industrial levels); (2) the probability \r\nwith which we want to ensure that warming is held below that limit (for example, a one-in-two, two-in-three, \r\nor higher chance), and (3) how successful we are in limiting emissions of other greenhouse gases that affect the \r\nclimate, such as methane or nitrous oxide. These choices can be informed by science, but ultimately represent \r\nsubjective choices. Once these choices have been made, to estimate the remaining carbon budget for a given \r\ntemperature goal, we can combine knowledge about: how much our planet has warmed already; the amount \r\nof warming per cumulative tonne of CO2; and the amount of warming that is still expected once global net \r\nCO2 emissions are brought down to zero. For example, to limit global warming to 1"


id: 49f6f2f828a055e88981ff515fb60b3e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 9044
content:
"5degC above pre-industrial \r\nlevels with either a  one-in-two (50%) or two-in-three (67%) chance, the remaining carbon budgets amount \r\nto 500 and 400 billion tonnes of CO2, respectively, from 1 January 2020 onward (FAQ 5.4, Figure 1). Currently, \r\nhuman activities are emitting around 40 billion tonnes of CO2 into the atmosphere in a single year."


id: cc2722e3f11a2da0032fabf0e6ecf596
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 29661
content:
"The remaining carbon budget depends on how much the world has already warmed to date. This past warming \r\nis caused by historical emissions, which are estimated by looking at the historical carbon budget - a scientific way \r\nto describe all past and present sources and sinks of CO2. It describes how the CO2 emissions from human activities \r\nhave redistributed across the various CO2 reservoirs of the Earth system. These reservoirs are the ocean, the land \r\nvegetation, and the atmosphere (into which CO2 was emitted). The share of CO2 that is not taken up by the \r\nocean or the land, and that thus increases the concentration of CO2 in the atmosphere, causes global warming. \r\nThe historical carbon budget tells us that, of the about 2560 billion tonnes of CO2 that were released into the \r\natmosphere by human activities between the years 1750 and 2019, about a quarter were absorbed by the ocean \r\n(causing ocean acidification) and about a third by the land vegetation"


id: 4435ae1204a61a4801cd656e520207b3
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 8151
content:
" About 45% of these emissions remain in \r\nthe atmosphere (see FAQ 5.1). Adding these historical CO2 emissions to estimates of remaining carbon budgets \r\nallows an estimate of the total carbon budget consistent with a specific global warming level."


id: 72aad7786cb4b28429b6a7073bbe99a4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 15657
content:
"Wenche Aas (Norway), Dimitris Akritidis (Greece), Robert J. Allen (United States of America), \r\nNicolas Bellouin (United Kingdom/France), Sophie Berger (France/Belgium), Sara M. Blichner (Norway), \r\nJosep G. Canadell (Australia), William Collins (United Kingdom), Owen R. Cooper (United States \r\nof America), Frank J. Dentener (EU/The Netherlands), Sarah Doherty (United States of America), \r\nJean-Louis Dufresne (France), Sergio Henrique Faria (Spain/Brazil), Piers Forster (United Kingdom), \r\nTzung-May Fu (China), Jan S. Fuglestvedt (Norway), John C. Fyfe (Canada), Aristeidis K. Georgoulias \r\n(Greece), Matthew J. Gidden (Austria/United States of America), Nathan P. Gillett (Canada), \r\nPaul Ginoux (United States of America), Paul T. Griffiths (United Kingdom), Jian He (United States \r\nof America/China), Christopher Jones (United Kingdom), Svitlana Krakovska (Ukraine), \r\nChaincy Kuo (United States of America), David S"


id: d5ae3ff6d27916c109c66e792c91cc0
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 31139
content:
" Lee (United Kingdom), Maurice Levasseur \r\n(Canada), Martine Lizotte (Canada), Thomas K. Maycock (United States of America), \r\nJean-Francois Muller (Belgium), Helene Muri (Norway), Lee T. Murray (United States of America), \r\nZebedee R. J. Nicholls (Australia), Jurgita Ovadnevaite (Ireland/Lithuania), Prabir K.  Patra \r\n(Japan/India), Fabien Paulot (United States of America/France, United States of America), \r\nPallav Purohit (Austria/India), Johannes Quaas (Germany), Joeri Rogelj (United Kingdom/Belgium), \r\nBjorn H.  Samset (Norway), Chris Smith (United Kingdom), Izuru Takayabu (Japan), \r\nMarianne  Tronstad Lund (Norway), Alexandra P. Tsimpidi (Germany/Greece), Steven Turnock \r\n(United Kingdom), Rita Van Dingenen (Italy/Belgium), Hua Zhang (China), Alcide Zhao \r\n(United Kingdom/China) "


id: 4d39722ffaacad6f482d43ed52242adc
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 9582
content:
"Short-lived climate forcers (SLCFs) affect climate and are, in most \r\ncases, also air pollutants. They include aerosols (sulphate, nitrate, \r\nammonium, carbonaceous aerosols, mineral dust and sea spray), \r\nwhich are also called particulate matter (PM), and chemically \r\nreactive gases (methane, ozone, some halogenated compounds, \r\nnitrogen oxides, carbon monoxide, non-methane volatile organic \r\ncompounds, sulphur dioxide and ammonia). Except for methane and \r\nsome halogenated compounds whose lifetimes are about a decade \r\nor more, SLCF abundances are spatially highly heterogeneous since \r\nthey only persist in the atmosphere from a few hours to a couple \r\nof months.  SLCFs are either radiatively active or influence the \r\nabundances of radiatively active compounds through chemistry \r\n(chemical adjustments), and their climate effect occurs predominantly \r\nin the first two decades after their emission or formation"


id: 2cc1f2554bf841ffb015f772b1e4c9db
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 4476
content:
" They can \r\nhave either a  cooling or warming effect on climate, and they also \r\naffect precipitation and other climate variables. Methane and some \r\nhalogenated compounds are included in climate treaties, unlike \r\nthe other SLCFs that are nevertheless indirectly affected by climate \r\nchange mitigation since many of them are often co-emitted with \r\nCO2 in combustion processes. This chapter assesses the changes, in \r\nthe past and in a selection of possible futures, of the emissions and \r\nabundances of individual SLCFs primarily on global to continental \r\nscales, and how these changes affect the Earth's energy balance \r\nthrough radiative forcing and feedback in the climate system. \r\nThe  attribution of climate and air-quality changes to emissions \r\nsectors and regions, and the effects of SLCF mitigations defined for \r\nvarious environmental purposes, are also assessed. "


id: 33028ab19b857dae7756b0314e0b580d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 5448
content:
"Over the last decade (2010-2019), strong shifts in the \r\ngeographical distribution of emissions have led to changes \r\nin atmospheric abundances of highly variable SLCFs (high \r\nconfidence). Evidence from satellite and surface observations \r\nshows strong regional variations in trends of ozone (O3), \r\naerosols and their precursors (high confidence). In particular, \r\ntropospheric columns of nitrogen dioxide (NO2) and sulphur dioxide \r\n(SO2) continued to decline over North America and Europe (high \r\nconfidence), and to increase over Southern Asia (medium confidence), \r\nbut have declined over Eastern Asia (high confidence). Global \r\ncarbon monoxide (CO) abundance has continued to decline (high \r\nconfidence). The concentrations of hydrofluorocarbons (HFCs) are \r\nincreasing (high confidence)"


id: bf6aee8ad3903d3d934058d034de12c9
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 27671
content:
" Global carbonaceous aerosol budgets \r\nand trends remain poorly characterized due to limited observations, \r\nbut sites representative of background conditions have reported \r\nmulti-year declines in black carbon (BC) over several regions of the \r\nNorthern Hemisphere. {6.2, 6.3, 2.2.4, 2.2.5, 2.2.6}"


id: 534b023b51fc5bdebba5141686281138
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 10574
content:
"In the next two decades, it is very likely that the SLCF \r\nemissions changes in the WGI core set of SSPs will cause \r\na  warming relative to 2019, whatever the SSPs, in addition \r\nto the warming from long-lived greenhouse gases. The net \r\neffect of SLCF and hydrofluorocarbon (HFC) changes on \r\nGSAT across the SSPs is a likely warming of 0.06degC-0.35degC in \r\n2040 relative to 2019. Warming over the next two decades \r\nis quite similar across the SSPs due to competing effects of \r\nwarming (methane, ozone) and cooling (aerosols) SLCFs. For \r\nthe scenarios with the most stringent climate and air pollution \r\nmitigations (SSP1-1.9 and SSP1-2.6), the likely near-term warming \r\nfrom the SLCFs is predominantly due to sulphate aerosol reduction, \r\nbut this effect levels off after 2040. In the absence of climate change \r\npolicies and with weak air pollution control (SSP3-7"


id: 5fa00c5dcc90220bd219175eb577159
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 12623
content:
"0), the likely\r\nnear-term warming due to changes in SLCFs is predominantly due \r\nto increases in methane, ozone and HFCs, with smaller contributions \r\nfrom changes in aerosols. SSP5-8.5 has the highest SLCF-induced \r\nwarming rates due to warming from methane and ozone increases \r\nand reduced aerosols due to stronger air pollution control compared \r\nto the SSP3-7.0 scenario. {6.7.2}"


id: d1288c9919b52546d8873da3318ec45b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 30443
content:
"Future air quality (in term of surface ozone and PM \r\nconcentrations) on global to local scales will be primarily \r\ndriven by changes in precursor emissions as opposed to \r\nclimate change (high confidence) and climate change is \r\nprojected to have mixed effects. A warmer climate is expected \r\nto reduce surface ozone in regions remote from pollution sources \r\n(high confidence) but is expected to increase it by a few parts per \r\nbillion over polluted regions, depending on ozone precursor levels \r\n(medium to high confidence). Future climate change is expected to \r\nhave mixed effects, positive or negative, with an overall low effect, \r\non global surface PM and more generally on the aerosol global \r\nburden (medium confidence), but stronger effects are not excluded in \r\nregions prone to specific meteorological conditions (low confidence)"


id: 3acdf45c3691a304d465819d2c7f14f0
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 6619
content:
" \r\nOverall, there is low confidence in the response of surface ozone and \r\nPM to future climate change due to the uncertainty in the response \r\nof the natural processes (e.g., stratosphere-troposphere exchange, \r\nnatural precursor emissions, particularly including biogenic volatile "


id: 3e60640abb8d1e15d591ee174a69e3fb
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 7186
content:
"Over time scales of 10 to 20 years, the global temperature \r\nresponse to a year's worth of current emissions of SLCFs is at \r\nleast as large as that due to a year's worth of CO2 emissions \r\n(high confidence). Sectors producing the largest SLCF-induced \r\nwarming are those dominated by methane emissions: fossil \r\nfuel production and distribution, agriculture and waste \r\nmanagement (high confidence). On these time scales, SLCFs \r\nwith cooling effects can significantly mask the CO2 warming in the \r\ncase of fossil fuel combustion for energy and land transportation, \r\nor completely offset the CO2 warming and lead to an overall net \r\ncooling in the case of industry and maritime shipping (prior to the \r\nimplementation of the revised fuel-sulphur limit policy for shipping \r\nin 2020) (medium confidence)"


id: f6a13b120aa10d39d10f9e927fd08a47
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 36314
content:
" Ten years after a one-year pulse of \r\npresent-day aviation emissions, SLCFs induce strong but short-lived \r\nwarming contributions to the GSAT response (medium confidence), \r\nwhile CO2 both gives a warming effect in the near term and dominates \r\nthe long-term warming impact (high-confidence). {6.6.1, 6.6.2}"


id: 795776b86fe78bf29a71d100edb43ffd
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 16659
content:
"Future changes in SLCFs are expected to cause additional \r\nwarming. This warming is stable after 2040 in scenarios leading \r\nto lower global air pollution as long as methane emissions \r\nare also mitigated, but the overall warming induced by SLCF \r\nchanges is higher in scenarios in which air quality continues to \r\ndeteriorate (induced by growing fossil fuel use and limited air \r\npollution control) (high confidence). If a strong air pollution control \r\nresulting in reductions in anthropogenic aerosols and non-methane \r\nozone precursors was considered in SSP3-7.0, it would lead to a likely\r\nadditional near-term global warming of 0.08 [0.00  to  0.10]  degC in \r\n2040. An additional concomitant methane mitigation (consistent \r\nwith SSP1's stringent climate change mitigation policy implemented \r\nin the SSP3 world) would not only alleviate this warming but would \r\nturn this into a cooling of 0.07degC with a likely range of [-0.02 to \r\n+0.14] degC (compared with SSP3-7.0 in 2040)"


id: 9d14cce7f849f315c2552c048df4a659
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 22253
content:
" Across the SSPs, the \r\ncollective reduction of methane, ozone precursors and HFCs can \r\nmake a difference of 0.2degC with a very likely range of [0.1 to 0.4] degC \r\nin 2040 and 0.8degC with a very likely range of [0.5 to 1.3] degC at the \r\nend of the 21st century (comparing SSP3-7.0 and SSP1-1.9), which is \r\nsubstantial in the context of the Paris Agreement. Sustained methane \r\nmitigation, wherever it occurs, stands out as an option that combines \r\nnear- and long-term gains on surface temperature (high confidence)\r\nand leads to air-quality benefits by reducing surface ozone levels \r\nglobally (high confidence). {6.6.3, 6.7.3, 4.4.4} "


id: a634a4fff5eb4ff499b61ba40834c736
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 23688
content:
"The effects of SLCFs decay rapidly over the first few decades \r\nafter pulse emission. Consequently, on time scales longer \r\nthan about 30 years, the net long-term global temperature \r\neffects of sectors and regions are dominated by CO2 (high \r\nconfidence). The global mean temperature response following \r\na climate change mitigation measure that affects emissions of both \r\nshort- and long-lived climate forcers depends on their atmospheric \r\ndecay times, how fast and for how long the emissions are reduced, \r\nand the inertia in the climate system. For SLCFs including methane, \r\nthe rate of emissions drives the long-term global temperature effect, \r\nas opposed to CO2 for which the long-term global temperature effect \r\nis controlled by the cumulative emissions"


id: ad09c1e62b4243dff74515b8e8db2b69
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 24770
content:
" About 30 years or more \r\nafter a one-year emission pulse occurs, the sectors contributing the \r\nmost to global warming are industry, fossil fuel combustion for energy \r\nand land transportation, essentially through CO2 (high confidence). \r\nCurrent emissions of SLCFs, CO2 and N2O from Eastern Asia and North \r\nAmerica are the largest regional contributors to additional net future \r\nwarming on both short (medium confidence) and long time scales \r\n(high confidence). {6.6.1, 6.6.2}"


id: 3a0f3c332c58597edf035b3724c481a5
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 6511
content:
"Rapid decarbonization strategies lead to air-quality \r\nimprovements but are not sufficient to achieve, in the near \r\nterm, air-quality guidelines set for fine PM by the World Health \r\nOrganization (WHO), especially in parts of Asia and in some \r\nother highly polluted regions (high confidence). Additional \r\nmethane and BC mitigation would contribute to offsetting the \r\nadditional warming associated with SO2 reductions that would \r\naccompany decarbonization (high confidence). Strong air pollution \r\ncontrol as well as strong climate change mitigation, implemented \r\nseparately, lead to large reductions in exposure to air pollution by the \r\nend of the century (high confidence). Implementation of air pollution \r\ncontrols, relying on the deployment of existing technologies, leads \r\nmore rapidly to air quality benefits than climate change mitigation \r\n(high confidence), which requires systemic changes"


id: 644ad0fb4a03f5ab90620f23010fae42
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 13351
content:
" However, \r\nin both cases, significant parts of the population are projected to \r\nremain exposed to air pollution exceeding the WHO guidelines (high \r\nconfidence). Additional policies envisaged to attain Sustainable \r\nDevelopment Goals (SDGs; e.g.,  access to clean energy, waste \r\nmanagement) bring complementary SLCF reduction. Only strategies \r\nintegrating climate, air quality, and development goals are found to \r\neffectively achieve multiple benefits. {6.6.3, 6.7.3, Box 6.2}"


id: 8b4e43e5c1ea2b2d3370ab56eb8aa3bb
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 19442
content:
"Emissions reductions associated with COVID-19 containment \r\nled to a discernible temporary improvement of air quality in \r\nmost regions, but changes to global and regional climate are \r\nundetectable above internal variability. Global anthropogenic \r\nNOx emissions decreased by a maximum of about 35% in April 2020 \r\n(medium confidence). There is high confidence that, with the \r\nexception of surface ozone, these emissions reductions have \r\ncontributed to improved air quality in most regions of the world. \r\nGlobal fossil CO2 emissions decreased by 7% (with a  range of \r\n5.8-13.0%) in 2020 relative to 2019, largely due to reduced emissions \r\nfrom the transportation sector (medium confidence). Overall, the net \r\nERF, relative to ongoing trends, from COVID-19 restrictions was likely \r\nsmall and positive for 2020 (less than 0"


id: e2440e1a295885111f0a19cc896cb510
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 33098
content:
"2 W m-2), thus temporarily \r\nadding to the total anthropogenic climate influence, with positive \r\nforcing from aerosol changes dominating over negative forcings \r\nfrom CO2, NOx and contrail cirrus changes. Consistent with this small \r\nnet radiative forcing, and against a  large component of internal \r\nvariability, Earth system model simulations show no detectable effect \r\non global or regional surface temperature or precipitation (high \r\nconfidence). {Cross-Chapter Box 6.1}"


id: df0aa463ad41a24e00a9bea508147465
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 32584
content:
"Figure 6.1 | Sources and processes leading to atmospheric short-lived climate forcer (SLCF) burden and their interactions with the climate system. Both \r\ndirect and indirect SLCFs and the role of atmospheric processes for the lifetime of SLCFs are depicted. Anthropogenic emissions sectors illustrated are: fossil fuel exploration, \r\ndistribution and use; biofuel production and use; waste; transport; industry; agricultural sources; and open biomass burning. Emissions from natural systems include those from \r\nopen biomass burning, vegetation, soil, ocean, lightning and volcanoes. SLCFs interact with solar or terrestrial radiation, surface albedo, and cloud or precipitation systems. \r\nThe radiative forcing due to individual SLCFs can be either positive or negative. Climate change induces changes in emissions from most natural systems as well as from some \r\nanthropogenic emissions sectors (e.g., agriculture) leading to a climate feedback (purple arrows)"


id: 31ecdf64b566ae81f2ff5dff8e3a0eff
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 5270
content:
" Climate change also influences atmospheric chemistry processes, such as \r\nchemical reaction rates or via circulation changes, thus affecting atmospheric composition leading to a climate feedback. Air pollutants influence emissions from terrestrial \r\nvegetation, including agriculture (grey arrow). "


id: fe3f5d96b7f8725138f72cf425c02434
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 37532
content:
"In the IPCC Special Report on Global Warming of 1.5degC (SR1.5; \r\nAllen et al., 2018a), Rogelj et al. (2018a) state that the evolution \r\nof methane and SO2 emissions strongly influences the chances \r\nof limiting warming to 1.5degC, and that, considering mitigation \r\nscenarios to limit warming to 1.5degC or 2degC, a weakening of aerosol \r\ncooling would add to future warming in the near term, but can be \r\ntempered by reductions in methane emissions (high confidence). In \r\naddition, as some SLCFs are co-emitted alongside CO2, especially \r\nin the energy and transport sectors, low CO2 scenarios, relying on \r\ndecline of fossil fuel use, can result in strong abatement of some \r\ncooling and warming SLCFs (Rogelj et al., 2018a). On the other hand, \r\nspecific reductions of the warming SLCFs (methane and BC) would, \r\nin the short term, contribute significantly to the efforts of limiting \r\nwarming to 1.5degC"


id: 5ea55297faa4faf6aa342e45b248bbce
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 12428
content:
" Reductions of BC and methane would have \r\nsubstantial co-benefits, improving air quality and therefore limiting \r\neffects on human health and agricultural yields. This would, in \r\nturn, enhance the institutional and socio-cultural feasibility of such \r\nactions in line with the United Nations' Sustainable Development \r\nGoals (SDGs; Coninck et al., 2018)."


id: 9619f4b43331bd006e581bacebfdd05d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 21123
content:
"As depicted in Figure  6.1, emissions of SLCFs are governed \r\nby anthropogenic activities and sources from natural systems \r\n(see Section 6.2 for details). Atmospheric chemistry in this context \r\nis both a  source and a  sink of SLCFs. For instance, ozone and \r\nsecondary aerosols are exclusively formed through atmospheric \r\nmechanisms (Sections 6.3.2 and 6.3.5 respectively). The hydroxyl \r\n(OH) radical, the most important oxidizing agent in the troposphere, \r\nacts as a  sink for SLCFs by reacting with them and thereby \r\ninfluencing their lifetime (Section  6.3.6). Through SLCF radiative \r\nforcing and feedbacks (Section 6.4), key climate parameters, such as \r\ntemperature, hydrological cycle and weather patterns are perturbed. \r\nClimate change also influences air quality (Section 6.5). As depicted \r\nin Figure 6.1, SLCFs affect both climate and air quality, hence SLCF \r\nmitigation has linkages to both issues (Section 6.6)"


id: 6d85066f6ec27df8b0ac53ca8e0bc566
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 14838
content:
" Socio-economic \r\nnarratives including air-quality policies determine future projections \r\nof SLCFs in the five core Shared Socio-economic Pathways (SSPs): \r\nSSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (described \r\nin Chapter 1), and in addition, a subset of SSP3 scenarios make it \r\npossible to isolate the effect of various SLCF mitigation trajectories \r\non climate and air quality (Section 6.7)."


id: 87e4d5bf5e15f9bce5ae86341b60067a
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
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content:
"Following SR1.5, the IPCC Special Report on Climate Change and \r\nLand (SRCCL; IPCC, 2019a) took into consideration the emissions \r\non land of three major SLCFs: mineral dust, carbonaceous aerosols \r\n(BC and OA) and biogenic volatile compounds (BVOCs) (Jia et al., \r\n2019)"


id: 4a851be22ba4e0c77108dffcdcc150ba
created_at: 2023-03-16 14:02:01
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vector_id: 9127
content:
" The SRCCL concluded that: (i) there is no agreement about \r\nthe direction of future changes in mineral dust emissions; (ii) fossil \r\nfuel and biomass burning, and secondary organic aerosols (SOA) \r\nfrom natural BVOC emissions are the main global sources of \r\ncarbonaceous aerosols whose emissions are expected to increase in \r\nthe near future due to possible increases in open biomass burning \r\nand increase in SOA from oxidation of BVOCs (medium confidence); \r\nand (iii) BVOCs are emitted in large amounts by forests and they are \r\nrapidly oxidized in the atmosphere to form less volatile compounds \r\nthat can condense and form SOA, and in a warming planet, BVOC \r\nemissions are expected to increase but magnitude is unknown and \r\nwill depend on future land-use change, in addition to climate (limited \r\nevidence, medium agreement)."


