# document

id: 27cebbab4394110e82ed5e40bd830e8f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 3673
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"Enhanced mid-ocean ridge magmatism and/or hydrothermal activity \r\nmodulated by sea level rise has recently been hypothesized to have \r\ncontributed to the deglacial CO2 rise (Crowley et al., 2015; Lund \r\net al., 2016; Huybers and Langmuir, 2017; Stott et al., 2019b). While \r\ngeological carbon release may have affected the ocean's radiocarbon \r\nbudget (Ronge et al., 2016; Rafter et al., 2019; Stott et al., 2019a), \r\nmodel results suggest that the potential contribution of geological \r\ncarbon sources to the atmosphere remained small (Roth and Joos, \r\n2012; Hasenclever et al., 2017)."


id: 3135095cac3f6cfd8cadaa2f8ded02cd
created_at: 2023-03-16 14:00:41
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vector_id: 5164
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"Simulations of Earth models of intermediate complexity (EMIC) with \r\ncoupled glacial-interglacial climate and the carbon cycle were able to \r\nreproduce first-order changes in the atmospheric CO2 content for the \r\nfirst time in recent years (Ganopolski and Brovkin, 2017; Khatiwala \r\net al., 2019). The most important processes accounting for the full \r\ndeglacial CO2 amplitude in the models include solubility changes, \r\nchanges in oceanic circulation and marine carbonate chemistry. The \r\neffect of the terrestrial carbon cycle, variable volcanic outgassing \r\nand the temperature dependence on the oceanic remineralization \r\nlength scale contribute less than 15 ppm CO2 between the glacial \r\nand interglacial intervals of the cycles. However, details in the \r\nsimulated response of the marine carbon cycle and atmospheric CO2\r\nconcentrations to changes in ocean circulation depend to a  large \r\ndegree on model parametrization (Gottschalk et al., 2019)."


id: c6fcabd40800cc19951dcb96f5b75138
created_at: 2023-03-16 14:00:41
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vector_id: 28887
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"Independent paleoclimatic evidence suggests with high confidence\r\nthat marine and terrestrial CH4 and N2O emissions are highly sensitive \r\nto climate on (sub)centennial time scales. Limited, yet internally \r\nconsistent ice core measurements indicate with medium confidence\r\nthat pulsed geologic CH4 release from continental margins associated \r\nwith warming remained negligible across the LDT. Multiple lines of \r\nevidence suggest with high confidence that CO2 was released from the \r\nocean interior on centennial time scales during the LDT in response \r\nto, or associated with warming, contributing to the transition out of \r\nthe last glacial stage to the current interglacial period."


id: 3985bc15ffef9f201ec48d30240433c0
created_at: 2023-03-16 14:00:41
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vector_id: 6425
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"Multiple lines of evidence inferred from marine sediment proxies \r\nindicate with low to medium confidence that the millennial rates of \r\nCO2 concentration change in the atmosphere during the last 56 Myr \r\nwere at least four to five times lower than during the last century \r\n(Figure  5.3). In spite of uncertainties in ice core reconstructions \r\nrelated to delayed enclosure of air bubbles, which tend to smooth \r\nthe records, there is high confidence that the rates of atmospheric \r\nCO2 and CH4 change during the last century were at least 10 and 5 "


id: 435439cb1655d3ed3a6b0b14b87f6daa
created_at: 2023-03-16 14:00:41
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"times faster, respectively, than the maximum centennial growth rate \r\naverages of those gases during the last 800 kyr (Fig. 5.4).\r\n5.2 "


id: 21ab7f868302888120405518ee6ac736
created_at: 2023-03-16 14:00:41
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vector_id: 2728
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"This section assesses the trends and variability in atmospheric \r\naccumulation of the three main greenhouse gases (GHGs) -  CO2, \r\nCH4 and N2O - their ocean and terrestrial sources and sinks as well \r\nas their budgets during the Industrial Era (1750-2019). Emphasis is \r\nplaced on the more recent contemporary period (1959-2019) where \r\nunderstanding is increasingly better constrained by atmospheric, \r\nocean and land observations. The section also assesses our increased \r\nunderstanding of the anthropogenic forcing and processes driving \r\nthe trends, as well as how variability at the seasonal to decadal \r\nscales provide insights on the mechanism governing long-term \r\ntrends and emerging biogeochemical-climate feedbacks with their \r\nregional characteristics."


id: d382f7a571562595a66aac91c0c67ff7
created_at: 2023-03-16 14:00:41
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"Fossil CO2 emissions have grown continuously since the beginning \r\nof the industrial era (Figure  5.5) with short intermissions due to \r\nglobal economic crises or social instability (Peters et al., 2012; \r\nFriedlingstein et al., 2020). In the most recent decade (2010-2019), \r\nfossil CO2 emissions reached an average 9.6 +-  0.5  PgC yr-1 and \r\nwere responsible for 86% of all anthropogenic CO2 emissions. In \r\n2019, fossil CO2 emissions were estimated to be 9.9 +- 0.5 PgC yr-1\r\nexcluding carbonation (Friedlingstein et al., 2020), the highest on \r\nrecord. These estimates exclude the cement carbonation sink of \r\naround 0.2 PgC yr-1. Fossil CO2 emissions grew at 0.9% yr-1 in the \r\n1990s, increasing to 3.0% yr-1 in the 2000s, and reduced to 1.2% \r\nfrom 2010 to 2019. The slower growth in fossil CO2 emissions in \r\nthe last decade is due to a  slowdown in growth from coal use. "


id: c06e2e9f15432cf15ae2f191c2606f92
created_at: 2023-03-16 14:00:41
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"occurring due to interannual variability or trends in environmental \r\nconditions (in particular, climate, CO2, and nutrient deposition) \r\n(Houghton, 2013)."


id: a454a5fc30f969a64551823dd1d6c52b
created_at: 2023-03-16 14:00:41
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"The global net flux from land-use change and land management \r\nis composed of carbon fluxes from land-use conversions, land \r\nmanagement and changes therein (Pongratz et al., 2018) and is \r\nequivalent to the LULUCF fluxes from the agriculture, forestry and \r\nother land use (AFOLU) sector (Jia et al., 2019). It consists of gross \r\nemissions (loss of biomass and soil carbon in clearing or logging, \r\nharvested product decay, emissions from peat drainage and \r\nburning, degradation) and gross removals (CO2 uptake in natural \r\nvegetation regrowing after harvesting or agricultural abandonment, \r\nafforestation). The LULUCF flux relates to direct human interference \r\nwith terrestrial vegetation, as opposed to the natural carbon fluxes "


id: 66031fa74215b75bf39b4a47724bdeb0
created_at: 2023-03-16 14:00:41
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"Land-use-related component fluxes can be verified by the growing \r\ndatabases of global satellite-based biomass observations in \r\ncombination with information on remotely sensed land cover change. "


id: 248a8c96d59136a06b7bd5b26727924f
created_at: 2023-03-16 14:00:41
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"pre-industrial times (Erb et al., 2018; see also Section 5.1.2.3). Low \r\nconfidence is assigned to pre-industrial emissions estimates."


id: 2bd372edbc7827de3706c32c58f4a544
created_at: 2023-03-16 14:00:41
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"However, they differ from bookkeeping and modelling with Dynamic \r\nGlobal Vegetation Models (DGVMs) in excluding legacy emissions \r\nfrom pre-satellite-era land-use change and land management, and \r\nneglecting soil carbon changes, often focusing on gross deforestation, \r\nnot regrowth (Jia et al., 2019)."


id: 661774ba3a13fc2d8320ea74b7129b
created_at: 2023-03-16 14:00:41
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content:
"Since AR5, evidence emerged that the LULUCF flux might have been \r\nunderestimated as DGVMs include anthropogenic land cover change, \r\nbut often ignore land management practices not associated with \r\na change in land cover; land management is more widely captured \r\nby bookkeeping models through use of observation-based carbon \r\ndensities (Ciais et al., 2013; Pongratz et al., 2018). Sensitivity studies \r\nshow that practices such as wood and crop harvesting increase \r\nglobal net LULUCF emissions (Arneth et al., 2017) and explain about \r\nhalf of the cumulative loss in biomass (Erb et al., 2018)."


id: abfa2116b8eafa8a8d4dd7b8333601de
created_at: 2023-03-16 14:00:41
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"Gross emissions are on average two to three times larger than the net \r\nflux from LULUCF, increasing from an average of 3.5 +- 1.2 PgC yr-1 for \r\nthe decade of the 1960s to an average of 4.4 +- 1.6 PgC yr-1 during \r\n2010-2019 (Friedlingstein et al., 2020). Gross removals partly \r\nbalance these gross emissions to yield the net flux from LULUCF and \r\nincrease from -2.0 +- 0.7 PgC yr-1 for the 1960s to -2.9 +- 1.2 PgC yr-1\r\nduring 2010-2019. These large gross fluxes show the relevance of \r\nland management, such as harvesting or rotational agriculture, and \r\nthe large potential to reduce emissions by halting deforestation \r\nand degradation."


id: 5a7ff0beeec6a3a6fcfcc1fa0b765325
created_at: 2023-03-16 14:00:41
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"Over the past six decades, the fraction of anthropogenic CO2\r\nemissions that has accumulated in the atmosphere (referred to as \r\nairborne fraction) has remained near constant at approximately \r\n44% (Figure 5.7) (Ballantyne et al., 2012; Ciais et al., 2019; Gruber \r\net al., 2019b; Friedlingstein et al., 2020). This suggests that the land \r\nand ocean CO2 sinks have continued to grow at a  rate consistent \r\nwith the growth rate of anthropogenic CO2 emissions, albeit with \r\nlarge interannual and sub-decadal variability dominated by the land \r\nsinks (Figure 5.7)."


id: 6a0c49fe665e24e09c8e98fe6af87ff8
created_at: 2023-03-16 14:00:41
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"Since AR5, an alternative observable diagnostic to the airborne \r\nfraction has been proposed to understand the trends in land and \r\nocean sinks in response to its driving atmospheric CO2 concentrations \r\n(Raupach et al., 2014; Bennedsen et al., 2019). It is the sink rate \r\nthat is defined as the combined ocean and land sink flux per unit of \r\natmospheric excess of CO2 above pre-industrial levels (Raupach et al., "


id: ef0ed202d40011f24f500c719133d108
created_at: 2023-03-16 14:00:41
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"(1960-2018), air-sea fluxes and storage of anthropogenic CO2 are \r\nlargely influenced by atmospheric CO2 concentrations, physical \r\nocean processes and physicochemical carbonate chemistry, which \r\ndetermines the unique properties of CO2 in seawater (Chapter 9 and \r\nCross-Chapter Box  5.3; Wanninkhof, 2014; DeVries et al., 2017; \r\nMcKinley et al., 2017, 2020, Gruber et al., 2019a, b; Hauck et al., \r\n2020). Here we assess three different approaches (Figures  5.8a,b \r\nand 5.9) that together provide high confidence that, during the \r\nhistorical period (1960-2018), the ocean carbon sink (Socean) and its \r\nassociated ocean carbon storage have grown in response to global \r\nanthropogenic CO2 emissions (Gruber et al., 2019a; Hauck et al., \r\n2020; McKinley et al., 2020)."


id: a3a37e4ce6db61f18db73371f2d0906c
created_at: 2023-03-16 14:00:41
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"SROCC Section  5.2.2.3.2; Bates et al., 2014; Sutton et al., 2016; \r\nFassbender et al., 2017; Landschutzer et al., 2018; Jiang et al., 2019). \r\nA recent model-based study suggests that re-emergence of previously \r\nstored anthropogenic CO2 is changing the buffering capacity of the \r\nmixed layer and reducing the ocean sink for anthropogenic CO2\r\nduring the historical period (Rodgers et al., 2020). This trend is \r\nnot reflected in observations-based products (Figure  5.8a), so we \r\nattribute a low confidence."


id: ea3e4b1427ef4b0db85c381fb13cbcc0
created_at: 2023-03-16 14:00:41
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"The second assessment approach makes use of six independent \r\nmethods to constrain the mean decadal ocean sink over the period \r\n1990-2019 (Figure  5.8b). This provides a  multi-decadal advance \r\non the 1990-1999 decadal constraint from (Denman et al., 2007) \r\nthat has been widely used as a model constraint for GOBMs used \r\nfor the global carbon budget (Hauck et al., 2020). The medium \r\nconfidence attributed by this assessment of the global multi-decadal \r\ntrend (Figure  5.8a) is further supported by the broad agreement \r\nin magnitude and trend of the decadal mean ocean CO2 uptake \r\nwith assessments that also include additional observations-based, \r\nindependent methods such as ocean CO2 inversion and atmospheric \r\nCO2 and O2/N2 measurements (Figure 5.8b; Supplementary Materials \r\nTables 5.SM.1 and 5.SM.2)."


id: 28bd5604947e47c021368cecb3dc08c3
created_at: 2023-03-16 14:00:41
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"also be a factor amplifying the global decadal perturbation of the \r\n1990s (Gloege et al., 2021). Therefore, while there is high confidence\r\nin the 1990s hiatus of the global ocean sink for anthropogenic CO2, \r\nand that the Southern Ocean makes an observable contribution to \r\nit, there is still low confidence in the attribution for the processes \r\nbehind the 1990s hiatus (Section 5.2.1.3.2). Observed increases in \r\nthe amplitude of the seasonal cycle of ocean pCO2 and reductions in \r\nthe mean global buffering capacity provide high confidence that the \r\ngrowing CO2 sink is also beginning to drive observable large-scale \r\nchanges in ocean carbonate chemistry (Jiang et al., 2019). However, \r\nthere is medium confidence that these changes which, depending \r\non the emissions scenario, could drive future ocean feedbacks, are \r\nstill too small to emerge from the historical multi-decadal observed \r\ngrowth rate of Socean (Sections 5.1.2; 5.3.2 and 5.4.2, and Figure 5.8a; "


id: 98622fc762a1fa91fd860705c6f36092
created_at: 2023-03-16 14:00:41
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"interior support the medium confidence assessment that the ocean \r\nanthropogenic carbon storage rates continue to be determined by the \r\nocean sink (Socean) in response to growing CO2 emissions (Figure 5.9; \r\nMcKinley et al., 2020)."


id: 9a1fc177cc332c2795e768a69a2fba1b
created_at: 2023-03-16 14:00:41
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"equatorial oceans' influence on global mean Fnet has a low correlation \r\nbecause, notwithstanding the coherence in interannual variability, it \r\ndoes not show the same global mean trend of strengthening sink \r\nin response to growing global emissions (Supplementary Materials \r\nFigure 5.SM.1d; Gregor et al., 2019). All regions, except the equatorial \r\nocean, contribute to varying extents to the multi-decadal trend of \r\ngrowth in the global ocean sink (Supplementary Materials Figure 5.\r\nSM.1). Data sparseness in the high latitudes and the relatively short \r\nlength of the observational records leads to low confidence in the \r\nattribution of the processes that link regional-global variability to \r\nclimate (Landschutzer et al., 2019; Gloege et al., 2021)."


id: 142fa79ccc38018c1db553f7ccde41ec
created_at: 2023-03-16 14:00:42
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content:
"the Southern Ocean, the storage rate of the anthropogenic CO2 was \r\nrather lower after 2005 than before (Section 9.2.3.2; Tanhua et al., \r\n2017; Bindoff et al., 2019). These changes have been predominantly \r\nascribed to the impact of changes in the MOC on the transport of \r\nanthropogenic CO2 into the ocean interior due to regional climate \r\nvariability, in addition to the increase in the atmospheric CO2\r\nconcentration (Section 9.2.3.1; Wanninkhof et al., 2010; Perez et al., \r\n2013; DeVries et al., 2017, 2019; Gruber et al., 2019b; McKinley \r\net al., 2020). However, the low frequency of carbon observations \r\nin the interior of the vast ocean leads to medium confidence in the \r\nassessment of temporal variability in the rate of regional ocean CO2\r\nstorage and its controlling mechanisms."


id: c50271e6e5d0ce02d9d3e89a09901a45
created_at: 2023-03-16 14:00:42
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"enhanced vegetation photosynthesis over the past decades (medium \r\nto high confidence) (Figure 5.10), including increasing satellite-derived \r\nvegetation greenness (e.g., see Chapter 2; Mao et al., 2016; Zhu et al., \r\n2016; Jia et al., 2019) and satellite-derived photosynthesis indicators \r\n(e.g., Badgley et al., 2017; Zhang et al., 2018), change in atmospheric \r\nconcentration of carbonyl sulphide (Campbell et al., 2017), enhanced \r\nseasonal CO2 amplitude (Graven et al., 2013; Forkel et al., 2016), \r\nobservation-driven inference of increasing photosynthesis CO2\r\nuptake based mostly on enhanced water use efficiency (Cheng et al., \r\n2017), and DGVM simulated increase of photosynthesis CO2 uptake \r\n(Anav et al., 2015)."


id: b03a11f962741e1ec82511d2422c81bc
created_at: 2023-03-16 14:00:42
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"Substantial progress has been made since AR5 on attributing change \r\nof the global net land CO2 sink. Increasing global net land CO2 sink \r\nsince the 1980s is mainly driven by the fertilization effect from rising \r\natmospheric CO2 concentrations (Schimel et al., 2015; Sitch et al., 2015; \r\nFernandez-Martinez et al., 2019; O'Sullivan et al., 2019; Tagesson \r\net al., 2020; Walker et al., 2021) (medium confidence). Increasing \r\nnitrogen deposition (de Vries et al., 2009; Devaraju et al., 2016; \r\nHuntzinger et al., 2017) or the synergy between increasing nitrogen \r\ndeposition and atmospheric CO2 concentration (O'Sullivan et al., \r\n2019) could have also contributed to the increasing global net land \r\nCO2 sink. The effects of climate change alone on the global net land \r\nCO2 sink is so divergent that even the signs (directions) of the effects \r\nare not the same across DGVMs (e.g., Huntzinger et al., 2017)."


id: 39bf507fd6d94769e33f743b1d892d9d
created_at: 2023-03-16 14:00:42
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"In summary, multiple lines of observational and modelling evidence \r\nprovide high confidence in the finding that the ocean sink for \r\nanthropogenic CO2 has increased quasi-linearly over the past 60 years \r\nin response to growing global emissions of anthropogenic CO2, with \r\na  mean fraction of 23% of total emissions. The high confidence\r\nassessment is moderated to medium confidence due to a  number \r\nof ocean CO2 flux terms yet to be adequately constrained. Observed \r\nchanges in the variability of ocean pCO2 and observed reductions in \r\nthe mean global buffering capacity provide high confidence that the \r\ngrowing CO2 sink is also beginning to drive observable large-scale \r\nchanges in ocean carbonate chemistry. However, there is medium \r\nconfidence that these changes which, depending on the emissions \r\nscenario, could drive future ocean feedbacks, are still too small to \r\nemerge from the historical multi-decadal observed growth rate of Socean."


id: d01e2ce96b10e2714ce6c0a6090cdbfc
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"Significant uncertainties remain for the land CO2 sink partition \r\nof processes due to challenges in reconciling multiple-scale \r\nevidence from experiments to the globe (Fatichi et al., 2019; Walker \r\net al., 2021), due to large spatial and inter-model differences in \r\ndiagnosing dominant driving factors affecting the net land CO2 sink "


id: 9a3ffe5767beb835233b5067de2e88c
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"Carbon uptake by vegetation photosynthesis exerts a  first-order \r\ncontrol over the net land CO2 sink. Several lines of evidence show "


id: c39ee1f775c329288b17a88df539a94
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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"Understanding the mechanisms driving interannual variability in \r\nthe carbon cycle has the potential to provide insights into whether \r\nand to what extent the carbon cycle can affect the climate (carbon-\r\nclimate feedback), with particular interests over the highly climate\u0002sensitive tropical carbon cycle (e.g.,  Cox et al., 2013; X.  Wang \r\net al., 2014; Fang et al., 2017; Jung et al., 2017; Humphrey et al., \r\n2018; Malhi et al., 2018; see Section 5.4). Consistent findings from \r\nstudies with atmospheric inversions, satellite observations and \r\nDGVMs (e.g.,  Malhi et al., 2018; Rodenbeck et al., 2018) lead to \r\nhigh confidence that the tropical net land CO2 sink is reduced under \r\nwarmer and drier conditions, particularly during El Nino events. \r\nInterannual variations in tropical land-atmosphere CO2 exchange \r\nare significantly correlated with anomalies of tropical temperature, \r\nwater availability and terrestrial water storage (X. Wang et al., 2014; "


id: 1e383c7eb6829b92a9bf6311b600babb
created_at: 2023-03-16 14:00:42
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"(Huntzinger et al., 2017; Fernandez-Martinez et al., 2019), and due \r\nto model deficiency in process representations (He et al., 2016). \r\nNitrogen dynamics, a major gap in DGVMs identified in AR5, have \r\nnow been incorporated in about half of the DGVMs contributing to \r\nthe carbon budget of the Global Carbon Project (GCP) (see Le Quere \r\net al. (2018a) for model characteristics) and a  growing number \r\nof ESMs (Arora et al., 2020). However, as the representations of "


id: 4b9a2937e09a9f80ceedfb8efc9fadcf
created_at: 2023-03-16 14:00:42
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"Jung et al., 2017; Humphrey et al., 2018; Piao et al., 2020), whose \r\nrelative contribution are difficult to separate due to covariations \r\nbetween these climatic factors. At continental scale, the dominant \r\nclimatic driver of interannual variations of tropical land-atmosphere "


id: 3b041cb0d9e9bedc9363fd197bd4f7ac
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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"CO2 exchange was temperature variations (Figure 5.11; Piao et al., \r\n2020), which could partly result from the spatial compensation of \r\nthe water availability effects on land-atmospheric CO2 exchange \r\n(Jung et al., 2017)."


id: 75655e260c7efddbb1ec0048a24b7fff
created_at: 2023-03-16 14:00:42
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content:
"Cross-Chapter Box 5.1 | Interactions Between the Carbon and Water Cycles, Particularly Under \r\nDrought Conditions"


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"Contributors: Josep G. Canadell (Australia), Philippe Ciais (France), Herve Douville (France), Sabine Fuss (Germany), Robert Jackson \r\n(United States of America), Annalea Lohila (Finland), Shilong Piao (China), Sonia I.  Seneviratne (Switzerland), Sergio M. Vicente\u0002Serrano (Spain), Sonke Zaehle (Germany)"


id: 42e84182450c56ffe134019efd133827
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 7920
content:
"This box presents an assessment of interactions between the carbon and water cycles that influence the dynamics of the biosphere \r\nand its interaction with the climate system. It also highlights carbon-water trade-offs arising from the use of land-based climate \r\nchange mitigation options. Individual aspects of the interactions between the carbon and water cycles are addressed in separate \r\nchapters (Sections 5.2.1, 5.4.1, 8.2.3, 8.3.1, 8.4.1 and 11.6). The influence of wetlands and dams on methane emissions is assessed \r\nelsewhere (Sections 5.2.2, 5.4.7 and 8.3.1), as well as the consequences of permafrost thawing (Section 9.5.2 and Box 5.1) and/or \r\nincreased flooding (Sections 8.4.1, 11.5 and 12.4) on wetland extent in the northern high latitudes and wet tropics."


id: 81b1da830544b873eae39138dc08dcdb
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 17938
content:
"Increasing atmospheric CO2 concentration enhances leaf photosynthesis and drives a partial closure of leaf stomata, leading to higher \r\nwater-use efficiency (WUE) at the leaf canopy and ecosystem scales (Norby and Zak, 2011; De Kauwe et al., 2013; Fatichi et al., 2016; \r\nKnauer et al., 2017; Mastrotheodoros et al., 2017). Since AR5 (Box 6.3), a growing body of evidence from tree-ring and carbon isotopes \r\nfurther confirms an increase of plant water-use efficiency over decadal to centennial time scales, with some evidence for a stronger \r\nenhancement of photosynthesis compared to stomatal reductions (Frank et al., 2015; Guerrieri et al., 2019; Adams et al., 2020)."


id: 582532552917200d9561435c83bee4a3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 11373
content:
"Leaf stomata closure can have large effects on land freshwater availability because of reduced plant transpiration, leading in some \r\nregions to higher soil moisture and runoff (Roderick et al., 2015; Milly and Dunne, 2016; Y. Yang et al., 2019). However, increased water \r\navailability is often not realized because other CO2 physiological effects that enhance ecosystem evapotranspiration might offset the \r\ngains. These effects include plant growth and leaf area expansion (Ainsworth and Long, 2005; Ukkola et al., 2016; McDermid et al., \r\n2021), lengthening of the vegetative growing season (Frank et al., 2015; Lian et al., 2021), and the effects of stomatal closure on \r\nnear-surface atmosphere that leads to increased air temperature and VPDs (Berg et al., 2016; Vogel et al., 2018; Zhou et al., 2019; \r\nGrossiord et al., 2020)."


id: f9e315fbd6926f2d48e22d25bea4e6cf
created_at: 2023-03-16 14:00:42
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vector_id: 36821
content:
"ESMs show no consensus about the net hydrological response to physiological CO2 effects. Some studies show water savings as \r\na consequence of the CO2 effects on leaf stomata closure (Swann et al., 2016; Lemordant et al., 2018), while other studies show \r\nthat increased leaf area offsets the gains from increased WUE (Mankin et al., 2019). However, these projections are subject to ESM \r\nuncertainties to quantify transpiration (Lian et al., 2021), among them the correct representations of plant hydraulic architecture such \r\nas changes in xylem anatomical properties and deep rooting (Nie et al., 2013; L. Liu et al., 2020)."


id: 65f59105db999ff1a8684eb9b65a397
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 13630
content:
"In conclusion, it is very likely that elevated CO2 leads to increased WUE at the leaf level, concurrent with enhanced photosynthesis. \r\nIncreased CO2 concentrations alleviate the effects of water deficits on plant productivity (medium confidence) but there is low \r\nconfidence for its role under extreme drought conditions. There is low confidence that increased WUE by vegetation will substantially \r\nreduce global plant transpiration and diminish the frequency and severity of soil moisture and streamflow deficits associated with the \r\nradiative effect of higher CO2 concentrations."


id: d6088a4923b732394afd872ab179dbc6
created_at: 2023-03-16 14:00:42
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content:
"How does drought affect the terrestrial CO2 sink?\r\nWater availability controls the spatial distribution of photosynthesis - gross primary productivity (GPP) - over a larger part of the globe \r\n(Beer et al., 2010) and, at local scale, drought decreases GPP more than respiration (Schwalm et al., 2012) over most ecosystem types. \r\nThis makes water availability a major climatic driver of variability in net ecosystem exchange (Jung et al., 2017; Humphrey et al., 2018). "


id: cbb4f1ad77256b3011729bc2c9f4ea0b
created_at: 2023-03-16 14:00:42
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content:
"In addition to suppressing photosynthesis, field evidence suggests that droughts reduce the land CO2 sink, also through increasing \r\nforest mortality and promoting wildfire (Allen et al., 2015; Brando et al., 2019; Abram et al., 2021)."


id: cf0ce684a439bc1e4f97a967f2e37c1f
created_at: 2023-03-16 14:00:42
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content:
"At the global scale, interannual variability in the atmospheric CO2 growth rate and global-scale terrestrial water storage from satellite \r\nshow that a  lower global net land CO2 sink is associated with below-average terrestrial water storage (Humphrey et al., 2018). \r\nAtmospheric inversions based on surface and satellite column CO2 measurements show significant carbon release during drought \r\nevents in pan-tropic areas (Phillips et al., 2009; Gatti et al., 2014; J. Liu et al., 2017; Palmer et al., 2019). Regional extreme droughts in \r\nthe mid-latitudes also decrease GPP and land CO2 sink (Ciais et al., 2005; Wolf et al., 2016; W. Peters et al., 2020; Flach et al., 2021). \r\nDroughts are not compensated by equivalent wet anomalies because of the non-linear response of the terrestrial carbon uptake to \r\nsoil moisture (Green et al., 2019)."


id: b8e971a92ec384020d328962d6d77aaa
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 26622
content:
"In conclusion, there is high confidence that the global net land CO2 sink is reduced on interannual scale when regional-scale \r\nreductions in water availability associated with droughts occur, particularly in tropical regions. There is also high confidence that the \r\nglobal land sink will become less efficient due to soil moisture limitations and associated drought conditions in some regions for high\u0002emissions scenarios, specially under global warming above 4degC. However, there is low confidence on how these water cycle feedbacks \r\nwill play out in lower emissions scenarios (at 2degC global warming or lower) due to uncertainties in regional rainfall changes and the \r\nbalance between the CO2 fertilization effect, through WUE, and the radiative impacts of greenhouse gases."


id: c577329d598885934461bfe0fa519f11
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 28651
content:
"Most of the water consumed in BECCS is used to grow the feedstock, with carbon capture and storage constituting a smaller portion \r\nacross all crops (Rosa et al., 2020), with an estimated evaporative loss of 260 km3 yr-1 for 3.3 GtC yr-1 (Smith et al., 2016). The \r\nsame authors also estimate water use for CDR through afforestation at 1040 km3 yr-1 for 3.3 GtC yr-1, including interception and \r\ntranspiration, adjusted for the original land cover's water use. "


id: 8b3d2b93d96ee73e57de5af7fc237b5f
created_at: 2023-03-16 14:00:42
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vector_id: 19433
content:
"The impacts of different CDR options on the water cycle depend crucially on regional climate, prior land cover, and scale of deployment \r\n(Trabucco et al., 2008). Extensive irrigation for afforestation in drier areas will have larger downstream impacts than in wetter regions, \r\nwith the difference in water use between the afforested landscapes and its previous vegetation determining the level of potential \r\nimpacts on evapotranspiration and runoff (Jackson et al., 2005; Teuling et al., 2017). Afforestation and reforestation sometimes "


id: 31338ff309a0b5d62181bac2c750f9f3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 5162
content:
"enhances precipitation through atmospheric feedbacks such as increased convection, at least in the tropics (Ellison et al., 2017) and \r\nthe increase in precipitation can, in some regions, even cancel out the increased evapotranspiration (Li et al., 2018). "


id: 52733f2d5431d0f808471be4b2af7028
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 10427
content:
"In conclusion, extensive deployment of BECCS and afforestation/reforestation will require larger amounts of freshwater resources \r\nthan used by the previous vegetation, altering the water cycle at regional scales (high confidence). Consequences of high water \r\nconsumption on downstream uses, biodiversity, and regional climate depend on prior land cover, background climate conditions, \r\nand scale of deployment (high confidence). Therefore, a regional approach is required to determine the efficacy and sustainability \r\nof CDR projects."


id: e192bcd094aad0a6ed65bdec462d81d0
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 32979
content:
"from land use, land-use change, and forestry (ELULUCF), the growth rate \r\nof CO2 in the atmosphere (Gatm), and the ocean (Socean) and natural \r\nland (Sland) CO2 sinks. An imbalance term (BImb) is required to ensure \r\nmass balance of the source and sinks that have been independently \r\nestimated: EFOS +  ELULUCF =  Gatm +  Socean +  Sland. +  BImb. All estimates \r\nare reported with 1 standard deviation (+-1s, 1 sigma) representing \r\na likelihood of 68%."


id: 261b4526051071c296112def5e49e7b4
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 3415
content:
"The global CO2 budget (Figure  5.12) encompasses all natural \r\nand anthropogenic CO2 sources and sinks. Table  5.1 shows the \r\nperturbation of the global carbon mass balance between reservoirs \r\nsince the beginning of the industrial era, circa 1750."


id: f1d486c98a4558be6c28ea3ba1f960c7
created_at: 2023-03-16 14:00:42
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vector_id: 35561
content:
"Since AR5 (Ciais et al., 2013), a number of improvements have led to the \r\nmore constrained carbon budget presented here. Some new additions \r\ninclude: (i) the use of independent estimates for the residual carbon \r\nsink on natural terrestrial ecosystems (Le Quere et al., 2018a); (ii) \r\nimprovements in the estimates of emissions from cement production \r\n(Andrew, 2019) and the sink associated with cement carbonation (Cao \r\net al., 2020); (iii) improved and new emissions estimates from forestry \r\nand other land use (Hansis et al., 2015; Gasser et al., 2020); (iv) the \r\nuse of ocean observation-based sink estimates and a  revised river \r\nflux partition between hemispheres (Friedlingstein et al., 2020); and \r\n(v) the expansion of constraints from atmospheric inversions, based on \r\nsurface networks and the use of satellite retrievals. "


id: edb6a106eae5029fd9a92ffa6bfde188
created_at: 2023-03-16 14:00:42
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vector_id: 34862
content:
"The budget, based on the annual assessment by the GCP \r\n(Friedlingstein et al., 2020), uses independent estimates of all major \r\nflux components: fossil fuel and carbonate emissions (EFOS), CO2 fluxes "


id: 457be6774e1235f37172db0120f86505
created_at: 2023-03-16 14:00:42
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vector_id: 8344
content:
"Table 5.1 | Global anthropogenic CO2 budget accumulated since the Industrial Revolution (onset in 1750) and averaged over the 1980s, 1990s, 2000s, \r\nand 2010s. By convention, a negative ocean or land to atmosphere CO2 flux is equivalent to a gain of carbon by these reservoirs. The table does not include natural exchanges \r\n(e.g., rivers, weathering) between reservoirs. Uncertainties represent the 68% confidence interval (Friedlingstein et al., 2020)."


id: c5ce3d70db48be9a1b2f31c788e5505c
created_at: 2023-03-16 14:00:42
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vector_id: 28696
content:
"Resplandy et al., 2018) and corrected to account for 0.2 PgC buried \r\nin ocean floor sediments. These terms are included in Figure  5.12. \r\nInclusion of other smaller fluxes could further constrain the carbon \r\nbudget (Ito, 2019; Friedlingstein et al., 2020)."


id: 4635fc6c59d96cc6720de2aa5942fced
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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content:
"land-ocean aquatic continuum comprising freshwaters, estuaries, \r\nand coastal areas. Natural and anthropogenic transfers of carbon \r\nfrom soils to freshwater systems are significant (2.4-5.1 PgC yr-1) \r\n(Regnier et al., 2013; Drake et al., 2018). Some of the carbon is \r\nburied in freshwater bodies (0.15 PgC) (Mendonca et al., 2017), and \r\na  significant proportion returns to the atmosphere via outgassing \r\nfrom lakes, rivers and estuaries (Raymond et al., 2013; Regnier et al., \r\n2013; Lauerwald et al., 2015). The net export of carbon from the \r\nterrestrial domain to the open oceans is estimated to be 0.80 PgC yr-1\r\n(medium confidence), based on the average of (Jacobson et al., 2007; "


id: 2673d73e4e9ee7243cc5dc73e506be28
created_at: 2023-03-16 14:00:42
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vector_id: 3460
content:
"Methane is a  much more powerful greenhouse gas than CO2\r\n(Chapter 7) and participates in tropospheric chemistry (Chapter 6). "


id: 123d519b5e84349de1b10e2d4a6c66b0
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 372
content:
"emissions from the oil and gas sectors declined by about 10 Tg yr-1\r\nthrough the 1990s, and atmospheric CH4 loss steadily increased \r\n(Dlugokencky et al., 2003; Simpson et al., 2012; Crippa et al., 2020; \r\nHoglund-Isaksson et al., 2020; Chandra et al., 2021). The methane \r\ngrowth rate began to increase again at 7 +- 3 ppb yr-1 during 2007-\r\n2016, the causes of which are highly debated since AR5 (Rigby et al., \r\n2008; Dlugokencky et al., 2011; Dalsoren et al., 2016; Nisbet et al., "


id: e5fb3f539dec545157edb84a93f29406
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 33673
content:
"This section discusses both bottom-up and top-down estimates of \r\nemissions and sinks. Bottom-up estimates are based on empirical \r\nupscaling of point measurements, emissions inventories and \r\ndynamical model simulations, while top-down estimates refer to \r\nthose constrained by atmospheric measurements and chemistry\u0002transport models in inversion systems. Since AR5, a  larger suite \r\nof atmospheric inversions using both in situ and remote sensing \r\nmeasurement have led to better understanding of the regional \r\nCH4 sources (Cross-Chapter Box  5.2). New ice core measurements \r\nof 14C-CH4 are used for estimating the geological sources of CH4\r\n(Table 5.2). Compared to the SRCCL (IPCC, 2019a; Jia et al., 2019), \r\nwe provide a whole atmospheric sources-sinks budget consisting of \r\nall emissions and losses."


id: c1324d022a63386da00f3132970a25b2
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 27978
content:
"Emissions from rice cultivation decreased from about 45 Tg yr-1 in the \r\n1980s to about 29 Tg yr-1 in the decade 2000-2009, but increased \r\nagain slightly to 31 Tg yr-1 during 2008-2017, based on inventories \r\ndata. However, ecosystem models showed a gradual increase with \r\ntime due to climate change (limited evidence, low agreement) \r\n(Crippa et al., 2020; Hoglund-Isaksson et al., 2020; Ito, 2020)."


id: e03fdbb1b1a38475153f10269213fd0c
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 32771
content:
"Biomass burning and biofuel consumption (including natural and \r\nanthropogenic processes) caused at least 30 Tg yr-1 emissions during \r\n2008-2017 and constituted up to about 5% of global anthropogenic \r\nCH4 emissions. Methane emissions from open biomass burning \r\ndecreased during the past two decades mainly due to reduction \r\nof burning in savanna, grassland and shrubland (van der Werf \r\net al., 2017; Worden et al., 2017). There is recent evidence from the \r\ntropics that fire occurrence is non-linearly related to precipitation, \r\nimplying that severe droughts will increase CH4 emissions from fires, \r\nparticularly from the degraded peatlands (Field et al., 2016)."


id: 9891a91d583dc3682a004e5707d8ece2
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 21519
content:
"Table 5.2 | Global CH4 budget. Sources and sinks of CH4 for the two most recent decades for wich data is available, from bottom-up and top-down estimations (in Tg CH4 yr-1). \r\nThe data are updated from Saunois et al. (2020), for the bottom-up anthropogenic emissions (FAO, 2019; US EPA, 2019; Crippa et al., 2020; Hoglund-Isaksson et al., 2020), \r\ntop-down geological emissions (Schwietzke et al., 2016; Petrenko et al., 2017; Hmiel et al., 2020), and top-down sinks from seven selected inverse models. The means (min-max) \r\nwith outliers removed from the range and the means are given. Outliers defined as >75th percentile + 3 x the interquartile range or <25th percentile - 3 x the interquartile \r\nrange. The top-down budget imbalances are calculated for each model separately and averaged. Note also the round-off error for the sources and sinks, which sometimes leads \r\nto last digit mismatch in the sums. For detailed information on datasets, see further details on data table 5.SM.6."


id: c5365aeae02e723614dd6045b03bec0d
created_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 28607
content:
"All geological sources around the world, including the coastal oceans \r\nand fjords, are estimated to emit CH4 in the range of 35-76 Tg yr-1\r\n(Etiope et al., 2019). There is evidence that the ventilation of geological \r\nCH4 is likely to be smaller than 15 Tg yr-1 (Petrenko et al., 2017; Hmiel \r\net al., 2020). A lower geological CH4 ventilation will reduce the gap \r\nbetween bottom-up and top-down estimates (Table 5.2), but widen \r\nthe gap in the ratio of fossil fuel-derived sources to the biogenic \r\nsources for matching the D14C-CH4 observations."


id: 17923292849fa171dfcb591d176ee4be
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 1205
content:
"Trees in upland and wetland forests contribute to CH4 emissions by \r\nabiotic production in the canopy, by the methanogenesis taking place \r\nin the stem, and by conducting CH4 from soil into the atmosphere \r\n(Covey and Megonigal, 2019). There is emerging evidence of the \r\nimportant role of trees in transporting and conducting CH4 from \r\nsoils into the atmosphere, especially in tropics (Pangala et al., 2017), \r\nwhereas direct production of CH4 by vegetation only has a  minor \r\ncontribution (limited evidence, high agreement) (Bruhn et al., 2012; \r\nCovey and Megonigal, 2019). The contribution of trees in transporting \r\nCH4 may further widen the gap between the bottom-up and top-down \r\nestimates in the global budget, particularly needing a re-assessment \r\nof emissions in the tropics and in forested wetlands of temperate and \r\nboreal regions (Pangala et al., 2017; Jeffrey et al., 2019; Welch et al., \r\n2019; Sjogersten et al., 2020)."


id: 839236a5ec1456af782b5af91c9ec0aa
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 18232
content:
"Microbial methane uptake by soil comprises up to 5% (30 Tg yr-1) \r\nof the total CH4 sink in 2008-2017 (Table  5.2). There is evidence \r\nfrom experimental and modelling studies of increasing soil microbial \r\nuptake due to increasing temperature (Yu et al., 2017), although \r\nevidence also exists for decreasing CH4 consumption, possibly linked \r\nto precipitation changes (Ni and Groffman, 2018). The estimate of \r\nglobal methane loss by microbial oxidation in upland soils has been \r\nlowered marginally by 4 Tg yr-1, compared to 34 Tg yr-1 in AR5, for \r\nthe period 2000-2009. Termites, an infraorder of insects (Isoptera) \r\nfound in almost all land masses, emitted about 9 Tg yr-1 of CH4 in \r\n2000-2009. Increased emissions from insects and other anthropods \r\nare projected (Brune, 2018)."


id: 8877103c5b26b550f25b961e017ca87e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 18999
content:
"the bottom-up estimates of their emissions (Thornton et al., 2016a). \r\nAlthough there is evidence that regional human activities and \r\nglobal warming both increase inland water CH4 emissions (Beaulieu \r\net al., 2019), the increase in the decadal emissions since AR5 (Ciais \r\net al., 2013) rather reflect improvements in the estimate (medium \r\nconfidence), due to updates in the datasets and new upscaling \r\napproaches (Saunois et al., 2020)."


id: fe1329eba2c9966bc55c2f1e3ddad4b0
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 37500
content:
"Recent analysis using D14C-CH4 in ice samples suggest that CH4\r\nemissions from fossil fuel exploitation are responsible for 30% of \r\ntotal CH4 emissions (Lassey et al., 2007; Hmiel et al., 2020), which is \r\nlargely inconsistent with sectorial budgets where fossil fuel emissions \r\nadd up to 20% only (Ciais et al., 2013). However, recent model \r\nsimulations produce fairly consistent d13C-CH4 values and trends, as \r\nobserved in the atmospheric samples using 20% fossil fuel emissions \r\nfraction (Ghosh et al., 2015; Warwick et al., 2016; Fujita et al., 2020; \r\nStrode et al., 2020). Further research is needed to clarify the relative \r\nroles of CH4 emissions from fossil fuel exploitation and freshwater \r\ncomponents. A key challenge is to accommodate the higher estimated \r\nemissions from these two components without a major increase in \r\nthe sinks, in order to be consistent with the observed changes in the \r\ncarbon and hydrogen isotopes."


id: 39322a478d3953c198975167740399e0
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 6374
content:
"A summary of top-down and bottom-up estimates of CH4 emissions \r\nand sinks for the period 2008-2017 is presented in Figure 5.14 (details \r\nin Table 5.2 and the associated text for the emissions). In addition to \r\n483-682 Tg yr-1 loss of CH4 in the troposphere by reaction with OH, \r\n1-35 Tg yr-1 of CH4 loss is estimated to occur in the lower troposphere \r\ndue to Cl but are not included in the top-down models as shown in \r\nTable 5.2 (Hossaini et al., 2016; Gromov et al., 2018; X. Wang et al., \r\n2019). The decadal mean CH4 burden/imbalance increased at the rate \r\nof 30, 12, 7 and 21 Tg yr-1 in the 1980s (1980-1989), 1990s (1990-\r\n1999), 2000s (2000-2009) and the most recent decade (2008-2017), \r\nrespectively (virtually certain), as can be estimated from observed \r\natmospheric growth rate (Cross-Chapter Box 5.2, Figure 1)."


