# document

id: a6d70d7ff498242a179d8d328dfba4f3
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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"The response of surface ozone to climate-induced Earth system \r\nchanges is complex due to counteracting effects. Studies considering \r\nthe individual effects of climate-driven changes in specific precursor \r\nemissions or processes show increases in surface ozone under \r\nwarmer atmosphere for some processes. This is indeed the case for \r\nenhanced STE and stratospheric ozone recovery (Sekiya and Sudo, \r\n2014; Hess  et al., 2015; Banerjee et al., 2016; Meul et al., 2018; \r\nMorgenstern et al., 2018; Akritidis et al., 2019) or the increase of \r\nsoil NOx emissions (Wu et al., 2008; Romer et al., 2018), which can \r\neach lead to 1 to 2 ppb increase in surface ozone. Other processes, \r\nin particular deposition or those related to emissions from natural \r\nsystems (Section  6.2.2) are expected to play a  key role in future \r\nsurface ozone and even the occurrence of pollution events (e.g., in the \r\ncase of wildfires) but their effects are difficult to quantify in isolation."


id: fc98019c73278ce2539885a99a13ccaa
created_at: 2023-03-16 14:00:43
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"This section relies on observational studies performed by analysing \r\nthe correlation between specific meteorological conditions projected \r\nto occur more frequently in the future and surface pollutants, and \r\nglobal- and regional-scale modelling studies considering solely \r\nclimate change in the future. We also assess the surface ozone and \r\nPM2.5 changes based on CMIP6 models analysed in AerChemMIP, \r\nconsidering climate change in isolation with emissions in 2050 from \r\nSSP3-7.0 scenario (Section 6.7.1). Air quality being highly variable in \r\nspace and time, the use of regional atmospheric chemistry models is \r\nnecessary to characterize the effect of future climate on air quality \r\nproperly. However, difficulties for such assessment arise from the "


id: 558fece91bb340d187c345f14ad5edb
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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"Over ozone-producing regions of the world, such as in North America, \r\nEurope and Eastern Asia, AR5 and post-AR5 model studies project \r\na general increase of surface ozone levels (climate change penalty \r\non ozone) in a  future warmer climate particularly during summer \r\n(Fu and Tian, 2019). However, in current regional models, using \r\nmore robust protocols, this increase of surface ozone, attributable \r\nto climate change, is of lower magnitude than in previous estimates \r\n(Lacressonniere et al., 2016; Garcia-Menendez et al., 2017). Climate \r\nchange enhances the efficiency of precursor emissions to generate \r\nsurface ozone in polluted regions (Schnell et al., 2016), and thus the \r\nmagnitude of this effect will depend on the emissions considered in \r\nthe study (present or future, and mitigated or not; Colette et al., 2015; \r\nFiore et al., 2015). "


id: dd2e2f8903d63c9c2cf190a8b3553faf
created_at: 2023-03-16 14:00:43
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"Consistent with AR5 findings, global mean surface ozone \r\nconcentration decreases range from 0.69 +- 0.16 ppb to \r\n2.28 +- 0.24 ppb due to the dominating role of ozone destruction \r\nby water vapour in a  four-member ensemble of CMIP6 ESM for \r\nsurface warmings of 1.5degC-2.5degC (Figure  6.14). This decrease is \r\ndriven by the ozone decrease over oceans, especially in the tropics \r\n(decrease of 1-5 ppb) and large parts of the continental unpolluted \r\nregions. The sensitivity of annual mean surface ozone to the level \r\nof surface warming over these remote areas varies spatially from \r\n-2 to -0.2 ppb oC-1 (Supplementary Material Figure 6.SM.1)."


id: 8fac029d354dc4de58ec1bda846bb106
created_at: 2023-03-16 14:00:43
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"et al., 2012) that would influence SOA concentrations, although this \r\neffect has been questioned by more recent evidence (B. Wang et al., \r\n2018; Z. Zhao et al., 2019). More generally, climate change will also \r\naffect dust concentration levels in the atmosphere (Section 6.2.2.4) \r\nand the occurrence of forest fires, both very large sources of aerosols \r\nto the global troposphere (Section 6.2.2.6)."


id: 573ec2f8aea8edad35c7024150f2501b
created_at: 2023-03-16 14:00:43
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"Wet deposition constitutes the main sink for atmospheric \r\nPM (Allen et al., 2016, 2019; Xu and Lamarque, 2018). In particular, \r\nprecipitation frequency has a  higher effect on PM wet deposition \r\nthan precipitation intensity (Hou et al., 2018). PM is also sensitive \r\nto wind speed and atmospheric stability conditions emphasizing \r\nthe importance of stagnation episodes and low planetary boundary \r\nlayer heights for increasing PM atmospheric concentrations (Porter \r\net al., 2015)."


id: c275c71df002752b0731b89668dbc24b
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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"At the global scale, depending on its magnitude, the warming leads \r\neither to a small increase in global mean PM concentration levels \r\n(about 0.21 ug m-3 in 2100 for RCP8.5), mainly controlled by sulphate \r\nand organic aerosols or a small decrease (-0.06 ug m-3 for RCP2.6, \r\nWestervelt et al. (2016) and Xu and Lamarque (2018)). On the other \r\nhand, Xu and Lamarque (2018) and Allen et al. (2016, 2019) found an \r\nincrease of aerosol burden and PM surface concentration throughout \r\nthe 21st Century, attributed to a decrease in wet-removal flux despite \r\nthe overall projected increase in global precipitation, on the ground \r\nof an expected shift of future precipitation towards more frequent \r\nheavy events. Based only on three models, the CMIP6 ensemble \r\nshows that for most land areas, there is low agreement between \r\nmodels on the sign of the effect of climate change on annual mean \r\nPM2.5 (Supplementary Material Figure 6.SM.2)."


id: 95069457d9121b97c95329b6f65306bf
created_at: 2023-03-16 14:00:43
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"Due to the typical atmospheric lifetime of PM in the atmosphere, \r\nof the order of a few days, most studies dealing with the future PM \r\nconcentration levels have a regional character and concern mainly \r\nEurope (Megaritis et al., 2013; Lacressonniere et al., 2016, 2017; \r\nLemaire et al., 2016; Cholakian et al., 2019), the USA (Penrod et al., \r\n2014; Fiore et al., 2015; Gonzalez-Abraham et al., 2015; Shen et al., \r\n2017; He et al., 2018; Nolte et al., 2018), Southern and Eastern Asia \r\n(Jiang et al., 2013; Nguyen et al., 2019) and India (Pommier et al., \r\n2018). No studies are available for other areas of the world."


id: 4a20ecfc3cb5befb950df03504d5be8d
created_at: 2023-03-16 14:00:43
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"Changes in the chemical composition of PM as a result of future climate \r\nchange can also be an important issue for the effects of PM on human \r\nhealth and the environment, but only sparse data are available in the \r\nliterature on this and the results are, as yet, inconclusive (Im et al., \r\n2012; Jiang et al., 2013; Megaritis et al., 2013; Gonzalez-Abraham \r\net al., 2015; Gao et al., 2018; He et al., 2018; Cholakian et al., 2019)."


id: b585e388e9f55aad4a83b4e2749bec3c
created_at: 2023-03-16 14:00:43
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"Changes in concentration and chemistry of particulate matter (PM) in \r\na changing climate depend in a complex manner on the response of \r\nthe multiple interactions of changes in emissions, chemical processes, \r\ndeposition and other factors (e.g.,  temperature, precipitation, \r\ncirculation patterns). These changes are difficult to assess and, at \r\nthe time of AR5, no confidence level was attached to the overall \r\nimpact of climate change on PM2.5 (Kirtman et al., 2013). Possible \r\nchanges induced by climate change may concern both atmospheric \r\nconcentration levels and chemical composition."


id: 5cd5b6bf1e2b19d9a12aa588cd92a6e
created_at: 2023-03-16 14:00:43
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"Overall, there is medium confidence (medium evidence, high \r\nagreement) in a  small effect, positive or negative, on PM global \r\nburden due to climate change. "


id: eac4e968e18361b035b2467775e36572
created_at: 2023-03-16 14:00:43
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"Higher temperatures increase the reaction rate of gaseous SO2 to \r\nparticulate sulphate conversion but also favour evaporation of \r\nparticulate ammonium nitrate (Megaritis et al., 2013). Also, higher \r\ntemperatures are expected to affect BVOC emissions (e.g., Pacifico "


id: ebdff8aedb8be8c2c2a46d8f0a19b5f3
created_at: 2023-03-16 14:00:43
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"Meteorological conditions, such as heatwaves, temperature \r\ninversions and atmospheric stagnation episodes favour air quality \r\nextremes and are influenced by changing climate (Fiore et al., 2015). \r\nThe body of literature on the connection between climate change and \r\nextreme anthropogenic pollution episodes is essentially based on \r\ncorrelation and regression applied to observation reanalysis but the \r\nmetrics and methodologies differ making quantitative comparisons \r\ndifficult. Many emission processes in the natural systems are sensitive \r\nto temperature, and bursts of emissions as a  reponse to extreme \r\nweather, as in the case of wildfires in dry conditions (Bondur et al., \r\n2020; Xie et al., 2020) can occur, which would then add to the risk \r\nof extreme air pollution but are not sufficiently constrained to be \r\nquantitatively assessed."


id: 8b80dc2a0283ec1c906a5b4f5d707ba3
created_at: 2023-03-16 14:00:43
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"Policies addressing the reduction of either SLCFs or LLGHGs, often \r\nprioritize mitigation of emissions from specific anthropogenic \r\nsources, such as energy production, industry, transportation, \r\nagriculture, waste management and residential fuel use. The choice \r\nof the targetted sector and chosen measures will determine the \r\nratios of emitted SLCFs and LLGHGs. These changes in emissions of \r\nco-emitted species will result in diverse responses driven by complex \r\nchemical and physical processes, and resulting climate perturbations. \r\nThe understanding of the co-benefits through sectoral mitigation \r\nefforts (as well as potential negative impacts) is essential to inform \r\npolicymaking. "


id: 496214eb8c249b0e0d5158aac744e6a
created_at: 2023-03-16 14:00:43
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"The discussion of targeted SLCF policies and their role in climate \r\nchange mitigation includes: critical evaluation of the climate  co\u0002benefits (Smith and Mizrahi, 2013; Pierrehumbert, 2014; Rogelj et al., \r\n2014b; Strefler et al., 2014; M.R. Allen et al., 2016); modelling the \r\npotential of dedicated BC and methane reductions in association with \r\nor without climate policy (Harmsen et al., 2020a; Smith et al., 2020); \r\nquantifying individual or multi-component mitigation in relation \r\nto natural variability (Samset et al., 2020); warning about the risk \r\nof diversion of resources from targeted LLGHG policies, especially \r\nthose targeting CO2 (e.g., Shoemaker et al., 2013); and seeing it as \r\nan opportunity to strengthen commitment and accelerate action on \r\nLLGHGs (Victor et al., 2015; Aakre et al., 2018). "


id: 6a7d981ac1e325d4dcdb54be48c36af7
created_at: 2023-03-16 14:00:43
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"The increase of frequency, duration and intensity of heatwaves \r\nis extremely likely on all continents for different future warming \r\nlevels (Section 11.3.5, Table 11.2). However, there is low confidence\r\nin projected changes in storm tracks, jets and blocking, and thus \r\ntheir influence on extreme temperatures in the mid-latitudes \r\n(Section 11.3.1). "


id: c91bd103b81fe5e0d514c82e329a7468
created_at: 2023-03-16 14:00:43
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"Over the last decade, research on air quality-climate interactions and \r\nfeedbacks has brought new attention from policy communities to the \r\npossibility of win-win mitigation policies that could both improve air \r\nquality and mitigate climate change, possibly also reducing the cost \r\nof interventions (Anenberg et al., 2012; Shindell et al., 2012, 2017b; \r\nSchmale et al., 2014a, b; Sadiq et al., 2017; Fay et al., 2018; Harmsen \r\net al., 2020b). Haines et al. (2017) and Shindell et al. (2017b) connect \r\nthe measures to mitigate SLCFs with the achievements of some of \r\nthe SDGs. Indeed, most studies on co-benefits to date focus on the "


id: d27bcae8d7274ab027e1e2d14478f9be
created_at: 2023-03-16 14:00:43
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"In conclusion, there is still medium confidence that climate\u0002driven changes in meterorological conditions, such as heatwaves \r\nor stagnations, will favour extreme air pollution episodes over \r\nhighly polluted areas, however, the relationship between these \r\nmeteorological conditions and high concentrations of ozone and \r\nPM2.5 have been shown to be regionally and metric dependant."


id: e6608e50a5abb4ec7955bb2e5c479d62
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"impacts of climate change mitigation strategies, in particular to meet \r\nNationally Determined Contributions (NDCs) and/or to remain below \r\nspecific global temperature targets, on air quality and human health \r\n(West et al., 2013; Rao et al., 2016; Shindell et al., 2016, 2017b; \r\nZhang et al., 2016; Chang et al., 2017; Haines et al., 2017; Li et al., \r\n2018; Markandya et al., 2018; Williams et al., 2018; Xie et al., 2018; \r\nLelieveld et al., 2019). Such co-benefits of climate change mitigation \r\nfor air quality and human health can offset the costs of the climate \r\nmeasures (Saari et al., 2015; Li et al., 2018). A growing number of \r\nstudies analyse the co-benefits of current and planned air-quality \r\npolicies on LLGHGs and global and regional climate change impacts \r\n(Lund et al., 2014; Akimoto et al., 2015; Lee et al., 2016; Maione et al., \r\n2016; Peng et al., 2017)."


id: a79b69683345c1cf0b31c1f2e35d84d2
created_at: 2023-03-16 14:00:43
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"This section assesses the effects of mitigating SLCFs, motivated \r\nby various objectives, discussing temperature response time, \r\ntemperature and air-quality attribution of SLCFs sources, and chosen \r\nmitigation approach. The air quality and climate effects of the \r\nmeasures to contain the spread of COVID-19 in 2020 are discussed in \r\nCross-Chapter Box 6.1 at the end of this section."


id: 15ac2b7cced6de29ccdb54bcc66346ed
created_at: 2023-03-16 14:00:43
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"The effect over time on GSAT following a mitigation effort affecting \r\nemissions of LLGHGs or SLCFs depends on the lifetimes of the \r\nLLGHGs and SLCFs, their radiative efficiencies, how fast the emissions \r\nare reduced, how long reductions last (limited time or sustained \r\nreduction), and the inertia of the climate system itself. Mitigation of \r\nSLCFs is often implemented through new legislation or technology \r\nstandards for the different emissions sectors and components, \r\nimplying that reductions are sustained over time. "


id: 6a27ddba9dd856a5fd40d46901dc96db
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"mixing into the deep ocean and therefore a substantial fraction of \r\natmospheric CO2 is only removed on millenium time scales. This \r\nmeans that for SLCFs including methane, the rate of emissions drives \r\nthe long-term stabilisation, as opposed to CO2 where the long-term \r\neffect is controlled by cumulative emissions (Allen et al., 2018b). \r\nMethods to compare rates of SLCF emissions with cumulative CO2\r\nemissions are discussed in Chapter 7 (Section 7.6.1.4)."


id: c3e27c759a0afa764471dae54febf60e
created_at: 2023-03-16 14:00:43
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"As a  consequence, in idealized ESM studies that assume an \r\ninstantaneous removal of all anthropogenic or fossil fuel-related \r\nemissions, a rapid change in aerosol levels occurs leading to large \r\nincreases in GSAT with the rate of warming lasting for several years. \r\nSimilarly, the thermal inertia causes the pulse emissions (Figure 6.15) \r\nof SLCFs to have a  significant effect on surface temperature even \r\nafter 10 years. "


id: 34689d21382b333536aee91bf95d95e3
created_at: 2023-03-16 14:00:43
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"In summary, for SLCFs with short lifetime (e.g., months), the response \r\nin surface temperature occurs strongly as soon as a sustained change \r\nin emissions is implemented and continues to grow for a  few \r\nyears, primarily due to thermal inertia in the climate system (high \r\nconfidence). Near its maximum, the response slows down but will \r\nthen take centuries to reach equilibrium (high confidence). For SLCFs \r\nwith longer lifetimes (e.g.,  a decade), a  delay equivalent to their \r\nlifetimes comes in addition to the delay due the thermal inertia \r\n(high confidence). "


id: acbf82124acb8ff421ed44a4d34f091e
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"Assessment of the temperature response to source emissions \r\nsectors is important for identifying priority mitigation measures and \r\ndesigning efficient mitigation strategies. "


id: dfdaabf4cc8698276dcfe0b050086c34
created_at: 2023-03-16 14:00:43
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"The net climate effect of a one-year pulse of current emissions from the \r\nresidential sector is warming in the near term of +0.0018degC +- 0.00084degC \r\nfrom fossil fuel use and +0.0014degC +- 0.0012degC from biofuel use. Over \r\na 100-year time horizon, this warming is +0.0017degC +- 0.00017degC \r\nand +0.0001degC +- 0.000079degC, respectively (Lund et al., 2020). This is \r\ndue to the effects of BC, methane, CO and NMVOCs, which add to \r\nthat of CO2, but the uncertainty in the sign of carbonaceous aerosol \r\nnet effects challenges overall quantitative understanding of this \r\nsector and leads to low confidence in this assessment. Residential \r\nsector emissions are an important source of indoor and outdoor air \r\npollution in Asia and globally (high confidence)."


id: c7f84fec20cc3dab046dadebf0d7c513
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"The residential and commercial sector is associated with SLCF \r\nemissions of carbonaceous aerosols, CO and NMVOCs, SO2 and NOx, \r\nand can be split by fuel type (biofuel or fossil fuel) where residential \r\nfossil fuel is also associated with CO2 and methane emissions \r\n(Section 6.2.1). "


id: 6275bf117f46b30699c682a0cda44981
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"The net effect of residential CO and NMVOC emissions is warming \r\nand that of SO2 and NOx is cooling of the atmosphere. However, the "


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"positive on a 20-year time horizon (AR5, Chapter 8; Lund et al., 2017). \r\nThis difference in net GSAT after 20 years in AR5 compared to AR6, \r\nresults primarily from a shorter time scale of the climate response in \r\nthe underlying AGTP calculations in Lund et al. (2020), which means \r\nthe initial, strong impacts of the most short-lived SLCFs, including the \r\nwarming by contrail-cirrus decay faster, in turn giving the net NOx\r\neffect a relatively higher importance after 20 years. On longer time \r\nhorizons, the net GSAT response switches back to positive, as CO2\r\nbecomes the dominating warming contribution."


id: 623a797b9a72a629104e22bff35da5d0
created_at: 2023-03-16 14:00:43
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"to a  net surface warming at 20- and 100-year horizons following \r\na  one-year pulse emission. This net temperature response was \r\ndetermined by similar contributions from contrails, contrail cirrus and \r\nCO2 over a 20-year time horizon, and dominated by CO2 in a 100-year \r\nperspective (Figure 8.34 in AR5, Myhre et al., 2013). "


id: 1d542e3961851866d2bc1ad6004a5513
created_at: 2023-03-16 14:00:43
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"In summary, the net aviation ERF is assessed to be +0.1 W m-2 (+-0.045) \r\nfor the year 2018 (low confidence). This confidence level is largely \r\na result of the fact that the SLCF-related terms, which account for \r\nmore than half (66%) of the net aviation ERF, are the most uncertain \r\nterms. The climate response to SLCF-related aviation terms exhibits \r\nsubstantial spatio-temporal heterogeneity in characteristics (high \r\nconfidence). Overall, cirrus and contrail cirrus warming, as well as \r\nNOx-induced ozone increase, induce strong but short-lived warming \r\ncontributions to the GSAT response 10 years after a one-year pulse of \r\npresent-day aviation emissions (medium confidence), while CO2 both \r\ngives a warming effect in the near term and dominates the long-term \r\nwarming impact (high confidence).    "


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"One of the most significant changes between AR5 and AR6 in terms \r\nof aviation SLCFs is the explicit calculation of a contrail cirrus ERF \r\nfound to be 35% of the corresponding RF (Bickel et al., 2020), \r\nconfirming the studies indicating smaller efficacy of linear contrails \r\n(Ponater et al., 2005; Rap et al., 2010). The net-NOx term is generally \r\nagreed to be a positive RF in the present day, although attribution \r\nin a non-linear chemical system is problematic (Grewe et al., 2019), \r\nbut Skowron et al. (2021) point out that the sign of net NOx term is \r\ndependent on background conditions and could be negative under \r\ncertain future scenarios."


id: 6d0f6d352d67ee90f2af516345e6be69
created_at: 2023-03-16 14:00:43
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"Shipping is also of importance for air pollution in coastal areas along \r\nthe major trade routes, especially in Europe and Asia (Corbett et al., \r\n2007; H. Liu et al., 2016, Figure 6.17; Jonson et al., 2020). Jonson \r\net al. (2020) estimated that shipping is responsible for 10% or more \r\nof the controllable PM2.5 concentrations and depositions of oxidised \r\nnitrogen and sulphur for many coastal countries. Widespread \r\nintroduction of low-sulphur fuels in shipping from 2020 (IMO, 2016) \r\nwill lead to improved air quality and reduction in premature mortality \r\nand morbidity (Sofiev et al., 2018)."