id: 6acb9df31fb86e32d471269f89f5adff
created_at: 2023-03-16 14:02:01
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content:
"This chapter discusses air quality from a global point of view with \r\na  focus on surface ozone and particulate matter concentrations. \r\nLocal and indoor air pollution, as well as the effect of air pollution \r\non health, are beyond the scope of this chapter. This Assessment \r\nis mainly based on results and studies relying on global models or \r\nobservation datasets operated through global networks or from \r\nsatellites. Global chemistry-climate models enable the quantification \r\nof changes in background concentrations, such as changes in \r\nsurface ozone due to large-scale changes in climate or methane, \r\nby considering comprehensively the physiochemical processes \r\n(Box 6.1). In addition, climate effects are often non-linear responses \r\nto concentrations which already respond non-linearly to emissions, \r\nwith per-mass unit effects often larger in pristine than in polluted \r\nregions, justifying the relevance of global models"


id: 7fea7cfd797ae81e5912bbcff130599
created_at: 2023-03-16 14:02:01
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" However, specific \r\naspects of urban air quality cannot be captured by global models \r\nand require high-resolution models that reproduce the temporal and \r\nspatial variability of emissions and abundances necessary to precisely \r\naccount for the non-linearity of the chemistry and the sensitivity of \r\nlocal air pollution to its drivers. Consequently, the sectoral analysis \r\nin Section  6.6 and the mitigation effects in Section  6.7 cannot be \r\ndirectly applied for local air-quality planning. "


id: 832aa07ce01866d14fa1a043ba3c47b4
created_at: 2023-03-16 14:02:01
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content:
"combined different emissions datasets. However, for the period \r\nafter 1990, the CMIP6 dataset shows for all species, except for \r\nSO2, CO, and (since 2011) for NOx, a  different trend than CMIP5 \r\n(i.e., continued strong growth of emissions driven primarily by \r\ndevelopments in Asia (Figure 6.19)). The unprecedented growth of \r\nemissions from Eastern and Southern Asia since 2000 changed the \r\nglobal landscape of emissions, making Asia the dominant SLCF source \r\nregion (Figures  6.3 and 6.19). The Representative Concentration \r\nPathways (RCP) scenarios used in AR5 started from the year 2000 \r\n(van Vuuren et al., 2011) and did not capture the SLCF emissions \r\nwhich actually occurred until 2015. The CEDS inventory (Hoesly et al., \r\n2018) includes improved representation of these trends and the \r\nestimate for 2014. These findings have been largely supported by \r\nseveral independent emissions inventory studies and remote-sensing \r\ndata analysis"


id: 53228942584e159b745f993163cf6dea
created_at: 2023-03-16 14:02:01
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content:
" However, for the last decade the decline of Asian \r\nemissions of SO2 and NOx appears underestimated while growth \r\nof BC and OC emissions in Asia and Africa seems overestimated in \r\nCMIP6, compared to most recent regional evaluations (Klimont et al., \r\n2017a; Zheng et al., 2018b; Elguindi et al., 2020; Kanaya et al., 2020; \r\nMcDuffie et al., 2020), which are largely considered in the updated \r\nrelease of the CEDS (Hoesly et al., 2019). Consequently, global CMIP6 \r\nanthropogenic emissions for 2014 are likely overestimated by about \r\n10% for SO2 and NOx and by about 15% for BC and OC. "


id: fd69ed7041c2fbcff452d9be928bb911
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 37383
content:
"Estimates of global anthropogenic (human-caused) SLCF emissions \r\nand their historical evolution that were used in AR5 (CMIP5; Lamarque \r\net al., 2010) have been revised for use in CMIP6 (Hoesly et al., 2018). \r\nThe update considered new data and assessment of the impact \r\nof environmental policies, primarily regarding air pollution control \r\n(R. Wang et al., 2014; S.X. Wang et al., 2014; Montzka et al., 2015; \r\nCrippa et al., 2016; Turnock et al., 2016; Klimont et al., 2017a; Zanatta \r\net al., 2017; Prinn et al., 2018). Additionally, Hoesly et al. (2018) have \r\nextended estimates of anthropogenic emissions back to 1750 and \r\ndeveloped an updated and new set of spatial proxies allowing for more \r\ndifferentiated (source sector-wise) gridding of emissions (Feng et al., \r\n2020)"


id: c3b91d7bc37cdf9f3477192d3771e521
created_at: 2023-03-16 14:02:01
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vector_id: 28366
content:
" The CMIP6 emissions inventory has been developed with the \r\nCommunity Emissions Data System (CEDS) that improves upon existing \r\ninventories with a  more consistent and reproducible methodology, \r\nsimilar to approaches used in, for example, the EDGAR database \r\n(Crippa et al., 2016) and the GAINS model (Amann et al., 2011; Klimont \r\net al., 2017a; Hoglund-Isaksson et al., 2020) where emissions of all \r\ncompounds are consistently estimated using the same emissions drivers \r\nand propagating individual components (activity data and emissions \r\nfactors) separately to capture fuel and technology trends affecting \r\nemissions trajectories over time. This contrasts with the approach used \r\nto establish historical emissions for CMIP5 where different datasets \r\navailable at the time were combined"


id: 4849561b330c75b33c87e8f5fc12449f
created_at: 2023-03-16 14:02:01
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content:
" The CMIP6 exercise is based on \r\nthe first release of the CEDS emissions dataset (version 2017-05-18, \r\nsometimes referred to hereafter as CMIP6 emissions) whose main \r\nfeatures regarding SLCFs are described hereafter. The CEDS has been \r\nand will be regularly updated and extended; the recent update of the \r\nCEDS (Hoesly et al., 2019) and consequences for this Assessment is \r\ndiscussed when necessary. Some details on how SLCF emissions \r\nhave been represented in scenarios used by IPCC assessments can be \r\nfound in Chapter 1 (Section 1.6.1 and Cross-Chapter Box 1.4 and in \r\nSection 6.7.1.1)."


id: 5d93ddde3219d7c0dafbac1cb8c6f1a5
created_at: 2023-03-16 14:02:01
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"Global emissions of NOx have been growing in spite of the \r\nsuccessful reduction of emissions in North America, Europe, Japan \r\nand Korea (Crippa et al., 2016; Turnock et al., 2016; Miyazaki \r\net al., 2017; Jiang et al., 2018), partly driven by continuous efforts \r\nto strengthen the emissions standards for road vehicles in most \r\ncountries (Figures 6.18 and 6.19). In many regions, an increase in \r\nvehicle fleet as well as non-compliance with emissions standards \r\n(Anenberg et al., 2017, 2019; Jonson et al., 2017; Jiang et al., \r\n2018), growing aviation (Grewe et al., 2019; Lee et al., 2021) and \r\ndemand for energy, and consequently a  large number of new \r\nfossil fuel power plants, have more than compensated for these \r\nreductions"


id: 3a96fee497ad53c896a2436ff0dcda52
created_at: 2023-03-16 14:02:01
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content:
" Since about 2011, global NOx emissions appear to \r\nhave stabilized or slightly declined (medium confidence) but the \r\nglobal rate of decline has been underestimated in the CEDS, as \r\nrecent data suggest that emissions reductions in China were larger \r\nthan included in the CEDS (Figure  6.19 and Hoesly et al., 2018). \r\nRecent bottom-up emissions estimates (Zheng et al., 2018b) largely \r\nconfirm what has been shown in satellite data (F. Liu et al., 2016; \r\nMiyazaki et al., 2017; Silver et al., 2018): a strong decline of NO2\r\ncolumn over eastern China (high confidence) (Section  6.3.3.1). "


id: a1b9869c626361f74af12045df8a4375
created_at: 2023-03-16 14:02:01
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vector_id: 23005
content:
"At a  global level, the estimated CEDS CO emissions trends are \r\ncomparable to NOx, which has been confirmed by several inverse \r\nmodelling studies (Section 6.3.3.2). The transport sector (including \r\ninternational shipping and aviation) was the largest anthropogenic \r\nsource of NOx (about 50% of the total) and also contributed over \r\n25% of CO emissions in 2014; Asia represented 50% and North \r\nAmerica and Europe about 20% of global total NOx and CO \r\nemissions (Figure 6.3).\r\nOil production-distribution and transport sectors have dominated \r\nanthropogenic NMVOC emissions for most of the 20th century \r\n(Hoesly et al., 2018) and still represent a large share (Figure 6.3). \r\nEfforts to control transport emissions (i.e"


id: 679922690a606cf07c8155e99515e51c
created_at: 2023-03-16 14:02:01
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content:
", increasing stringency \r\nof vehicle emissions limits) were largely offset by the fast growth \r\nof emissions from chemical industries and solvent use, as well as \r\nfrom fossil fuel production and distribution, resulting in continued \r\ngrowth of global anthropogenic NMVOC emissions since 1900 \r\n(high confidence) (Figure 6.18). Since AR5, there is high confidence\r\nthat motor vehicle NMVOC emissions have sharply declined in \r\nNorth America and Europe in the last decades (Rossabi and Helmig, \r\n2018), for example, by about an order of magnitude in major US \r\ncities since 1990 (Bishop and Haugen, 2018; McDonald et al., \r\n2018). Increasing (since 2008) oil- and gas-extraction activities in \r\nNorth America lead to a strong growth of NMVOC emissions (high \r\nconfidence) as shown by analysis of ethane column data (Franco \r\net al., 2016), but absolute emission amounts remain uncertain \r\n(Petron et al., 2014; Tzompa-Sosa et al., 2019)"


id: def8e155f5482833a7d0e9ed47f91332
created_at: 2023-03-16 14:02:01
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vector_id: 32576
content:
" In Eastern Asia, \r\nthere is medium confidence in a decreasing trend of motor vehicle \r\nemissions, suggested by ambient measurements in Beijing since \r\n2002 (Wang et al., 2015) and by bottom-up estimates (Zheng et al., \r\n2018b), and a  decrease in residential heating emissions due to \r\ndeclining coal and biofuel use since 2005 (Zheng et al., 2018b; M. \r\nLi et al., 2019). However, total anthropogenic NMVOC emissions \r\nhave increased steadily in China since the mid-20th century, \r\nlargely due to the growing importance of the solvent-use and \r\nindustrial sectors (medium evidence, high agreement) (Sun et al., \r\n2018; Zheng et al., 2018b; M. Li et al., 2019). Resulting changes \r\nin the NMVOC speciated emissions might be underestimated in \r\nthe current regional and global inventories. For  example, in the \r\nUSA, a recent study suggested an emergent shift in urban NMVOC \r\nsources from transportation to chemical products (i.e., household \r\nchemicals, personal care products, solvents, etc"


id: 36ad318b158d28d63bce750aff54f362
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vector_id: 5988
content:
"), which is not in \r\naccordance with emissions inventories currently used (McDonald \r\net al., 2018). In many European regions and cities, wood burning has \r\nbeen increasingly used for residential heating, partly for economic \r\nreasons and because it is considered CO2-neutral (Athanasopoulou \r\net al., 2017); in situ measurements in several cities, including Paris, \r\nsuggest that wood burning explains up to half of the NMVOC \r\nemissions during winter (Kaltsonoudis et al., 2016; Languille et al., \r\n2020). Due to the vast heterogeneity of sources and components \r\nof NMVOCs, uncertainty in regional emissions and trends is higher \r\nthan for most other components.\r\nEmissions of carbonaceous aerosols (BC, OC) have been steadily \r\nincreasing and their emissions have almost doubled since 1950 \r\n(medium confidence) (Hoesly et al., 2018). Before 1950, North \r\nAmerica and Europe contributed about half of the global total "


id: e4a34d287c2dd88d177b5c5fb517586a
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 33447
content:
"but successful introduction of diesel particulate filters on road \r\nvehicles (Fiebig et al., 2014; Robinson et al., 2015; Klimont et al., \r\n2017a) and declining reliance on solid fuels for heating brought in \r\nlarge reductions (high confidence) (Figure  6.19). Currently, global \r\ncarbonaceous aerosol emissions originate primarily from Asia and \r\nAfrica (Bond et al., 2013; Hoesly et al., 2018; Elguindi et al., 2020; \r\nMcDuffie et al., 2020), representing about 80% of the global total \r\n(high confidence) (Figure 6.3). Consideration, in CMIP6, of emissions \r\nfrom kerosene lamps and gas flaring, revised estimates for open \r\nburning of waste, regional coal consumption, and new estimates for \r\nRussia (Stohl et al., 2013; Huang et al., 2015; Huang and Fu, 2016; \r\nKholod et al., 2016; Conrad and Johnson, 2017; Evans et al., 2017; \r\nKlimont et al., 2017a) resulted in over 15% higher global emissions \r\nof OC and BC than in the CMIP5 estimates for the first decade of \r\nthe 21st century (Figure  6"


id: ae99999edba7e5fc28a284a6de37cf10
created_at: 2023-03-16 14:02:01
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vector_id: 24998
content:
"18). However, the continued increase \r\nof BC emissions over Eastern Asia after 2005, estimated in CMIP6 \r\n(Figure 6.19), has been questioned recently as a steady decline of \r\nBC concentrations was measured in the air masses flowing out from \r\nthe east coast of China (Kanaya et al., 2020), which has been also \r\nestimated in recent regional bottom-up and top-down inventories \r\n(Zheng et al., 2018a; Elguindi et al., 2020; McDuffie et al., 2020). \r\nSince AR5, confidence in emissions estimates and trends in North \r\nAmerica and Europe has increased, but high uncertainties remain for \r\nAsia and Africa, despite their major contribution to global emissions. \r\nThe size distribution of emitted species, of importance for climate \r\nand health impacts, remains uncertain and the CEDS inventory \r\ndoes not provide such information"


id: 6a89c26c941fa193ceec0d8312dce411
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 14376
content:
" Overall, a factor two uncertainty \r\nin global estimates of BC and OC emissions remains, with post\u00022005 emissions overestimated in Asia (high confidence) and Africa \r\n(medium confidence). "


id: 7d45fdbb72571e6e484d792abb442210
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 17287
content:
"Agricultural production (livestock and mineral nitrogen fertilizer \r\napplication) is the primary source of ammonia in the atmosphere \r\nwith more than half of present-day emissions originating in Asia \r\n(Hoesly et al., 2018; Figure 6.3, EC-JRC/PBL, 2020; Vira et al., 2020). \r\nNH3 emissions are estimated to have grown strongly since 1850, \r\nespecially since 1950, driven by continuously increasing livestock \r\nproduction, widespread application of mineral nitrogen fertilizers, \r\nand lack of action to control ammonia (high confidence) (Erisman \r\net al., 2008; Riddick et al., 2016; Hoesly et al., 2018; Fowler et al., \r\n2020). The trends estimated in CMIP5 and CMIP6 are similar, while \r\nin absolute terms, CMIP6 has somewhat higher emissions as it \r\nincludes emissions from wastewater and human waste that were \r\nlargely missing in CMIP5 (Hoesly et al., 2018). CMIP6 has improved \r\nspatial and temporal distribution of emissions (Lamarque et al., \r\n2013a) relying on the EDGAR v4"


id: a7b6f349e0a002ba9578aeca558d78b0
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 23896
content:
"Figure 6.3 | Relative regional and sectoral contributions to the present day (year 2014) anthropogenic emissions of short-lived climate forcers (SLCFs).\r\nEmissions data are from the Community Emissions Data System (CEDS; Hoesly et al., 2018). Emissions are aggregated into the following sectors: fossil fuel production and \r\ndistribution (coal mining, oil and gas production, upstream gas flaring, gas distribution networks), fossil fuel combustion for energy (power plants), residential and commercial \r\n(fossil and biofuel use for cooking and heating), industry (combustion and production processes, solvent-use losses from production and end use), transport (road and off-road \r\nvehicles), shipping (including international shipping), aviation (including international aviation), agriculture (livestock and crop production), waste management (solid waste, \r\nincluding landfills and open trash burning, residential and industrial waste water), and other"


id: e17c1fb0898da54b149803b79a35d999
created_at: 2023-03-16 14:02:01
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vector_id: 32969
content:
" Further details on data sources and processing are available in the chapter data \r\ntable (Table 6.SM.3)."


id: ec791429e14d4b96969680fe04cdea2d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 34685
content:
"Since AR5, the quality and completeness of activity and emission\u0002factor data and applied methodology, including spatial allocation \r\ntogether with independent satellite-derived observations, have \r\nimproved, raising confidence in methods used to derive emissions. \r\nThere is high confidence in the sign of global trends of SLCF emissions \r\nuntil the year 2000. However, only medium confidence for the rate \r\nof change in the two last decades, owing primarily to uncertainties \r\nin the actual application of reduction technologies in fast-growing \r\neconomies of Asia. At a  regional level, bottom-up derived SLCF \r\nemissions trends, and magnitudes in regions with strong economic \r\ngrowth and changing air-quality regulation, are highly uncertain but \r\ncan be better constrained with top-down methods (Section 6.3)"


id: 1181a9cab0979eba06495f34d0d84e23
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 241
content:
" For \r\nmost SLCF species, there is high confidence in trends and magnitudes \r\nfor affluent countries from the Organisation for Economic Co\u0002operation and Development (OECD) regions where accurate and \r\ndetailed information about drivers of emissions exists; medium \r\nconfidence is assessed for regional emissions of NH3, methane and \r\nNMVOC. "


id: 2011cf9bd7f8c50fe4a7ef9a3f05edf6
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 2490
content:
"This section assesses our current understanding of SLCF emissions \r\nby natural systems. Many naturally occurring emission processes in \r\nthe Earth system have been perturbed by the growing influence of \r\nhuman activities either directly (e.g., deforestation, agriculture) or \r\nvia human-induced atmospheric CO2 increase and climate change, \r\nand therefore cannot be considered as purely natural emissions. \r\nThe  temporal evolution and spatial distribution of natural SLCF \r\nemissions are highly variable and their estimates rely on models \r\nwith rather uncertain parametrizations for production mechanisms. \r\nFor  some SLCFs, the natural processes by which emissions occur \r\nare also not well understood. In the following sections, we assess \r\nthe level of confidence in present-day SLCF emissions by natural \r\nsystems, in their perturbation since the pre-industrial period and \r\ntheir sensitivity to future changes"


id: 15ed45f80f2940b7f1de2030ab7776b4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 972
content:
" When available, the assessment \r\nalso includes estimates from the CMIP6 model ensemble. Note that \r\nvolcanic SO2 emissions are discussed in Section 2.2.2 and natural \r\nsources of methane and N2O are assessed in Sections 5.2.2 and \r\n5.2.3."


id: 76ca130c41abe531959a876adf38d519
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 16267
content:
"A wide range of BVOCs are emitted from vegetation with the \r\ndominant compounds being isoprene and monoterpenes but also \r\nincluding sesquiterpenes, alkenes, alcohols, aldehydes and ketones. \r\nThe photooxidation of BVOC emissions plays a  fundamental role \r\nin atmospheric composition by controlling the regional and global \r\nbudgets of ozone and organic aerosols, and impacting the lifetime \r\nof methane and other reactive components (Arneth et al., 2010b; \r\nHeald and Spracklen, 2015). Substantial uncertainty exists across \r\ndifferent modelling frameworks for estimates of global total BVOC \r\nemissions and individual compound emissions (Messina et al., 2016). \r\nGlobal isoprene emissions estimates differ by a factor of two from \r\n300-600 TgC yr-1 and global monoterpene emissions estimates by \r\na factor of five from 30-150 TgC yr-1 (Messina et al., 2016)"


id: 892918d64a42d7da0b2d1e26831bf839
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 19118
content:
" A main \r\ndriver of the uncertainty ranges is the choice of basal emissions rates \r\nassigned to different plant functional types in the model; however, \r\nthe smaller uncertainty range for isoprene than for monoterpenes is \r\nnot fully understood (Arneth et al., 2008). The evaluation of global \r\nBVOC emissions is challenging because of poor measurement data \r\ncoverage in many regions and the lack of year-round measurements \r\n(Unger et al., 2013). Several observational approaches have been \r\ndeveloped in the past few years to improve understanding of BVOC \r\nemissions, including indirect methods such as the measurement of \r\nthe OH loss rate in forested environments (Yang et al., 2016) and \r\napplication of the variability in satellite formaldehyde concentrations \r\n(Palmer et al., 2006; Barkley et al., 2013; Stavrakou et al., 2014). \r\nDirect space-borne isoprene retrievals using infrared radiance (IR) \r\nmeasurements have very recently become available (Fu et al., 2019; \r\nWells et al., 2020)"


id: afef0a276ecf1811271e4c3a6cc4b704
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 25206
content:
" Collectively these approaches have identified \r\nweaknesses in the ability of the parametrizations in global models \r\nto reproduce BVOC emissions hotspots (Wells et al., 2020). However, \r\nnone of the current observational approaches have yet been able to \r\nreduce the uncertainty ranges in global emissions estimates."


id: 3ef3b3f3db768193c34a82a270c9623e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 7286
content:
"Global BVOC emissions are highly sensitive to environmental changes \r\nincluding changes in climate, atmospheric CO2\r\n, and vegetation \r\ncomposition and cover changes in natural and managed lands. Recent \r\nglobal modelling studies agree that global isoprene emissions have \r\ndeclined since the pre-industrial period, driven predominantly by \r\nanthropogenic land-use and land-cover change (LULCC) with results \r\nconverging on a 10-25% loss of isoprene emissions between 1850 \r\nand the present day (Lathiere et al., 2010; Unger, 2013, 2014; Acosta \r\nNavarro et al., 2014; Heald and Geddes, 2016; Hantson et al., 2017; \r\nHollaway et al., 2017; Scott et al., 2017). The historical evolution of \r\nmonoterpene and sesquiterpene emissions is less well studied and \r\nthere is no robust consensus on even the sign of the change (Acosta \r\nNavarro et al., 2014; Hantson et al., 2017)"


id: 92ec7af1fffddcab366e6a30b39b2de0
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 20627
content:
" Future global isoprene \r\nand monoterpene emissions depend strongly on the climate and \r\nland-use scenarios considered (Hantson et al., 2017; Szogs et al., \r\n2017). BVOC emissions will be sensitive to future land-based climate \r\nchange mitigation strategies including afforestation and bioenergy, \r\nwith impacts of bioenergy depending on the choice of crops (Szogs \r\net al., 2017). "


id: e4b06a1dec4318f9cd5aa75eac91b025
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 33457
content:
"At the plant level, BVOC emissions rates and composition depend \r\nstrongly on plant species with plants tending to emit either isoprene \r\nor monoterpenes but not both. Photosynthetic activity is a  main \r\ndriver of isoprene and monoterpene production. Therefore, radiation \r\nand temperature, along with leaf-water status, phenological state \r\nand atmospheric CO2 mixing ratio, affect emissions directly (on the \r\nleaf scale) and indirectly (via plant productivity; Guenther et al., 2012; \r\nLoreto et al., 2014; Niinemets et al., 2014). CO2 directly influences \r\nthe isoprene-synthesis process, with inhibition under increasing \r\natmospheric CO2 (Rosenstiel et al., 2003; Possell et al., 2005; \r\nWilkinson et al., 2009). Direct CO2 inhibition has been observed for \r\nsome monoterpene compounds (Loreto et al., 2001; Llorens et al., \r\n2009)"


id: 67869c47c7883f68344639bada5bdb05
created_at: 2023-03-16 14:02:01
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vector_id: 13878
content:
" Severe/long-term water stress may reduce emissions whilst \r\nmild/short-term water stress may temporarily amplify or maintain \r\nBVOC emissions to protect plants against ongoing stress (Penuelas \r\nand Staudt, 2010; Potosnak et al., 2014; Genard-Zielinski et al., 2018). \r\nFurthermore, observations in the Amazon indicate that the chemical \r\ncomposition of monoterpene emissions could also change under \r\nelevated temperature conditions (Jardine et al., 2016). In addition, all \r\nthese processes are investigated over short time scales but the long\u0002term response of BVOC emissions depends on how the vegetation "


id: 77f065184b68a66516221d66f17ce5af
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 16431
content:
"An extremely limited number of studies have explored the evolution \r\nof global dust sources since pre-industrial times (Mahowald et al., \r\n2010; Stanelle et al., 2014). A modelling study estimated a  25% \r\nincrease in global dust emissions between the late 19th century and \r\nthe present, due to agricultural land expansion and climate change \r\n(Stanelle et al., 2014). CMIP5 models were unable to capture the \r\nobserved variability of annual and longer time scales in North African \r\ndust emissions (Evan et al., 2014), however, more recent ESMs with \r\nprocess-based dust emissions schemes that account for changes in \r\nvegetation and climate in a more consistent manner, better match \r\nthe observations (Kok et al., 2014; Evans et al., 2016). Feedbacks \r\nbetween the global dust cycle and the climate system (Section 6.4.5) \r\ncould account for a substantial fraction of the total aerosol feedbacks \r\nin the climate system with an order of magnitude enhancement \r\non a  regional scale (Kok et al"


id: dffaf5e73c1946e63f480481eef66fe8
created_at: 2023-03-16 14:02:01
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vector_id: 32730
content:
", 2018). In  summary, there is high \r\nconfidence that atmospheric dust source and loading are sensitive to \r\nchanges in climate and land use, however, there is low confidence in \r\nquantitative estimates of dust emission response to climate change. "