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content:
"Figure 5.14 | Global methane (CH4) budget (2008-2017). Values and data sources as in Table  5.2 (in TgCH4). The atmospheric stock is calculated from mean CH4\r\nconcentration, multiplying a factor of 2.75 +- 0.015 Tg ppb-1, which accounts for the uncertainties in global mean CH4 (Chandra et al., 2021). Further details on data sources and \r\nprocessing are available in the chapter data table (Table 5.SM.6)."


id: efbfcd6e4d8eb0d698c6b06a70b619a
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 35223
content:
"Contributors: Prabir K. Patra (Japan/India), Josep G. Canadell (Australia), Frank J. Dentener (European Union, The Netherlands), Xin \r\nLan (United States of America/China), Vaishali Naik (United States of America)"


id: 139e80d696f5e75fa9903248365f6c84
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
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vector_id: 594
content:
"Cross-Chapter Box 5.2, Figure 1 shows the decadal CH4 budget derived from the Global Carbon Project (GCP)-CH4 synthesis for 1980s, \r\n1990s and 2000s (Kirschke et al., 2013), and for 2010-2017 (Saunois et al., 2020). The imbalance of the sources and sinks estimated \r\nby atmospheric inversions (dark blue bars) can be used to explain the changes in CH4 concentration increase rates between the \r\ndecades (Table 5.2)."


id: caf07716d63cfa3c42706932ebc3b426
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 29491
content:
"Since AR5, many studies have discussed the role of different source categories in explaining the increase in CH4 growth rate since \r\n2007 and a coincident decrease of d13C-CH4 and dD-CH4 isotopes (Figure 5.13; Rice et al., 2016). Both 13C and D are enriched in "


id: dceb71630d6162886073d016279af4d3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32253
content:
"mass-weighted average source signatures for CH4 emissions from thermogenic sources (e.g., coal mining, oil and gas industry) and \r\npyrogenic (biomass burning) sources, and depleted in biogenic (e.g., wetlands, rice paddies, enteric fermentation, landfill and waste) \r\nsources. Proposed hypotheses for CH4 growth (2007-2017) are inconclusive and vary from a concurrent decrease in thermogenic and \r\nincrease in wetland and other biogenic emissions (Nisbet et al., 2016; Schwietzke et al., 2016), an increase in emissions from agriculture \r\nin the tropics (Schaefer et al., 2016), a concurrent reduction in pyrogenic emissions and an increase in thermogenic emissions (Worden \r\net al., 2017), or an emissions increase from biogenic sources and a slower increase in emissions from thermogenic sources compared \r\nto inventory emissions (Patra et al., 2016; Thompson et al., 2018; Jackson et al., 2020; Chandra et al., 2021)."


id: 6dae67baa4da2ce732f11f2b61078cf0
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 14863
content:
"In natural ecosystems, nitrous oxide (N2O) is primarily produced as \r\na by-product during the remineralization of organic matter via the \r\nprimary processes of nitrification and denitrification (Butterbach\u0002Bahl et al., 2013; Voss et al., 2013). The net N2O production is highly \r\nsensitive to local environmental conditions such as temperature, \r\noxygen concentrations, pH and the concentrations of ammonium and \r\nnitrate, among others, causing strong variability of N2O emissions in \r\ntime and space, even at small scales. Changes in the atmospheric \r\nabundance of N2O result largely from the balance of the net N2O \r\nsources on land and ocean, and the photochemical destruction of \r\nN2O in the stratosphere."


id: 2d36053c8deea9994f049681b1851f6
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 4558
content:
"Since AR5 (WGI, Section  6.4.3), improved understanding of N2O \r\nsources allows for a  more comprehensive assessement of the \r\nglobal N2O budget (Table 5.3). This progress is based on extended \r\natmospheric observations (Francey et al., 2003; Elkins et al., 2018; \r\nPrinn et al., 2018), improved atmospheric N2O inversions (Saikawa \r\net al., 2014; Thompson et al., 2019), updated and expanded \r\ninventories of N2O sources (Winiwarter et al., 2018; Janssens\u0002Maenhout et al., 2019), as well as improved bottom-up estimate of \r\nfreshwater, ocean and terrestrial sources (Martinez-Rey et al., 2015; \r\nLandolfi et al., 2017; Buitenhuis et al., 2018; Lauerwald et al., 2019; \r\nMaavara et al., 2019; Tian et al., 2019)."


id: 60fd86fb4a3fe70f319003fc59d86b8b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 12829
content:
"As assessed by SRCCL (IPCC, 2019a), combined firn, ice, air and \r\natmospheric measurements show that the 15N/14N isotope ratio \r\n(robust evidence, high agreement) and the predominant position of \r\nthe 15N atom in atmospheric N2O (limited evidence, low agreement) \r\nin N2O has changed since 1940 (Figure 5.15b, c) whereas they were \r\nrelatively constant in the pre-industrial period (Ishijima et al., 2007; \r\nPark et al., 2012; Prokopiou et al., 2017, 2018). The SRCCL concluded \r\nthat this change indicates a shift in the nitrogen-substrate available \r\nfor denitrification, and the relative contribution of nitrification to \r\nthe global N2O source (robust evidence, high agreement), which are \r\nassociated with increased fertilizer use in agriculture (Park et al., \r\n2012; Snider et al., 2015; Prokopiou et al., 2018)."


id: 7a4623415295515916dd92adfff4e778
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 16804
content:
"The human perturbation of the natural nitrogen cycle through the \r\nuse of synthetic fertilizers and manure, as well as nitrogen deposition \r\nresulting from land-based agriculture and fossil fuel burning has \r\nbeen the largest driver of the increase in atmospheric N2O of 31.0 \r\n+-  0.5 ppb (10%) between 1980 and 2019 (robust evidence, high \r\nagreement) (Tian et al., 2020). The long atmospheric lifetime of N2O \r\nimplies that it will take more than a  century before atmospheric \r\nabundances stabilize after the stabilization of global emissions. \r\nThe rise of atmospheric N2O is of concern, not only because of its \r\ncontribution to the anthropogenic radiative forcing (Chapter 7) but \r\nalso because of the importance of N2O in stratospheric ozone loss \r\n(Ravishankara et al., 2009; Fleming et al., 2011; W. Wang et al., 2014)."


id: bd2a040b74b50344203260b52bd7cb05
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 27330
content:
"Since AR5 (WGI, Section 6.4.3), the mean atmospheric lifetime of N2O \r\nhas been revised to 116 +- 9 years (Prather et al., 2015). The small \r\nnegative feedback of the N2O lifetime to increasing atmospheric \r\nN2O results in a slightly lower residence time (109 +- 10 years) of N2O "


id: 85b561d6455a56933ddad5028a060dad
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18576
content:
"perturbations compared with that assessed by AR5 (118-131 years) \r\n(Prather et al., 2015). The dominant N2O loss occurs through \r\nphotolysis and oxidation by O(1D) radicals in the stratosphere and \r\namounts to approximately 13.1 (12.4-13.6) TgN yr-1 (Minschwaner \r\net al., 1993; Prather et al., 2015; Tian et al., 2020)."


id: f3a76604727a0c63cca2acfa4e6d496c
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 35848
content:
"Biomass burning from crop residue burning, grassland, savannah and \r\nforest fires, as well as biomass burnt in household stoves, releases N2O \r\nduring the combustion of organic matter. Updated inventories since \r\nAR5 (WGI, Section 6.4.3) result in a lower range of the decadal mean \r\nemissions of 0.6 (0.5-0.8) TgN yr-1 (van der Werf et al., 2017; Tian \r\net al., 2020). The attribution of grassland, savannah or forest fires to \r\nnatural or anthropogenic origins is uncertain, preventing a separation \r\nof the biomass burning source into natural and anthropogenic."


id: cfb01aaa82e9cca89b8f854e9a6fb61a
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 30210
content:
"account for up to 50% of the global ocean source (Codispoti, 2010; \r\nArevalo-Martinez et al., 2015; Babbin et al., 2015). However, recent \r\nglobal-scale analyses estimate lower contributions (4-7%, Battaglia \r\nand Joos, 2018b; Buitenhuis et al., 2018). Further investigation \r\nis required to reconcile these estimates and provide improved \r\nconstraints on the N2O source from low-oxygen zones."


id: df6739cb9f4eb0c65ec58d37334f0b36
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32646
content:
"Since AR5 (WGI, Section  6.4.3), new estimates of the global ocean \r\nN2O source derived from ocean biogeochemistry models are 3.4 (2.5-\r\n4.3) TgN yr-1 for the period 2007-2016 (Figure 5.16; Manizza et al., \r\n2012; Suntharalingam et al., 2012; Martinez-Rey et al., 2015; Landolfi \r\net al., 2017; Buitenhuis et al., 2018; Tian et al., 2020). This is slightly \r\nlower than climatological estimates from empirically based methods \r\nand surface ocean data syntheses (Bianchi et al., 2012; S. Yang et al., \r\n2020). Nitrous oxide processes in coastal upwelling zones continue \r\nto be poorly represented in global estimates of marine N2O emissions \r\n(Kock et al., 2016), but may account for an additional 0.2-0.6 TgN yr-1\r\nof the global ocean source (Seitzinger et al., 2000; Nevison et al., 2004)."


id: 76e67ae962cbeb75d2d592e8f0c3da3b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 16287
content:
"Atmospheric deposition of anthropogenic N on oceans can stimulate \r\nmarine productivity and influence ocean emissions of N2O.  New \r\nocean model analyses since AR5 (WGI, 6.4.3), suggest a  relatively \r\nmodest global potential impact of 0.01-0.32 TgN yr-1 (pre-industrial \r\nto present-day) equivalent to 0.5-3.3% of the global ocean N2O \r\nsource (Suntharalingam et al., 2012; Jickells et al., 2017; Landolfi \r\net al., 2017). However, larger proportionate impacts are predicted in \r\nnitrogen-limited coastal and inland waters downwind of continental \r\npollution outflow, such as the Northern Indian Ocean (Jickells et al., \r\n2017; Suntharalingam et al., 2019)."


id: f5cb07078329e11dd494977bac101914
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 36188
content:
"In the oxic ocean (>97% of ocean volume), nitrification is believed \r\nto be the primary N2O source (Freing et al., 2012). In sub-oxic ocean \r\nzones (Section  5.3), where denitrification prevails, higher N2O \r\nyields and turnover rates make these regions potentially significant \r\nsources of N2O (Arevalo-Martinez et al., 2015; Babbin et al., 2015; \r\nJi et al., 2015). The relative proportion of ocean N2O from oxygen\u0002minimum zones is highly uncertain (Zamora et al., 2012). Estimates \r\nderived from in situ sampling, particularly in the eastern tropical \r\nPacific, suggest significant fluxes from these regions, and potentially "


id: a0a25f676858b8998d599eeb4966faab
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 22815
content:
"Inland waters and estuaries are generally sources of N2O as \r\na  result of nitrification and denitrification of dissolved inorganic \r\nnitrogen, however, they can serve as N2O sinks in specific conditions \r\n(Webb et al., 2019). Since AR5 (WGI, 6.4.3), improved emissions \r\nfactors, including their spatio-temporal scaling, and consideration \r\nof transport within the aquatic system allows for better constraint "


id: 2b7e60fca779c3fa4e8631c907e0ef61
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 4284
content:
"of these emissions (Murray et al., 2015; Hu et al., 2016; Lauerwald \r\net al., 2019; Maavara et al., 2019; Kortelainen et al., 2020; Yao et al., \r\n2020). Despite uncertainties because of the side effects of canals and \r\nreservoirs on nutrient cycling, these advances permit attribution of \r\na fraction of inland water N2O emissions to anthropogenic sources \r\n(Tian et al., 2020), which contributes to the increased anthropogenic \r\nshare of the global N2O source in this report compared to AR5 (Ciais \r\net al., 2013). As an indirect consequence of agricultural nitrogen use \r\nand waste-water treatment, the anthropogenic emissions from inland \r\nwaters have increased by about a quarter (0.1 TgN yr-1) between the \r\n1980s and 2007-2016 (Tian et al., 2020)."


id: 3b69a97aa53d99ad7efd2c33edaaa507
created_at: 2023-03-16 14:00:42
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vector_id: 6699
content:
"show that global warming has led to increased soil N2O emissions \r\nof 0.8 (0.3-1.3) TgN yr-1 since approximately pre-industrial times, of \r\nwhich about half occurred since the 1980s (limited evidence, high \r\nagreement) (Tian et al., 2019, 2020)."


id: 9f69102d2384438e6e563331802f6380
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 22623
content:
"The SRCCL assessed that deforestation and other forms of land-use \r\nchange significantly alter terrestrial N2O emissions through emission \r\npulses following conversions, generally resulting in long-term \r\nreduced emissions in unfertilized ecosystems (medium evidence, \r\nhigh agreement). This conclusion is supported by a  recent study \r\ndemonstrating that the deforestation-pulse effect is offset by the \r\neffect of reduced area of mature tropical forests (Tian et al., 2020)."


id: f6b0d2a062c5c5d29214cbda4786602b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 36328
content:
"Uncertainties remain in process-based models with respect to their \r\nability to capture the complicated responses of terrestrial N2O \r\nemissions to rain pulses, freeze-thaw cycles and the net consequences \r\nof elevated levels of CO2 accurately (Tian et al., 2019). Emerging \r\nliterature suggests that permafrost thaw may contribute significantly \r\nto arctic N2O emissions (Voigt et al., 2020), but these processes are \r\nnot yet adequately represented in models and upscaling to large\u0002scale remains a significant challenge."


id: ef76a88905bf54bb429a49a6f62aaacf
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 35162
content:
"The synthesis of bottom-up estimates of N2O sources (Sections 5.2.3.2-\r\n5.2.3.4 and Figure  5.17) yields a  global source of 17.0 (12.2 to \r\n23.5) TgN yr-1 for the years 2007-2016 (Table  5.3). This estimate \r\nis comparable to AR5, but the uncertainty range has been reduced \r\nprimarily due to improved estimates of ocean and anthropogenic N2O "


id: 735e49ce1a580e54a4d4bfd14bb1c8d2
created_at: 2023-03-16 14:00:42
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content:
"Figure 5.17 | Global nitrous oxide (N2O) budget (2007-2016). Values and data sources as in Table 5.3. The atmospheric stock is calculated from mean N2O concentration, \r\nmultiplying a factor of 4.79 +- 0.05 Tg ppb-1 (Prather et al., 2012). Pool sizes for the other reservoirs are largely unknown. Further details on data sources and processing are \r\navailable in the chapter data table (Table 5.SM.6)."


id: 8d9e3035f32164ebb7c769ffdd946744
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 19810
content:
"decades. For the period 1960-2019, the relative contribution to the \r\ntotal effective radiative forcing (ERF) was 63% for CO2, 11% for \r\nCH4, 6% for N2O, and 17% for the halogenated species (Chapter 7; \r\nFigure 5.18). The systematic decline in the relative contribution to \r\nERF for CH4 since 1850 is caused by a slower increase rate of CH4\r\nin the recent decades, at 6, 10 and 5 ppb yr-1 during 1850-2019, \r\n1960-2019 and 2000-2019, respectively, in comparison with the \r\nincreasing rate of CO2 (at 0.7, 1.6 and 2.2 ppm yr-1, respectively) \r\nand N2O (at 0.4, 0.7 and 0.9 ppb yr-1, respectively; Figure  5.4). \r\nOwing to the shorter lifetime of CH4, the effect of a reduction in \r\nthe emissions increase rate on the ERF increase is evident at inter\u0002decadal time scales."


id: bd936cfc0f7f111278fc372b084dc902
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 27395
content:
"(CH4), nitrous oxide (N2O) - were discussed in the previous sections. \r\nThis section compares the balance of the sources and sinks of these \r\nthree gases and their regional net flux contributions to the radiative \r\nforcing. CO2 has multiple residence times in the atmosphere - from one \r\nyear to many thousands of years (Box 6.1 in Ciais et al., 2013) - and \r\nN2O has a mean lifetime of 116 years. They are both long-lived GHGs, \r\nwhile CH4 has a lifetime of 9.1 years and is considered a short-lived \r\nGHG (see Chapter 2 for lifetime of GHGs, Chapter 6 for CH4 chemical \r\nlifetime, and Chapter 7 for effective radiative forcing of all GHGs)."


id: d634304a6162f2447740d41eb43dd2e3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 31214
content:
"Figure 5.18 shows the contribution to radiative forcing of CO2, CH4, \r\nN2O, and the halogenated species since the 1900s to more recent "


id: 8eb3d89d0aadf2aa6b2223eb51f67dbb
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 19976
content:
"The Paleocene-Eocene Thermal Maximum (PETM) was an episode of \r\nglobal warming exceeding pre-industrial temperatures by 4degC-8degC \r\n(McInerney and Wing, 2011; Dunkley Jones et al., 2013) that occurred \r\n55.9-55.7  Ma. The PETM involved a  large pulse of geologic CO2\r\nreleased into the ocean-atmosphere system in 3-20 kyr (Zeebe et al., \r\n2016; Gutjahr et al., 2017; Kirtland Turner et al., 2017; Kirtland Turner, \r\n2018; Gingerich, 2019; Section 5.2.1.1). In response, observationally \r\nconstrained model simulations report an increase in atmospheric \r\nCO2 concentrations ranging from about 900  ppm to >2000  ppm \r\n(Chapter 2; Gutjahr et al., 2017; Cui and Schubert, 2018; Anagnostou \r\net al., 2020). The PETM thus provides a test for our understanding of \r\nthe ocean's response to the increase in carbon (and heat) emissions \r\nover geologically short time scales."


id: 547ca14327e1a61994e30e0d9bda954b
created_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 10752
content:
"Recent model outputs and globally distributed geochemical data \r\nreveal with medium confidence widespread ocean deoxygenation \r\nduring the PETM (Dickson et al., 2012, 2014; Winguth et al., 2012; \r\nChang et al., 2018; Remmelzwaal et al., 2019), with parts of the \r\nocean potentially becoming drastically oxygen-depleted (anoxic; \r\nYao et al., 2018; Clarkson et al., 2021). Deoxygenation affected the \r\nsurface ocean globally (including the Arctic Ocean; Sluijs et al., 2006), \r\ndue to vertical and lateral expansion of OMZs (Zhou et al., 2014) that \r\nresulted from warming and related changes in ocean stratification.\r\nExpansion of OMZs may have stimulated N2O production through \r\nwater-column (de)nitrification (Junium et al., 2018). The degree to \r\nwhich N2O production impacted PETM warming, however, has not \r\nyet been established."


id: 5f599dde737ff80f610436456a149cfc
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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content:
"This section assesses past events of ocean acidification and \r\ndeoxygenation (Section  5.3.1), the historical trends and spatial \r\nvariability for the upper ocean (Section 5.3.2) and the ocean interior \r\n(Section  5.3.3). Future projections for ocean acidification and the \r\ndrivers in the coastal ocean are assessed in Sections  5.3.4 and \r\n5.3.5, respectively."


id: f739e901f47364587c392070a3064f82
created_at: 2023-03-16 14:00:42
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vector_id: 36415
content:
"The feedbacks associated with recovery from the PETM are uncertain, \r\nyet could include drawdown associated with silicate weathering \r\n(Zachos et al., 2005) and regrowth of terrestrial and marine organic \r\ncarbon stocks (Bowen and Zachos, 2010; Gutjahr et al., 2017)."


id: 114afff45c96b6ac2184b1a2199e6894
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 203
content:
"emissions. Nitrogen stable isotope measurements on N2O extracted \r\nfrom ice cores suggest that approximately one-third (of the order of \r\n0.7 +- 0.3TgN yr-1) of the deglacial increase in N2O emissions relates \r\nto oceanic sources (Schilt et al., 2014; H. Fischer et al., 2019)."


id: d25b92d14fc8ffed8d088e14ac322fc7
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 30623
content:
"The Last Deglacial Transition (LDT) is the best documented climatic \r\ntransition in the past associated with a substantial atmospheric CO2\r\nrise ranging from 190 to 265 ppm between 18-11 ka (Marcott et al., \r\n2014). The amplitude of the deglacial CO2 rise is thus on the same \r\norder of magnitude as the increase since the Industrial Revolution."


id: 3d5ad6caaba1466b9350db3bf4bb4a03
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 7013
content:
"Boron isotope (d11B) data suggest a 0.15-0.05 unit decrease in sea \r\nsurface pH (Honisch and Hemming, 2005; Henehan et al., 2013) \r\nacross the LDT, an average rate of decline of about 0.002 units per \r\ncentury compared with the current rate of more than 0.1 units \r\nper century (Bopp et al., 2013; Gattuso et al., 2015). Planktonic \r\nforaminiferal shell weights decreased by 40% to 50% (Barker \r\nand Elderfield, 2002), and coccolith mass decreased by about \r\n25% (Beaufort et al., 2011) across the LDT.  Independent proxy \r\nreconstructions thus highlight with high confidence that pH values \r\ndecreased as atmospheric CO2 concentrations increased across the \r\nLDT. There is, however, low confidence in the inferred rate of ocean \r\nacidification owing to multiple sources of uncertainties affecting \r\nrates estimates based on marine sediments (Section 5.1.2.1)."


id: 9c0a63f4eed4cbfc3ff5e30bc6e5b014
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vector_id: 22108
content:
"Geochemical and micropaleontological evidence suggest that \r\nintermediate-depth OMZs almost vanished during the Last Glacial \r\nMaximum (LGM) (Jaccard et al., 2014). However, multiple lines of \r\nevidence suggest with medium confidence that the deep (>1500 m) \r\nocean became depleted in O2 (concentrations were possibly lower \r\nthan 50 mmol kg-1) globally (Jaccard and Galbraith, 2012; Hoogakker \r\net al., 2015, 2018; Gottschalk et al., 2016, 2020a; Anderson et al., \r\n2019) as a  combined result of sluggish ventilation of the ocean \r\nsubsurface (Gottschalk et al., 2016, 2020a; Skinner et al., 2017) and \r\na generally more efficient marine biological carbon pump (Buchanan \r\net al., 2016; Yamamoto et al., 2019; Galbraith and Skinner, 2020)."


id: 2cfa9e6c2c768699b82790ee176c8669
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4511
content:
"Overall, many of the records show a  highly variable seawater pH \r\nunderlaying strong imprints of internal climate variability (high \r\nconfidence) and, in most instances, superimposed on a decreasing \r\nd11B trend that is indicative of anthropogenic ocean acidification in \r\nrecent decades (medium confidence). The robustness of seawater \r\npH reconstructions is currently limited by the uncertainty on the \r\ncalibration of the d11B proxy in different tropical coral species."


id: 3d298ed7dc8e096d4c142530b095682a
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 6987
content:
"The SROCC (Section  5.2.2.3) indicated that it is virtually certain\r\nthat  the ocean has undergone acidification globally in response \r\nto ocean CO2 uptake, and concluded that pH in open ocean \r\nsurface water has changed by a  virtually certain range of -0.017 \r\nto -0.027 pH units per decade since the late 1980s. Since SROCC, \r\nevidence of the progress of acidification across all regions of the \r\noceans has been further strengthened by continued observations \r\nof seawater carbonate chemistry at ocean time series stations, \r\nand compiled shipboard studies providing temporally resolved and \r\nmethodologically consistent datasets (Jiang et al., 2019) (Figure 5.20; \r\nSupplementary Material Table 5.SM.3; Section 2.3.3.5)."


id: efcbbef6cbb5d99dc6ccf327ac5c50
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 35236
content:
"In the subtropical open oceans, decreases in pH have been reported \r\nwith a  very likely rate range from -0.016 to -0.019 pH units per \r\ndecade since 1980s, which equates to approximately 4 % increase \r\nin hydrogen ion concentration ([H+]) per decade. Accordingly, the \r\nsaturation state O  (=[Ca2+][CO3\r\n2-]/Ksp) of seawater with respect to \r\ncalcium carbonate mineral aragonite has been declining at rates \r\nranging from -0.07 to -0.12 per decade (Gonzalez-Davila et al., \r\n2010; Feely et al., 2012; Bates et al., 2014; Takahashi et al., 2014; "


id: a471a5bdcaef5463370f451ec2cf7fa3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 23436
content:
"In open subpolar and polar zones, the very likely range (-0.003 to \r\n-0.026 pH unit per decade) and uncertainty (up to 0.010) observed \r\nin pH decline are larger than in the subtropics, reflecting the complex \r\ninterplay between physical and biological forcing mechanisms \r\n(Olafsson et al., 2009; Midorikawa et al., 2012; Bates et al., 2014; "


id: 65437b2aec4bdd6fb60a2063022895e9
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 13510
content:
"Mid-latitudinal zones within the subtropical cells and tropical regions \r\npresent a  relatively deep and shallow detection, respectively. A  pH \r\ndecrease has also been observed on the Antarctic continental shelf \r\n(Hauck et al., 2010; Williams et al., 2015). Acidification is also underway \r\nin the subsurface to intermediate layers of the Arctic Ocean due to \r\nthe inflow of ventilated waters from the North Atlantic and the North \r\nPacific (Qi et al., 2017; Ulfsbo et al., 2018)."


id: e18651fab5b7828ce19f18b3f8e270f4
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32973
content:
"Overall, in agreement with SROCC, it is virtually certain from these \r\nobservational studies that ocean surface waters undergo acidification \r\nglobally with the CO2 increase in the atmosphere. These sustained \r\nmeasurements over the past decades, and campaign studies of ocean \r\ncarbonate chemistry, also highlight with robust evidence that trends \r\nof acidification have been modulated by the variability and changes \r\nin physical and chemical states of ocean, including those affected by \r\nthe warming of the cryosphere, and need to be better understood."


id: 273179463d73a9e9f513984cd4e72d2
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 3563
content:
"These trends of acidification in the ocean interior lead to high \r\nconfidence in shoaling of the saturation horizons of calcium carbonate \r\nminerals where O  = 1. In the Pacific Ocean where the aragonite \r\nsaturation horizon is shallower (a few hundred metres to 1200 m; \r\nFigure 5.21c), the rate of its shoaling is in the order of 1-2 m yr-1\r\n(Feely et al., 2012; Ross et al., 2020). In contrast, shoaling rates of \r\n4 m yr-1 to 1710 m for 1984-2008 and of 10-15 m yr-1 to 2250 m for \r\n1991-2016 have been observed in the Iceland sea and the Irminger \r\nsea, respectively (Olafsson et al., 2009; Perez et al., 2018)."


id: 8eb70187c15e1a94ad23ee5b9b4326d7
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 19978
content:
"Advances in observations and modelling for ocean physics and \r\nbiogeochemistry and established knowledge of ocean carbonate \r\nchemistry show with very high confidence that anthropogenic CO2\r\ntaken up into the ocean surface layer is further spreading into the \r\nocean interior through ventilation processes, including vertical \r\nmixing, diffusion, subduction and meridional overturning circulations \r\n(Sections 2.3.3.5, 5.2.1.3 and 9.2.2.3; Sallee et al., 2012; Bopp et al., \r\n2015; Nakano et al., 2015; Iudicone et al., 2016; Toyama et al., 2017; \r\nPerez et al., 2018; Gruber et al., 2019b) and is causing acidification in \r\nthe ocean interior. The net change in oxygen consumption by aerobic \r\nrespiration of marine organisms further influences acidification by \r\nreleasing CO2 (Section  5.3.3.2; Chen et al., 2017; Breitburg et al., \r\n2018; Robinson, 2019)."


id: 828683cdf2dec343d6aa3c9ffead2432
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18062
content:
"In summary, ocean acidification is spreading into the ocean interior. \r\nIts rates at depths are controlled by the ventilation of the ocean \r\ninterior as well as anthropogenic CO2 uptake at the surface, thereby \r\ndiminishing with depth (very high confidence) (Figure 5.21). Variability \r\nin ocean circulation modulates the trend of ocean acidification at \r\ndepths through the changes in ventilation and their impacts on \r\nmetabolic CO2 content. However, the large knowledge gap around \r\nventilation changes leads to low confidence in their impacts in many \r\nocean regions (Sections 5.3.3.2; 9.2.2.3 and 9.3.2)."


id: 4f985478c2a923c54dc3b7df690b3458
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9954
content:
"Observations over past decades of basin-wide and global syntheses \r\nof ocean interior carbon show that the extent of acidification due to \r\nanthropogenic CO2 invasion tends to diminish with depth (very high \r\nconfidence) (Section 5.2.1.3.3 and Figure 5.21; Woosley et al., 2016; \r\nCarter et al., 2017; Lauvset et al., 2020). The regions of deep convection \r\nsuch as subpolar North Atlantic and Southern Ocean present the \r\ndeepest acidification detections below 2000 m (medium confidence). "


id: 2d10cf636dbe2365133f6d518934f29e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4530
content:
"As summarized in SROCC (Section  5.2.2.4), there is a  growing \r\nconsensus that between 1970 and 2010 the open ocean has \r\nvery likely lost 0.5-3.3% of its dissolved oxygen in the upper \r\n1000 m depth (Section 2.3.3.6; Helm et al., 2011; Ito et al., 2017; "


id: 9c6894069a9f4106c116b1a89910638b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 22163
content:
"regions remains challenging (Levin, 2018; Oschlies et al., 2018). \r\nEarth system models (ESMs) in CMIP5 and CMIP6 corroborate the \r\ndecline in ocean oxygen, and project a continuing and accelerating \r\ndecline with a  strong impact of natural climate variability under \r\nhigh-emissions scenarios (Bopp et al., 2013; Long et al., 2016; \r\nKwiatkowski et al., 2020). However, CMIP5 models did not reproduce \r\nobserved patterns for oxygen changes in the tropical thermocline, \r\nand generally simulated only about half the oxygen loss inferred \r\nfrom observations (Oschlies et al., 2018). CMIP6 models have \r\na  more realistic simulated mean state of ocean biogeochemistry \r\nthan CMIP5 models due to improved ocean physical processes \r\nand better representation of biogeochemical processes (Seferian \r\net al., 2020). They also exhibit enhanced ocean warming as a result "


id: d4c0c78f431d93c78ed15a781fce87f1
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 30996
content:
"Schmidtko  et al.,  2017; Bindoff et al., 2019). Regionally, the \r\nequatorial and North Pacific, the Southern Ocean and the South \r\nAtlantic have shown the greatest oxygen loss of up to 30 mol m-2 per \r\ndecade (Schmidtko et al., 2017). Warming - via solubility reduction \r\nand circulation changes - mixing and respiration are considered the \r\nmajor drivers, with 50% of the oxygen loss for the upper 1000 m of \r\nthe global oceans attributable to the solubility reduction (Schmidtko \r\net al., 2017). Climate variability also modifies the oxygen loss on \r\ninterannual and decadal time scales especially for the tropical \r\nocean OMZs (Deutsch et al., 2011, 2014; Llanillo et al., 2013) and \r\nthe North Pacific subarctic zone (Whitney et al., 2007; Sasano et al., \r\n2018; Cummins and Ross, 2020). However, quantifying the oxygen \r\ndecline and variability and attributing them to processes in different "


id: bec5b58176c4014e4d0fcb779852172b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 27569
content:
"uncertainties (Babbin et al., 2015; Trimmer et al., 2016; Landolfi et al., \r\n2017). Furthermore, the correlation between N2O and oxygen varies \r\nwith microorganisms present, nutrient concentrations, and other \r\nenvironmental variables (Voss et al., 2013)."


id: cde1d92c7808e7a4c5ed07a02c07b55c
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 29909
content:
"of an increase in the equilibrium climate sensitivity (ECS) of \r\nCMIP6 relative to CMIP5 models, which contributes to increased \r\nstratification and reduced subsurface ventilation (Sections  4.3.1, \r\n4.3.4, 5.3.3.2, 7.4.2, 7.5.6, 9.2.1, and TS2.4). Consequently, CMIP6 \r\nmodel ensembles reproduce the ocean deoxygenation trend \r\nof -0.30  to -1.52 mmol m-3 per decade between 1970-2010 \r\nreported in SROCC (Section 5.2.2.4) with a very likely range, and \r\nalso project 32-71% greater subsurface (100-600  m) oxygen \r\ndecline relative to their Representative Concentration Pathway \r\n(RCP) analogues in CMIP5, reaching to the likely range of decline of \r\n6.4 +- 2.9 mmol m-3 under SSP1-2.6 and 13.3 +- 5.3 mmol m-3 under \r\nSSP5-8.5, from 1870-1899 to 2080-2099 (Kwiatkowski et al., \r\n2020). It is concluded that the oxygen content of subsurface ocean \r\nis projected to transition to historically unprecedented condition \r\nwith decline over the 21st century (medium confidence)."


id: 497d17f7df86e437303022c8108468d6
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 8950
content:
"In summary, total oceanic N2O emissions were projected to decline \r\nby 4-12% from 2005-2100 (Martinez-Rey et al., 2015) and by 24% \r\nfrom the pre-industrial era to 2100 (Landolfi et al., 2017) under \r\nRCP8.5. However, there is low confidence in the reduction in N2O \r\nemissions to the atmosphere, because of large conceptual and \r\nparametric uncertainties, a limited number of modelling studies that \r\nexplored this process, and greater oxygen losses simulated in CMIP6 \r\nmodels than in CMIP5 models (Kwiatkowski et al., 2020)."


id: 843a1121a913a01c923b7ed8b29a7e3a
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18339
content:
"In surface ocean, changes in the amplitude of seasonal variations in \r\npH are also projected to occur with high confidence. ESMs in CMIP6 \r\nshow +73 +-  12% increase in the amplitude of seasonal variation \r\nin hydrogen ion concentration ([H+]) but 10 +- 5% decrease in the \r\nseasonal variation in pH (= -log [H+]) from 1995-2014 to 2080-\r\n2099 under SSP5-8.5. The simultaneous amplification of [H+] and \r\nattenuation of pH seasonal cycles is counterintuitive but is the \r\nconsequence of a  greater increase in the annual mean [H+] due \r\nto anthropogenic CO2 invasion than the corresponding increase \r\nin its seasonal amplitude. These changes are consistent with the \r\namplification/attenuation of the seasonal variation of +81 +-16% for "


id: 96bd2f0bb49937eb11c59c45bae80116
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 21237
content:
"to decrease as atmospheric CO2 decreases (Sections  5.4.5, 5.4.10 \r\nand 5.6.2.1; Mathesius et al., 2015; Tokarska and Zickfeld, 2015). \r\nHowever, because of the long time scales of the ocean turnover that \r\ntransfers CO2 from the upper to the deep ocean, excess carbon will \r\ncontinue to accumulate in the deep ocean even after a decrease in \r\natmospheric CO2 (Cao et al., 2014; Mathesius et al., 2015; Tokarska \r\nand Zickfeld, 2015; T. Li et al., 2020). There is thus high confidence\r\nthat CO2 emissions leave a long-term legacy in ocean acidification, \r\nand are therefore irreversible at multi-human generational scales, \r\neven with aggressive atmospheric CO2 removal."


id: c4e48b1a842c683df699d85446eca3ba
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 28551
content:
"The coastal ocean, from the shoreline to the isobath of 200  m, is \r\nhighly heterogeneous due to the complex interplay between \r\nphysical, biogeochemical and anthropogenic factors (Gattuso et al., \r\n1998; Chen and Borges, 2009; Durr et al., 2011; Laruelle et al., 2014; \r\nMcCormack et al., 2016). These areas, according to SROCC (Bindoff \r\net al., 2019) are, with high confidence, already affected by ocean \r\nacidification and deoxygenation. This section assesses the drivers \r\nand spatial variability of acidification and deoxygenation based on \r\nnew observations and data products."


id: 44eb15e3c0c2a8dc8af2351c153d4c69
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 8271
content:
"Overall, with the rise of atmospheric CO2, the physics of CO2 transfer \r\nacross the air-sea interface, the carbonate chemistry in seawater, \r\nthe trends of ocean acidification being observed in the past decades \r\n(Section 5.3.3.2) and modelling studies described in this section, it \r\nis virtually certain that ocean acidification will continue to grow. \r\nHowever, the magnitude and sign (direction) of many of ocean \r\ncarbon-climate feedbacks are still poorly constrained (Matear and \r\nLenton, 2014, 2018), leading to low confidence in their significant \r\nand long-lasting impacts on ocean acidification."


id: b71fa25e19a1d02f153db09e977fb00
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26345
content:
"Reversing the increase in atmospheric CO2 concentrations through \r\nnegative emissions (Section  5.6) will reverse ocean acidification \r\nat the sea surface (high confidence) but will not result in \r\nrapid amelioration of ocean acidification in the deeper ocean \r\n(Section 5.3.3.2). The ocean's uptake of atmospheric CO2 will start "


id: ec44f1532ccde5bf8b609c3d1c3b37ee
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 34664
content:
"Since AR5, (Ciais et al., 2013) and in agreement with SROCC (IPCC, \r\n2019b), there is now high agreement (robust evidence) that coastal "


id: 55f78910dfb0037bffa36fada44ce00
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 11013
content:
"temperate north-east Pacific and north-west Atlantic, the mean time \r\nof emergence for acidification is above two decades (Sutton et al., \r\n2019; Turk et al., 2019)."


id: edffab8a9a9f0e6d2f154f53e4ac7fc3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 34923
content:
"ocean acidification, whether induced only by increasing atmospheric \r\nCO2 or exacerbated by eutrophication or upwelling, has negative \r\neffects on specific groups of marine organisms such as reef-building \r\ncorals, crabs, pteropods, and sessile fauna (AR6 WGII, Chapter  3; \r\nDupont et al., 2010; Bindoff et al., 2019; Bednarsek et al., 2020; \r\nOsborne et al., 2020), especially when combined with stressors \r\nsuch as temperature and deoxygenation, and potentially increased \r\nbioavailability of toxic elements such as arsenic and copper (Millero \r\net al., 2009; Boyd et al., 2015; Breitburg et al., 2018)."


id: 7b700b418bca02c6459f78ad193bb3d4
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 17014
content:
"Many coastal tropical areas are under heavy anthropogenic \r\neutrophication induced by the effluents from large cities, or \r\nreceive large riverine inputs of freshwater, nutrients, and organic \r\nmatter (such as Amazon, Mississippi, Orinoco, Congo, Mekong, or \r\nChangjiang rivers). Under strong eutrophication, often sub-surface \r\nand bottom waters present pH values lower than average surface \r\nopen ocean (about 8.0) because increased respiration decreases pH \r\n(high agreement, robust evidence), despite a  net atmospheric CO2\r\nsink in shallow and vertically stratified coastal areas (Kone et al., \r\n2009; Wallace et al., 2014; Cotovicz Jr. et al., 2015, 2018; Fennel and \r\nTesta, 2019; Lowe et al., 2019; Section 5.3.5.1)."


id: ed34ab22bc84a466accf622bfbe82959
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 34798
content:
"There is high agreement (robust evidence) that heterogeneity implies \r\ndifferent responses of coastal regions to increasing atmospheric CO2, \r\ndecreasing seawater pH and calcium carbonate saturation state, and \r\ndeoxygenation (Duarte et al., 2013; Regnier et al., 2013; Breitburg \r\net al., 2018; Laruelle et al., 2018; Carstensen and Duarte, 2019)."


id: 3e1aec0c53e99bdc136f601eae7e9d79
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 7133
content:
"There is high agreement that long-time series of observations \r\nutilizing standard methods are needed to distinguish the climate \r\nchange signal in seawater carbonate chemistry from the natural \r\nvariability of coastal sites (Duarte et al., 2013; Salisbury and Jonsson, \r\n2018; IOC, 2019; Sutton et al., 2019; Tilbrook et al., 2019; Turk et al., \r\n2019). Despite the increasing availability of data and sea-air CO2\r\nflux budgets for the coastal ocean (Sections  5.3.5.1 and 5.2.3.1), \r\nadditional long-term observations are required to constrain the \r\nglobal time of emergence of coastal acidification. There is high \r\nagreement (medium evidence) that, for the coastal subtropical to "


id: bb516eef1da338412c29b262ad14485e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 27022
content:
"There is medium evidence from observations and models that \r\nthe coastal north-western Antarctic Peninsula (Southern Ocean) \r\nwill experience calcium carbonate undersaturation by 2060, \r\nconsidering that anthropogenic emissions reach an atmospheric CO2\r\nconcentration of about 500 pm at that date (Lencina-Avila et al., \r\n2018; Monteiro et al., 2020a). The synergies among warming, melt \r\nwater, sea-air CO2 equilibrium and circulation may, to some extent, "


id: d698f2413b33c01390c9370d37de7929
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31271
content:
"offset the coastal ocean acidification trends in Antarctica (Henley \r\net al., 2020). In the coastal western Arctic Ocean, there is increasing \r\nrobust evidence that ocean acidification is driven by sea-air CO2 fluxes \r\nand sea-ice melt, and increasing intrusions since the 1990s of low\u0002alkalinity Pacific water, lowering aragonite saturation state (Qi et al., \r\n2017, 2020; Cross et al., 2018). The Bering Sea (north-eastern Pacific) \r\nshows decreasing trends in calcium carbonate saturation, associated \r\nto the increasing atmospheric CO2 uptake combined with riverine \r\nfreshwater and carbon inputs (high agreement, robust evidence) \r\n(Pilcher et al., 2019; H. Sun et al., 2020)."


id: bc501f4b780689667253d7d9337af34d
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 27207
content:
"(Section  5.4.7), combined feedback assessment (Section  5.4.8), \r\npossible biogeochemical abrupt changes (Section 5.4.9), long-term \r\ncarbon cycle projections (Section 5.4.10), and near-term prediction of \r\nocean and land carbon sinks (5.4.11)."