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"In summary, a year's worth of present-day global shipping emissions \r\n(i.e., without the implementation of the 2020 clean fuel standards) \r\ncause a net global cooling (-0.0024 +- 0.0025degC) on 10-20 year time \r\nhorizons (high confidence) but its magnitude is of low confidence."


id: 4b0d9c4979bdbebdad2c5e6d941fc617
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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"term effects (and a 20-year horizon is presented in Supplementary \r\nMaterial Figure 6.SM.3). Other time-horizon choices may affect the \r\nrelative importance, and even sign in the case of the NOx effect, \r\nof the temperature response from some of the SLCFs, or be more \r\nrelevant for certain applications. GSAT response is calculated using \r\nthe concept of AGTP (Section 7.6.2.2). Further details of the AGTP \r\nemulator applied in Figure 6.16 are provided in Lund et al. (2020) \r\nand Supplementary Material 6.SM.4 (Section 7.6.1.2, Cross-Chapter \r\nBox 7.1 and Supplementary Material 7.SM.7.2). As discussed by Lund \r\net al. (2020), the AGTP framework is primarily designed to study \r\nthe relative importance of individual emissions and sources, but the \r\nabsolute magnitude of temperature responses should be interpreted \r\nwith care due to the linearity of the AGTP, which does not necessarily \r\ncapture all the non-linear effects of SLCFs emissions on temperature."


id: d096ce1d66fb4e7f2dac2f6c9d33c56d
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 30342
content:
"The on-road and off-road transportation sectors have a net warming \r\nimpact on climate over all time scales (Berntsen and Fuglestvedt, \r\n2008; Fuglestvedt et al., 2008; Unger et al., 2010; Lund et al., 2020). \r\nA one-year pulse of present-day emissions has a  small net global \r\ntemperature effect on short time scales (+0.0011degC +- 0.0045degC), \r\npredominantly driven by CO2 and BC warming offset by NOx-induced \r\ncooling through methane lifetime reductions (Lund et al., 2020)."


id: 23c830414aec3f6062940c93507c09b1
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: 3075
content:
"The vehicle tailpipe emissions profiles of diesel and gasoline are \r\ndistinctly different. Diesel air pollutant emissions are dominated by \r\nBC and NOx whereas gasoline air pollutant emissions are dominated \r\nby CO and NMVOCs, especially when distribution and upstream losses \r\nare considered. Thus, the net radiative effect of the on-road vehicle \r\nfleets depends upon the share of different fuels used, in particular \r\ngasoline and diesel (Lund et al., 2014; Huang et al., 2020). The net \r\nSLCF for year-2010 emissions from the global diesel vehicle fleet \r\nhave been estimated to be +28 mW m-2 (Lund et al., 2014). Huang \r\net al. (2020) estimated net global radiative effects of SLCFs (including \r\naerosols, ozone, and methane) from the gasoline and diesel vehicle \r\nfleets in the year 2015 to be +13.6 and +9.4 mW m-2, respectively, \r\nwith similar fractional contributions of SLCFs to the total global \r\nclimate impact including CO2 on the 20-year time scale (14-15%)."


id: 2f759f20cc5c6e8d3e4b95feaa1ad791
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: 4891
content:
"Differences in the mix of emissions result in net effects on GSAT \r\nthat vary substantially, in both magnitude and sign, between sectors \r\nand regions. SLCFs contribute substantially to the GSAT effects of \r\nsectors on short time horizons (10-20 years) but CO2 dominates on \r\nlonger time horizons (Figure 6.16). As the effect of the SLCFs decays \r\nrapidly over the first few decades after emission, the net long-term \r\ntemperature effect is predominantly determined by CO2. N2O adds \r\na  small contribution to the long-term effect of agriculture. CO2\r\nemissions cause an important contribution to near-term warming \r\nthat is not always fully acknowledged in discussions of LLGHGs and \r\nSLCFs (Lund et al., 2020). "


id: 6237a79a43d2661baa344f2b59026335
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: 13008
content:
"In summary, the present-day global land-based transport pulse \r\nemissions cause a  net global warming on all time scales (high \r\nconfidence) and are detrimental to air quality (high confidence)."


id: 414c2442ceaa62ad7085b15f36791c3e
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: 7669
content:
"Figure 6.16 presents the GSAT response to an idealized pulse of year\u00022014 emissions of individual SLCF and LLGHG. The GSAT response \r\nis calculated for 11 sectors and 10 regions accounting for best \r\navailable knowledge and geographical dependence of the forcing \r\nefficacy of different SLCFs (Lund et al., 2020). Two time horizons \r\nare shown (of 10  and 100 years) to represent near- and long\u0002"


id: e10c4a34be054e89c897a528160bbd8e
created_at: 2023-03-16 14:00:43
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content:
"year time horizons. Results for the 20-year time horizon are provided \r\nin the Supplementary Material 6.SM.4. Compared to the 10-year time \r\nhorizon, there are some changes in ranking, especially of sectors and \r\nregions with a strong SO2 contribution, which decays substantially \r\nbetween 10 and 20 years. Aviation is the sector with the most \r\ndistinct difference between 10- and 20-year time horizons, such that \r\nthe net GSAT effect after 20 years becomes small but negative. This \r\nis due to a switch in sign for the NOx AGTP for this sector and the \r\nstronger effect of short-lived ozone response over these two short\u0002term horizons in the case of aviation compared with other sectors."


id: 7a892058b29c8986ef7e636a514d3a05
created_at: 2023-03-16 14:00:43
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vector_id: 16849
content:
"is an important contributor to ambient PM2.5 in Europe and North \r\nAmerica, while open biomass burning is a  major contributor in \r\nSouth East Asia and Developing Pacific, North America as well as \r\nLatin America. These results are consistent with several global and \r\nregional studies, where contribution of emissions sources to ambient \r\nPM2.5 or premature mortality was estimated at different scales \r\n(e.g., Guttikunda et al., 2014; Lelieveld et al., 2015b; Amann et al., \r\n2017; Qiao et al., 2018; Venkataraman et al., 2018; Wu et al., 2018). "


id: ca18f496180a056a4366b9f72723d676
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: 29354
content:
"In terms of source regions, the largest contributions to net short\u0002term warming are caused by emissions in Eastern Asia, Latin \r\nAmerica and North America, followed by Africa, Eastern Europe, \r\nWest-Central Asia and South East Asia (medium confidence). \r\nHowever, the relative contributions from individual species vary. \r\nIn Eastern Asia, North America, Europe and Southern Asia, the effect \r\nof current emissions of cooling and warming SLCFs approximately \r\nbalance in the near term and these regions cause comparable net \r\nwarming effects on 10- and 100-year time horizons (Figure  6.16). \r\nIn Latin America, Africa, and South East Asia and Developing Pacific, \r\nmethane and BC emissions are currently high while emissions of CO2\r\nand cooling aerosols are low compared to other regions, resulting in \r\na net warming effect after 10 years that is substantially higher than \r\nthat of CO2 alone. "


id: 9741175ba301dbbb893d7764be8257f8
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Overall, the global sectors that contribute the largest warming on \r\nshort time scales are the methane-dominated sources, that is energy \r\nproduction (fossil fuel mining and distribution), and agriculture \r\nand waste management (high confidence). On short time scales, \r\nother net warming sectors are residential fossil fuel and energy \r\ncombustion (dominated by CO2), and aviation and residential biofuel \r\n(dominated by SLCFs) (medium confidence). On short time scales, \r\nglobal emissions from industry and shipping cause a  net cooling \r\neffect despite a  considerable warming from CO2 emissions (high \r\nconfidence). On longer time horizons, the sectors that contribute the \r\nlargest warming are energy combustion and industry due to the large \r\nCO2 emissions (high confidence). "


id: f1fe10d750d32ddbb8baea3d5bf55fd1
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Residential and commercial cooking and heating are among the \r\nmost important anthropogenic sources of ambient PM2.5, except in \r\nthe Middle East and Asia-Pacific Developed (high confidence) and \r\nagriculture is the dominant source in Europe and North America \r\n(medium confidence). Energy and industry are important PM2.5\r\ncontributors in most regions, except Africa (high confidence). Energy \r\nand land transportation are the major anthropogenic sources of \r\nozone across many world regions (medium to high confidence)."


id: fa95eed343263f03a8c08e1b22f3e47
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"the carbon price and climate change mitigation target, further \r\nreduction of methane from waste and agriculture will also be \r\npart of such scenarios. The limitations of RCP scenarios (where \r\ncontinuous strengthening of air-quality legislation was assumed \r\nresulting in lack of futures where global and regional air \r\nquality deteriorates) for the analysis of air quality and potential \r\nfor mitigation of SLCFs have been discussed in literature \r\n(e.g.,  Amann et al., 2013; von Schneidemesser et al., 2015). \r\nSSP scenarios consider various levels of air pollution control, in \r\naccordance with their socio-economic narrative, and thus cover \r\na wider span of SLCF trajectories (Section 6.7). The economic cost \r\nof the implementation of these scenarios and their co-benefits \r\non air quality and SDGs are assessed in the AR6 WGIII Report \r\n(Chapter 3). "


id: a31bc79ca25483087c1e66dfff494c2f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Several SLCF-emissions reduction strategies have been explored in \r\nthe literature or are already pursued as part of environmental and \r\ndevelopment policies, including air quality, waste management, \r\nenergy poverty and climate change. The effects of various policies \r\nand strategies have been addressed in a limited number of modelling \r\nstudies with different objectives that range from assessment of \r\nspecific policies and their regional effects (UNEP and WMO, 2011; \r\nShindell et al., 2012, 2017b; AMAP, 2015a, b; Haines et al., 2017; \r\nUNEP and CCAC, 2018; UNEP, 2019; Harmsen et al., 2020a) to large\u0002scale global-scenario studies with varying levels of SLCF control \r\n(e.g.,  Sand et al., 2016; Rogelj et al., 2018b; Shindell and Smith, \r\n2019). They could be grouped into: "


id: a8cc0beeec3891cbe1ba07a866f2f1a5
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"level of exposure to pollution and subsequent effects varies \r\nsignificantly from one region to another. Therefore, air-quality \r\npolicies, regional climate impact concerns and development \r\npriorities, as well as the consequent level of mitigation of \r\nparticular SLCF species, will differ regionally and source-wise with \r\nrespect to the emissions sources, influence of inter-continental \r\ntransport of pollution, and spatial physical heterogeneities (Lund \r\net al., 2014; AMAP, 2015a; Sand et al., 2016; Turnock et al., 2016; \r\nSofiev et al., 2018; WMO, 2018). This is also the case at a finer \r\nlocal and regional scale where priorities and scope for SLCF \r\nmitigation will differ (e.g., Amann et al., 2017; UNEP, 2019). "


id: dc82f2a9cefe3b4a4c847204daa53671
created_at: 2023-03-16 14:00:43
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content:
"The Kigali Amendment (UNEP, 2016): with the adoption of the Kigali \r\nAmendment to the Montreal Protocol (UN, 1989) in 2016, parties \r\nagreed to the phase-down of HFCs, substances that are not ozone \r\ndepleting but are climate-forcing agents (Papanastasiou et al., \r\n2018). Baseline scenarios, in the absence of controls or only pre\u0002Kigali national legislation, projected increased use and emissions \r\nof HFCs. All recent baseline projections are significantly higher than \r\nthose used in the Representative Concentration Pathways (RCP) \r\nscenarios (Figure  6.18; Meinshausen et al., 2011). There is low "


id: ebf9394661c9c372a991d6290839b406
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 34623
content:
"SDGs has only started (Haines et al., 2017). Note that mitigation \r\nscenarios outside of the SSP framework are assessed here while \r\nthose within the SSPs are assessed in Section 6.7.3. "


id: 413b1a6e84f630de68d4c405642688ce
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: 7657
content:
"confidence that the high baseline (assuming absence of controls, \r\nlack of technical progress and high growth) as developed by Velders \r\net al. (2009), resulting in additional warming of about 0.5degC by \r\n2100 (Xu et al., 2013; WMO, 2018), is plausible. Evolution of HFC \r\nemissions along the baselines consistent with Velders et al. (2009) \r\nand Velders et al. (2015) would result in a global average warming, \r\ndue to HFCs, relative to 2000, of about 0.1degC-0.12degC by 2050 and \r\n0.35degC-0.5degC and 0.28degC-0.44degC by 2100, respectively (Xu  et al., \r\n2013). The  baseline implementation considered in SSP5-8.5 \r\n(Section 6.7.1.1) is comparable to the lower bound of projections by \r\nVelders et al. (2015; Figure 6.18) and several other studies (Gschrey \r\net al., 2011; Purohit and Hoglund-Isaksson, 2017; EPA, 2019; Purohit \r\net al., 2020) and result in additional warming of 0.15degC-0.3degC by \r\n2100 (medium confidence) (Figure 6.22). "


id: e5406894cf7a3d35c9efe0a5878f8a79
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: 33566
content:
"Focusing on air quality, specifically addressing aerosols, by introducing \r\nthe best available technology reducing PM2.5, SO2 and NOx in most \r\nAsian countries within the 2030-2050 time frame (a strategy that \r\nhas indeed shown reduction in PM2.5 exposure in China) comes, \r\nin many regions, short of national regulatory PM2.5 concentration \r\nstandards (often set at 35 ug  m-3 for annual mean; UNEP, 2019). \r\nSimilarly, global studies (Rafaj et al., 2018; Amann et al., 2020) show \r\nthat strengthening current air-quality policies, that address primarily \r\naerosols and their precursors, will not enable the achievement of \r\nWHO air quality guidelines (annual average concentration of PM2.5\r\nbelow 10 ug m-3) in many regions. "


id: 98d379cebb0acaf88d798e9b420e0698
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 21559
content:
"There is a consensus in the literature that mitigation of SLCF emissions \r\nplays a central role in simultaneous mitigation of climate change, air \r\nquality and other development goals including SDG targets (UNEP \r\nand WMO, 2011; Shindell et al., 2012, 2017b; Rogelj et al., 2014b, \r\n2018b; AMAP, 2015a; Haines et al., 2017; Klimont et al., 2017b; \r\nMcCollum et al., 2018; Rafaj et al., 2018; UNEP and CCAC, 2018; UNEP, \r\n2019). There is less agreement in the literature with respect to the \r\nactual mitigation potential (or its potential rate of implementation), \r\nnecessary policies to trigger successful implementation, and resulting \r\nclimate impacts. Most studies agree that climate policies, especially \r\nthose aiming to keep warming below 1.5degC or 2degC, trigger large SLCF \r\nmitigation co-benefits, (e.g., Rogelj et al., 2014b, 2018b), however, \r\ndiscussion of practical implementation of respective policies and "


id: bf058295a029b4ebc3bc100c81b1dec3
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: 27604
content:
"While there is robust evidence that air-quality policies resulting \r\nin reductions of aerosols and ozone can be beneficial for human \r\nhealth but can lead to 'disbenefits' for near-term climate change, the \r\nexistence of such trade-offs in response to climate change mitigation \r\npolicies is less certain (Shindell and Smith, 2019). Recent studies \r\nshow that very ambitious but plausible gradual phasing out of fossil \r\nfuels in 1.5degC-compatible pathways with little or no overshoot, lead \r\nto a near-term future warming of less than 0.1degC, when considering "


id: 3f0f04dd2fb2fbaac3bb122b1821805e
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: 7307
content:
"associated emissions reduction of both warming and cooling species. \r\nThis suggests that there may not be a strong conflict, at least at the \r\nglobal scale, between climate and air-quality benefits in the case of \r\na worldwide transition to clean energy (Shindell and Smith, 2019; Smith \r\net al., 2019). However, at the regional scale, the changes in spatially \r\nvariable emissions and abundance changes might result in different \r\nresponses, including implications for precipitation and monsoons \r\n(Chapter 8), especially over Southern Asia (e.g., Wilcox et al., 2020)."


id: 1a7e89c1a4d026a8466dc78ee20a5d6b
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 1652
content:
"negative CO2 emissions and, with currently known and effectively \r\napplied emissions-control technologies, this would also have \r\nimmediate and significant air-quality benefits, reducing premature \r\ndeaths worldwide (Shindell et al., 2018). For  a  2degC-compatible \r\npathway, Vandyck et al. (2018) estimated 5% and 15% reduction \r\nin premature mortality due to PM2.5 in 2030 and 2050, respectively, \r\ncompared to reference scenarios."