id: 2538f915a33a94510ced61dde9de5635
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 3287
content:
"Oceans are a  significant source of marine aerosols that influence \r\nclimate directly by scattering and absorbing solar radiation or \r\nindirectly through the formation of cloud condensation nuclei \r\n(CCN) and ice nucleating particles (INPs). Marine aerosols consist \r\nof primary sea-spray particles and secondary aerosols produced by \r\nthe oxidation of emitted precursors, such as dimethylsulphide (DMS) \r\nand numerous other BVOCs. Sea-spray particles, composed of sea \r\nsalt and primary organic aerosols (POA), are produced by wind\u0002induced wave breaking as well as the direct mechanical disruption \r\nof waves. The understanding of sea-spray emissions has increased \r\nsubstantially over the last five years, however, the knowledge of \r\nformation pathways and factors influencing their emissions continue \r\nto have large uncertainties (Forestieri et al., 2018; Saliba et al., 2019). \r\nThe emission rate of sea-spray particles is predominantly controlled \r\nby wind speed"


id: aa978c6d4f38242e55bf24639621fc5e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 24368
content:
" Since AR5, the influence of other factors, including \r\nsea surface temperature, wave history and salinity is increasingly \r\nevident (Callaghan et al., 2014; Grythe et al., 2014; Ovadnevaite et al., \r\n2014; Salter et al., 2014; Barthel et al., 2019). Marine POA, often the \r\ndominant submicron component of sea spray, are emitted as a result \r\nof oceanic biological activity, however the biological processes by \r\nwhich these particles are produced remain poorly characterized \r\ncontributing to large uncertainties in global marine POA emissions \r\nestimates (Tsigaridis et al., 2014; Cravigan et al., 2020; Hodzic et al., \r\n2020). Furthermore, the particle size and chemical composition of \r\nsea-spray particles, and how these evolve in response to changing \r\nclimate factors and dynamic oceanic biology, continue to have \r\nlarge uncertainties. "


id: 1bd89ba4640ca0d8f8cc5ec0e4a01c02
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 1841
content:
"For the last two decades, model-based emissions estimates are \r\nconstrained by remote-sensing capacity to detect active fires and \r\narea burned. In AR5, biomass burning emissions were derived from \r\na satellite product (Lamarque et al., 2010). Since then, improvements \r\nin detection of small fires has enhanced the agreement with higher\u0002resolution and ground-based data on burned area in several regions \r\n(Randerson et al., 2012; Mangeon et al., 2015), especially for areas \r\nsubjected to agricultural waste burning (Chuvieco et al., 2016, 2019). \r\nThe updated emissions factors and the contribution of forest versus \r\nsavanna fires lead to significantly higher global emissions of NOx\r\nand lower emissions of OC and CO in CMIP6, compared with CMIP5. \r\nA recent compilation and assessment of emissions factors (Andreae, \r\n2019) indicates that the emissions factors from Akagi et al"


id: 7124616f9c51b1fe938592136a4326c3
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 15434
content:
" (2011), \r\nprimarily used to produce the CMIP6 datasets, differ by +-50% for \r\nCO, OC, BC and NOx, depending on the biome, and would imply, for \r\nexample, up to 10-30% higher OC and BC emissions from tropical \r\nforest fires. "


id: 88f93b1e4971b84f768581262140a158
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 19092
content:
"This section assesses the evolution of atmospheric abundance1 of \r\nSLCFs since AR5 based on observations and modelling, our knowledge \r\nof SLCF burden and distribution, and our understanding of the trends \r\nover longer time scales. In addition to emissions (Section  6.2), \r\natmospheric chemistry (gas and heterogeneous chemistry), deposition \r\n(including wet and dry removal), and transport processes play a major \r\nrole in determining the atmospheric distribution, budget and lifetime \r\nof SLCFs. The distribution and lifetime of SLCFs are further influenced \r\nby the modulation of chemical and physical processes in response \r\nto a changing climate. Therefore, the time evolution of atmospheric \r\nabundance of SLCFs is characterized by many complex non-linear \r\ninteractions occurring at varying temporal and spatial scales"


id: bd2013f2489eed0450794941181c3776
created_at: 2023-03-16 14:02:01
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vector_id: 27323
content:
" For this \r\nAssessment, global-scale, long-term measurements are employed \r\nonly for a  few gaseous SLCFs while for most short-lived species \r\nregional-scale observations and global models are relied upon. "


id: 9a4070e662c9cd1a7002ddb004901d29
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 21792
content:
"Process-level understanding of tropospheric gas and aerosol chemistry developed through laboratory and simulation chamber \r\nexperiments, as well as quantum chemical theory, is used to generate chemical mechanisms. Atmospheric simulation chambers are \r\ndesigned to identify the chemical pathways and quantify reaction kinetics in isolation from atmospheric transport, deposition and \r\nemission processes. Ideally the chemical regimes studied are representative for ambient atmospheric complexity and concentrations \r\n(e.g., McFiggans et al., 2019). Recently, quantum chemical theory has advanced to a level that it can provide kinetic and product \r\ninformation in a parameter range not possible with laboratory experiments (Vereecken et al., 2015). Iterative and interlinked use of \r\nsimulation chamber and quantum chemical theory has led to improved knowledge of chemical mechanisms (Peeters et al., 2009, \r\n2014; Nguyen et al., 2010; Fuchs et al., 2013)"


id: a521c3e91b654cc9eb9fb7c1b74819c2
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 23546
content:
" For application in chemistry-climate models (CCMs), the chemical mechanisms need \r\nto be computationally efficient, requiring simplifications. Such simplifications include reduced hydrocarbon representations, the \r\napplication of lumping techniques (one compound or a chemical structure representing a family of compounds, for example, as done \r\nfor parametrizing SOA formation) and/or the implementation of artificial operators representing key steps of the chemistry (Emmerson \r\nand Evans, 2009; Xia et al., 2009; Stockwell et al., 2020). Additionally, aerosol microphysical processes (nucleation, coagulation, \r\ncondensation, evaporation and sedimentation) that determine the evolution of aerosol number concentrations and size particle \r\ndistribution are represented in parametrized forms in global models with varying levels of complexity (Mann et al., 2014)."


id: d40e72e7e991ce38a35ab7f7f65d8d1b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 30881
content:
"Global three-dimensional CCMs (Box 6.1, Figure 1) represent the full coupling of chemistry with climate physics (e.g., Morgenstern \r\net al., 2017) with different levels of complexity (e.g., interactive aerosols with or without tropospheric and/or stratospheric chemistry). \r\nMethane concentrations are typically prescribed or constrained to observations while emissions of other SLCFs (or their precursors) are \r\neither prescribed or calculated interactively in the current generation of CCMs (Collins et al., 2017). CCMs, now part of Earth system \r\nmodels (ESMs), are applied extensively to simulate the distribution and evolution of chemical compounds on a variety of spatial and \r\ntemporal scales to improve current knowledge, make future projections and investigate global scale chemistry-climate interactions \r\nand feedbacks (Section  3.8.2.2). CCMs are also used to interpret observations to disentangle the processes that drive observed \r\nvariability and trends"


id: 484b872e6ea1808085ff1662d919d689
created_at: 2023-03-16 14:02:01
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vector_id: 8745
content:
" Some aspects of air quality, such as diurnal peaks or local threshold violations, strong gradient in chemical \r\nregimes and coupling between processes cannot be captured by relatively coarse spatial resolution (>50 km) global CCMs (Markakis \r\net al., 2014) and necessitate subsequent downscaling modelling exercises."


id: fdef0999fe75915736e3668634b88967
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
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vector_id: 37472
content:
"model parametrizations (e.g.,  chemical mechanisms, photolysis schemes, parametrizations for mixing and convective transport, \r\nand deposition), model input parameters (e.g., reaction rate constants, emissions) and an incomplete understanding of the physical \r\nand chemical processes that determine SLCF distributions (Brasseur and Jacob, 2017; Young et al., 2018). CCMs can therefore not \r\ncapture every aspect of atmospheric chemical composition, but are expected to represent, as faithfully as possible, the sensitivity \r\nof chemical compounds to their drivers (e.g.,  anthropogenic emissions). Models are evaluated in multiple ways to identify their \r\nstrengths and weaknesses in explaining the evolution of SLCF abundances"


id: 3af1bf09a72935062c33420a131a203d
created_at: 2023-03-16 14:02:01
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content:
" For example, CCM simulations are performed in the \r\nnudged or offline meteorology mode, that is, driven by observed or reanalysed meteorology rather than in the free-running mode, \r\nfor consistent comparison of modelled chemical composition with observations for a specific time period (Dameris and Jockel, 2013). \r\nHowever, caution is exercised as nudging can alter the model climate resulting in unintentional impacts on the simulated atmospheric \r\nphysics and/or chemistry (Orbe et al., 2018; Chrysanthou et al., 2019). Chemical mechanisms implemented in CCMs are evaluated \r\nand intercompared to assess their skill in capturing relevant chemistry features (e.g., Brown-Steiner et al., 2018). The multi-model \r\nensemble approach, employed for evaluating climate models, has been particularly useful for characterizing errors in CCM simulations \r\nof SLCFs related to structural uncertainty and internal variability (Naik et al., 2013; Shindell et al., 2013; Young et al., 2013; Turnock \r\net al"


id: addf8bcc23e0d0c9ba7515e7c94b1a62
created_at: 2023-03-16 14:02:01
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vector_id: 24893
content:
", 2020). However, as discussed in Box 4.1, this approach is unable to capture the full uncertainty range. "


id: 206203d0e443815ebd4c097bfb1c97fc
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 2543
content:
"This assessment draws upon results from single-model studies and recent multi-model intercomparisons (e.g., AeroCom, CCMI), in \r\nparticular those endorsed by CMIP6 (see Table 1.3), which then allows for the full consideration of robustness and uncertainty due \r\nto model structures and processes. Based on the collective information provided in this body of literature, the CMIP6 multi-model \r\nensemble is largely fit-for-purpose of evaluating the influence of SLCFs on radiative forcing, climate and non-CO2 biogeochemical \r\nfeedbacks. Additionally, CMIP6 models are fit for capturing the global air pollution response to changes in emissions and meteorology, \r\nbut have difficulty in simulating the mean state (Turnock et al., 2020). The set of CMIP6 simulations has been used to update the \r\nrelations between emissions and surface temperature at the heart of the emulators (Cross-Chapter Box 7.1) and update emissions \r\nmetrics (Section 7.6)"


id: e02e7a1cba4cd8d51da40d98db36a62d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 32763
content:
" Emulators and emissions metrics are used in this chapter (Sections 6.6 and 6.7) to assess more specifically the \r\neffect of the individual SLCFs for each sector and region, which would be of prohibitive computing cost with CCMs. CCMs are also \r\nused to build global source-receptor models which use relations between surface concentrations and emissions. Such a model is used \r\nto assess the impact of various mitigation policies on air quality (Sections 6.5 and 6.7)."


id: 1620ab6d370a65bb638a5ffb4c1f50b4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 1004
content:
"The global chemical methane sink, essentially due to tropospheric \r\nOH, required to calculate the chemical lifetime is estimated by either \r\nbottom-up global CCMs and ESMs (BU) or top-down observational \r\ninversion methods (TD). BU global models represent the coupled \r\nchemical processes and feedbacks that determine the chemical sinks \r\nbut show large diversity in their estimates, particularly the tropospheric \r\nOH sink (Y. Zhao et al., 2019; Stevenson et al., 2020). TD inversion \r\nmethods, on the contrary, provide independent observational \r\nconstraints on the methane sink due to tropospheric OH over large \r\nspatio-temporal scales, but are prone to observational uncertainties \r\nand do not account for the chemical feedbacks on OH (Prather and \r\nHolmes, 2017; Naus et al., 2019)"


id: 7f5d7ded78c928c4c2ff9d6c5db8b45b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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content:
" The central estimate of mean chemical \r\nmethane loss over the period 2008-2017 varied from 602 [minimum \r\nand maximum range of 507-803] Tg yr-1 from BU chemistry-climate \r\nmodels in the Chemistry-Climate Modelling Initiative (CCMI) to \r\n514 [474-529] Tg yr-1 from TD inverse modelling (Section 5.2.2 and \r\nTable 5.2). The smaller range in the TD estimate (11%) results from the \r\nuse of a common climatological mean OH distribution (Saunois et al., \r\n2020; Zhao et al., 2020a), while the larger range in the BU estimate (49%) \r\nreflects the diversity in OH concentrations from different chemical"


id: c19ca21005eca22a1f37e96905db8276
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 28050
content:
"About 10% of the total atmospheric ozone column resides in the \r\ntroposphere. The ozone forcing on climate strongly depends on its \r\nvertical and latitudinal distribution in the troposphere. The lifetime \r\nof ozone in the troposphere ranges from a  few hours in polluted \r\nurban regions to up to few months in the upper troposphere. \r\nObserved tropospheric ozone concentrations range from less than \r\n10 ppb over the tropical Pacific Ocean to as much as 100 ppb in \r\nthe upper troposphere and more than 100 ppb downwind of major \r\nozone precursor emissions regions. An ensemble of five CMIP6 \r\nmodels including whole atmospheric chemistry and interactive \r\nozone has been shown to simulate consistently the present-day \r\nozone distribution (north to south and latitudinal gradients) and its \r\nseasonal variability when compared with observations from sondes, \r\nbackground surface stations and satellite products (Griffiths et al., \r\n2021)"


id: e2b3c81ed5fe863b6d6bce2ffb659dc6
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 33173
content:
" The biases, whose magnitude is similar to AR5, are lower than \r\n15% against climatological seasonal cycles from ozonesondes with \r\nan overestimate in the Northern Hemisphere and an underestimate \r\nin the Southern Hemisphere (Griffiths et al., 2021). The CMIP6 multi\u0002model ensemble estimate of the global mean lifetime of ozone for \r\npresent-day conditions is 25.5 +- 2.2 days (Griffiths et al., 2021), which \r\nis within the range of previous multi-model estimates (Stevenson \r\net al., 2006; Young et al., 2013), indicating a high level of confidence."


id: 6d5bea422de9fa0b11f165467ee27fbf
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 14821
content:
"The methane perturbation lifetime (tpert) is defined as the e-folding \r\ntime it takes for the methane burden to decay back to its initial value \r\nafter being perturbed by a change in methane emissions. Perturbation \r\nlifetime is longer than the total atmospheric lifetime of methane, as an \r\nincrease in methane emissions decreases tropospheric OH, which in \r\nturn increases the lifetime and therefore the methane burden (Prather, \r\n1994; Fuglestvedt et al., 1996; Holmes et al., 2013; Holmes, 2018). \r\nSince perturbation lifetime relates changes in emissions to changes \r\nin burden, it is used to determine the emissions metrics assessed in \r\nChapter  7 (Section  7.6). The perturbation lifetime is related to the \r\natmospheric lifetime as tpert = f * ttotal where f is the feedback factor \r\nand is calculated as f = 1/(1-s), where s = d (ln ttotal)/d (ln[CH4]) (Prather \r\net al., 2001)"


id: d2a701be20ad5b2c1a9600a8e5a3df03
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 30664
content:
" Since there are no observational constraints for either \r\ntpert or f, these quantities are derived from CCMs or ESMs. AR5 used \r\nf = 1.34 +- 0.06 based on a combination of multi-model (mostly CTMs \r\nand a few CCMs) estimates (Holmes et al., 2013). A recent model study \r\nexplored new aspects of methane feedbacks finding that the strength \r\nof the feedback, typically treated as a constant, varies in space and \r\ntime but will in all likelihood remain within 10% over the 21st century \r\n(Holmes, 2018). For this Assessment, the value of f is assessed to be \r\n1.30 +- 0.07 based on a six-member ensemble of AerChemMIP ESMs "


id: b8c6df8a7c745d62e641f0fda86fb2fd
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 26593
content:
"Ninety percent of the total column ozone (TCO) resides in the \r\nstratosphere. The chemical lifetime of ozone in the stratosphere \r\nranges from less than a  day in the upper stratosphere to several \r\nmonths in the lower stratosphere (Bekki and Lefevre, 2009). Global \r\nstratospheric ozone trends based on observations are assessed in \r\nChapter 2 (Section 2.2.5.2). The CMIP6 model ensemble shows that \r\nglobal TCO has slightly changed from 1850-1960 (Keeble et al., 2021). \r\nThe rapid decline in the 1970s and 1980s due to halogenated ozone\u0002depleting substances (ODSs, as assessed in Section  2.2.5.2 from \r\nobservations) until the end of the 1990s, followed by a slight increase \r\nsince then, is captured by the models (Keeble et al., 2021). Overall, \r\nthe observed climatology patterns and annual cycle amplitudes are \r\nwell represented in the CMIP6 ensemble mean"


id: 31c70adde280a851dd62f95f2cc2e6ae
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 5241
content:
" The CMIP6 ensemble \r\noverestimates the observed TCO values by up to 6% (10-20 Dobson \r\nUnits (DU)) globally in the NH and SH mid-latitudes, and in the \r\ntropics, but the trend in these regions is well captured between 1960 \r\nand 2014. However, there is poor agreement between the individual \r\nCMIP6 models in the pre-industrial period and throughout the \r\nhistorical period, with model TCO values spread across a range of \r\nabout 60 DU. The global stratospheric ozone column decreased by \r\n14.3 +- 8.7 DU from 1850-2014 (Keeble et al., 2021). "


id: f146d6d1d9862d6029ee8100ed882259
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 35463
content:
"The distribution of tropospheric NOx is highly variable in space and \r\ntime owing to its short lifetime coupled with highly heterogeneous \r\nemission and sink patterns. NOx undergoes chemical processing, \r\nincluding the formation of nitric acid (HNO3), nitrate (NO- ), and 3\r\norganic nitrates (e.g., alkyl nitrate and peroxyacyl nitrate), atmospheric \r\ntransport, and deposition. Despite challenges in retrieving quantitative \r\ninformation from satellite observations (Duncan et al., 2014; Lin et al., \r\n2015; Lorente et al., 2017; Silvern et al., 2018), improved accuracy and \r\nresolution of satellite-derived tropospheric NO2 columns over the past \r\ntwo decades have advanced understanding of the global distribution, \r\nlong-term trends and source attribution of NOx. Long-term average \r\ntropospheric NO2 column based on multiple satellite-borne instruments \r\n(Figure 6"


id: 4590f8b7514854893c754fdef67dbfdf
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vector_id: 8354
content:
"6a) reveals the highest NO2 levels over the most populated, \r\nurbanized and industrialized regions of the world corresponding to high \r\nNOx emissions source regions (Krotkov et al., 2016; Georgoulias et al., \r\n2019). Enhanced but highly variable NO2 columns are also associated \r\nwith biomass-burning regions as well as areas influenced by lightning \r\nactivity (Miyazaki et al., 2014; Tanimoto et al., 2015)."


id: 1d63f10cd70f11adea124b8d0d0b4edd
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vector_id: 2068
content:
"The AR5 reported NO2 decreases by 30-50% in Europe and North \r\nAmerica, and increases by more than a  factor of two in Asia, over \r\nthe 1996-2011 period based on satellite observations (Hartmann \r\net al., 2013). Extension of this analysis covering the time period up to \r\n2015 reveals that NO2 has continued to decline over the USA, Western \r\nEurope and Japan (Schneider et al., 2015; Duncan et al., 2016; Krotkov \r\net al., 2016) because of effective fossil fuel NOx emissions controls \r\n(Section 6.2), although this rate of decline has slowed down post-2011 \r\n(Jiang et al., 2018). Satellite observations also reveal a 32% decline in \r\nNO2 column over China after peaking in 2011 (Figure 6.6b), consistent \r\nwith declining NOx emissions (Section 6.2) due to the implementation \r\nof emissions-control strategies (de Foy et al., 2016; Irie et al., 2016; \r\nF. Liu et al., 2016)"


id: d43952081b411597f8341115b3595a5
created_at: 2023-03-16 14:02:01
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content:
" Over Southern Asia, tropospheric NO2 levels have \r\ngrown rapidly with increases of 50% during 2005-2015, largely \r\ndriven by hotspot areas in India experiencing rapid expansion of \r\nthe power sector (Duncan et al., 2016; Krotkov et al., 2016). Further \r\nanalysis indicates that many parts of India have also undergone \r\na  reversal in NO2 trends since 2011 that has been attributed to \r\na combination of factors, including a slowdown in economic growth, \r\nimplementation of cleaner technologies, non-linear NOx chemistry, \r\nand meteorological variability (Georgoulias et al., 2019). Satellite data "


id: 7ac716f726cfc2a0ae2272fa841c538e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 16897
content:
"The AR5 reported a  global CO decline of about 1% yr-1 based on \r\nsatellite data from 2002-2010, but biases in instruments rendered \r\nlow confidence in this trend. The AR5 also indicated a  small CO \r\ndecrease from in situ networks but did not provide quantitative \r\nestimates. New analysis of CO trends performed since AR5 and based \r\non different observational platforms and assimilation products show \r\na decline globally and over most regions during the last one to two \r\ndecades with varying amplitudes partly depending on the period of \r\nanalysis (Table 6.4). Inversion-based analysis attributes the global CO \r\ndecline during the past two decades to decreases in anthropogenic \r\nand biomass-burning CO emissions despite probable increase in \r\natmospheric CO chemical production (Gaubert et al., 2017; Jiang \r\net al., 2017; Zheng et al., 2019). Furthermore, Buchholz et al"


id: 4acb0a8213de110a4797c2ceb0e823f3
created_at: 2023-03-16 14:02:01
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vector_id: 9186
content:
" (2021) \r\nreport a  slowdown in global CO decline in 2010-2018 compared \r\nto 2002-2010, although the magnitude and sign of this change in \r\nthe trend varies regionally. Global models prescribed with emissions \r\ninventories developed prior to the CMIP6 inventory capture the \r\ndeclining observed CO trends over North America and Europe but \r\nnot over Eastern Asia (Strode et al., 2016). CMIP6 models driven by \r\nCMIP6 emissions simulate a negative trend in global CO burden over \r\nthe 1990-2020 period (Griffiths et al., 2021), however the simulated \r\ntrends have not yet been evaluated against observations. "


id: fca7a8813cec6fce99fb554a5fd8e98c
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 37263
content:
"Since AR5, advances in satellite retrievals (e.g.,  Worden et al., \r\n2013; Warner et al., 2014; Buchholz et al., 2021), ground-based \r\ncolumn observations (e.g.,  Zeng et al., 2012; Te et al., 2016), \r\nairborne platforms (e.g.,  Cohen et al., 2018; Petetin et al., 2018), \r\nsurface measurement networks (e.g., Andrews et al., 2014; Schultz \r\net al., 2015; Prinn et al., 2018; Petron et al., 2019) and assimilation \r\nproducts (e.g.,  Deeter et al., 2017; Flemming et al., 2017; Zheng \r\net al., 2019) have resulted in better characterization of the present\u0002day atmospheric CO  distribution. Typical annual mean surface \r\nCO concentrations range from around 120 ppb in the Northern \r\nHemisphere to around 40 ppb in the Southern Hemisphere (Petron \r\net al., 2019). The sub-regional patterns in CO reflect the distribution of \r\nemissions sources. Seasonal hotspots are linked to areas of biomass \r\nburning in tropical South America, equatorial Africa, South East Asia \r\nand Australia"


id: 61719970cb6e286efec59efd1dd1b4cc
created_at: 2023-03-16 14:02:01
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vector_id: 12901
content:
" A study using data assimilation techniques estimates \r\na global mean CO burden of 356 +- 27 Tg over the 2002-2013 period \r\n(Gaubert et al., 2017). "


id: afc0216a8d40d45225b5c26cfacc2c13
created_at: 2023-03-16 14:02:01
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vector_id: 25167
content:
"Formaldehyde (HCHO) is a short-lived, high-yield product of NMVOC \r\noxidation, and formaldehyde column data from satellite instruments \r\ncan therefore inform on trends in anthropogenic NMVOC abundances \r\nover very industrialized regions. The AR5 reported significant positive \r\ntrends in formaldehyde between 1997 and 2009 over northeastern \r\nChina (4% yr-1) and negative trends over northeastern US cities. \r\nSince AR5, there is robust evidence and high agreement of an upward \r\ntrend of HCHO over eastern China, though large regional disparities \r\nexist in the trends (De Smedt et al., 2015; Shen et al., 2019) with \r\na possible negligible or decreasing trend over Beijing and the Pearl \r\nRiver Delta. In other world regions, in particular North America, there \r\nis limited to medium evidence for significant changes in the HCHO \r\ncolumns, except in regions where the trend is particularly strong \r\n(e.g., the Houston area: -2.2% yr-1 over 2005-2014) and the Alberta \r\noil sands (+3"


id: 82fc1423277871081bdd18edf8e5921a
created_at: 2023-03-16 14:02:01
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vector_id: 18137
content:
"8% yr-1; Zhu et al., 2017). Over the northeastern USA, \r\neven the sign of the trend differs between studies (De Smedt et al., \r\n2015; Zhu et al., 2017) for reasons that are unclear. "


id: 7ac82315530cd9298a1e044e3edf390
created_at: 2023-03-16 14:02:01
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vector_id: 16899
content:
"Of the atmospheric VSLSs, brominated and iodinated species \r\nare predominantly of oceanic origin, while chlorinated species \r\nhave significant additional anthropogenic sources (Carpenter \r\net al., 2014; Hossaini et al., 2015). Global mean chlorine from the \r\nVSLSs has increased in the troposphere from about 91  ppt in \r\n2012 to about 110  ppt in 2016 (Engel et al., 2018). This increase \r\nis mostly due to dichloromethane (CH2Cl2), a  species that has \r\npredominantly anthropogenic sources reflected by three-times higher \r\nconcentrations in the Northern Hemisphere than in the Southern \r\nHemisphere (Hossaini  et al., 2017). The upward dichloromethane \r\ntrend is corroborated by upper-tropospheric aircraft data over \r\nthe period 1998-2014 (Elvidge et al., 2015b; Oram et al., 2017)"


id: a77342f8b8b01d26b2bc7ea228b31119
created_at: 2023-03-16 14:02:01
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vector_id: 23851
content:
" \r\nThe observations from the surface networks show that the abundance \r\nof dichloromethane continued to increase until 2019 (Annex III), \r\nalthough the accuracy of global abundance of VSLSs is limited by the \r\nscarce coverage by networks. No long-term changes of the bromine\u0002containing VSLSs have been observed (Engel et al., 2018)."