id: d26d26d872fac01b9a9c5332942c91fa
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 30639
content:
"The spatial distribution of hypoxic areas is highly heterogeneous \r\nin the coastal ocean, and there is high agreement, robust evidence\r\nthat more severe hypoxia or anoxia is often associated with highly \r\npopulated coastal areas, or local circulation and upwelling, and \r\nseasonal stratification leading to an accumulation of organic matter \r\nin subsurface waters (Ciais et al., 2013; Rabalais et al., 2014; M. Li \r\net al., 2016; Breitburg et al., 2018; Bindoff et al., 2019; SROCC \r\nChapter 5). The causes and trends of coastal deoxygenation can only \r\nbe assessed by making available long-term time series combined \r\nwith regional modelling (Fennel and Testa, 2019), as in the California \r\ncurrent system (Wang et al., 2017), the East China Sea (Chen et al., \r\n2007; Qian et al., 2017), the Namibian or along the north-western \r\nAtlantic shelves (Claret et al., 2018). Other coastal upwelling sites \r\nsuch as the Arabian Sea display seasonal hypoxia but no worsening \r\ntrends (Gupta et al., 2016)."


id: f61e2170be20b219ee248b1dd8ea14ee
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 36230
content:
"The Baltic Sea is the largest semi-enclosed sea where hypoxia is \r\nreported to have happened before the 1950s (Carstensen et al., \r\n2014; Rabalais et al., 2014; Lukawska-Matuszewska et al., 2019). \r\nThe frequency and volume of seawater inflow from the North Sea \r\ndecreased after 1950, leading to an expansion of hypoxic areas from \r\n40,000 to 60,000 km2 in combination with increasing eutrophication \r\n(Carstensen et al., 2014). From the available observations, there is \r\nrobust evidence that many areas in the Baltic Sea are experiencing \r\ndeoxygenation despite efforts to reduce nutrient loads (Lennartz \r\net al., 2014; Jokinen et al., 2018)."


id: 7b8f7ea804449d13f2adcb5404074c31
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 17035
content:
"There is medium agreement (medium evidence) that simply reducing \r\nanthropogenic nutrient inputs may lead to less severe coastal \r\nhypoxic conditions, as observed in the coastal north-western Adriatic \r\nSea (Djakovac et al., 2015). However, low-oxygen sediments may \r\nremain a  long-term source of phosphorus and ammonium to the \r\nwater column, and in this way fuelling primary production (Jokinen \r\net al., 2018; Fennel and Testa, 2019; Limburg et al., 2020)."


id: e503efede864c2f70bbcdece7627173f
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 33522
content:
"This section covers biogeochemical feedbacks on climate change, which \r\nrepresent one of the largest sources of uncertainty in climate change \r\nprojections. The relevant processes are discussed (Sections  5.4.1 \r\nto 5.4.4), prior to discussing the simulation and projection of the \r\ncarbon cycle in Earth system models (Section  5.4.5), emergent \r\nconstraints on future projections (Section 5.4.6), non-CO2 feedbacks "


id: 2aa6219de4d691b3561293cf55144085
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4136
content:
"Our understanding of the various biological processes that affect \r\nthe strength of the CO2 fertilization effect on photosynthesis and \r\nits impact on carbon storage in vegetation and soils, (in particular \r\nregarding the limitations imposed by nitrogen and phosphorus \r\navailability), has developed since AR5 (WGI, Box  6.2). Based on \r\nconsistent behaviour across all CMIP6 ESMs, there is high confidence \r\nthat CO2 fertilization of photosynthesis acts as an important negative \r\nfeedback on anthropogenic climate change, by reducing the rate at \r\nwhich CO2 accumulates in the atmosphere. Since AR5 (WGI, Box 6.2), \r\nan increasing number of CMIP6 ESMs account for nutrient cycles. \r\nThe consistent results found in their model projections suggests with \r\nhigh confidence that limited nutrient availability will limit the CO2\r\nfertilization effect (Arora et al., 2020). The magnitude of the direct \r\nCO2 effect on land carbon uptake, and its limitation by nutrients, \r\nremains uncertain."


id: 5ebca5d5548231fcadfce87575789c4d
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 12445
content:
"In AR5 (WGI, Section 6.4.2) there was high agreement that CMIP5 \r\nESMs project continued ocean CO2 uptake through to 2100, with \r\nhigher uptake corresponding to higher concentration or emissions \r\npathways. There has been no significant change in the magnitude \r\nof the sensitivity of ocean carbon uptake to increasing atmospheric \r\nCO2, or in the inter-model spread, between the CMIP5 and CMIP6 era \r\n(Arora et al., 2020). The analysis from emissions and concentration\u0002driven CMIP5 model projections show that the ocean sink stops \r\ngrowing beyond 2050 across all emissions scenarios (Section 5.4.5.3). \r\nCMIP6 models also show a similar time evolution of global ocean \r\nCO2 uptake to CMIP5 models over the 21st century (Figure  5.25) \r\nwith decreasing net ocean CO2 uptake ratio to anthropogenic CO2\r\nemissions under SSP5-8.5."


id: e07b3cf2839225fae11103b09a862dff
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 32817
content:
"Consistent with AR5 (WGI, Section 6.4.2), the CO2 fertilization effectis \r\nthe dominant cause for the projected increase in land carbon uptake \r\nbetween 1860 and 2100 in ESMs (Figures 5.26 and 5.27, and Table 5.5; \r\nArora et al., 2020). In the CMIP6 ensemble, the increase of land carbon \r\nstorage due to CO2 fertilization is a global phenomenon but is strongest \r\nin the tropics (Figure 5.26). The resulting increase of productivity is a key \r\ndriver of increases in vegetation and soil carbon storage. However, \r\nconsistent with earlier findings (Todd-Brown et al., 2013; Friend et al., \r\n2014; Hajima et al., 2014), processes affecting vegetation carbon-use \r\nefficiency and turnover, such as allocation changes, mortality, and \r\nvegetation structural changes, as well as the pre-industrial soil carbon \r\nturnover time, also play an important role (Arora et al., 2020)."


id: e3a579527c32e5b927868e223ffd497
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 33298
content:
"Projected increases in ocean DIC due to anthropogenic CO2 uptake \r\namplify the sensitivity of carbonate system variables to perturbations \r\nof DIC in the surface ocean, for example via the amplitude of the \r\nseasonal cycle of pCO2, which impacts the mean annual air-sea fluxes \r\n(Hauck et al., 2015; Fassbender et al., 2018; Landschutzer et al., 2018; \r\nSROCC, Section 5.2.2.3). A larger amplification of the surface ocean \r\npCO2 seasonality occurs in the subtropics where pCO2 seasonality is \r\ndominated by temperature seasonality, with the summer increase \r\nin the difference in pCO2 between surface water and the overlying \r\natmosphere reaching 3matm per decade between 1990 and 2030 \r\nunder RCP8.5 (Schlunegger et al., 2019; Rodgers et al., 2020). In \r\ncontrast, the impact of biological production on the seasonal cycle of \r\npCO2 in summer in the Southern Ocean strengthens the drawdown \r\nof CO2 (Hauck et al., 2015)."


id: 3c203d577dc597323267738c81fe719
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 6817
content:
"its control by leaf stomata, and the resulting soil and plant tissue \r\nwater content. Thus the direct effect of warming on photosynthesis \r\ncan be positive, negative, or invariant depending on the environmental \r\ncontext (Lin et al., 2012; Yamori et al., 2014; Smith and Dukes, 2017; \r\nGrossiord et al., 2020). Observations and models suggest that the \r\nvapour pressure deficit effects are stronger than direct temperature \r\neffects on enzyme activities (Smith et al., 2020), and that acclimation \r\nof photosynthetic optimal temperature may mitigate productivity \r\nlosses of tropical forests under climate change (Kattge and Knorr, \r\n2007; Tan et al., 2017; Kumarathunge et al., 2019). Some models have \r\nbegun to include these acclimation responses in photosynthesis and \r\nautotrophic respiration (Lombardozzi et al., 2015; Smith et al., 2015; \r\nHuntingford et al., 2017; Mercado et al., 2018)."


id: e132585b0a6b6a38d0b277e7baa1f872
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32928
content:
"Overall, there is medium confidence on three outcomes in the ocean \r\nfrom projected CO2 uptake under medium to high CO2 concentration \r\nscenarios: (i) a weakening of the buffering capacity, which impacts \r\nthe airborne fraction via the reduction of the ocean CO2 buffering \r\ncapacity due to cumulative ocean CO2 uptake, which reduces \r\nthe net ocean CO2 uptake ratio to anthropogenic CO2 emissions \r\n(Katavouta et al., 2018; Arora et al., 2020; Rodgers et al., 2020); \r\n(ii) an amplification of the seasonal cycle of CO2 variables, which \r\nimpacts both the ocean sink and ocean acidification (Hauck et al., \r\n2015); (iii) a decrease in the aragonite and calcite saturation levels in \r\nthe ocean, which negatively impacts the calcification rates of marine \r\norganisms (high confidence) and forms a negative feedback on the \r\nuptake of CO2 (McNeil and Sasse, 2016) (Cross-Chapter Box 5.3)."


id: 64e8dc10e7f60ac661b7df9e4040c8f3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 13444
content:
"The SRCCL assessed that climate change is playing an increasing role \r\nin determining wildfire regimes alongside human activity (medium \r\nconfidence), with future climate variability expected to enhance the \r\nrecurrence and severity of wildfires in many biomes, such as tropical \r\nrainforests (high confidence). Projections of increased fire weather in \r\na warmer climate are widespread (Section 12.3.2.8) and may drive \r\nincreased fire frequency and severity in several regions, including \r\nArctic and boreal ecosystems (Gauthier et al., 2015; X.J.  Walker \r\net al.,  2019), Mediterranean-type ecosystems (Turco et al., 2014; \r\nJin et al., 2015), degraded tropical forests (Aragao et al., 2018), and \r\ntropical forest-savanna transition zones (Lehmann et al., 2014)."


id: fb7b78d4ae75b25172e000a6bc38eae2
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 37066
content:
"Wildfire is included in some CMIP6 ESMs (Table  5.4) and is thus \r\nonly partially represented in estimates of carbon-climate feedbacks \r\nfrom these models. The CMIP5 ESMs that include fire project an \r\n8-58% increase of fire carbon emissions under future scenarios, with \r\nhigher emissions under higher warming scenarios; the ensemble \r\nspread is driven by differing factors such as population density, fire \r\nmanagement, and other land-use processes (Kloster and Lasslop, \r\n2017). Fire dynamics in CMIP6 models, as evaluated in land-only \r\nconfigurations of CMIP6-generation land surface models, also show \r\nlarge variations but better agreement with observations (Teckentrup \r\net al., 2019; Hantson et al., 2020; Lasslop et al., 2020)."


id: d1a6741551de5f24fa89746c244df5df
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 30516
content:
"The AR5 assessed with medium confidence that future climate \r\nchange will decrease land carbon uptake relative to the case with \r\nconstant climate, but with a  poorly constrained magnitude (AR5 \r\nWGI, Chapter  6, Executive Summary). Ongoing uncertainty in the \r\nmagnitude and geographic pattern of the feedbacks (Section 5.4.5), \r\ncontinues to support a medium confidence assessment that future \r\nclimate change will decrease land carbon uptake relative to the case \r\nwith constant climate."


id: 6fed51f5ea25d8d20da3bfebffe81c4f
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15227
content:
"Plant productivity is highly dependent on local climate. In cold \r\nenvironments, warming has generally led to an earlier onset of the \r\ngrowing season, and with it an increase in early season vegetation \r\nproductivity (e.g., Forkel et al., 2016). However, this trend is affected \r\nby the adverse effects of climate variability, and other emerging \r\nlimitations on vegetation production by water, energy and nutrients, \r\nwhich may gradually reduce the effects of warming (Piao et al., 2017; \r\nBuermann et al., 2018; Liu et al., 2019). At centennial time scales, \r\nboreal forest expansion may act as a  climate-driven carbon sink \r\n(Pugh et al., 2018)."


id: 916ae2a80e699f760ea14622a258709c
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 20414
content:
"et al., 2018; Varney et al., 2020), although experimental soil warming \r\nin tropical forests suggest high sensitivity of decomposition to \r\nwarming in those regions as well (Nottingham et al., 2020)."


id: dd827cbddd3c1b0703b177d32bb64371
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 32351
content:
"Allen et al., 2015; McDowell et al., 2018; Grossiord et al., 2020), and \r\nthat these processes are not well represented in ESMs (Powell et al., \r\n2013; Fisher et al., 2018). An ensemble of land models that includes \r\necological processes such as forest demography shows that changes \r\nto mortality may be a more important driver of carbon dynamics than \r\nchanges to productivity (Friend et al., 2014)."


id: 82b0e7c8fc3c2f46bea09f58d4964e0e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 18089
content:
"Peat soils, where thick organic layers build up due to saturated \r\nand anoxic conditions, represent another possible source of carbon \r\nto the atmosphere. Peats could dry, and decompose or burn as \r\na  result of climate change in both high (Chaudhary et al., 2020) \r\nand tropical (Cobb et al., 2017) latitudes, and in combination with \r\nanthropogenic drainage of peatlands (Warren et al., 2017). Peat \r\ncarbon dynamics are not included in the majority of CMIP6 ESMs."


id: eaef531da3f3b5893b2defad662f0dde
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 34471
content:
"Overall, climate change will force widespread increases in fire weather \r\nthroughout the world (Section  12.3.2.8). Because of incomplete \r\ninclusion of fire in ESMs, a  separate compilation of fire-driven \r\ncarbon-climate feedback estimates is shown in Figure 5.29, based \r\non results from Eliseev et al. (2014a) and Harrison et al. (2018). There \r\nis low agreement in magnitude and medium agreement in sign which \r\nleads to an assessment of medium confidence that fire represents \r\na positive carbon-climate feedback, but very low confidence in the \r\nmagnitude of that feedback. Other disturbances such as tree mortality \r\nwill increase across several ecosystems (medium agreement) with \r\ndecreased vegetation carbon (medium confidence). However, the \r\nlack of model agreement and key process representation in ESMs \r\nleads to a  low confidence assessment in the projected magnitude \r\nof this feedback."


id: 957e4f5316a927f2b858f4689b3ab526
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 21018
content:
"Soil microbial dynamics shift in response to temperature, giving \r\nrise to complex longer-term trophic effects that are more complex \r\nthan the short-term sensitivity of decomposition to temperature. \r\nSuch responses are observed in response to long-term warming \r\nexperiments (Melillo et al., 2017). While most CMIP6 ESMs do not \r\ninclude microbial dynamics, simplified global soil models that do \r\ninclude such dynamics show greater uncertainty in projections of \r\nsoil carbon changes, despite agreeing more closely with current \r\nobservations, than the linear models used in most ESMs (Wieder \r\net al., 2013; Guenet et al., 2018)."


id: e5bd2195f0a783dd206f45024fafa4a6
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 33650
content:
"Changes to soil carbon stocks in response to climate change are \r\na potentially strong positive feedback (Cox et al., 2000). Since AR5 \r\n(WGI, Section 6.4.2), progress has been made in understanding soil \r\ncarbon dynamics, and associated feedbacks. Advances include: (i) \r\nan increased understanding of and ability to quantify high-latitude \r\nsoil carbon feedbacks (Box 5.1); (ii) increased understanding of the \r\ncauses responsible for soil carbon persistence on long time scales, \r\nparticularly the interactions between decomposers and soil organic \r\nmatter and mineral assemblages (Kleber et al., 2007; Schmidt \r\net al., 2011; Luo et al., 2016); and (iii) increased understanding of \r\nsoil carbon dynamics in subsurface layers (Hicks Pries et al., 2017; \r\nBalesdent et al., 2018)."


id: e1e1ada3bc61f79ec1272966f1aaf901
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 33037
content:
"The high agreement and multiple lines of evidence that warming \r\nincreases decomposition rates lead to high confidence that \r\nwarming will, overall, result in carbon losses relative to a constant \r\nclimate and contribute to the positive carbon-climate feedback \r\n(Section 5.4.8). However, the wide spread in ESM projections and the \r\nlack of model representation of key processes that may amplify or \r\nmitigate soil carbon losses on longer time scales (including microbial \r\ndynamics, permafrost, peatlands, and nutrients) lead to low confidence\r\nin the magnitude of global soil carbon losses with warming."


id: 7fe7661a2433906b054598134c0b46fb
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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content:
"What is permafrost carbon and why should we be concerned about it?\r\nSoils in the Arctic and other cold regions contain perennially frozen layers, known as permafrost. Soils in the northern permafrost region \r\nstore a large amount of organic carbon, estimated at 1460-1600 PgC across surface soils and deeper deposits (Hugelius et al., 2014; \r\nStrauss et al., 2017; Mishra et al., 2021). Of that carbon, permafrost soils and deposits store 1070-1360 PgC, of which 300-400 PgC \r\nare in the first metre, and the rest at depth. The remaining 280-340 PgC are in permafrost-free soils within the permafrost region. \r\nThese carbon deposits have accumulated over thousands of years due to the slow rates of organic matter decomposition in frozen \r\nand/or waterlogged soil layers, but these frozen soils are highly decomposable upon thaw (Schadel et al., 2014)."


id: 577a59635b2529e3203cb13c0933d94f
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 11257
content:
"Is permafrost carbon already thawing and emitting greenhouse gases?\r\nThe permafrost region was a historic carbon sink over centuries to millennia (high confidence) (Loisel et al., 2014; Lindgren et al., \r\n2018). Currently though, thawing soils due to anthropogenic warming are losing carbon from the decomposition of old frozen organic \r\nmatter, as found via carbon 14 (14C) signature of respiration at sites undergoing rapid permafrost thaw (Hicks Pries et al., 2013), of \r\ndissolved organic carbon in rivers draining watersheds with permafrost thaw (Vonk et al., 2015; Wild et al., 2019), and of methane \r\n(CH4) produced in thawing lakes (Walter Anthony et al., 2016)."


id: d674f443ce44b6b133d612c9c48ac9e7
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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content:
"Since AR5, evidence of a more active carbon cycle in the northern high-latitude regions has also been observed through the increased \r\namplitude of CO2 seasonal cycles. However, the relative roles of local sources versus influence from mid-latitudes makes it difficult to infer \r\nchanges to Arctic ecosystems from these observations (Graven et al., 2013; Forkel et al., 2016; Takata et al., 2017; Bruhwiler et al., 2021). \r\nEstimates of CO2 fluxes with atmospheric inversion models showed an enhanced seasonal cycle amplitude but no significant trends in \r\nannual total fluxes, in agreement with flux tower measurements over one decade (2004-2013) (Welp et al., 2016; Takata et al., 2017)."


id: ccec1e3430893280d53c8c26aa5a9525
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 29784
content:
"Atmospheric measurements and inversions performed at the global and regional scales do not show any detectable trends in annual \r\nmean CH4 emissions from the permafrost region over the past 30 years (Jackson et al., 2020; Saunois et al., 2020; Bruhwiler et al., \r\n2021), consistent with atmospheric measurements in Alaska that showed no significant annual trends, despite significant increase in air \r\ntemperature (Sweeney et al., 2016). Atmospheric inversions and biospheric models do not show any clear trends in CH4 emissions for \r\nwetland regions of the high latitudes during the period 2000-2016 (Patra et al., 2016; Poulter et al., 2017; Jackson et al., 2020; Saunois \r\net al., 2020). Large uncertainties on wetland extent and limited data constraints place low confidence in these modelling approaches."


id: 45403517b507000643fbf4cf55dffe5e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 8324
content:
"The SROCC also assessed with high confidence that CH4 fluxes have been under-observed due to their high variability at multiple scales \r\nin both space and time, and that there is a persistent mismatch between top-down and bottom-up methane budgets, with emissions \r\ncalculated by upscaling ground observations typically higher than emissions inferred from large-scale atmospheric observations \r\n(Thornton et al., 2016a; Saunois et al., 2020)."


id: 87b3e9aebc4101495d8ae39d6ba74988
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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content:
"In conclusion, there is high confidence that the permafrost region has acted as a historic carbon sink over centuries to millennia, and \r\nhigh confidence that some permafrost regions are currently net sources of CO2. There is robust evidence that some CH4 emissions \r\nsources for some regions have increased over the past decades (medium confidence). For the northern permafrost-wide region, \r\nno multi-decadal trend has been detected on CO2 and CH4 fluxes but, given the low resolution and sparse observations of current \r\nobservations and modelling sytems, we place low confidence in this statement."


id: 7777189cbcf13db8cd3fb1c46d4acbcc
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 16351
content:
"Since AR5, there have been new studies showing that permafrost thaw also leads to nitrous oxide (N2O) release from soil (Abbott and \r\nJones, 2015; Karelin et al., 2017; Wilkerson et al., 2019), a previously unaccounted source. However, this release is unquantified at the \r\npan-Arctic scale."


id: 8c8089dcdd7f720e276c9f2b20f22f29
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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content:
"Large areas of Alaska and Siberia are underlain by frozen, glacial-age, ice- and carbon-rich deposits, and many of these areas show \r\nevidence of thermokarst processes during Holocene warm periods. Rapid warming of high northern latitudes contributed to permafrost \r\nthaw, liberating labile organic carbon to the atmosphere (Kohler et al., 2014; Crichton et al., 2016; Winterfeld et al., 2018; Meyer et al., \r\n2019), supporting the vulnerability of these areas to further warming (Strauss et al., 2013, 2017)."


id: d56c6c4b51b06b7de47b5b7048f97b41
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 31100
content:
"In conclusion, several independent lines of evidence indicate that permafrost thaw did not release vast quantities of fossil CH4\r\nassociated with the transient warming events of the LDT. This suggests that large emissions of CH4 from old carbon sources will not \r\noccur in response to future warming (medium confidence)."


id: 9af79fb63fdc9b0627f4909af7f09097
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
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content:
"What level of emissions do we expect in the future?\r\nNear-surface permafrost is projected to decrease significantly under future global warming scenarios (high confidence) (Section 9.5.2), \r\nthus creating the potential for releasing CO2 and CH4 to the atmosphere, and act as a positive carbon-climate feedback."


id: 35070558cf42073574e1da4ea16fea64
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 5756
content:
"Through the Coupled Model Intercomparison Project Phase 5 (CMIP5), Earth system models (ESMs) had not included permafrost carbon \r\ndynamics. This remains largely true in Coupled Model Intercomparison Project Phase 6 (CMIP6), with most models not representing \r\npermafrost carbon processes, a small number representing the active-layer thickening effect on decomposition (Table 5.4), and no \r\nESMs representing thermokarst or fire-permafrost-carbon interactions. The CMIP6 ensemble mean predicts a negative carbon-climate \r\nfeedback in the permafrost region. However, those that do include permafrost carbon show a positive carbon-climate feedback in the "


id: 8adbbb4060137e88e6b6d9e8b8a9aec4
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 19384
content:
"permafrost region (Figure 5.27). Given the current limited ESM capacity to assess permafrost feedbacks, estimates in this report are \r\nbased on published permafrost-enabled land surface model results."


id: 30b659e0275eb78ca84fe608bc8c25ab
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content:
"The SROCC assessed that warming under a high-emissions scenario (RCP8.5 or similar) would result in a loss of permafrost carbon by \r\n2100 of 10s to 100s of PgC, with a maximum estimate of 240 PgC and a best estimate of 92 +- 17 PgC (Meredith et al., 2019; SROCC, \r\nFigure 3.11). Under lower emissions scenarios, Schneider von Deimling et al. (2015) estimated permafrost feedbacks of 20-58 PgC of \r\nCO2 by 2100 under an RCP2.6 scenario, and 28-92 PgC of CO2 under an RCP4.5 scenario."


id: cf9c6d7004993618f42523943b85cd40
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 30193
content:
"This new assessment, based on studies included in or published since SROCC (Schaefer et al., 2014; Koven et al., 2015c; Schneider von \r\nDeimling et al., 2015; Schuur et al., 2015; MacDougall and Knutti, 2016a; Gasser et al., 2018; Yokohata et al., 2020), estimates that \r\nthe permafrost CO2 feedback per degree of global warming (Figure 5.29) is 18 [3.1 to 41, 5-95% range] PgC degC-1. The assessment \r\nis based on a  wide range of scenarios evaluated at 2100, and an assessed estimate of the permafrost CH4-climate feedback at \r\n2.8 [0.7 to 7.3] PgCeq degC-1 (Figure 5.29). This feedback affects the remaining carbon budgets for climate stabilization and is included \r\nin their assessment (Section 5.5.2)."


id: ad1fc376e381fee04231aa0ecc7fe166
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 24781
content:
"Beyond 2100, models suggest that the magnitude of the permafrost carbon feedback strengthens considerably over the period \r\n2100-2300 under a high-emissions scenario (Schneider von Deimling et al., 2015; McGuire et al., 2018). Schneider von Deimling et al. \r\n(2015) estimated that thawing permafrost could release 20-40 PgC of CO2 in the period from 2100 to 2300 under an RCP2.6 scenario, \r\nand 115-172 PgC of CO2 under an RCP8.5 scenario. The multi-model ensemble (McGuire et al., 2018) projects a much wider range of \r\npermafrost soil carbon losses of 81-642 PgC (mean 314 PgC) for an RCP8.5 scenario from 2100 to 2300, and of a gain of 14 PgC to \r\na loss of 54 PgC (mean loss of 17 PgC) for an RCP4.5 scenario over the same period."


id: dbdd593c673c706f38a3fb166375d183
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32108
content:
"Methane release from permafrost thaw (including abrupt thaw) under a  high-warming RCP8.5 scenario has been estimated at \r\n836-2614 Tg CH4 over the 21st century and 2800-7400 Tg CH4 from 2100-2300 (Schneider von Deimling et al., 2015), and as \r\n5300 Tg CH4 over the 21st century and 16,000 Tg CH4 from 2100-2300 (Turetsky et al., 2020). For RCP4.5, these numbers are 538-\r\n2356 Tg CH4 until 2100 and 2000-6100 Tg CH4 from 2100-2300 (Schneider von Deimling et al., 2015), and 4100 Tg CH4 until 2100 \r\nand 10,000 Tg CH4 from 2100-2300 (Turetsky et al., 2020)."


id: 450a412476c5ca894bfd7c78e629f02b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 8287
content:
"A key uncertainty is whether permafrost carbon feedbacks scale roughly linearly with warming (Koven et al., 2015c), or instead scale at \r\na greater (MacDougall and Knutti, 2016b; McGuire et al., 2018) or smaller rate (e.g., CH4 emissions estimated by Turetsky et al., 2020). \r\nIt also remains unclear whether the permafrost carbon pool represents a coherent global tipping element of the Earth system with a single \r\nabrupt threshold (Drijfhout et al., 2015) at a given level of global warming, or a local scale tipping point without abrupt thresholds when \r\naggregated across the pan-Arctic region, as is suggested by recent model results (e.g., Koven et al., 2015a; McGuire et al., 2018)."


id: 3118d647f17e95ccf24ad3130feb6dbf
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 5146
content:
"In conclusion, thawing terrestrial permafrost will lead to carbon release under a warmer world (high confidence). However, there \r\nis low confidence on the timing, magnitude and linearity of the permafrost climate feedback owing to the wide range of published \r\nestimates and the incomplete knowledge and representation in models of drivers and relationships. It is projected that CO2 released \r\nfrom permafrost will be 18 (3.1-41) PgC degC-1 by 2100, with the relative contribution of CO2 vs CH4 remaining poorly constrained. \r\nPermafrost carbon feedbacks are included among the under-represented feedbacks quantified in Figure 5.29."


id: 2dbb03541e5f783bf716de0698971e08
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4634
content:
"reduces the solubility of CO2, which increases pCO2 and increases the \r\nstratification of the mixed layer, both acting as positive feedbacks \r\nweakening the ocean sink (Section  9.2.1 and Cross-Chapter \r\nBox 5.3; Arora et al., 2020); (ii) changing the temporal and spatial \r\ncharacteristics of wind stress and storms alters mixing - entrainment \r\nin, and across the bottom of, the mixed layer (Bronselaer et al., 2018); \r\nand (iii) warming and wind stress influence the large-scale meridional \r\noverturning circulation (MOC) circulation, which modifies the rate \r\nof ventilation, storage or outgassing of ocean carbon in the ocean \r\ninterior (Section 5.2.3.1; Gruber et al., 2019b; Arora et al., 2020). The \r\nland-to-ocean riverine flux and the carbon burial in ocean sediments "


id: c8038467b993329031e6cccb31572dfe
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 29050
content:
"The principal contribution to increasing global ocean carbon is the \r\nair-sea flux of CO2, which changes the dissolved inorganic carbon \r\n(DIC) inventory (Section 5.4.2; Arora et al., 2020). The processes that \r\ninfluence the variability and trends of the ocean carbon-heat nexus \r\nare assessed in Cross-Chapter Box 5.3. Climate has three important \r\nimpacts on the ocean uptake of anthropogenic CO2: (i) ocean warming "


id: 59e2a68bb90c3ee00eaa0ff1757debbb
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 11649
content:
"play a  minor role (low confidence) (Arora et al., 2020). Based on \r\nhigh agreement of projections by coupled climate models, there is \r\nhigh confidence that the resultant climate-carbon cycle feedbacks \r\nare positive, but the extent of the ocean sink weakening is scenario \r\ndependent (Arora et al., 2020)."


id: df70ef15aa8ce41992357a43cd457d33
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32656
content:
"SSP5-8.5 scenario. The mechanisms driving these changes vary widely \r\nbetween models due to differences in parametrization of particle \r\nformation, remineralization and plankton community structure."


id: fe1735c3b3ab2028b2a124461a08d937
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 37504
content:
"Regionally, the Southern Ocean is a  major sink of anthropogenic \r\nCO2 (Figure  5.8a), although challenges in modelling its circulation \r\nand Antarctic sea ice transport (Sections 3.4.1.2, 9.2.3.2 and 9.3.2) \r\ngenerate uncertainty in the response of its sink to future carbon-\r\nclimate feedbacks. Increased freshwater input may cause a slowdown \r\nof the lower overturning circulation, leading to increased Southern \r\nOcean biological carbon storage (Ito et al., 2015); alternatively, \r\nincreased winds may intensify the overturning circulation, reducing \r\nthe net CO2 sink in the Southern Ocean (Bronselaer et al., 2018; \r\nSaunders et al., 2018). On centennial time scales, there is thus low \r\nconfidence in the overall effect of intensifying winds in the Southern \r\nOcean on CO2 uptake."


id: db983e081775d04222f1f52837c2b4d
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32068
content:
"While physical drivers control the present-day anthropogenic carbon \r\nsink, biological processes are responsible for the majority of the \r\nvertical gradient in DIC (natural carbon storage). A  small fraction \r\nof the organic carbon fixed by primary production (PP) reaches the \r\nsea floor, where it can be stored in sediments on geological time \r\nscales, making the biological carbon pump (BCP) an important \r\nmechanism for very long-term CO2 storage. Projected reductions in \r\nocean ventilation (Section  9.2.1.4) would lengthen residence time \r\nand lead to DIC accumulating in the deep ocean due to organic \r\ncarbon remineralization."


id: 880cff0f81085a40ec0a49a595620b01
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18925
content:
"Based on high agreement across multiple lines of evidence and \r\nphysical understanding there is high confidence that feedbacks to \r\nclimate will arise from alterations to the magnitude and efficiency \r\nof the BCP changing PP, and the depth of remineralization. However, \r\nthe complexity of the mechanisms involved in the export and \r\nremineralization of POC result in low confidence in the magnitude \r\nand sign of biological feedbacks to climate. Nevertheless, improved \r\nmodel representation of PP and the BCP is required (which requires "


id: 3f1246e6389d620566437513ee244355
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 7312
content:
"In CMIP5 models run under RCP8.5, particulate organic carbon (POC) \r\nexport flux is projected to decline by 1-12% by 2100 (Taucher and \r\nOschlies, 2011; Laufkoetter et al., 2015). Similar values are predicted \r\nin 18 CMIP6 models, with declines of 2.5-21.5% (median -14%) or \r\n0.2-2 GtC (median -0.8 GtC) between 1900 and 2100 under the "


id: d50e4760bbddee454aa4bda67bbad3b7
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31050
content:
"better observational constraints), as the contribution of biological \r\nprocesses to CO2 uptake is expected to become more significant with \r\ncontinued climate change (Hauck et al., 2015)."


id: 94f7394adc72359ff87668ab458a9e4d
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20943
content:
"of the land and ocean carbon cycle models used in CMIP6 ESMs (Arora \r\net al., 2020). In CMIP6, most ocean carbon cycle models (8 of 11) track \r\nthree or more limiting nutrients (most often nitrogen, phosphorus, \r\nsilicon, iron), and include two or more phytoplankton types. More \r\nthan half of the land carbon cycle models (6 of 11) now include an \r\ninteractive nitrogen cycle, and almost half (5 of 11) represent forest \r\nfires. However, even for CMIP6, very few models explicitly represent \r\nvegetation dynamics (3 of 11) or permafrost carbon (2 of 11). Despite \r\nthese remaining limitations, the carbon cycle components of CMIP6 \r\nrepresent an advance on those in CMIP5, as they represent additional \r\nimportant processes (e.g.,  nitrogen limitations on the land carbon \r\nsink, and iron limitations on ocean ecosystems)."


id: ae7666b3e3e42a31d3a1043d07e6640c
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 24978
content:
"This section summarizes future projections of land and ocean \r\ncarbon sinks from the latest ESMs. ESMs are the basis for century \r\ntime-scale projections (Chapter 4), and for detection and attribution \r\nstudies (Chapter 3). These models aim to simulate the evolution of \r\nthe carbon sources and sinks on land and in the ocean, in addition \r\nto the physical components of the climate system. ESMs include \r\ninteractions between many of the processes and feedbacks described \r\nin Sections 5.4.1 to 5.4.4."


id: aea4f13b73404aa7cd3fa7d6d36d5338
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25003
content:
"ESMs can be driven by anthropogenic CO2 emissions ('emissions-driven' \r\nruns), in which case atmospheric CO2 concentration is a  predicted \r\nvariable; or by prescribed time-varying atmospheric concentrations \r\n('concentration-driven' runs). In concentration-driven runs, simulated \r\nland and ocean carbon sinks respond to the prescribed atmospheric \r\nCO2 and resulting changes in climate, but do not feed back through \r\nchanges in the atmospheric CO2 concentration. Concentration-driven \r\nruns are used to diagnose the carbon emissions consistent with \r\nthe Shared Socio-economic Pathways (SSPs) and other prescribed \r\nconcentration scenarios (Section 5.5). In this subsection we specifically \r\nanalyse results from concentration-driven ESM projections."


id: a0c6f7f9eaa90d89ac137e504da588b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 22844
content:
"ESMs are now integral to the Coupled Model Intercomparison Project. \r\nModel output data from CMIP5 was analysed in AR5, while data from \r\nCMIP6 forms the basis for the analysis presented in this subsection. The \r\nCMIP5 ESMs discussed in AR5 (WGI, Section 6.4.2) produced a wide \r\nrange of projections of future CO2 (Friedlingstein et al., 2014b) primarily \r\nassociated with different magnitudes of carbon-climate and carbon\u0002concentration feedbacks (Arora et al., 2013), but also exacerbated by \r\ndifferences in the simulation of the net carbon release from land-use \r\nchange (Brovkin et al., 2013). A  key deficiency of almost all CMIP5 \r\nESMs was the neglect of nutrient limitations on CO2-fertilization of \r\nland plant photosynthesis (Section 5.4.1; Zaehle et al., 2015)."


id: a194f375518f4f254d479393cdb60adc
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 22979
content:
"To give confidence in their projections, models need to be \r\ncompared to the widest possible array of observational benchmarks. \r\nThis is particularly the case for highly uncertain land carbon cycle "


id: 939d981fe59caf6a7a2bea2bb6df05c6
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20738
content:
"Some CMIP6 models considered in this report now include nitrogen \r\nlimitations on land vegetation growth, along with many other added \r\nprocesses compared to CMIP5. Table 5.4 summarizes characteristics "


id: 3fe9d08ebff3d5ea27e915e802bba085
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 7441
content:
"Table 5.4 | Properties of the CMIP6 Earth system models (ESMs), focusing on the land and ocean carbon cycle components of these models (Arora \r\net al., 2020). Characteristics listed under each ESM are: number of vegetation carbon pools (veg C pools); number of soil and litter carbon pools (dead C pools); number of \r\nPlant Functional Types (PFTs); whether wildfire is represented (fire); whether vegetation dynamics is represented (dynamic veg); whether permafrost carbon is represented \r\n(permafrost C); whether the nitrogen cycle is represented (nitrogen cycle); the number of phytoplankton types (phytoplankton); the number of zooplankton types (zooplankton); \r\nand the list of ocean nutrients represented (limiting nutrients)."


id: 6fa7c8e9899ad292f693b2885bef0cd1
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15193
content:
"feedbacks (Arora et al., 2013; Friedlingstein et al., 2014b). Land \r\nmodels within ESMs can be compared to multiple different datasets \r\nthat test different aspects of the models. These include fluxes, such as \r\ngross carbon uptake, and states, such as leaf area and carbon stocks, \r\nwhich influence carbon fluxes and are diagnostic of carbon turnover \r\ntimes. Comparisons can also be made between between carbon and \r\nwater cycles and other aspects of the terrestrial carbon cycle. To \r\nprovide these multiple orthogonal constraints, a model benchmarking \r\nsystem - the international land model benchmarking (ILAMB) - has \r\nbeen developed (Collier et al., 2018)."


id: 5c12c1effc581bba1ddafac6a28a5e86
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 11997
content:
"The land carbon cycle components of historical ESM simulations \r\nshow a larger range, with simulated cumulative land carbon uptake \r\n(1850-2014) spanning the range from -47 to +21 GtC, compared to \r\nthe GCP estimate of -12 +- 50 GtC (Figure 5.23b). This range is due in \r\npart to the complications of simulating the difference between carbon \r\nuptake by intact ecosystems and the direct release of carbon due \r\nto land-use change (Hajima et al., 2020a). There is high confidence\r\nthat the land continues to dominate the overall uncertainty in the \r\nprojected response of the global carbon cycle to climate change."


id: f8117816fa183901a6e83be16ae5f194
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 36544
content:
"Figure 5.22 shows an overview set of land (Figure 5.22a) and ocean \r\n(Figure  5.22b) benchmarks applied to both the CMIP5 and CMIP6 \r\nhistorical simulations. There is good evidence of an improvement in \r\nmodel performance from CMIP5 (in yellow) to CMIP6 (in green), \r\nin both the land and ocean, based on these benchmarks. The mean of \r\nthe CMIP6 land models outperforms or performs equivalently to the \r\nmean of the CMIP5 land models on all available metrics."


id: d563b4af134565bd49d510c7157ddd29
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31096
content:
"This section evaluates concentration-driven historical simulations \r\nof changes in land and ocean cumulative carbon uptake, against \r\nobservation-based estimates from the Global Carbon Project (GCP; \r\nLe Quere et al., 2018a). For each model, common historical land-use \r\nchanges were prescribed (Jones et al., 2016a)."


id: f861a2a285cf00f6d9d1cf11a85ae21e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 36593
content:
"Figure  5.23 shows global annual mean values from CMIP6 \r\nconcentration-driven runs for 1850 to 2014. The ocean carbon cycle \r\nmodels reproduce historical carbon uptake well, with the model range \r\nfor the global ocean carbon sink in 2014 (2.3-2.7 GtC yr-1) clustering \r\naround the central GCP estimate of 2.6 +-  0.5 GtC yr-1. Simulated \r\ncumulative ocean carbon uptake (1850-2014) ranges from 110 to \r\n166 GtC, with a model mean of 131 +- 17 PgC, which is lower than \r\nthe GCP estimate of 150 +- 25 GtC (Figure 5.23a). This suggests that \r\nCMIP6 models may slightly underestimate historical ocean carbon \r\nuptake (Watson et al., 2020)."


id: e4177c06ded9e64f0017e1a366b6dcae
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 33367
content:
"It has been previously noted that AR5 models tended to overestimate \r\nland uptake in the tropics and underestimate uptake in the northern \r\nmid-latitudes, compared to inversion estimates. The inclusion of \r\nnitrogen limitations on CO2-fertilization within CMIP6 models was \r\nexpected to reduce this discrepancy (Anav et al., 2013). There is indeed \r\nsome evidence that the CMIP6 ensemble (red wedge in Figure 5.24b) \r\ncaptures the northern land carbon sink more clearly than CMIP5 "


id: df422e2806c58f4115afcfc6e8607fef
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 32625
content:
"model projections of cumulative land carbon uptake (Figure 5.25f). The \r\nfive-year ensemble mean net land carbon sink is projected to decline \r\nto 0.4 +- 1.0 GtC yr-1 by 2100 under SSP1-2.6, and to reach around \r\n5.6 +- 3.7 GtC yr-1 under SSP5-8.5 (Figure 5.25c). Cumulative net land \r\ncarbon uptake from 1850 is projected to saturate at approximately \r\n150 +- 35 GtC under SSP1-2.6, and to reach 310 +- 130 GtC by 2100 \r\nunder SSP5-8.5. Significant uncertainty remains in the future of \r\nthe global land carbon sink, but there has been a notable reduction \r\nin the model spread from CMIP5 to CMIP6."


id: 20e7e6b268d808e3aa47ac5d70dae0e1
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 2610
content:
"(blue wedge in Figure 5.24b), but there remains a tendency for the \r\nESMs to place more of the global land carbon sink in the tropics than \r\nthe mid-latitudes, compared to the inversion estimates. Based on \r\na consistent signal across CMIP6 ESMs, there is medium confidence\r\nthat land carbon cycle models continue to underestimate the \r\nNorthern Hemisphere land carbon sink, when compared to estimates \r\nfrom atmospheric inversion (Ciais et al., 2019)."


id: f878f27779b9f584dec2139f991bbd60
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 36604
content:
"Land and ocean carbon uptake are driven primarily by increases in \r\natmospheric CO2 (Figure 5.25). As a result, the evolution of land and \r\nocean carbon sinks differs significantly between the SSP scenarios. \r\nUnder scenarios that have greater increases in atmospheric CO2 (such \r\nas SSP5-8.5 and SSP3-7.0) the absolute values of the sinks are larger, \r\nbut the fraction of implied emissions taken up by the sinks declines \r\nthrough the 21st century. By contrast, scenarios that assume CO2\r\nstabilization in the 21st century (such as SSP1-2.6 or SSP2-4.5), have \r\nsmaller absolute sinks, but these sinks take up an increasing fraction \r\nof the implied emissions (Figure  5.25d). These general principles \r\napply to the ocean and land carbon sinks."