id: d251e4fe69b3095a867b50a7c7cf6f7d
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: 30617
content:
"Neither ambitious climate change policy nor air-quality abatement \r\npolicy can automatically yield co-benefits without integrated policies \r\naimed at co-beneficial solutions (Zusman et al., 2013; Schmale et al., \r\n2014a; Melamed et al., 2016), particularly in the energy generation \r\nand transport sectors (Rao et al., 2013; Thompson et al., 2016; Shindell \r\net al., 2018; Vandyck et al., 2018). Integrated policies are necessary \r\nto yield multiple benefits of mitigating climate change, improving air \r\nquality, protecting human health and achieving several SDGs."


id: ce0abb2e594ccbdaef625d283c43eacb
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: 29934
content:
"Striving to achieve air-quality and climate targets will bring significant SLCF reductions. These reductions contribute first and foremost \r\nto the attainment of SDGs targeting improved human health and sustainable cities (SDGs 3 and 11), specifically related to PM \r\nexposure (goals 3.9 and 11.6; Lelieveld, 2017; Amann et al., 2020), but also access to affordable and clean energy, responsible \r\nconsumption and production, and climate, as well as reducing nutrient losses and consequently protecting biodiversity (SDG 7, 12, \r\n13, 14 and 15; UNEP, 2019; Amann et al., 2020). Furthermore, declining SLCF emissions will result in reduced crop losses (SDG 2; zero \r\nhunger) due to decrease of ozone exposure (Feng and Kobayashi, 2009; Ainsworth et al., 2012; Emberson et al., 2018). "


id: 1e950cab30928b860acb1037270717a0
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: 2255
content:
"However, the design of suitable policies addressing these SDGs can be difficult because of the complexity of linking emissions to \r\nimpacts on human health, ecosystems, equity, infrastructure and costs. Beyond the fact that several species are co-emitted, interlinkage "


id: 1c2b91d353baeca65407515615f4f64c
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: 1891
content:
"SLCF reduction (high confidence). Sustained methane mitigation, \r\nwherever it occurs, stands out as an option that combines near- and \r\nlong-term gains on surface temperature (high confidence) and leads \r\nto an air pollution benefit by reducing ozone levels globally (high \r\nconfidence)."


id: 305efc0e0556ca4483c41864a5f4907e
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: 5039
content:
"In summary, there is high confidence that effective decarbonization \r\nstrategies could lead to air-quality improvements but are not sufficient \r\nto achieve, in the near term, air-quality WHO guideline values set for \r\nfine particulate matter, especially in parts of Asia and in some highly \r\npolluted regions. Additional policies (e.g.,  access to clean energy, \r\nwaste management) envisaged to attain SDGs bring complementary "


id: 8c54be940eb8a43ffb0f11d51d9ed3f8
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 19589
content:
"Cross-Chapter Box 6.1 | Implications of COVID-19 Restrictions for Emissions, Air Quality \r\nand Climate"


id: e53c2714662318353bfef7a0f76427c3
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: 33561
content:
"Contributors: Josep G. Canadell (Australia), Sergio Henrique Faria (Spain/Brazil), Piers Forster (UK), Sandro Fuzzi (Italy), Nathan P. Gillett \r\n(Canada), Christopher Jones (UK), Zbigniew Klimont (Austria/Poland), Svitlana Krakovska (Ukraine), Prabir Patra (Japan/India), \r\nJoeri Rogelj (Austria/Belgium), Bjorn Samset (Norway), Sophie Szopa (France), Izuru Takayabu (Japan), Hua Zhang (China)"


id: f68bcb53a8fe909f1a867ef501498f68
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: 36306
content:
"In response to the outbreak of COVID-19 (officially the severe acute respiratory syndrome-coronavirus 2 or SARS-CoV-2), which was \r\ndeclared a pandemic on March 11 2020 by the World Health Organization (WHO), regulations were imposed by many countries to \r\ncontain the spread of COVID-19. Restrictions were implemented on the movement of people, such as closing borders or requiring \r\nthe majority of population to stay at home, for periods of several months. This Cross-Chapter Box assesses the influence of the \r\nCOVID-19 containment on short-lived climate forcers (SLCFs) and long-lived greenhouse gases (LLGHGs), and related implications for \r\nthe climate. Note that this assessment was developed late in the AR6 WGI process and is based on the available emerging literature."


id: 36e172cc41c62b16f73722204485b1c2
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: 6024
content:
"Global fossil CO2 emissions are estimated to have declined by 7% (medium confidence) in 2020 compared to 2019 emissions, with \r\nestimates ranging from 5.8% to 13.0% based on various combinations of data on energy production and consumption, economic \r\nactivity and proxy activity data for emissions and their drivers (Forster et al., 2020; Friedlingstein et al., 2020; Le Quere et al., 2020; Liu \r\net al., 2020). However, the concentration of atmospheric CO2 continued to grow in 2020 compared to previous years (Dlugokencky and \r\nTans, 2021). Given the large natural interannual variability "


id: f6890ccd3098da2e7c9da2693fab77c0
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: 36303
content:
"of CO2 (Section 5.2.1), and the small expected impact of emissions in the CO2 growth rate, there were no observed changes in CO2\r\nconcentration that could be attributed to COVID-19 containment (medium confidence) (Chevallier et al., 2020; Tohjima et al., 2020). "


id: 6af788a21eaf55027c2fa627d7371e
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 14451
content:
"Global daily CO2 emissions from fossil fuel sources had a maximum decline of 17% in early April, compared with the mean 2019 \r\nlevels, and coinciding with the global peak pandemic lockdown (Le Quere et al., 2020). The reductions in CO2 emissions in 2020 were \r\ndominated by the drop in emissions from surface transport followed, in order of absolute emissions reductions, by industry, power and \r\naviation (Le Quere et al., 2020; Liu et al., 2020). Residential emissions showed little change (Liu et al., 2020) or rose slightly (Forster \r\net al., 2020; Le Quere et al., 2020). Aviation had the biggest relative drop in activity. CO2 emissions due to land use (based on early and \r\nuncertain evidence on deforestation and forest fires) were higher than average in 2020 (Amador-Jimenez et al., 2020). "


id: 50d8fae434f95c087f829378ab202034
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Using similar methodologies, Forster et al. (2020) assembled activity data and emissions estimates for other greenhouse gases and \r\naerosols and their precursors. Anthropogenic NOx emissions, which are largely from the transport sector, are estimated to have \r\ndecreased by a maximum of 35% in April (medium confidence). Species whose emissions are dominated by other sectors, such as \r\nmethane and NH3 from agriculture, saw smaller reductions. "


id: 3e64ecb5b478b053607486dd1a9ff54c
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 7189
content:
"Owing to the short atmospheric lifetimes of SLCFs relevant to air quality, changes in their concentrations were detected within \r\na few days after lockdowns had been implemented (e.g., Bauwens et al., 2020; Venter et al., 2020; Gkatzelis et al., 2021; Shi et al., \r\n2021). The COVID-19-driven economic slowdown has illustrated how complex the relationship is between emissions and air pollutant \r\nconcentrations due to non-linearity in the atmospheric chemistry leading to secondary compound formation (Section 6.1, Box 6.1; \r\nKroll et al., 2020)."


id: 508566edc38cbd6c29c8fedb8efc967c
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 10112
content:
"Several studies have examined the effect of COVID-19 containment on air quality, showing that multi-year datasets with proper \r\nstatistical/modelling analysis are required to discriminate the effects of meteorology from that of emissions reduction (Dhaka et al., \r\n2020; L. Li et al., 2020; Wang et al., 2020; Zhao et al., 2020b). Accounting for meteorological influences and with an increasing \r\nstringency index, the median observed change in NO2 decreased from -13% to -48%, and in PM2.5 decreased from -10% to -33%, \r\nwhereas the median change in ozone increased from 0% to 4% (Gkatzelis et al., 2021). The latter can be explained by the decrease of \r\nNO emissions that titrate ozone in specific highly polluted areas, leading to the observed increase in surface ozone concentration in \r\ncities (Le et al., 2020; Sicard et al., 2020; Huang et al., 2021)."


id: e165068eedd6b50a2d217eabd3968860
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 32953
content:
"The temporary decrease of PM2.5 concentrations should be put in perspective of the sustained reduction (estimated at 30-70%), which \r\ncould be achieved by implementing policies addressing air quality and climate change (Section 6.6.3). Such sustained reductions can \r\nlead to multiple benefits and simultaneously achieve several SDGs (Section 6.6.3). These policies would also result in reduction of \r\nground-level ozone by up to 20% (Section 6.7.1.3)."


id: 8c28349e72612c6c4927189f5a712acd
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Except for ozone, temporary improvement of air quality during lockdown periods was observed in most regions of the world (high \r\nconfidence), resulting from a combination of interannual meteorological variability and the impact of COVID-19 containment measures \r\n(high confidence). Estimated air pollution reductions associated with lockdown periods are lower than what can be expected from \r\nintegrated mitigation policy leading to lasting reductions (medium confidence)."


id: 352b4b7f9f34e909e095694eaf39a96c
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"COVID-19-related emissions changes primarily exerted effective radiative forcing (ERF) through reduced emissions rates of CO2 and \r\nmethane, altered abundance of SLCFs, notably ozone, NO2 and aerosols, and through other changes in anthropogenic activities, \r\nnotably a reduction in the formation of aviation-induced cirrus clouds. "


id: 8c50e8a494023c3829d0008e5a3a3c9f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Forster et al. (2020) combined the FaIR emulator (Cross-Chapter Box 7.1) with emissions changes for a range of species, relative to \r\na continuation of Nationally Determined Contributions (Rogelj et al., 2017). They found a negative ERF from avoided CO2 emissions that \r\nstrengthens through 2020 to -0.01 W m-2. During the spring lockdown, they found a peak positive ERF of 0.1 W m-2 from loss of aerosol\u0002induced cooling, and a peak negative ERF of -0.04 W m-2 from reductions in tropospheric ozone (from reduced photochemical production \r\nvia NOx). Overall, they estimated a net ERF of +0.05 W m-2 for spring 2020, declining to +0.025 W m-2 by the end of the year."


id: f6d45b0a61a4bc6482a052ec9cda93bf
created_at: 2023-03-16 14:00:43
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content:
"Gettelman et al. (2021) extended Forster et al.'s (2020) results using two ESMs, and found a spring peak aerosol-induced ERF ranging \r\nfrom 0.12 to 0.3 W m-2, depending on the aerosol parametrization. They also estimated an ERF of -0.04 W m-2 from loss of contrail \r\nwarming. Overall, they report a peak ERF of 0.04 to 0.2 W m-2, and a subsequent decline to around half the peak value. Two independent \r\nESM studies Weber et al. (2020) and Yang et al. (2020) found consistent results in time evolution and component contributions but \r\nincluded fewer forcing components."


id: d1ab327fa6e62b3156cbde4f634428e
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 30521
content:
"The available studies are in broad agreement on the sign and magnitude of contributions to ERF from COVID-19-related emissions \r\nchanges during 2020. The range in peak global mean ERF in spring 2020 was [0.025 to 0.2] W m-2 (medium confidence), composed \r\nof a  positive forcing from aerosol-climate interactions of [0.1 to 0.3]  W  m-2, and negative forcings from CO2 (-0.01 W m-2), \r\nNOx (-0.04 W m-2) and contrail cirrus (-0.04 W m-2) (limited evidence, medium agreement). By the end of 2020, the ERF was at half \r\nthe peak value (medium confidence). "


id: 963da091ba51b9c4d0abde47039f8833
created_at: 2023-03-16 14:00:43
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vector_id: 21142
content:
"Changes in atmospheric composition due to COVID-19 emissions reductions are not thought to have caused a detectable change \r\nin global temperature or rainfall in 2020 (high confidence). A large ensemble of Earth system model (ESM) simulations show an \r\nensemble average reduction in Aerosol Optical Depth (AOD) in some regions, notably Eastern and Southern Asia (Fyfe et al., 2021). \r\nThis result is supported by observational studies finding decreases in optical depth in 2020 (Gkatzelis et al., 2021; Ming et al., 2021; \r\nvan Heerwaarden et al., 2021), which may have contributed to observed increases in solar irradiance (van Heerwaarden et al., 2021) \r\nor solar clear-sky reflection (Ming et al., 2021). "


id: 70aa4e0ca8f3cf63210327f2d1f8e79c
created_at: 2023-03-16 14:00:43
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content:
"All the RCP trajectories started in 2005 and relied on the assumption \r\nthat economic growth will bring rapid strengthening of air pollution \r\nlegislation, effectively reducing emissions of non-methane SLCFs \r\n(e.g., Chuwah et al., 2013). While in the long-term such trends are \r\nexpected if more ambitious air pollution control goes on par with \r\neconomic growth. The near-term developments, however, might be \r\nmuch more diverse across regions and species, as has been observed \r\nin the last three decades (Amann et al., 2013; Rafaj et al., 2014; Rafaj \r\nand Amann, 2018; Ru et al., 2018), especially in several fast-growing \r\neconomies, leading to the difference between CMIP6 historical "


id: ec92d0d799c1fbda193e4cdc7e5b4a32
created_at: 2023-03-16 14:00:43
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content:
"The trajectory of future SLCF emissions is driven by the evolution of \r\nsocio-economic drivers described in Section  1.6.1.1 but dedicated, \r\nSSP-specific, air pollution policy storylines can change the regional \r\nand global trends (Rao et al., 2017). Additionally, assumptions \r\nabout urbanization (Jiang and O'Neill, 2017) will affect the spatial \r\ndistribution of emissions and consequently air quality."


id: 95df87f72ada4a370f2d5a5c35068dfa
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
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content:
"densities. The pace of change varies across regions and SSPs resulting \r\nin a  wider range of future air pollutants evolution (Figure  6.18), \r\nreflecting the differences in assumed levels of air pollution controls \r\nacross regions (Figure  6.19). At the end of the century, the range \r\nacross the SSPs is about four times that of RCPs for SO2 and NOx, two \r\nto four times for BC and NMVOCs, and up to three times for CO and \r\nOC, while indicating a slightly smaller range than RCPs for methane \r\n(Figure  6.18). The originally developed SSP scenarios (Rao et al., \r\n2017) have been harmonized with the CMIP6 historical emissions \r\n(Hoesly et al., 2018) and include updated SO2 emissions to account \r\nfor the recent decline in China (Gidden et al., 2019)."


id: 554668e513dae17cb0862bd0ef49114c
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: 10884
content:
"All SSP scenarios (Figure 6.18), except SSP3-7.0, project decline in \r\nglobal total emissions for all SLCFs by the end of the 21st century, \r\nexcept for ammonia and for HFCs (more on HFCs below). Similar to \r\nRCPs, ammonia emissions continue to increase in most SSPs, except "


id: 6bddf674479a86895cc88df98ed38336
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 14595
content:
"Figure 6.19 (continued): Emissions for the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 1850-2014 are based on Hoesly et al. (2018) and van Marle \r\net al. (2017) and emissions for CMIP5 for the period 1850-2005 are from Lamarque et al. (2010). Projections originate from the Shared Socio- economic Pathway (SSP) database \r\n(Riahi et al., 2017; Rogelj et al., 2018a; Gidden et al., 2019) and Representative Concentration Pathway (RCP) database (van Vuuren et al., 2011). Further details on data sources \r\nand processing are available in the chapter data table (Table 6.SM.3)."


id: dc96785922c3c00e7fcb8c1f211ace45
created_at: 2023-03-16 14:00:43
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vector_id: 32208
content:
"An additional scenario, based on the SSP3-7.0, has been designed \r\nspecifically to assess the effect of a strong SLCF emissions abatement \r\nand is called SSP3-7.0-lowNTCF in the literature (Collins et al., 2017; \r\nGidden et al., 2019). It has been applied in the modelling studies \r\n(e.g.,  AerChemMIP) with or without consideration of additional \r\nmethane reduction and we refer here to these scenarios, respectively, \r\nas SSP3-7.0-lowSLCF-lowCH4 or SSP3-7.0-lowSLCF-highCH4. In these \r\nscenarios, aerosols, their precursors, and non-methane tropospheric \r\nozone precursors are mitigated by applying the same emissions \r\nfactors as in SSP1-1.9. "


id: 8f4b47535599e6e6544c238e50d351be
created_at: 2023-03-16 14:00:43
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content:
"About 50% of remaining BC emissions in SSP1-1.9 or SSP1-2.6 are \r\nprojected to originate from waste and open biomass burning of \r\nwhich open burning of waste represent a  significant part. Some \r\nstudies suggest this might be pessimistic as, for example, efficient \r\nwaste management (consistent with SDG goals) could potentially \r\neliminate the open burning of solid residues (Gomez-Sanabria et al., \r\n2018), which accounts for over 30% of BC emissions in SSP1-1.9 \r\nin 2050 or 2100."


id: dce31238e3a2f89e86ed2bff6b12a0d0
created_at: 2023-03-16 14:00:43
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content:
"in SLCF-abundance projections include scenario uncertainties, or \r\nparametric and structural uncertainties in the model representation \r\nof the processes affecting simulated abundances with implications \r\nfor radiative forcing and air quality. The evolution of methane \r\nabundances in SSP scenarios, for example, is derived from integrated \r\nassessment models (IAMs) which do not include the effects from \r\nbiogeochemical feedbacks (e.g., climate-driven changes in wetland \r\nemissions; Meinshausen et al., 2020) introducing uncertainty. "


id: 54d8701e2472bbcae1d6afb71f8ee17
created_at: 2023-03-16 14:00:43
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vector_id: 10805
content:
"In summary, in SSPs, in addition to the socio-economic development \r\nand climate change mitigation policies shaping the GHG emissions \r\ntrajectories, the SLCF emissions trajectories are also steered by \r\nvarying levels of air pollution control originating from SSP narratives \r\nand independent from climate change mitigation. Consequently, \r\nSSPs span a wider range of SLCF emissions than considered in the \r\nRCPs, better covering the diversity of future options in air pollution \r\nmanagement and SLCF-induced climate effects (high confidence). \r\nIn addition to SSP-driven emissions, the future evolution of SLCFs \r\nabundance is also sensitive to chemical and biogeochemical \r\nfeedbacks involving SLCFs, particularly natural emissions, whose \r\nmagnitude and sign are poorly constrained."


id: 1e3cdd7b264a51446be9dda7359454b7
created_at: 2023-03-16 14:00:43
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content:
"The projection of air-quality relevant abundances (surface ozone and \r\nPM2.5) under the SSP scenarios are assessed here. Future changes in \r\nglobal and regional annual mean surface ozone and PM2.5 driven by \r\nthe evolution of emissions as well as by climate change have been \r\nquantified by CMIP6 models analysed in AerChemMIP (Allen et al., \r\n2020, 2021; Turnock et al., 2020). "


id: 9a0c811c74677e1688de5dc10c48bacf
created_at: 2023-03-16 14:00:43
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content:
"The SSP SLCF trajectories reflect the effect of recent legislation and \r\nthe assumed evolution thereof in the longer term, however, they do \r\nnot necessarily reflect the full mitigation potential for several SLCFs, \r\nwithin particular SSPs (Figure 6.18), that could be achieved with air\u0002quality- or SDG-targeted policies (Amann et al., 2013; Rogelj et al., \r\n2014a; Haines et al., 2017; Klimont et al., 2017b; Rafaj and Amann, \r\n2018; Shindell et al., 2018; Tong et al., 2020). Such policies could \r\nbring more rapid mitigation of SLCFs, independent of the climate \r\nstrategy (Section 6.6.3)."