id: fd41a269ab7e3b5abd6a8ae50406285f
created_at: 2023-03-16 14:02:01
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vector_id: 37354
content:
"controls since the mid-1980s, and increases over Asia. From about \r\n2000, global mean AOD stabilized in the models, driven by soaring \r\nemissions in Southern Asia and declining emissions in Eastern Asia \r\n(Section  6.2.1). Trends after around 2010 are difficult to assess \r\nfrom CMIP6 models because the historical simulations end at 2014. \r\nNevertheless, the strong decline in anthropogenic SO2 emissions over \r\nEastern Asia since 2011 is underestimated in the CMIP6 emissions \r\ndatabase (Hoesly et al., 2018), indicating that the observed AOD \r\nchange over Eastern Asia may not be captured accurately by CMIP6 \r\nmodels (Wang et al., 2021). While all CMIP6 models simulate the \r\nincrease of AOD between 1850 and 2014 there is strong inter-model \r\ndiversity in the simulated AOD change since 1850 ranging from \r\n0.01 (15%) to 0.08 (53%) in 2014. Some models therefore lie outside \r\nthe 68% confidence interval of 0.02 (15%) to 0"


id: f91ab8f0e4abd826f040ef4561eb97c5
created_at: 2023-03-16 14:02:01
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vector_id: 36701
content:
"04 (or 30%) for \r\nglobal AOD change in 2005-2015 compared to 1850, estimated by \r\nBellouin et al. (2020) based on observational and model (excluding \r\nCMIP6) lines of evidence. In addition to the horizontal distribution \r\nof aerosols documented by AOD, their number size distribution, \r\nvertical distribution, optical properties, hygroscopicity, ability to act \r\nas CCN, chemical composition, mixing state and morphology are key \r\nelements to assess their climate effect (Section 6.4). "


id: 9b8ed4c66ec21eba27e00b4b1aaccaf3
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 22032
content:
"Based on long-term surface-based in situ observations, AR5 \r\nreported a  strong decline in sulphate aerosols in Europe and the \r\nUSA over 1990-2009, with the largest decreases occurring before \r\n2000 in Europe and post-2000 in the USA. Since AR5, atmospheric \r\nmeasurements in conjunction with model results have provided \r\ninsights into the spatial and temporal distribution of sulphate and \r\nsulphur deposition (Vet  et al., 2014; Tan et al., 2018; Aas et al., \r\n2019). The in situ observations in North America and Europe reveal \r\nsubstantial reduction since the measurements started around \r\n1980, though the trends have not been linear through this period \r\n(Table 6.5). Several regional studies agree with these trend estimates \r\nfor Europe (Banzhaf et al., 2015; Theobald et al., 2019) and North \r\nAmerica (Sickles II and Shadwick, 2015; Paulot et al., 2016). Further, \r\nthe concentrations of primary emitted SO2 (Section  6.3.3"


id: 97cea499a8fffc13d403529c309ab9c6
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vector_id: 21403
content:
"5) show \r\ngreater decreases than secondary sulphate aerosols over these \r\nregions due to a  combination of higher oxidation rate (hence \r\nmore SO2 converted to SO4\r\n2-) and increased dry deposition rate of \r\nSO2 (Fowler et al., 2009; Banzhaf et al., 2015). In situ observations \r\nover other parts of the world are scattered (Figure  6.7), and the \r\nlack of observations makes it too uncertain to quantify regional "


id: 69b6dbe6170346704c4c816aff966380
created_at: 2023-03-16 14:02:01
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vector_id: 14228
content:
"Ammonium nitrate is semi-volatile, which results in complex spatial \r\nand temporal patterns in its concentrations (Putaud et al., 2010; \r\nHand et al., 2012a; H. Zhang et al., 2012), reflecting variations in \r\nits precursors, NH3 and HNO3, as well as SO4\r\n2-, non-volatile cations, \r\ntemperature and relative humidity (Nenes et al., 2020). High relative \r\nhumidity and low temperature as well as elevated fine particulate \r\nmatter loading (Huang et al., 2014; Petit et al., 2015; H. Li et al., 2016; \r\nSandrini et al., 2016) favour nitrate production. Measurements reveal \r\nthe high contribution of NO3\r\n-\r\n to surface PM2.5 (>30%) in regions with \r\nelevated regional NOx and NH3 emissions, such as the Paris area \r\n(Beekmann et al., 2015; Zhang et al., 2019), northern Italy (Masiol \r\net al., 2015; Ricciardelli et al., 2017), Salt Lake City (Kuprov et al., \r\n2014; Franchin et al., 2018), the North China Plains (Guo et al., \r\n2014; Chen et al., 2016) and New Delhi (Pant et al., 2015)"


id: f4cb0864da164d031620daf349e7d5ac
created_at: 2023-03-16 14:02:01
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vector_id: 36020
content:
" Recent \r\nobservations also show that ammonium nitrate contributes to the \r\nAsian tropopause aerosol layer (Vernier et al., 2018; Hopfner et al., \r\n2019). Model diversity in simulating the present-day global fine-mode \r\nNO3\r\n-\r\n burden is large with two multi-model intercomparison studies \r\nreporting estimates in the range of 0.14-1.88 Tg and 0.08-0.93 Tg \r\nrespectively (Bian et al., 2017; Gliss et al., 2021). Models differ in their \r\nestimates of the global tropospheric nitrate burden by up to a factor \r\nof 13 with differences remaining nearly the same across the CMIP5 \r\nand CMIP6 generation of models (Bian et al., 2017; Gliss et al., 2021). \r\nWhile regional patterns in the concentration of fine-mode NO3\r\n-\r\n are \r\nqualitatively captured by models, the simulation of fine-mode NO3\r\n-\r\nis generally worse than that of NH4\r\n+ or SO42- (Bian et al., 2017)"


id: 630d0d92439b0e70884017032cc4bc95
created_at: 2023-03-16 14:02:01
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vector_id: 13154
content:
" This \r\ncan be partly attributed to the semi-volatile nature of ammonium \r\nnitrate and biases in the simulation of its precursors (Heald et al., \r\n2014; Paulot et al., 2016), including the sub-grid scale heterogeneity \r\nin NOx and NH3 emissions (Zakoura and Pandis, 2018). "


id: 4e271e2f11ddd3159575082ecd338d85
created_at: 2023-03-16 14:02:01
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vector_id: 9719
content:
"An extensive review on BC (Bond et al., 2013) discussed limitations in \r\ninferring its atmospheric abundance and highlighted inconsistencies \r\nbetween different terminology and related measurement techniques \r\n(Petzold et al., 2013; Sharma et al., 2017). Due to a lack of global \r\nobservations, AR5 only reported declining total carbonaceous aerosol \r\ntrends from the USA and a declining BC trend from the Arctic based \r\non data available up to 2008. Since AR5, the number of observation \r\nsites has grown worldwide (Figure  6.7) but datasets suitable for \r\nglobal trend analyses remain limited (Reddington et al., 2017; \r\nLaj et al., 2020). Locally, studies based on observations from rural and \r\nbackground sites have reported decreasing surface carbonaceous \r\naerosol trends in the Arctic, Europe, the USA, Japan and India \r\n(Table 6.6). Increases in carbonaceous aerosol concentrations in some \r\nrural sites of the western USA have been associated with wildfires \r\n(Hand et al., 2013; Malm et al"


id: 232bf119e37999a400beeee5b8f4c7bb
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vector_id: 2980
content:
", 2017). Long-term OA observations \r\nare scarce, so their trends outside of the USA are difficult to assess. \r\nIce-core analysis has provided insight into carbonaceous aerosol \r\ntrends predating the satellite and observation era over the Northern \r\nHemisphere (Section 2.2.6, Figure 2.9b). "


id: 9a540abbbe783b8cd7e0e7a35b319eb8
created_at: 2023-03-16 14:02:01
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vector_id: 21810
content:
"Simulated OA burdens also show a  large spread among global \r\nmodels, with Gliss et al. (2021) reporting a  multi-model median of \r\n1.91 +- 0.65 Tg for the year 2010. The large spread reflects the wide \r\nrange in the complexity of the OA parametrizations, particularly for \r\nSOA formation, as well as in the primary OA emissions (Tsigaridis \r\net al., 2014; Gliss et al., 2021). The uncertainties are particularly large \r\nin model estimates of SOA production rates, which vary between \r\n10 and 143 Tg yr-1 (Tsigaridis et al., 2014; Hodzic et al., 2016; Tilmes \r\net al., 2019). While the level of complexity in the representation of OA \r\nin global models has increased since AR5 (Shrivastava et al., 2017; \r\nHodzic et al., 2020), limitations in process-level understanding of \r\nthe formation, ageing and removal of organic compounds lead to \r\nuncertainties in the global model predictions of global OA burden \r\nand distribution as well as the relative contribution of POA and \r\nSOA to OA. Jo et al"


id: 15e826f63c7fe4bd22fb739f25418fcb
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 966
content:
" (2016) estimated that BrC contributes about \r\n20% of total OA burden. That would give BrC a burden similar to \r\nthat of BC (low confidence), enhancing the overall forcing exerted by \r\ncarbonaceous aerosol absorption (Zhang et al., 2020)."


id: 671f4ca0cc4143f19301827fe574a777
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 13817
content:
"BC and OA lifetimes are estimated to be 5.5 days +- 35% and 6.0 \r\ndays +- 29% (median +- 1 standard deviation), respectively, based \r\non an ensemble of 14 models (Gliss et al., 2021). Disagreement \r\nin  simulated lifetime leads to horizontal and vertical variations in \r\npredicted carbonaceous aerosol concentrations, with implications for \r\nradiative forcing (Samset et al., 2013; Lund et al., 2018b). Airborne \r\ncampaigns have provided valuable vertical-profile measurements \r\nof carbonaceous aerosol concentrations (Schwarz et al., 2013; \r\nFreney et al., 2018; Hodgson et al., 2018; Schulz et al., 2019; D. Zhao \r\net al., 2019; Morgan et al., 2020). Compared to those measurements, \r\nmodels tend to transport BC too high in the atmosphere, suggesting \r\nthat lifetimes are not larger than 5.5 days (Samset et al., 2013; Lund \r\net al., 2018b). Newly developed size-dependent wet-scavenging \r\nparametrization for BC (Taylor et al., 2014; Schroder et al., 2015; \r\nOhata et al., 2016; G. Zhang et al"


id: b04d2453d90c8884bf7534b616355bda
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vector_id: 25259
content:
", 2017; Ding et al., 2019; Moteki \r\net al., 2019; Motos et al., 2019) may lead to decreased BC lifetimes \r\nand improve agreement with observed vertical profiles. "


id: 101ce46618939497afeecefb8ab13812
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 23
content:
"unclear what level of sophistication is required to properly quantify \r\naerosol effects on climate (Boucher et al., 2013). Since the AR5, \r\nEkman (2014) found that the CMIP5 models with the most complex \r\nrepresentations of aerosol impacts on cloud microphysics had \r\nthe largest reduction in biases in surface temperature trends. \r\nCMIP6-generation CCMs that simulate aerosol and cloud-size \r\ndistributions better represent the effect of a  volcanic eruption \r\non lower atmosphere clouds than a  model with aerosol-mass \r\nonly (Malavelle et al., 2017). This highlights the need for skilful \r\nsimulation of conditions underlying aerosol-cloud interactions, \r\nsuch as the distribution, transport and properties of aerosol \r\nspecies, in addition to the interactions themselves (Chapter  7). \r\nIn advance of CMIP6, representations of aerosol processes and \r\naerosol-cloud interactions in ESMs have generally become more \r\ncomprehensive (Meehl et al., 2020; Gliss et al., 2021; Thornhill \r\net al"


id: 9b8b9573969a009c1b37f425bf684b7a
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 22029
content:
", 2021b; see also Section 1.5), with enhanced links to aerosol \r\nemissions and gas-phase chemistry. Many CMIP6 models (Annex \r\nII: Table AII.5) now simulate aerosol number size distribution, in \r\naddition to mass distribution, which is a prerequisite for accurately \r\nsimulating number concentrations of CCN (Bellouin et al., 2013), \r\nwhile some CMIP6 models use prescribed aerosol optical properties \r\nto constrain aerosol forcing (e.g., Stevens et al., 2017). Hence, the \r\nrange of complexity in aerosol modeling noted in CMIP5 is still \r\npresent in the CMIP6 ensemble. Although simulated CCN have \r\nbeen compared to surface (Fanourgakis et al., 2019) and aircraft \r\n(Reddington et al., 2017) measurements, with mixed results, \r\nthe lack of global coverage limits confidence in the evaluations. \r\nEvaluations of AOD have been more wide-ranging (Section 6.3.5; \r\nGliss et al"


id: 22fd9c2f85895efa47aa53f3ae6bb118
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 2950
content:
", 2021) but are less relevant to aerosol-cloud interactions \r\nas they do not allow the evaluation of vertical profiles, aerosol\u0002cloud overlap regions, aerosol type or number. Nevertheless, biases \r\nin simulated patterns and trends in AOD, alongside biases in cloud \r\nfractions (Vignesh et al., 2020), likely affect quantifications of the \r\naerosol-cloud interactions. "


id: 9f43f2e9b8272483f09d1e3b349f003a
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 22597
content:
"In AR5, the confidence in the spatial patterns of aerosol and ozone \r\nforcing was lower than that for the global mean because of the large \r\nspread in the regional distribution simulated by global models, and \r\nwas assessed as medium. The AR5 assessment was based on aerosol \r\nand ozone RFs, and aerosol ERFs (with fixed SSTs) from ACCMIP and \r\na small sample of CMIP5 models (Myhre et al., 2013; Shindell et al., \r\n2013). For this assessement, the spatial distribution of aerosol ERF \r\ndue to human-induced changes in aerosol concentrations over 1850-\r\n2014 is quantified based on results from a seven-member ensemble \r\nof CMIP6 ESMs including interactive gas and aerosol chemistry \r\nanalysed in AerChemMIP. There is insufficient information to estimate \r\nthe spatial patterns of ozone ERF from CMIP6, however, the spatial \r\npatterns in SLCF ERF are dominated by that from aerosol ERF over \r\nmost regions (e.g.,  Shindell et al., 2015)"


id: a46439f3771ac93053a124d0374548ac
created_at: 2023-03-16 14:02:01
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vector_id: 23427
content:
" The aerosol ERF includes \r\ncontributions from both direct aerosol-radiation (ERFari) and indirect \r\naerosol-cloud interactions (ERFaci; Section 7.3.3), and is computed \r\nas the difference between radiative fluxes from simulations with \r\ntime-evolving aerosol and their precuror emissions, and identical \r\nsimulations but with these emissions held at their 1850 levels (Collins \r\net al., 2017). Both simulations are driven by time-evolving sea surface \r\ntemperatures (SSTs) and sea ice from the respective coupled model \r\nhistorical simulation, and therefore, differ from ERFs computed using \r\nfixed pre-industrial SST and sea ice fields (Section  7.3.1), but the \r\neffect of this difference is generally small (Forster et al., 2016). A \r\ncorrection for land surface temperature change (Section 7.3.1) is not \r\navailable from these data to explicitly quantify the contribution from \r\nadjustments"


id: ad34e8eaa36b72a0247bcaf633bf2308
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content:
" The ESMs included here used the CMIP6 anthropogenic \r\nand biomass-burning emissions for ozone and aerosol precursors \r\nbut varied in their representation of the natural emissions, chemistry \r\nand climate characteristics contributing to spread in the simulated \r\nconcentrations (Section 6.3) and resulting forcings, partly reflecting \r\nuncertainties in the successive processes (Thornhill et al., 2021b)."


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vector_id: 23128
content:
"The geographical distribution of the ensemble-mean aerosol ERF \r\nover the 1850-2014 period is highly heterogeneous (Figure 6.10a) in \r\nagreement with AR5. Negative ERF is greatest over and downwind of \r\nmost industrialized regions in the Northern Hemisphere and to some \r\nextent over tropical biomass-burning regions, with robust signals. \r\nThe largest negative forcing occurs over Eastern Asia and Southern \r\nAsia, followed by Europe and North America, reflecting the changes \r\nin anthropogenic aerosol emissions in recent decades (Section 6.2). \r\nPositive ERFover high albedo areas, including cryosphere, deserts and \r\nclouds, also found in AR5 and attributed to absorbing aerosols, are \r\nnot robust across the small CMIP6 ensemble applied here. Regionally \r\naggregated shortwave (SW) and longwave (LW) components of \r\nthe aerosol ERF exhibit similar large variability across regions \r\n(Figure 6.10b)"


id: dd738a0b49ba4afc3e15f463c6492c88
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 32342
content:
" The SW flux changes come from aerosol-radiation and \r\naerosol-cloud interactions while the small positive LW flux changes \r\ncome from aerosol-cloud interactions (related to liquid-water path \r\nchanges (Section 7.3.2.2). These spatial patterns in aerosol ERF are \r\nsimilar to the patterns reported in AR5. "


id: 8c72eaefa046c5b34148062daf8b1ff
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 19613
content:
"The best estimate for the ERF due to emissions of BC is reduced from \r\nthe AR5, and is now estimated to be 0.11 [-0.20 to 0.42] W  m-2\r\nwith an uncertainty also including negative values. As discussed in \r\nSection  7.3.3.1.2, a  significant portion of the positive BC forcing \r\nfrom aerosol-radiation interactions is offset by negative atmospheric \r\nadjustments due to cloud changes, as well as lapse rate and \r\natmospheric water vapour changes, resulting in a smaller positive net \r\nERF for BC compared with AR5. The large range in the forcing estimate \r\nstems from variation in the magnitude and sign of atmospheric \r\nadjustments across models and is related to the differences in the \r\nmodel treatment of different processes affecting BC (e.g.,  ageing, \r\nmixing) and its interactions with clouds and cryosphere (Section 7.3.3; \r\nThornhill et al., 2021b). The emissions-based ERF for organic carbon \r\naerosols is -0.21 [-0.44 to +0"


id: e9c07c8f01f6d7fe00870212ef692ec4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 34275
content:
"02] W m-2, a weaker estimate compared \r\nwith AR5 attributed to stronger absorption by OC (Section 7.3.3.1.2)."


id: b9ad6e398e4adc82590e2042656c8853
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 26756
content:
"The emissions-based contributions to GSAT change (Figure  6.12b) \r\nwere not assessed in AR5, but with the ERF from aerosol-cloud \r\ninteractions attributed to the emitted compounds there is now \r\na better foundation for this assessment. The contribution to emissions\u0002based ERF at 2019 (Figure 6.12a) is scaled by the historical emissions \r\n(over the period 1750-2019) of each compound to reconstruct \r\nthe historical time series of ERF. An impulse response function (Cross\u0002Chapter Box 7.1, Supplementary Material 7.SM5.2) is then applied \r\nto obtain the contribution of SLCF emissions to the GSAT response. \r\nDue  to the non-linear chemical and physical processes described \r\nabove relating emissions to ERF, and the additional non-linear \r\nrelations between ERF and GSAT, these emissions-based estimates \r\nof GSAT responses strongly depend on the methodology applied to \r\nestimate ERF and GSAT (Supplementary Material 6.SM.2)"


id: 2c9bde747b71f474c8c7b7d1ffbe751b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 4448
content:
" Therefore, \r\nthe relative contribution of each compound through its primary \r\nemissions versus secondary formation or destruction (e.g.,  for \r\nmethane emissions its ozone versus methane contributions), by \r\nconstruction (omitting the non-linear processes), will be equal for \r\nERF and GSAT. Uncertainties in the GSAT response are estimated \r\nusing the assessed range of the equilibrium climate sensitivity (ECS) \r\nfrom Chapter 7 of this report. For most of the emitted compounds the \r\nuncertainty in the GSAT response is dominated by the uncertainty in \r\nthe relationship between emissions and the ERF."


id: e1c376988627d9884579e31350b17cd2
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 33014
content:
"On the global scale, as assessed in Chapter 3, anthropogenic aerosols \r\nhave likely cooled GSAT since 1850-1900 driven by the negative \r\naerosol forcing, while it is extremely likely that human-induced \r\nstratospheric ozone depletion has primarily driven stratospheric cooling \r\nbetween 1979 and the mid-1990s. Multiple modelling studies support \r\nthe understanding that present-day emissions of SO2, a  precursor \r\nfor sulphate aerosols, are the dominant driver of near- surface air \r\ntemperature responses in comparison to BC or OC even though, for \r\nsome regions, BC forcing plays a key role (Baker et al., 2015; Samset \r\net al., 2016; Stjern et al., 2017; Zanis et al., 2020). Further, there is \r\nhigh confidence that the aerosol-driven cooling has led to detectable \r\nlarge-scale water-cycle changes since at least the mid-20th century \r\nas assessed in Chapter 8"


id: 7285e58fc3edf6b518aba2a464311ca0
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 15638
content:
" The overall effect of surface cooling from \r\nanthropogenic aerosols is to reduce global precipitation and alter large\u0002scale atmospheric circulation patterns (high confidence), primarily \r\ndriven by the cooling effects of sulphate aerosols (Section  8.2.1).\r\nIn addition, there is high confidence that darkening of snow through \r\nthe deposition of black carbon and other light-absorbing particles \r\nenhances snowmelt (Section 7.3.4.3; SROCC Chapter 3)."