id: 7a3be3b1743ba25a5e571492046e984b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 16799
content:
"Geographical patterns of carbon changes for four SSP scenarios are \r\nshown in Figure 5.26, with cleared areas (no diagonal lines) showing \r\nagreement on the sign of the change by at least 80% of the models. \r\nIn all scenarios the ocean sink is strongest in the Southern Ocean and \r\nNorth Atlantic. The land carbon sink occurs primarily where there are \r\npresent-day forests. In the mid- and high-northern latitudes, a carbon \r\nsink is projected as a result of the combined impacts of increasing CO2\r\nand warming (Section 5.4.5.5). Changes in land carbon storage in the \r\ntropics also depend strongly on the assumed rate of deforestation \r\nwhich varies in magnitude across the SSPs, from relatively low rates \r\nin SSP1-2.6 to relatively high rates in SSP3-7.0."


id: 8707e89802ff497f3a03e625fe8f7135
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18774
content:
"The concentration-driven CMIP6 ESMs agree well on the evolution \r\nof the global ocean carbon sink through the 21st century for four \r\nSSP scenarios (Figure 5.25). The five-year ensemble mean ocean sink \r\ndeclines to 0.6 +- 0.2 GtC yr-1 by 2100 under SSP1-2.6, and peaks \r\naround 2080 at 5.4 +- 0.4 GtC yr-1 under SSP5-8.5. Cumulative ocean \r\ncarbon uptake from 1850 is projected to saturate at approximately \r\n290 +- 30 GtC under SSP1-2.6, and to reach 520 +- 40 GtC by 2100 \r\nunder SSP5-8.5 (Figure 5.25e)."


id: 298fe4c23f0340949d2d7c9c92ca45ba
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 3975
content:
"In summary, oceanic and terrestrial carbon sinks are projected \r\nto continue to grow with increasing atmospheric concentrations \r\nof CO2, but the fraction of emissions taken up by land and ocean \r\nis expected to decline as the CO2 concentration increases (high \r\nconfidence). In the ensemble mean, ESMs suggest approximately \r\nequal global land and ocean carbon uptake for each of the SSP \r\nscenarios. However, the range of model projections is much \r\nlarger for the land carbon sink. Despite the wide range of model \r\nresponses, uncertainty in atmospheric CO2 by 2100 is dominated \r\nby future anthropogenic emissions rather than carbon-climate \r\nfeedbacks (high confidence)."


id: 302ae647c6faf235da5ed3fa5eeb2a16
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 5004
content:
"The ensemble mean changes in land and ocean sinks are qualitatively \r\nsimilar, but the land shows much higher interannual variability in \r\ncarbon uptake (Figure 5.25c) and also a much larger spread in the "


id: 72408d52b00b259aab0ffe566d606006
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15599
content:
"b and g values are diagnosed from idealized runs in which a 1% per \r\nyear increase in atmospheric CO2 concentration is prescribed, as for \r\nAR5 (WGI, Box  6.4; Arora et al., 2013). Values of b are calculated \r\nfrom 'biogeochemical' runs in which the prescribed CO2 increases do \r\nnot affect climate, and these are then used to isolate g  values in \r\nfully coupled runs where both climate and CO2 change (Friedlingstein \r\net al., 2003)."


id: e248245642d5499684e4d2d7df70aae
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 33103
content:
"To diagnose the causes of the varying time-evolution of \r\ncarbon  sinks,  the traditional linear feedback approach is \r\nadopted (Friedlingstein et al., 2003), as used previously to analyse \r\nC4MIP (Friedlingstein et al., 2006) and CMIP5 models (Arora et al., \r\n2013). Changes in land carbon storage (DCL) and changes in ocean \r\ncarbon storage (DCo) are decomposed into contributions arising from \r\nwarming (DT) and increases in CO2 (DCO2):"


id: cb91c94752c79ce41a85b448450b5b87
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 29575
content:
"where bL (bo) and gL (go) are coefficients that represent the sensitivity \r\nof land (ocean) carbon storage to changes in CO2 and global mean \r\ntemperature respectively. This feedback formalism is one of several \r\nthat have been proposed for analysing climate-carbon cycle \r\nfeedbacks (Lade et al., 2018)."


id: 3982457712a72e055318762f55e6fb0d
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 6422
content:
"This quasi-equilibrium framework is scenario dependent because \r\nof the time scales associated with land and ocean carbon uptake, \r\nas discussed in AR5 (WGI, Box 6.4). However, it is retained here for \r\ntraceability with AR5. This approach has been used to define a number \r\nof emergent constraints on carbon cycle feedbacks (Section  5.4.6) \r\nand to reconstruct the transient climate response to cumulative CO2\r\nemissions (TCRE) (Jones and Friedlingstein, 2020), as in Section 5.5. \r\nTo minimize the confounding effect of the scenario dependence, "


id: c6fbc228154795595d0d58fb42169139
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 28886
content:
"Table 5.5 | Diagnosed global feedback parameters for CMIP6 ESMs based \r\non 1% per year runs to 4xCO2 (Arora et al., 2020). The last two rows show the \r\nmean and standard deviation across the CMIP6 and CMIP5 models, respectively."


id: 420245c3dc45742696d487692082888d
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 7789
content:
"Table 5.5 shows the global land and global ocean values of b and g for \r\neach of the CMIP6 ESMs (Arora et al., 2020). The last two rows show \r\nthe ensemble means and standard deviation across the ensemble for \r\nCMIP6 and CMIP5. In both ensembles, the largest uncertainties are \r\nin the sensitivity of land carbon storage to CO2 (bL) and the sensitivity \r\nof land carbon storage to temperature (gL). The more widespread \r\nmodelling of nitrogen limitations in CMIP6 was expected to lead \r\nto reductions in both of these feedback parameters. There is some \r\nevidence for that, with ensemble mean gL moving from -58 +-  38 \r\nGtC K-1 to -33 +-  33 GtC K-1. Between CMIP5 and CMIP6, there \r\nare also reductions in ensemble mean bo (0.82 to 0.77 GtC ppm-1), \r\nbL (0.93 to 0.89 GtC ppm-1) and go (-17.3 to -16.9 GtC K-1), but \r\nthese are progressively less significant compared to the model \r\nspread in each case."


id: daf14a7457c856496069fa352a3076e1
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31968
content:
"carbon storage in the boreal zone, but has a compensating negative \r\nimpact on the carbon sink in tropical and subtropical lands, and in \r\nthe North Atlantic Ocean (g, Figure  5.27b). Overall, the ensemble \r\nmean of the CMIP6 ESMs model indicates increasing carbon storage \r\nwith CO2 in almost all locations (Figure 5.27c)."


id: 60051a1268889d509e6c83826b5470ea
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 12683
content:
"Emergent constraints are based on relationships between observable \r\naspects of the current or past climate (such as trends or variability), \r\nand uncertain aspects of future climate change (such as the strength \r\nof particular feedbacks). These relationships are evident across \r\nan ensemble of models. When combined with an observational \r\nestimate of the trend or variability in the real climate, such emergent \r\nrelationships can yield 'emergent constraints' on future climate \r\nchange (Hall et al., 2019). At the time of AR5 (WGI, Section 9.8.3), \r\nthere had been relatively few applications of the technique to \r\nconstrain carbon cycle sensitivities, but there have been many studies \r\npublished since (e.g., the summary in Cox, 2019). Figure 5.28 shows \r\nsome key published emergent constraints on the carbon cycle in ESMs."


id: e62c8cd8175b1b56f5c244ac965b1c3f
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 33708
content:
"In these idealized 1% per year CO2 runs, the CMIP6 models show \r\nreasonable agreement on the patterns of carbon uptake and also on \r\nthe separate impacts of CO2 increase and climate change (Figure 5.27). \r\nFor the ensemble mean, increasing atmospheric CO2 increases carbon \r\nuptake by the oceans, especially in the Southern Ocean and the North \r\nAtlantic Ocean, and on the land, especially in tropical and boreal \r\nforests (b, Figure  5.27a). Climate change further enhances land "


id: e93d5fc93bd61276216b4c04821c75df
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 34215
content:
"permafrost thaw are estimated to contribute an additional CH4-\r\nclimate feedback of 0.01 [0.003 to 0.04, 5-95% range] W m-2 degC-1\r\n(limited evidence, low agreement). "


id: b70dc1f7e599b26a91674ddc92c68715
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26328
content:
"Sources and sinks of CH4 and N2O respond both directly and indirectly \r\nto atmospheric CO2 concentration and climate change, and thereby \r\ngive rise to additional biogeochemical feedbacks in the climate \r\nsystem, which may amplify or attenuate climate-carbon cycle \r\nfeedbacks (Gasser et al., 2017; Lade et al., 2018; Denisov et al., 2019). \r\nMany of these of feedbacks are only partially understood, and thus \r\nwere only partially addressed in AR5 (WGI, Sections 6.3.3, 6.3.4 and \r\n6.4.7). Since AR5, a growing body of estimates from ESMs, as well \r\nas independent modelling and observation-based studies, enable \r\nimproved estimates of the associated feedbacks."


id: 108ddc54a6ae83110d88dcd3e45ec6a1
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 85
content:
"Methane release from wildfires may increase by up to a factor of 1.5 \r\nduring the 21st century (Eliseev et al., 2014a, b; Kloster and Lasslop, \r\n2017). However, given the contemporary estimate for CH4 from \r\nwildfires of no more than 16 TgCH4 yr-1 (van der Werf et al., 2017; \r\nSaunois et al., 2020), this feedback is small, adding no more than \r\n40 ppb to the atmospheric CH4 by the end of the 21st century (medium \r\nconfidence). Methane emissions from pan-Arctic freshwater bodies is \r\nalso estimated to increase by 16 TgCH4 yr-1 in the 21st century (Tan \r\nand Zhuang, 2015). Emissions from subsea and permafrost methane \r\nhydrates are not expected to change substantially in the 21st century \r\n(Section 5.4.9.1.3)."


id: ce9cb16e6f6a97deae0578287948f9
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 30049
content:
"The goal of this section is to assess the climate feedback parameters \r\na, as it is defined in Section 7.4.1.1, for CH4 and N2O biogeochemical \r\nfeedbacks. The strength of the feedbacks is estimated in a  linear \r\nframework (Gregory et al., 2009), using the radiative forcing equations \r\nfor CO2, CH4 and N2O (Etminan et al., 2016). In addition to estimates \r\nfrom ESMs, the feedback parameter a is estimated from independent \r\nestimates of surface emission climate sensitivities and atmospheric \r\nbox models, following (Arneth et al., 2010; Thornhill et al., 2021). \r\nThese assessed feedback parameters are used in Section 7.4.2.5."


id: b279847b17bb724374bfde7d7b929514
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25595
content:
"Climate change will also affect N2O production in the ocean (Codispoti, \r\n2010; Freing et al., 2012; Bopp et al., 2013; Rees et al., 2016; Breider et al., \r\n2019). Model projections in the 21st century show a 4-12% decrease \r\nin ocean N2O emissions under RCP8.5 due to a combination of factors, \r\nincluding increased ocean stratification, decreased ocean productivity, \r\nand the impact of increasing atmospheric N2O abundance on the air-\r\nsea flux, corresponding to an ocean N2O climate feedback of -0.008 \r\n+-  0.002 W m-2 degC-1 (limited evidence, high agreement) (Martinez\u0002Rey et al., 2015; Landolfi et al., 2017; Battaglia and Joos, 2018b). \r\nOn millennial time scales, the ocean N2O climate feedback may be \r\npositive, owing to ocean deoxygenation and long-term increases in \r\nremineralization (Battaglia and Joos, 2018b)."


id: d348702ec64ccc7a646979aa6d412578
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 30768
content:
"Based-on these studies, there is medium confidence that the \r\ncombined climate feedback parameter for CH4 and N2O is positive, \r\nbut there is low confidence in the magnitude of the estimate (0.05 \r\n[0.02 to 0.09] W m-2 degC-1, 5-95% range)."


id: 4e7d32c86826d2d95a7e18a5c4d86c81
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9779
content:
"This section assesses the magnitude of the combined biogeochemical \r\nfeedback in the climate system (Figure 5.29) by integrating evidence \r\nfrom: carbon-cycle projections represented in Earth system models \r\n(Section  5.4.5.5), independent estimates of CO2 emissions due to "


id: bb2c190fcd5401ded299be54faab3bb0
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26990
content:
"permafrost thaw (Box  5.1) and fire (Section  5.4.3.2), natural CH4\r\nand N2O emissions (Section  5.4.7), and aerosol and atmospheric \r\nchemistry (Section  6.3.6). We derive a  physical climate feedback \r\nparameter a, as defined in Section 7.4.1.1, for CO2-based feedbacks \r\nusing the linear framework proposed by Gregory et al. (2009), using \r\nthe radiative forcing equations for CO2 (Etminan et al., 2016)."


id: 38c640eb0da719922198d4f992cadf9
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 6302
content:
"The applicability of the linear feedback framework (Section 5.4.5.5) \r\nsuggests that large-scale biogeochemical feedbacks are approximately \r\nlinear in the forcing from changes in CO2 and climate. Nevertheless, \r\nregionally the biosphere is known to be capable of producing \r\nabrupt changes or even 'tipping points' (Higgins and Scheiter, \r\n2012; Lasslop et al., 2016). Abrupt change is defined as a  change \r\nin the system that is substantially faster than the typical rate of the \r\nchanges in its history (Section 1.4.5). A related matter is a tipping \r\npoint: a critical threshold beyond which a system reorganizes, often \r\nabruptly and/or irreversibly. Possible abrupt changes in the Earth \r\nsystem include those related to ecosystems and biogeochemistry \r\n(Lenton et al., 2008; Steffen et al., 2018): tropical and boreal forest "


id: dac8c745ecbca72611f6d61b249ac7d0
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31947
content:
"CO2 and non-CO2 biogeochemical feedbacks are an important \r\ncomponent of the assessment of TCRE and the remaining carbon \r\nbudget (Section 5.5). The feedbacks of the carbon cycle of CO2 and \r\nclimate are implicitly taken account in the TCRE assessment, because \r\nthey are represented in the various underlying lines of evidence. "


id: 8418af16544953f56bb1865b71949234
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 18318
content:
"Table 5.6 | Examples of possible biogeochemical abrupt changes and tipping points in the Earth system. The fourth and sixth columns provide upper estimates \r\nof the impact of each example on the evolution of atmospheric GHGs in the 21st century. These upper estimates are therefore very unlikely but provide a useful comparison to \r\nthe impact of direct anthropogenic emissions (currently 2.5 ppm yr-1)."


id: 80af7818e816674fd16fd4754c273bf8
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 17767
content:
"dieback; and release of greenhouse gases (GHGs) from permafrost \r\nand methane clathrates (Table 5.6). In this section we therefore focus \r\non estimating upper limits on the possible impact of abrupt changes on \r\nthe  evolution of atmospheric GHGs out to 2100, for comparison \r\nto the impact of direct anthropogenic emissions."


id: 74a7516a80613dee885591bae0225d87
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15951
content:
"Boreal forest dieback is not expected to change the atmospheric CO2\r\nconcentration substantially because forest loss at the south is partly \r\ncompensated by: (i) temperate forest invasion into previously boreal \r\nareas; and (ii) boreal forest gain at the north (Friend et al., 2014; \r\nKicklighter et al., 2014; Schaphoff et al., 2016) (medium confidence). \r\nAn upper estimate of this magnitude, based on statistical modelling \r\nof climate change alone, is of 27 Pg vegetation carbon loss in \r\nthe southern boreal forest, which is roughly balanced by gains in the \r\nnorthern zone (Koven, 2013). Carbon release from vegetation and \r\nsoil due to wildfires in boreal regions (Eliseev et al., 2014b; Turetsky \r\net al., 2015; X.J. Walker et al., 2019) is also not expected to change \r\nthis estimate substantially because of its small present-day value of \r\nabout 0.2 PgC yr-1 (van der Werf et al., 2017), and because of likely\r\nincreases in precipitation in boreal regions (Section 4.5.1)."


id: 2e123ed2d85c9b8adb3e5343c081909e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 4689
content:
"To estimate an upper limit on the impact of Amazon forest dieback \r\non atmospheric CO2, we consider the very unlikely limiting case of \r\nnegligible direct-CO2 effects (Section  5.4.1). Emergent constraint \r\napproaches (Section 5.4.6) may be used to estimate an overall loss of \r\ntropical land carbon due to climate change alone, of around 50 PgC \r\nper degC of tropical warming (Cox et al., 2013; Wenzel et al., 2014). \r\nThis implies an upper limit to the release of tropical land carbon of \r\n<200 PgC over the 21st century (assuming tropical warming of <4degC, \r\nand no CO2-fertilization), which translates to dCO2/dt <0.5 ppm yr-1."


id: 70f3388c8ed434dd0456a417ac48709e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15403
content:
"and Eliseev, 2020), which precludes a substantial response to global \r\nwarming over the next few centuries and associated emissions."


id: f5921c2c89c7f0b9ffbf6900d3df8a78
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 36156
content:
"Thus, it is very unlikely that CH4 emissions from clathrates will \r\nsubstantially warm the climate system over the next few centuries."


id: 19814389d7eb5bb258db6b9a0c4a9f7
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 1489
content:
"Projecting abrupt changes is intrinsically difficult, because by \r\ndefinition abrupt changes occur in a small region of the parameter \r\nand/or forcing space. At the time of AR5 there was no available \r\nsystematic study of abrupt changes or tipping points in ESMs. An \r\nanalysis of ESMs since AR5 has identified a number of abrupt changes \r\nin the CMIP5 ensemble (Drijfhout et al., 2015; Bathiany et al., 2020). \r\nThese include abrupt changes in tropical forests and high-latitude \r\ngreening, permafrost thaw, and vegetation composition change \r\n(Bathiany et al., 2020). Most modelled abrupt changes were detected \r\nin boreal and tundra regions, with few models showing Amazon \r\nforest dieback (Bathiany et al., 2020)."


id: eddcec0421345963a372379a1d5fe5c8
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 34892
content:
"Based on the evidence presented in this section, we conclude that \r\nabrupt changes and tipping points in the biogeochemical cycles lead \r\nto additional uncertainty in 21st century GHG concentrations changes. \r\nHowever, these are very likely to be small compared to the uncertainty \r\nassociated with future anthropogenic emissions (high confidence)."


id: ac3c713c4e00334a49b603b6f8b628c4
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 24638
content:
"Figure  5.30 shows carbon cycle changes to 2300 under three SSP \r\nscenarios with long-term extensions: SSP5-8.5, SSP5-3.4-overshoot, \r\nand SSP1-2.6, for four CMIP6 ESMs and one EMIC.  Under all three \r\nscenarios, all five models project a reversal of the terrestrial carbon cycle \r\nfrom a sink to a source. However, the reasons for these reversals under \r\nvery high emissions and low/negative emissions are  very  different. \r\nUnder the SSP5-8.5 scenario, the terrestrial carbon-climate \r\nfeedback is projected to strengthen, while the carbon-concentration "


id: 997d3b90b1b53c300b8c4c122a134387
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 21675
content:
"carbon budget estimates. There is growing evidence that the potential \r\npredictive skill of air-land CO2 flux is maintained out to a lead-time \r\nof two years (Lovenduski et al., 2019b); this predictability horizon \r\nis also supported by perfect model studies (Seferian et al., 2018a; \r\nSpring and Ilyina, 2020). The origins of this interannual predictability \r\nare not yet fully understood. However, they seem to be associated \r\nwith the oscillatory behaviour of ENSO (Seferian et al., 2014) and \r\nthe drivers of terrestrial carbon flux predictability, such as ecosystem \r\nrespiration and gross primary production (Lovenduski et al., 2019a). \r\nInitialized simulations suggest that observed variability in the land \r\ncarbon sink is improved through initialization of prediction systems \r\nwith the observed state of the physical climate."


id: 27d5648e18c7a1a1cb5bebfb132a817e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 3678
content:
"The AR5 (WGI, Section  11.3.2) assessed near-term  climate \r\npredictability based on ESMs initialized from the observed \r\nclimate  state. Since AR5, a  growing number of prediction systems \r\nhave been developed based on ESMs that include the ocean and \r\nland carbon cycle components. Predictability of key physical climate \r\nvariables (assessed in Chapter  4) provides a  platform to establish \r\npredictive skill for interannual variations in the strength of the natural \r\ncarbon sinks in response to internal climate variability. In most \r\nsystems the carbon cycle components are only indirectly initialized \r\nand respond to the initialized climate variations (Li et al., 2019). This \r\nsubsection synthesizes information on predictability of the land and \r\nocean carbon sinks using both the idealized potential predictability \r\nand the actual predictability skill measures."


id: 5a2967c16b51f2f5030d00192d2464a
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 11693
content:
"The predictability horizon of variations in atmospheric CO2 growth \r\nrate is not yet fully established in the literature. However, predictive \r\nskill of the land and ocean carbon sinks show a potential to establish \r\npredictability of variations in atmospheric CO2 up to two years in \r\nadvance in the initialized prediction systems, with an upper bound of \r\nup to three years in a perfect-model study (Spring and Ilyina, 2020); \r\nthis skill is primarily limited by the terrestrial carbon sink predictability."


id: 97c4186443e480b53172707b5874aadc
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 21399
content:
"Seasonal predictability of air-land CO2 flux up to 6-8 months is driven \r\nby the state of El Nino-Southern Oscillation (ENSO) (Zeng et al., \r\n2008; Betts et al., 2018). Fewer land carbon initialized predictions \r\nare available from decadal prediction systems, yet they tend to \r\noutperform the uninitialized simulations in capturing the major year\u0002to-year variations, as indicated by higher correlations with the global "


id: a6c922ae4714a27c374d390097c11d4b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 23758
content:
"forcing from CO2, and ocean heat uptake) are governed by complex \r\nmechanisms that all evolve in time (Sections 3.5, 4.3, 4.5, 5.4, and \r\n7.3, and Cross-Chapter Box 5.3; Gregory et al., 2009). Starting with \r\nan initial description in AR5 (M. Collins et al., 2013; T.F. Stocker et al., \r\n2013), a  body of literature has since expanded the understanding \r\nof physical mechanisms from which a  simple proportional \r\nrelationship between cumulative emissions of CO2 and change in \r\nglobal temperature arises - expressed in either global mean surface \r\ntemperature (GMST) or global surface air temperature (GSAT)."


id: c3a537026a2c8189a40c4a101c0b1011
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 28352
content:
"Studies have focused on two key features of the transient climate \r\nresponse to cumulative CO2 emissions (TCRE) relationship: (i) why \r\nthe relationship is nearly constant in time (Goodwin et al., 2015; \r\nMacDougall and Friedlingstein, 2015; Williams et al., 2016; Ehlert \r\net al., 2017; Katavouta et al., 2018); and (ii) why, and under which \r\nconditions, the relationship is independent of the historical rate (or \r\npathway) of CO2 emissions (MacDougall, 2017; Seshadri, 2017)."


id: 9181f5ca34fd24e4647ff02c374c1558
created_at: 2023-03-16 14:00:42
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vector_id: 20431
content:
"The land carbon cycle does not appear to play a fundamental role \r\nin the origin of the linearity and path-independence of TCRE (Goodwin \r\net al., 2015; MacDougall and Friedlingstein, 2015; Ehlert et al., 2017) \r\nbut, in contrast to the ocean sink, dominates the uncertainty in the \r\nmagnitude of TCRE by modulating the cumulative airborne fraction of \r\ncarbon (Goodwin et al., 2015; Williams et al., 2016; Katavouta et al., \r\n2018; Jones and Friedlingstein, 2020). Some terrestrial carbon cycle \r\nfeedbacks (such as the permafrost carbon feedback; Section 5.4.8, \r\nBox 5.1) have the potential to alter both the linearity and pathway \r\nindependence of TCRE, if such feedbacks significantly contribute \r\ncarbon to the atmosphere (Sections 5.5.1.2.3 and 5.4.8, and Box 5.1; \r\nMacDougall and Friedlingstein, 2015). A  recent study also shows \r\nhow the value of TCRE can depend on the effect of ocean ventilation \r\nmodulating ocean heat uptake (Katavouta et al., 2019)."


id: 6330aba852aef4ed9be5b161b0cfd635
created_at: 2023-03-16 14:00:42
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vector_id: 13172
content:
"The question of whether, and under which conditions, the TCRE \r\nrelationship is independent of the historical rate of CO2 emissions \r\n(also referred to as 'pathway independence of TCRE') has been \r\nexamined by using simple mathematically tractable models \r\n(MacDougall, 2017; Seshadri, 2017). Based on the assumption "


id: 7778f746551f744522ba9c629d859223
created_at: 2023-03-16 14:00:42
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content:
"Contributors: Pedro M.S. Monteiro (South Africa), Jean-Baptiste Sallee (France), Piers Foster (United Kingdom), Baylor Fox-Kemper \r\n(United States of America), Helen T. Hewitt (United Kingdom), Masao Ishii (Japan), Joeri Rogelj (United Kingdom/Belgium), Kirsten \r\nZickfeld (Canada/Germany)"


id: 36f945889571212236cd91d1b0d42f43
created_at: 2023-03-16 14:00:42
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vector_id: 6041
content:
"In the past 60  years, the ocean has taken up and stored 23 +-  5% of anthropogenic carbon emissions (medium confidence) \r\n(Section 5.2.1.3) as well as more than 90% of the heat that has accumulated in the Earth system (referred to as excess heat) since the \r\n1970s (Sections 7.2.2, 9.2.2 and 9.2.3, and Box 7.2; Frolicher et al., 2015; Talley et al., 2016; Gruber et al., 2019b; Hauck et al., 2020). The \r\ninterplay between heat and CO2 uptake by the ocean has played a major role in slowing the rate of global warming, and also provides \r\na first order influence in determining the unique properties of a metric of the coupled climate-carbon cycle response - transient \r\nclimate response to cumulative CO2 emissions (TCRE) - which is critical to setting the future remaining carbon emissions budget \r\n(Sections 5.5.1.3 and 5.5.4). This role of the ocean in the uptake of heat and anthropogenic CO2 and related feedbacks is what we "


id: 8ad4f745c0c4cbf944cd7ab6172daf2e
created_at: 2023-03-16 14:00:42
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vector_id: 19378
content:
"term the 'ocean carbon-heat nexus'. The ocean processes behind this nexus are important in shaping and understanding the near\u0002linear relationship between cumulative CO2 emissions and global warming (TCRE) as well as the uncertainties in future projections \r\nof TCRE properties (Zickfeld et al., 2016; Bronselaer and Zanna, 2020; Jones and Friedlingstein, 2020), its path independence \r\n(MacDougall, 2017), and the warming commitment after cessation of greenhouse gas emissions - the zero emissions commitment \r\n(ZEC; Section 5.5.2; Zickfeld et al., 2016; Ehlert and Zickfeld, 2017). In this box, we assess the role of the ocean and its physical and \r\nchemical thermodynamic processes that shape these striking characteristics."


id: b24e9a47f08bf4758bcde855a7c16cdb
created_at: 2023-03-16 14:00:42
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content:
"Large-scale regional and global ocean circulation shape the spatial pattern of the uptake and storage of both CO2 and heat (see \r\nFigure 5.8 for carbon; Figure 9.6 for heat observations; Section 9.2; Frolicher et al., 2015; Bronselaer and Zanna, 2020). This coherence \r\nof spatial patterns driven by the large-scale ocean circulation has three aspects. First, notwithstanding interannual-decadal variability \r\nin heat and CO2 uptake, there is a spatial coherence of the temporally integrated uptake at the air-sea boundary, particularly in the \r\nSouthern Ocean (Cross-Chapter Box 5.3, Figure 1; Talley et al., 2016; Keppler and Landschutzer, 2019; Auger et al., 2021). Second, the \r\nimportance of the meridional overturning circulation in the subsequent storage of both heat and CO2 in mode, intermediate and deep \r\nwaters of the ocean interior (Section 9.2). Third, of particular note, the roles of the North Atlantic Ocean (Section 9.2.3.1) and the "


id: 1f1933cab6588d2acb208048d3db9339
created_at: 2023-03-16 14:00:42
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content:
"Southern Ocean (Section 9.2.3.2) in linking the spatial pattern of air-sea fluxes, the storage of heat and carbon, and ultimately in \r\nunderstanding and predicting the sensitivity of the carbon-heat nexus to climate change (Frolicher et al., 2015; Thomas et al., 2018; \r\nWu et al., 2019)."


id: d5f60b1529f165085cb80b43ddfa5855
created_at: 2023-03-16 14:00:42
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content:
"amounts of cumulative emissions. However, Tokarska et al. (2016) \r\nsuggested that a  large decrease in TCRE for high cumulative \r\nemissions is only associated with some EMICs; in the four ESMs \r\nanalysed in their study, the TCRE remained approximately constant \r\nup to 5000 PgC, owing to stronger declines in the efficiency of ocean \r\nheat uptake in ESMs compared to EMICs."


id: eb86336ec05c10c5e98b38c409202418
created_at: 2023-03-16 14:00:42
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content:
"Overall, there is high agreement between multiple lines of evidence \r\n(robust evidence) resulting in high confidence that TCRE remains \r\nconstant for the domain of increasing cumulative CO2 emissions \r\nuntil at least 1500  PgC, with medium confidence of it remaining \r\nconstant up to 3000 PgC because of less agreement across available \r\nlines of evidence."


id: ac1d78c9c43d3fb72efee4799a631475
created_at: 2023-03-16 14:00:42
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content:
"pathways (Huppmann et al., 2018) and noting the lack of literature \r\nthat disentangles these various contributions, there is medium \r\nevidence and high agreement resulting in medium confidence that \r\nthe TCRE remains a good predictor of CO2-induced warming when \r\napplied in the context of emissions reduction pathways, provided that \r\nZEC and long-term Earth system feedbacks are adequately accounted \r\nfor when emissions decline towards zero (see also Section 5.5.1.2.3)."


id: 84f14c5fe56fdfc7dc42656c07b49a19
created_at: 2023-03-16 14:00:42
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content:
"considered in individual or all ESMs, could cause a further increase \r\nor decrease of TCRE (Matthews et al., 2020). These are discussed in \r\ndetail in Section 5.4, but their quantitative effects on TCRE have not \r\nyet been explored by the literature."


id: 73b704077bc19a6f28b451b36bb1388a
created_at: 2023-03-16 14:00:42
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content:
"The AR5 (M. Collins et al., 2013) assessed that the TCRE is likely to \r\nfall in the range of 0.8degC -2.5degC per 1000 PgC (or per exagrams of \r\ncarbon, EgC-1) for cumulative emissions up to 2000 PgC, based on \r\nmultiple lines of evidence. These include estimates based on ESMs \r\nof varying complexity (Matthews et al., 2009; Gillett et al., 2013; \r\nZickfeld et al., 2013), simple climate modelling approaches (Allen \r\net al., 2009; Rogelj et al., 2012) or observational constraints and \r\nattributable warming (Gillett et al., 2013)."


id: 3064d68792bf80ed67ec33e9d88a9727
created_at: 2023-03-16 14:00:42
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"Table 5.7 | Overview of results from studies estimating the transient climate response to cumulative CO2 emissions (TCRE). GSAT = Global mean surface air \r\ntemperature increase, SAT = surface air temperature (e.g., over land only), SST = sea surface temperature, ECS = equilibrium climate sensitivity. Studies that do not isolate the \r\nCO2-induced warming contribution in their TCRE estimates are not included."


id: 7354dd63f51bc268b7832db1b6e5edd
created_at: 2023-03-16 14:00:42
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content:
"equilibrium climate sensitivity (ECS) or transient climate response \r\n(TCR) - is among the most important contribution to uncertainty in \r\nTCRE, with uncertainty in the strength of the land carbon feedback \r\nand ocean heat uptake or ventilation having also been identified as \r\ncrucial to uncertainty in TCRE (Matthews et al., 2009; Gillett et al., \r\n2013; Ehlert et al., 2017; MacDougall et al., 2017; R.G.  Williams \r\net al., 2017a, 2020; Katavouta et al., 2019; Arora et al., 2020; Jones \r\nand Friedlingstein, 2020; Spafford and Macdougall, 2020). Finally, \r\ninternal variability has been shown to affect the maximum accuracy \r\nof TCRE estimates by +-0.1degC per 1000 PgC (5-95% range; Tokarska \r\net al., 2020)."


id: 7281f8d40d86b353a2634c55aadb8bbb
created_at: 2023-03-16 14:00:42
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content:
"2015; Rogelj et al., 2015a, b; MacDougall, 2016; Simmons and \r\nMatthews, 2016; Ehlert et al., 2017; Matthews et al., 2017, 2021; \r\nMillar et al.,  2017b; Goodwin et al., 2018; Mengis et al., 2018; \r\nPfleiderer et al., 2018; Tokarska et al., 2018; Cain et al., 2019). These \r\ncontributing factors are integrated in an overarching assessment of \r\nremaining carbon budgets for limiting global average warming to \r\nlevels ranging from 1.5degC to 2.5degC relative to pre-industrial levels \r\nprovided in Section  5.5.2.3. Box  5.2 provides an overview of the \r\nmethodological advances since AR5 (W.J. Collins et al., 2013)."


id: 7a4e834da6bfe54931b79a301ec24f05
created_at: 2023-03-16 14:00:42
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content:
"The AR6 Glossary (Annex VII) defines remaining carbon budgets \r\nas the maximum amount of cumulative net global anthropogenic \r\nCO2 emissions expressed from a  recent specified date that would \r\nresult in limiting global warming to a  given level with a  given \r\nprobability, taking into account the effect of other anthropogenic \r\nclimate forcers, consistent with the definition used inSR1.5 (Rogelj \r\net al., 2018b). Studies, however, apply a variety of definitions that \r\nresult in published remaining carbon budget estimates informing to \r\ncumulative emissions at the time when global-mean temperature \r\nincrease would reach, exceed, avoid, or peak at a  given warming \r\nlevel with a given probability (M. Collins et al., 2013; T.F. Stocker et al., \r\n2013; Clarke et al., 2014; Friedlingstein et al., 2014a; IPCC, 2014; \r\nRogelj et al., 2016; Millar et al., 2017b)."


id: d411d27db602d039699f365c0ebbfca9
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"This section provides an assessment of remaining carbon budgets \r\nconsistent with the AR6 Glossary definition (Annex VII). Given that \r\nsome feedbacks are time dependent, the values in this section \r\napply to limiting warming over the 21st century, consistent with \r\nrecent studies highlighting the usefulness of time-limited carbon \r\nbudgets (Sanderson, 2020). Irrespective of the exact definition of \r\nthe remaining carbon budget, the finding that higher cumulative \r\nCO2 emissions lead to higher temperatures implies that annual net \r\nCO2 emissions have to decline to close to zero in order to halt global \r\nwarming, whether at 1.5degC, 2degC or another level (Allen et al., 2018)."


id: 3c2c43909686c0a9b39b66b23538eb9d
created_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 6825
content:
"Estimates of remaining carbon budgets consistent with holding global \r\nwarming below a specific temperature threshold depend on a range \r\nof factors which are increasingly being studied and quantified. These \r\nfactors include: (i) well-understood methodological and definitional \r\nchoices (Sections  5.5.2.1 and 5.5.2.2; Friedlingstein et al., 2014a; \r\nRogelj et al., 2016, 2018b); and (ii) a set of contributing factors such \r\nas historical warming, the TCRE and its limitations, the ZEC (the \r\namount of warming projected to occur following a complete cessation \r\nof emissions; see Section 4.7.1.1), as well as contributions of non\u0002CO2 climate forcers (Section 5.5.2.2; MacDougall and Friedlingstein, "


id: 52e068cea97ce0d41dbea0859460f510
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vector_id: 10496
content:
"Millar et al., 2017b; Gasser et al., 2018; Lowe and Bernie, 2018; \r\nTokarska et al., 2018; Nicholls et al., 2020)."


id: f8a5d1d9e254a17a369118b332015f11
created_at: 2023-03-16 14:00:42
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"reference periods (Cross-Chapter Box 1.2) or temperature metrics \r\nand updated data products (Cross-Chapter Box  2.3) can result \r\nin a  different estimated historical warming and thus a  changed \r\nremaining carbon budget."


id: 48c79e82acb1a96f0be1a3922798c99
created_at: 2023-03-16 14:00:42
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content:
"Recent studies suggest further changes to this framework by \r\nincluding non-linear adjustments to the TCRE contribution (Nicholls \r\net al., 2020), or including non-CO2 forcers in different ways by \r\naccounting for their different forcing effects (Matthews et al., 2021). \r\nFigure  5.31 provides a  conceptual schematic of how the various \r\nindividually assessed contributions are combined into a consolidated \r\nassessment of the remaining carbon budget. Together with estimates \r\nof historical CO2 emissions to date (Section 5.2.1), these remaining \r\ncarbon budgets provide the overall amount of cumulative CO2\r\nemissions consistent with limiting global warming to specific levels. \r\nA comparison with the approach applied in AR5 (M. Collins et al., \r\n2013; Clarke et al., 2014) is available in SR1.5 Section 2.2.2 (Rogelj \r\net al., 2018b) as well as Box 5.2."


id: a905da03235167289bd5b7e0eadd1bb5
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"Remaining carbon budgets are assessed through the combination of \r\nfive separate components (Forster et al., 2018; Rogelj et al., 2018b). \r\nEach component is discussed and assessed separately in the sections \r\nbelow, based on all available lines of evidence. Box 5.1 details the \r\ndifferences compared to AR5 and SR1.5 estimates (W.J. Collins et al., \r\n2013; Rogelj et al., 2018b)."


id: c0fb9fdf2f02963a88c2faa6806d2cd7
created_at: 2023-03-16 14:00:42
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"The first and central component for estimating remaining carbon \r\nbudgets is the TCRE. Based on the assessment in Section 5.5.1.4, an \r\nassessed likely range for TCRE of 1.0degC-2.3degC per 1000  PgC with \r\na normal distribution is used."


id: 49fb8725a37b646d825fab2d237074b2
created_at: 2023-03-16 14:00:42
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content:
"Application of the SR1.5 method (Forster et al., 2018; Rogelj et al., \r\n2018b) with AR6-calibrated emulators (Box 7.1) suggests a median \r\nadditional non-CO2 warming contribution at the time global CO2\r\nemissions reach net zero levels of about 0.1degC-0.2degC relative to \r\n2010-2019. Uncertainty surrounding this range due to geophysical \r\nuncertainties such as non-CO2-forcing uncertainties and TCR is of the \r\norder of +-0.1degC. Differences in the choices of mitigation strategies \r\nconsidered in low-emissions scenarios (Huppmann et al., 2018) result \r\nin a potential additional variation around the central range of at least \r\n+-0.1degC (spread across scenarios, referred to as non-CO2 scenario \r\nuncertainty in Table 5.8)."


id: 50ed10ec8cd3a4175edcf895415c322c
created_at: 2023-03-16 14:00:42
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"independent Earth system feedbacks combine to a  feedback of \r\nabout 7  +- 27  PgC  K-1 (1-sigma range, or 26 +-  97  GtCO2 degC-1). \r\nOverall, Section  5.4.8 assessed there to be low confidence in the \r\nexact magnitude of these feedbacks and they represent identified \r\nadditional amplifying factors that scale with additional warming, and \r\nmostly increase the challenge of limiting global warming to or below \r\nspecific temperature levels."


id: 38445e37e1ac441b9c6e20cc15dbd8be
created_at: 2023-03-16 14:00:42
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content:
"Section  5.5.1.2 highlighted recent literature describing potential \r\nimpacts of Earth system feedbacks that have typically not been \r\nincluded in standard ESMs (MacDougall and Friedlingstein, \r\n2015; Schneider von Deimling et al., 2015; Schadel et al., 2016; Burke \r\net al., 2017b; Mahowald et al., 2017; Comyn-Platt et al., 2018; Gasser \r\net al., 2018; Lowe and Bernie, 2018), the most important of which \r\nis carbon release from thawing permafrost. The SR1.5 estimated \r\nunrepresented Earth system processes to result in a  reduction \r\nof remaining carbon budgets of up to 100  GtCO2 over the course \r\nof this century, and more thereafter (Rogelj et al., 2018b). Here \r\nthis assessment is updated based on the Earth system feedback \r\nassessment of Section  5.4.8 and synthesized in Figure  5.29 by \r\napplying the reverse method by Gregory et al. (2009)."


id: d0b36fb51f41aaae4260c79971ecd0a0
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"The assessment in Section 5.4 and Box 5.1 highlights the different \r\nnature, magnitude and uncertainties surrounding additional \r\nEarth system feedback. The remaining carbon budgets reported in \r\nTable 5.8 account for these feedbacks, including corrections due to \r\npermafrost CO2 and CH4 feedbacks as well as those due to aerosol \r\nand atmospheric chemistry (Section 5.4.8). Two of these additional \r\nfeedbacks (tropospheric ozone and methane lifetime feedbacks) \r\nare included in the projections of non-CO2 warming carried out \r\nwith AR6-calibrated emulators (Box  7.1). The remainder of these "


id: e84a8a96d0a8399a19f62a1edf2ecda1
created_at: 2023-03-16 14:00:42
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"Table 5.8 | The assessed remaining carbon budget and corresponding uncertainties. Assessed estimates are provided for additional human-induced warming \r\nexpressed as global average surface air temperature since the recent past (2010-2019), which likely amounted to 0.8 to 1.3 with a best estimate of 1.07degC relative to \r\n1850-1900 (Table 3.1 in Chapter 3)."


id: 4213b677109c47c50875ab917ff83db8
created_at: 2023-03-16 14:00:42
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"a\r\n Human-induced global surface air temperature increase between 1850-1900 and 2010-2019 is assessed at 0.8-1.3degC (likely range; Chapter 3) with a best estimate of \r\n1.07degC. Warming reflects changes in GSAT, as TCRE and other estimates are GSAT-based. Combined with a central estimate of TCRE (1.65degC EgC-1) the uncertainty in historical \r\nhuman-induced GSAT warming results in a potential variation of remaining carbon budgets of +-150 PgC or +-550 GtCO2."


id: 43302e1c6ebf199fe49fcd0f460ba799
created_at: 2023-03-16 14:00:42
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"f\r\n Remaining carbon budget variation due to geophysical uncertainty in forcing and temperature response of non-CO2 emissions of the order of +-0.1degC, very likely range (5-95%) \r\nof non-CO2 response (Section 5.5.2.2.3). Combined with a central estimate of TCRE (1.65degC EgC-1) this results in at least +-60 PgC or +-220 GtCO2.\r\ng\r\n The variation due to the ZEC is estimated for a central TCRE value of 1.65degC EgC-1 and a 1-sigma ZEC range of 0.19degC. In real-world pathways, the magnitude of this effect \r\nwill depend on the pace of CO2 emissions reductions to net zero."