id: 1f46ee386e516725c41723c20ade887f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"The projections of future SLCF abundances typically follow their \r\nemissions trajectories except for SLCFs that are formed from \r\nprecursor reactions (e.g.,  tropospheric ozone) or are influenced by \r\nbiogeochemical feedbacks (Sections 6.2.2 and 6.4.5). According to \r\nmulti-model CMIP6 simulations, total column ozone (reflecting mostly \r\nstratospheric ozone) is projected to return to 1960s values by the \r\nmiddle of the 21st century under the SSP2-4.5, SSP3-7.0, SSP4-3.4, \r\nSSP4-6.0 and SSP5-8.5 scenarios (Keeble et al., 2021). ESMs project \r\nincreasing tropospheric ozone burden over the 2015-2100 period \r\nfor the SSP3-7.0 scenario (Figure 6.4; Griffiths et al., 2021), there is, \r\nhowever, a large spread in the magnitude of this increase reflecting \r\nstructural uncertainties associated with the model representation of \r\nprocesses that influence tropospheric ozone. Sources of uncertainties "


id: c77dbf924669311dd7244782429c6d6b
created_at: 2023-03-16 14:00:43
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content:
"The strong abatement of ozone precursor emissions (except those of \r\nmethane; SSP3-7.0-lowSLCF-highCH4) lead to a  decrease of global \r\naverage surface ozone by 15% (6 ppb) between 2015 and 2055 (Allen \r\net al., 2020), and ozone decreases in all regions except Southern Asia. \r\nHowever, this decrease is twice as large when methane emissions are \r\nabated simultaneously (SSP3-7.0-lowSLCF-lowCH4), underlying the \r\nimportance of methane emissions reduction as an important lever to \r\nreduce ozone pollution (high confidence) (Section 6.6.4). "


id: a2d8807521d857c849c11c201a3962a2
created_at: 2023-03-16 14:00:43
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content:
"spread over regions with large natural aerosol sources, for example, \r\nin North Africa, where dust sources are important. The mitigation of \r\nnon-methane SLCFs in the SSP3-7.0-lowSLCF-highCH4 scenario is \r\npredicted to reduce PM2.5 by 25% (in 2055, relative to the SSP3-7.0 \r\nscenario) over global land surface areas (Allen et al., 2020)."


id: bb5121339f594d99d270ba1090a42ac0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"The magnitude of the annual mean change in surface ozone and PM2.5\r\nfor all the SSPs (accounting for both emissions and climate change) is \r\ngreater than that expected from climate change in isolation (Turnock \r\net al., 2020). The uncertainty in the projections comes from how \r\nnatural emissions will respond to climate change. However, multiple \r\nlines of evidence (along with Sections 6.2.2, 6.5, and 6.7.1) provide \r\nhigh confidence (compared to medium in AR5) that changes in \r\nemissions, and in particular in human-induced emissions, will drive \r\nfuture air pollution levels rather than physical climate change. "


id: 32fcb135f46e6c2c0b979a86ac24f525
created_at: 2023-03-16 14:00:43
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content:
"A decrease in surface PM2.5 concentrations is estimated for SSP1-1.9, \r\nSSP1-2.6 and SSP2-4.5 (Turnock et al., 2020) (Figure 6.21). A decrease \r\nin PM2.5, is also projected in SSP5-8.5, which does not consider any \r\nclimate change mitigation but has a  strong air pollution control. \r\nThe decrease is largest in the regions with the highest 2005-2014 \r\nmean concentrations (the Middle East, Southern Asia and Eastern \r\nAsia). Under the SSP3-7.0 scenario, PM2.5 is predicted to increase or \r\nremain at near present-day values across Asia; regions where present\u0002day concentrations are currently the highest. There is large model "


id: b4359bdd2e9fe2dd468807cb4d4e3fb4
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"surface ozone and PM (when air pollution and climate change are \r\nboth strongly mitigated, e.g., SSP1-2.6) to no improvement and even \r\ndegradation (high confidence) (when no climate change mitigation \r\nand only weak air pollution control are considered, SSP3-7.0). In \r\nthe latter case, PM levels are estimated to increase until 2050 over \r\nlarge parts of Asia and surface ozone pollution worsens over all \r\ncontinental areas throughout the whole century (high confidence). \r\nIn scenarios without climate change mitigation but with strong air \r\npollution control (SSP5-8.5), high methane levels hamper the decline \r\nin global surface ozone in the near term and only PM levels decrease \r\n(high confidence)."


id: e25e2bde657dd30876ec7dedd9592ad9
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: 33120
content:
"This section assesses how the different spatial and temporal evolution \r\nof SLCF emissions in the SSPs affects the future global and regional \r\nERFs, and GSAT and precipitation responses. In CMIP6, only a very \r\nlimited set of simulations (all based on the SSP3-7.0 scenario) have \r\nbeen carried out with coupled ESMs to specifically address the future \r\nrole of SLCFs (Sections 4.3 and 4.4; Collins et al., 2017). Note that \r\nthe ScenarioMIP simulations (Section 4.3) include the SLCF emissions \r\n(as shown in Figures 6.18 and 6.19), however, they cannot be used \r\nto quantify the effect of individual forcers. Coupled ESMs can in \r\nprinciple be used for this through a series of sensitivity simulations \r\n(e.g., Allen et al., 2020, 2021), but the amount of computer time "


id: 7ac8b4e21bbad09c74bfccc0b4d5b495
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
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content:
"required has made this approach prohibitive across the full SSP range. \r\nTherefore, to quantify the contribution from emissions of individual \r\nforcers spanning the range of the SSP scenarios to GSAT response, \r\nthe analysis is mainly based on estimates using a two-layer emulator \r\nconfiguration derived from the medians of MAGICC7 and FaIRv1.6.2 \r\n(Section 1.5.3.4, Cross-Chapter Box 7.1 and Supplementary Material \r\n7.SM5.2). The contribution from SLCFs to changes in GSAT have been \r\ncalculated based on the global mean ERF for the various components \r\nas assessed in Section  7.3.5, using the two-layer emulator for the \r\nclimate response. "


id: 49cb2d496412dbf14afab969acfbed3
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
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content:
"The projections of GSAT for a broad group of forcing agents (aerosols, \r\nmethane, tropospheric ozone and HFCs with lifetimes lower than \r\n50 years) for the SSP scenarios show how much of the future \r\nwarming or cooling (relative to 2019) can be attributed to the SLCFs \r\n(Figure 6.22). Note that during the first two decades, some of these \r\nchanges in GSAT are due to emissions before 2019, in particular \r\nfor the longer-lived SLCFs such as methane and HFCs (Figure 6.15). \r\nThe scenarios SSP3-7.0-lowSLCF-highCH4 and SSP3-7.0-lowSLCF\u0002lowCH4 are special cases of the SSP3-7.0 scenario with strong, but \r\nrealistic, reductions in non-methane SLCFs and all SLCFs, respectively \r\n(Gidden et al., 2019)."


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content:
"As discussed in Sections 6.2, 6.3 and 6.4, there are uncertainties \r\nrelating emissions of SLCFs to changes in abundance (Box 6.2) and \r\nfurther to ERF, in particular for aerosols and tropospheric ozone. \r\nFurthermore, there are uncertainties related to climate sensitivity, \r\nthat is, the relation between ERF and change in GSAT. Uncertainties \r\nin the ERF are assessed in Chapter 7 and calibrated impulse response \r\nfunction also includes the assessed range (Box 7.1). There are also \r\nuncertainties related to the radiative efficacies of the different SLCFs \r\nand time scales for the response, in particular for regional emissions \r\n(Schwarber et al., 2019; Yang et al., 2019b) that cannot be accounted \r\nfor with the simple models used here. "


id: 77859250390700ae7f7b62718bef3fe0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 16356
content:
"Historical emissions have been updated until 2019 (see \r\nSupplementary Material 7.SM.1.3.1) and used for ERF for calculating \r\nGSAT in Figure 6.22. The year 2019 has been chosen as the base year \r\nto be consistent with the attributed temperature changes since 1750 \r\n(Figure 7.8). The warming attributed to SLCFs (methane, ozone and \r\naerosols) over the last decade (Figure 7.8) constitutes about 30% of \r\nthe peak SLCF-driven warming in the most stringent scenarios (SSP1), \r\nin good agreement with Shindell and Smith (2019), and supported by \r\nthe recent observed decline in AOD (Section 2.2.6). "


id: 13a8c576f84acf3940ee8e8f2260411
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 599
content:
"Regionally inhomogeneous ERFs can lead to regionally dependent \r\nresponses (Section  6.4.3). Mitigation of non-methane SLCFs over \r\nthe period 2015-2055 (SSP3-7.0-lowSLCF-highCH4 versus SSP3-7.0) \r\nwill lead to positive ERF over land regions (Allen et al., 2020). There \r\nare large regional differences in the ERF from no significant trend \r\nover northern Africa to about 0.5 W m-2 decade-1 for Southern Asia. \r\nThe differences are mainly driven by differences in the reductions \r\nof sulphate aerosols. There is no strong correspondence between \r\nregional warming and the ERF trends. As expected, the sensitivity \r\n(temperature change per unit ERF) increases towards higher latitudes \r\ndue to climate feedbacks and teleconnections. Regionally, the \r\nwarming rates are higher over continental regions, with the highest \r\nincrease in temperatures for Central and northern Asia and the Arctic "


id: b6a34cad925bbcac0e67dd72ff9a62d7
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 26252
content:
"From 2019 and until about 2040, SLCFs and HFCs will contribute \r\nto increase GSAT in the WGI core set of SSP scenarios, with a very \r\nlikely range of 0.04degC-0.41degC relative to 2019. The warming is most \r\npronounced in the strong mitigation scenarios (i.e., SSP1-1.9 and \r\nSSP1-2.6) due to rapid cuts in aerosols. In scenario SSP3-7.0, there is \r\nno reduction of aerosols until mid-century and it is the increases in \r\nmethane and ozone that give a net warming in 2040. The warming \r\nis similar in magnitude to that in the SSP1-scenarios, in which the \r\nreduction in aerosols is the main driver. Contributions to warming \r\nfrom methane, ozone, aerosols and HFCs make SSP5-8.5 the scenario \r\nwith the highest warming in 2040 and throughout the century. "


id: 999566dcc982484e731fe787be61a754
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"In the near term (2035-2040), it is unlikely that differences in the \r\nsocio-economic developments and emissions controls induced \r\nby policies (as embedded in the SSPs) can lead to a  discernible \r\ndifference in the net effect of changes of SLCFs on GSAT. This is \r\nbecause the inter-model spread in the estimated net effect of SLCFs \r\non GSAT is as large as the difference between the scenarios due to \r\nthe compensating effects of change in emissions leading to cooling \r\nand warming. However, in the longer term, there is high confidence\r\nthat the net warming induced by changes in SLCFs will be lower in \r\nthe scenario considering strong climate change mitigation (SSP1-1.9 \r\nand SSP1-2.6 that include reductions in methane emissions) than in \r\nthe high CO2 emissions scenarios (SSP3-7.0 and SSP5-8.5). "


id: 7c052f1f3e19cedb669e36fa8f5b5ea
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 17117
content:
"in 2055 relative to 2015. The models agree on an increasing global \r\nmean trend in precipitation due to SLCFs, however precipitation \r\ntrends over land are more uncertain (Allen et al., 2020), in agreement \r\nwith the relationship between aerosol and precipitation trends \r\nassessed in Chapter 8."


id: 8615a509f15f9cafe893a95e0e139d22
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"ESM estimates of future concentrations of various SLCFs vary \r\nconsiderably even when using the same future emissions scenarios, \r\nwhich is related to sources of model structural uncertainty in \r\nthe several physical, chemical and natural emissions model \r\nparametrizations. The general uncertainties in understanding and \r\nrepresenting chemical and physical processes governing the life \r\ncycle of SLCFs (Box  6.1) necessarily also applies to simulations of \r\nfuture concentrations and ERF. In addition, how the models are able \r\nto simulate climate changes (i.e., circulation and precipitation) that \r\naffect the dispersion and removal of SLCFs constitute a  structural \r\nuncertainty in the models. Also SLCF-related climate feedbacks \r\n(e.g., NOx from lightning or BVOCs from vegetation; Section 6.4.5) \r\nadd to the uncertainty. "


id: 611b89018360740f44f786db7ff5d7ed
created_at: 2023-03-16 14:00:43
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vector_id: 12852
content:
"For SLCFs with lifetimes shorter than typical mixing times in the \r\natmosphere (days to weeks), the effects on secondary forcing agents \r\n(e.g., tropospheric ozone, sulphate and nitrate aerosols) depend on \r\nwhere and when the emissions occur due to non-linear chemical and "


id: a2b9927cd9dc2f489f69ba65c11fb490
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 23168
content:
"Figure 6.23 | Contribution from regional emissions of short-lived climate forcers (SLCFs) to changes in global surface air temperature (GSAT) in 2040 \r\n(upper row) and 2100 (lower row), relative to 2020 for four Shared Socio-economic Pathways (SSPs). Adapted from Lund et al. (2020). NOx, CO, and NMVOC \r\naccount for the impact through changes in ozone and methane, NOx additionally includes the impact through formation of nitrate aerosols. BC, SO2 and OC accounts for \r\nthe direct aerosol effect (aerosol-radiation interactions), as well as an estimate of the semi-direct effect for BC due to rapid adjustments and indirect effect (aerosol-cloud \r\ninteractions) of sulphate aerosols. Regions are the same as shown in the map in Figure 6.7. Further details on data sources and processing are available in the chapter data \r\ntable (Table 6.SM.3)."


id: ff5090464599e14f444335e4572377e1
created_at: 2023-03-16 14:00:43
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vector_id: 37716
content:
"physical processes. Also, the ERF following a change in concentrations \r\ndepends on the local conditions (Sections 6.2, 6.3 and 6.4). "


id: e64ef5d403ec8a436d82aa7f1f53fd5e
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 33725
content:
"As illustrated in Figure 2.2 of SR1.5 (Rogelj et al., 2018a), the total \r\naerosol ERF change in stringent mitigation pathways is expected to \r\nbe positive and to contribute to a warming since it is dominated by \r\nthe effects from the phase-out of SO2 (Figure 6.24, Section 6.7.2.2). \r\nRecent emissions inventories and observations of trends in AOD \r\n(Sections 2.2.6 and 6.2.1) show that it is very likely that there have \r\nbeen reductions in global SO2 emissions and in aerosol burdens over \r\nthe last decade. Here, we use 2019 as the reference year rather than \r\nthe 'Recent Past' defined as the average over 1995-2014 (Section 4.1) \r\nin order to exclude the recent emissions reductions when discussing \r\nthe different possible futures. "


id: d44024383ef7483adae10d9e0d3a15c3
created_at: 2023-03-16 14:00:43
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content:
"For the SSP3-7.0-lowSLCF-highCH4 and SSP3-7.0-lowSLCF-lowCH4\r\nscenarios, a  five-ESM ensemble has been analysed relative to the \r\nstandard SSP3-7.0 scenario (Allen et al., 2020, 2021). For SSP3-7.0\u0002lowSLCF-highCH4, in which the methane emissions are as in the \r\nstandard SSP3-7.0 scenario, Allen et al. (2021) found an enhanced \r\nglobal mean surface warming of 0.23degC +- 0.05degC by mid-century \r\nand 0.21degC +- 0.03degC by 2100 relative to the warming in the standard \r\nSSP3-7.0 scenario. Also including strong mitigation of methane \r\nemissions, the same models (Allen et al., 2021) find that the warming \r\nis offset resulting in a net cooling of 0.15degC +- 0.05degC at mid-century "


id: a0d3a629864014f05c7a6ea5efcceabb
created_at: 2023-03-16 14:00:43
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vector_id: 22851
content:
"Air-quality policies lead to a decrease in emissions of both warming \r\nand cooling SLCFs. Here we assess the contribution of SLCFs to the \r\ntotal warming (also including the LLGHGs) in the case of stringent \r\nSLCF mitigation to improve air quality in scenarios with continued \r\nhigh use of fossil fuels (e.g.,  SSP3-7.0-lowSLCF and SSP5-8.5). \r\nConversely, we also assess the effect on air quality of strategies \r\naiming to mitigate air pollution or climate change under the SSP3-7.0 \r\nframework (using the SSP3-7.0-lowSLCF-lowCH4, SSP3-7.0-lowSLCF\u0002highCH4 and SSP3-3.4 scenarios). "


id: 1a0d25c4197b42a93e7fb78175e6aeb0
created_at: 2023-03-16 14:00:43
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content:
"(2050-2059) and 0.50degC +- 0.02degC at the end of the century (2090-\r\n2099) relative to SSP3-7.0."


id: 8888c118038ba5e158e67444c5394db5
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 19010
content:
"means that emissions of aerosols and methane increase until 2030 \r\nand are reduced quickly thereafter (Fujimori et al., 2017). The effect \r\non GSAT (relative to 2019) is shown in Figures 6.22 and 6.24. The net \r\nGSAT response to the SLCFs is dominated by the aerosols, with an \r\ninitial cooling until 2030, then a fast rebound for 15 years followed \r\nby a  very moderate warming reaching 0.21degC in 2100. The ozone \r\nreduction causes a slight cooling (up to 0.06degC), in contrast to the \r\nwarming in the SSP3-7.0-lowSLCF-highCH4 scenario in which the \r\nmethane emissions increase until 2100."


id: b4c54412324961cb5ce6f1236833c8c4
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: 25974
content:
"The SSP3 storyline assumes 'regional rivalry' (Section 1.6.1.1) with \r\nweak air pollution legislation and no climate change mitigation, \r\nand is compared here against SSP3-7.0-lowSLCF-lowCH4 (strong \r\nair pollution control) and SSP3-3.4 (the most ambitious climate \r\npolicy feasible under the SSP3 narrative). In the SSP3-3.4 scenario, \r\nall emissions follow the SSP3-7.0 scenario until about 2030 and \r\nthen deep and rapid cuts in fossil fuel use are imposed (Fujimori \r\net al., 2017). In the case of climate change mitigation, such as in \r\nthe SSP3-3.4 scenario, the decrease of SLCF emissions is a co-benefit \r\nfrom the targeted decrease of CO2 (when SLCFs are co-emitted), \r\nbut also directly targeted as in the case of methane. For SLCFs, this "


id: 140d8f83b4d8c1764174288f1fb1e29f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"is essential, but the production of accurate emissions inventories \r\nremain a  challenge for lots of rapidly growing urban areas. The \r\nemissions reporting now planned in the official mandate of the Task \r\nForce on National Greenhouse Gas Inventories (TFI) can be a step \r\nin this direction if accompanied by efforts on spatial distribution of \r\nemissions. An integrated modelling framework associating global and \r\nhigh-resolution chemistry-transport models with shared protocols is \r\nmissing to allow a systematic assessment of future changes on air \r\nquality at this scale. "


id: 7579f2b764d9be2c99b75d327b0f9428
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 16070
content:
"In parallel, opportunities of progress may emerge from big-data \r\nacquisition. Big data and their mining can inform practices related \r\nto emissions or can document pollution levels if associated with \r\nmassive deployment of low-cost sensors through citizen science. \r\nNew generation satellite data will also give access to sub-kilometre\u0002scale air pollution observations."