id: 92f327aa27a9e7f30dd42afad2a27d63
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 20633
content:
"The Arctic region is warming considerably faster than the rest of \r\nthe globe (Atlas 11.2.2) and, generally, studies indicate that this \r\namplification of the temperature response toward the Arctic has \r\nan important contribution from local and remote aerosol forcing \r\n(Stjern et al., 2017; Westervelt et al., 2018). Several studies indicate \r\nthat changes in long-range transport of sulphate and BC from \r\nnorthern mid-latitudes can potentially explain a significant fraction \r\nof Arctic warming since the 1980s (e.g., Navarro et al., 2016; Breider \r\net al., 2017; Ren et al., 2020). Modelling studies show that changes \r\nin mid-latitude aerosols have influenced Arctic climate by changing \r\nthe radiative balance through aerosol-radiation and aerosol-cloud \r\ninteractions, and enhancing poleward heat transport (Navarro et al., \r\n2016; Ren et al., 2020)"


id: f8f745bf979a7b07ea97bca2c9c87c49
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 36680
content:
" Idealized aerosol-perturbation studies have \r\nshed further light on the sensitivity of Arctic temperature response to \r\nindividual aerosol species. Studies show relatively large responses in \r\nthe Arctic to BC perturbations and reveal the importance of remote \r\nBC forcing by rapid adjustments (Sand et al., 2013b; Stjern et al., \r\n2017; L. Liu et al., 2018; Yang et al., 2019b). Perturbations in SO2\r\nemissions over major emitting regions in the Northern Hemisphere \r\nhave been shown to produce the largest Arctic temperature responses \r\n(Kasoar et al., 2018; Lewinschal et al., 2019). "


id: db470462e8c13097167d9fca0b0fca0e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 32007
content:
"the temperature response is more evenly distributed in line with the \r\nresults of CMIP5 models including the temperature response to ozone \r\nchanges (Shindell et al., 2015). The ensemble-mean global mean \r\nsurface temperature decreases by 0.66degC +- 0.51degC while decreasing \r\nby 0.97degC +- 0.54degC for the Northern Hemisphere and 0.34degC +- 0.2degC \r\nfor the Southern Hemisphere. The zonal-mean temperature response \r\nis negative at all latitudes (high confidence) and becomes more \r\nnegative with increasing latitude, with a maximum ensemble-mean \r\ndecrease of around 2.7degC at northern polar latitudes. The zonal\u0002mean response is not directly proportional to the zonal-mean forcing, \r\nespecially in the Arctic where the temperature response is cooling \r\nwhile the local ERF is positive (Figure 6.10). This is consistent with \r\nprior studies showing that the Arctic, in particular, is highly sensitive \r\nto forcing at NH mid-latitudes (e.g.,  Shindell and Faluvegi, 2009; \r\nSand et al"


id: f4c187469a6f7c81ff2bc838dc6abf5b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 36014
content:
", 2013a) and with results from CMIP5 models (more on the \r\nArctic below; Shindell et al., 2015). Thus, there is high confidence that \r\nthe temperature response to aerosols is more asymmetric than the \r\nresponse to WMGHGs and negative at all latitudes."


id: f5ca21766873291bc0fa9de304c11662
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 36187
content:
"Tropospheric aerosols influence the land and ocean ecosystem \r\nproductivity and the carbon cycle through changing physical climate \r\nand meteorology (Jones, 2003; Cox et al., 2008; Mahowald, 2011; \r\nUnger et al., 2017) and through changing deposition of nutrients \r\nincluding nitrogen, sulphur, iron and phosphorous (Mahowald \r\net al., 2017; Kanakidou et al., 2018). There is robust evidence and \r\nhigh agreement from field (Oliveira et al., 2007; Cirino et al., 2014; \r\nRap et al., 2015; X. Wang et al., 2018) and modelling (Mercado et al., \r\n2009; Strada and Unger, 2016; Lu et al., 2017; Yue et al., 2017) studies \r\nthat aerosols affect plant productivity through increasing the diffuse \r\nfraction of downward shortwave radiation, although the magnitude \r\nand importance to the global land carbon sink is controversial"


id: c2118b20ef3f57f9cc6777e3216bf5f4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 28113
content:
" At large \r\nscales the dominant effect of aerosols on the carbon cycle is likely\r\na global cooling effect of the climate (medium confidence) (Jones, \r\n2003; Mahowald, 2011; Unger et al., 2017). We  assess that these \r\ninteractions between aerosols and the carbon cycle are currently too \r\nuncertain to constrain quantitatively the indirect CO2 forcing."


id: 6ccb097169b51fea83884e28b791470a
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 14732
content:
"Ozone uptake itself damages photosynthesis and reduces plant \r\ngrowth with consequences for the carbon and water cycles \r\n(Ainsworth et al., 2012; Emberson et al., 2018). The AR5 concluded \r\nthere was robust evidence of the effect of ozone on plant physiology \r\nand subsequent alteration of the carbon storage, but considered \r\ninsufficient quantification of and a lack of systematic incorporation \r\nof the ozone effect in carbon-cycle models as a limitation to assess \r\nthe terrestrial carbon balance (Ciais et al., 2013). Since AR5 several \r\nmore ESMs have incorporated interactive ozone-vegetation damage \r\nresulting in an increase in evidence to support the influence of ozone \r\non the land carbon cycle"


id: d18ca196e8080d2489a5ef9b0429d403
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 30500
content:
" The new modelling studies tend to focus \r\non ozone effects on plant productivity rather than the land carbon \r\nstorage and agree that ozone-induced gross-primary productivity \r\n(GPP) losses are largest today in the eastern USA, Europe and eastern \r\nChina, ranging from 5-20% on the regional scale (low confidence) \r\n(Yue and Unger, 2014; Lombardozzi et al., 2015; Yue et al., 2017; \r\nOliver et al., 2018). There is medium evidence and high agreement\r\nbased on observational studies and models that ozone-vegetation \r\ninteractions further influence the climate system, including water and \r\ncarbon cycles by affecting stomatal control over plant transpiration of \r\nwater vapour between the leaf surface and atmosphere (Wittig et al., \r\n2007; Sun et al., 2012; VanLoocke et al., 2012; Lombardozzi et al., \r\n2013; Hoshika et al., 2015; Arnold et al., 2018)"


id: 18178d44a177b59743cc3da8fbff85c8
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 1282
content:
" While some modelling \r\nstudies suggest that the unintended Nr deposition fertilization effect \r\nin forests may potentially offset the ozone-induced carbon losses \r\n(Felzer et al., 2007; de Vries et al., 2017), complex interactions have \r\nbeen observed between ozone and Nr deposition to ecosystems that \r\nhave not yet been included in ESMs. For some plants, the effects \r\nof increasing ozone on root biomass become more pronounced as "


id: cae966523cd45321c9e43afee05fc8f6
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 29485
content:
"climate feedbacks are assessed in Section 7.4.2. Additionally, non-CO2\r\nbiogeochemical feedbacks due to climate-driven changes in methane \r\nsources and N2O sources and sinks are assessed in Section  5.4.7. \r\nThe goal of this section is to estimate the feedback parameter \r\n(a  as defined in section  7.4.1.1) from climate-induced changes in \r\natmospheric abundances or lifetimes of SLCFs mediated by natural \r\nprocesses or atmospheric chemistry. These non-CO2 biogeochemical \r\nfeedbacks act on time scales of years to decades and have important \r\nimplications for climate sensitivity and emissions-abatement policies. \r\nThe feedback parameter is quantified entirely from ESMs that \r\nexpand the complexity of CCMs by coupling the physical climate and \r\natmospheric chemistry to land and ocean biogeochemistry. In AR5, \r\na for non-CO2 biogeochemical feedbacks was estimated from an \r\nextremely limited set of modelling studies with much less confidence \r\nassociated with the estimate"


id: ecc7603d2db6d234c7cd71a120991ef2
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 34731
content:
" Since AR5, ESMs have advanced to \r\ninclude more feedback processes, facilitating a relatively more robust \r\nassessment of a. CMIP6 ESMs participating in AerChemMIP performed \r\ncoordinated sets of experiments (Collins et al., 2017) facilitating the \r\nconsistent estimation of a (Thornhill et al., 2021a) and we rely on this \r\nmulti-model analysis for the best estimates (Table 6.8). Considering \r\nthe consistent methodology, the assessed central values and 5-95% \r\nranges for a are based on the AerChemMIP estimates. The full range \r\nof model uncertainty is not captured in AerChemMIP because of the \r\nrelatively small ensemble size, therefore estimates from studies using \r\nother models or with different protocols are discussed to reinforce or \r\ncritique these values. "


id: a2a4e1f4db701add4e8ed416d21ca261
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 23152
content:
"effects on DMS fluxes produce diverging estimates of changes in \r\nDMS emissions strength under climate change resulting in large \r\nuncertainties in the DMS-sulphate-cloud albedo feedback (Bopp et al., \r\n2004; Kloster et al., 2007; Gabric et al., 2013). In AR5, the climate\u0002DMS feedback parameter was estimated to be -0.02 W m-2 degC-1 based \r\non a single model. Since AR5, new modelling studies using empirical \r\nrelationships between pH and total DMS production find that global \r\nDMS emissions decrease due to combined ocean acidification and \r\nclimate change, leading to a  strong positive climate feedback (Six \r\net al., 2013; Schwinger et al., 2017). However, another study argues \r\nfor a much weaker positive feedback globally due to complex and \r\ncompensating regional changes in marine ecosystems (S. Wang et al., \r\n2018). The AerChemMIP multi-model analysis suggests small positive \r\nfeedback (Table 6"


id: 3c911f1b3acc51283ea304fbd9bd7f75
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 6898
content:
"8), consistent with these recent studies, but with \r\nlarge uncertainties in the magnitude of a. "


id: b864c188807b059fec784d5f4e3dabf3
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 26541
content:
"Climate-dust feedback: Mineral dust is the most abundant aerosol \r\ntype in the atmosphere, when considering aerosol mass, and affects \r\nthe climate system by interacting with both longwave and shortwave \r\nradiation as well as contributing to the formation of CCN and \r\nINP. Because dust emissions are sensitive to climate variability \r\n(e.g.,  through changes in the extent of arid land; Section  6.2.2.4), \r\nit has been hypothesized that the climate-dust feedback could be \r\nan important feedback loop in the climate system. Since AR5, an \r\nimproved understanding of the shortwave absorption properties \r\nof dust as well as a  consensus that dust particles are larger than \r\npreviously thought has led to a  revised understanding that the \r\nmagnitude of radiative forcing due to mineral dust is small (Kok et al., \r\n2017; Ryder et al., 2018)"


id: 41ead247d0666f0668ae3b9c1e5667b9
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 7770
content:
" A recent study notes that global models \r\nunderestimate the amount of coarse dust in the atmosphere and \r\naccounting for this limitation raises the possibility that dust emissions \r\nwarm the climate system (Adebiyi and Kok, 2020). Model predictions \r\nof dust emissions in response to future climate change range from an \r\nincrease (Woodward et al., 2005) to a decrease (Mahowald and Luo, \r\n2003), thus leading to high uncertainties on the sign of the climate\u0002dust feedback. Since AR5, Kok et al. (2018) estimated the direct \r\ndust-climate feedback parameter, from changes in the dust direct \r\nradiative effect only, to be in the range -0.04 to +0.02W m-2 degC-1. \r\nThe assessed central value and the 5-95% range of the climate-dust \r\nfeedback parameter based on AerChemMIP ensemble (Table  6.8) \r\nis within the range of the published estimate, however both the \r\nmagnitude and sign of a are model-dependent. "


id: 931578b1e2660bba502d337773f53091
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 20654
content:
"Climate-sea-spray feedback: Sea-spray emissions from ocean \r\nsurfaces influence climate directly or indirectly through the formation \r\nof CCN as discused in Section 6.2.1.2. They are sensitive to SST and \r\nsea ice extent, as well as to wind speed, and are therefore expected \r\nto feedback on climate (Struthers et al., 2013). However, there are \r\nlarge uncertainties in the strength of climate feedback from sea\u0002spray aerosols because of the diversity in the model representation \r\nof emissions (many represent sea-salt emissions only) and their \r\nfunctional dependence on environmental factors noted above, in situ \r\natmospheric chemical and physical processes affecting the sea-spray \r\nlifetime, and aerosol-cloud interactions (Struthers et al., 2013; Soares \r\net al., 2016; Nazarenko et al., 2017)"


id: 7793ce43b9e92954e97410a8598279f0
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 16368
content:
" Additional work is needed to \r\nidentify how sea-spray and POA emissions respond to shifts in ocean \r\nbiology and chemistry in response to warming, ocean acidification \r\nand changes in circulation patterns (Cochran et al., 2017), and \r\naffect CCN and INP formation (DeMott et al., 2016). AerChemMIP \r\nmodels, representing only the sea-salt emissions, agree that the \r\nsea-salt-climate feedback is negative, however there is a large range \r\nin the feedback parameter indicating large uncertainties (Table 6.8)."


id: acbc50006bca9c3f135fb122f20c3a56
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 24724
content:
"climate will lead to decreases in the tropical lower troposphere \r\nowing to increased water vapour, and increases in the subtropical \r\nto mid-latitude upper troposphere due to increases in lightning \r\nand stratosphere-to-troposphere transport (Stevenson et al., 2013). \r\nA small positive feedback is estimated from climate-induced changes \r\nin global mean tropospheric ozone (Dietmuller et al., 2014) while \r\na small negative feedback is estimated by Heinze et al. (2019) based \r\non the model results of Stevenson et al. (2013). Additionally, these \r\nozone feedbacks induce a  change in stratospheric water vapour \r\namplifying the feedback due to stratospheric ozone (Stuber et al., \r\n2001). Since AR5, several modelling studies have estimated the \r\nintensity of meteorology-driven ozone feedbacks on climate from \r\neither combined tropospheric and stratospheric ozone changes or \r\nseparately with contrasting results. One study suggests no change \r\n(Marsh et al"


id: bc4bfe2b1d15bcdbb9a1ae027a4a2234
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 27202
content:
", 2016), while other studies report reductions of ECS \r\nranging from 7-8% (Dietmuller et al., 2014; Muthers et al., 2014) \r\nto 20% (Nowack et al., 2015). The estimate of this climate-ozone \r\nfeedback parameter is very strongly model-dependent with values \r\nranging from -0.13 to -0.01 W m-2 degC-1 though there is agreement \r\nthat it is negative. The assessed central value and the 5-95% range of \r\nclimate-ozone feedback parameter based on AerChemMIP ensemble \r\nis within the range of these published estimates, but closer to the \r\nlower bound. This climate-ozone feedback factor does not include \r\nthe feedback on ozone from lightning changes which is discussed \r\nseparately below. "


id: c12a9cc865eed1410d0483ac0569ae45
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 27973
content:
"Climate-fire feedback: Wildfires are a  major source of SLCF \r\nemissions (Section  6.2.2.6). Climate change has the potential to \r\nenhance fire activity (Sections 12.4 and 5.4.3.2) thereby enhancing \r\nSLCF emissions leading to feedbacks. Climate-driven increases in fire \r\ncould potentially lead to offsetting feedback from increased ozone \r\nand decreased methane lifetime (due to increases in OH) leaving \r\nthe feedback from aerosols to dominate with an uncertain net \r\neffect (e.g., Landry et al., 2015). The AR5 assessment of climate-fire \r\nfeedbacks included a value of a due to fire aerosols to be in the range \r\nof -0.03 to + 0.06 W m-2 degC-1 based on Arneth et al. (2010a). A recent \r\nstudy estimates climate feedback due to fire aerosols to be greater \r\nthan that due to BVOCs, with a value of a equal to -0.15 (-0.24 to \r\n-0.05) W m-2 degC-1 (Scott et al., 2018)"


id: 8460d12d1761ccbca9942b78bddc518a
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 18361
content:
" Clearly, the assessment of fire\u0002related non-CO2 biogeochemical feedbacks is very uncertain because \r\nof limitations in the process understanding of the interactions \r\nbetween climate, vegetation and fire dynamics, and atmospheric \r\nchemistry and their representation in the current generation \r\nESMs. Some AerChemMIP ESMs include the representation of fire \r\ndynamics but do not activate their interaction with atmospheric \r\nchemistry. Given the large uncertainty and lack of information from \r\nAerChemMIP ESMs, we do not include a quantitative assessment of \r\nclimate-fire feedback for AR6. "


id: fc86db73c78d3776cb497dc70c97d578
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 37243
content:
"Climate-BVOC feedback: BVOCs, such as isoprene and \r\nterpenes, are produced by land vegetation and marine plankton \r\n(Sections 6.2.2.3 and 6.2.2.5). Once in the atmosphere, BVOCs and \r\ntheir oxidation products lead to the formation of secondary organic \r\naerosols (SOA) exerting a  negative forcing, and increased ozone \r\nconcentrations and methane lifetime exerting a  positive forcing. \r\nBVOC emissions are suggested to lead to a climate feedback in part \r\nbecause of their strong temperature dependence observed under \r\npresent-day conditions (Kulmala et al., 2004; Arneth et al., 2010a). \r\nTheir response to future changes in climate and CO2 levels remains \r\nuncertain (Section 6.2.2.3). Estimates of the climate-BVOC feedback \r\nparameter are typically based on global models which vary in their \r\nlevel of complexity of emissions parametrization, BVOC speciation, \r\nthe mechanism of SOA formation and the interaction with ozone \r\nchemistry (Thornhill et al., 2021a)"


id: 52122a7b96fa779935648775b3f660a7
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 10368
content:
" Since AR5, observational studies \r\n(Paasonen et al., 2013) and models (Scott et al., 2018) estimate the \r\nfeedback due to biogenic SOA (via changes in BVOC emissions) to \r\nbe in the range of about -0.06 to -0.01 W m-2 degC-1. The assessed \r\ncentral estimate of the climate-BVOC feedback parameter based on \r\nthe AerChemMIP ensemble suggests that climate-induced increases \r\nin SOA from BVOCs will lead to a  strong cooling effect that will \r\noutweigh the warming from increased ozone and methane lifetime, \r\nhowever the uncertainty is large (Thornhill et al., 2021a). "


id: b54394b5806bfe813d7f95a8834fdd37
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 26063
content:
"For marine cloud brightening (MCB), seeded aerosols may affect both \r\ncloud microphysical and macrophysical properties (Section 7.3.3.2). \r\nBy principle, MCB relies on ERFaci through the so-called Twomey \r\neffect (Twomey, 1977), but ERFari may be of equal magnitude \r\nas shown in studies that consider spraying of sea salt outside \r\ntropical marine cloud areas (Jones and Haywood, 2012; Partanen \r\net al., 2012; Alterskjaer and Kristjansson, 2013; Ahlm et al., 2017). \r\nThe maximum negative ERF estimated from modelling is within the \r\nrange of -5.4 to -0.8 W m-2 (Latham et al., 2008; Rasch et al., 2009; \r\nJones et al., 2011; Partanen et al., 2012; Alterskjaer and Kristjansson, \r\n2013). For dry sea salt, the ERF efficiency is estimated to be within \r\nthe range of -3 to -10 W m-2/(Pg yr-1), when emitted over tropical \r\noceans in  ESMs in the Geoengineering Intercomparison Project \r\n(GeoMIP; Ahlm et al., 2017)"


id: 367abad9a21d7d499f75fc54b003d842
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 5964
content:
" Cloud-resolving models reveal the \r\ncomplex behaviour and response of stratocumulus clouds to seeding, \r\nin that the ERF efficiency depends on meteorological conditions, \r\nand the ambient aerosol composition, where lower background \r\nparticle concentrations may increase the ERFaci efficiency (Wang \r\net al., 2011). Seeding could suppress precipitation formation and \r\ndrizzle, and hence increase the lifetime of clouds, preserving their \r\ncooling effect (Ferek et al., 2000). In contrast, cloud lifetime could \r\nbe decreased by making the smaller droplets more susceptible to \r\nevaporation. Modelling studies have shown that a  positive ERFaci\r\n(warming) could also result from seeding clouds with too large \r\naerosols (Pringle et al., 2012; Alterskjaer and Kristjansson, 2013)"


id: 8481693a1dacfb2dd391ab171e6b7768
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 18378
content:
" \r\nThese individual and combined processes are not well understood, \r\nand may have a limited representation in models, or counteracting \r\nerrors (Mulmenstadt and Feingold, 2018), lending low to medium \r\nconfidence to the ERF estimates."


id: e75d2f3203e1bff19d8ed39eea6ec098
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 33894
content:
"Stratospheric aerosol injections (SAI) have the potential to achieve \r\na  high negative global ERF, with maximum ERFs ranging from \r\n-5  to  -2  W m-2 (Niemeier and Timmreck, 2015; Weisenstein \r\net al., 2015; Niemeier and Schmidt, 2017; Kleinschmitt et al., 2018). \r\nThe  magnitude of the maximum achievable ERF depends on the \r\nchosen aerosol type and mixture, internal structure and size, or \r\nprecursor gas (e.g., SO2), as well as the injection strategy (latitude, \r\naltitude, magnitude and season of injections), plume dispersal, \r\nmodel representation of aerosol microphysics, and ambient aerosol \r\nconcentrations (Rasch et al., 2008; Robock et al., 2008; Pierce et al., \r\n2010; Weisenstein et al., 2015; Laakso et al., 2017; MacMartin et al., \r\n2017; Dai et al., 2018; Kleinschmitt et al., 2018; Vattioni et al., 2019; \r\nVisioni et al., 2019). For sulphur, the radiative forcing efficiency is \r\nof around -0.1 to -0.4 W  m-2/(TgS yr-1) (Niemeier and Timmreck, \r\n2015; Weisenstein et al"


id: e6ae725d9bb65ce886b5108ac9508d1d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 33778
content:
", 2015; Niemeier and Schmidt, 2017). \r\nDifferent manufactured aerosols, such as ZrO2, TiO2 and Al2O3, have \r\ndifferent ERF efficiencies compared to sulphate (Ferraro et al., 2011; \r\nWeisenstein et al., 2015; Dykema et al., 2016; Jones et al., 2016). \r\nThe aerosol size distribution influences the optical properties of an "


id: 1f38133280bc3a98c4776eea59c86927
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 2352
content:
"Air pollutants can be impacted by climate change through physical \r\nchanges affecting meterorological conditions, chemical changes \r\naffecting their lifetimes, and biological changes affecting their natural \r\nemissions (Kirtman et al., 2013). Changes in meteorology affect air \r\nquality directly through modifications of atmospheric transport \r\npatterns (e.g.,  occurrence and length of atmospheric blocking \r\nepisodes, ventilation of the polluted boundary layer), extent of mixing \r\nlayer and stratosphere-troposphere exchange (STE) for surface ozone \r\n(von Schneidemesser et al., 2015), and through modifications of the \r\nrate of reactions that generate secondary species in the atmosphere. \r\nChanging precipitation patterns in a  future climate also influence \r\nthe wet removal efficiency, in particular for atmospheric aerosols \r\n(Hou  et al., 2018). Processes at play in non-CO2 biogeochemical \r\nfeedbacks (Section  6.4"


id: b087e7c7942811e1fb30ee5c9b902d28
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 25294
content:
"Since the AR5, several studies have investigated the net effect of \r\nclimate change on surface ozone, based on either global or regional \r\nmodel projections. A systematic and quantitative comparison of the \r\nozone change, however, is difficult due to the variety of models with \r\ndifferent complexities in the representation of natural emissions, \r\nchemical mechanisms and physical processes, as well as the surface \r\nozone metrics applied for analysis. Ozone response to climate change \r\nhas been shown to be particularly sensitive to model representation \r\nof processes like BVOC emissions, deposition, and isoprene chemistry\r\n(Squire et al., 2015; Val Martin et al., 2015; Schnell et al., 2016; \r\nPommier et al., 2018). More robust protocols are now used more \r\ncommonly comprising, notably, longer simulations necessary to \r\nseparate change from interannual variability (Barnes et al., 2016; \r\nLacressonniere et al., 2016; Garcia-Menendez et al., 2017)"


id: bc3695f88efc540431c71c273bbb36fe
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 27191
content:
"Figure 6.14 | Multi-model annual mean change in surface O3 (ppb) concentrations at different warming levels. Changes are shown for (a) 1.0degC, (b) 1.5degC, (c)\r\n2.0degC and (d) 2.5degC increases in global mean surface air temperature. CMIP6 models include GFDL-ESM4, GISS-E2-1-G, MRI-ESM2-0 and UKESM1-0-LL. For each model, the \r\nchange in surface O3 is calculated as the difference between two AerChemMIP experiments - one with evolving future emissions and sea surface temperatures (SSTs) under \r\nthe SSP3-7.0 scenario and the other with the same setup but with fixed present-day SSTs. The difference is calculated as a 20-year mean in surface O3 around the year when \r\nthe temperature threshold in each model is exceeded. The multi-model change in global annual mean surface O3 concentrations with +- 1 standard deviation are shown within \r\nparentheses"


id: dc200011eb9413e08a69754ca7e61bb
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 32144
content:
" Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, three out of four models agree on sign of change; \r\ndiagonal lines indicate regions with low model agreement, where three out of four models agree on sign of change. For more information on the simple approach, please refer \r\nto the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)."