id: 12f0cb9d451769dbbe5bf7c9613b3525
created_at: 2023-03-16 14:00:42
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"h\r\n Historical emissions uncertainty reflects the +-10% uncertainty in the historical emissions estimate since 1 January 2015."


id: 3b8aad767e2acf26b6123a75695895aa
created_at: 2023-03-16 14:00:42
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vector_id: 6718
content:
"Box 5.2 | Implications of Methodological Advancements in Estimating the Remaining Carbon \r\nBudget since the IPCC's Fifth Assessment Report (AR5)"


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"Methodological advancements since AR5 (M. Collins et al., 2013; IPCC, 2013b, 2014; T.F. Stocker et al., 2013; Clarke et al., 2014) \r\nresult in an updated and strengthened assessment of remaining carbon budgets. Methods and approaches at the time of AR5 are \r\ndescribed in Section  5.5.2.1. Since AR5, strengths and weaknesses of various approaches have been more clearly articulated in \r\nthe literature (e.g., in Rogelj et al., 2016; Millar et al., 2017b; Tokarska and Gillett, 2018; Matthews et al., 2020), resulting in a new \r\nconsolidated framework applied in SR1.5 (Rogelj et al., 2018b, 2019) that is also used in AR6. This framework incorporates five \r\nmethodological advancements compared to AR5, the implications of which are discussed in this box."


id: 1a11c03ef5da96a9d60abb3564154766
created_at: 2023-03-16 14:00:42
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"First, publications since AR5 applied methods that limit the effect of uncertainties in historical, diagnosed emissions in coupled Earth \r\nsystem models (ESMs) on estimates of the remaining carbon budget (Millar et al., 2017b; Tokarska and Gillett, 2018). These new \r\nmethods express remaining carbon budget estimates relative to a recent reference period instead of relative to the pre-industrial \r\nperiod (Millar et al., 2017b; Tokarska et al., 2019b). Estimates of the full carbon budget since the pre-industrial period can still be \r\nobtained by adding estimates of historical CO2 emissions (Table 5.1) to the estimates in Table 5.8. This methodological update resulted, \r\nall other aspects being equal, in median estimates of remaining carbon budgets being about 350-450 GtCO2 larger compared to AR5 \r\n(IPCC, 2014; Millar et al., 2017b)."


id: 988b67ce9f4b21bd26f0621e0fe6d224
created_at: 2023-03-16 14:00:42
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content:
"At the time of AR5, Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012) provided global surface air temperature \r\n(GSAT) projections for the representative concentration pathways (Meinshausen et al., 2011c), which were used to determine carbon \r\nbudgets while taking into account the effects of non-CO2 forcers (T.F.  Stocker et al., 2013). Their use came with two recognized \r\nlimitations: first, the model spread of the CMIP5 represents an ensemble of opportunity with limited statistical value (Tebaldi and \r\nKnutti, 2007); and second, the evolution of non-CO2 emissions as a function of cumulative CO2 emissions can differ markedly between \r\nhigh and low emissions pathways (Meinshausen et al., 2011c; Friedlingstein et al., 2014a; Rogelj et al., 2016; Matthews et al., 2017). \r\nSolutions to these two limitations have been published since AR5 and represent the second and third methodological improvement \r\ncompared to AR5."


id: 3ed355ccef624a37e6209adf0df87ff3
created_at: 2023-03-16 14:00:42
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content:
"The third methodological improvement is a more direct estimation of the warming contribution of non-CO2 emissions, consistent with \r\npathways that bring global CO2 emissions down to net zero. Instead of deriving this contribution implicitly from the CMIP5 ensemble, \r\nclimate emulators (Meinshausen et al., 2011b; C.J. Smith et al., 2018; Schwarber et al., 2019) that are calibrated to the combined \r\nAR6 assessment (Cross-Chapter Box 7.1) are used to estimate the non-CO2 contribution across a wide variety of stringent mitigation \r\nscenarios (Huppmann et al., 2018). The specific relative effect of this advance compared to AR5 is not calculable because CMIP5 data \r\ndoes not isolate non-CO2 from CO2-induced warming."


id: 780fe4ae9831906eae485278df0efa13
created_at: 2023-03-16 14:00:42
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content:
"Between SR1.5 and AR6, each of the five components described in Section  5.5.2.1 and Figure  5.31 have been re-assessed (see \r\nSections 5.5.2.2.1-5.5.2.2.5). Their updated assessments in turn affect the assessment of the remaining carbon budget. The new "


id: 62018eae87e163e643625ea663b28066
created_at: 2023-03-16 14:00:42
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content:
"and narrower assessment of TCRE in AR6 compared to SR1.5 (likely range of 1.0degC-2.3degC EgC-1 compared to 0.8degC-2.5degC EgC-1, \r\nrespectively, with the same central estimate) leads to no change in median estimates and about a 50 and 100 GtCO2 increase in \r\nremaining carbon budgets estimates at the 67th percentile in AR6 compared to SR1.5 for 1.5degC and 2degC of global warming, respectively."


id: d8d7d58a94ce377ac74450b48d37d21c
created_at: 2023-03-16 14:00:42
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content:
"All methodological improvements and new evidence combined result in median and 67th percentile remaining carbon budget \r\nestimates for limiting warming to 1.5degC being about 300-350 GtCO2 larger compared to an assessment that would use the evidence \r\nand methods available at the time of the AR5. For limiting warming to 2degC, the difference is about 400-500 GtCO2. Since SR1.5, fewer \r\nkey advancements had to be integrated. In a like-with-like comparison, the combined effects of all AR6 updates result in median \r\nremaining carbon budget estimates for limiting warming to 1.5degC and 2degC being the same and about 60 GtCO2 smaller, respectively, \r\nin AR6 compared to SR1.5. At the 67th percentile, remaining carbon budget estimates for limiting warming to 1.5degC and 2degC are about \r\n40 to 60 GtCO2 larger, respectively, mainly as a result of a narrower assessed TCRE range."


id: b0f28c0cb7cf3bb4f92b2b4ff693a35
created_at: 2023-03-16 14:00:42
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vector_id: 25351
content:
"(Glossary). CDR and SRM are referred to as 'geoengineering' in some \r\nof the literature, and are considered separately in this report."


id: e46d5412fbaae4330b46d778230baa9
created_at: 2023-03-16 14:00:42
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content:
"This section assesses the implications of CDR and SRM for \r\nbiogeochemical cycles. CDR has received growing interest as an \r\nimportant mitigation option in emissions scenarios consistent with \r\nmeeting the Paris Agreement climate goals (SR1.5, SRCCL). The \r\nclimate effects of CDR and SRM are assessed in Chapter  4, and \r\na  detailed assessment of the socio-economic dimensions of these \r\noptions is presented in AR6 WGIII, Chapters 7 and 12."


id: fec62aeb038d4732e7fd72b0e5a9fefb
created_at: 2023-03-16 14:00:42
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vector_id: 37627
content:
"Carbon dioxide removal (CDR) refers to anthropogenic activities that \r\nseek to remove CO2 from the atmosphere and durably store it in \r\ngeological, terrestrial or ocean reservoirs, or in products (Glossary). \r\nCO2 is removed from the atmosphere by enhancing biological or \r\ngeochemical carbon sinks or by direct capture of CO2 from air and \r\nstorage. Solar radiation modification (SRM) refers to the intentional, \r\nplanetary-scale modification of the Earth's radiative budget with \r\nthe aim of limiting global warming. Most proposed SRM methods \r\ninvolve reducing the amount of incoming solar radiation reaching \r\nthe surface, but others also act on the longwave radiation budget by \r\nreducing optical thickness and cloud lifetime (Glossary). SRM does \r\nnot fall within the IPCC definitions of mitigation and adaptation "


id: fa82a7d9fdfd2b9379a86961174ec06e
created_at: 2023-03-16 14:00:42
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content:
"The scope of this section is to assess the general and methods-specific \r\neffects of CDR on the global carbon cycle and other biogeochemical \r\ncycles. The focus is on Earth system feedbacks that either amplify "


id: 804096c1216ac77fe88ac8b70d611d3f
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 17707
content:
"IPCC, 2018), SRCCL (Chapter  6, IPCC, 2019a) and SROCC (Bindoff \r\net al., 2019) for the assessment of potentials and side effects of \r\nspecific CDR methods. Emerging literature on deliberate methane \r\nremoval is also briefly discussed."


id: c72cd09f821ad962192c5e26fb4df5b8
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 28925
content:
"or reduce carbon sequestration potentials of specific CDR methods, \r\nand determine their effectiveness in reducing atmospheric CO2 and \r\nmitigating climate change. Technical carbon sequestration potentials \r\nof CDR methods are assessed on a qualitative scale; a comprehensive \r\nquantitative assessment is left to the AR6 Working Group III Report \r\n(Chapters 7 and 12). Biogeochemical and biophysical side effects of \r\nCDR methods are assessed here, while the co-benefits and trade\u0002offs for biodiversity, water and food production are briefly discussed \r\nfor completeness, but a  comprehensive assessment is left to WGII \r\n(Chapters 2 and 5) and WGIII (Chapters 7 and 12). The assessment \r\nin this chapter emphasizes literature published since the AR5 WGI \r\nreport (Chapter  6) for the assessment of the global carbon cycle \r\nresponse to CDR, and literature published since SR1.5 (Chapter  4; "


id: bc0cce9896337f9e47bf01f183545fdb
created_at: 2023-03-16 14:00:42
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vector_id: 27149
content:
"In this chapter, CDR methods are categorised by the carbon \r\ncycle processes that result in CO2 removal: (i) enhanced net \r\nbiological production and storage by land ecosystems; (ii) enhanced \r\nnet biological production and storage in the open and coastal \r\nocean; (iii) enhanced geochemical processes on land and in the \r\nocean; and (iv) direct air capture and storage by chemical processes. \r\nA  subset of CDR methods that restore or sustainably manage \r\nnatural or modified ecosystems while providing human well-being "


id: a6b5b37a082d674b5ac4a35fc3143d49
created_at: 2023-03-16 14:00:42
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vector_id: 23755
content:
"Table 5.9 | Characteristics of carbon dioxide removal (CDR) methods. Termination effects refer to the possible effects of a hypothetical, sudden and sustained \r\ntermination of the CDR method."


id: a01f89db7c7fd15629f713e6c59d6e9b
created_at: 2023-03-16 14:00:42
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vector_id: 22728
content:
"and biodiversity benefits are also referred to as natural or nature\u0002based solutions (Glossary; Griscom et al., 2017, 2020; Fargione \r\net al., 2018). CDR methods commonly discussed in the literature are \r\nsummarized in Table 5.9. Other CDR options have been suggested, \r\nbut there is insufficient literature for an assessment. These include \r\nocean biomass burial, ocean downwelling, removal of CO2 from \r\nseawater with storage, and cloud alkalinization (Keller et al., \r\n2018a; GESAMP, 2019)."


id: 148072e0fb69b0a1508c30965ccd1a77
created_at: 2023-03-16 14:00:42
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content:
"This subsection assesses three aspects of the climate-carbon cycle \r\nresponse to CDR: the time-dependent behaviour of CO2 fluxes \r\nin scenarios with CDR, the effectiveness of CDR in drawing down \r\natmospheric CO2 and cooling global mean temperature, and the \r\nsymmetry of the climate-carbon cycle response to positive and \r\nnegative CO2 emissions."


id: 721bb955cc8b1c2aa9881b894342a212
created_at: 2023-03-16 14:00:42
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content:
"During the industrial era, CO2 emitted by the combustion of fossil fuels and land-use change has been redistributed between \r\natmosphere, land, and ocean carbon reservoirs due to carbon cycle processes (Box 5.3, Figure 1b and Figure 5.13). Over the past \r\ndecade (2010-2019), 46% of the emitted CO2 remained in the atmosphere, 23% was taken up by the ocean, and 31% by the terrestrial \r\nbiosphere (Section 5.2.1.5). When carbon dioxide removal (CDR) is applied during periods in which human activities are net CO2\r\nsources to the atmosphere, and the amount of emissions removed by CDR is smaller than the net source (net positive CO2 emissions), \r\nCDR acts to reduce the net emissions (Box 5.3 Figure 1c). In this scenario, part of the CO2 emissions in the atmosphere are removed \r\nby the land and ocean sinks, as has been the case historically."


id: de03c59415371c48e21a097fc5d429ac
created_at: 2023-03-16 14:00:42
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content:
"When CDR removes more CO2 emissions than human activities emit (net negative CO2 emissions), and atmospheric CO2 declines, the \r\nland and ocean sinks initially continue to take up CO2 from the atmosphere. This is because carbon sinks, particularly the ocean, exhibit \r\ninertia and continue to respond to the prior trajectory of rising atmospheric CO2 concentration. After some time, which is determined \r\nby the magnitude of the removal and the rate and amount of CO2 emissions prior to the CDR application, land and ocean carbon \r\nreservoirs begin to release CO2 into the atmosphere, making CDR less effective (Box 5.3, Figure 1d)."


id: 1d76aa728756fb0a45100b9fc214e712
created_at: 2023-03-16 14:00:42
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content:
"Box 5.3, Figure 1| Schematic representation of carbon fluxes between atmosphere, land, ocean and geological reservoirs. Different system conditions \r\nare shown: (a) an unperturbed Earth system; and changes in carbon fluxes for (b) an Earth system perturbed by fossil fuel carbon dioxide (CO2) emissions; (c) an Earth \r\nsystem in which fossil fuel CO2 emissions are partially offset by carbon dioxide removal (CDR); (d) an Earth system in which CDR exceeds CO2 emissions from fossil \r\nfuels ('net negative' CO2 emissions). Carbon fluxes depicted in (a) (solid and dashed black lines) also occur in (b-d). The question mark in the land-to-ocean carbon \r\nflux perturbation in (c) and (d) indicates that the effect of CDR on this flux is unknown. Note that box sizes do not scale with the size of carbon reservoirs. Adapted \r\nfrom Keller et al. (2018a). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6)."


id: 672119d87c47855fd7001bf946e4e043
created_at: 2023-03-16 14:00:42
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content:
"Idealized 'pulse' removal Earth system model simulations are useful \r\nfor understanding the carbon cycle response to CDR.  Figure  5.32 \r\nillustrates the response of atmospheric CO2, land and ocean carbon \r\nsinks to an instantaneous CO2 removal applied from a pre-industrial \r\nequilibrium state. Following CO2 removal from the atmosphere, \r\nthe atmospheric CO2 concentration declines rapidly at first and "


id: 365204672bc875660bd768e6034e33c6
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updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 5938
content:
"requires a  reference simulation to evaluate the effect of the CO2\r\nremoval. When CDR is applied from an equilibrium state, the PAF and \r\nAF are equivalent measures."


id: 5c7bad7071bcabe2bbb366fffc567b55
created_at: 2023-03-16 14:00:42
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vector_id: 12066
content:
"-1000 PgC). Data for the UVic ESCM is from Zickfeld et al. (2021). (b) Perturbation \r\nairborne fraction (see text for definition) for model simulations where CDR is applied \r\nfrom four Representative Concentration Pathways (RCPs) (shown on the horizontal \r\naxis in terms of their cumulative CO2 emissions during 2020-2099). Symbols indicate \r\nresults for four CDR scenarios, which differ in terms of the magnitude and rate of CDR \r\n(see Jones et al. (2016b) for details). Results are based on simulations with the Hadley \r\nCentre Simple Climate-Carbon Model and are shown for the year 2100. Data from \r\nJones et al. (2016b). Further details on data sources and processing are available in \r\nthe chapter data table (Table 5.SM.6)."


id: 9e77f998543c7b8a60df48735dd58482
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content:
"It is commonly assumed that the climate-carbon cycle response \r\nto a  negative CO2 emission (i.e.,  removal from the atmosphere) \r\nis equal in magnitude and opposite in sign to the response to \r\na  positive CO2 emission of equal magnitude -  that is, symmetric. \r\nIf the response were symmetric, a  positive CO2 emission could be \r\noffset by a  negative emission of equal magnitude. This subsection \r\nassesses the symmetry in the coupled climate-carbon cycle response "


id: 6653c2d0bedfeda81a6bb5c5d9d732a8
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content:
"conservation initiatives, accelerating breeding for tree health and \r\nproductivity (Isabel et al., 2020)."


id: f28fe6e236f63bacf98e70a1821e0b93
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content:
"In response to increasing risks to permanence of carbon stocks of some \r\ntypes of afforestation practices and the competition for land, there \r\nhas been an increasing recognition that secondary forest regrowth \r\nand restoration of degraded forests and non-forest ecosystems can \r\nplay a  large role in carbon sequestration (high confidence). The \r\nrational for this focus builds on their high carbon stocks and rates of \r\nsequestration (Griscom et al., 2017; Lewis et al., 2019; Maxwell et al., \r\n2019; Pugh et al., 2019), high resilience to disturbances (Dymond \r\net al., 2014; Messier et al., 2019), and additional benefits such as \r\nenhanced biodiversity (Strassburg et al., 2020)."


id: 53c3e2177870905b2f27455b26cdde86
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content:
"The global sequestration potential of forestation varies substantially \r\ndepending on the scenario-assumptions of available land and of \r\nbackground climate (AR6 WGIII, Section 7.5). Afforestation of native \r\ngrasslands, savannas, and open-canopy woodlands leads to the \r\nundesirable loss of unique natural ecosystems with rich biodiversity, \r\ncarbon storage and other ecosystem services (Veldman et al., 2015; \r\nIPBES, 2018). Comprehensive approaches to assess the effectiveness \r\nof land-based carbon removal options need to be based on the \r\nwhole carbon cycle, covering both carbon stocks and flows, and \r\nestablishing the links between human activities and their impacts on \r\nthe biosphere and atmosphere (Keith et al., 2021)."


id: 782b904eef3247c6dbcd01ec8c7b0b16
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 16475
content:
"Biological CDR methods, introduced in Table  5.9, seek to increase \r\ncarbon storage on land by enhancing net primary productivity and/or \r\nreducing CO2 sources to the atmosphere."


id: 19e9b9dd7f485e354ab7e72b2cd29f09
created_at: 2023-03-16 14:00:42
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vector_id: 1545
content:
"Forest-based methods include afforestation, reforestation, and forest \r\nmanagement (Table 5.9). Building on previous work that emphasized \r\nthe global potentials of various options, more recent advances have \r\nfocused on the limits of those global potentials in light of ecological \r\nand climate risks that can threaten the long-term permanence of \r\ncarbon stocks (Boysen et al., 2017b; Anderegg et al., 2020). Some \r\nof those risks arise from droughts, fires, insect outbreaks, diseases, \r\nerosion, and other disturbances (Thompson et al., 2009)."


id: b536f8cb3896e8ccd6e356c17e9a1ebb
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content:
"Sustainable forest management can help to manage some of these \r\nvulnerabilities, while in some cases it can increase and maintain \r\nforest sinks through harvest, transfer of carbon to wood products \r\nand their use to store carbon and substitute emissions-intensive \r\nconstruction materials (Churkina et al., 2020). Forest genomics \r\ntechniques can increase the success of both reforestation and "


id: 9ad05d515f34c862a17f9cb293ea03db
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 21889
content:
"In conclusion, land-based CDR methods that rely on enhanced net \r\nbiological uptake and storage of carbon, have a  wide range of \r\nbiogeochemical and biophysical side effects. These side effects can \r\n(directly or indirectly) strengthen or weaken the climate change \r\nmitigation effect of a  given method, or affect water quality and \r\nquantity, food supply and biodiversity (Figure  5.36). With the \r\nexception of weakened ocean carbon sequestration, there is low \r\nconfidence in the Earth system feedbacks of these methods. Most "


id: 8716cff404d93281d2aa4bcb8fd484eb
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content:
"methods are associated with a  range of biogeochemical and \r\nbiophysical side effects and co-benefits and trade-offs, but these \r\nare often highly dependent on local context, management regime, \r\nprior land use, and scale (high confidence). Highest co-benefits are \r\nobtained with methods that seek to restore natural ecosystems and \r\nimprove soil carbon sequestration (Figure 5.36) while highest trade\u0002off possibilities (symmetry with the highest co-benefits) occur for \r\nreforestation or afforestation with monocultures and BECCS, again \r\nwith strong dependence on scale and context (medium confidence)."


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content:
"immediate threat to the integrity of coastal carbon stocks (Smale \r\net al., 2019). Blue carbon restoration seeks to increase the rate of \r\ncarbon sequestration, although restoration may be challenging, \r\nbecause of ongoing use of coastal land for human settlement, \r\nconversion to agriculture and aquaculture, shoreline hardening and \r\nport development."


id: 6972768625e5ade62b066116864fa866
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updated_at: 2023-03-16 14:00:42
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content:
"Biogeochemical factors affecting reliable quantification of the \r\nclimatic benefits of coastal vegetation include the variable production \r\nof CH4 and N2O by such ecosystems (Adams et al., 2012; Keller, 2018; \r\nRosentreter et al., 2018), uncertainties regarding the provenance \r\nof the carbon that they accumulate (Macreadie et al., 2019), and \r\nthe release of CO2 by biogenic carbonate formation in seagrass \r\necosystems (Kennedy et al., 2018). While coastal habitat restoration \r\npotentially provides significant mitigation of national emissions for \r\nsome countries (Taillardat et al., 2018; Serrano et al., 2019), the global \r\nsequestration potential of blue carbon approaches is <0.02 PgC yr-1\r\n(medium confidence) (Figure 5.36; SROCC, Section 5.5.1.2; Griscom \r\net al., 2017; Gattuso et al., 2018; GESAMP, 2019; NASEM, 2019)."


id: b6ced1807646143c6b61117201a5da06
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content:
"Restoration of vegetated coastal ecosystems (sometimes referred \r\nto as 'blue carbon' -  see Glossary) refers to the potential for \r\nincreasing carbon sequestration by plant growth and burial \r\nof organic carbon in the soil of coastal wetlands (including salt \r\nmarshes and mangroves) and seagrass ecosystems. Wider usage of \r\nthe term blue carbon occurs in the literature, for example, including \r\nseaweeds (macroalgae), shelf sea sediments and open ocean \r\ncarbon exchanges. However, such systems are less amenable to \r\nmanagement, with many uncertainties relating to the permanence \r\nof their carbon stores (Windham-Myers et al., 2018; Lovelock and \r\nDuarte, 2019; SROCC, Section 5.5.1.1)."


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vector_id: 11032
content:
"Ocean alkalinization, via the deposition of alkaline minerals \r\n(e.g., olivine) or their dissociation products (e.g., quicklime) at the \r\nocean surface, can increase surface total alkalinity and thus increase \r\nCO2 uptake and storage (Glossary; Supplementary Material Text \r\n5.SM.3; AR6 WGIII Chapter 12; GESAMP, 2019; Keller, 2019). Ocean \r\nalkalinization ameliorates surface ocean acidification (high confidence) \r\n(Hauck et al., 2016; Tran et al., 2020), but there are also negative side \r\neffects on the marine ecosystem, most of which are poorly understood \r\nor quantified (Figure 5.36 and Supplementary Materials Table 5.SM.4; \r\nBach et al., 2019). Although ocean alkalinization could potentially \r\nsequester large amounts of carbon (>=1  PgC yr-1; Figure  5.36; and \r\nSupplementary Materials Table 5.SM.5) there is no new evidence to "


id: eff7c0c076e57801d8bc7ceec3dc9636
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 35262
content:
"revisit the SROCC (SROCC Section 5.5.1.2.4) conclusion that there is \r\nlow confidence that ocean alkalinization is a viable climate change \r\nmitigation approach."


id: afe853ac0723799c74bab09d50275ad3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25201
content:
"Proposals to remove CH\r\n4 from the atmosphere are emerging \r\n(de Richter et al., 2017; Jackson et al., 2019). CH\r\n4 removal methods \r\nseek to capture CH\r\n4 directly from ambient air, similarly to DACCS \r\nfor CO\r\n2 using, for example, zeolite trapping, but instead of storing \r\nit, CH\r\n4 would be chemically oxidized to CO2 (Jackson et al., 2019). \r\nMethane can be also removed microbially by supporting naturally \r\noccurring processes, such as by enhancing the soil microbial uptake \r\nthrough afforestation (J. Wu et al., 2018) or by directing the venting \r\nair from a  cow barn into the soil bed of a  nearby greenhouse, \r\nutilizing microbial CH\r\n4 oxidation (Nisbet et al., 2020). Microbial CH4\r\noxidation could also be used for removal of CH\r\n4 leaked from point \r\nsources by building biocatalytic polymers which include methane\u0002oxidizing enzymes (Blanchette et al., 2016). Methane removal is, \r\nhowever, still in its infancy and the available literature is insufficient \r\nfor an assessment."


id: 9c090ea3b9f4e51535123b24bb4fc488
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 22107
content:
"CDR me\r\nAfforestationand forest m\r\nSoil carbon s\r\nBiochar\r\nBioenergy wcapture and (BECCS)\r\nPeatland resBlue carbon \r\nDirect air carand storage \r\nOcean alkaliArtificial ocea\r\nCharacteristicremoval (CDR\r\nEnhanced w\r\nOcean iron f\r\nDecades \r\nto\r\ncenturies\r\nCenturies\r\nto \r\nmillenia\r\n10 to 100\r\nthousand\r\nyears\r\nPotentially\r\npermanent\r\nTimescale of \r\ncarbon storage\r\nFigure 5.36 | Characterismethods) sequestration potfor some methods also consunderlying this classification biogeochemical, biophysical, \r\nwhether the feedbacks streincreases or decreases the mquantity, food production anavailable in the chapter data "


id: b8347801f151eecd7f8e5c4611a17302
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26513
content:
"in response to initiation or termination of SRM. Most literature on \r\nthe biogeochemical responses to SRM focuses on stratospheric \r\naerosol injection (SAI), and only a  few studies have investigated \r\nthe biogeochemical responses to marine cloud brightening (MCB) \r\nand cirrus cloud thinning (CCT). At the time of AR5, there were only \r\na few studies on the biogeochemical responses to SRM. The main \r\nassessment of AR5 (Ciais et al., 2013) was that SRM will not interfere \r\nwith the direct biogeochemical effects of increased CO2, such as \r\nocean acidification and CO2 fertilization, but could affect the carbon "


id: 7442502c19b1d4f958293d4b299ed6aa
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15884
content:
"This section assesses the possible consequences of solar radiation \r\nmodification (SRM) on the biosphere and global biogeochemical \r\ncycles. The SRM options and the physical climate response to SRM is \r\nassessed in detail in Section 4.6.3 and Table 4.7. Section 6.3.6 assesses \r\nthe potential effective radiative forcing of aerosol-based SRM \r\noptions and Section 8.6.3 assesses the abrupt water cycle changes "


id: f5755cf95561ac4f77c8e4fdd2a3fa3c
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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vector_id: 36141
content:
"Figure 5.37 | Cumulative carbon dioxide (CO2) uptake by land and ocean carbon sinks in response to stratospheric sulphur dioxide (SO2) injection. Results \r\nare shown for a scenario with 50-year (2020-2069) continuous stratospheric SO2 injection at a rate of 5 Tg yr-1 appplied to a RCP4.5 baseline scenario (GeoMIP experiment G4; \r\nKravitz et al., 2011), followed by termination in year 2070. Anomalies are shown relative to RCP4.5 for the multi-model ensemble mean and for each of six Earth system models \r\n(ESMs) over the 50-year period of stratospheric SO2 injection (left-hand side), and over 20 years after termination of SO2 injection (right-hand side). Adapted from Plazzotta et al. \r\n(2019). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6)."


id: bcdc64689c7306a36adb50accf321c0c
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 27281
content:
"cycle through climate-carbon feedbacks. Overall, AR5 concluded that \r\nthe level of confidence on the effects of SRM on carbon and other \r\nbiogeochemical cycles is very low (Ciais et al., 2013). Since AR5, more \r\nmodelling work has been conducted to examine various aspects of \r\nthe global biogeochemical cycle responses to SRM. "


id: af47fb6fa5dbaad4a984dd68078fb593
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25090
content:
"would increase the global land carbon sink relative to a  high-CO2\r\nworld without SRM (high confidence)."


id: 12c4a702adf71467e02b781ba38dfdf1
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 450
content:
"Based on available evidence, SRM with elevated CO2 would increase \r\nglobal mean NPP and carbon storage on land relative to an unperturbed \r\nclimate, mainly because of CO2 fertilization of photosynthesis (high \r\nconfidence) (Glienke et al., 2015; Tjiputra et al., 2016; Dagon and \r\nSchrag, 2019; Duan et al., 2020; C.-E. Yang et al., 2020). However, the \r\namount of increase is uncertain as it depends on the extent to which \r\nCO2 fertilization of land plants is limited by nutrient availability."


id: b6b83bd57ef6ae647becd258b40ada78
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26270
content:
"Relative to a  high-greenhouse gas (GHG) world without solar \r\nradiation modification (SRM), SRM would affect the carbon cycles \r\nthrough changes in sunlight, climate (e.g., temperature, precipitation, \r\nsoil moisture, ocean circulation), and atmospheric chemistry \r\n(e.g.,  ozone; Section  4.6.3.3; Cao, 2018). Net SRM effects on the \r\ncarbon cycle, relative to a world without SRM, depend on the change \r\nof individual factors, and interactions among them."


id: 655c94f27dd1d41d2d586ac0b86c21e5
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 13523
content:
"Relative to a  high-CO2 world without SRM, SRM would also  have \r\ncompensating effects on crop yields. SRM is expected to \r\nhave  a  positive impact on crop yields by diminishing heat stress \r\n(Pongratz et al., 2012). However, reductions in light availability will \r\nproduce a  counteracting reduction in crop yields, especially if the \r\ncrop type does not benefit appreciably from diffuse-light fertilization \r\n(Proctor et al., 2018). The balance between these effects varies \r\nmarkedly across crop types and regions, from projected increases \r\nin maize production in China (Xia et al., 2014) to reductions in \r\ngroundnut yields in parts of India (Yang et al., 2016). Because of \r\nthese diverging results from a limited set of studies, there is overall \r\nlow confidence in the effect of SRM on crop yields."


id: 33ef4fa9225ab90553d9bd9815120b1b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 5589
content:
"Consistent with the AR5 assessment, there is high confidence that \r\nSRM would not mitigate CO2-induced ocean acidification (Ciais \r\net al., 2013). Some studies even suggest an acceleration of deep\u0002ocean acidification as a result of ocean circulation change (Tjiputra \r\net al., 2016; Lauvset et al., 2017). There are large differences in the \r\nsimulated spatial pattern of ocean NPP change in response to SRM, \r\nwhich depends strongly on the SRM method that is considered \r\n(Partanen et al., 2016; Lauvset et al., 2017)."


id: 5949a5f489cd69b3b078062bc7beddbb
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 11536
content:
"to reduce the rate of release of CO2 and CH4 from permafrost thaw \r\n(Lee et al., 2019; Chen et al., 2020)."


id: b6ccb03cc8cf2c81022358766b789c28
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26283
content:
"A hypothetical sudden and sustained termination of SRM would \r\ncause a rapid increase in global warming that would pose great risks \r\nto biodiversity (A.  Jones et al., 2013; McCusker et al., 2014; Trisos \r\net al., 2018) (Section  4.6.3.3). It would also weaken carbon sinks, \r\naccelerating atmospheric CO2 accumulation and inducing further \r\nwarming (Figure 5.37; Matthews and Caldeira, 2007; Tjiputra et al., \r\n2016; Muri et al., 2018; Plazzotta et al., 2019). However, a scenario with \r\ngradual phase-out of SRM under emissions reduction could reduce \r\nthe large negative effect of sudden SRM termination (MacMartin \r\net al., 2014; Keith and MacMartin, 2015; Tilmes et al., 2016), though \r\nthis would be limited by how rapidly emissions reductions can be \r\nscaled-up (Ekholm and Korhonen, 2016)."


id: fbabb7cd7ab6fc5b0b4db27127c9c187
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 37100
content:
"SAI is found to reduce global average surface ozone concentration \r\n(Xia et al., 2017) mainly as a result of aerosol-induced reduction in \r\nstratospheric ozone at polar regions, resulting in reduced transport \r\nof ozone from the stratosphere (Pitari et al., 2014; Tilmes et al., \r\n2018). The reduction in surface ozone, together with alteration to \r\nultraviolet (UV) radiation, would have important implications for \r\nvegetation response (Xia et al., 2017). A  modelling study shows \r\nthat sea salt aerosol injection for MCB would reduce hydroxyl (OH) \r\nconcentration and increase CH4 lifetime, and hence, have potential \r\nimplications for surface ozone pollution (Horowitz et al., 2020). It \r\nhas also been reported that the use of SAI to limit global mean \r\nwarming from 2degC to 1.5degC would reduce fire weather in many \r\nareas (Burton et al., 2018)."


id: c24fd0a728bc373419edcf1ee2e8c4dc
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 28154
content:
"SRM would alter the global carbon cycle through SRM-induced climate \r\neffect, such as changes in sunlight, temperature, precipitation, and \r\nocean circulation. Compared to a high-CO2 world without SRM, SRM \r\nwould enhance the net uptake of CO2 by the terrestrial biosphere \r\nand ocean, thus acting to reduce atmospheric CO2 (high confidence). \r\nSRM would also affect surface ozone, UV radiation, and atmospheric \r\nchemistry. Due to complex interplays between SRM-induced changes \r\nin direct and diffuse sunlight, temperature, the coupling of water\u0002carbon-nitrogen cycles, and atmospheric chemistry, there is large \r\nuncertainty in the overall response of the terrestrial biosphere \r\nresponse to SRM. Thus, the level of confidence on the effect of SRM \r\non carbon and other biogeochemical cycles is low."


id: 4f365d82beddec369601bc3dd7c4b247
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9901
content:
"For decades, about half of the carbon dioxide (CO2) that human activities have emitted to the atmosphere \r\nhas been taken up by natural carbon sinks in vegetation, soils and oceans. These natural sinks of CO2 have \r\nthus roughly halved the rate at which atmospheric CO2 concentrations have increased, and therefore slowed \r\ndown global warming. However, observations show that the processes underlying this uptake are beginning to \r\nrespond to increasing CO2 in the atmosphere and climate change in a way that will weaken nature's capacity \r\nto take up CO2 in the future. Understanding of the magnitude of this change is essential for projecting how the \r\nclimate system will respond to future emissions and emissions reduction efforts."


id: 3346699fce19a6e8970b3b5d1eff965a
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 5484
content:
"Direct observations of CO2 concentrations in \r\nthe atmosphere, which began in 1958, show that the \r\natmosphere has only retained roughly half of the CO2 \r\nemitted by human activities, due to the combustion of \r\nfossil fuels and land-use change such as deforestation \r\n(FAQ 5.1, Figure 1). Natural carbon cycle processes on \r\nland and in the oceans have taken up the remainder \r\nof these emissions. These land and ocean removals or \r\n'sinks' have grown largely in proportion to the increase \r\nin CO2 emissions, taking up 31% (land) and 23% \r\n(ocean) of the emissions in 2010-2019, respectively \r\n(FAQ 5.1, Figure 1). Therefore, the average proportion \r\nof yearly CO2 emissions staying in the atmosphere \r\nhas remained roughly stable at 44% over the last six \r\ndecades, despite continuously increasing CO2 emissions \r\nfrom human activities."


id: ef732044db28b4c31e8fa7c8f6f51fc6
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 35157
content:
"No, natural carbon sinks have taken up a near constant \r\nfraction of our carbon dioxide (CO2) emissions over the \r\nlast six decades. However, this fraction is expected to \r\ndecline in the future if CO2 emissions continue to increase."


id: f899834f0e63479e7a52edf4d2cf1ebb
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 36625
content:
"FAQ 5.1, Figure  1 | Atmospheric carbon dioxide (CO2) and natural \r\ncarbon sinks. (Top) Global emissions of CO2 from human activities and the \r\ngrowth rate of CO2 in the atmosphere; (middle) the net land and ocean CO2\r\nremoval (natural sinks); and (bottom) the fraction of CO2 emitted by human \r\nactivities remaining in atmosphere from 1960 to 2019. Lines are the five \r\nyears running mean, error bars denote the uncertainty of the mean estimate. \r\nSee Table 5.SM.6 for more information on the data underlying this figure."


id: d69261f30e459ad43aa33654f3b28dd8
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31267
content:
"several factors control how much CO2 is captured: the difference in CO2 partial pressure between the atmosphere \r\nand the surface ocean; wind speeds at the ocean surface; the chemical composition of seawater (that is, its \r\nbuffering capacity), which affects how much CO2 can be taken up; and the use of CO2 in photosynthesis by \r\nseawater microalgae. The CO2-enriched surface ocean water is transported to the deep ocean in specific zones \r\naround the globe (such as the Northern Atlantic and the Southern Ocean), effectively storing the CO2 away from \r\nthe atmosphere for many decades to centuries. The combined effect of warmer surface ocean temperatures on \r\nthese processes is to weaken the future ocean CO2 sink. "


id: c9abb19e657be5e698d6e8df67d719b6
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 29285
content:
"The ocean carbon sink is better quantified than the land sink, thanks to direct ocean and atmospheric carbon \r\nobservations. The land carbon sink is more challenging to monitor globally, because it varies widely, even \r\nregionally. There is currently no direct evidence that the natural sinks are slowing down, because observable \r\nchanges in the fraction of human emissions stored on land or in oceans are small compared to year-to-year \r\nand decadal variations of these sinks. Nevertheless, it is becoming more obvious that atmospheric and climate \r\nchanges are affecting the processes controlling the land and ocean sinks."


id: f8bd34da74656441fb0f2e2ca701f077
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 36647
content:
"Since the land and ocean sinks respond to the rise in atmospheric CO2 and to human-induced global warming, \r\nthe absolute amount of CO2 taken up by land and ocean will be affected by future CO2 emissions. This also implies \r\nthat, if countries manage to strongly reduce global CO2 emissions, or even remove CO2 from the atmosphere, \r\nthese sinks will take up less CO2 because of the reduced human perturbation of the carbon cycle. Under future \r\nhigh-warming scenarios, it is expected that the global ocean and land sinks will stop growing in the second \r\nhalf of the century as climate change increasingly affects them. Thus, the total amount of CO2 emitted to the \r\natmosphere and the responses of the natural CO2 sinks will both determine what efforts are required to limit \r\nglobal warming to a certain level (see FAQ 5.4), underscoring how important it is to understand the evolution of \r\nthese natural CO2 sinks."


id: 52795ba7a92dd0363cd103ceed2666e2
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 10435
content:
"In the Arctic, large amounts of organic carbon are stored in permafrost - ground that remains frozen throughout \r\nthe year. If significant areas of permafrost thaw as the climate warms, some of that carbon may be released \r\ninto the atmosphere in the form of carbon dioxide or methane, resulting in additional warming. Projections \r\nfrom models of permafrost ecosystems suggest that future permafrost thaw will lead to some additional \r\nwarming - enough to be important, but not enough to lead to a 'runaway warming' situation, where permafrost \r\nthaw leads to a dramatic, self-reinforcing acceleration of global warming."


id: bc12f18b94f12e2e5ffa58549f1de5ca
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 27154
content:
"The Arctic is the biggest climate-sensitive carbon pool on Earth, storing twice as much carbon in its frozen soils, \r\nor permafrost, than is currently stored in the atmosphere. As the Arctic region warms faster than anywhere else \r\non Earth, there are concerns that this warming could release greenhouse gases to the atmosphere and therefore \r\nsignificantly amplify climate change."


id: db1780a1d34405e67d5ba09a1360455f
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31982
content:
"The carbon in the permafrost has built up over thousands of years, as dead plants have been buried and \r\naccumulated within layers of frozen soil, where the cold prevents the organic material from decomposing. As \r\nthe Arctic warms and soils thaw, the organic matter in these soils begins to decompose rapidly and return to the \r\natmosphere as either carbon dioxide or methane, which are both important greenhouse gases. Permafrost can \r\nalso thaw abruptly in a given place, due to melting ice in the ground reshaping Arctic landscapes, lakes growing \r\nand draining, and fires burning away insulating surface soil layers. Thawing of permafrost carbon has already \r\nbeen observed in the Arctic, and climate models project that much of the shallow permafrost (<3 m depth) \r\nthroughout the Arctic would thaw under moderate to high amounts of global warming (2degC-4degC)."


id: 40bab3cd7a5f8274ad5c7e145ea4f4a9
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 7563
content:
"Overall, thawing permafrost in the Arctic appears to be an important additional source of heat-trapping gases \r\nto the atmosphere, more so than undersea hydrates. Climate and carbon cycle models are beginning to consider \r\npermafrost processes. While these models disagree on the exact amount of the heat-trapping gases that will be \r\nreleased into the atmosphere, they agree that: (i) the amount of such gases released from permafrost will increase \r\nwith the amount of global warming; and (ii) the warming effect of thawing permafrost is significant enough to \r\nbe considered in estimates of the remaining carbon budgets for limiting future warming."


id: 646c6b0c6e90297de356fa13360d0d72
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 13367
content:
"Deliberate removal of carbon dioxide (CO2) from the \r\natmosphere could reverse (i.e.,  change the direction \r\nof) some aspects of climate change. However, this \r\nwill only happen if it results in a net reduction in the \r\ntotal amount of CO2 in the atmosphere, that is, if \r\ndeliberate removals are larger than emissions. Some \r\nclimate change trends, such as the increase in global \r\nsurface temperature, would start to reverse within \r\na  few years. Other aspects of climate change would \r\ntake decades (e.g.,  permafrost thawing) or centuries \r\n(e.g., acidification of the deep ocean) to reverse, and \r\nsome, such as sea level rise, would take centuries to \r\nmillennia to change direction."


id: fc09e2baafed94dec38770801107535
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 37148
content:
"Removing more carbon dioxide (CO2) from the atmosphere \r\nthan is emitted into it could reverse some aspects of climate \r\nchange, but some changes would continue in their current \r\ndirection for decades to millennia."