id: 46bb5c865368a732187608e936891770
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 8515
content:
"A systematic emissions modeling framework is needed to assess the \r\nLLGHG emissions changes associated with SLCF reductions induced \r\nby air pollution control in the SSP framework. The SLCF-mediated \r\neffects of large-scale technology deployment to allow climate change \r\nmitigation, such as hydrogen energy production, carbon capture and \r\nstorage though amine filters, or changes in agricultural practices \r\nto limit GHG emissions and/or produce bioenergy are also not \r\nconsidered in the emissions scenarios. "


id: 16485890594969bd813760bbad55fae0
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 1035
content:
"Strong air pollution control as well as strong climate change \r\nmitigation, implemented independently, lead to large reduction of the \r\nexposure to air pollution by the end of the century (high confidence). \r\nImplementation of air pollution control, relying on the deployment of \r\nexisting technologies, leads more rapidly to air-quality benefits than \r\nclimate change mitigation which requires systemic changes but, in \r\nboth cases, significant parts of the population remain exposed to air \r\npollution exceeding the WHO guidelines (high confidence)."


id: 9cf87fb72301c8b721fd2c35d72033cf
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Ice-core analyses can now inform trends for more SLCFs over the \r\nlast millennium (such as light NMVOCs or CO) and more proxies are \r\navailable to inform about past emissions. However, pre-industrial \r\nlevels of SLCFs are still relatively poorly constrained. In addition, \r\nrecent trends in abundances of the various types of aerosols and \r\nof NMVOCs suffer from the scarcity of observation networks in \r\nvarious parts of the world, in particular in the Southern Hemisphere. \r\nSuch  network development is necessary to record and understand \r\nthe evolution of atmospheric composition. "


id: c63cc20817807e2918a9462b38e79fa2
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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content:
"Assessment of future air pollution changes at the urban level \r\nrequires the use of a  high-resolution models to properly account \r\nfor non-linearities in chemistry, specific urban structures and local \r\nmeteorology as well as temporal and spatial variations in emissions \r\nand population exposure. To assess the relevance of air pollution \r\nreduction policies, regional air-quality models are necessary and \r\nare still not implemented in many developing countries. To properly \r\napply such models, the quality of spatialized emissions inventories "


id: acf276e4419ee24ba3d896ae6f16dc81
created_at: 2023-03-16 14:00:43
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vector_id: 24756
content:
"Short-lived climate forcers (SLCFs) are compounds such as methane and sulphate aerosols that warm or cool the \r\nEarth's climate over shorter time scales - from days to years - than greenhouse gases like carbon dioxide, whose \r\nclimatic effect lasts for decades, centuries or more. Because SLCFs do not remain in the atmosphere for very \r\nlong, their effects on the climate are different from one region to another and can change rapidly in response \r\nto changes in SLCF emissions. As some SLCFs also negatively affect air quality, measures to improve air quality \r\nhave resulted in sharp reductions in emissions and concentrations of those SLCFs in many regions over the few \r\nlast decades."


id: 2202a71d62d0be44e7d3737833aefeb4
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: 2795
content:
"The SLCFs include gases as well as tiny particles called aerosols, and they can have a warming or cooling effect \r\non the climate (FAQ 6.1, Figure 1). Warming SLCFs are either greenhouse gases (e.g., ozone or methane) or \r\nparticles like black carbon (also known as soot), which warm the climate by absorbing energy and are sometimes \r\nreferred to as short-lived climate pollutants. Cooling SLCFs, on the other hand, are mostly made of aerosol \r\nparticles (e.g.,  sulphate, nitrate and organic aerosols) that cool down the climate by reflecting away more \r\nincoming sunlight. "


id: b262126fbc38fbc8d259d3d095e0241f
created_at: 2023-03-16 14:00:43
updated_at: 2023-03-16 14:00:43
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vector_id: 25580
content:
"Some SLCFs do not directly affect the climate but produce climate-active compounds and are referred to as \r\nprecursors. SLCFs are emitted both naturally and as a result of human activities, such as agriculture or extraction \r\nof fossil fuels. Many of the human sources, particularly those involving combustion, produce SLCFs at the \r\nsame time as carbon dioxide and other long-lived greenhouse gases. Emissions have increased since the start \r\nof industrialization, and humans are now the dominant source for several SLCFs and SLCF precursors, such as \r\nsulphur dioxide (which produces sulphate aerosols) and nitrogen oxides (which produce nitrate aerosols and \r\nozone), despite strong reductions over the last few decades in some regions due to efforts to improve air quality. "


id: 4c346934eb4015e3d31b0d488cf3d921
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: 9412
content:
"The climatic effect of a  chemical compound in the atmosphere depends on two things: (i) how effective it \r\nis at cooling or warming the climate (its radiative efficiency) and (ii) how long it remains in the atmosphere \r\n(its lifetime). Because they have high radiative efficiencies, SLCFs can have a strong effect on the climate even \r\nthough they have relatively short lifetimes of up to about two decades after emission. Today, there is a balance \r\nbetween warming and cooling from SLCFs, but this can change in the future."


id: 8c6c008e748ccf4e238edfa5a2836fba
created_at: 2023-03-16 14:00:43
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vector_id: 19607
content:
"The short lifetime of SLCFs constrains their effects in both space and time. First, of all the SLCFs, methane and \r\nthe short-lived halocarbons persist the longest in the atmosphere: up to two decades (FAQ 6.1, Figure 1). This is \r\nlong enough to mix in the atmosphere and to spread globally. Most other SLCFs only remain in the atmosphere \r\nfor a few days to weeks, which is generally too short for mixing in the atmosphere, sometimes even regionally. \r\nAs a result, the SLCFs are unevenly distributed and their effects on the climate are more regional than those of \r\nlonger-lived gases. Second, rapid (but sustained) changes in emissions of SLCFs can result in rapid climatic effects. "


id: 7b5570c8dba66bcd5072ac40ef3a03ad
created_at: 2023-03-16 14:00:43
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content:
"In addition to the direct warming and cooling effects, SLCFs have many other consequences for the climate system \r\nand for air quality (see FAQ 6.2). For instance, deposition of black carbon on snow darkens its surface, which \r\nsubsequently absorbs more solar energy, leading to more melting and more warming. Aerosols also modify the \r\nproperties of clouds, which has indirect cooling effects on the climate and causes changes in local rainfall (see \r\nFAQ 7.2). Climate models indicate that SLCFs have altered atmospheric circulation on local and even hemispheric \r\nscales (e.g., monsoons) as well as regional precipitation. For instance, recent observations show that regional \r\nweather is influenced by strong regional contrasts in the evolution of aerosol concentrations, particularly over \r\nSouth and East Asia. "


id: e1c9a5d174ce5ba7647b30fe584fadbc
created_at: 2023-03-16 14:00:43
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content:
"Although policies to limit climate change and discussions of the so-called remaining carbon budgets primarily \r\nfocus on carbon dioxide (see FAQ 5.4), SLCFs can significantly affect temperature changes. It is therefore important \r\nto understand how SLCFs work and to quantify their effects. Because reducing some of the SLCF emissions, such as \r\nmethane, can simultaneously reduce warming effects and adverse effects on air quality as well as help attaining \r\nSustainable Development Goals, mitigation of SLCFs is often viewed as a favourable 'win-win' policy option."


id: a0f01544d5ee773f701aa5e8802aacf2
created_at: 2023-03-16 14:00:43
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vector_id: 22870
content:
"FAQ 6.1: What are short-lived climate forcers and how do they affect the climate?\r\nShort lived climate forcers do not remain for very long in the atmosphere, thus an increase or decrease in their emissions \r\nrapidly affects the climate system."


id: 1fb9bbe68c31460616dcf2f323df9665
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content:
"FAQ 6.1, Figure 1 | Main short-lived climate forcers, their sources, how long they exist in the atmosphere, and their relative contribution to \r\nglobal surface temperature changes between 1750 and 2019 (area of the globe). By definition this contribution depends on the lifetime, the warming/\r\ncooling potential (radiative efficiency), and the emissions of each compound in the atmosphere. Blue indicates cooling and orange indicates warming. Note that, \r\nbetween 1750 and 2019, the cooling contribution from aerosols (blue diamonds and globe) was approximately half the warming contribution from carbon dioxide."


id: e3d9ac12d2a62c10e920ecadcefbf7bd
created_at: 2023-03-16 14:00:43
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index: climate_gpt_only_giec
vector_id: 33327
content:
"Climate change and air quality are intimately linked. Many of the human activities that produce long-lived \r\ngreenhouse gases also emit air pollutants, and many of these air pollutants are also 'short-lived climate forcers' \r\nthat affect the climate. Therefore, many options for improving air quality may also serve to limit climate change \r\nand vice versa. However, some options for improving air quality cause additional climate warming, and some \r\nactions that address climate change can worsen air quality. "


id: c4516c3f1a4c36cbf66b5318635329c8
created_at: 2023-03-16 14:00:43
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content:
"Climate change and air pollution are both critical environmental issues that are already affecting humanity. \r\nIn 2016, the World Health Organization attributed 4.2 million deaths worldwide every year to ambient (outdoor) \r\nair pollution. Meanwhile, climate change impacts water resources, food production, human health, extreme \r\nevents, coastal erosion, wildfires, and many other phenomena. "


id: 2a241207d871c174441ed7f0dffe53f2
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vector_id: 4053
content:
"Most human activities, including energy production, agriculture, transportation, industrial processes, waste \r\nmanagement and residential heating and cooling, result in emissions of gaseous and particulate pollutants that \r\nmodify the composition of the atmosphere, leading to degradation of air quality as well as to climate change. \r\nThese air pollutants are also short-lived climate forcers - substances that affect the climate but remain in the \r\natmosphere for shorter periods (days to decades) than long-lived greenhouse gases like carbon dioxide (see \r\nFAQ 6.1). While this means that the issues of air pollution and climate change are intimately connected, air \r\npollutants and greenhouse gases are often defined, investigated and regulated independently of one another in \r\nboth the scientific and policy arenas. "


id: 7ab402261c89b133e9b47398f40b328c
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: 16884
content:
"Many sources simultaneously emit carbon dioxide and air pollutants. When we drive our fossil fuel vehicles \r\nor light a fire in the fireplace, it is not just carbon dioxide or air pollutants that are emitted, but always both. \r\nIt is therefore not possible to separate emissions into two clearly distinct groups. As a result, policies aiming at \r\naddressing climate change may have benefits or side effects for air quality, and vice versa. "


id: be6aa0106a758b52ca386608685f592b
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: 27506
content:
"For example, some short-term 'win-win' policies that simultaneously improve air quality and limit climate change \r\ninclude the implementation of energy efficiency measures, methane capture and recovery from solid-waste \r\nmanagement and the oil and gas industry, zero-emissions vehicles, efficient and clean stoves for heating and \r\ncooking, filtering of soot (particulate matter) for diesel vehicles, cleaner brick-kiln technology, practices that \r\nreduce burning of agricultural waste, and the eradication of burning of kerosene for lighting."


id: 9a5bf6d3dd3716abc93533b96b26ab4
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: 21814
content:
"There are, however, also 'win-lose' actions. For example, wood burning is defined as carbon neutral because \r\na tree accumulates the same amount of carbon dioxide throughout its lifetime as is released when wood from \r\nthat tree is burned. However, burning wood can also result in significant emissions of air pollutants, including \r\ncarbon monoxide, nitrogen oxides, volatile organic compounds, and particulate matter, that locally or regionally \r\naffect the climate, human health and ecosystems (FAQ 6.2, Figure 1). Alternatively, decreasing the amount of \r\nsulphate aerosols produced by power and industrial plants, and from maritime transport, improves air quality \r\nbut results in a warming influence on the climate, because those sulphate aerosols contribute to cooling the \r\natmosphere by blocking incoming sunlight. "


id: 5d981b8e92f8a86735ec7eb245ecd848
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: 12249
content:
"Air quality and climate change represent two sides of the same coin, and addressing both issues together could \r\nlead to significant synergies and economic benefits while avoiding policy actions that mitigate one of the two \r\nissues but worsen the other."


id: ac1c4b33e1583c5fa3200aae83c01141
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: 24603
content:
"Kyle Armour (United States of America), William Collins (United Kingdom), Jean-Louis Dufresne \r\n(France), David Frame (New Zealand), Daniel J. Lunt (United Kingdom), Thorsten Mauritsen (Sweden/\r\nDenmark), Matthew D. Palmer (United Kingdom), Masahiro Watanabe (Japan), Martin  Wild \r\n(Switzerland), Hua Zhang (China)"


id: 85a87ca54e4b3962a05e270f448bc85e
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: 18568
content:
"Review Editors:\r\nRobert Colman (Australia), H. Damon Matthews (Canada), Venkatachalam Ramaswamy (United \r\nStates of America)"


id: f0407b7f5437461c9ad6b506d95bc211
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: 35302
content:
"Forster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, \r\nM. Watanabe, M. Wild, and H. Zhang, 2021: The Earth's Energy Budget, Climate Feedbacks, and Climate \r\nSensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth \r\nAssessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, \r\nS.L. Connors, C. Pean, S. Berger, N.  Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, \r\nJ.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 923-1054, doi:10.1017/9781009157896.009."


id: a1b0fbc0a4f7cc163c94b3daef2e881d
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: 23002
content:
"The global energy inventory increased by 282 [177 to \r\n387] Zettajoules (ZJ; 1021 Joules) for the period 1971-2006 and \r\n152 [100 to 205] ZJ for the period 2006-2018. This corresponds \r\nto an Earth energy imbalance of 0.50 [0.32 to 0.69] W m-2 for the \r\nperiod  1971-2006, increasing to 0.79 [0.52 to 1.06] W m-2 for \r\nthe  period 2006-2018, expressed per unit area of Earth's surface. \r\nOcean heat uptake is by far the largest contribution and accounts for \r\n91% of the total energy change. Compared to AR5, the contribution \r\nfrom land heating has been revised upwards from about 3% to \r\nabout 5%. Melting of ice and warming of the atmosphere account \r\nfor about 3% and 1% of the total change respectively. More \r\ncomprehensive analysis of inventory components and cross-validation \r\nof global heating rates from satellite and in situ observations lead \r\nto a  strengthened assessment relative to AR5 (high confidence). \r\n{Box 7.2, 7.2.2, Table 7.1, 7.5.2.3}"


id: b97aabe5fa2e4fd7fe8b2e843aebbe0e
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: 26724
content:
"Improved quantification of effective radiative forcing, the climate \r\nsystem radiative response, and the observed energy increase in \r\nthe Earth system for the period 1971-2018 demonstrate improved \r\nclosure of the global energy budget compared to AR5. Combining \r\nthe likely range of ERF with the central estimate of radiative response \r\ngives an expected energy gain of 340 [47 to 662] ZJ. Combining the \r\nlikely range of climate response with the central estimate of ERF gives \r\nan expected energy gain of 340 [147 to 527] ZJ. Both estimates are \r\nconsistent with an independent observation-based assessment of \r\nthe global energy increase of 284 [96 to 471] ZJ, (very likely range) \r\nexpressed relative to the estimated 1850-1900 Earth energy imbalance \r\n(high confidence). {7.2.2, Box 7.2, 7.3.5, 7.5.2}"


id: 91858abfd80d638898cfcae026da2537
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: 20435
content:
"Since AR5, additional evidence for a  widespread decline \r\n(or dimming) in solar radiation reaching the surface is found \r\nin the observational records between the 1950s and 1980s, \r\nwith a  partial recovery (brightening) at many observational \r\nsites thereafter (high confidence). These trends are neither \r\na local phenomenon nor a measurement artefact (high confidence). \r\nMulti-decadal variation in anthropogenic aerosol emissions are \r\nthought to be a  major contributor (medium confidence), but \r\nmulti-decadal variability in cloudiness may also have played a role. \r\nThe downward and upward thermal radiation at the surface has \r\nincreased in recent decades, in line with increased greenhouse \r\ngas concentrations and associated surface and atmospheric warming \r\nand moistening (medium confidence). {7.2.2}"


id: 1e2de27856857c808a75458e47748832
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: 2186
content:
"For carbon dioxide, methane, nitrous oxide and \r\nchlorofluorocarbons, there is now evidence to quantify the \r\neffect on ERF of tropospheric adjustments (e.g., from changes \r\nin atmospheric temperatures, clouds and water vapour). \r\nThe assessed ERF for a doubling of carbon dioxide compared \r\nto 1750 levels (3.93 +- 0.47 W m-2) is larger than in AR5. Effective \r\nradiative forcings (ERF), introduced in AR5, have been estimated for \r\na larger number of agents and shown to be more closely related to the \r\ntemperature response than the stratospheric-temperature adjusted \r\nradiative forcing. For carbon dioxide, the adjustments include the \r\nphysiological effects on vegetation (high confidence). {7.3.2}"


id: e79efbd072b37dc14665353b6b125266
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: 33940
content:
"The net effect of changes in clouds in response to global \r\nwarming is to amplify human-induced warming, that is, the \r\nnet cloud feedback is positive (high confidence). Compared to \r\nAR5, major advances in the understanding of cloud processes \r\nhave increased the level of confidence and decreased the \r\nuncertainty range in the cloud feedback by about 50%.\r\nAn assessment of the low-altitude cloud feedback over the subtropical \r\noceans, which was previously the major source of uncertainty in the \r\nnet cloud feedback, is improved owing to a combined use of climate \r\nmodel simulations, satellite observations, and explicit simulations \r\nof clouds, altogether leading to strong evidence that this type of \r\ncloud amplifies global warming. The net cloud feedback, obtained \r\nby summing the cloud feedbacks assessed for individual regimes, \r\nis 0.42 [-0.10 to +0.94] W m-2 degC-1. A net negative cloud feedback is \r\nvery unlikely (high confidence). {7.4.2, Figure 7.10, Table 7.10}"