id: 46236a1e8747e8c4dd0e4195676b7e6a
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 8416
content:
"Considering anthropogenic emissions of precursors globally higher \r\nthan the current emissions (SSP3-7.0 in 2050; Figure 6.20), the CMIP6 \r\nensemble confirms the surface ozone penalty due to climate change\r\nover regions close to anthropogenic pollution sources or close to \r\nnatural emissions sources of ozone precursors (e.g., biomass-burning \r\nareas), with a penalty of a few ppb for the annual mean, proportional \r\nto warming levels (Figure  6.14). This rate ranges regionally from \r\n0.2-2 ppb degC-1 (Supplementary Material Figure  6.SM.1). The CMIP6 \r\nESMs show this consistently for South East Asia (in line with Hong et al. \r\n(2019) and Schnell et al. (2016)) and for India (in line with Pommier \r\net al., 2018) as well as in parts of Africa and South America, close to \r\nenhanced BVOC emissions (at least three out of four ESMs agree on \r\nthe sign of change)"


id: 5a78026112b65652d4502f17d1b1ac7f
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 11747
content:
" The results are mixed in polluted regions of Europe \r\nand US because of lower anthropogenic precursor emissions which \r\nleads to a very low sensitivity of surface ozone to climate change (-0.5 \r\nppb degC-1 to 0.5 ppb degC-1; Supplementary Material Figure 6.SM.1) and \r\nthus the ESMs can disagree on sign of changes for a given warming \r\nlevel. This heterogeneity in the results is also found in regional studies \r\nover North America (Gonzalez-Abraham et al., 2015; Val Martin et al., \r\n2015; Schnell et al., 2016; He et al., 2018; Nolte et al., 2018; Rieder \r\net al., 2018) or over Europe (Colette et al., 2015; Lacressonniere et al., \r\n2016; Schnell et al., 2016; Fortems-Cheiney et al., 2017)."


id: 51628d89a4d4646e4bd1978924ac895b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 10260
content:
"Overall, warmer climate is expected to reduce surface ozone in \r\nunpolluted regions as a result of greater water vapour abundance \r\naccelerating ozone chemical loss (high confidence). Over regions \r\nwith high anthropogenic and/or natural ozone precursor emissions, \r\nthere is prevailing evidence that climate change will introduce \r\na surface O3 penalty increasing with increasing warming levels (with \r\na  magnitude ranging regionally from 0.2-2 ppb degC-1) (medium to\r\nhigh confidence). Yet, there are uncertainties in processes affected \r\nin a warmer climate which can impact and modify future baseline \r\nand regional/local surface ozone levels. The response of surface \r\nozone to future climate change through stratosphere-troposphere \r\nexchange, soil NOx emissions and wildfires is positive (medium \r\nconfidence)"


id: 79be0873982de19d4ed08d59b9456e6d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 16723
content:
" In addition, there is low confidence in the magnitude \r\nof the effect of climate change on surface ozone through biosphere \r\ninteractions (natural methane, non-methane BVOC emissions and \r\nozone deposition) and lightning NOx emissions. "


id: 3aef4e05667a40d9b272aef70777aef2
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 6638
content:
"or a high percentile) as the sensitivity of peak values to meteorological \r\nconditions can be different from sensitivity of the median or mean \r\n(Porter et al., 2015). The AR5 assessed with medium confidence\r\nthat uniformly higher temperatures in polluted environments will \r\ntrigger regional feedbacks in chemistry and local emissions that will \r\nincrease peak ozone and PM pollution, but assessed low confidence\r\nin projecting changes in meteorological blocking associated with \r\nthese extreme episodes. \r\nMeteorological conditions, such as heatwaves, temperature \r\ninversions and atmospheric stagnation episodes favour air quality \r\nextremes and are influenced by changing climate (Fiore et al., 2015). \r\nThe body of literature on the connection between climate change and \r\nextreme anthropogenic pollution episodes is essentially based on \r\ncorrelation and regression applied to observation reanalysis but the \r\nmetrics and methodologies differ making quantitative comparisons \r\ndifficult"


id: 1833c37b3800e347776f0621278af6a1
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 1293
content:
" Many emission processes in the natural systems are sensitive \r\nto temperature, and bursts of emissions as a  reponse to extreme \r\nweather, as in the case of wildfires in dry conditions (Bondur et al., \r\n2020; Xie et al., 2020) can occur, which would then add to the risk \r\nof extreme air pollution but are not sufficiently constrained to be \r\nquantitatively assessed.\r\n6.6 Air Quality and Climate Response \r\nto SLCF Mitigation\r\nLong-lived greenhouse gas (LLGHG) emissions reductions are \r\ntypically motivated by climate change mitigation policies, whereas \r\nSLCF reductions mostly result from air pollution control and climate \r\npolicies (FAQ6.2), as well as policies focusing on achieving UN \r\nSustainable Development Goals (SDGs; Box 6.2)"


id: 49414421229cfa4cc77c52ac025e7510
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 8915
content:
" The management \r\nof several SLCFs (BC, methane, tropospheric ozone and HFCs) is \r\nconsidered in the literature as a  fast-response, near-term measure \r\nto curb climate change, while reduction of emissions of LLGHGs \r\nis an essential measure for mitigating long-term climate warming \r\n(Shindell et al., 2012, 2017b; Shoemaker et al., 2013; Rogelj et al., \r\n2014b; Lelieveld et al., 2019). Note that the term short-lived climate \r\npollutants (SLCPs), referring only to warming SLCFs, has been used \r\nwithin the policy arena. The SR1.5 report states that limiting warming \r\nto 1.5degC to achieve Paris Agreement goals, implies net-zero CO2\r\nemissions around 2050 and concurrent deep reductions in emissions \r\nof non-CO2 forcers, particularly methane (Rogelj et al., 2018a). \r\nIn addition, several SLCFs are key air pollutants or precursors of fine \r\nparticulate matter (PM2.5) and tropospheric ozone, and therefore \r\nsubject to control driven by air-quality targets. "


id: 8550fe54331bf0cba81efccbcb309a33
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 18502
content:
"Since AR5, published studies provide augmented evidence for \r\nthe connections between extreme ozone and PM pollution events \r\nand high temperatures, especially long-lasting heatwaves, whose \r\nfrequency is increasing due to a warming climate (Lelieveld et al., \r\n2014; Porter et al., 2015; Hou and Wu, 2016; Jing et al., 2017; Schnell \r\nand Prather, 2017; Sun et al., 2017; H. Zhang et al., 2017). However, \r\nthe relationship between air pollution and individual meteorological \r\nparameters is exaggerated because of covariation on synoptic time \r\nscales (Fiore et al., 2015). For example, heatwaves are often associated \r\nwith clear skies and stagnation, making clear attribution to specific \r\nmeteorological variables complicated. In Asia, future changes in \r\nwinter conditions have also been shown to favour more particulate \r\npollution (Cai et al., 2017; Zou et al., 2017). The relationship between \r\nthe occurrence of stagnation episodes and high concentrations \r\nof ozone and PM2"


id: bcd316480ce82c03f22ad934ed69ca91
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 27279
content:
"5 has been shown to be regionally and metric \r\ndependant (Oswald et al., 2015; Sun et al., 2017; Kerr and Waugh, \r\n2018; Schnell et al., 2018; Garrido-Perez et al., 2019)."


id: eaed4da25bfc7aade2d4e61da5dd7f1e
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 34468
content:
"It is often perceived that the full climatic response following \r\nmitigation of SLCFs will occur almost immediately. However, the \r\ninertia of the climate system strongly modifies the short-term and \r\nlong-term response. SLCFs with lifetimes shorter than the time scales \r\nfor inter-hemisheric mixing (1-2 years) can cause a more spatially \r\nheterogeneous forcing than LLGHGs and thus different regional \r\npatterns of the climate response (Section  6.4.3). The temporal \r\nresponse in GSAT to a radiative forcing can be quantified using linear \r\nimpulse response functions (Cross-Chapter Box 7.1; Geoffroy et al., \r\n2013; Olivie and Peters, 2013; Smith et al., 2018). Figure 6.15 shows \r\nthe GSAT response for sustained step reduction in emissions of \r\nidealised SLCFs with different lifetimes. The response is relative to \r\na  baseline with constant emissions, so effects of emissions before \r\nthe step reduction is not shown"


id: 9a5af5379d0bf58f93ac5f6f57093bd3
created_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 21812
content:
" For SLCFs with lifetimes shorter \r\nthan a  few years, the concentrations quickly reach a  new steady \r\nstate and the response time is primarily governed by the thermal \r\ninertia and thus the time scales of the climate system. For compounds \r\nwith lifetime on the order of 10 years (e.g., methane), there is about \r\na 10-year delay in the response during the first decades, compared \r\nto compounds with lifetimes less than one year. However on longer \r\ntime scales the response is determined solely by the time scales of \r\nthe climate system itself. For CO2 (dashed line in Figure  6.15) the \r\ntemporal response is very different due to the long time scale for "


id: 31c99b17371802211fee5c78fd2d28ed
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 5244
content:
"sign of the net global radiative effects of carbonaceous aerosols from \r\nthe residential sector and solid-fuel cookstove emissions (warming \r\nor cooling) is not well constrained based on evidence from recent \r\nglobal atmospheric modelling studies. Estimates of direct aerosol-\r\nradiation and aerosol-cloud effects from the global residential \r\nsector range from -20 to +60 mW m-2 (Kodros et al., 2015) and \r\n-66 to +21 mW m-2 (Butt et al., 2016) and from -20 to +10 mW m-2\r\n(Kodros et al., 2015) and -52 to -16 mW m-2 (Butt et al., 2016), \r\nrespectively. Uncertainties are due to assumptions about the aerosol \r\nemissions masses, size distribution, aerosol optical properties \r\nand mixing states (Section  6.3.5.3). Allowing BC to act as an INP \r\nin a  global model leads to a  much larger global forcing estimate \r\nfrom -275 to +154 mW m-2 with a large uncertainty range due to \r\nuncertainty in the plausible range of maximum freezing efficiency \r\nof BC (Huang et al., 2018)"


id: cdb0fe3e0fd522395ebba2f54fcbcb62
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 29887
content:
" The residential biofuel sector is a major \r\nconcern for indoor air quality (Bonjour et al., 2013). In addition, several \r\natmospheric modelling studies find that this sector is also important \r\nfor outdoor air quality and even a dominant source of population\u0002weighted outdoor PM2.5 in India and China (Lelieveld et al., 2015b; \r\nSilva et al., 2016; Spracklen et al., 2018; Reddington et al., 2019)."


id: 9d830f8d6d269d08933f918294c8af08
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 22315
content:
"Temperature effects of emissions can be quantified for the historical \r\ncontribution to the present temperature impact (Section 6.4.2), for \r\nidealized one-year pulses of emissions or for continued (sustained) \r\nemissions at present levels and for changes during a specific time \r\nperiod, for emissions from future scenarios with various hypotheses, \r\ngiving complementary information to feed mitigation strategies. \r\nThe AR5 assessed the net global temperature impact of source \r\nemissions sectors from a  one-year pulse (a single year's worth) \r\nof year 2008 emissions and found that the largest contributors to \r\nwarming on 50-100-year time scales are the energy, industrial and \r\non-road transportation sectors. Sectors that emit large amounts of \r\nmethane (agriculture and waste management) and black carbon \r\n(residential biofuel) are important contributors to warming over \r\nshort time horizons up to 20 years"


id: 5244d1cb07bcb5686658d255c563b284
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 10398
content:
" Below, we discuss the effect on \r\nERFs, temperature and air pollution of selected key sectors estimated \r\nto have large non-CO2 forcing, including agriculture, residential and \r\ncommercial, and transport (aviation, shipping, land transportation)."


id: 1845b1d8f1bbc1542dc5ada7d4312728
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 1307
content:
"According to the SRCCL assessment (Jia et al., 2019), agriculture, \r\nforestry and other land use (AFOLU) are a  significant net source \r\nof GHG emissions (high confidence), with more than half of these \r\nemissions attributed to non-CO2 GHGs from agriculture. With respect \r\nto SLCFs, agricultural activities are major global sources of methane \r\nand NH3 (Section  6.2.1). The agriculture sector exerts strong near\u0002term warming due to large methane emissions that is slightly \r\noffset by a small cooling from secondary inorganic aerosols formed \r\nnotably from the NH3 emissions (Heald and Geddes, 2016; Lund \r\net al., 2020). For present-day emissions, agriculture is the second \r\nlargest contributor to warming on short time scales but with a small \r\npersisting effect on surface temperature (+0.0012degC +- 0.00028degC) \r\nafter a pulse of current emissions (Figure 6.16, see detailed description \r\nin Section  6.6.2.3.4; Lund et al., 2020)"


id: d8294242565ae08a3707cfe971e4627
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 31531
content:
" Aerosols produced from \r\nagricultural emissions, released after nitrogen fertilizer application \r\nand from animal husbandry, influence surface air quality and make \r\nan important contribution to surface PM2.5 in many densely populated \r\nareas ( Figure 6.17; Lelieveld et al., 2015b; Bauer et al., 2016)."


id: 22027b5481f38ec67c97ec2c37bfca12
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vector_id: 2794
content:
"Aviation is associated with a range of SLCFs, in particular emissions \r\nof NOx and aerosol particles, alongside emissions of water vapour \r\nand CO2. The largest SLCF effects are those from the formation \r\nof persistent condensation trails (contrails) and NOx emissions. \r\nPersistent contrails are ice-crystal clouds, formed around aircraft \r\nsoot particles (and water vapour from the engine), injected in \r\nambient cold and ice-supersaturated atmosphere, which can spread \r\nand form contrail cirrus clouds. The 'net NOx' effect arises from the \r\nformation of tropospheric ozone, counterbalanced by the destruction \r\nof ambient methane and associated cooling effects of reductions \r\nin stratospheric water vapour and background ozone. The AR5 \r\nassessed the radiative forcing from persistent linear contrails to be \r\n+0.01 [+0.005  to  +0.03] W m-2 for the year 2011, with medium \r\nconfidence (Boucher et al., 2013)"


id: a2382692ac5fa6da2ec0b52dece09170
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 23090
content:
" The combined linear contrail and \r\ntheir subsequent evolution to contrail cirrus radiative forcing from \r\naviation was assessed to be +0.05 [+0.02 to +0.15] W m-2, with low \r\nconfidence. An additional forcing of +0.003 W m-2 due to emissions \r\nof water vapour in the stratosphere by aviation was also reported \r\n(Boucher et al., 2013). The aviation sector was also estimated to lead "


id: 1b13ae49d95fec8747a41d7322f44592
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 1732
content:
"Our assessment is built upon Lee et al. (2021). Their study consists of \r\nan updated, comprehensive assessment of aviation climate forcing \r\nin terms of RF and ERF based on a large number of studies and the \r\nmost recent air-traffic and fuel-use datasets available (for 2018), \r\nnew calculations and the normalization of values from published \r\nmodelling studies, and combining the resulting best estimates via \r\na Monte-Carlo analysis. Lee et al. (2021) report a net aviation ERF \r\nfor year-2018 emissions of +0.101 [0.055-0.145] W m-2 with major \r\ncontributions from contrail cirrus (0.057 W m-2), CO2 (0.034 W m-2) \r\nand NOx (0.017 W m-2). Contrails and aviation-induced cirrus yield \r\nthe largest individual positive ERF followed by CO2 and NOx emissions \r\n(Lee  et al. 2021). The confidence level in ERF due to contrails \r\nand aviation-induced cirrus is assessed to be low in Chapter  7 \r\n(Section 7.3.4.2) due to potential missing processes"


id: 16eb0887c30c17572fb6f1c9f1cd5abe
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 1131
content:
" The formation \r\nand emission of sulphate aerosols yield a negative (cooling) term. \r\nSLCF forcing terms contribute about eight times more than CO2 to \r\nthe uncertainty in the aviation net ERF in 2018 (Lee et al., 2021). \r\nThe largest uncertainty in assessing aviation climate effects is on the \r\ninteractions of BC and sulphate aerosols on cirrus and mixed-phase \r\nclouds, for which no best estimates of the ERFs were provided (Lee \r\net al., 2021)."


id: 242efcbbc593edbfa4e8a8715d79e423
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 3135
content:
"Quantifying the effects of shipping on climate is particularly \r\nchallenging because (i) the sulphate cooling impact is dominated \r\nby aerosol-cloud interactions and (ii) ship emissions contain NOx, \r\nSOx and BC, which lead to mixed particles. Previous estimates \r\nof the sulphate radiative effects from present-day shipping \r\nspan the range -47 to -8 mW m-2 (direct radiative effect) and \r\n-600 to -38 mW m-2 (indirect radiative effects) (Lauer et al., 2007; \r\nBalkanski et al., 2010; Eyring et al., 2010; Lund et al., 2012). Partanen \r\net al. (2013) reported a  global mean ERF for year-2010 shipping \r\naerosol emissions of -390 mW m-2. The temperature change has \r\nbeen shown to be highly sensitive to the choice of aerosol-cloud \r\nparametrization (Lund  et al., 2012). One year of global present\u0002day shipping emissions, not considering the impact of recent low \r\nsulphur fuel regulation (IMO, 2016), are estimated to cause net \r\ncooling in the near term (-0.0024degC +- 0"


id: 7d8cd6292c52db3ed838c3d03d97e64d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 17327
content:
"0025degC) and slight warming \r\n(+0.00033degC +- 0.00015degC) on a 100-year horizon (Lund et al., 2020). "


id: 1dce621aa3ccb6537f654a9d66ec8ae6
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 2126
content:
"The best estimate ERFs from aviation (Lee et al., 2021) have been used \r\nto calculate aviation-specific Absolute Global Temperature change \r\nPotential (AGTP) using the method described in Lund et al. (2020) \r\nand subsequently compute the effect of a one-year pulse of aviation \r\nemissions on global mean surface temperature on a 10- and 100\u0002year time horizon (Section 6.6.2.3.4 and Figure 6.16). The effect of \r\ncontrail-cirrus is most important for the estimated net-GSAT response \r\nafter the first decade, followed by similar warming contributions \r\nfrom NOx and CO2 emissions. At a  20-year time horizon, the net \r\ncontribution from aviation to GSAT has switched from a positive to \r\na small negative effect (see Supplementary Material 6.SM.4)"


id: f71033f45d0c25a6478adb4e9bbbd1f1
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 36382
content:
" This is \r\ndue to the combination of rapidly decaying contrail-cirrus warming \r\nand the complex time variation of the net temperature response to \r\nNOx emissions, which changes sign between 10 and 20 years due \r\nto the balance between the positive short-lived ozone forcing and \r\nnegative forcing from changes in methane and methane-induced \r\nchanges in ozone and stratospheric water vapour. The net GSAT \r\nresponse to aviation emissions has previously been estimated to be "


id: 762e61ff743801f7b5cb3b800f53558d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 16195
content:
"There is consensus that on-road transportation sector emissions, \r\nincluding gasoline and diesel, are important anthropogenic \r\ncontributors to elevated surface ozone and PM2.5 concentrations \r\n(Chambliss et al., 2014; Lelieveld et al., 2015b; Silva et al., 2016; \r\nAnenberg et al., 2019). At a  global scale, land transportation has \r\nbeen estimated to be the dominant contributor to surface ozone \r\nconcentrations in populated areas (Silva et al., 2016) and ozone\u0002induced vegetation damages (Section  6.4.4; Unger et al., 2020). \r\nFurthermore, it is now well established that real-world diesel NOx\r\nemissions rates are substantially higher, the so-called 'excess NOx', \r\nin all regional markets than in laboratory tests, worsening air quality \r\n(Anenberg et al., 2017; Jonson et al., 2017; Chossiere et al., 2018) \r\nand contributing to slightly larger warming on the scale of years and \r\nsmaller warming at the decadal scale (Tanaka et al., 2018)"


id: c45b3df82bdbf11d1330b7e18689ce3
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 28465
content:
" Excess \r\nNOx emissions from key global diesel markets are estimated at \r\n4.6 Tg yr-1 in 2015, with annual mean ozone and PM2.5 increases of \r\n1 ppb and 1ug m-3 across large regions of Europe, India and China \r\n(Anenberg et al., 2017)."


id: b66eb687c6ff35336b734d8f46000a5
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 26224
content:
"The global sectoral ranking for near- and long-term global \r\ntemperature effects is similar to the AR5 assessment despite regional \r\nreductions in aerosol precursor emissions between 2008 and 2014. \r\nThis report has applied updated climate policy metrics for SLCFs \r\nand treatment of aerosol-cloud interactions for SO2, BC and OC \r\n(Lund et al., 2020). By far the largest 10-year GSAT effects are from \r\nthe energy production (fossil fuel mining and distribution), agriculture \r\nand waste management sectors (high confidence). Methane is the \r\ndominant contributor in the energy production, agriculture and \r\nwaste management sectors. On the 10-year time horizon, other net \r\nwarming sectors are residential fossil fuel and energy combustion \r\n(dominated by CO2) and aviation and residential biofuel (dominated \r\nby SLCFs and cloud) (medium confidence)"


id: f3926bce27ba46225526ce8679d252e9
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 35834
content:
" The total residential and \r\ncommercial sector, including biofuel and fossil fuels, is the fourth \r\nlargest contributor to warming globally on short time horizons of \r\n10-20 years. The energy combustion sector has considerable cooling \r\nfrom high emissions of SO2 that result in a relatively small net GSAT \r\ntemperature effect on short time horizons, despite the high CO2\r\nemissions from this activity. On the 10-year time horizon, global \r\nemissions from industry and shipping cause a  net cooling effect \r\ndespite a considerable warming from CO2 emissions. On the 100-year \r\ntime horizon, the net effects of agriculture and waste management are \r\nsmall, while energy combustion is the largest individual contributor \r\nto warming due to its high CO2 emissions. The second largest driver \r\nof long-term temperature change is industry, demonstrating the \r\nimportance of non-CO2 emissions for shaping relative weight over \r\ndifferent time frames"


id: b61cc04496149193245ce6ada977f6be
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 26180
content:
" Transport contributes a small net warming on \r\nthe 10-year time horizon that increases by a factor of three on the \r\n100-year time horizon. In contrast, the aviation sector contribution to \r\nwarming shrinks by about a factor of three between the 10- and 100-"


id: e61bd1eca3b830c3d01ef021d6daba2b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 33693
content:
"Natural sources contribute more than 50% to surface ozone in all \r\nregions except Southern Asia and South East Asia. Southern Asia, \r\nEastern Asia and the Middle East experience the highest surface \r\nozone levels of all regions. For ozone, the anthropogenic sectoral \r\nattribution is more uniform across regions than for PM2.5, except \r\nfor Southern and South East Asia, where land transportation \r\nplays a  larger role, and Eastern Asia, where the most significant \r\ncontribution is from energy and industry. Land transportation and \r\nenergy are the most important contributors to ozone across many \r\nof the regions, with smaller contributions from agriculture, biomass \r\nburning, waste management and industry. Open biomass burning is \r\nnot a major contributor to surface ozone, except for in Africa, Latin \r\nAmerica and South East Asia where its contribution is estimated at \r\nabout 5-10% of anthropogenic sources"


id: 22884cceb9cd9a07f9c0097c3eef3e8e
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 2885
content:
" The relative importance of \r\nnatural and anthropogenic emissions sources on surface ozone has \r\nbeen assessed in several studies (Uherek et al., 2010; Zare et al., \r\n2014; Mertens et al., 2020; Unger et al., 2020) and the results are \r\ncomparable with the estimates of the TM5-FASST used here."