id: f159889a55db84760635554c5f3c1d0e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 35475
content:
"In the absence of deliberate CO2 removal, the CO2\r\nconcentration in the atmosphere (a measure of \r\nthe amount of CO2 in the atmosphere) results from \r\na  balance between human-caused CO2 release and \r\nthe removal of CO2 by natural processes on land and \r\nin the ocean (natural 'carbon sinks'; see FAQ 5.1). \r\nIf  CO2 release exceeds removal by carbon sinks, the \r\nCO2 concentration in the atmosphere would increase; "


id: d41f248791e65447bdda544094a6d6b8
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 30892
content:
"FAQ 5.3, Figure 1 | Changes in aspects of climate change in response \r\nto a  peak and decline in the atmospheric CO2 concentration (top \r\npanel). The vertical grey dashed line indicates the time of peak CO2\r\nconcentration in all panels. It shows that the reversal of global surface warming \r\nlags the decrease in the atmospheric CO2 concentration by a few years, the \r\nreversal of permafrost area decline lags the decrease in atmospheric CO2 by \r\ndecades, and ocean thermal expansion continues for several centuries. The \r\nquantitative information in the figure (i.e., numbers on vertical axes) is not to \r\nbe emphasized as it results from simulations with just one model and will be \r\ndifferent for other models. The qualitative behaviour, however, can be expected \r\nto be largely model independent."


id: ee05743016b149fd1b5477ca9a133ff1
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 34929
content:
"if CO2 release equals removal, the atmospheric CO2 concentration would stabilize; and if CO2 removal exceeds \r\nrelease, the CO2 concentration would decline. This applies in the same way to net CO2 emissions - that is, the sum \r\nof human-caused releases and deliberate removals."


id: ac0d1a3745f7a0dcd529298280085c62
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
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index: climate_gpt_only_giec
vector_id: 24591
content:
"'Overshoot' scenarios are a  class of future scenarios that are receiving increasing attention, particularly in \r\nthe context of ambitious climate goals, such as the global warming limits of 1.5degC or 2degC included in the Paris \r\nAgreement. In these scenarios, a slow rate of reduction in emissions in the near term is compensated by net \r\nnegative CO2 emissions in the later part of this century, which results in a temporary breach or 'overshoot' of \r\na given warming level. Due to the delayed reaction of several climate system components, it follows that the \r\ntemporary overshoot would result in additional climate changes compared to a scenario that reaches the goal \r\nwithout overshoot. These changes would take decades to many centuries to reverse, with the reversal taking \r\nlonger for scenarios with larger overshoot."


id: 952a1c8a55e1fe17e53bc07727db0a2b
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20972
content:
"Removing more CO2 from the atmosphere than is emitted into it would indeed begin to reverse some aspects \r\nof climate change, but some changes would still continue in their current direction for decades to millennia. \r\nApproaches capable of large-scale removal of CO2 are still in the state of research and development or unproven \r\nat the scales of deployment necessary to achieve a  net reduction in atmospheric CO2 levels. CO2 removal \r\napproaches, particularly those deployed on land, can have undesired side effects on water, food production \r\nand biodiversity."


id: 49cb41f3ca12ec1dcc7952ddbc258faf
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9007
content:
"There are several types of carbon budgets. Most often, the term refers to the total net amount of carbon dioxide \r\n(CO2) that can still be emitted by human activities while limiting global warming to a specified level (e.g., 1.5degC \r\nor 2degC above pre-industrial levels). This is referred to as the 'remaining carbon budget'. Several choices and value \r\njudgements have to be made before it can be unambiguously estimated. When the remaining carbon budget is \r\ncombined with all past CO2 emissions to date, a 'total carbon budget' compatible with a specific global warming \r\nlimit can also be defined. A third type of carbon budget is the 'historical carbon budget', which is a scientific way \r\nto describe all past and present sources and sinks of CO2."


id: 744d513a03656d41139b8cfbba4cfbfa
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15894
content:
"The term remaining carbon budget is used to describe the total net amount of CO2 that human activities can \r\nstill release into the atmosphere while keeping global warming to a specified level, like 1.5degC or 2degC relative \r\nto pre-industrial temperatures. Emissions of CO2 from human activities are the main cause of global warming. \r\nA remaining carbon budget can be defined because of the specific way CO2 behaves in the Earth system. That \r\nis, global warming is roughly linearly proportional to the total net amount of CO2 emissions that are released \r\ninto the atmosphere by human activities - also referred to as cumulative anthropogenic CO2 emissions. Other \r\ngreenhouse gases behave differently and have to be accounted for separately."


id: f94091c8d99e926e7742f4518d6b5f6
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 36722
content:
"In summary, determining a remaining carbon budget - that is, how much CO2 can be released into the atmosphere \r\nwhile stabilizing global temperature below a chosen level - is well understood but relies on a set of choices. \r\nHowever, it is clear that, for limiting warming below 1.5degC or 2degC, the remaining carbon budget from 2020 \r\nonwards is much smaller than the total CO2 emissions released to date."


id: a612e7de037abdf342aae83794075204
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 23675
content:
"The term carbon budget is used in several ways. Most often the term refers to the total net amount of carbon dioxide \r\n(CO2) that can still be emitted by human activities while limiting global warming to a specified level. "


id: 7368b2ec58ba208b25660e9f9de498e3
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15783
content:
"FAQ 5.4, Figure 1 | Various types of carbon budgets. Historical cumulative carbon dioxide (CO2) emissions determine to a large degree how much the \r\nworld has warmed to date, while the remaining carbon budget indicates how much CO2 could still be emitted while keeping warming below specific temperature \r\nthresholds. Several factors limit the precision with which the remaining carbon budget can be estimated. Therefore, estimates need to specify the probability with \r\nwhich they aim at limiting warming to the intended target level (e.g., limiting warming to 1.5degC with a 67% probability)."


id: 70aa32f1027f946ce141c622f0a20daa
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 15345
content:
"Bhupesh Adhikary (Nepal), Paulo Artaxo (Brazil), Terje Berntsen (Norway), William D. Collins \r\n(United States of America), Sandro Fuzzi (Italy), Laura Gallardo (Chile), Astrid Kiendler-Scharr \r\n(Germany/Austria), Zbigniew Klimont (Austria/Poland), Hong Liao (China), Nadine Unger \r\n(United Kingdom/United States of America), Prodromos Zanis (Greece)"


id: c385d809f91b410e1e143a8a2e95d60f
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 28333
content:
"Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, \r\nH. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. \r\nContribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change \r\n[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, \r\nK. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge \r\nUniversity Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817-922, doi:10.1017/9781009157896.008."


id: 90c032e5ef93a114cf9c58a46392f0fe
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20314
content:
"OH cannot be measured directly and is inferred from Earth system and \r\nchemistry-climate models (ESMs, CCMs) constrained by emissions \r\nand from observationally constrained inversion methods. There is \r\nconflicting information from these methods for the 1980-2014 \r\nperiod. ESMs and CCMs concur on a positive trend since 1980 (about \r\na  9% increase over 1980-2014) and there is medium confidence\r\nthat this trend is mainly driven by increases in global anthropogenic \r\n(human-caused) nitrogen oxide (NOx) emissions and decreases in \r\nanthropogenic CO emissions. The observation-constrained methods \r\nsuggest either positive trends or the absence of trends based on \r\nlimited evidence and medium agreement. Future changes in global \r\nOH, in response to SLCF emissions and climate change, will depend \r\non the interplay between multiple offsetting drivers of OH. {6.3.6 and \r\nCross-Chapter Box 5.1}"


id: d4f473b7afa058c0f004b757223d56fa
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31035
content:
"Over the historical period, changes in aerosols and their \r\neffective radiative forcing (ERF) have primarily contributed to \r\na surface cooling, partly masking the greenhouse gas-driven \r\nwarming (high confidence). Radiative forcings induced by aerosol \r\nchanges lead to both local and remote temperature responses (high \r\nconfidence). The temperature response preserves the south to north \r\ngradient of the aerosol ERF - hemispherical asymmetry - but is more \r\nuniform with latitude and is strongly amplified towards the Arctic \r\n(medium confidence). {6.4.1, 6.4.3}"


id: 62ea9d9a2958a0c949c796311678b045
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 13138
content:
"Since the mid-1970s, trends in aerosols and their precursor \r\nemissions have led to a shift from an increase to a decrease of \r\nthe magnitude of the negative globally averaged net aerosol \r\nERF (high confidence). However, the timing of this shift varies by \r\ncontinental-scale region and has not occured for some finer regional \r\nscales. The spatial and temporal distribution of the net aerosol ERF \r\nfrom 1850 to 2014 is highly heterogeneous, with stronger magnitudes \r\nin the Northern Hemisphere (high confidence). {6.4.1}"


id: 5fe8ed115e2348459d019a97384ce9d7
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 12671
content:
"For forcers with short lifetimes (e.g.,  months) and not \r\nconsidering chemical adjustments, the response in surface \r\ntemperature occurs strongly as soon as a  sustained change \r\nin emissions is implemented, and that response continues to \r\ngrow for a few years, primarily due to thermal inertia in the \r\nclimate system (high confidence). Near its maximum, the response \r\nslows down but will then take centuries to reach equilibrium (high \r\nconfidence). For SLCFs with longer lifetimes (e.g., a decade), a delay \r\nequivalent to their lifetimes is appended to the delay due to thermal \r\ninertia (high confidence). {6.6.1}"


id: 4f4cc3c91834b3d66b0727b9b6fa5536
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 9914
content:
"Over the 1750-2019 period, changes in SLCF emissions, \r\nespecially of methane, NOx and SO2, have substantial effects \r\non effective radiative forcing (ERF) (high confidence). The \r\nnet global emissions-based ERF of NOx is negative and that of \r\nnon-methane volatile organic compounds (NMVOCs) is positive, in \r\nagreement with the AR5 Assessment (high confidence). For methane, \r\nthe emissions-based ERF is twice as high as the abundance-based ERF \r\n(high confidence) attributed to chemical adjustment mainly via ozone \r\nproduction. SO2 emissions changes make the dominant contribution "


id: e498a44294c45dedc3d028086c01d33c
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 33442
content:
"There is no significant trend in the global mean tropospheric \r\nconcentration of hydroxyl (OH) radical - the main sink for many \r\nSLCFs, including methane (CH4) - from 1850 up to around 1980 \r\n(low confidence) but OH has remained stable or exhibited \r\na positive trend since the 1980s (medium confidence). Global "


id: d9cd903edad6f9a9b551718551296ff0
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 31788
content:
"to the ERF from aerosol-cloud interactions (high confidence).\r\nOver the 1750-2019 period, the contributions from the emitted \r\ncompounds to changes in global surface air temperature (GSAT) \r\nbroadly match their contributions to the ERF (high confidence). Since \r\na peak in emissions-induced SO2 ERF has already occurred recently \r\nand since there is a delay in the full GSAT response, changes in SO2\r\nemissions have a slightly larger contribution to GSAT change than \r\nCO2 emissions, relative to their respective contributions to ERF. {6.4.2, \r\n6.6.1 and 7.3.5} "


id: b5934a6bb3bb4001e1c2938d48884021
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 26099
content:
"organic compounds, wildfire-emitted precursors, land and marine \r\naerosols, and lightning NOx) to climate change. {6.3, 6.5}"


id: f3a92ee326376b18b6b145bd61357eae
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 35849
content:
"The SSPs span a  wider range of SLCF emissions than the \r\nRepresentative Concentration Pathways (RCPs), representing \r\nbetter the diversity of future options in air pollution \r\nmanagement (high confidence). In the SSPs, the socio-economic \r\nassumptions and climate change mitigation levels primarily drive \r\nfuture emissions, but the SLCF emissions trajectories are also \r\nsteered by varying levels of air pollution control originating from \r\nthe SSP narratives, independently from climate change mitigation.\r\nConsequently, SSPs consider a  large variety of regional ambitions \r\nand effectiveness in implementing air pollution legislation and result \r\nin wider range of future air pollution levels and SLCF-induced climate \r\neffects. {6.7.1}"


id: 421b5ca35ed704c8ec3114c1cba3c617
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 7801
content:
"Reactive nitrogen, ozone and aerosols affect terrestrial \r\nvegetation and the carbon cycle through deposition and \r\neffects on large-scale radiation (high confidence). However, \r\nthe magnitude of these effects on the land carbon sink, ecosystem \r\nproductivity and hence their indirect CO2 forcing remain uncertain \r\ndue to the difficulty in disentangling the complex interactions \r\nbetween the individual effects. As such, these effects are assessed \r\nto be of second order in comparison to the direct CO2 forcing (high \r\nconfidence), but effects of ozone on terrestrial vegetation could \r\nadd a substantial (positive) forcing compared with the direct ozone \r\nforcing (low confidence). {6.4.4}"


id: e264e99e41fba16f12cdb4a482907057
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 33123
content:
"Air pollution projections range from strong reductions in global \r\nsurface ozone and PM (e.g., SSP1-2.6, with strong mitigation \r\nof both air pollution and climate change) to no improvement \r\nand even degradation (e.g., SSP3-7.0 without climate change \r\nmitigation and with only weak air pollution control) (high \r\nconfidence). Under the SSP3-7.0 scenario, PM levels are projected \r\nto increase until 2050 over large parts of Asia, and surface ozone \r\npollution is projected to worsen over all continental areas through \r\n2100 (high confidence). Without climate change mitigation but with \r\nstringent air pollution control (SSP5-8.5), PM levels decline through \r\n2100, but high methane levels hamper the decline in global surface \r\nozone at least until 2080 (high confidence). {6.7.1}"


id: 1365d3e2f3fdd5622487a2a5a85f7ecd
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 563
content:
"Climate feedbacks induced from changes in emissions, \r\nabundances or lifetimes of SLCFs mediated by natural \r\nprocesses or atmospheric chemistry are assessed to have an \r\noverall cooling effect (low confidence), that is, a total negative \r\nfeedback parameter of -0.20 [-0.41 to +0.01] W m-2 degC-1. These \r\nnon-CO2 biogeochemical feedbacks are estimated from ESMs, which \r\nhave advanced since AR5 to include a consistent representation of \r\nbiogeochemical cycles and atmospheric chemistry. However, process\u0002level understanding of many chemical and biogeochemical feedbacks \r\ninvolving SLCFs, particularly natural emissions, is still emerging, \r\nresulting in low confidence in the magnitude and sign of most SLCF \r\nclimate feedback parameters. {6.2.2, 6.4.5} "


id: a03d40e45737e94c82e9a9977729bc6e
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 22745
content:
"At present, emissions from the residential and commercial \r\nsectors (fossil and biofuel use for cooking and heating) and \r\nthe energy sector (fossil fuel production, distribution and \r\ncombustion) contribute the most to the world population's \r\nexposure to anthropogenic fine PM (high confidence), \r\nwhereas emissions from the energy and land transportation \r\nsectors contribute the most to ozone exposure (medium to\r\nhigh confidence). The contribution of different sectors to PM varies \r\nacross regions, with the residential sector being the most important \r\nin Southern Asia and Africa, agricultural emissions dominating in \r\nEurope and North America, and industry and energy production \r\ndominating in Central and Eastern Asia, Latin America and the \r\nMiddle East. Energy and industry are important PM2.5 contributors in \r\nmost regions, except Africa (high confidence). Sector contributions to \r\nsurface ozone concentrations are similar for all regions. {6.6.2}"


id: 26782f984d44fe5a7c4e9197eb043cc2
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 3462
content:
"and span a  range from very high to very low emissions. In \r\nthe scenarios without climate change mitigation (SSP3-7.0 and \r\nSSP5-8.5) the likely range of the estimated warming due to SLCFs \r\nin 2100 relative to 2019 is 0.4degC-0.9degC {6.7.3, 6.7.4}. 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 warming in the first half \r\nof the century. For the scenarios considering the most stringent \r\nclimate and air pollution mitigations (SSP1-1.9 and SSP1-2.6), the \r\nreduced warming from reductions in methane, ozone and HFCs partly \r\nbalances the warming from reduced aerosols, and the overall SLCF \r\neffect is a likely increase in GSAT of 0.0degC-0.3degC in 2100, relative \r\nto 2019. The SSP2-4.5 scenario (with moderate climate change and \r\nair pollution mitigations) results in a likely warming of 0.2degC-0.5degC \r\nin 2100 due to SLCFs, with the largest warming from reductions in \r\naerosols. {6.7.3}"


id: 8993b7f79c9f9d7de27f05f75f4474fa
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 19181
content:
"Assuming implementation and efficient enforcement of both \r\nthe Kigali Amendment to the Montreal Protocol on Ozone \r\nDepleting Substances and current national plans to limit \r\nemissions (as in SSP1-2.6), the effects of HFCs on GSAT, relative \r\nto 2019, would remain below +0.02degC from 2050 onwards \r\nversus about +0.04degC to +0.08degC in 2050 and +0.1degC to +0.3degC \r\nin 2100 considering only national HFC regulations decided \r\nprior to the Kigali Amendment (as in SSP5-8.5) (medium \r\nconfidence). Further improvements in the efficiency of refrigeration \r\nand air-conditioning equipment during the transition to low-global\u0002warming-potential refrigerants would bring additional greenhouse \r\ngas reductions (medium confidence) resulting in benefits for climate \r\nchange mitigation and to a lesser extent for air quality due to reduced \r\nair pollutant emissions from power plants. {6.6.3, 6.7.3} "


id: 5609b443dc71e0f43a9de30d7c920603
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 11027
content:
"ammonium (NH+\r\n4), carbonaceous aerosols (e.g.,  black carbon (BC), \r\norganic aerosols (OA)), mineral dust, and sea spray (see Table 6.1) \r\nand can be present as internal or external mixtures and at sizes from \r\nnano-meters to tens of micro-meters. SLCFs can be emitted directly \r\nfrom natural systems and anthropogenic sources (primary) or can be \r\nformed by reactions in the atmosphere (secondary; Figure 6.1). "


id: 67f5ca869585ab5377b1828d020cc4d
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 13944
content:
"Short-lived climate forcers (SLCFs) are a  set of chemically and \r\nphysically reactive compounds with atmospheric lifetimes typically \r\nshorter than two decades but differing in terms of physiochemical \r\nproperties and environmental effects. SLCFs can be classified as \r\ndirect or indirect, with direct SLCFs exerting climate effects through \r\ntheir radiative forcing and indirect SLCFs being precursors of direct \r\nclimate forcers. Direct SLCFs include methane (CH4), ozone (O3), short\u0002lived halogenated compounds, such as hydrofluorocarbons (HFCs), \r\nhydrochlorofluorocarbons (HCFCs), and aerosols. Indirect SLCFs \r\ninclude nitrogen oxides (NOx), carbon monoxide (CO), non-methane \r\nvolatile organic compounds (NMVOCs), sulphur dioxide (SO2), and \r\nammonia (NH3). Aerosols consist of sulphate (SO2\r\n4\r\n-\r\n), nitrate (NO- ), 3"


id: 9562afa3195ca33edae8151e1669ef1
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 20999
content:
"greenhouse gases (GHGs) in Chapters 2, 5, and 7). In contrast to well\u0002mixed GHGs, such as CO2, methane and some HFCs, the radiative \r\nforcing effects of most SLCFs are largest at regional scales and \r\nclimate effects predominantly occur in the first two decades after "


id: b39393aabf93c2e64c8b100f300d95d7
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 25762
content:
"their emissions or formation. However, changes in their emissions \r\ncan also induce long-term climate effects, for instance by altering \r\nsome biogeochemical cycles. Therefore, the temporal evolution of \r\nradiative effects of SLCFs follows that of emissions, that is, when SLCF "


id: 6dd64a36d6f65d60e1eecbc85f9df6f8
created_at: 2023-03-16 14:00:42
updated_at: 2023-03-16 14:00:42
content_type: text
index: climate_gpt_only_giec
vector_id: 14895
content:
"Table 6.1 | Overview of SLCFs of interest for Chapter 6. For each SLCF, its source types, lifetime in the atmosphere, and associated radiatively active agent is given. Source \r\ntype can be primary (emitted) and/or secondary (formed through multiple atmospheric mechanisms). Unless otherwise noted, the stated lifetime refers to tropospheric lifetime.* \r\nClimate effect of increased SLCFs is indicated as '+' for warming and '-' for cooling. 'Direct' is used for SLCFs exerting climate effects through their radiative forcing and \r\n'Indirect' for SLCFs which are precursors affecting the atmospheric burden of other climatically active compounds. Other processes through which SLCFs affect climate are listed \r\nwhere applicable. The World Health Organization (WHO) guidelines for air quality (AQ) are given, where applicable, to show which SLCFs are regulated for air-quality purposes. "


id: d9bf66f040a831728b2e747dbae5f602
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 31777
content:
"* For lifetimes reported in this table, it is assumed that the compounds are uniformly mixed throughout the troposphere, however, this assumption is unlikely for compounds \r\nwith lifetimes <1 year and, therefore, the reported values should be viewed as approximations (Prather et al., 2001). "


id: 99e6f99906573890f65349f12fc2d9b4
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 21741
content:
" a Clouds: effect on clouds through aerosol-cloud interactions, Ecosystems: effect on ecosystems through changes in radiation and deposition, Cryo: effect on planetary albedo \r\nthrough deposition on snow and ice; b Krzyzanowski and Cohen (2008); c regulated through Kyoto/Montreal protocols; d For Particulate Matter with diameter <2.5 um (PM2.5): \r\n10 ug m-3 annual mean or 25 ug m-3 24-hour mean (99th percentile) and for Particulate Matter with diameter <10 um (PM10): 20 ug m-3 annual mean or 50 ug m-3 24-hour \r\nmean (99th percentile)."


id: 8cb54bbf63272bcc779596b968df64ae
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 19653
content:
"emissions decline to zero their atmospheric abundance and radiative \r\neffects decline towards zero. The total influence of individual SLCF \r\nemissions on radiative forcing and climate includes their effects \r\non the abundances of other forcers through chemistry (chemical \r\nadjustments)."


id: 15566276cb6316955b71dd1be9c8d9c0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 877
content:
"remained of very low to low confidence. The AR5 also reported that \r\nforcing agents such as aerosols and ozone are highly heterogeneous \r\nspatially and temporally, and these patterns affect global and \r\nregional temperature responses as well as other aspects of climate \r\nresponse such as the hydrologic cycle (Myhre et al., 2013)."


id: bcfc1b2b25fd8524b97a69db5299c347
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
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vector_id: 27295
content:
"SLCFs can affect climate by interacting with radiation or by \r\nperturbing other components of the climate system (e.g.,  the \r\ncryosphere and carbon cycle through deposition, or the water cycle \r\nthrough modifications of cloud properties via cloud condensation \r\nnuclei or ice nuclei). SLCFs can have either net warming or net \r\ncooling effects on climate. In addition to altering the Earth's radiative \r\nbalance, many SLCFs are also air pollutants with adverse effects \r\non human health and ecosystems. SLCFs are of interest for climate \r\npolicies (e.g.,  methane, HFCs), and are regulated as air pollutants \r\n(e.g., aerosols, ozone) or because of their deleterious influence on \r\nstratospheric ozone (e.g., HCFCs). The list of SLCFs assessed in this \r\nchapter and their effects are provided in Table 6.1. "


id: 59c691f77302171e765ef3578169ad12
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 11626
content:
"The AR5 WGI also evaluated the air quality-climate interaction \r\nthrough the projected trends of surface ozone and PM2.5. Kirtman \r\net al. (2013) concluded with high confidence that the response of air \r\nquality to climate-driven changes is more uncertain than the response \r\nto emissions-driven changes, and also that locally higher surface \r\ntemperatures in polluted regions will trigger regional feedbacks in \r\nchemistry and local emissions that will increase peak levels of ozone \r\nand PM2.5 (medium confidence)."


id: 794127433842a47f3219b0e47daf263a
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 16643
content:
"Although ozone, aerosols and their precursors have been considered \r\nin previous IPCC assessment reports, AR5 considered SLCFs as \r\na  specific category of climate-relevant compounds but referred \r\nto them as near-term climate forcers (NTCFs; Myhre et al., 2013). \r\nIn AR5, the linkages between air quality and climate change were \r\nalso considered in a  more detailed and quantitative way than in \r\nprevious reports (Kirtman et al., 2013; Myhre et al., 2013)."


id: 54460a147b19551da44b9a9e19833c59
created_at: 2023-03-16 14:00:43
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vector_id: 10720
content:
"The AR5 WGI assessed radiative forcings for short-lived gases, \r\naerosols, aerosol precursors and aerosol-cloud interactions as well \r\nas the evolution of confidence levels in the forcing mechanisms from \r\nSAR to AR5. Whereas the forcing mechanisms for ozone and aerosol-\r\nradiation interactions were estimated to be characterized with high \r\nconfidence, the ones induced by aerosols through other processes "


id: d0fcd1bb02a941cbf6fd34c3c977622d
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 30413
content:
"and other light-absorbing particles enhances snowmelt in the Arctic \r\n(Meredith et al., 2019), but that there is limited evidence and low \r\nagreement that long-term changes in glacier mass of high mountain \r\nareas are linked to light-absorbing particles (Hock et al., 2019)."


id: 631930a9134a7a2abc44b92049376aa2
created_at: 2023-03-16 14:00:43
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content:
"Specific aspects of SLCFs can also be found in other chapters of this \r\nreport: the evolution of ozone, HFCs and aerosols, as well as the long\u0002term evolution of methane, dust and volcanic aerosols are discussed\r\nin Chapter  2; near-term climate projections and SLCFs are \r\ndiscussed in Chapter 4; the global budget of methane is addressed in \r\nChapter 5; aerosol-cloud and aerosol-precipitation interactions are \r\ntreated in Chapters 7 and 8, respectively; the global radiative forcing \r\nof SLCFs is assessed in Chapter  7; some aspects of downscaling \r\nmethodology in climate modelling concerning SLCFs are discussed \r\nin Chapter 10. The WGII report assesses how climate change affects \r\nair pollution and its impacts on human health and the WGIII report \r\nassesses the role of SLCFs in abatement strategies and their cost\u0002effectiveness, the implications of mitigation efforts on air pollution \r\nas well as the articulation between air pollution policies and \r\nGHG mitigation. "


id: 9fdc0f6d6e78f1a3f0b9034458afcc66
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 22689
content:
"Due to their short lifetimes, SLCF trends and effects are strongly \r\nrelated to the localization and evolution of the emissions sources. \r\nTo better link the drivers of emissions evolution and SLCFs, Chapter 6 \r\nmakes use of regions defined by the WGIII in most of the analysis. \r\nAn exception is made for the effect of SLCFs on the climate, for which \r\nanalysis relies on WGI Atlas regions."


id: 37dcb43d097ef2e62e1d2979df8e827f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
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vector_id: 6163
content:
"SLCF emissions originate from a  variety of sources driven by \r\nanthropogenic activities and natural processes. The natural sources \r\ninclude vegetation, soil, fire, lightning, volcanoes and oceans. \r\nChanges in SLCF emissions from natural systems occur either due \r\nto human activities, such as land-use change, or due to global \r\nchanges. Their sensitivity to climate change thus induces climate \r\nfeedbacks (see Section 6.4.5 for a quantification of these feedbacks). \r\nThis section reviews the current understanding of historical emissions \r\nfor anthropogenic, natural, and open biomass burning sources. \r\nA detailed discussion of methane sources, sinks, trends are provided \r\nin Chapter 5, Section 5.2.2. "


id: 35255226b9750c5a93b1299011d96fb5
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 5770
content:
"For SO2, independent emissions inventories and observational \r\nevidence show that on a  global scale strong growth of Asian \r\nemissions has been countered by reduction in North America and \r\nEurope (Reis et al., 2012; Amann et al., 2013; Crippa et al., 2016; \r\nAas et al., 2019). However, Chinese emissions declined by nearly \r\n70% between about 2006 and 2017 (high confidence) (Silver et al., \r\n2018; Zheng et al., 2018b; Mortier et al., 2020; Tong et al., 2020). \r\nThe estimated reduction in China contrasts with continuing strong \r\ngrowth of SO2 emissions in Southern Asia (Figure 6.19). In 2014, over \r\n80% of anthropogenic SO2 emissions originated from power plants \r\nand industry, with Asian sources contributing more than 50% of the \r\ntotal (Figure 6.3)."


id: 9712d771d7620609a3b3a137395a2dac
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"For most of the SLCF species, the global and regional anthropogenic \r\nemissions trends developed for CMIP6 for the period 1850-2000 are \r\nnot substantially different from those used in CMIP5 (Figures 6.18 \r\nand 6.19) despite the different method used to derive them. Hoesly \r\net al. (2018, CEDS) developed independent time series capturing \r\ntrends in fuel use, technology and level of control, whereas CMIP5 "


id: 2ae87b38748d96e1140062449e52cb8b
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 4178
content:
"Bottom-up global emissions estimates of methane (Lamarque \r\net al., 2010; Hoesly et al., 2018; Janssens-Maenhout et al., 2019; \r\nHoglund-Isaksson et al., 2020) for the last two decades are higher \r\nthan top-down assessments (e.g.,  Saunois et al., 2016, 2020) but \r\ntrends from the two methods are similar and indicate continued \r\ngrowth (high confidence). Larger discrepancies exist at the sectoral \r\nand regional levels, notably for coal mining (Peng et al., 2016; \r\nMiller et al., 2019) and the oil and gas sector due to the growth of \r\nunconventional production and higher loss estimates (Section 5.2.2; \r\nFranco et al., 2016; Alvarez et al., 2018; Dalsoren et al., 2018). "


id: 4cf14dfd0072486cdcf64a6d96ae479a
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 9527
content:
"To summarize, there are significant differences in spatial and temporal \r\npatterns of SLCF emissions across global regions (Figure 6.18). Until \r\nthe 1950s, the majority of SLCF emissions were associated with \r\nfossil fuel use (SO2, NOx, NMVOCs, CO) and about half of BC and OC \r\noriginated from North America and Europe (Lamarque et al., 2010; \r\nHoesly et al., 2018). Since the 1990s a large redistribution of emissions \r\nwas associated with strong economic growth in Asia and declining \r\nemissions in North America and Europe due to air-quality legislation "


id: e87ddda00ef2c13e684dd6e3164f8077
created_at: 2023-03-16 14:00:43
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content:
"but important uncertainties remain for regionally specific temporal \r\npatterns (Riddick et al., 2016; Liu et al., 2019; Feng et al., 2020; \r\nVira et al., 2020). The continuing increase in global NH3 emissions \r\nis driven primarily by growing livestock and crop production in Asia \r\nwhile emissions in the USA and Europe remain about constant or \r\nhave slightly declined in the last decade (Hoesly et al., 2018). Recent \r\nsatellite and ground observations support trends estimated in CMIP6 \r\ndataset (Section 6.3.3.4). "


id: e97b69ca9e6e07cdd868d48871b26452
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
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vector_id: 34244
content:
"and the declining capacity of energy-intensive industry; currently more \r\nthan 50% of anthropogenic emissions of each SLCF species (including \r\nmethane and NH3) originates from Asia (Figure  6.3; Amann et al., \r\n2013; Bond et al., 2013; Fiore et al., 2015; Crippa et al., 2016, 2018; \r\nKlimont et al., 2017a; Hoesly et al., 2018). The dominance of Asia for \r\nSLCF emissions is corroborated by growing remote-sensing capacity \r\nthat has been providing an independent evaluation of estimated \r\npollution trends in the last decade (Duncan et al., 2013; Lamsal et al., \r\n2015; Luo et al., 2015; Fioletov et al., 2016; Geddes et al., 2016; Irie \r\net al., 2016; Krotkov et al., 2016; Wen et al., 2018)."


id: 1cdc0992d31036bf596d7146d351056
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
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vector_id: 1991
content:
"ozone and OH, and on the lifetime of methane compared with surface \r\nNOx emissions. Whereas the global spatial and temporal distribution \r\nof lightning flashes can be characterized thanks to satellite-borne \r\nand ground sensors (Virts et al., 2013; Cecil et al., 2014), constraining \r\nthe amount of NOx produced per flash (Miyazaki et al., 2014; Medici \r\net al., 2017; Nault et al., 2017; Marais et al., 2018; D.J. Allen et al., \r\n2019; Bucsela et al., 2019) and its vertical allocation (Koshak et al., \r\n2014; Medici et al., 2017) has been more elusive. "


id: 35bc1e0a79e3f782f4b571c49a98aba3
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 5864
content:
"Atmospheric Chemistry and Climate Model Intercomparison \r\nProject (ACCMIP) models in CMIP5 used a range of LNOx between \r\n1.2-9.7 TgN y-1 (Lamarque et al., 2013b). In CMIP6, the corresponding \r\nLNOx range is between 3.2-7.6 TgN y-1 (Griffiths et al., 2021). \r\nAll CMIP6 models (as well as most models included in CMIP5, Young \r\net al., 2013) apply a parametrization that relates cloud-top height \r\nto lightning intensity (Price and Rind, 1992), projecting an increase \r\nin LNOx in a warmer world in the range of 0.27-0.61 TgN yr-1 degC-1\r\n(Thornhill et al., 2021a). However, models using parametrizations \r\nbased on convection (Grewe et al., 2001), updraft mass flux (Allen \r\nand Pickering, 2002) or ice flux (Finney et al., 2016a) show either \r\nmuch less sensitivity or a  negative response (Finney et al., 2016b, \r\n2018; Clark et al., 2017). "


id: 4ab639779cf90b568028f1c5cd7f1968
created_at: 2023-03-16 14:00:43
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vector_id: 9161
content:
"In summary, the total present-day global lightning NOx emissions are \r\nstill estimated to be within a factor of two. There is high confidence\r\nthat LNOx are perturbed by climate change; however, there is low \r\nconfidence in the sign of the change due to fundamental uncertainties \r\nin parametrizations."


id: 580998be9860349c193f9b0f129daa7f
created_at: 2023-03-16 14:00:43
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vector_id: 11353
content:
"Soil NOx (SNOx) emissions occur in connection with complex \r\nbiogenic/microbial nitrification and denitrification processes (Ciais \r\net al., 2013), which in turn are sensitive - in a non-linear manner - \r\nto temperature, precipitation, soil moisture, carbon and nutrient \r\ncontent, and the biome itself (e.g., Hudman et al., 2012). Global SNOx\r\nestimates, based on observationally constrained chemistry-transport \r\nmodel and vegetation model studies, show a broad range between \r\n4.7-16.8 TgN yr-1 (Young et al., 2018). This estimate is generally \r\nlarger than the current source strength used in CMIP6 simulations, \r\nwhich is prescribed using an early empirical estimate, typically scaled \r\nto about 5 TgN yr-1 (Yienger and Levy, 1995). "


id: c2d469afa6674b00c3a6a4beaa80a017
created_at: 2023-03-16 14:00:43
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vector_id: 28232
content:
"By the end of the 21st century, the overall nitrogen fixation in non\u0002agricultural ecosystems could be 40% larger than in 2000, due to \r\nincreased enzyme activity with growing temperatures, but the \r\nemission rates of NO (and N2O) could be dominated by changes in \r\nprecipitation patterns and evapotranspiration fluxes (Fowler et al., \r\n2015). Current Earth system models (ESMs) incorporate biophysical \r\nand biogeochemical processes only to a  limited extent (Jia et al., \r\n2019), precluding adequate climate sensitivity studies for SNOx. \r\nHence, while the current strength source of soil NOx has been \r\nbetter constrained over the last decade, adequate representations \r\nof SNOx and how it escapes from the canopy, which could provide \r\nquantitative estimates of climate-driven changes in SNOx, are still \r\nmissing in ESMs."


id: 44b21d4778020fe23c4772ba4f8e1433
created_at: 2023-03-16 14:00:43
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vector_id: 8230
content:
"Lightning contributes approximately 10% of the total NOx emissions \r\n(Murray, 2016). Since lightning NOx (LNOx) is predominantly released \r\nin the upper troposphere, it has a disproportionately large impact on "


id: 6c2d714ec700e0d184d12220df9b6d30
created_at: 2023-03-16 14:00:43
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vector_id: 14649
content:
"Most CMIP6 models use overly simplistic parametrizations and \r\nproject an increase in global BVOC emissions in response to warming \r\ntemperatures (Turnock et al., 2020). This good agreement actually \r\nreflects the lack of diversity in BVOC-emissions parametrizations in \r\nglobal models that do not fully account for the complex processes \r\ninfluencing emissions that are discussed above."


id: 9b62dc84adc5ffb498c0552b42fdf3d4
created_at: 2023-03-16 14:00:43
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content:
"Overall, we assess that historical global isoprene emissions declined \r\nbetween the pre-industrial period and the present day by 10-25% \r\n(low confidence) but historical changes in global monoterpenes and \r\nsesquiterpenes are too uncertain to provide an assessment. Future \r\nchanges in BVOCs depend strongly on the evolution of climate and \r\nland use and are strongly sensitive to land-based climate change \r\nmitigation strategies. However, the net response of BVOC emissions \r\nis uncertain due to the complexity of processes that are hard to \r\nconstrain observationally and are considered with various degrees \r\nof details in models."


id: d4175ee723101b28f1d71bf729e8b652
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 30889
content:
"The emission of dust particles into the atmosphere results from \r\na natural process, namely saltation bombardment of the soil by large \r\nwind-blown particles, such as sand grains, and from disintegration \r\nof saltating particle clusters (Kok et al., 2012). The occurrence \r\nand intensity of dust emissions are controlled by soil properties, \r\nvegetation and near-surface wind, making dust emissions sensitive \r\nto climate change and LULCC (Jia et al., 2019). In addition, dust can \r\nbe directly emitted through human activities, such as agriculture, \r\noff-road vehicles, building construction and mining, and indirectly \r\nemitted through hydrological changes due to human actions such as \r\nwater diversion for irrigation (e.g., Ginoux et al., 2012). Estimates of \r\nthe anthropogenic fraction of global dust vary from less than 10% to \r\nover 60% suggesting that the human contribution to the global dust "


id: 12dcd2c747a90c96d2ab2b5ce396bc5b
created_at: 2023-03-16 14:00:43
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vector_id: 472
content:
"budget is quite uncertain (Ginoux et al., 2012; Stanelle et al., 2014; \r\nXi and Sokolik, 2016). Reconstruction of global dust (deposition) \r\nfrom paleo records indicate factor of two to four changes between \r\nthe different climate regimes in the glacial and interglacial periods \r\n(Section 2.2.6). "


id: 2fb51bd073e668d9db77d910e1774a3b
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 4926
content:
"water to the atmosphere due to wind-induced mixing of surface \r\nwater. DMS oxidizes to produce sulphate aerosols and contributes \r\nto the formation of CCN. Since AR5, the range in global DMS flux \r\nestimates reduced from 10-40 TgS yr-1 to 9-34 TgS yr-1 with a very \r\nlikely range of 18-24 TgS yr-1 based on sea-surface measurements \r\nand satellite observations (Lana et al., 2011). DMS production, \r\nand consequently emissions, have been shown to respond to \r\nmultiple stressors, including climate warming, eutrophication, and \r\nocean acidification. However, large uncertainties in process-based \r\nunderstanding of the mechanisms controlling DMS emissions, from \r\nphysiological to ecological, limit our knowledge of past variations \r\nand our capacity to project future changes."


id: 41f56861009d4389c797ac39bf81e4ac
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 7775
content:
"Overall, there is low confidence in the magnitude and changes in \r\nmarine aerosol emissions in response to shifts in climate and marine \r\necosystem processes."


id: bc3b108390f2f7d86f3ebe145be5eea2
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 27194
content:
"Emissions from open biomass burning (including forest, grassland, \r\npeat fires and agricultural waste burning) represent about 30%, 10%, \r\n15% and 40% of present-day global emissions of CO, NOx, BC and \r\nOC, respectively (van Marle et al., 2017; Hoesly et al., 2018). Wildfires \r\nalso play an important role in several atmospheric chemistry-climate \r\nfeedback mechanisms (Bowman et al., 2009; Fiore et al., 2012) and \r\nfire events occurring near populated areas induce severe air pollution \r\nepisodes (Marlier et al., 2020; Rooney et al., 2020; Yu et al., 2020)."


id: c9b19231b0c8134fb5796091fc2f0bc7
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 29287
content:
"The historical (pre-satellite era) dataset for CMIP6 considers advances \r\nin knowledge of past fire dynamics (new fire proxy datasets, such as \r\ncharcoal in sediments and levoglucosan in ice cores) and visibility \r\nrecords from weather stations (Marlon et al., 2016; van Marle et al., \r\n2017). At a  global level, CMIP5 and CMIP6 emissions trends are \r\nsimilar, however, there are substantial differences at the regional \r\nlevel, especially for the USA, South America (south of Amazonia) and \r\nSouthern Hemisphere Africa (van Marle et al., 2017)."


id: 82ed658f1b439c0f14c173957b0c1fc2
created_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 18124
content:
"Globally, the CMIP5 estimates (Lamarque et al., 2010), indicated \r\na  gradual decline of open biomass burning emissions from 1920 "


id: 971ea1186ec51517f0bafc7d3e6fee88
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 21293
content:
"satellite era is assessed. There is low to medium confidence in SLCF \r\nemissions from biomass burning from the pre-industrial period to the \r\n1980s, which rely on the incorporation of several proxy data, with \r\nlimited spatial representativeness. Nevertheless, uncertainties in \r\nthe absolute value of pre-industrial emissions remain high, limiting \r\nconfidence in radiative forcing estimates. "


id: e33065a262141a80ed705f3b7e4785d2
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 33239
content:
"to about 1950 and then steady, and stronger than CMIP6, increase \r\ntowards 2000. In contrast, CMIP6 biomass burning emissions \r\n(van Marle et al., 2017) increase only slightly over 1750-2015 - they \r\npeak during the 1990s after which they decrease gradually, which is \r\nconsistent with the assessment of fire trends in Chapter 5. Therefore, \r\nthe CMIP6 evolution has a smaller difference between pre-industrial \r\nand present-day emissions than CMIP5, resulting in a lower radiative \r\nforcing of biomass burning SLCFs, possibly leading to a lower effect \r\non climate (van Marle et al., 2017). "


id: 9317a127efac8ec8960b005b5deca317
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 20656
content:
"Climate warming, especially through change in temperature and \r\nprecipitation, will generally increase the risk of fire (Jia et al., 2019, \r\nsee also Chapter 12) and can also affect the fire injection and plume \r\nheight (Veira et al., 2016), but occurrence of fires and their emissions \r\nin the future strongly depends on anthropogenic factors, such as \r\npopulation density, land use and fire management (Veira et al., \r\n2016). Consequently, future emissions vary widely with increases \r\nand decreases amongst the SSP scenarios due to different land-use \r\nchange scenarios. "


id: f27674dcef02ea96e5763456bcb14f1a
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 35671
content:
"In summary, there has been an improvement in the knowledge of \r\nbiomass burning emissions by reducing key uncertainties highlighted \r\nin AR5. However, systematic assessment of remaining uncertainties \r\nis limited, with a lower limit of uncertainties due to emissions factors \r\nof 30%, and larger uncertainties due to burning-activity estimates, \r\nespecially at regional level. Overall, a medium confidence in current \r\nglobal biomass burning SLCF emissions and their evolution over the "


id: 90090087df72cff5a356e61799934508
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 20191
content:
"Changes in the atmospheric distribution of SLCFs determine their radiative forcing, and climate and air-quality impacts. This box \r\nprovides an overview of how process-level understanding of the distribution and evolution of chemical compounds is derived and \r\nwhere uncertainties come from. "


id: ac21afd28db14da7518c18a83615d70f
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content:
"A wide range of in situ and remotely sensed observations are used to characterize atmospheric chemical composition. Measurements \r\nmade routinely as part of long-term monitoring programmes are particularly useful for assessing long-term trends and variability, and \r\nspatial distributions (Sections 2.2, 6.3 and 7.3.3), while intensive field campaigns provide a more comprehensive view of atmospheric "


id: 9ad172ba44c1bbee7239640678358506
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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"composition at a specific location for a limited time, facilitating an improved process-level understanding. Retrieval of atmospheric \r\nconcentrations from satellites, in particular, has been tremendously useful for providing global continuous coverage, although the \r\nretrievals themselves depend on prior information of atmospheric composition usually derived from models. Over the last decade \r\nor so, observations of atmospheric concentrations have been combined with information from global chemistry-climate models to \r\nproduce global assimilation and forecasting systems with the purpose of producing chemical reanalysis or improving model inputs \r\n(i.e., emissions or boundary conditions) and forecasts (Miyazaki et al., 2015; Randles et al., 2017; Inness et al., 2019). "


id: 29febd1852171d87c454edc5a1c31096
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 4037
content:
"The skill of CCMs is typically assessed by their ability to reproduce observed abundance, trends and variability of chemical compounds. \r\nHowever, uncertainty remains large because of observation limitations (errors and uncertainties, spatial and temporal coverage), "


id: 50aa6e8d74b4c550371c484c14916e1a
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 10134
content:
"Box 6.1, Figure 1 | Knowledge exchange between laboratory/theoretical studies, observations and global climate-chemistry models (CCMs) to \r\ninform our understanding of short-lived climate forcers (SLCFs)."