id: d1c1dc3901eb58494858b1f60dacfb5d
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: 30531
content:
"Radiative feedbacks, particularly from clouds, are expected \r\nto become less negative (more amplifying) on multi-decadal \r\ntime scales as the spatial pattern of surface warming \r\nevolves, leading to an ECS that is higher than was inferred \r\nin AR5 based on warming over the instrumental record. \r\nThis new understanding, along with updated estimates \r\nof historical temperature change, ERF, and Earth's energy \r\nimbalance, reconciles previously disparate ECS estimates \r\n(high confidence). However, there is currently insufficient evidence \r\nto quantify a  likely range of the magnitude of future changes to \r\ncurrent climate feedbacks. Warming over the instrumental record \r\nprovides robust constraints on the lower end of the ECS range \r\n(high  confidence), but owing to the possibility of future feedback \r\nchanges it does not, on its own, constrain the upper end of the range, \r\nin contrast to what was reported in AR5. {7.4.4, 7.5.2, 7.5.3}"


id: a596bf6fd4a4669d69fd347aa7d21f8a
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: 23625
content:
"Based on multiple lines of evidence the best estimate of ECS is \r\n3degC, the likely range is 2.5degC to 4degC, and the very likely range \r\nis 2degC to 5degC. It is virtually certain that ECS is larger than 1.5degC. \r\nSubstantial advances since AR5 have been made in quantifying ECS \r\nbased on feedback process understanding, the instrumental record, "


id: 9d99706ef26ffb5df1c88b6e2d0ea5e4
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: 22328
content:
"Global ocean heat uptake is a  smaller source of uncertainty in \r\ncentennial-time scale surface warming (high confidence). {7.5.7}"


id: 4e08cd70ce1ccb4150462a385505a07b
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: 9700
content:
"paleoclimates and emergent constraints. There is a  high level of \r\nagreement among the different lines of evidence. All lines of evidence \r\nhelp rule out ECS values below 1.5degC, but currently it is not possible \r\nto rule out ECS values above 5degC. Therefore, the 5degC upper end of \r\nthe very likely range is assessed to have medium confidence and the \r\nother bounds have high confidence. {7.5.5}"


id: f4fc5073fe2e5082d8ff49c0d6ac5130
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: 36054
content:
"The assessed historical and future ranges of GSAT change in this \r\nReport are shown to be internally consistent with the Report's \r\nassessment of key physical-climate indicators: greenhouse gas \r\nERFs, ECS and TCR. When calibrated to match the assessed ranges \r\nwithin the assessment, physically based emulators can reproduce \r\nthe best estimate of GSAT change over 1850-1900 to 1995-2014 to \r\nwithin 5% and the very likely range of this GSAT change to within \r\n10%. Two physically based emulators match at least two-thirds of the \r\nChapter  4-assessed projected GSAT changes to within these levels \r\nof precision. When used for multi-scenario experiments, calibrated \r\nphysically based emulators can adequately reflect assessments \r\nregarding future GSAT from Earth system models and/or other lines of \r\nevidence (high confidence). {Cross-Chapter Box 7.1}"


id: 851d91de72dabe80bbc2d25b70610dbb
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: 18472
content:
"Based on process understanding, warming over the instrumental \r\nrecord, and emergent constraints, the best estimate of TCR is \r\n1.8degC, the likely range is 1.4degC to 2.2degC and the very likely\r\nrange is 1.2degC to 2.4degC (high confidence). {7.5.5}"


id: c4fdf8bec4183c5db615efcbff5d5c53
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: 28570
content:
"The assessed global warming potentials (GWP) and global \r\ntemperature-change potentials (GTP) for methane and nitrous \r\noxide are slightly lower than in AR5 due to revised estimates \r\nof their lifetimes and updated estimates of their indirect \r\nchemical effects (medium confidence). The assessed metrics now \r\nalso include the carbon cycle response for non-CO2 gases. The carbon \r\ncycle estimate is lower than in AR5, but there is high confidence in \r\nthe need for its inclusion and in the quantification methodology. \r\nMetrics for methane from fossil fuel sources account for the extra \r\nfossil CO2 that these emissions contribute to the atmosphere and so \r\nhave slightly higher emissions metric values than those from biogenic \r\nsources (high confidence). {7.6.1}"


id: 22f21ea84ac7e5b613c450380492dd2c
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: 2944
content:
"The total human-forced GSAT change from 1750 to 2019 \r\nis calculated to be 1.29 [0.99 to 1.65] degC. This calculation \r\nis  an emulator-based estimate, constrained by the historic \r\nGSAT and ocean heat content changes from Chapter  2 and \r\nthe ERF, ECS and TCR from this chapter. The calculated GSAT \r\nchange is composed of a  well-mixed greenhouse gas warming of \r\n1.58 [1.17  to 2.17] degC (high confidence), a  warming from ozone \r\nchanges of 0.23  [0.11 to 0.39] degC (high confidence), a  cooling of \r\n-0.50 [-0.22 to -0.96] degC from aerosol effects (medium confidence), \r\nand a -0.06 [-0.15 to +0.01] degC contribution from surface reflectance \r\nchanges from land-use change and light-absorbing particles on \r\nice and snow (medium confidence). Changes in solar and volcanic \r\nactivity are assessed to have together contributed a small change of \r\n-0.02 [-0.06 to +0.02] degC since 1750 (medium confidence). {7.3.5}"


id: c33129d206705484db170ab1b4c5b7cb
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: 28281
content:
"New emissions metric approaches such as GWP* and the \r\ncombined-GTP (CGTP) are designed to relate emissions rates \r\nof short-lived gases to cumulative emissions of CO2. These \r\nmetric approaches are well suited to estimate the GSAT \r\nresponse from aggregated emissions of a  range of gases \r\nover time, which can be done by scaling the cumulative CO2 \r\nequivalent emissions calculated with these metrics by the \r\ntransient climate response to cumulative emissions of CO2.\r\nFor a given multi-gas emissions pathway, the estimated contribution \r\nof emissions to surface warming is improved by using either these "


id: a732675f5b7450ac7ca3047fd9912c69
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: 23815
content:
"Uncertainties regarding the true value of ECS and TCR are \r\nthe dominant source of uncertainty in global temperature \r\nprojections over the 21st  century under moderate to high \r\ngreenhouse gas emissions scenarios. For scenarios that reach \r\nnet zero carbon dioxide emissions, the uncertainty in the \r\nERF values of aerosol and other short-lived climate forcers \r\ncontribute substantial uncertainty in projected temperature."


id: a022a30a065a2c6f206a9a2f54128c17
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: 22730
content:
"new metric approaches or by treating short- and long-lived GHG \r\nemissions pathways separately, as compared to approaches that \r\naggregate emissions of GHGs using standard GWP or GTP emissions \r\nmetrics. By contrast, if emissions are weighted by their 100-year \r\nGWP or GTP values, different multi-gas emissions pathways with the \r\nsame aggregated CO2 equivalent emissions rarely lead to the same \r\nestimated temperature outcome (high confidence). {7.6.1, Box 7.3}"


id: 4fc04ce0dd10c25b4a4908c448c7d2e3
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: 9987
content:
"The choice of emissions metric affects the quantification of net \r\nzero GHG emissions and therefore the resulting temperature \r\noutcome after net zero emissions are achieved. In general, \r\nachieving net zero CO2 emissions and declining non-CO2 radiative \r\nforcing would be sufficient to prevent additional human-caused \r\nwarming. Reaching net zero GHG emissions as quantified by \r\nGWP-100 typically results in global temperatures that peak and \r\nthen decline after net zero GHGs emissions are achieved, though \r\nthis outcome depends on the relative sequencing of mitigation of \r\nshort-lived and long-lived species. In contrast, reaching net zero GHG \r\nemissions when quantified using new emissions metrics such as \r\nCGTP or GWP* would lead to approximate temperature stabilization \r\n(high confidence). {7.6.2}"


id: 9aef5f8d36944fcfbdcee75b35c7ef49
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: 21908
content:
"and greenhouse gas fluxes in terrestrial ecosystems (SRCCL; IPCC, \r\n2019b), as well as community-led assessments (e.g., Bellouin et al. \r\n(2020) covering aerosol radiative forcing and Sherwood et al. (2020) \r\ncovering equilibrium climate sensitivity)."


id: d18325c3f0f6feeb9741f93e543494a9
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: 30498
content:
"This chapter assesses the major physical processes that affect the \r\nevolution of Earth's energy budget and the associated changes in \r\nsurface temperature and the broader climate system, integrating \r\nelements that were dealt with separately in previous reports."


id: 7ab4865a7dd35216bdde979ccd1928fa
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: 16885
content:
"The top-of-atmosphere (TOA) energy budget determines the net amount \r\nof energy entering or leaving the climate system. Its time variations \r\ncan be monitored in three ways, using: (i) satellite observations of the \r\nradiative fluxes at the TOA; (ii) observations of the accumulation of \r\nenergy in the climate system; and (iii) observations of surface energy \r\nfluxes. When the TOA energy budget is changed by a human or natural \r\ncause (a 'radiative forcing'), the climate system responds by warming \r\nor cooling (i.e., the system gains or loses energy). Understanding \r\nof changes in the Earth's energy flows helps understanding of the \r\nmain physical processes driving climate change. It also provides \r\na fundamental test of climate models and their projections."


id: 817ec06bec59ab8ffbad7ccca62c664f
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: 17901
content:
"This chapter principally builds on the IPCC Fifth Assessment Report \r\n(AR5; Boucher, 2012; Church et al., 2013; M. Collins et al., 2013; Flato \r\net al., 2013; Hartmann et al., 2013; Myhre et al., 2013b; Rhein et al., \r\n2013). It also builds on the subsequent IPCC Special Report on Global \r\nWarming of 1.5degC (SR1.5; IPCC, 2018), the Special Report  on \r\nthe Ocean and Cryosphere in a  Changing Climate (SROCC; IPCC, \r\n2019a) and the Special Report on climate change, desertification, \r\nland degradation, sustainable land management, food security, "


id: 6bc526a01642c888450ad718d8edf980
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: 14582
content:
"Figure 7.1 (continued): Panel (b) Visual abstract of the chapter, illustrating why the Earth's energy budget matters and how it relates to the underlying chapter assessment. \r\nThe methods used to assess processes and key new findings relative to AR5 are highlighted. Upper schematic adapted from Von Schuckmann et al. (2020)."


id: 8926dc2e54b97989dbbd73926f597a04
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: 19117
content:
"In Box  7.1 an energy budget framework is introduced, which \r\nforms the basis for the discussions and scientific assessment in the \r\nremainder of this chapter and across the Report. The framework \r\nreflects advances in the understanding of the Earth system response \r\nto climate forcing since the publication of AR5. A schematic of this \r\nframework and the key changes relative to the science reported in \r\nAR5 are provided in Figure 7.1."


id: bab9d365d4afb405286455637ced1dae
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: 26919
content:
"Regional changes in temperature, rainfall, and climate extremes \r\nhave been found to correlate well with the forced changes in GSAT \r\nwithin Earth System Models (ESMs; Section 4.6.1; Giorgetta et al., \r\n2013; Tebaldi and Arblaster, 2014; Seneviratne et  al., 2016). While \r\nthis so-called 'pattern scaling' has important limitations arising from, \r\nfor instance, localized forcings, land-use changes, or internal climate \r\nvariability (Deser et al., 2012; Luyssaert et al., 2014), changes in GSAT \r\nnonetheless explain a substantial fraction of inter-model differences \r\nin projections of regional climate changes over the 21st  century \r\n(Tebaldi and Knutti, 2018). This Chapter's assessments of TCR and \r\nECS thus provide constraints on future global and regional climate \r\nchange (Chapters 4 and 11)."


id: b5efb4e0dbb2fb2b95c6bd48dbea904f
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: 26153
content:
"The forcing and response energy budget framework provides a methodology to assess the effect of individual drivers of global surface \r\ntemperature response, and to facilitate the understanding of the key phenomena that set the magnitude of this temperature response. \r\nThe framework used here is developed from that adopted in previous IPCC reports (see Ramaswamy et al., 2019 for a discussion). \r\nEffective Radiative Forcing (ERF), introduced in AR5 (Boucher et al., 2013; Myhre et al., 2013b) is more explicitly defined in this \r\nReport and is employed as the central definition of radiative forcing (Sherwood et al., 2015, Box 7.1, Figure 1a). The framework has also \r\nbeen extended to allow variations in feedbacks over different time scales and with changing climate state (Sections 7.4.3 and 7.4.4)."


id: 511c9c73922ef8cacefdedf135fdd0ac
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: 10214
content:
"ERF is the TOA energy budget change resulting from the perturbation, excluding any radiative response related to a change in GSAT \r\n(i.e., DT = 0). Climate feedbacks (a) represent those processes that change the TOA energy budget in response to a given DT."


id: 46b04a3cac99e6aa562d9a636ac8233
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: 8505
content:
"The effective radiative forcing, ERF (DF; units: W m-2) quantifies the change in the net TOA energy flux of the Earth system due to \r\nan imposed perturbation (e.g., changes in greenhouse gas or aerosol concentrations, in incoming solar radiation, or land-use change). \r\nERF is expressed as a change in net downward radiative flux at the TOA following adjustments in both tropospheric and stratospheric \r\ntemperatures, water vapour, clouds, and some surface properties, such as surface albedo from vegetation changes, that are uncoupled to"


id: 6ede1327b7a6ad75060ebb5fd79354fa
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: 15070
content:
"The feedback parameter, a (units: W m-2 degC-1) quantifies the change in net energy flux at the TOA for a given change in GSAT. Many \r\nclimate variables affect the TOA energy budget, and the feedback parameter can be decomposed, to first order, into a sum of terms "


id: 52487a1008b346e006eba4b30b227316
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: 10401
content:
"A change in process x amplifies the temperature response to a forcing when the associated feedback parameter ax is positive (positive \r\nfeedback) and dampens the temperature response when ax is negative (negative feedback). New research since AR5 emphasizes how \r\nfeedbacks can vary over different time scales (Section 7.4.4) and with climate state (Section 7.4.3), giving rise to the concept of an \r\n'effective feedback parameter' that may be different from the equilibrium value of the feedback parameter governing ECS (Section 7.4.3)."


id: b2ac63b605b21873e9548ca54a347cd8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 25629
content:
"to the TOA energy fluxes, Earth's energy budget  al.o includes the \r\ninternal flows of energy within the climate system, which characterize \r\nthe climate state. The surface energy budget consists of the net solar \r\nand thermal radiation as well as the non-radiative components such as \r\nsensible, latent and ground heat fluxes (Figure 7.2, upper panel). It is \r\na key driver of the global water cycle, atmosphere and ocean dynamics, \r\nas well as a variety of surface processes."


id: 2f0e8e35105b7c7233d11f2e814f7add
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 4833
content:
"Figure 7.2 (upper panel) shows a schematic representation of Earth's \r\nenergy budget for the early 21st century, including globally averaged \r\nestimates of the individual components (Wild et al., 2015). Clouds are \r\nimportant modulators of global energy fluxes. Thus, any perturbations \r\nin the cloud fields, such as forcing by aerosol-cloud interactions \r\n(Section  7.3) or through cloud feedbacks (Section  7.4) can have \r\na strong influence on the energy distribution in the climate system. \r\nTo illustrate the overall effects that clouds exert on energy fluxes, \r\nFigure 7.2 (lower panel) also shows the energy budget in the absence "


id: 8a3bae4a9e663fd785ef7a4072248adc
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 19283
content:
"of clouds, with otherwise identical atmospheric and surface radiative \r\nproperties. It has been derived by taking into account information \r\ncontained in both in situ and satellite radiation measurements taken \r\nunder cloud-free conditions (Wild et al., 2019). A comparison of the \r\nupper and lower panels in Figure  7.2 shows that without clouds, \r\n47  W m-2 less solar radiation is reflected back to space globally \r\n(53 +- 2 W m-2 instead of 100 +- 2 W m-2), while 28 W m-2 more thermal \r\nradiation is emitted to space (267 +- 3 W m-2 instead of 239 +- 3 W m-2). \r\nAs a result, there is a 20 W m-2 radiative imbalance at the TOA in the \r\nclear-sky energy budget (Figure  7.2, lower panel), suggesting that \r\nthe Earth would warm substantially if there were no clouds."


id: c5524e73d039c26016b2c566eca886fa
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 28626
content:
"ESMs also show larger discrepancies in their surface energy fluxes than \r\nat the TOA due to weaker observational constraints, with a spread of \r\ntypically 10-20 W m-2 in the global average, and an even greater \r\nspread at regional scales (J.-L.F. Li et al., 2013; Wild et al., 2013; Boeke \r\nand Taylor, 2016; Wild, 2017, 2020; C. Zhang et al., 2018). Differences \r\nin the land-averaged downward thermal and solar radiation in CMIP5 \r\nESMs amount to more than 30 and 40 W m-2, respectively (Wild et al., \r\n2015). However, in the global multi-model mean, the magnitudes of \r\nthe energy budget components of the CMIP6 ESMs generally show \r\nbetter agreement with reference estimates than previous model \r\ngenerations (Wild, 2020)."


id: 757d8e67d85708db759c7654e5caf0a6
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 16073
content:
"In summary, since AR5, the magnitudes of the global mean energy \r\nbudget components have been quantified more accurately, not only at \r\nthe TOA, but also at the Earth's surface, where independent estimates \r\nof the radiative components have converged (high confidence). \r\nConsiderable uncertainties remain in regional surface energy budget \r\nestimates as well as their representation in climate models."


id: ff8399001947d714ef8ab0a39bf88c99
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 37744
content:
"Since 2000, changes in top-of-atmosphere (TOA) energy fluxes can \r\nbe tracked from space using CERES satellite observations (Figure 7.3). \r\nThe variations in TOA energy fluxes reflect the influence of internal \r\nclimate variability, particularly that of El Nino-Southern Oscillation "


id: f419b446349021cd4e794923c253e04f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 35912
content:
"(ENSO), in addition to radiative forcing of the climate system and \r\nclimate feedbacks (Allan et al., 2014; Loeb et al., 2018b). For example, \r\nglobally, the reduction in both outgoing thermal and reflected solar \r\nradiation during La Nina conditions in 2008/2009 led to an energy \r\ngain for the climate system, whereas enhanced outgoing thermal and \r\nreflected solar radiation caused an energy loss during the El Ninos of \r\n2002/2003 and 2009/2010 (Figure 7.3; Loeb et al., 2018b). An ensemble \r\nof CMIP6 models is able to track the variability in global mean TOA "


id: 8d66e82412aa9455a43642b42ec9e7a9
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 19767
content:
"fluxes observed by CERES, when driven with prescribed sea surface \r\ntemperatures (SSTs) and sea ice concentrations (Figure 7.3; Loeb et al., \r\n2020). Under cloud-free conditions, the CERES record shows a near \r\nzero trend in outgoing thermal radiation (Loeb et al., 2018b), which - \r\ncombined with an increasing surface upwelling thermal flux - implies \r\nan increasing clear-sky greenhouse effect (Raghuraman et al., 2019). \r\nConversely, clear-sky solar reflected TOA radiation in the CERES record \r\ncovering March 2000 to September 2017 shows a decrease due to "


id: 129a70419cd71598675c8d38323748d0
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 425
content:
"reductions in aerosol optical depth in the Northern Hemisphere and \r\nsea ice fraction (Loeb et al., 2018a; Paulot et al., 2018)."