id: 6e42744f6857d988ff43dc0f3edfc7b8
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 14969
content:
"The attribution of present-day surface PM2.5 and ozone concentrations \r\nto sectors and regions (Figure  6.17) is based on 2014 CMIP6 \r\nemissions used in the TM5-FASST model (Van Dingenen et al., 2018) \r\nthat has been widely applied to analyse air quality in regional and \r\nglobal scenarios (e.g., Van Dingenen et al., 2009; Rao et al., 2016, \r\n2017; Vandyck et al., 2018; Harmsen et al., 2020b). Regions with the \r\nlargest year-2014 population-weighted annual average surface PM2.5\r\nconcentrations are Southern Asia, Eastern Asia and the Middle East. \r\nThe dominant anthropogenic source of ambient PM2.5 in Southern \r\nAsia are the residential and commercial sectors (biomass and coal \r\nfuel-based cooking and heating) with secondary contributions from \r\nenergy and industry. In Eastern Asia, the main anthropogenic sources \r\nof ambient PM2.5 are energy, industry and residential sources. Natural \r\nsources, predominantly dust, are the most important PM2"


id: 1bb36afe103eac17e7d06039612cb3b8
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 1818
content:
"5 source in \r\nthe Middle East, Africa and Eurasia, contributing about 40-70% of \r\nambient annual average concentrations (Figure  6.17). Agriculture "


id: f221ca6d3e96f9dd4e3649c90b2ab9bb
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 35626
content:
"affected by significant air pollutant burden changes due to air-quality \r\npolicy in selected regions. Turnock et al. (2016) estimated that the \r\nstrong decrease in NOx, SO2 and PM2.5 emissions in Europe, induced \r\nby air-quality policies resulting in implementation of abatement \r\nmeasures since the 1970s, have caused a  surface warming of \r\n+0.45degC +- 0.11degC and increase of precipitation +13 +- 0.8 mm yr-1 \r\nover Europe, compared to the scenario without such policies. While \r\nthe temperature increase is likely overestimated since the impact of \r\nthe increase in ammonium nitrate was not considered in this study, \r\nthe simulated European all-sky TOA radiative effect of the European \r\nair pollutant mitigation over the period 1970-2009 is 2.5 times the \r\nchange in global mean CO2 radiative forcing over the same period \r\n(Myhre et al., 2013)"


id: 6f403308b7209d05d41c25b072e4cd34
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 15131
content:
" Other studies found that the recent measures to \r\nreduce pollution over China have induced a decrease of aerosols and \r\nincrease of ozone over east China (K. Li et al., 2019, 2020), resulting \r\nin an overall warming effect mainly due to the dominant effect of \r\nsulphate reductions in the period 2012-2017 (Dang and Liao, 2019). "


id: 556738db24a6e975945a7b3103c1aee1
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 10907
content:
"International shipping emissions regulation: from January 2020, \r\na  new global standard, proposed by the International Maritime \r\nOrganisation, limits the sulphur content in marine fuels to \r\n0.5%  against the previous 3.5% (IMO, 2016). This legislation is \r\nconsidered in the SSP5 and SSP2-4.5 and with a delay of few years \r\nin SSP3-lowSLCF, SSP1-1.9, and SSP1-2.6, and in other SSP-emissions \r\nscenarios achieved by the mid-21st century. This global measure \r\naims to reduce the formation of sulphate (and consequently PM2.5) \r\nand largely reduce the health exposure to PM2.5, especially over \r\nIndia, east China and coastal areas of Africa, and the Middle East \r\n(Sofiev  et al., 2018). Sofiev et al. (2018) used a  high spatial-and\u0002temporal resolution chemistry climate model and estimated a  net \r\ntotal ERF of +71 mW m-2 associated with this measure and due to \r\nlower direct aerosol cooling (+3.9 mW m-2) and lower cloud albedo \r\n(+67 mW m-2)"


id: 53be2f649a7f5b3c570f6060a9e74488
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 10640
content:
" This value, which correponds to an 80% decrease of \r\nthe cooling effect of shipping induced by about 8 Tg of SO2 of avoided \r\nemissions, is consistent with older estimates which considered \r\nsimilar reduction of emitted sulphur. However, there is considerable \r\nuncertainty in the indirect forcing since small changes in aerosols, \r\nacting as CCNs in a clean environment, can have disproportionally \r\nlarge effects on the radiative balance. Since sulphate is by far the \r\nlargest component of the radiative forcing (Fuglestvedt et al., 2008) \r\nand of surface temperature effect (Figure 6.16) due to ship emissions \r\nover a short time scale, limiting the co-emitted SLCFs can not offset \r\nthe warming by sulphur reductions. The reduction of sulphur emissions \r\nfrom shipping is assessed to lead to a slight warming mainly due to \r\naerosol-cloud interactions (medium evidence, medium agreement)."


id: 5d55375c03e83102dc1ce6267fda3673
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 12206
content:
"In the following subsections, we assess the SLCF mitigation and its \r\neffects as identified in regional and global studies evaluating past \r\nand current air quality and other SLCF regulations (Sections 6.6.3.1, \r\n6.6.3.2 and 6.6.3.3). Development policies, independent from the \r\nCMIP6 assessment framework, including peer-reviewed studies and \r\ninitiatives like the United Nations Environment Programme (UNEP), \r\nanalysing win-win climate, air-quality and SDG-motivated strategies \r\nare discussed in Section 6.6.3.4. Note that sensitivity studies where \r\nimpacts of complete removal of particular species are analysed \r\n(e.g., Samset et al., 2018) are used sparingly in this assessment. While \r\nsuch analysis can be useful for assessing the effect of a zero-emissions \r\ncommitment (Chapter 4.7.1.1), they do not correspond to a realistic \r\nSLCF-mitigation strategy with plausible pace of implementation and \r\nremoval of co-emitted species (Shindell and Smith, 2019)"


id: ad7892e83c9efca4ad25e81af440032d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 24833
content:
" Discussion \r\nof climate and air-quality implications of SLCF reductions in SSP \r\nscenarios is provided in Section 6.7. "


id: 9be91523179d821c8aa062fdc528486c
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 22084
content:
"Air-quality policies emerged several decades ago focusing on \r\nemissions mitigation, first driven by local- then by regional-scale \r\nair-quality and ecosystem-damage concerns, that is, health impacts, \r\nacidification and eutrophication. They have made it possible to reduce \r\nor limit pollution exposure in many megacities or highly populated \r\nregions, for example, in Los Angeles, Mexico City and Houston in North \r\nAmerica (Parrish et al., 2011), Santiago in Chile (Gallardo et al., 2018), \r\nSao Paulo in Brazil (Andrade et al., 2017), Europe (Reis et al., 2012; \r\nCrippa et al., 2016; Serrano et al., 2019), and over Eastern Asia during \r\nthe last decade (Silver et al., 2018; Zheng et al., 2018b). However, very \r\nfew studies have quantified the impact of these policies on climate. \r\nThe AR5 concluded that air-quality control will have consequences on \r\nclimate including strong regional variability, however, no estimates \r\nof impacts of specific air-quality policy were available"


id: 44423872613ee7359744d1615bbcd4b
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 8161
content:
"Efficient implementation of the Kigali Amendment and national and \r\nregional regulations has been projected to reduce global average \r\nwarming in 2050 by 0.05degC-0.07degC (Klimont et al., 2017b; WMO, \r\n2018) and by 0.2degC-0.4degC in 2100 compared with the baseline \r\n(see Figure  2.20 of WMO, 2018). Analysis of SSP scenarios based \r\non an emulator (Section  6.7.3) shows a  comparable mitigation \r\npotential of about 0.02degC-0.07degC in 2050 and about 0.1degC-0.3degC \r\nin 2100 (Figure  6.22, SSP5-8.5 versus SSP1-2.6). Furthermore, the \r\nenergy efficiency improvements of cooling equipment alongside the \r\ntransition to low-global-warming potential alternative refrigerants \r\nfor refrigeration and air-conditioning equipment could potentially \r\nincrease the climate benefits from the HFC phasedown under the \r\nKigali Amendment (Shah et al., 2015; Hoglund-Isaksson et al., \r\n2017; Purohit and Hoglund-Isaksson, 2017; WMO, 2018). Purohit \r\net al"


id: 18e28596dd2185e8e230a52a4cc2f6f
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 1420
content:
" (2020) estimated that depending on the expected rate of \r\ntechnological development, improving the energy efficiency of \r\nstationary cooling technologies and compliance with the Kigali \r\nAmendment could bring future global electricity savings of more than \r\n20% of the world's expected electricity consumption beyond 2050 \r\nor cumulative reduction of about 75-275 Gt CO2-eq over the period \r\n2018-2100 (medium confidence). This could potentially double the \r\nclimate benefits of the HFC phase-down of the Kigali Amendment as \r\nwell as result in small air-quality improvements due to reduced air \r\npollutant emissions from the power sector (i.e., 8-16% reduction of \r\nPM2.5, SO2 and NOx; Purohit et al., 2020)."


id: ba036912226a696b9a05095d0d53e20a
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 26806
content:
"A multi-model study (four ESMs and six CTMs) found a consistent \r\nresponse to the removal of SO2 emissions that resulted in a global \r\nmean surface temperature increase of 0.69degC (0.4degC-0.84degC). \r\nHowever, results are mixed for a  global BC-focused deep SLCF \r\nreduction without SO2 and methane mitigation which remain as in the \r\nbaseline (see ECLIPSE in Figure 6.18). BC contributed about -0.022degC \r\ntemperature reduction for the decade 2041-2050 based on the \r\nassumption that mitigation of the non-methane species contributed \r\nonly about 10% of the global temperature reduction for the strategy \r\nwhere methane mitigation was also included (-0.22degC  +-  0.07degC; \r\nStohl et al., 2015). These results are consistent with studies analysing \r\nsimilar strategies using emulators (e.g.,  Smith and Mizrahi, 2013; \r\nRogelj et al., 2014b). Stohl et al. (2015) also analysed the impact of \r\nBC-focused mitigation on air quality, estimating large-scale regional \r\nreduction in PM2"


id: 293fd4124e2b0f4f38a9cac8ff4894f4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 3925
content:
"Local response to global reduction can be higher than the global \r\ntemperature response, particularly for regions subjected to rapid \r\nchanges. Hence, mitigation of rapid warming in the Arctic has been \r\nsubject to an increasing number of studies (Sand et al., 2013b, 2016; \r\nJiao et al., 2014; AMAP, 2015a, b; Mahmood et al., 2016; Christensen \r\net al., 2019). Considering maximum technically feasible reductions \r\n(MTFR) for methane globally and an idealized strategy reducing key \r\nglobal anthropogenic sources of BC (about 80% reduction by 2030 \r\nand sustained thereafter) and precursors of ozone was estimated to \r\njointly bring a reduction of Arctic warming, averaged over the 2041-\r\n2050 period, between 0.2degC and 0.6degC (AMAP, 2015a; Sand et al., \r\n2016). Stohl et al. (2015) have estimated that a global SLCF mitigation \r\nstrategy (excluding further reduction of SO2) would lead to about \r\ntwice as high a temperature reduction (-0.44 (-0.39 to -0"


id: 933c75b985756d5712124de961044e0f
created_at: 2023-03-16 14:02:01
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vector_id: 20677
content:
"There is robust evidence that reducing atmospheric methane will \r\nbenefit climate and improve air quality through near-surface ozone \r\nreduction (Fiore et al., 2015; Shindell et al., 2017a) and wide agreement \r\nthat strategies reducing methane offer larger (and less uncertain) \r\nclimate benefits than policies addressing BC (e.g., Smith and Mizrahi, \r\n2013; Rogelj et al., 2014b, 2018b; Stohl et al., 2015; Christensen \r\net al., 2019; Shindell and Smith, 2019). SR1.5 (Rogelj et al., 2018b) \r\nhighlighted the importance of methane mitigation in limiting warming \r\nto 1.5oC in addition to net zero CO2 emissions by 2050. Implementation \r\nof the identified maximum technically feasible reductions (MTFR) \r\npotential for methane globally, estimated at nearly 50% reduction \r\n(or 205 Tg CH4 in 2050) of anthropogenic emissions from the baseline, \r\nwould lead to a reduction in warming, calculated as the differences \r\nbetween the baseline and MTFR scenario, for the 2036-2050 period \r\nof about 0"


id: e221917cc253216af1ef506e3ad3d3bf
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 33073
content:
"20degC +- 0.02degC globally (AMAP, 2015b). Plausible levels of \r\nmethane mitigation, achieved with proven technologies, can increase \r\nthe feasibility of achieving the Paris Agreement goal through slightly \r\nslowing down the pace of CO2 reductions (but not changing the final \r\nCO2 reduction goal) while this benefit is enhanced by the indirect \r\neffects of methane mitigation on ozone levels (Collins et al., 2018). \r\nAdressing methane mitigation appears even more important in view of \r\nrecently observed growth in atmospheric concentrations that is linked \r\nto increasing anthropogenic emissions (Section 5.2.2). "


id: ee95db3f0914e4f508c9e6111370796
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 35008
content:
"Decarbonization of energy supply and end-use sectors is among key \r\npillars of any ambitious climate change mitigation strategy and it \r\nwould result in improved air quality owing to associated reduction of \r\nco-emitted SLCF emissions (e.g., McCollum et al., 2013; Rogelj et al., \r\n2014b; Braspenning Radu et al., 2016; Rao et al., 2016; Stechow \r\net al., 2016; Lelieveld et al., 2019; Shindell and Smith, 2019). Regional \r\nstudies (Lee et al., 2016; Shindell et al., 2016; Chen et al., 2018; Li et al., \r\n2018), where significant CO2 reductions were assumed for 2030 and \r\n2050, show consistently reduced of PM2.5 and ozone concentrations \r\nresulting in important health benefits. However, these improvements \r\nare not sufficient to bring PM2.5 levels in agreement with the WHO air\u0002quality guidelines in several regions. Amann et al"


id: 541b3d85292b0c3f12c00d4baef7fad0
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 10697
content:
" (2020) and UNEP \r\n(2019) highlight that only the combination of strong air-quality, \r\ndevelopment and climate policies, including societal transformations, \r\ncould pave the way towards the achievement of such a  target at \r\na regional and global level. "


id: 7ad7f0d87066b34821cd221ecca476ea
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 16919
content:
"At a  global level, Rao et al. (2016) showed that climate policies, \r\ncompatible with Copenhagen pledges and a long-term CO2 target of \r\n450 ppm, result in important air-quality benefits, reducing the share \r\nof the global population exposed to PM2.5 levels above the WHO \r\nTier 1 standard (35 ug m-3) in 2030 from 21% to 5%. The impacts \r\nare similar to a  strong air-quality policy but still leave large parts \r\nof population, especially in Asia and Africa, exposed to levels well \r\nabove the WHO air quality guideline level of 10 ug m-3. The latter can \r\nbe partly alleviated by combining such climate policy with strong air\u0002quality policy. Shindell et al. (2018) analysed more ambitious climate \r\nchange mitigation scenarios than Rao et al. (2016) and highlighted \r\nthe opportunities to improve air quality and avert societal effects \r\nassociated with warmer climate by accelerated decarbonization \r\nstrategies"


id: 5536d6c2776906ec0f4f2e4950ab2f48
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 10868
content:
" Most climate change mitigation strategies compatible with \r\nlimiting global warming to well below 2degC rely on future negative CO2\r\nemissions postponing immediate reduction. Alternatively, a  faster \r\ndecarbonization could allow the achievement of a 2degC goal without "


id: 8c6c0600a884f513fd7a917f3511372a
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 19608
content:
"between species, such as through atmospheric chemistry, can weaken the benefit of emissions reduction efforts. An illustration lies \r\nin the recent (2013-2017) reduction of aerosols over China (Silver et al., 2018; Zheng et al., 2018b) resulting from the strategy to \r\nimprove air quality ('Clean Air Action'); this has successfully reduced the level of PM2.5 but has led to a concurrent increase in surface \r\nozone, partly due to declining heterogeneous interactions of ozone precursors with aerosols (K. Li et al., 2019; Yu et al., 2019). This side \r\neffect on ozone has been addressed since then by amending the legislation to target NMVOC sources, especially solvent use. Complex \r\ninteractions between anthropogenic and biogenic volatile compounds are also at play and reduction of certain SLCFs could possibly \r\npromote new particle formation from organic vapours (e.g., Lehtipalo et al., 2018)"


id: f821c201167c624b1de90ef522810207
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 36553
content:
" Finally, a recent example of this complexity is the \r\nmixed effects on ozone pollution induced by NOx decrease during the COVID-19 pandemic (Cross-Chapter Box 6.1). Thus, the climate \r\nand air pollution effects of policies depend strongly on the choice of regulated compounds and the degree of reduction. Such policies \r\nhave to be informed by strong science support, including for example multi-model analyses such as HTAP (UNECE, 2010) and AMAP \r\n(AMAP, 2015a, b), based on global and regional CCMs. This is essential to capture the complexity and inform the policy development \r\nprocess."


id: cd113ab9f30a0eebe6f9818ccf42d6cf
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 29814
content:
"In addition, pursuing SDG objectives, apparently decoupled from air pollution, such as improved waste management, access to clean \r\nenergy, or improved agricultural practices, would also stimulate and lead to mitigation of SLCFs. Amann et al. (2020) show that a global \r\nstrategy to achieve the WHO air quality guidelines, cannot only rely on air pollution control but also on a combination of SDG-aligned \r\npolicies. Such actions would include energy efficiency improvements, increased use of renewables, reduction of methane from waste \r\nmanagement and agriculture, and CO2 and methane due to lower fossil fuel consumption, resulting in climate co-benefits. Consideration \r\nof SDGs including local air-quality co-benefits, creates an opportunity to support and gain acceptance for ambitious climate change \r\nmitigation (Jakob and Steckel, 2016; Stechow et al., 2016; Vandyck et al., 2018)"


id: 42bced4e3781f0c0cb36756c6c88706f
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 7891
content:
" Such near-term policies targeting SDGs and air quality \r\nwould enable longer-term transformations necessary to achieve climate goals (Chapter 17, WGIII)."


id: 39348b8686e957f000815286ce3c240d
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
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vector_id: 6375
content:
"Cross-Chapter Box 6.1, Figure 1 | Emissions reductions and their effect on aerosols and climate in response to COVID-19. Estimated reductions in \r\nemissions of CO2, SO2 and NOx are shown in panel (a) based on reconstructions using activity data (updated from Forster et al., 2020). Eight Earth system models \r\n(ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until \r\nAugust 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. \r\nPanel (b) shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel (c) shows ESM-simulated GSAT anomalies during 2020; curves \r\ndenote the ensemble mean result for each model with shading used for +-1 standard deviation for each model. ESM data from these simulations ('ssp245-covid') \r\nis archived on the Earth System Grid CMIP6 database"


id: 7761cd7ab058b7f31582b32ae74a1a6
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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index: climate_gpt_only_giec
vector_id: 16342
content:
" Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, \r\nwhere >=80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. \r\nFor more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1."


id: dbf8e9da7895474c8d994671051789ff
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 32126
content:
"Model simulations of the response to COVID-19 emissions reductions indicate a small warming of global surface air temperature \r\n(GSAT) due to a decrease in sulphate aerosols (Forster et al., 2020; Fyfe et al., 2021), balanced by cooling due to an ozone decrease \r\n(Forster et al., 2020; Weber et al., 2020), black carbon decrease (Weber et al., 2020) and CO2 decrease. It is noted that observational \r\nstudies report little SO2 change, at least locally near the surface (Shi et al., 2021), and do not correlate with emissions inventory-based \r\nchanges (Gkatzelis et al., 2021). One study suggests a small net warming while another using idealized simulations suggests a small \r\ncooling (Weber et al., 2020). Simulated GSAT and rainfall changes are unlikely to be detectable in observations (high confidence) \r\n(Samset et al., 2020; Fyfe et al., 2021). Multi-model ESM simulations based on a realistic COVID-19 containment forcing scenario \r\n(Forster et al"


id: 75afed9ce220909e809a195ba346c74
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
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vector_id: 16109
content:
", 2020) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross-Chapter \r\nBox 6.1)."


id: dda2f128805030f0407b1ce12be1bdb4
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 32374
content:
"Growing urbanization worldwide has strongly modified the spatial \r\ndistribution and intensity of SLCF emissions. The effect and extent \r\nof urbanization on air pollution and other emissions species are \r\ncaptured within Integrated Assessment Models (IAMs) at varying \r\nlevels of complexity. In most cases, models use a  combination of \r\nproxies and assumptions of end-use efficiency and technological \r\nimprovement assumptions to estimate emissions arising from rural\u0002to-urban migration and population growth within cities, utilizing \r\nquantifications of urbanization for the SSPs (Jiang and O'Neill, 2017). \r\nIn addition, spatial patterns of future rural and urban population \r\ngrowth, migration, and decline have been quantified for the SSPs \r\nusing a  gravity model (Jiang and O'Neill, 2017)"


id: 26f587e2a7102b56eda6f72496bef814
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 3522
content:
" However, linking \r\nthese spatial patterns with IAM regional emissions pathways is still \r\nan ongoing area of study and has not yet been represented in spatial \r\nemissions estimates provided by IAMs (Riahi et al., 2017; Gidden \r\net al., 2019; Feng et al., 2020). As described in Feng et al. (2020), \r\nspatial emissions estimates derived for CMIP6 are largely a product of \r\nexisting spatial patterns of population, but do not vary dynamically in \r\nfuture patterns. To the extent urbanization is accounted for in gridded \r\nemissions, IAM native region resolution (varying, for example, from \r\n11 world regions to more than 30, depending on the model) provides \r\nurbanization-based dynamics"


id: 3d3391ee8fa0824a1f0f9cc36095ad70
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 6993
content:
" Despite the interest of studying the \r\neffect of well-planned, densely populated urban centres, which \r\ncan help to maximize the benefits of agglomeration, by providing \r\nproximity to infrastructure and services, the opportunity for energy \r\nsaving, and providing a frame for air-quality control, IAM realizations \r\nof SSPs are not sufficient to assess this effect. The  opportunities \r\nand risks associated with this rapid urbanization for SLCF emissions \r\nand air quality are analysed in the Chapter 6 of the WGII report and \r\nChapter 8 of the WGIII report."