id: eff2d600da41ab1804c9e28493c45036
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 35257
content:
"The global mean surface mixing ratio of methane has increased by \r\n156% since 1750 (Section  2.2.3.4 and Annex III). Since AR5, the \r\nmethane mixing ratio has increased by about 3.5% from 1803 +- 2 ppb \r\nin 2011 to 1866 +- 3 ppb in 2019 (Section 2.2.3.3.2) largely driven by \r\nanthropogenic activities as assessed in Chapter 5 (Section 5.2.2 and \r\nCross-Chapter Box 5.2). "


id: 9b223551a59410305f75386718944ff0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 21948
content:
"An assessment of the global methane budget is provided in Chapter 5, \r\nwhile this section assesses methane atmospheric lifetime and \r\nperturbation time (Prather et al., 2001). The AR5 based its assessment \r\nof methane lifetime on Prather et al. (2012). The methane chemical \r\nlifetime due to tropospheric OH, the primary sink of methane, was \r\nassessed to be 11.2 +- 1.3 years constrained by surface observations \r\nof methyl chloroform (MCF), and lifetimes due to stratospheric loss,2\r\ntropospheric halogen loss and soil uptake were assessed to be \r\n150 +- 50 years, 200 +- 100 years, and 120 +- 24 years, respectively \r\n(Myhre et al., 2013). Considering the full range of individual lifetimes, \r\nthe total methane lifetime was assessed in AR5 to be 9.25 +- 0.6 years."


id: 38c7dc09403e09d9f5aef431563342da
created_at: 2023-03-16 14:00:43
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vector_id: 6304
content:
"Table 6.2 | Methane lifetime due to chemical losses, soil uptake and total atmospheric lifetime based on CMIP6 multi-model analysis, and bottom-up \r\nand top-down methane budget estimates in Table 5.2. Bottom-up and top-down methane lifetimes are calculated using the central estimates of the respective sinks for \r\nthe mean 2008-2017 period in Table 5.2 together with the mean 2008-2017 global methane concentration of 1815 ppb (see Annex III) converted to methane burden using \r\na fill-factor of 2.75 Tg/ppb from Prather et al. (2012). Values in parentheses show the minimum and maximum range."


id: ab7a4ded04f518d9dc1cc4992041e409
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"mechanisms implemented in the global models (Y. Zhao et al., 2019). \r\nSee Section 6.3.6 for further discussion on the conflicting information \r\non OH from CCMs/ESMs and TD inversion approaches. Further work is \r\nrequired to reconcile differences between BU and TD estimates of the \r\nchemical methane sink. "


id: 4d1cad5d28e1ce181625347cbac4d4e5
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 9559
content:
"(Thornhill et al., 2021b). This f value is slightly smaller but within the \r\nrange of the AR5 value. This results in an overall perturbation methane \r\nlifetime of 11.8 +- 1.8 years, within the range of the AR5 value of \r\n12.4 +- 1.4 years. The methane perturbation lifetime assessed here is \r\nused in the calculation of emissions metrics in Section 7.6. "


id: 911bb203841f73cf1dc9012f38bd60a7
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 20367
content:
"The present-day BU methane chemical lifetime shows a larger spread \r\nthan that in the TD estimates (Table 6.2) in line with the spread seen \r\nin the sink estimates. The spread in the methane lifetime calculated \r\nby three CMIP6 ESMs is narrower and is enclosed within the spread \r\nof the BU CCMI model ensemble. Based on the consideration that the \r\nsmall imbalance in total methane sources versus sinks derived from \r\nTD estimates is close to the observed atmospheric methane growth \r\nrate (Table 5.2), the TD values are assessed to be the best estimates \r\nfor this assessment. The relative uncertainty (+- 1 standard deviation) \r\nis taken to be the same as that in AR5, that is, 11.8%, 33% and 10% \r\nfor chemical, soil and total lifetime, respectively. The central estimate \r\nof the total atmospheric methane lifetime assessed here is the same \r\nas that in AR5. "


id: b5a0ad55adf56c3b7438d17ae880e53
created_at: 2023-03-16 14:00:43
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content:
"The AR5 assessed the tropospheric ozone burden to be 337 +- 23 Tg for \r\nthe year 2000 based on the ACCMIP ensemble of model simulations \r\n(Myhre et al., 2013). Multiple satellite products, ozonesondes and \r\nCCMs are used to estimate tropospheric ozone burden (Table 6.3). \r\nSatellite products provide lower-bound values as they exclude regions \r\nunder polar night conditions (Gaudel et al., 2018). The tropospheric \r\nozone burden values from multi-model exercises are within the range "


id: d9cc41911c4f7e923455eb5e57ba567
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Table 6.3 | Global tropospheric ozone budget terms and burden based on multi-model estimates and observations for present conditions. All uncertainties \r\nquoted as +- 1 standard deviation. Values of tropospheric ozone burden with asterisk indicate average over the latitudinal zone 60degN-60degS. STE = stratospheric-tropospheric \r\nexchange."


id: 19ca3778743759d93ec6c4efd242d965
created_at: 2023-03-16 14:00:43
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"of the observational estimates despite different definitions of the \r\ntropopause for multi-model estimates which can lead to differences \r\nof about 10% on the ozone-burden model estimates (Griffiths et al., \r\n2021). Weighted by their number of members, CMIP6 and CCMI \r\nmulti-model estimates and observational estimates of tropospheric \r\nozone burden in about the year 2010, lead to an assessment of the \r\ntropospheric ozone burden of 347 +- 28 Tg for 2010. "


id: 8d8de00daac68e96e2c9c93c911bd50b
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 19796
content:
"between 1850 and the present (Myhre et al., 2013). Based on limited \r\nisotopic evidence, Chapter  2 assesses that the global tropospheric \r\nozone increased by less than 40% between 1850 and 2005 (low "


id: 6d09384f7c77e8d2a9172953210ea577
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 14779
content:
"The tropospheric ozone budget is controlled by chemical production and \r\nloss, by stratospheric-tropospheric exchange (STE), and by deposition \r\nat the Earth's surface, whose magnitude are calculated by CCMs \r\n(Table 6.3). Despite the high agreement of the model ensemble mean \r\nwith observational estimates in the present-day tropospheric ozone \r\nburden, the values of individual budget terms can vary widely across \r\nmodels in CMIP6, consistent with previous model intercomparison \r\nexperiments (Young et al., 2018). Furthermore, single-model studies \r\nhave shown that the halogen chemistry, which is typically neglected \r\nfrom model chemistry schemes in CCMs, may have a notable impact on \r\nthe ozone budget, as halogens, particularly of marine origin, take part \r\nin efficient ozone-loss catalytic cycles in the troposphere (Saiz-Lopez \r\net al., 2012; Sarwar et al., 2015; Sherwen et al., 2016)."


id: 14d8ddf0c93166e0ad9a37d9999e9ffc
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 791
content:
"Because of the heterogeneous distribution of ozone, limited \r\nobservations or proxies do not provide accurate information about \r\nthe global pre-industrial abundance, posing a  challenge to the \r\nestimation of the historical evolution of tropospheric ozone. Therefore, \r\nglobal CCMs complemented by observations are relied upon for \r\nestimating the long-term changes in tropospheric ozone. The AR5 \r\nconcluded that anthropogenic changes in ozone precursor emissions \r\nare unequivocally responsible for the increase in tropospheric ozone "


id: 62c071bfaa74c1065c93f3e9f8553e01
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 13105
content:
"confidence) (Section  2.2.5.3). The CMIP6 models are in line with \r\nthis increase of tropospheric ozone with an ensemble-mean value \r\nof 109  +- 25 Tg (model range) from 1850-1859 to 2005-2014 \r\n(Figure 6.4). This increase is higher than the AR5 value of 100 +- 25 Tg \r\nfrom 1850-2010 due to higher ozone precursor emissions in CMIP6. \r\nHowever, the AR5 and CMIP6 values are close when considering the \r\nreported uncertainties. The uncertainties are equivalent in CMIP6 and \r\nAR5 despite enhanced inclusion of coupled processes in the CMIP6 \r\nESMs (e.g., biogenic NMVOC emissions or interactive stratospheric \r\nozone chemistry). "


id: 3dd046d4bac5f8f3799315e5079b0f36
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 7081
content:
"In summary, there is high confidence in the estimated present\u0002day (about 2010) global tropospheric ozone burden based on an \r\nensemble of models and observational estimates (347 +- 28 Tg), but \r\nthere is medium confidence among the individual models for their \r\nestimates of the tropospheric ozone-related budget terms. "


id: 85ba90f84bdb7a5f64c8c5350ead72f6
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 18578
content:
"Evidence from successive multi-model intercomparisons and the \r\nlimited isotopic evidence agree on the magnitude of the increase \r\nof the tropospheric ozone burden from 1850 to the present day in \r\nresponse to anthropogenic changes in ozone precursor emissions \r\ncorroborating AR5 findings. This increase is assessed to be 109 +- 25 Tg \r\n(medium confidence). The CMIP6 model ensemble shows a constant \r\nglobal increase since the mid-20th century whose rate is consistent \r\nwith that derived from observations since the mid-1990s. "


id: e3494c71eb261452f3e1f73b47e0fedd
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 33253
content:
"Since the mid-20th century, the CMIP6 model ensemble shows \r\na higher global trend (Figure 6.4). Since the mid-1990s, the trends \r\nare better documented by observations (Section  2.2.5.3) and \r\nindicate spatial heterogeneity. In particular, in situ observations \r\nat remote surface sites and in the lower free troposphere indicate \r\npositive trends that are far more common than negative trends, \r\nespecially in the northern tropics and across Southern and Eastern \r\nAsia (Figure  6.5). The CMIP6 ensemble and observations largely \r\nagree on the magnitude of the global positive trend since 1997 \r\n(0.82 +- 0.13 Tg yr-1 in the model ensemble; 0.70 +- 0.15 Tg yr-1 in the \r\nozonesonde dataset; 0.83 +- 0.85 Tg yr-1 in the satellite ensemble) and \r\nqualitatively reproduce positive trends in the Southern Hemisphere \r\n(Griffiths et al., 2021). More analyses are needed for evaluation in \r\nother parts of the world to assess the skill of the recent ensemble \r\nbased on CMIP6 emissions. "


id: a8475104a4e4d532a53986dec04d82e8
created_at: 2023-03-16 14:00:43
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vector_id: 24002
content:
"Model simulations attribute about half of the observed upper\u0002stratospheric ozone increase after 2000 to the decline of ODS since \r\nthe late 1990s while the other half of the ozone increase is attributed \r\nto the slowing of gas-phase ozone destruction cycles due to cooling \r\nof the upper stratosphere by increasing GHGs (Aschmann et al., \r\n2014; Oberlander-Hayn et al., 2015). "


id: 5e99f11d87f8e5b5dfb5cc4cbd50623d
created_at: 2023-03-16 14:00:43
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vector_id: 12420
content:
"Figure 6.5 | Decadal tropospheric ozone trends since 1994. Trends are shown \r\nat 28 remote and regionally representative surface sites (Cooper et al., 2020) and in \r\n11 regions of the lower free troposphere (650 hPa, about 3.5 km) as measured by \r\nIn-Service Aircraft for a Global Observing System (IAGOS) above Europe, north-eastern \r\nUSA, south-eastern USA, western North America, north-east China, South East Asia, \r\nsouthern India, the Persian Gulf, Malaysia/Indonesia, the Gulf of Guinea and northern \r\nSouth America (Gaudel et al., 2020). High-elevation surface sites are >1500 m above \r\nsea level. All trends end with the most recently available year but begin in 1995 \r\nor 1994. The sites and datasets are the same as those used in Figure 2.8, further \r\ndetails on data sources and processing are available in the Chapter  2 data table \r\n(Table 2.SM.1). "


id: cd188269d774e79c01963ba775a7c0c6
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 29820
content:
"In summary, global stratospheric ozone column has decreased from \r\npre-industrial period to present day in response to the ODS-induced \r\nozone rapid decline in the 1970s and 1980s, followed by slow, and \r\nstill incomplete, recovery. There is medium confidence that global \r\nstratospheric ozone column has changed by 14.3 +- 8.7 DU between \r\n1850 and 2014. "


id: f6f760992889ff24e5e7f92561eafcc0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 36362
content:
"Observational constraints derived from the isotopic composition \r\nof atmospheric nitrate inferred from ice cores provide evidence of \r\nincreasing anthropogenic NOx sources since pre-industrial times \r\n(Hastings et al., 2009; Geng et al., 2014). Global NOx emissions trends \r\nin bottom-up inventories (Section 6.2.1) as well as model simulations \r\nof nitrogen deposition (Lamarque et al., 2013a) are in qualitative \r\nagreement with these observational constraints. CMIP6 ESMs exhibit \r\nstable NOx burden over the first half of the 20th century and then \r\na sharp increase driven by a factor of three increase in emissions, \r\nhowever, the magnitude of this increase remains uncertain due to \r\npoor observational constraints on pre-industrial concentrations of \r\nNOx (Griffiths et al., 2021)."


id: 8dbb99f3f085cbf713017048f32ce1ce
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"reveals spatially heterogeneous NO2 trends over the Middle East with \r\nan overall increase over 2005-2010 and a decrease over large parts \r\nof the region after 2011-2012. The reasons for trend reversal within \r\nindividual areas are diverse, including warfare, imposed sanctions, \r\nand air-quality controls (Lelieveld et al., 2015a; Georgoulias et al., \r\n2019). Satellite-derived tropospheric NO2 levels over Africa and Latin \r\nAmerica do not show a clear trend; both increasing and decreasing \r\ntrends are observed over large agglomerations in these regions since \r\nthe early 2000s (Schneider et al., 2015; Duncan et al., 2016). "


id: 92882df24543e6eb35db0494ba32a291
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 20578
content:
"In summary, global tropospheric NOx abundance has increased from \r\n1850-2015 (high confidence). Satellite observations of tropospheric \r\nNOx indicate strong regional variations in trends over 2005-2015. \r\nThere is high confidence that NO2 has declined over the USA and \r\nWestern Europe since the mid-1990s and increased over China until \r\n2011. NO2 trends have reversed (declining) over China beginning in \r\n2012 and NO2 has increased over Southern Asia by 50% since 2005 \r\n(medium confidence). "


id: 6423fd1c884f0e407e237975eb604dfb
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 13320
content:
"An increase of global CO burden of about 50% for the year 2000 \r\nrelative to 1850 is found in CMIP6 (Griffiths et al., 2021). "


id: bfa08bafa1b44f47f265fc6b3a11e867
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 27708
content:
"About half of the atmospheric CO burden is due to its direct emissions \r\nand the remainder is due to the atmospheric oxidation of methane \r\nand NMVOCs. Reaction with OH is the primary sink of CO with \r\na smaller contribution from dry deposition."


id: 39ce1ae9909e2dfaf44927755b420d53
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 6472
content:
"Global models generally capture the global spatial distribution \r\nof the observed CO concentrations but have regional biases of \r\nup to 50% (e.g.,  Emmons et al., 2020; Horowitz et al., 2020). \r\nDespite updated emissions datasets, the global multi-model and \r\nsingle-model simulations persistently underestimate observed CO \r\nconcentrations at northern high and mid-latitudes as well as in the \r\nSouthern Hemisphere, but with smaller biases compared with that \r\nin the Northern Hemisphere (Naik et al., 2013; Stein et al., 2014; \r\nMonks et al., 2015; Strode et al., 2015). Models are biased high in the \r\ntropics, particularly over highly polluted areas in India and Eastern \r\nAsia (Strode et al., 2016; Yarragunta et al., 2017)."


id: 430bcd9e50fe0787818d865dc60707b8
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 7944
content:
"In summary, our understanding of present-day global CO distribution \r\nhas increased since AR5 with newer and improved observations and \r\nreanalysis. There is high confidence that global CO burden is declining \r\nsince 2000. Evidence from observational CO reanalysis suggests \r\nthis decline is driven by reductions in anthropogenic CO emissions, \r\nhowever this is yet to be corroborated by global ESM studies with the \r\nmost recent emissions inventories. "


id: ce546745f56d620956ccf3a2187b0f40
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 29994
content:
"NMVOCs encompass thousands of compounds with lifetimes from \r\nhours to days to months, and abundances and chemical composition \r\nhighly variable with respect to space and time. Although the biogenic \r\nsource (Section  6.2.2) dominates the global NMVOC budget, \r\nanthropogenic activities are the main driver of long-term trends in \r\nthe abundance of many compounds."


id: 1db9946d18f70ca383ac441db75cdce0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 2114
content:
"Estimates of global CO burden simulated by global models generally \r\nfall within the range of that derived from data assimilation \r\ntechniques, though the spread across the models is large (Naik et al., \r\n2013; Stein et al., 2014; Zeng et al., 2015; Myriokefalitakis et al., \r\n2016). There is a large diversity in model-simulated CO budget driven \r\nby uncertainties in CO sources and sinks, particularly those related to \r\nin situ production from NMVOCs and loss due to reaction with OH \r\n(Stein et al., 2014; Zeng et al., 2015; Myriokefalitakis et al., 2016). \r\nGlobal CO budget analysis from a multi-model ensemble for more \r\nrecent years, including results from the CMIP6 model runs, are not \r\nyet available. "


id: d4582f880f67ee9fef6d23435ecadf6c
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 30928
content:
"Information on the global distribution of individual NMVOCs is scarce, \r\nexcept for the less reactive compounds having lifetimes of several \r\ndays to months. Based on measurements from polar firn air samples \r\nand ground-based networks, AR5 reported that the abundances of \r\nthe predominantly anthropogenic light alkanes (C2-C5) increased \r\nuntil 1980 and declined afterwards. The decline was attributed to \r\nair-quality emissions controls and to fugitive emissions decreases \r\nfollowing the collapse of the Soviet Union (Simpson et al., 2012). \r\nSince AR5, scarce ground-based measurements have shown that the \r\ndecline in C2-C3 alkanes ended around 2008 and their abundances \r\nare since growing again, which is primarily attributed to increasing \r\nNorth American emissions (Section  6.2.1). Furthermore, since AR5 \r\nthe evolution of ethane levels during the past millennium was made \r\naccessible by analysis of ice-core samples (Nicewonger et al., 2016). "


id: 17a38098805cf8efe64c6101c2e2fb31
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 1214
content:
"Reconstructions of CO concentrations based on limited ice-core \r\nsamples in the Northern Hemisphere high latitudes suggest CO mole \r\nfractions of about 145 ppb in the 1950s, which rose by 10-15 ppb \r\nin the mid- 1970s, and then declined by about 30-130 ppb by 2008 \r\n(Petrenko et al., 2013). The negative trends since the 1990s are often \r\nattributed to emissions regulations from road transportation in North \r\nAmerica and Europe. Due to limited observations prior to the satellite \r\nera, long-term global CO trends are based on estimates from models. "


id: 23802104235e6a1f4dc03c36740bdcb2
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 3036
content:
"The large observed interpolar ratio of ethane in pre-industrial times \r\n(3.9) corroborates a  large geologic source of ethane previously \r\nput forward by (Etiope and Ciccioli, 2009), and narrows down its \r\nlikely global magnitude (Nicewonger et al., 2018) (low to medium \r\nconfidence). The incorporation of geologic emissions in CCMs is \r\nnot yet systematic though a one-model study has shown improved \r\nagreement of the results with observations (Dalsoren et al., 2018). "


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vector_id: 11041
content:
"emissions inventories (Van Damme et al., 2018). NH3 exhibits a strong \r\nvertical gradient, with a  maximum in the boundary layer (Schiferl \r\net al., 2016), and can be transported into the upper troposphere and \r\nlower stratosphere (UTLS), particularly in the Asian Monsoon region, \r\nas indicated by observations (Froyd et al., 2009; Hopfner et al., 2016, \r\n2019) and theoretical considerations (Ge et al., 2018). There is a large \r\nrange in the present-day NH3 burden (from 0.04-0.7 TgN) simulated \r\nby CCMs, highlighting deficiencies in the process-level representation \r\nof NH3 in current global models (Bian et al., 2017). The underestimate \r\nof surface NH3 concentrations (Bian et al., 2017) further highlights such \r\ndeficiencies and the limitations in comparing site-specific observations \r\nwith relatively coarse-resolution models."


id: a4910398634b6d42c4ca847f98b112a2
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 23455
content:
"Observations show that NH3 concentration has been increasing \r\nin recent decades in the USA (Butler et al., 2016; Warner et al., 2016; \r\nYu et al., 2018), Western Europe (van Zanten et al., 2017; Warner et al., \r\n2017; Wichink Kruit et al., 2017; Tang et al., 2018), and China (Warner \r\net al., 2017; M. Liu et al., 2018). This trend has been attributed to \r\na combination of increasing ammonia emissions (Sutton et al., 2013; \r\nFowler et al., 2015) and decreases in the chemical reaction of NH3\r\nwith nitric and sulphuric acids associated with reductions in  SO2\r\nand NOx emissions whose rate depends on the region (Warner et al., \r\n2017; Yao and Zhang, 2019). Over longer time scales, CCMs simulate \r\nan increase of the NH3 burden by a factor of two to seven since pre\u0002industrial conditions (Xu and Penner, 2012; Hauglustaine et al., 2014). "


id: 1dee06d1aa690f325923215f8f3ffc1
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 2138
content:
"In summary, progress has been made in the understanding of the \r\nspatio-temporal distribution of ammonia, though representation of \r\nNH3 remains rather unsatisfactory due to process-level uncertainties. \r\nEvidence from observations and models suggests that ammonia \r\nconcentrations have been increasing over recent decades due to \r\nemissions and chemistry. There is high confidence that the global NH3\r\nburden has increased considerably from the pre-industrial period to the \r\npresent day, although the magnitude of the increase remains uncertain."


id: 4894460f8b883d58a03c5c7ec10f5563
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 8794
content:
"In summary, after a decline between 1980 and 2008, abundances of \r\nlight NMVOCs have increased again over the Northern Hemisphere \r\ndue to the extraction of oil and gas in North America (high \r\nconfidence). Trends in satellite HCHO observations, used as a proxy of \r\nanthropogenic NMVOC over industrialized areas, show a significant \r\npositive trend over eastern China (high confidence) but also indicate \r\nlarge regional disparities in the magnitude of the trends over China \r\nand even in their signs over North America."


id: 20e6051d4aae5f7fc42bec6f9d581f77
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 2607
content:
"The AR5 did not assess trends in SO2 concentrations. Trends in SO2\r\nabundances are consistent with the overall anthropogenic emissions \r\nchanges as presented in Section  6.2 and Figure  6.18. Long-term \r\nsurface-based in situ observations in North America and Europe \r\nshow reductions of more than 80% since the measurements began \r\naround 1980 (Table 6.5). Europe had the largest reductions in the first \r\npart of the period while the highest reduction came later in North \r\nAmerica. Observed trends are qualitatively reproduced by global \r\nand regional models over North America and Europe during the \r\nperiod 1990-2015 for which emissions changes are well quantified \r\n(Table 6.5; Aas et al., 2019)."


id: a65a035e1b097b1e0e57216d90a470a5
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 23703
content:
"Ammonia is the most abundant alkaline gas in the atmosphere. \r\nIts present-day source is dominated by livestock and crop production \r\n(Section  6.2). Ammonia reacts with nitric acid and sulphuric acid \r\nto produce ammonium sulphate and ammonium nitrate, which \r\ncontribute to the aerosol burden (Section 6.3.5.2), promotes aerosol \r\nnucleation by stabilizing sulphuric acid clusters (Kirkby et al., 2011), \r\nand contributes to nitrogen deposition (Section 6.4.4; Sheppard et al., \r\n2011; Flechard et al., 2020). Trends in NH3 were not assessed in AR5."


id: 55c123053eeb35a0b9b69e6ce3d59dce
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 10976
content:
"Considerable expansion of satellite (Clarisse et al., 2009; Shephard \r\nand Cady-Pereira, 2015; Warner et al., 2016) and ground-based \r\nobservations (Miller et al., 2014; Y. Li et al., 2016; Pan et al., 2018) has \r\nimproved our understanding of the spatial distribution and seasonal to \r\ninterannual variability of ammonia, and advanced its representations \r\nin models (e.g., Zhu et al., 2015). Regionally, peak NH3 concentrations \r\nare observed over large agricultural (e.g.,  northern India, the USA \r\nMidwest and Central Valley) and biomass-burning regions, in good \r\nqualitative agreement with emissions inventories (Van Damme \r\net al., 2015, 2018). However, several large agricultural and industrial \r\nhotspots have been found to be missing or greatly underestimated in "


id: 2fdf5bac009a408ca279474d0c725bc0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 4956
content:
"In situ observations over other parts of the world are scattered. \r\nHowever, the limited in situ observations in Eastern Asia indicate an \r\nincrease in atmospheric SO2 up to around 2005 and then a decline \r\n(Aas et al., 2019). This is confirmed by satellite observations (Krotkov \r\net al., 2016), which further reveal a rapid decline in SO2 since around \r\n2012 or 2013 (Krotkov et al., 2016; Zheng et al., 2018b). In India, on \r\nthe other hand, SO2 levels have doubled between 2005 and 2015 \r\n(Krotkov et al., 2016). "


id: c93dfe434cf250d2e0ae27d4664ac0cc
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 29224
content:
"Table 6.5 | Summary of changes or trends in atmospheric abundance of sulphur dioxide (SO2) and sulphate (SO4\r\n2-) aerosols based on in situ and satellite \r\nobservations."


id: a0d5cfffe417829f37bf2bc7001662e4
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 22853
content:
"VSLSs are halogenated substances with atmospheric lifetimes less \r\nthan half a  year. While longer-lived ODSs account for most of the \r\npresent-day stratospheric halogen loading, there is robust evidence\r\nthat VSLSs contribute to stratospheric bromine and chlorine \r\n(Carpenter et al., 2014; Elvidge et al., 2015a; Hossaini et al., 2015), \r\nthus also contributing to stratospheric ozone depletion. "


id: bc70f8946371612d2d1064587d6bf4e6
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 27227
content:
"In summary, surface and satellite observations indicate strong \r\nregional variations in trends of atmospheric SO2 abundance. The SO2\r\nconcentrations in North America and Europe have declined over \r\n1980-2015 with slightly stronger reductions in North America \r\n(70 +- 20%) than in Europe (58 +- 32%) over 2000-2015, though \r\nEurope had larger reductions than the US in the prior decade (1990-\r\n2000). In Asia, the SO2 trends are more scattered, though there is \r\nmedium confidence that there was a strong increase up to around \r\n2005, followed by a  steep decline in China, while over India, the \r\nconcentrations are increasing steadily."


id: d345c0a34ac175b687aa8ddb16f4ad13
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 30766
content:
"Halogenated species are emitted in the atmosphere in the form \r\nof the synthetically produced chlorofluorocarbons (CFCs), halons, \r\nhydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and \r\nothers. Their historical global abundances are provided in Annex III \r\nand discussed in Chapter 2 (Section 2.2.4 and Table 2.3). In summary, \r\nfor the period 2011-2019, the abundance of total chlorine from \r\nHCFCs has continued to increase in the atmosphere with decreased \r\ngrowth rates; total tropospheric bromine from halons and methyl \r\nbromide continued to decrease while abundances of most currently \r\nmeasured HFCs increased significantly, consistent with expectations \r\nbased on the ongoing transition away from the use of ODSs. Here, \r\nemphasis is given on the very short-lived halogenated species \r\n(VSLSs). The trends for these species were not discussed in IPCC AR5. "


id: 3c68644eb28a0aa85cbbb0059ae61dd5
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
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vector_id: 6867
content:
"Models participating in Phase III of the AeroCom intercomparison \r\nproject were found to underestimate present-day AOD by about \r\n20% (Gliss et al., 2021), although different remote-sensing estimates \r\nobtain different estimates of global mean AOD. Gliss et al. (2021) also \r\nhighlight the considerable diversity in the simulated contribution of \r\nvarious aerosol types to total AOD. However, models simulate regional \r\ntrends in AODs that agree well, when expressed as percentage change, \r\nwith ground- (Mortier et al., 2020; Gliss et al., 2021) and satellite\u0002based (Cherian and Quaas, 2020; Gliss et al., 2021) observations. \r\nAOD  trends simulated by CMIP6 models are more consistent with \r\nsatellite-derived trends than CMIP5 models for several sub-regions, \r\nthanks to improved emissions estimates (Cherian and Quaas, 2020). "


id: b5699cbe1cb208f6f7b014af09bd9c3c
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 26076
content:
"This section assesses trends in the atmospheric distribution of \r\naerosols and improvements in relevant physical and chemical \r\nprocesses. The observed large-scale temporal evolution of aerosols \r\nis assessed in Section 2.2.6. Since AR5, long-term measurements of \r\naerosol mass concentrations from regional global surface networks \r\nhave continued to expand and provide information on the distribution \r\nand trends in aerosols (Figure 6.7). There is large spatial variability in \r\naerosol mass concentration, expressed as PM2.5, dominant aerosol \r\ntype and aerosol composition, consistent with the findings in AR5. "


id: d2fde82343ce4dd6f18b8e5739867d40
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 30716
content:
"Remote-sensing instruments provide a larger-scale view of aerosol \r\ndistributions and trends than ground-based monitoring networks by \r\nretrieving the aerosol optical depth (AOD), which is indirectly related \r\nto aerosol mass concentrations. AOD is the column-integrated \r\nmeasure of extinction of the solar intensity due to aerosols at a given \r\nwavelength, and is therefore relevant to the estimation of the \r\nradiative forcing of aerosol-radiation interactions (Section 7.3.3.1). "


id: b90f92da1d8684ab97dbf59456f18520
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 26647
content:
"All CMIP6 models simulate a positive trend in global mean AOD from \r\n1850, with a  strong increase after the 1950s coinciding with the \r\nmassive increase in anthropogenic SO2 emissions (Figure 6.8). Global \r\nmean AOD increases have slowed since 1980, or even reversed in \r\nsome models, as a result of a compensation between SO2 emissions \r\ndecreases over the USA and Europe in response to air-quality "


id: 124abe7482951a7250df2d85d7cc1b7f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 381
content:
"layer (Kremser et al., 2016). SO2 emissions from volcanic eruptions are \r\na significant source of stratospheric sulphate loading (see Chapter 2 \r\nfor reconstruction of stratospheric aerosol optical depth and \r\nChapter  7 for radiative forcing of volcanic aerosols). Furthermore, \r\nstudies suggest sulphate contributions from anthropogenic SO2\r\nemissions transported into the stratosphere could have a consequent \r\nimpact on radiative forcing (Myhre et al., 2004; Yu et al., 2016). \r\nHowever, there is significant uncertainty in the relative importance of \r\nthis stratospheric sulphate source (Kremser et al., 2016)."


id: a6eee2aa27f93c503b04564aa8013cd8
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 23777
content:
"Process understanding of sulphate production pathways from SO2\r\nemissions has seen some progress since AR5. More specifically, \r\nmany global climate models now have a more complete description \r\nof chemical reactions such that oxidant levels (including ozone) \r\nare better described, include a  pH-dependence of SO2 oxidation \r\n(e.g., Kirkevag et al., 2018; Bauer et al., 2020), and implement explicit \r\ndescriptions of ammonium and nitrate aerosol components, which \r\nmay influence the partitioning of sulphate (Bian et al., 2017; Lund \r\net al., 2018a). The pH influences the heterogeneous chemistry as well \r\nas the physical properties of the aerosols, and this topic has been \r\na subject of growing interest since AR5 (Cheng et al., 2016; Freedman \r\net al., 2019; Nenes et al., 2020). Increases in cloudwater pH have \r\nbeen shown to significantly increase the radiative forcing due to \r\nsulphate aerosols (Turnock et al., 2019). "


id: b36c17856de93ef6ca12da3711632574
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 25734
content:
"Sulphate is removed from the atmosphere by dry deposition and \r\nwet scavenging, and these processes depend on the characteristics \r\nof the Earth's surface, and the intensity, frequency and amount of \r\nprecipitation (Boucher et al., 2013). Even though there have been \r\nsome improvements since AR5, representation of atmospheric \r\ntransport and of wet scavenging and related cloud processes remains \r\na key source of uncertainty in the simulated aerosol distribution and \r\nlifetime, with further consequences for the sulphate forcing estimates \r\n(Kristiansen et al., 2016; Lund et al., 2018a). There are also still \r\nrelatively large uncertainties in the emission height used in models \r\naffecting the simulated aerosol distribution (Yang et al., 2019a)."


id: 16d27b06d0bfcd0fd514aab83ee5671e
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 1112
content:
"Sulphate aerosols (or sulphate-containing aerosols) are emitted \r\ndirectly or formed in the atmosphere by gas- and aqueous-phase \r\noxidation of precursor sulphur gases, including SO2, DMS and carbonyl \r\nsulphide (OCS), emitted from anthropogenic and natural sources \r\n(Section 6.2). Sulphate aerosols influence climate forcing directly by \r\neither scattering solar radiation or absorbing longwave radiation, \r\nand indirectly by influencing cloud micro- and macrophysical \r\nproperties and precipitation (Boucher et al., 2013; Myhre et al., 2013). \r\nAdditionally, sulphate aerosols and sulphate deposition have a large \r\nimpact on air quality and ecosystems (Reis et al., 2012). The majority \r\nof sulphate particles are formed in the troposphere, however, SO2 and \r\nother longer-lived natural precursors, such as OCS, transported into \r\nthe stratosphere, contribute to the background stratospheric aerosol "


id: 630c84190d031c7433475a6897516a41
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 13153
content:
"representative trends (Hammer  et al., 2018). However, limited in \r\nsitu observations in Eastern Asia indicate an increase in atmospheric \r\nsulphate up to around 2005 and then a decline (Aas et al., 2019), \r\nwhich is confirmed by satellite observations of SO2 (Section 6.3.3.5). \r\nIn India, on the other hand, satellite observations indicate a rapid \r\nincrease in the SO2 levels (Krotkov et al., 2016), and long-term \r\nmeasurements of sulphate in precipitation in India further provide \r\nevidence of an increasing trend from 1980-2010 (Bhaskar and \r\nRao, 2017; Aas et al., 2019). Further improvements in global trend \r\nassessments are expected with new integrated reanalysis products \r\nfrom the Earth-system data assimilation projects (Randles et al., \r\n2017; Inness et al., 2019). "


id: de52a51bc6cfb32eefa2298ee87652ba
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 32470
content:
"produced in the atmosphere by the oxidation of NOx and SO2\r\nrespectively. Ammonium nitrate is formed only after H2SO4 is fully \r\nneutralized. NH4\r\n+ and NO3-\r\n aerosols produced via these gas-to-particle \r\nreactions are a major fraction of fine-mode particles (with diameter \r\n<1um) affecting air quality and climate. Coarse-mode nitrate, \r\nformed by the heterogeneous reaction of nitric acid with dust and \r\nsea salt, dominates the overall global nitrate burden, but has little \r\nradiative effect (Hauglustaine et al., 2014; Bian et al., 2017). Trends in \r\nammonium (NH4\r\n+) and nitrate (NO3-\r\n) were not assessed in AR5."


id: 483d65978796380f6cc1f04db99f8c58
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 8736
content:
"Global model present-day estimates of the global NH4\r\n+ burden range \r\nfrom 0.1-0.6 TgN (Bian et al., 2017). Models generally simulate \r\nsurface NH4\r\n+ concentrations better than surface NH3 concentrations \r\n(Bian et al., 2017), which reflects its thermodynamic control by SO4\r\n2-\r\nrather than NH3 (Shi et al., 2017). The concomitant increases of NH3, \r\nSO2, and NOx emissions (see Section  6.2) have led to a  factor of \r\nthree to nine increase in the simulated NH4\r\n+ burden from 1850-2000 \r\n(Hauglustaine et al., 2014; Lund et al., 2018a), driven primarily by \r\nammonium sulphate (70-90%). The increases in the NH3 and NH4\r\n+\r\nburdens are indirectly supported by the observed increase of NH4\r\n+\r\nconcentration in ice cores in mid- to high latitudes (Kang et al., 2002; \r\nKekonen et al., 2005; Lamarque et al., 2013; Iizuka et al., 2018). "


id: 3c3b707243c2022735e3cd35e213732b
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 6842
content:
"Indirect evidence of decadal trends in the atmospheric loading of \r\nsulphur are provided by Alpine ice cores, mainly influenced by \r\nEuropean sources (Engardt et al., 2017), and ice cores from Svalbard \r\n(Samyn et al., 2012) and Greenland (Patris et al., 2002; Iizuka et al., \r\n2018) influenced by sources in Europe and North America. These \r\nshow similar patterns with a  weak increase from the end of the \r\n19th  century up to around 1950, followed by a  steep increase up \r\nto around 1980, and then a significant decrease over the next two \r\ndecades (see also Section 2.2.6). This general trend is consistent with \r\nthe emissions of SO2 in North America and Europe (Figures 6.18 and \r\n6.19; Hoesly et al., 2018)."


id: f6310f617cd830f1d19b5bc25e6198a6
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 36241
content:
"Global and regional models qualitatively reproduce observed trends \r\nover North America and Europe for the period 1990-2015 for which \r\nemissions changes are generally well quantified (Aas et al., 2019; \r\nMortier et al., 2020), building confidence in the relationship between \r\nemissions, concentration, deposition and radiative forcing derived \r\nfrom these models. However, the models seem to systematically \r\nunderestimate sulphate (Bian et al., 2017; Lund et al., 2018a) and \r\nAOD (Lund et al., 2018a; Gliss et al., 2021), and there are quite \r\nlarge differences in the models' distribution of the concentration \r\nfields of sulphate driven by differences in the representation of \r\nphotochemical production and sinks of aerosols. One global model \r\nstudy also highlighted biases in simulated sulphate trends over the \r\n2001-2015 period over eastern China due to uncertainties in the \r\nCEDS anthropogenic SO2 emissions trends (Paulot et al., 2018a)."


id: f6c2ba0f997ba7e72a0c9a5323b88d69
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 36333
content:
"In summary, there is high confidence that the global tropospheric \r\nsulphate burden increased from 1850 to around 2005, but there \r\nare large regional differences in the magnitude. Sulphate aerosol \r\nconcentrations in North America and Europe have declined over 1980-\r\n2015 with slightly stronger reductions in North America (47 +- 20%) \r\nthan in Europe (40 +- 30%) over 2000-2015, though Europe had \r\nlarger reductions in the prior decade (1990-2000; 52 +- 21% and \r\n21 +- 14% respectively for Europe and North America). In Asia, the \r\ntrends are more scattered, though there is medium confidence\r\nthat there was a  strong increase up to around 2005, followed by \r\na  steep decline in China, while over India, the concentrations are \r\nincreasing steadily."


id: 926d3172be7c387fe13f859576b587e4
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 20559
content:
"Models indicate that the burden of fine-mode NO3\r\n-\r\n has increased \r\nby a factor of two to five from 1850-2000 (Xu and Penner, 2012; \r\nHauglustaine et al., 2014; Lund et al., 2018a), an increase that has "


id: ab7099ee01cafe19c835fe0e80d90209
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 24487
content:
"accelerated between 2001 and 2015 (Lund et al., 2018a; Paulot et al., \r\n2018b). The sensitivity of NO3\r\n-\r\n to changes in NH3, SO4\r\n2-, and HNO3\r\nis determined primarily by aerosol pH, temperature, and aerosol \r\nliquid water (Guo et al., 2016, 2018; Weber et al., 2016; Nenes et al., \r\n2020). In regions where aerosol pH is high, changes in NO3\r\n-\r\n follow \r\nchanges in NOx emissions, consistent with the observed increase of \r\nammonium nitrate in northern China from 2000-2015 (Wen et al., \r\n2018) and its decrease in the US Central Valley (Pusede et al., 2016). \r\nIn contrast, the decrease in SO2 emissions in the south-east USA has \r\ncaused little change in NO3\r\n- from 1998-2014 as nitric acid largely \r\nremains in the gas phase due to highly acidic aerosols (Weber et al., \r\n2016; Guo et al., 2018). "


id: 60f97d831a74e7ba20eee7a061c6ca22
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 12825
content:
"Carbonaceous aerosols are black carbon (BC)3, which is soot \r\nmade almost purely of carbon, and organic aerosols4 (OA), which \r\nalso contain hydrogen and oxygen and can be of both primary \r\n(POA) or secondary (SOA) origin. BC and a  fraction of OA called \r\nbrown carbon (BrC) absorb solar radiation. The various components \r\nof carbonaceous aerosols have different optical properties, so the \r\nknowledge of their partition, mixing, coating and ageing is essential \r\nto assess their climate effect (Section 7.3.3.1.2). "


id: ad49c915fb8cfcbaeca6e0751476745f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 24806
content:
"Carbonaceous aerosols receive attention in the scientific and policy \r\narena due to their radiative forcing, and their sizeable contribution \r\nto PM in an air-quality context (Rogelj et al., 2014b; Harmsen et al., \r\n2015; Shindell et al., 2016; Haines et al., 2017; Myhre et al., 2017). \r\nBC  exerts a  positive forcing, but the forcing from carbonaceous \r\naerosol as a  whole is negative (Bond et al., 2013; Thornhill et al., \r\n2021b). On average, carbonaceous aerosols account for 50-70% \r\nof PM with a  diameter lower than 1 um in polluted and pristine \r\nareas (Zhang et al., 2007; Carslaw et al., 2010; Andreae et al., 2015; \r\nMonteiro dos Santos et al., 2016; Chen et al., 2017)."