id: 54d37308f051782d0cd1991fc7e2457b
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 10801
content:
"An effort to reconstruct variations in net TOA fluxes back to 1985, \r\nbased on a combination of satellite data, atmospheric reanalysis and \r\nhigh-resolution climate model simulations (Allan et  al., 2014; Liu \r\net  al., 2020), exhibits strong interannual variability associated with \r\nthe volcanic eruption of Mount Pinatubo in 1991 and the ENSO events \r\nbefore 2000. The same reconstruction suggests that Earth's energy \r\nimbalance increased by several tenths of a W m-2 between the periods \r\n1985-1999 and 2000-2016, in agreement with the assessment of \r\nchanges in the global energy inventory (Section 7.2.2.2, and Box 7.2, \r\nFigure  1). Comparisons of year-to-year variations in Earth's energy \r\nimbalance estimated from CERES and independent estimates based \r\non ocean heat content change are significantly correlated with similar \r\nphase and magnitude (Johnson et al., 2016; Meyssignac et al., 2019), \r\npromoting confidence in both satellite and in situ-based estimates \r\n(Section 7.2.2.2)."


id: f15485b9bbd3db7fe845cb004320a7b4
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 35472
content:
"In summary, variations in the energy exchange between Earth and space \r\ncan be accurately tracked since the advent of improved observations \r\nsince the year 2000 (high confidence), while reconstructions indicate \r\nthat the Earth's energy imbalance was larger in the 2000s than in the \r\n1985-1999 period (high confidence)."


id: a6a33f8b065909a0bbc8da2eea85a1eb
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23741
content:
"The global energy inventory quantifies the integrated energy gain \r\nof the climate system associated with global ocean heat uptake, \r\nwarming of the atmosphere, warming of the land, and melting of ice. \r\nDue to energy conservation, the rate of accumulation of energy in \r\nthe Earth system (Section  7.1) is equivalent to the Earth energy \r\nimbalance (DN in Box  7.1, Equation 7.1). On annual and longer \r\ntime scales, changes in the global energy inventory are dominated \r\nby changes in global ocean heat content (OHC; Rhein et al., 2013; \r\nPalmer and McNeall, 2014; Johnson et al., 2016). Thus, observational \r\nestimates and climate model simulations of OHC change are critical \r\nto the understanding of both past and future climate change \r\n(Sections 2.3.3.1, 3.5.1.3, 4.5.2.1 and 9.2.2.1)."


id: 6d30374232c281438abe94a85f49bb66
created_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 14798
content:
"The assessed changes in the global energy inventory (Box  7.2, \r\nFigure 1, and Table 7.1) yields an average value for Earth's energy \r\nimbalance (N in Box 7.1, Equation 7.1) of 0.57 [0.43 to 0.72] W m-2\r\nfor the period 1971-2018, expressed relative to Earth's surface \r\narea (high  confidence). The estimates for the periods 1993-2018 \r\nand 2006-2018 yield substantially larger values of 0.72  [0.55  to \r\n0.89] W m-2 and 0.79 [0.52 to 1.06] W m-2, respectively, consistent with \r\nthe increased radiative forcing from GHGs (high confidence). For the "


id: e23febaf766e9fd0d0cbfd2c14d41ed7
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 33091
content:
"Table 7.1 | Contributions of the different components of the global energy inventory for the periods 1971-2018, 1993-2018 and 2006-2018 (Box 7.2 \r\nand Cross-Chapter Box 9.1). Energy changes are computed as the difference between annual mean values or year mid-points. The total heating rates correspond to Earth's \r\nenergy imbalance and are expressed per unit area of Earth's surface."


id: 100e61ae6fdfb58c76c698f735400da5
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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content:
"period 1971-2006, the total energy gain was 282 [177 to 387] ZJ, with \r\nan equivalent Earth energy imbalance of 0.50 [0.32 to 0.69] W m-2. \r\nTo  put these numbers in context, the 2006-2018 average Earth \r\nsystem heating is equivalent to approximately 20 times the annual \r\nrate of global energy consumption in 2018.1"


id: f3899cb7b3921ef5b0e3370d6b9ae8ef
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 35826
content:
"Consistent with AR5 (Rhein et al., 2013), this Report finds that ocean \r\nwarming dominates the changes in the global energy inventory \r\n(high confidence), accounting for 91% of the observed change for \r\nall periods considered (Table 7.1). The contributions from the other \r\ncomponents across all periods are approximately 5% from land \r\nheating, 3% for cryosphere heating and 1% associated with warming \r\nof the atmosphere (high confidence). The assessed percentage \r\ncontributions are similar to the recent study by von Schuckmann et al. \r\n(2020) and the total heating rates are consistent within the assessed \r\nuncertainties. Cross-validation of heating rates based on satellite and \r\nin situ observations (Section 7.2.2.1), and closure of the global sea \r\nlevel budget using consistent datasets (Cross-Chapter Box 9.1 and \r\nTable 9.5), strengthen scientific confidence in the assessed changes \r\nin the global energy inventory relative to AR5."


id: 8a70806f4783eb8373683e94d371945d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 19316
content:
"The AR5 (Hartmann et  al., 2013) reported pronounced changes \r\nin multi-decadal records of in situ observations of surface solar \r\nradiation, including a  widespread decline between the 1950s and \r\n1980s, known as 'global dimming', and a partial recovery thereafter, \r\ntermed 'brightening' (Section 12.4). These changes have interacted \r\nwith closely related elements of climate change, such as global and \r\nregional warming rates (Z. Li et al., 2016; Wild, 2016; Du et al., 2017; \r\nZhou et al., 2018a), glacier melt (Ohmura et al., 2007; Huss et al., \r\n2009), the intensity of the global water cycle (Wild, 2012) and \r\nterrestrial carbon uptake (Mercado et  al., 2009). These observed \r\nchanges have also been used as emergent constraints to quantify \r\naerosol effective radiative forcing (Section 7.3.3.3)."


id: 7609b346acf1ea06c44ecd8c16482e7f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 16175
content:
"Since AR5, there is additional evidence that strong decadal changes \r\nin surface solar radiation have occurred under cloud-free conditions, \r\nas shown for long-term observational records in Europe, USA, China, \r\nIndia and Japan (Xu et al., 2011; Gan et al., 2014; Manara et al., 2016; "


id: 9bdbcda6e66c1bdf9e2511c47888b519
created_at: 2023-03-16 14:00:44
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"line with what would be expected from an increased radiative forcing \r\nfrom GHGs and the warming and moistening of the atmosphere. \r\nUpdates of the longest observational records from the Baseline \r\nSurface Radiation Network continue to show an increase at the \r\nmajority of sites, in line with an overall increase predicted by ESMs \r\nof the order of 2 W m-2 per decade (Wild, 2016). Upward longwave \r\nradiation at the surface is rarely measured but is expected to have \r\nincreased over the same period due to rising surface temperatures."


id: 793208559492ccca0c8603f219cd1d11
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 16635
content:
"ESMs and reanalyses often do not reproduce the full extent of \r\nobserved dimming and brightening (Wild and Schmucki, 2011; Allen \r\net al., 2013; Zhou et al., 2017a; Storelvmo et al., 2018; Moseid et al., \r\n2020; Wohland et al., 2020), potentially pointing to inadequacies in \r\nthe representation of aerosol mediated effects or related emissions \r\ndata. The inclusion of assimilated aerosol optical depth inferred from \r\nsatellite retrievals in the MERRA2 reanalysis (Buchard et al., 2017; \r\nRandles et al., 2017) helps to improve the accuracy of the simulated \r\nsurface solar radiation changes in China (Feng and Wang, 2019). \r\nHowever, non-aerosol-related deficiencies in model representations \r\nof clouds and circulation, and/or an underestimation of natural \r\nvariability, could further contribute to the lack of dimming and \r\nbrightening in ESMs (Wild, 2016; Storelvmo et al., 2018)."


id: 581e8cb9503ba381fbdae5a1a6168724
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 11370
content:
"In summary, since AR5, multi-decadal decreasing and increasing trends \r\nin surface solar radiation of up to several percent per decade have \r\nbeen detected at many more locations, even in remote areas. There is \r\nhigh confidence that these trends are widespread, and not localized \r\nphenomena or measurement artefacts. The origin of these trends is \r\nnot fully understood, although there is evidence that anthropogenic \r\naerosols have made a substantial contribution (medium confidence). \r\nThere is medium confidence that downward and upward thermal \r\nradiation has increased since the 1970s, while there remains low \r\nconfidence in the trends in surface sensible and latent heat."


id: fed485b1b763076af497b5c8e1f455a1
created_at: 2023-03-16 14:00:44
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vector_id: 37636
content:
"This box assesses the present knowledge of the global energy budget for the period 1971-2018, that is, the balance between radiative \r\nforcing, the climate system radiative response and observations of the changes in the global energy inventory (Box 7.2, Figure 1a,d)."


id: 5e508febeeaeba42a4fd6f24431774a1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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content:
"The net effective radiative forcing (ERF) of the Earth system since 1971 has been positive (Section 7.3 and Box 7.2, Figure 1b,e), mainly \r\nas a result of increases in atmospheric greenhouse gas concentrations (Sections 2.2.8 and 7.3.2). The ERF of these positive forcing \r\nagents have been partly offset by that of negative forcing agents, primarily due to anthropogenic aerosols (Section 7.3.3), which \r\ndominate the overall uncertainty. The net energy inflow to the Earth system from ERF for the period 1971-2018 is estimated to be \r\n937 ZJ (1 ZJ = 1021 J) with a likely range of 644 to 1259 ZJ (Box 7.2, Figure 1b)."


id: 1352d5b5ac158aebe32cbd0a10f91780
created_at: 2023-03-16 14:00:44
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content:
"The ERF-induced heating of the climate system results in increased thermal radiation to space via the Planck response, but the picture \r\nis complicated by a variety of climate feedbacks (Section 7.4.2 and Box 7.1) that also influence the climate system radiative response \r\n(Box 7.2, Figure 1c). The total radiative response is estimated by multiplying the assessed net feedback parameter, a, from process-based \r\nevidence (Section 7.4.2 and Table 7.10) with the observed GSAT change for the period (Cross Chapter Box 2.3) and time-integrating \r\n(Box 7.2, Figure 1c). The net energy outflow from the Earth system associated with the integrated radiative response for the period \r\n1971-2018 is estimated to be 621 ZJ with a likely range of 419 to 823 ZJ. Assuming a pattern effect (Section 7.4.4) on a of -0.5 W m-2 degC-1\r\nwould lead to a systematically larger energy outflow by about 250 ZJ."


id: 844f334fd862f0107be197313ae06a50
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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"Combining the likely range of integrated radiative forcing (Box  7.2, Figure  1b) with the central estimate of integrated radiative \r\nresponse (Box 7.2, Figure 1c) gives a central estimate and likely range of 340 [47 to 662] ZJ (Box 7.2, Figure 1f). Combining the likely \r\nrange of integrated radiative response with the central estimate of integrated radiative forcing gives a likely range of 340 [147 to \r\n527] ZJ (Box 7.2, Figure 1f). Both calculations yield an implied energy gain in the climate system that is consistent with an independent \r\nobservation-based assessment of the increase in the global energy inventory expressed relative to the estimated 1850-1900 "


id: 68a77ad1a7177d19cf0304159bbaa5d2
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 14061
content:
"In addition to new and extended observations (Section 7.2.2), confidence in the observed accumulation of energy in the Earth system \r\nis strengthened by cross-validation of heating rates based on satellite and in situ observations (Section  7.2.2.1) and closure of \r\nthe global sea level budget using consistent datasets (Cross-Chapter Box 9.1 and Table 9.5). Overall, there is high confidence that \r\nthe global energy budget is closed for 1971-2018 with improved consistency compared to AR5."


id: cdcf4fa3976267f00f543d05c6cf0d39
created_at: 2023-03-16 14:00:44
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vector_id: 29901
content:
"Effective radiative forcing (ERF) quantifies the energy gained or lost by \r\nthe Earth system following an imposed perturbation (for instance in \r\nGHGs, aerosols or solar irradiance). As such it is a fundamental driver \r\nof changes in the Earth's TOA energy budget. ERF is determined by \r\nthe change in the net downward radiative flux at the TOA (Box 7.1) \r\nafter the system has adjusted to the perturbation but excluding the \r\nradiative response to changes in surface temperature. This section \r\noutlines the methodology for ERF calculations (Section  7.3.1) and \r\nthen assesses the ERF due to greenhouse gases (Section  7.3.2), \r\naerosols (Section 7.3.3) and other natural and anthropogenic forcing \r\nagents (Section 7.3.4). These are brought together in Section 7.3.5 \r\nfor an overall assessment of the present-day ERF and its evolution \r\nover the historical time period from 1750 to 2019. The same section \r\nalso evaluates the surface temperature response to individual ERFs."


id: f5159974640abb8cc1dab27d6f8ad747
created_at: 2023-03-16 14:00:44
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vector_id: 36070
content:
"Instantaneous radiative forcing (IRF) is defined here as the change \r\nin the net TOA radiative flux following a perturbation, excluding any \r\nadjustments. SARF is defined here as the change in the net radiative \r\nflux at TOA following a  perturbation including the response to \r\nstratospheric temperature adjustments. These differ from AR5 where \r\nthese quantities were defined at the tropopause (Myhre et al., 2013b). \r\nThe net IRF values will be different using the TOA definition. The net \r\nSARF values will be the same as with the tropopause definition, \r\nbut will have a  different partitioning between the longwave and \r\nshortwave. Defining all quantities at the TOA enables consistency in \r\nbreaking down the ERF into its component parts."


id: 7ec1ac72cccd4f339389329c4692dbb2
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 17506
content:
"As introduced in Box 7.1, AR5 (Boucher et al., 2013; Myhre et al., \r\n2013b) recommended ERF as a more useful measure of the climate \r\neffects of a  physical driver than the stratospheric-temperature\u0002adjusted radiative forcing (SARF) adopted in earlier assessments. \r\nThe AR5 assessed that the ratios of surface temperature change to \r\nforcing resulting from perturbations of different forcing agents were \r\nmore similar between species using ERF than SARF. ERF extended the \r\nSARF concept to account for not only adjustments to stratospheric \r\ntemperatures, but also responses in the troposphere and effects on \r\nclouds and atmospheric circulation, referred to as 'adjustments'. \r\nFor more details see Box 7.1. Since circulation can be affected, these \r\nresponses are not confined to the locality of the initial perturbation \r\n(unlike the traditional stratospheric-temperature adjustment)."


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vector_id: 18343
content:
"The assessment of ERFs in AR5 was preliminary because ERFs were \r\nonly available for a few forcing agents, so for many forcing agents the \r\nReport made the assumption that ERF and SARF were equivalent. This \r\nsection discusses the body of work published since AR5. This work has \r\ncomputed ERFs across many more forcing agents and models; closely \r\nexamined the methods of computation; quantified the processes \r\ninvolved in causing adjustments; and examined how well ERFs predict "


id: a767ec1b316e6f8d10b0b7df3c123cf3
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 23844
content:
"For comparison with offline radiative transfer calculations the SARFs \r\ncan be approximated by removing the adjustment terms (apart from \r\nstratospheric temperature) from the ERFs using radiative kernels to \r\nquantify the adjustment for each meteorological variable. Kernel \r\nanalysis by Chung and Soden (2015) suggested a  large spread in \r\nCO2 SARF across climate models, but their analysis was based on \r\nregressing variables in a  coupled-ocean experiment rather than \r\nusing a fSST approach which leads to a large spread due to natural \r\nvariability (Forster et al., 2016). Adjustments computed from radiative "


id: 2e4c98d6afaf9fcdaeaa1a6d2c00d483
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 4721
content:
"heating. It is not possible with offline radiation models to account \r\nfor other adjustments. The high-resolution model calculations of \r\nSARF for carbon dioxide, methane and nitrous oxide have been \r\nupdated since AR5, which were based on Myhre et al. (1998). The \r\nnew calculations include the shortwave forcing from methane and \r\nupdates to the water vapour continuum (increasing the total SARF \r\nof methane by 25%) and account for the absorption band overlaps \r\nbetween carbon dioxide and nitrous oxide (Etminan et  al., 2016). \r\nThe  associated simplified expressions, from a  re-fitting of the \r\nEtminan et al. (2016) results by Meinshausen et al. (2020), are given \r\nin Supplementary Material, Table 7.SM.1. The shortwave contribution \r\nto the IRF of methane has been confirmed independently (Collins \r\net al., 2018). Since they incorporate known missing effects we assess \r\nthe new calculations as being a  more appropriate representation \r\nthan Myhre et al. (1998)."


id: 8c92576cf772a8044e407d071dc1870e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 19638
content:
"As described in Section  7.3.1, ERFs can be estimated using ESMs, \r\nhowever the radiation schemes in climate models are approximations \r\nto high spectral resolution radiative transfer models with variations \r\nand biases in results between the schemes (Pincus et  al., 2015). \r\nHence ESMs alone are not sufficient to establish ERF best estimates \r\nfor the well-mixed GHGs (WMGHGs). This assessment therefore \r\nestimates ERFs from a combined approach that uses the SARF from \r\nradiative transfer models and adds the tropospheric adjustments \r\nderived from ESMs."


id: 8d704a1473a03cd60310b1f457b29a1c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 19776
content:
"In AR5, the main information used to assess components of ERFs \r\nbeyond SARF was from Vial et  al. (2013) who found a  near-zero \r\nnon-stratospheric adjustment (without correcting for near-surface \r\ntemperature changes over land) in 4xCO2 CMIP5 model experiments, \r\nwith an uncertainty of +-10% of the total CO2 ERF. No calculations \r\nwere available for other WMGHGs, so ERF was therefore assessed to \r\nbe approximately equal to SARF (within 10%) for all WMGHGs."


id: a098f89a1c7d90742f770f41980be244
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 22805
content:
"The effect of WMGHGs in ESMs can extend beyond their direct \r\nradiative effects to include effects on ozone and aerosol chemistry and \r\nnatural emissions of ozone and aerosol precursors, and in the case of \r\nCO2 to vegetation cover through physiological effects. In some cases \r\nthese can have significant effects on the overall radiative budget \r\nchanges from perturbing WMGHGs within ESMs (Myhre et al., 2013b; \r\nZarakas et al., 2020; O'Connor et al., 2021; Thornhill et al., 2021a). \r\nThese composition adjustments are further discussed in Chapter  6 \r\n(Section 6.4.2)."