id: fe626a12b77fc283f8ebc049b377dd93
created_at: 2023-03-16 14:02:01
updated_at: 2023-03-16 14:02:01
content_type: text
index: climate_gpt_only_giec
vector_id: 37559
content:
"This section assesses the 21st-century projections of SLCF emissions, \r\nabundances and responses in terms of climate and air quality \r\nfollowing the SSPs (Chapter  1, Section  1.6.1.3 and Cross-Chapter \r\nBox 1.5; Riahi et al., 2017; Gidden et al., 2019). The future evolution \r\nof atmospheric abundances and the resulting climate and AQ \r\nresponses is driven mainly by anthropogenic emissions and by natural \r\nemissions modulated by chemical, physical and biological processes \r\nas discussed in Sections 6.2 and 6.3. Like the RCP scenarios used in \r\nAR5, the SSP emissions scenarios consider only direct anthropogenic \r\n(including biomass burning) emissions and do not project natural \r\nemissions changes due to climate or land-use changes; ESMs \r\nintrinsically consider these biogeochemical feedbacks to varying \r\ndegrees (Section  6.4.5)"


id: dc0711a97e2cf40356e59702be5b17ba
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 32130
content:
" We rely on future projections based on \r\nCMIP6 ESMs with comprehensive representation of chemistry, \r\naerosol microphysics and biospheric processes that participated \r\nin the ScenarioMIP (O'Neill et al., 2016) and AerChemMIP (Collins \r\net al., 2017). However, due to the high computational costs of \r\nrunning coupled ESMs, they cannot be used for quantifying the \r\ncontributions from individual species, regions and sectors, and across \r\nthe scenarios. Therefore, reduced complexity models (Box  1.3 and \r\nCross-Chapter Box 7.1), which represent chemistry and complex ESM \r\ninteractions in parametrized forms updated since the AR5, are also \r\napplied here. "


id: 3ecf009dfb7f88079073c65b6b2711f
created_at: 2023-03-16 14:02:02
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content_type: text
index: climate_gpt_only_giec
vector_id: 7265
content:
"estimates for the post-2000 period (Hoesly et al., 2018) and those \r\nused in RCPs (Figure 6.18). Since several SLCFs are also air pollutants, \r\nthe narrow range of the RCP emissions trajectories in the future \r\nallowed for only limited analysis of near-future air quality (Amann \r\net al., 2013; Chuwah et al., 2013; von Schneidemesser et al., 2015). \r\nHowever, the range of storylines in the SSPs lead to a wider range \r\nof assumed pollution-control policies in the SSPs (Rao et al., 2017; \r\nRiahi et al., 2017). In SSP1 and SSP5, strong air-quality policies are \r\nassumed to minimize the adverse effects of pollution on population \r\nand ecosystems. In SSP2, a medium pollution control, with lower than \r\ncurrent policy targets, is considered. Only weak, regionally varied, air \r\npollution policies are applied in the SSP3 and SSP4. Additional climate \r\npolicies introduced to reach defined radiative forcing targets will also \r\naffect SLCF emissions. The SSP SLCF-emissions trajectories (Rao et al"


id: 876e73d8cf89c356a03343b5576bfba4
created_at: 2023-03-16 14:02:02
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vector_id: 18847
content:
", \r\n2017; Gidden et al., 2019) assume a long-term coupling of economic \r\ngrowth and specific emissions indicators, such as sectoral emission "


id: 14e2584e91429207ea3411a008328c1d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 792
content:
"For global methane emissions, the range is similar for SSPs and RCPs \r\nover the entire century (Figure  6.18), with highest projections in \r\nSSP3-7.0 (slightly below RCP8.5) estimating doubling of the current \r\nemissions and a reduction of about 75% by 2100 in scenarios consistent \r\nwith 1.5degC to 2degC targets; similar to RCP2.6. At the regional level, the \r\nevolution of methane emissions in climate change mitigation scenarios \r\nis comparable to RCPs but there are significant differences for some \r\nregions with respect to high CO2 emissions scenarios. In  particular, \r\nprojections for Eastern Asia differ significantly, the highest SSP3-7.0 \r\nis about half of the highest RCP by 2100 (Figure 6.19), which is due to \r\nmuch lower projections of coal use in China driven largely by efforts \r\nduring the last decade to combat poor air quality"


id: a2ac10d22e05b719f1e0a445ce45159c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 23160
content:
" At the same time, \r\nthe SSP scenarios without climate change mitigation project faster \r\ngrowth in methane emissions in Africa, the Middle East and Latin \r\nAmerica (Figure 6.19) driven by developments in agriculture, the oil \r\nand gas sectors, and, especially in Africa, waste management. There \r\nare significant differences in the assessment and feasibility of rapid \r\nmethane mitigation. Hoglund-Isaksson et al. (2020) review most \r\nrecent studies and assess feasibility of rapid widespread mitigation, \r\nconcluding that significant (over 50%) reductions are attainable but \r\nthe feasibility of such reductions could be constrained in the short \r\nterm due to locked capital. This might have implications for near-term \r\nevolution assumed in, for example, SSP1-1.9 or SSP3-lowSLCF-lowCH4, \r\nwhere emissions drop very quickly due to fast decarbonization and \r\nreductions in agriculture. Such high reduction potential in agriculture \r\nhas been also assumed in other studies (Lucas et al"


id: e9d802a2226ecfd4761974f2e21fbda6
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vector_id: 34285
content:
", 2007; Harmsen \r\net al., 2020a) but is questioned by Hoglund-Isaksson et al. (2020) \r\nwho indicate that widespread implementation (within decades) of \r\npolicies bringing about institutional and behavioural changes would \r\nbe important for transition towards very low methane emissions from \r\nlivestock production."


id: 9f5984b2fe0f601ec6be3ca40a9a65cc
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 22613
content:
"SSP1 and SSP2, accounting for the expected growth in food demand \r\nand a general lack of effective policies targeting agricultural emissions. \r\nAdditionally, mitigation potential for NH3 is generally smaller than \r\nfor other species owing to fugitive and widely distributed sources \r\n(Pinder et al., 2007; Klimont and Winiwarter, 2015; Mohankumar \r\nSajeev et al., 2018; Sajeev et al., 2018). Most significant changes of \r\nSLCF emissions in the near and long-term compared to the present \r\nday are expected for SO2. This is due to ever more stringent (and \r\nenforced) legislation in China's power sector, extended recently to \r\nindustrial sources (Zheng et al., 2018b; Tong et al., 2020), declining \r\ncoal use in most SSPs, recently announced stricter emissions limits \r\nfor the power sector in India, and reduction of the sulphur content of \r\noil fuel used in international shipping from 2020 (IMO, 2016). For the \r\nlower forcing targets (e.g., SSP1-2"


id: 4c95c39fcfbb5ce17434e07bf32ed4dd
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 9474
content:
"6), the SO2 trajectories are similar \r\nto the RCPs resulting in over 50-90% decline by 2050 and 2100, \r\nrespectively, while for the scenarios with no climate policies, the SSPs \r\nshow a large spread even at the end of the century."


id: ae7421dd1e9e5b17b0259ba9081550a0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 24976
content:
"Until the mid-21st century, SSP3-7.0 and SSP5-8.5 scenarios project \r\nno reduction in NOx emissions at the global level with decline in \r\nmost OECD countries and Eastern Asia, driven by existing legislation \r\nin power, industry and transportation (e.g.,  Tong et al., 2020), \r\nand continued increase in the rest of the world (Figures  6.18 and \r\n6.19). Towards the end of the century, similar trends continue \r\nin SSP3-7.0 while emissions in SSP5 decline strongly owing to \r\nfaster technological progress and stronger air-quality action (Rao \r\net al., 2017; Riahi et al., 2017). By 2100, the 'Regional Rivalry' (SSP3) \r\nscenario emissions of NOx (and most other SLCFs, except ammonia) \r\nare typically twice as high as the next highest SSP projection, both at \r\nthe global (Figure 6.18) and regional levels (Figure 6.19). In emissions \r\npathways consistent with Paris Agreement goals (SSP1-1.9 or SSP1\u00022.6; Section 1.6.1), NOx drops, compared to 2015, by 50% in SSP1-2.6 \r\nand by 65% in SSP1-1"


id: 7a570efed2c43ceb1d0f10ca873e9374
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 16819
content:
"9 by 2050, is reduced by about 70% by 2100, \r\nresulting in global emissions levels comparable to the 1950s and \r\nbelow the RCP range. In these pathways considering strong climate \r\nchange mitigation, similar reductions are projected at the regional \r\nlevel, except in Africa (less than 50% decline) due to its high share of \r\nbiomass emissions as well as strong growth in population and fossil \r\nfuel use. The trends in anthropogenic and biomass-burning emissions \r\nfor other ozone precursors (NMVOC, CO) are similar to that of NOx. "


id: d52dc71fdcecc0c0b66dbf2cb2f71cdd
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 31069
content:
"Global emissions of carbonaceous aerosols are projected to decline \r\nin all SSP scenarios (Figure 6.18) except SSP3-7.0. In that scenario, \r\nwhich also has much higher emissions than any of the RCPs, about \r\nhalf of the anthropogenic BC originates from cooking and heating on \r\nsolid fuels, mostly in Asia and Africa (Figure 6.19), where only limited \r\nprogress in access to clean energy is achieved. Slow progress in \r\nimproving waste management, high coal use in energy and industry, \r\nand no further progress in controlling diesel engines in Asia, Africa \r\nand Latin America contributes most of the remaining emissions, \r\nresulting in about 90% of anthropogenic BC emitted in the non\u0002OECD world by 2100 in SSP3-7.0. A similar picture emerges for OC \r\nbut with greater contribution of the waste management sector and \r\nbiomass burning, and lower impact of transportation and industry \r\ndevelopments. Since scenarios compliant with Paris Agreement goals \r\n(SSP1-1.9 or SSP1-2.6; Section 1.6"


id: 659eca9df78f8dd578996424114e662d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 13564
content:
"1) include widespread access to \r\nclean energy already by 2050, the global and regional emissions of \r\nBC decline by 70-75% by 2050 and 80% by 2100 relayive to 2015. \r\nThe decline in the residential sector (about 90% by 2050 and over \r\n95% by 2100) is accompanied by a  strong reduction in transport \r\n(over 98%) and the decarbonization of the industry and energy sector. "


id: dc72c5c8061905480bdf3f2d3f919fbd
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vector_id: 32184
content:
"The SSP scenarios draw on the HFC projections developed by Velders \r\net al. (2015) considering, in climate change mitigation scenarios, the \r\nprovisions of the Kigali Amendment (2016) to the Montreal Protocol \r\nleading to phase-down of HFCs (Section  6.6.3.2; Papanastasiou \r\net al., 2018). The SSP scenarios without climate change mitigation \r\n(e.g, SSP3-7.0, SSP5-8.5) show a range in HFC emissions of 3.2-5.3 \r\nGtCO2-eq yr-1 in 2050 and about 4-7.2 GtCO2-eq yr-1 by 2100 while in \r\ndeep climate change mitigation scenarios (SSP1-1.9 and SSP1-2.6), \r\nconsistent with the 1.5-2degC targets, they are expected to drop to \r\n0.1-0.35 GtCO2-eq yr-1 (Figure  6.18). In SSP1-1.9, the extent of \r\nreduction and its pace is more ambitious than current estimates of \r\nthe effect of the fully implemented and enforced Kigali Amendment \r\n(Figure 6.18; Hoglund-Isaksson et al., 2017; Purohit and Hoglund\u0002Isaksson, 2017)"


id: 1e87695636ee74bef7728e2a316324c2
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 2244
content:
" The best representation of the HFC emissions \r\ntrajectories in the SSP framework compliant with the Kigali \r\nAmendment is the SSP1-2.6 and the baseline (including only pre\u0002Kigali national legislation; Section  6.6.3) is best represented by \r\nSSP5-8.5 (Figure 6.18). However, since HFC emissions in SSPs were \r\ndeveloped shortly after the Kigali Amendment had been agreed, \r\nnone of these projections represents accurately the HFC emissions \r\ntrajectory corresponding to the phase-out emissions levels agreed \r\nto in the Kigali Amendment (Meinshausen et al., 2020), leading to \r\nmedium confidence in the assessment of the benefits of the Kigali \r\nAmendment when using SSP projections for HFCs. "


id: 942493423e5abbe8e249b6a4cac6f0d8
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 20820
content:
"Surface ozone increases continuously until 2050 across most regions \r\nin SSP3-7.0 and SSP5-8.5, (Turnock et al., 2020), particularly over \r\nEastern Asia, Southern Asia, the Middle East, Africa, South East Asia \r\nand Developing Pacific, where this increase can reach and even exceed \r\n5 ppb for annual mean averaged over land areas (Figure 6.20). After \r\n2050, surface ozone concentrations decrease in SSP5-8.5, reaching \r\nlevels below their 2005-2014 mean levels in most regions, but level \r\noff or continue to increase under SSP3-7.0. The increase in surface \r\nozone in the SSP5-8.5 scenario occurs despite an emissions decrease \r\nof several ozone precursors because the methane emissions increase \r\nuntil about 2080 in the absence of climate change mitigation. Ozone \r\ndecreases over all regions in response to strong emissions mitigation \r\nin SSP1-1.9 and SSP1-2.6 (Turnock et al"


id: ea3edf9a0ee7c26681bcd926722ad20e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 34361
content:
", 2020), with decreases of \r\n5  to 10 ppb as soon as 2030 in North America, Europe, Eurasia, \r\nEastern Asia, the Middle East and Southern Asia in their annual \r\nmeans over land areas. In most regions surface ozone is reduced \r\nslightly or remains near present day values in the middle of the road \r\nscenario, SSP2-4.5. In 2100, the largest differences in surface ozone \r\nchanges across the scenarios occur for the Middle East, Southern \r\nAsia and Eastern Asia with differences ranging up to 40 ppb between \r\nSSP3-7.0 and SSP1-1.9 at the end of the century. In each scenario, \r\ndespite discrepancies in the magnitude of changes, especially over \r\nNorth America, Europe, Eurasia, Eastern Asia and Southern Asia, the \r\nmodels are in high agreement regarding the signs of the changes and \r\nare thus assessed as of high confidence."


id: e51fdd5011c115f994a067387ecc1ca6
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 33538
content:
"After about 2040, it is likely that, across the scenarios, the net effect \r\nof the removal of aerosols is a further increase in GSAT. However, \r\ntheir contribution to the rate of change decreases towards the end \r\nof the century (from up to 0.2degC per decade before 2040 to about \r\n0.03degC per decade after 2040). After 2040, the changes in methane, \r\nHFCs and tropospheric ozone become equally important as the \r\nchanges in the aerosols for the GSAT trends. In the low-emissions \r\nscenarios (SSP1-1.9 and SSP1-2.6), the contribution to warming from \r\nthe SLCFs peaks around 2040 with a very likely range of 0.04degC to \r\n0.34degC. After the peak, the reduced warming from reductions in \r\nmethane and ozone dominates, giving a best total estimate warming \r\ninduced by SLCF and HFC changes of 0.12degC and 0.14degC respectively, \r\nin 2100, with a very likely range of -0.07degC to +0.45degC (Figure 6.22)"


id: ecfa18bf4b7c6fa8036eae6685d3f87a
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 34761
content:
" \r\nHowever, in the longer term towards the end of the century there \r\nare very significant differences between the scenarios. In SSP3-7.0 \r\nthere is a near-linear warming due to SLCFs of 0.08degC per decade, \r\nwhile for SSP5-8.5 there is a more rapid early warming. In SSP3-7.0, \r\nthe limited reductions in aerosols, but a steady increase in methane, \r\nHFCs and ozone lead to a nearly linear contribution to GSAT reaching \r\na best estimate of 0.5degC in 2100. Contributions from methane and \r\nozone decrease towards 2100 in SSP5-8.5, however the warming \r\nfrom HFCs still increase and the SSP5-8.5 has the largest SLCF and \r\nHFC warming in 2100 with a best estimate of 0.6degC. In the SSP2-4.5 \r\nscenario, a reduction in aerosols contributes to about 0.3degC warming \r\nin 2100, while contributions from ozone and methane in this scenario \r\nare small."


id: 3e0a538865143e66dfe0521de7be02fb
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 7121
content:
"The simplified approach used to estimate the contributions to GSAT in \r\nFigure 6.22 has been supplemented with ESM simulations driven by \r\nthe two versions of the SSP3-7.0-lowSLCF scenario (Section 6.7.1.1). \r\nResults from five CMIP6 ESMs with fully interactive atmospheric \r\nchemistry and aerosols for the high-methane scenario show (Allen \r\net al., 2020, 2021) that reductions in emissions of air pollutants \r\nwould lead to an additional increase in GSAT by 2055 relative to 2015 \r\ncompared to the standard SSP3-7.0 scenario, with a best estimate \r\nof 0.23degC +- 0.05degC, and a corresponding increase in global mean \r\nprecipitation of 1.3 +- 0.17% (note that uncertainties from the work \r\nof Allen et al. here and elsewhere are reported as twice standard \r\ndeviation). Including methane mitigation (SSP3-7.0-lowSLCF-lowCH4) \r\nwould lead to a small increase in global precipitation (0.7 +- 0.1%) \r\nby mid-century despite a decrease in GSAT (Section 6.7"


id: 8c7a435f2d7333c0d5052745272b622c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 19622
content:
"3), which is \r\nrelated to the higher sensitivity of precipitation to sulphate aerosols \r\nthan greenhouse gases (Section 8.2.1; Allen et al., 2021). "


id: 4b1e05f76ebf764decae51350a3f2013
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 9239
content:
"While the emulators used for GSAT projections shown in Figure 6.22 do \r\nnot take the regional perspective into account, the set of simulations \r\nperformed within the Hemispheric Transport of Air Pollutants Phase \r\n2 (HTAP2) project (Galmarini et al., 2017) allows for this perspective. \r\nThe results from the chemistry-transport model OsloCTM3 taking \r\npart in the HTAP2 have been used by Lund et al. (2020) to derive \r\nregion-specific absolute global warming potentials (AGTPs; cf. \r\nAamaas et al., 2016) for each emitted SLCF and each HTAP2 region. \r\nWith this set of AGTPs, Lund et al. (2020) estimate the transient \r\nresponse in GSAT to the regional anthropogenic emissions. There are \r\nimportant differences in the contributions to GSAT in 2040 and 2100 \r\n(relative to 2020) between the regions and scenarios, mainly due to \r\nthe differences in the mixture of emitted SLCFs (Figure 6.23)"


id: 7e7cc3b3e203852e536127cc92c6c2a8
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 17463
content:
" There is \r\noverall good agreement between the total net contribution from all \r\nregions to GSAT and the estimate based on global ERF and the two\u0002layer emulator (Figure 6.22). "


id: 200f7ba6dd12fde194b5b6eab9dbfeaa
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 2487
content:
"The role of the different SLCFs, and also the net of all the SLCFs \r\nrelative to the total warming in the scenarios, is quite different across \r\nthe SSP scenarios varying with the summed levels of climate change \r\nmitigation and air pollution control (Figure  6.24). In the scenario \r\nwithout climate change mitigation but with strong air pollution \r\ncontrol (SSP5-8.5) all the SLCFs (methane, aerosols and tropospheric \r\nozone) and the HFCs (with lifetimes less than 50 years) add to \r\nthe warming, while in the strong climate change and air pollution \r\nmitigation scenarios (SSP1-1.9 and SSP1-2.6), the emissions controls \r\nact to reduce methane, ozone and BC, and these reductions thus \r\ncontribute to cooling. In  all scenarios, except SSP3-7.0, emissions \r\ncontrols lead to a  reduction of the aerosols relative to 2019, \r\ncausing a warming. However, the warming from aerosol reductions \r\nis stronger in the SSP1 scenarios (with best estimates of 0.21degC in \r\n2040 and 0.4degC in 2100 in SSP1-2"


id: 84bade9dd576109d6a93c51b1e5cb04e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 18411
content:
"6) because of higher emissions \r\nreductions from stronger decrease of fossil fuel use in these \r\nscenarios than in SSP5-8.5 (0.13degC in 2040 and 0.22degC in 2100). The \r\nchanges in methane abundance contribute a warming of 0.14degC in \r\nSSP5-8.5, but a cooling of 0.14degC in SSP1-2.6 by the end of the 21st \r\ncentury relative to 2019. Furthermore, under SSP5-8.5, HFCs induce \r\na warming of 0.06degC with a very likely range of [0.04 to 0.08] degC in \r\n2050 and 0.2 [0.1 to 0.3] degC by the end of the 21st century, relative \r\nto 2019, while under SSP1-2.6, warming due to HFCs is negligible \r\n(below 0.02degC) (high confidence). This assessment relies on these \r\nestimates, which are based on updated ERFs and HFC lifetimes. It is in \r\naccordance with previous estimates (Section 6.6.3.2) of the efficiency \r\nof the implementation of the Kigali Amendment and national \r\nregulations. It  is very likely that under a  stringent climate and air \r\npollution mitigation scenario (SSP1-2"


id: 29da01a0b46cc9e53ad94145b1310be3
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 4013
content:
"6), the warming induced by \r\nchanges in methane, ozone, aerosols and HFCs towards the end of \r\nthe 21st century, will be very low compared with the warming they \r\nwould cause under the SSP5-8.5 scenario (0.14degC in SSP1-2.6 versus \r\n0.62degC in SSP5-8.5). "


id: aad0a285e7541ba46faa03ef10510505
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 24398
content:
"In the low- and medium-emissions scenarios (SSP1-2.6 and SSP2-4.5), \r\nthe warming effects induced by changes in SLCFs on GSAT are \r\ndominated by emissions in North America, Europe and Eastern Asia \r\n(Figure 6.23). In SSP1-2.6 the emissions of all SLCFs in all regions \r\ndecrease and the net effect of the changes in SLCFs from all of these \r\nthree regions is an increase in GSAT of about 0.02degC (per region) \r\nin 2040 and about 0.04degC in 2100. For SSP2-4.5, emissions of most \r\nSLCFs continue to increase in Southern Asia (Figure  6.19), leading \r\nto a net cooling in the near term (-0.03degC in 2040), while in 2100, \r\nNorth America, Europe, Eastern and Southern Asia all contribute \r\nto a warming, most pronounced from Eastern Asia (0.05degC). In the \r\nSSP3-7.0 scenario, the net effect induced by changes in SLCFs in all \r\nregions is an enhanced warming towards the end of the century, \r\ndriven predominantly by change in methane"


id: 5e4d6ed8b26378b0067515e694459f58
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 12367
content:
" Africa is the region \r\ncontributing the most to predicted global warming due to SLCF \r\nchanges in 2100 (0.24degC). In SSP5-8.5, methane emissions increase \r\nin North America, Europe and Africa, while there is a  decrease \r\nin the Asian regions. For North America and Europe, the methane \r\nincrease combined with a reduction in aerosol leads to highest net \r\ncontribution to GSAT in this scenario (0.06degC and 0.04degC in 2100, \r\nrespectively). The high growth in methane makes Africa the region \r\nwith the largest contribution to future warming by SLCFs (0.18degC in \r\n2100 versus 2020) in this scenario. "


id: 80758e7e977dcd62a8ae3b83686d7449
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 17739
content:
"There is robust evidence and high agreement that non-methane \r\nSLCF mitigation measures, through reductions in aerosols and \r\nnon-methane ozone precursors to improve air quality (SSP3-7.0\u0002lowSLCF-highCH4 vs SSP3-7.0), would lead to additional near-term \r\nwarming with a range of 0.1degC-0.3degC. Methane mitigation that also \r\nreduces tropospheric ozone, stands out as an option that combines \r\nnear- and long-term gains on surface temperature (high confidence). \r\nWith stringent but realistic methane mitigation (SSP3-7.0-lowSLCF\u0002lowCH4), it is very likely that warming (relative to SSP3-7.0) from non\u0002methane SLCFs can be offset (Figure 6.24; Allen et al., 2021). Due to \r\nthe slower response to the methane mitigation, this offset becomes \r\nmore robust over time and it is very likely to be an offset after \r\n2050"


id: 5e3b14833a923589e91b6a0ef8dbff0a
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 12351
content:
" However, when comparing to the present day, it is unlikely\r\nthat methane mitigation can fully cancel out the warming over the \r\n21st century from reduction of non-methane cooling SLCFs."


id: 5ef1a37cb7b3070b88b9983a62356e63
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 12479
content:
"To assess the effect of dedicated air-quality versus climate policy \r\non air quality, PM2.5 and ozone indicators were estimated for three \r\nSSP3 scenarios by applying a widely used approach for the analysis \r\nof air-quality implications for given emissions scenarios (Rao \r\net al., 2017; Van Dingenen et al., 2018; Vandyck et al., 2018) and \r\nwhose sensitivity of surface concentrations to emissions changes is \r\ncomparable to that in the ESM ensemble (Supplementary Material \r\n6.SM.5). The assessment shows that both strong air pollution control \r\nand strong climate change mitigation, implemented independently, \r\nlead to a large reduction of exposure to PM2.5 and ozone by the end \r\nof the century (high confidence) (Figures 6.25 and 6.26)"


id: 909cc96fde315a4c59241d34a48368f1
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 20287
content:
" However, \r\nimplementation of air pollution control, relying on the deployment \r\nof existing technologies, leads to benefits more rapidly than climate \r\nchange mitigation (high confidence), which requires systemic changes \r\nand is thus implemented later in this scenario. Notably, under the \r\nunderlying SSP3 context, significant parts of the population remain \r\nexposed to air quality exceeding the WHO guidelines for PM2.5 over \r\nthe whole century (high confidence), in particular in Africa, Eastern \r\nand Southern Asia and the Middle East, and for ozone only a small \r\nimprovement in population exposure is expected in Africa and Asia. "