id: f844c7b31ee1fbe08f458953fd103c7e
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 36577
content:
"In summary, there is high confidence that the NH4\r\n+ and NO3-\r\n burdens \r\nhave increased from the pre-industrial period to the present day, \r\nalthough the magnitude of the increase is uncertain especially \r\nfor NO3\r\n-\r\n. The sensitivity of NH4\r\n+ and NO3-\r\n to changes in NH3, H2SO4\r\nand HNO3 is well understood theoretically. However, it remains \r\nchallenging to represent in models, in part because of uncertainties \r\nin the simulation of aerosol pH, and only a minority of ESMs consider \r\nnitrate aerosols in CMIP6."


id: dc39ea58bc696148d847d1a7f6265b99
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 32160
content:
"et al. (2021), based on 14 AeroCom models, is 0.131 +- 0.047 Tg \r\n(median  +- 1 standard deviation). The range encompasses values \r\nreported by independent single-model estimates (Huang et al., 2013; \r\nLee et al., 2013; Sharma et al., 2013; Q. Wang et al., 2014; Tilmes \r\net al., 2019). "


id: b70d9987e95a57b4d7909b04d648410e
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 26327
content:
"Knowledge of carbonaceous aerosol atmospheric abundance \r\ncontinues to rely on global models due to a  lack of global-scale \r\nobservations. For BC, models agree within a  factor of two with \r\nmeasured surface mass concentrations in Europe and North America, \r\nbut underestimate concentrations at the Arctic surface by one to two \r\norders of magnitude, especially in winter and spring (Lee et al., 2013; \r\nLund et al., 2018a). For OA, AeroCom models underestimate surface \r\nmass concentrations by a factor of two over urban areas, as their low \r\nhorizontal resolution prevents them from resolving local pollution \r\npeaks (Tsigaridis et al., 2014; Lund et al., 2018a). Models agree \r\nwithin a  factor of two with OA surface concentrations measured \r\nat remote sites, where surface concentrations are more spatially \r\nuniform (Tsigaridis et al., 2014). "


id: b2c1a5f44f46923700fa8ece5e8a3143
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 25644
content:
"In summary, the lack of global-scale observations of carbonaceous \r\naerosols, the complexity of processes influencing them, and the large \r\nspread in their simulated global budget and burdens means that \r\nthere is only low confidence in the quantification of the present-day \r\natmospheric distribution of individual components of carbonaceous \r\naerosols. Global trends in carbonaceous aerosols cannot be \r\ncharacterized due to limited observations, but sites representative of \r\nbackground conditions have reported multi-year declines in BC over \r\nseveral regions of the Northern Hemisphere."


id: dcc9103614b425e203266238d1444f3c
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 32237
content:
"The atmospheric oxidising capacity is determined primarily by \r\ntropospheric hydroxyl (OH) radical and to a smaller extent by NO3\r\nradical, ozone, hydrogen peroxide (H2O2) and halogen radicals. OH \r\nis the main sink for many SLCFs, including methane, halogenated \r\ncompounds (HCFCs and HFCs), CO and NMVOCs, controlling their \r\nlifetimes and consequently their abundance and climate influence. \r\nOH-initiated oxidation of methane, CO and NMVOCs in the presence \r\nof NOx leads to the production of tropospheric ozone. OH also \r\ncontributes to the formation of aerosols from oxidation of SO2 to \r\nsulphate and NMVOCs to secondary organic aerosols. The evolution \r\nof the atmospheric oxidising capacity of the Earth driven by human \r\nactivities and natural processes is, therefore, of significance for \r\nclimate and air-quality concerns. "


id: 52217e44f9865bbd4ca27b6379812f5c
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 10375
content:
"Simulated BC burdens show a  large spread among models \r\n(Gliss  et al., 2021), despite using harmonised primary emissions, \r\nbecause of differences in BC removal efficiency linked to different \r\ntreatment of ageing and mixing, particularly in strong source regions. \r\nThe multi-model median BC burden for the year 2010 from Gliss "


id: 45358136822caca96f6789ac40dc1fd7
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 8316
content:
"The main source of tropospheric OH is the photoexcitation of \r\ntropospheric ozone that creates an electronically excited oxygen atom \r\nwhich reacts with water vapour producing OH. A secondary source of \r\nimportance for global OH is the recycling of peroxy radicals formed by "


id: bf5e7aef7fa4dec564f4e3c26294d4d4
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 27662
content:
"Oscillation, also contribute to OH variability via changes in lightning \r\nNOx emissions and deep convection (Turner et al., 2018), and fire \r\nemissions (Rowlinson et al., 2019). "


id: 172637a9b4ae64228081dc265ca572e1
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 1155
content:
"the reaction of OH with reduced and partly oxidized species, including \r\nmethane, CO and NMVOCs. In polluted air, NOx emissions control the \r\nsecondary OH production, while in pristine air it occurs via other \r\nmechanisms involving, in particular, isoprene (Lelieveld et al., 2016; \r\nWennberg et al., 2018). Knowledge of the effect of isoprene oxidation \r\non OH recycling has evolved tremendously over the past decade, \r\nfacilitating mechanistic explanation of elevated OH concentrations \r\nobserved in locations characterised by low NOx levels (Hofzumahaus \r\net al., 2009; Paulot et al., 2009; Peeters et al., 2009, 2014; Fuchs et al., \r\n2013). Since AR5, the inclusion of improved chemical mechanisms \r\nin some CTMs suggest advances in understanding of the global OH \r\nbudget, however, these improvements have yet to be incorporated in \r\nCMIP6-generation ESMs. "


id: 11cf7a59ce20d724aaf9c284223e9e44
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
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vector_id: 300
content:
"Global-scale OH observations are non-existent because of its \r\nextremely short lifetime (around 1 second) and therefore global \r\nOH abundance and its time variations are either inferred from \r\natmospheric measurements of methyl chloroform (MCF; Prinn et al., \r\n2018 and references therein) or derived from global atmospheric \r\nchemistry models (Lelieveld et al., 2016). The AR5 reported small \r\ninterannual OH variations in the 2000s based on atmospheric \r\ninversions of MCF observations (within +-5%) and global CCMs and \r\nCTMs (within +-3%) (Ciais et al., 2013). "


id: 39c63179406d1d3d5b21f48c5974ab0c
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 6465
content:
"Since AR5, there is much closer agreement in the estimates of \r\ninterannual variations in global mean OH derived from atmospheric \r\ninversions, empirical reconstruction, and global CCMs and ESMs, \r\nwith an estimate of 2-3% over the 1980-2015 period (Table 6.7). \r\nWhile the different methodologies agree on the occurrence of small \r\ninterannual variations, there is much debate over the longer-term \r\nglobal OH trend. Two studies using multi-box model inversions of \r\nMCF and methane observations suggest large positive and negative \r\ntrends since the 1990s in global mean OH (Rigby et al., 2017; \r\nTurner et al., 2017), however, both find that observational "


id: 985420d7124e57c79e259d56fc55ddac
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 21478
content:
"As a  result of the complex photochemistry, tropospheric OH \r\nabundance is sensitive to changes in SLCF emissions as well as \r\nclimate. Increases in methane, CO and NMVOCs reduce OH while \r\nincreases in water vapour and temperature, incoming solar radiation, \r\nNOx and tropospheric ozone enhance OH. The OH level thus responds \r\nto climate change and climate variability via its sensitivity to \r\ntemperature and water vapour, as well as the influence of climate \r\non natural emissions (e.g.,  wetland methane emissions, lightning \r\nNOx, BVOCs, fire emissions) with consequent feedbacks on climate \r\n(Section 6.4.5). Climate modes of variability, like El Nino-Southern "


id: 434001472a4bfe2e5f306b29387a0683
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 7985
content:
"CO over the 2002-2013 period (with CO declining trends) into \r\na CCM (Section 6.3.4; Gaubert et al., 2017). Multi-model sensitivity \r\nanalysis suggests that increasing OH since 1980 is predominantly \r\ndriven by changes in anthropogenic SLCF emissions with the \r\ncomplementary influence of increasing NOx and decreasing CO \r\nemissions (Stevenson et al., 2020). "


id: a76e17e4e7e1ef41be577748848fc147
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 23847
content:
"constraints are weak, such that a wide range of multi-annual OH \r\nvariations are possible. Indeed, Naus et al. (2019) find an overall \r\npositive global OH trend over the past two decades (Table  6.7) \r\nafter accounting for uncertainties and biases in atmospheric MCF \r\nand methane inversions, confirming the weakness in observational \r\nconstraints for deriving OH trends. Global ESMs, CCMs and CTMs \r\nexhibit increasing global OH after 1980 contrary to the lack of \r\ntrend derived from some atmospheric inversions and empirical \r\nreconstructions (Table 6.7). In particular, a three-member ensemble \r\nof ESMs participating in the AerChemMIP/CMIP6 agrees that global \r\nOH has increased since 1980 by around 9% (Figure 6.9) with an \r\nassociated reduction in methane lifetime (Stevenson et al., 2020). \r\nThis positive OH trend is in agreement with the OH increase of about \r\n7% derived by assimilating global-scale satellite observations of "


id: ce0c9e93d34ee472d4a3a788c74775db
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 29936
content:
"Over paleo time scales, proxy-based observational constraints from \r\nmethane and formaldehyde suggest tropospheric OH to be a factor \r\nof two to four lower in the Last Glacial Maximum (LGM) relative \r\nto pre-industrial levels, though these estimates are highly uncertain \r\n(Alexander and Mickley, 2015). Global models, in contrast, exhibit no \r\nchange in tropospheric OH (and consequently in methane lifetime) \r\nat the LGM relative to the pre-industrial period (Murray et al., 2014; "


id: 443b8021b20350c368ee91e9d0efe04
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 8159
content:
"Quiquet et al., 2015), however, the sign and magnitude of OH changes \r\nare sensitive to model predictions of changes in natural emissions, \r\nincluding lightning NOx and BVOCs, and model representation of \r\nisoprene oxidation chemistry (Achakulwisut et al., 2015; Hopcroft \r\net al., 2017). "


id: 766486dfa227dacec81b7184d9a7a507
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 16218
content:
"Regarding change since the pre-industrial era, at the time of the \r\nAR5, the ensemble mean of 17 global models participating in \r\nACCMIP indicated little change in tropospheric OH from 1850-2000. \r\nThis was due to the competing and finally offsetting changes in \r\nfactors enhancing or reducing OH with a consequent small decline \r\nin methane lifetime (Naik et al., 2013; Voulgarakis et al., 2013). \r\nHowever, there was large diversity in both the sign and magnitude \r\nof past OH changes across the individual models attributed to the \r\ndisparate implementation of chemical and physical processes (Nicely \r\net al., 2017; Wild et al., 2020). Analysis of historical simulations from \r\nthree CMIP6 ESMs indicates little change in global mean OH from \r\n1850 to about 1980 (Stevenson et al., 2020). However, there is no \r\nobservational evidence of changes in global OH since 1850 up to the \r\nearly 1980s to evaluate the ESMs. "


id: 3a400f940d968b7b307a7086e2067b2b
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 6533
content:
"In summary, global mean tropospheric OH does not show a significant \r\ntrend from 1850 up to around 1980 (low confidence). There is \r\nconflicting information from global models constrained by emissions \r\nversus observationally constrained inversion methods over the 1980-\r\n2014 period. A positive trend since 1980 (about 9% increase over \r\n1980-2014) is a  robust feature among ESMs and CCMs and there \r\nis medium confidence that this trend is mainly driven by increases in \r\nglobal anthropogenic NOx emissions and decreases in CO emissions. \r\nThere is limited evidence and medium agreement for positive trends \r\nor absence of trends inferred from observation-constrained methods. \r\nOverall, there is medium confidence that global mean OH has remained \r\nstable or exhibited a positive trend since the 1980s. "


id: c53000093ff6a8497c7f64d3bba1cbf3
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 28603
content:
"In summary, CMIP6 models generally represent more processes \r\nthat drive aerosol-cloud interactions than the previous generation \r\nof climate models, but there is only medium confidence that those \r\nenhancements improve their fitness for the purpose of simulating \r\nradiative forcing due to aerosol-cloud interactions because only a few \r\nstudies have identified the level of sophistication required to do so. \r\nIn addition, the challenge of representing the small-scale processes \r\ninvolved in aerosol-cloud interactions, and a lack of relevant model\u0002data comparisons, does not allow a  quantitative assessment of \r\nthe progress of the models from CMIP5 to CMIP6 in simulating the \r\nunderlying conditions relevant for aerosol-cloud interactions at this \r\ntime. "


id: 828497674c046c19d8d00da6090e19e8
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 18058
content:
"The radiative forcing on the climate system introduced by SLCFs is \r\ndistinguished from that of long-lived greenhouse gases (LLGHGs) by \r\nthe diversity of forcing mechanisms for SLCFs, and the challenges \r\nof constraining these mechanisms via observations and of inferring \r\ntheir global forcings from available data. Chapter  7 assesses the \r\nglobal estimates of effective radiative forcing (ERF) due to SLCF \r\nabundance changes. This section assesses the characteristics \r\n(e.g.,  spatial patterns, temporal evolution) of forcings, emissions\u0002based SLCF forcings, climate response and feedbacks due to SLCFs \r\nrelying primarily on results from CMIP6 models. Additionally, the \r\nERFs for several aerosol-based forms of solar -radiation modification \r\n(SRM) are discussed in Section 6.4.6. "


id: fbff7bc2e1c6b3a8a97cb4ccf9848502
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 37140
content:
"Forcing and climate response due to changes in SLCFs are typically \r\nestimated from global models that vary in their representation of \r\nthe various chemical, physical and radiative processes (Box  6.1) \r\naffecting the causal chain from SLCF emissions to climate response \r\n(Figure  6.1). The AR5 noted that the representation of aerosol \r\nprocesses varied greatly in CMIP5 models and that it remained "


id: b88e568676103530ea67600138764aa0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 26563
content:
"The highly heterogeneous distribution of SLCF abundances \r\n(Section 6.3) translates to strong heterogeneity in the spatial pattern \r\nand temporal evolution of forcing and climate responses due to \r\nSLCFs. This section assesses the spatial patterns of the current forcing \r\ndue to aerosols and their historical evolution by region. "


id: bf5ad36a959177d99078cfeb14f8c08d
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 27658
content:
"of ERF trends is consistent with the regional long-term trends in \r\naerosol precursor emissions (Section 6.2; Figures 6.18 and 6.19) and \r\ntheir abundances (Section  6.3). However, at finer regional scales, \r\nthere are regions where sulphate aerosols are still following an \r\nupward trend (e.g., Southern Asia; Section 6.3.5) implying that the \r\ntrends in ERF may not have shifted for these regions. The continental\u0002scale ERF trends are also in line with the satellite-observed AOD \r\ntrends assessed in Section 2.2.6. Global mean ERF reaches maximum "


id: c986ef617a3cb61de82b7be2290ea393
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 29266
content:
"Time evolution of 20-year means of regional net aerosol ERF shows \r\nthat the regions are divided into two groups depending on whether \r\nthe mean ERF attains its negative peak value in the 1970s-1980s \r\n(e.g., Europe, North America) or in the late 1990s-2000s (e.g., Asia, \r\nSouth America; Figure 6.11). Qualitatively, this shift in the distribution "


id: 496a4088f6670027a31c6f5901f4ecca
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 8940
content:
"formation and oxidation capacity with feedbacks on the methane \r\nlifetime, depend non-linearly on the concentrations of NOx, CO and \r\nNMVOCs. This means that the results from the model simulations \r\ndepend to some extent on the chosen methodology. In AR5 (based \r\non Shindell et al., 2009; Stevenson et al., 2013) the attribution was \r\ndone by removing the anthropogenic emissions of individual species \r\none  by one from a  control simulation for present-day conditions. \r\nFurther, only the radiative forcings, and not the ERF (mainly including \r\nthe effect of aerosol-cloud interactions) were attributed to the \r\nemitted compounds."


id: ee74123ed4e7d71910adf7d7d4c88da0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 34983
content:
"Since AR5, the emissions estimates have been revised and extended \r\nfor CMIP6 (Hoesly et al., 2018), the models have been further \r\ndeveloped, the period has been extended (1750-2019, versus 1750-\r\n2011 in AR5) and the experimental setup for the model simulations \r\nhas changed (Collins et al., 2017), making a  direct comparison of \r\nresults difficult. Figure  6.12a shows the global and annual mean \r\nERF attributed to emitted compounds over the period 1750-2019 \r\nbased on AerChemMIP simulations (Thornhill et al., 2021b) where \r\nanthropogenic emissions or concentrations of individual species \r\nwere perturbed from 1850 to 2014 levels (methodology described in \r\nSupplementary Material 6.SM.1). "


id: 1797bdf1d2b5a1ce4949d2454127d45f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 1206
content:
"The ERF based on primary CO2 emissions is slightly lower than the \r\nabundance-based estimate (Section 7.3.2.1) because the abundance\u0002based ERF combines the effect of primary CO2 emissions and a small \r\nadditional secondary contribution from atmospheric oxidation of \r\nmethane, CO, and NMVOCs (4%) of fossil origin, consistent with \r\nAR5 findings."


id: 42ddff67a9b54bd624e6be6d70e42d50
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 7909
content:
"negative values in the mid-1970s and its magnitude gradually \r\ndecreases thereafter. This weakening of the negative forcing since \r\n1990 agrees with findings that attribute this to a  reduction in \r\nglobal mean SO2 emissions combined with an increase in global \r\nBC (Myhre et al., 2017). Uncertainties in model-simulated aerosol \r\nERF distribution and trends can result from inter-model variations \r\nin the representation of aerosol-cloud interactions and aerosol \r\nmicrophysical processes as also demonstrated by Bauer et al. (2020). "


id: 33fb104e9bac1f0923abf208efd38c46
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 5596
content:
"Ozone-depleting substances, such as N2O and halocarbons, cause \r\na  reduction in stratospheric ozone, which affects ozone and OH \r\nproduction in the troposphere through ultraviolet radiation changes \r\n(and thus affect methane). They also have indirect effects on aerosols \r\nand clouds (Karset et al., 2018), since changes in oxidants induce \r\nchanges in the oxidation of aerosol precursors. "


id: 7f3004f868feea9815da73520eaeede0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 17562
content:
"In summary, the spatial and temporal distribution of the net aerosol \r\nERF from 1850-2014 is highly heterogeneous (high confidence). \r\nGlobally, there has been a  shift from increase to decrease of the \r\nnegative net aerosol ERF driven by trends in aerosol and their \r\nprecursor emissions (high confidence). However, the timing of this \r\nshift varies by continental-scale region and has not occurred for some \r\nfiner regional scales. "


id: db41addd604ea89be6680cd02ac3eb92
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 32005
content:
"The net ERF from N2O emissions is estimated to be 0.24 [0.13 to 0.34] \r\nW m-2, which is very close to the abundance-based estimate of \r\n0.21 W m-2 (Section  7.3.2.3). The indirect contributions from N2O \r\nare relatively minor with negative (methane-lifetime) and positive \r\n(ozone-and-cloud) effects nearly compensating each other. Emissions \r\nof halogenated compounds, including CFCs and HCFCs, were assessed \r\nas very likely causing a net-positive ERF in the AR5. However, recent \r\nstudies (Morgenstern et al., 2020; O'Connor et al., 2021; Thornhill \r\net al., 2021b) find strong adjustments in Southern Hemisphere \r\naerosols and clouds, such that the very likely range in the emission\u0002based ERF for CFC + HCFCs + HFCs now also include negative values. "


id: 1a36802469c133f14447b66337eb0465
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 1589
content:
"The ERFs attributable to emissions versus concentrations for several \r\nSLCFs including ozone and methane are different. A concentration \r\nchange, used to assess the abundance-based ERF, results from the\r\nchanges in emissions of multiple species and subsequent chemical \r\nreactions. The corollary is that the perturbation of a single emitted \r\ncompound can induce subsequent chemical reactions and affect the \r\nconcentrations of several climate forcers (chemical adjustments); \r\nthis  is what is accounted for in emissions-based ERF. Due to non\u0002linear chemistry (Section  6.3) and non-linear aerosol-cloud \r\ninteractions (Section  7.3.3.2), the ERF attributed to the individual \r\nspecies cannot be precisely defined and can only be estimated \r\nthrough model simulations. For example, the ERF attributed to \r\nmethane emissions, which includes indirect effects through ozone "


id: 89f83051db27012d050bd0e301ed7a67
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 19233
content:
"For methane emissions, in addition to their direct effect, there are \r\nindirect positive ERFs from methane enhancing its own lifetime, \r\ncausing ozone production, enhancing stratospheric water vapour, \r\nand influencing aerosols and the lifetimes of HCFCs and HFCs (Myhre \r\net al., 2013; O'Connor et al., 2021). The ERF from methane emissions \r\nis considerably higher than the ERF estimate resulting from its \r\nabundance change. The central estimate with the very likely range is \r\n1.19 [0.81 to 1.58] W m-2 for the emissions-based estimate compared "


id: ef092eb62331acdc2f55f8b1341a3dd
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 35080
content:
"Anthropogenic emissions of SO2 lead to the formation of sulphate \r\naerosols and a negative ERF through aerosol-radiation and aerosol-\r\ncloud interactions. The emissions-based ERFaci, which was not \r\npreviously considered in AR5, is now included. The estimated ERF is \r\nthus considerably more negative than the AR5 estimate with a radiative \r\nforcing of -0.4 W m-2, despite the decline of ERF due to aerosols since \r\n2011 (Section 7.3.3.1.3, Figure 6.12a). SO2 emissions are estimated \r\nto contribute to a negative ERF of -0.94 [-1.63 to -0.25] W m-2, with \r\n-0.23 W m-2 from aerosol-radiation interactions and -0.70 W m-2 \r\nfrom aerosol-cloud interactions. Emissions of NH3 lead to formation \r\nof ammonium-nitrate aerosols with an estimated ERF of -0.03 W m-2. "


id: 11d559591a02d28fb835d7cd67d13a35
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 303
content:
"with 0.54 W m-2 for the abundance-based estimate (Section 7.3.5). \r\nThe abundance-based ERF estimate for methane results from \r\ncontributions of its own emissions and the effects of several other \r\ncompounds, some decreasing methane lifetime, notably NOx, which \r\nimportantly reduce the methane abundance-based ERF. Emissions of \r\nCO and NMVOCs both indirectly contribute to a positive ERF through \r\nenhancing ozone production in the troposphere and increasing \r\nthe methane lifetime. For CO and NMVOCs of fossil origin there is \r\nalso a 0.07 W m-2 contribution to CO2 from their oxidation. The very \r\nlikely total ERF of CO and NMVOCs emissions is estimated to be \r\n0.44 [0.22 to 0.67] W m-2. "


id: fe0ec0d58edf030dde00715c4463134b
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 37498
content:
"NOx causes a  positive ERF through enhanced tropospheric ozone \r\nproduction and a negative ERF through enhanced OH concentrations \r\nthat reduce the methane lifetime. There is also a small negative ERF \r\ncontribution through the formation of nitrate aerosols, although \r\nonly three of the AerChemMIP models include nitrate aerosols. The \r\nbest estimate of the net ERF from changes in anthropogenic NOx\r\nemissions is -0.27 [-0.55 to 0.01] W m-2. The magnitude is somewhat \r\ngreater than the AR5 estimate (-0.15 [-0.34 to +0.02] W m-2) but \r\nwith a similar level of uncertainty. The difference between AR6 and \r\nAR5 estimates is possibly due to the different modeling protocols \r\n(see Supplementary Material: 6.SM.1)."


id: d9b12d5d9a84f34bcb9d6d5da8928039
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 31765
content:
"This section briefly discusses the climate response to SLCFs, in \r\nparticular to changes in aerosols, and gathers complementary \r\ninformation and assessments from Chapters 3, 7, 8 and 10. "


id: f7a3f728ad75db3d5242f73010c5cef
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 36473
content:
"Warming or cooling atmospheric aerosols, such as BC and sulphate, \r\ncan affect temperature and precipitation in distinct ways by \r\nmodifying the shortwave and longwave radiation, the lapse rate \r\nof the troposphere, and influencing cloud microphysical properties \r\n(Section  10.1.4.1.4, Box  8.1). An important distinction between \r\nscattering and absorbing aerosols is the opposing nature of their \r\ninfluences on circulation, clouds and precipitation, besides surface \r\ntemperature as evident from the contrasting regional climate \r\nresponses to regional aerosol emissions (e.g., Lewinschal et al., 2019; \r\nSand et al., 2020; also see Chapters 8 and 10). "


id: 4d093566a6113de3bf22c01e5166215
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 9546
content:
"The contributions from the emitted compounds to GSAT broadly follow \r\ntheir contributions to the ERF, mainly because their evolution over the \r\npast decades have been relatively similar and slow enough compared \r\nto their lifetimes to be reflected similarly in their ERF and GSAT despite \r\nthe delay of the GSAT response to ERF changes (Section  6.6.1). \r\nHowever, for some SLCFs (e.g., SO2) that have been reduced globally, \r\ntheir contribution to GSAT change is slightly higher compared with that \r\nof CO2 than their relative contribution to ERF because the peak in their \r\nERF change has already occurred (Section 6.4.1) whereas the peak of \r\ntheir GSAT effect started to decline recently (Figure 7.9). This is due \r\nto the inertia of the climate system delaying the full response of GSAT \r\nto a change in forcing (Figure 6.15). "


id: 34d328e0e60d6a046aa4a3a3866795d9
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 5718
content:
"In summary, emissions of SLCFs, especially methane, NOx and SO2, \r\nhave substantial effects on effective radiative forcing (ERF) (high \r\nconfidence). The net global emissions-based ERF of NOx is negative \r\nand that of NMVOCs is positive, in agreement with the AR5 assessment \r\n(high confidence). For methane, the emissions-based ERF is twice as \r\nhigh as the abundance-based ERF (high confidence). SO2 emissions \r\nmake the dominant contribution to the ERF associated with the \r\naerosol-cloud interaction (high confidence). The contributions from \r\nthe emitted compounds to GSAT broadly follow their contributions to \r\nthe ERF (high confidence). However, due to the inertia of the climate \r\nsystem delaying the full GSAT response to a  change in forcing, \r\nthe contribution to GSAT change due to SO2 emissions is slightly \r\nhigher compared with that due to CO2 emissions (than their relative \r\ncontributions to ERF) because the peak in emission-induced SO2 ERF \r\nhas already occurred."


id: 9e0d3d2b10c33a9ab8cef069ddc5fed5
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 22391
content:
"In AR5, there was low confidence in the overall understanding of \r\nclimate response to spatially varying patterns of forcing, though \r\nthere was medium to high confidence in some regional climate \r\nresponses, such as the damped warming of the NH and shifting of \r\nthe ITCZ from aerosols, and positive feedbacks enhancing the local \r\nresponse from high-latitude snow and ice albedo changes. Since \r\nAR5, the relationship between inhomogeneous forcing and climate \r\nresponse is better understood, providing further evidence of the \r\nclimate influence of SLCFs (aerosols and ozone in particular) on \r\nglobal to regional scales (Collins et al., 2013; Shindell et al., 2015; \r\nAamaas et al., 2017; Kasoar et al., 2018; Persad and Caldeira, 2018; \r\nWilcox et al., 2019) which differ from the relatively homogeneous \r\nspatial influence from LLGHGs. "


id: ceadddffea6615246e5ca9badf56276
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 30060
content:
"Large geographical variations in aerosol ERFs (Section 6.4.1) affect \r\nglobal and regional temperature responses (Myhre et al., 2013; \r\nShindell et al., 2015). Multi-model CMIP6 ensemble-mean results \r\n(Figure  6.13) show cooling over almost all areas of the globe in \r\nresponse to increases of aerosol and their precursor emissions from \r\n1850 to the recent past (1995-2014). While the ERF has hotspots, "


id: 47410cedfd89bcf3a9a5ea348b638841
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 8591
content:
"The asymmetric aerosol and greenhouse gas forcing on regional\u0002scale climate responses have also been assessed to lead to \r\ncontrasting effects on precipitation in Chapter  8. The asymmetric \r\nhistorical radiative forcing due to aerosols led to a southward shift in \r\nthe tropical rain belt (high confidence) and contributed to the Sahel \r\ndrought from the 1970s to the 1980s (high confidence). Furthermore, \r\nthe asymmetry of the forcing led to contrasting effects in monsoon \r\nprecipitation changes over West Africa, Southern Asia and Eastern \r\nAsia over much of the mid-20th century due to GHG-induced \r\nprecipitation increases counteracted by anthropogenic aerosol\u0002induced decreases (high confidence) (see Section 8.3 and Box 8.1). "


id: 8c381e2fdd468c59935349d118d852af
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 19566
content:
"The effects of changes in aerosols on local and remote changes in \r\ntemperature, circulation and precipitation are sensitive to a number \r\nof model uncertainties affecting aerosol sources, transformation \r\nand resulting radiative efficacy. Therefore, regional climate effects \r\nin global model studies must be interpreted with caution. When \r\ninvestigating the climate response to regional aerosol emissions, \r\nsuch uncertainties are likely to be confounded even further by \r\nthe variability between models in regional climate and circulation "


id: 8b7cc7036601a6e09d8ce12911acbb3e
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 19464
content:
"patterns, leading to greater inter-model spread at regional scales \r\nthan at a global scale (Baker et al., 2015; Kasoar et al., 2016). "


id: 57d4ff60c26ea4c9affc9d0ad0ffd671
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 11318
content:
"Nr deposition increased, and the beneficial effects of Nr on root \r\ndevelopment were lost at higher ozone treatments (Mills et al., \r\n2016). Reducing uncertainties in ozone vegetation damage effects \r\non the carbon cycle requires improved information on the sensitivity \r\nof different plant species to ozone, and measurements of ozone \r\ndose-response relationships for tropical plants, which are currently \r\nlacking. Surface ozone effect on the land carbon sink and indirect CO2\r\nforcing, therefore, remains uncertain. Collins et al. (2010) showed that \r\nadding in the effects of surface ozone on vegetation damage and \r\nreduced uptake of CO2 added about 10% to the methane emissions \r\nmetrics and could change the sign of the NOx metrics. However, this \r\nestimate has to be considered as an upper limit due to limitation of \r\nthe paramaterization used by Sitch et al. (2007) considering more \r\nrecent knowledge and is thus not included in the current metrics \r\n(Section 7.6.1.3)."


id: e81b4f150ee66f32a2710008af3e9056
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 34020
content:
"In summary, over the historical period, changes in aerosols and \r\ntheir ERF have primarily contributed to cooling, partly masking \r\nthe human-induced warming (high confidence). Radiative forcings \r\ninduced by aerosol changes lead to both local and remote changes in \r\ntemperature (high confidence). The temperature response preserves \r\nhemispheric asymmetry of the ERF but is more latitudinally uniform \r\nwith strong amplification of the temperature response towards the \r\nArctic (medium confidence)."


id: e9f96f249995e9e32e463dccd5a0fcf
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 34309
content:
"Deposition of reactive nitrogen (Nr; i.e., NH3 and NOx) increases the \r\nplant productivity and carbon sequestration in N-limited forests and \r\ngrasslands, and also in open and coastal waters and open ocean. Such \r\ninadvertent fertilization of the biosphere can lead to eutrophication \r\nand reduction in biodiversity in terrestrial and aquatic ecosystems. \r\nThe AR5 assessed that it is likely that Nr deposition over land currently \r\nincreases natural CO2 sinks, in particular in forests, but the magnitude \r\nof this effect varies between regions (Ciais et al., 2013). Increasing \r\nNr deposition or the synergy between increasing Nr deposition \r\nand atmospheric CO2 concentration could have contributed to the \r\nincreasing global-net land CO2 sink (Section 5.2.1.4.1). "


id: a7a78d16a065596313121949f279b6c6
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 23884
content:
"In summary, reactive nitrogen, ozone and aerosols affect terrestrial \r\nvegetation and the carbon cycle through deposition and effects on \r\nlarge-scale radiation (high confidence) but the magnitude of these \r\neffects on the land carbon sink, ecosystem productivity and indirect \r\nCO2 forcing remain uncertain due to the difficulty in disentangling \r\nthe complex interactions between the effects. As such, we assess the \r\neffects to be of second order in comparison to the direct CO2 forcing \r\n(high confidence) but, at least for ozone, it could add a substantial \r\n(positive) forcing compared with its direct forcing (low confidence). "


id: 283f826713a7727a200e4185076a8a40
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 3741
content:
"Climate change-induced changes in atmospheric composition and \r\nforcing due to perturbations in natural processes constitute an \r\nEarth system feedback amplifying (positive feedback) or diminishing \r\n(negative feedback) the initial climate perturbation (Ciais et al., 2013; \r\nHeinze et al., 2019). Quantification of these biogeochemical feedbacks \r\nis important to allow for a better estimate of the expected effects \r\nof emissions reduction policies for mitigating climate change and \r\nthe effect on the allowable global carbon budget (Lowe and Bernie, \r\n2018). Biogeochemical feedbacks due to changes in the carbon cycle \r\nare assessed in Section  5.4.5, while physical and biogeophysical "


id: 44fac1b9718a83dc6f80dde4dfd4f462
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 8282
content:
"Climate-ozone feedback: Changes in ozone concentrations in \r\nresponse to projected climate change have been shown to lead to \r\na  potential climate-atmospheric chemistry feedback. Chemistry-\r\nclimate models consistently project a  decrease in lower tropical \r\nstratospheric ozone levels due to enhanced upwelling of ozone\u0002poor tropospheric air associated with surface warming-driven \r\nstrengthening of the Brewer-Dobson circulation (Bunzel and \r\nSchmidt, 2013). Further, models project an increase in middle and \r\nextratropical stratospheric ozone due to increased downwelling \r\nthrough the strengthened Brewer-Dobson circulation (Bekki et al., \r\n2013; Dietmuller et al., 2014). These stratospheric ozone changes \r\ninduce a  net-negative global mean ozone radiative feedback \r\n(Dietmuller et al., 2014). Tropospheric ozone shows a  range of \r\nresponses to climate with models generally agreeing that warmer "


id: 7e323311016903ccfee68ac601d036aa
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 17418
content:
"Climate-DMS feedback: Dimethyl sulphide (DMS) is produced \r\nby marine phytoplankton and is emitted to the atmosphere where \r\nit can lead to the subsequent formation of sulphate aerosol and \r\nCCN (Section  6.2.2.5). Changes in DMS emissions from ocean \r\ncould feedback on climate through their response to changes in \r\ntemperature, solar radiation, ocean mixed-layer depth, sea ice extent, \r\nwind speed, nutrient recycling or shifts in marine ecosystems due to \r\nocean acidification and climate change, or atmospheric processing \r\nof DMS into CCN (Heinze et al., 2019). Models with varying degrees \r\nof representation of the relevant biogeochemical processes and "


id: bc53c634c0a0003712e2a25f8539385a
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 27212
content:
"parametrization of lightning in ESMs (Section 6.2.1.2; Finney et al., \r\n2016b; Clark et al., 2017). AerChemMIP multi-model ensemble mean \r\nestimate a net-negative climate feedback from increases in lightning \r\nNOx in a warming world (Thornhill et al., 2021a). All AerChemMIP \r\nmodels use a  cloud-top height lightning parametrization that \r\npredicts increases in lightning with warming. However, a  positive \r\nclimate-lightning NOx feedback cannot be ruled out because of \r\nthe dependence of the response to lightning parametrizations as \r\ndiscussed in Section 6.2.2.1. "


id: a0a5bd2fe15523c86db307fa0468ee59
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 22817
content:
"Climate-methane lifetime feedback: Warmer and wetter climate \r\nwill lead to increases in OH and oxidation rates leading to reduced \r\natmospheric methane lifetime  - a  negative feedback (Naik et al., \r\n2013; Voulgarakis et al., 2013). Furthermore, since OH is in turn \r\nremoved by methane, the climate-methane lifetime feedback will be \r\namplified (Section 6.3.1; Prather, 1996). Based on the multi-model \r\nresults of Voulgarakis et al. (2013), a for climate-methane lifetime \r\nis estimated to be -0.030 +- 0.01 W m-2 degC-1 by Heinze et al. (2019). \r\nThe assessed central value of a based on the AerChemMIP ensemble \r\nis within the range of this estimate but with greater uncertainty \r\n(Thornhill et al., 2021a). "


id: 7908673a364a144693181dcfa9297b47
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 16595
content:
"In summary, climate-driven changes in emissions, atmospheric \r\nabundances or lifetimes of SLCFs are assessed to have an\r\noverall cooling effect, that is, a  negative feedback parameter of \r\n-0.20  [-0.41  to + 0.01] W m-2 degC-1, thereby reducing climate \r\nsensitivity (Section 7.4.2.5.1). This net feedback parameter is obtained \r\nby summing the assessed estimates for the individual feedback given \r\nin Table 6.8. Confidence in the magnitude and the sign of most of the \r\nindividual as well as the total non-CO2 biogeochemical feedbacks \r\nremains low as evident from the large range in the value of a. This \r\nlarge uncertainty is attributed to the diversity in model representation \r\nof the relevant chemical and biogeochemical processes based on \r\nlimited process-level understanding. "


id: 246d5af9f3756be14a1f5132ec1358bc
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 3175
content:
"Climate-lightning NOx feedback: As discussed in Section 6.2.2.1, \r\nclimate change influences lightning NOx emissions. Increases in \r\nlightning NOx emissions will not only increase tropospheric ozone \r\nand decrease methane lifetime but also increase the formation \r\nof sulphate and nitrate aerosols, via oxidant changes, offsetting \r\nthe positive forcing from ozone. The response of lightning NOx to \r\nclimate change remains uncertain and is highly dependent on the "


id: ac1c37aafc62b7da4ed0152bcf86bf15
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 24602
content:
"Table 6.8 | Assessed central estimates and the very likely ranges (5-95%) of non-CO2 biogeochemical feedback parameter (ax) based on the AerChemMIP \r\nensemble estimates (Thornhill et al., 2021a). As in Section 7.4.1.1, ax (W m-2 degC-1) for a feedback variable x is defined as ax = dN\r\ndx\r\ndx\r\ndT where dNdx is the change in TOA energy \r\nbalance in response to a change in x induced by a change in surface temperature (T). The 5-95% range is calculated as mean +- standard deviation x 1.645 for each feedback. \r\nThe level of confidence in these estimates is low owing to the large intermodel spread. Published estimates of ax are also shown for comparison. "


id: 99a11522983a9a397a00456a3d458566
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 21695
content:
"aerosol layer, and hence the ERF efficiency, which also depends \r\non the dispersion, transport, and residence time of the aerosols. "


id: d14e18849da2fe698e90781c2f228628
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 30466
content:
"Solar radiation modification (SRM; Sections 4.6.3.3 and 8.6.3) has \r\nthe potential to exert a  significant ERF on the climate, mainly by \r\naffecting the SW component of the radiation budget (e.g., Caldeira \r\net al., 2013; NRC, 2015; Lawrence et al., 2018). The possible ways \r\nand the extent to which the most commonly discussed options may \r\naffect radiative forcing is addressed in this section. Side effects of \r\nSRM on stratospheric ozone and changes in atmospheric transport \r\ndue to radiative heating of the lower stratosphere are discussed in \r\nSection 4.6.3.3. "


id: 166b3f2e031cf08809ff5db9d1698c28
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 1051
content:
"need for long simulations that include complex chemistry-natural \r\nsystem interactions with high computational cost, in addition \r\nto the difficulty related to the regionalization of climate change \r\n(Section  10.3.1.2). Changes in the occurrence of weather patterns \r\ninfluencing air pollution (e.g.,  anticyclonic stagnation conditions, \r\ntransport pathways from pollution sources, convection) due to \r\nclimate change are assessed in Chapters 4 and 11. "


id: 9bb53403294091fb18f09144597f80c8
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 22060
content:
"Modelled ERFaci associated with cirrus cloud thinning (CCT) cover \r\na wide range in the literature, and the maximum are of the order of \r\n-0.8 to -3.5 W m-2, though they are of low confidence, with some \r\nstudies using more simplified representations (Mitchell and Finnegan, \r\n2009; Storelvmo et al., 2013; Kristjansson et al., 2015; Jackson et al., \r\n2016; Muri et al., 2018; Gasparini et al., 2020). ERFaci for CCT is mainly \r\naffected by particle seeding concentrations, with an optimum around \r\n20 L-1, according to limited evidence from models (Storelvmo et al., \r\n2013). Seeding leading to higher particle concentrations could lead \r\nto a warming (Storelvmo et al., 2013; Penner et al., 2015; Gasparini \r\nand Lohmann, 2016). The lack of representation of processes related \r\nto, for example, heterogeneous and homogeneous freezing and their \r\nprevalence, is a dominant source of uncertainty in ERF estimates, in \r\naddition to less research activity. "


id: 23466e5fa1fda4f1d7ab8491af607055
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 3001
content:
"The AR5 assessed with high confidence that in unpolluted regions, \r\nhigher water vapour abundances and temperatures in a  warmer \r\nclimate would enhance ozone chemical destruction, leading to lower \r\nbaseline5 surface ozone levels (Kirtman et al., 2013). In polluted \r\nregions, AR5 assessed with medium confidence that higher surface \r\ntemperatures will trigger regional feedbacks in chemistry and local \r\nemissions that will increase surface ozone and the intensity of surface \r\nozone peaks. "


id: 4b7425526901c0220f87251b445d0f30
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
content_type: text
index: climate_gpt_only_giec
vector_id: 9285
content:
"In summary, the aerosol and cloud microphysics involved with SRM \r\nare not well understood, notably due to insufficient (and varying \r\ndegrees of) representation of relevant processes in models. ERF of up \r\nto several W m-2 is reported in the literature, with SAI at the higher \r\nend and CCT with lower potentials, though it remains a challenge to \r\nestablish ERF potentials and efficacies with confidence. Modelling \r\nstudies have been published with more sophisticated treatment of \r\nSRM since AR5, but the uncertainties, such as cloud-aerosol radiation \r\ninteractions, remain large (high confidence)."