id: ac6cfcc281dcd7371754e10e4428a783
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 24663
content:
"The SARF for carbon dioxide (CO2) has been slightly revised due to \r\nupdates to spectroscopic data and inclusion of the absorption band \r\noverlaps between N2O and CO2 (Etminan et al., 2016). The formulae \r\nfitting to the Etminan et  al. (2016) results in Meinshausen et  al. \r\n(2020) are used. This increases the SARF due to doubling CO2 slightly \r\nfrom 3.71 W m-2 in AR5 to 3.75 W m-2. Tropospheric responses to \r\nCO2 in fSST experiments have been found to lead to an approximate \r\nbalance in their radiative effects between an increased radiative \r\nforcing due to water vapour, cloud and surface-albedo adjustments \r\nand a  decrease due to increased tropospheric temperature and \r\nland surface temperature response (Table  7.3; Vial et  al., 2013; "


id: 8c6aa1683bb49d1a888417c92bbec1e8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 19606
content:
"High spectral resolution radiative transfer models provide the most \r\naccurate calculations of radiative perturbations due to greenhouse \r\ngases (GHGs), with errors in the instantaneous radiative forcing (IRF) \r\nof less than 1% (Mlynczak et al., 2016; Pincus et al., 2020). They can \r\ncalculate IRFs with no adjustments, or SARFs by accounting for the \r\nadjustment of stratospheric temperatures using a  fixed dynamical "


id: 573693f9461157e4aec05c6d945c8c78
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 11207
content:
"Table 7.3 | Adjustments to the top-of-atmosphere (TOA) carbon dioxide forcing due to changes in stratospheric temperature, surface and tropospheric \r\ntemperatures, water vapour, clouds, and surface albedo, as a  fraction of the stratospheric-temperature-adjusted radiative forcing (SARF). Effective \r\nradiative forcing (ERF) is defined in this Report as excluding the surface temperature response."


id: 4c61dd67588a737ddaaec42ba373cdde
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 9444
content:
"Table 7.4 | Assessed effective radiative forcing (ERF), stratospheric-temperature-adjusted radiative forcing (SARF) and tropospheric adjustments to \r\n2xCO2 change since pre-industrial times compared to the AR5 assessed range (Myhre et al., 2013b). Adjustments are due to changes in tropospheric temperatures, \r\nwater vapour, clouds and surface albedo and land cover and are taken from Smith et al. (2018b) and assessed as a percentage of SARF (Table 7.3). Uncertainties are based on \r\nmulti-model spread in Smith et al. (2018b). Note some of the uncertainties are anticorrelated, which means that they do not sum linearly."


id: 67f1d550236971e259eead6c6502e6db
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 13942
content:
"The SARF for methane (CH4) has been substantially increased \r\ndue to updates to spectroscopic data and inclusion of shortwave \r\nabsorption (Etminan et al., 2016). Adjustments have been calculated \r\nin nine climate models by Smith et al. (2018b). Since CH4 is found \r\nto absorb in the shortwave near infrared, only adjustments from \r\nthose models including this absorption are taken into account. "


id: 457f72a6a4299c0d2d4dc0e15848e174
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 8347
content:
"(WMO, 2018) updated the lifetimes of many halogenated compounds \r\nand these were used in Hodnebrog et al. (2020a)."


id: b158a07aaca22a8cc86500978356ade
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 25404
content:
"Halogenated compounds containing chlorine and bromine lead \r\nto ozone depletion in the stratosphere which will reduce the \r\nassociated ERF (Morgenstern et  al., 2020). Chapter  6 (Section  6.4 \r\nand Figure 6.12) assesses the ERF contributions due to the chemical \r\neffects of reactive gases."


id: c9866193ab7ce198ffd8e01a19e7cb08
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 29264
content:
"The tropospheric adjustments to nitrous oxide (N2O) have been \r\ncalculated from 5 ESMs as 7% +- 13% of the SARF (Hodnebrog et al., \r\n2020b). This value is therefore taken as the assessed adjustment, but \r\nwith low confidence. The radiative modelling uncertainty is +-10% \r\n(Etminan et al., 2016), giving an overall uncertainty of +-16%. Nitrous \r\noxide concentrations have increased from 270 ppb in 1750 to 332 ppb \r\nin 2019 (Section  2.2.3.3). The historical ERF estimate from N2O is \r\nrevised upwards from 0.17 +- 0.06 W m-2 (1750-2011) in AR5 to \r\n0.21 +- 0.03 W m-2 (1750-2019) in this assessment, of which 0.02 W m-2\r\nis due to the 7 ppb increase in concentrations, and 0.02 W m-2 to the \r\ntropospheric adjustment. As the adjustments are assessed to be small \r\nthere remains high confidence in the overall assessment."


id: 4a20aa38d1e3a9a07852a63527adcce8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 9065
content:
"Increased nitrous oxide leads to ozone depletion in the upper \r\nstratosphere which will make a  positive contribution to the \r\ndirect ERF here (Section  6.4.2 and Figure  6.12) when considering \r\nemissions-based estimates of ERF."


id: c17e531349efac1b56878f17f09c9803
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 28028
content:
"The stratospheric-temperature-adjusted radiative efficiencies (SARF \r\nper ppb increase in concentration) for halogenated compounds \r\nare reviewed extensively in Hodnebrog et al. (2020a), an update to \r\nthose used in AR5. Many halogenated compounds have lifetimes \r\nshort enough that they can be considered short-lived climate forcers \r\n(SLCFs; Table 6.1). As such, they are not completely 'well-mixed' and \r\ntheir vertical distributions are taken into account when determining \r\ntheir radiative efficiencies. The World Meteorological Organization "


id: babf1f02ec917b2fb21092d7f33c91c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 26922
content:
"Without sufficient information to assess whether the ERFs differ \r\nfrom SARF, this assessment relies on offline radiative transfer \r\ncalculations of SARF for both tropospheric and stratospheric \r\nozone. Checa-Garcia et  al. (2018) found SARF of 0.30 W m-2 for \r\nchanges in ozone (1850-1860 to 2009-2014). These were based \r\non precursor emissions and ODS concentrations from the Coupled \r\nChemistry Model Initiative (CCMI) project (Morgenstern et al., 2017). \r\nSkeie et al. (2020) calculated an ozone SARF of 0.41 +- 0.12 W m-2\r\n(1850-2010; from five climate models and one chemistry transport \r\nmodel) using CMIP6 precursor emissions and ODS concentrations \r\n(excluding models without fully interactive ozone chemistry and \r\none model with excessive ozone depletion). The ozone precursor \r\nemissions are higher in CMIP6 than in CCMI, which explains much of \r\nthe increase compared to Checa-Garcia et al. (2018)."


id: 7125fd133557867f443a44440c5b9a9e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 15435
content:
"Previous assessments have split the ozone forcing into tropospheric \r\nand stratospheric components. This does not correspond to the division \r\nbetween ozone production and ozone depletion and is sensitive to \r\nthe choice of tropopause (high confidence) (Myhre et  al., 2013b). \r\nThe  contributions to total SARF in CMIP6 (Skeie et  al., 2020) are \r\n0.39 +- 0.07 and 0.02 +- 0.07 W m-2 for troposphere and stratosphere \r\nrespectively (using a  150 ppb ozone tropopause definition). This \r\nsmall positive (but with uncertainty encompassing negative values) \r\nstratospheric ozone SARF is due to contributions from ozone \r\nprecursors to lower stratospheric ozone and some of the CMIP6 models \r\nshowing ozone depletion in the upper stratosphere, where depletion \r\ncontributes a positive radiative forcing (medium confidence)."


id: 71bc37c5933e2d69fd6aa226ccd198c4
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 15523
content:
"Stratospheric water vapour may also change as an adjustment to \r\nspecies that warm or cool the upper troposphere-lower stratosphere \r\nregion (Forster and Joshi, 2005; Stuber et al., 2005), in which case it \r\nshould be included as part of the ERF for that compound. Changes in \r\nGSAT are also associated with changes in stratospheric water vapour \r\nas part of the water-vapour-climate feedback (Section 7.4.2.2)."


id: 275a08ae9cd24e3738c597e90bed79d3
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 3594
content:
"In summary, although there is insufficient evidence to quantify \r\nadjustments, there is high confidence in the assessed range of ERF \r\nfor ozone changes over the 1750-2019 period, giving an assessed \r\nERF of 0.47 [0.24 to 0.70] W m-2."


id: 86cb9b3e140e7104c6baf1bbd0b1b131
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 18742
content:
"ice nucleating particles (INPs) in the atmosphere may also have \r\noccurred, and thereby contributed to ERFaci by affecting properties of \r\nmixed-phase and cirrus (ice) clouds. In the following, an assessment \r\nof IRFari and ERFari (Section  7.3.3.1) focusing on observation\u0002based (Section 7.3.3.1.1) as well as model-based (Section 7.3.3.1.2) \r\nevidence is presented. The same lines of evidence are presented \r\nfor IRFaci and ERFaci in Section 7.3.3.2. These lines of evidence are \r\nthen compared with TOA energy budget constraints on the total \r\naerosol ERF (Section  7.3.3.3) before an overall assessment of the \r\ntotal aerosol ERF is given in Section  7.3.3.4. For the model-based \r\nevidence, all estimates are generally valid for 2014 relative to 1750 \r\n(the time period spanned by CMIP6 historical simulations), while for \r\nobservation-based evidence the assessed studies use slightly different \r\nend points, but they all generally fall within a decade (2010-2020)."


id: 65c7a22dd950ffeda7fa2bc48f43bc3e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 13595
content:
"in the anthropogenic fraction of total AOD remain, improved \r\nknowledge of anthropogenic absorption results in a slightly narrower \r\nvery likely range here than in AR5. The assessed best estimate and \r\nvery likely IRFari range from observation-based evidence is therefore \r\n-0.4 +- 0.4 W m-2, but with medium confidence due to the limited \r\nnumber of studies available."


id: c42d4faea064c87a1f1ea603f4e7f0ad
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 28429
content:
"While observation-based evidence can be used to estimate IRFari, global \r\nclimate models are needed to calculate the associated adjustments \r\nand the resulting ERFari, using the methods described in Section 7.3.1. "


id: 7cf1ebd512dbdd1a4d7d5f64e7286777
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 17256
content:
"A range of developments since AR5 affect model-based estimates \r\nof IRFari. Global emissions of most major aerosol compounds and \r\ntheir precursors are found to be higher in the current inventories, \r\nand with increasing trends. Emissions of the sulphate precursor \r\nSO2 are a notable exception; they are similar to those used in AR5 \r\nand approximately time-constant in recent decades (Hoesly et  al., \r\n2018). Myhre et  al. (2017) showed, in a  multi-model experiment, \r\nthat the net result of these revised emissions is an IRFari trend that \r\nis relatively flat in recent years (post-2000), a finding confirmed by \r\na single-model study by Paulot et al. (2018)."


id: a44afc954e2f3ff73c8024c311e4d92b
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23405
content:
"Since AR5, deeper understanding of the processes that govern \r\naerosol radiative properties, and thus IRFari, has emerged. Combined \r\nwith new insights into adjustments to aerosol forcing, this progress \r\nhas informed new observation- and model-based estimates of ERFari \r\nand associated uncertainties."


id: 1c117a130bad6c46c2a421c78e9de056
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 1876
content:
"The more negative estimate of Remy et al. (2018) is due to neglecting \r\na small positive contribution from absorbing aerosols above clouds \r\nand obtaining a  larger anthropogenic fraction than Kinne (2019). \r\nRemy et al. (2018) also did not update their assumptions on black \r\ncarbon anthropogenic fraction and its contribution to absorption to \r\nreflect recent downward revisions (Section 7.3.3.1.2). Kinne (2019) \r\nmade those revisions, so more weight is given to that study to assess \r\nthe central estimate of satellite-based IRFari to be only slightly \r\nstronger than reported in AR5 at -0.4 W m-2. While uncertainties "


id: f49c455a0c1f1b4d6e48cf0cca99a7d1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 35989
content:
"The above estimates support a  less negative central estimate and \r\na slightly narrower range compared to those reported for IRFari from \r\nESMs in AR5 of -0.35 [-0.6 to -0.13] W m-2. The assessed central \r\nestimate and very likely IRFari range from model-based evidence \r\nalone is therefore -0.2 +- 0.2 W m-2 for 2014 relative to 1750, with \r\nmedium confidence due to the limited number of studies available. \r\nRevisions due to stronger organic aerosol absorption, further \r\ndeveloped BC parameterizations and somewhat reduced sulphate \r\nemissions in recent years."


id: b2ac699df3324f267624fc667f929a48
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 25630
content:
"Table 7.6 | Present-day effective radiative forcing (ERF) due to changes \r\nin aerosol-radiation interactions (ERFari) and changes in aerosol-cloud \r\ninteractions (ERFaci), and total aerosol ERF (ERFari+aci) from GCM CMIP6 \r\n(2014 relative to 1850; Smith et  al., 2020b and later model results) and CMIP5 \r\n(year 2000 relative to 1860; Zelinka et al., 2014). CMIP6 results are simulated as part \r\nof RFMIP (Pincus et al., 2016). An additional 5% is applied to the CMIP5 and CMIP6 \r\nmodel results to account for land-surface cooling (Figure 7.4; Smith et al., 2020a)."


id: 573a4ef074cc304c3228d34d8d2311eb
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 11213
content:
"This assessment is consistent with the 5-95% confidence range for \r\nERFari in Bellouin et al. (2020) of -0.71 to -0.14 W m-2, and notably \r\nimplies that it is very likely that ERFari is negative. Differences relative \r\nto Bellouin et al. (2020) reflect the range of estimates in Table 7.6 and \r\nthe fact that an ERFari more negative than -0.6 W m-2 would require \r\nadjustments that considerably augment the assessed IRFari, which is \r\nnot supported by the assessed literature."


id: 7083b101a79d6da07476730fd495713e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 15318
content:
"The observation-based assessment of IRFari of -0.4 +- 0.4 W m-2 and \r\nthe corresponding model-based assessment of -0.2 +- 0.2 W m-2 can \r\nbe compared to the range of -0.45 to -0.05 W m-2 that emerged \r\nfrom a  comprehensive review in which an observation-based \r\nestimate of anthropogenic AOD was combined with model-derived \r\nranges for all relevant aerosol radiative properties (Bellouin et  al., \r\n2020). Based on the above, IRFari is assessed to be -0.25 +- 0.2 W m-2\r\n(medium confidence)."


id: 25a89b3e8e5833525c12d349c5bd464a
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 3350
content:
"Anthropogenic aerosol particles primarily affect water clouds by \r\nserving as additional cloud condensation nuclei (CCN) and thus \r\nincreasing cloud drop number concentration (Nd; Twomey, 1959). \r\nIncreasing Nd while holding liquid water content constant reduces cloud \r\ndrop effective radius (re), increases the cloud albedo, and induces an \r\ninstantaneous negative radiative forcing (IRFaci). The  clouds are "


id: bc9151848cfd893d9961228a05044aca
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 27246
content:
"ERFari from model-based evidence is -0.25 +- 0.25 W m-2, which \r\nsuggests a  small negative adjustment relative to the model-based \r\nIRFari estimate, consistent with the literature discussed in \r\nSection 7.3.3.1.2. Adding this small adjustment to our assessed IRFari \r\nestimate of -0.25 W m-2, and accounting for additional uncertainty in \r\nthe adjustments, ERFari is assessed to -0.3 +- 0.3 (medium confidence). "


id: aadc4b229dfa12eda21dcbf19fcac23d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 24400
content:
"Table 7.7 | Studies quantifying aspects of the global effective radiative \r\nforcing due to aerosol-cloud interactions ERFaci that are mainly based on \r\nsatellite retrievals and were published since AR5. All forcings/adjustments are \r\npresented as global annual mean values in W m-2. Most studies split the ERFaci into \r\ninstantaneous radiative forcing (IRFaci) and adjustments in liquid water path (LWP) \r\nand cloud fraction (Cf) separately. All published studies only considered liquid clouds. \r\nSome studies assessed the IRFaci and the LWP adjustment together and called this \r\n'intrinsic forcing' (Christensen et al., 2017) and the cloud fraction adjustment 'extrinsic \r\nforcing'. Published uncertainty ranges are converted to 5-95% confidence intervals, \r\nand 'n/a' indicates that the study did not provide an estimate for the relevant IRF/ERF."


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vector_id: 27510
content:
"thought to subsequently adjust by a slowing of the drop coalescence \r\nrate, thereby delaying or suppressing rainfall. Rain generally reduces \r\ncloud lifetime and thereby liquid water path (LWP, i.e., the vertically \r\nintegrated cloud water) and/or cloud fractional coverage (Cf; Albrecht, \r\n1989), thus any aerosol-induced rain delay or suppression would \r\nbe expected to increase LWP and/or Cf. Such adjustments could \r\npotentially lead to an ERFaci considerably larger in magnitude than \r\nthe IRFaci alone. However, adding aerosols to non-precipitating clouds \r\nhas been observed to have the opposite effect (i.e., a reduction in LWP \r\nand/or Cf) (Lebsock et  al., 2008; Christensen and Stephens, 2011). \r\nThese findings have been explained by enhanced evaporation of the \r\nsmaller droplets in the aerosol-enriched environments, and resultant \r\nenhanced mixing with ambient air, leading to cloud dispersal."


id: 7dd4284e883cc44a6f6ef96fa67fe22e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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content:
"A small subset of aerosols can also serve as ice nucleating particles \r\n(INPs) that initiate the ice phase in supercooled water clouds, and \r\nthereby alter cloud radiative properties and/or lifetimes. However, \r\nthe ability of anthropogenic aerosols (specifically BC) to serve \r\nas INPs in mixed-phase clouds has been found to be negligible in \r\nrecent laboratory studies (e.g.,  Vergara-Temprado et  al., 2018). \r\nNo  assessment of the contribution to ERFaci from cloud phase \r\nchanges induced by anthropogenic INPs will therefore be presented."


id: 45e42b4e733dc0749b131a33fbb30b24
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 8384
content:
"In ice (cirrus) clouds (cloud temperatures less than -40degC), INPs can \r\ninitiate ice crystal formation at relative humidity much lower than \r\nthat required for droplets to freeze spontaneously. Anthropogenic \r\nINPs can thereby influence ice crystal numbers and thus cirrus cloud \r\nradiative properties. At cirrus temperatures, certain types of BC \r\nhave in fact been demonstrated to act as INPs in laboratory studies \r\n(Ullrich et al., 2017; Mahrt et al., 2018), suggesting a non-negligible \r\nanthropogenic contribution to INPs in cirrus clouds. Furthermore, \r\nanthropogenic changes to drop number also alter the number of \r\ndroplets available for spontaneous freezing, thus representing \r\na  second pathway through which anthropogenic emissions could \r\naffect cirrus clouds."


id: 4783b5a2fc160a14bac0e92c93e1ad17
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 8638
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"Since AR5, the analysis of observations to investigate aerosol-cloud \r\ninteractions has progressed along several axes: (i) The framework of \r\nforcing and adjustments introduced rigorously in AR5 has helped better \r\ncategorize studies; (ii) the literature assessing statistical relationships \r\nbetween aerosol and cloud in satellite retrievals has grown, and \r\nretrieval uncertainties are better characterized; (iii) advances have \r\nbeen made to infer causality in aerosol-cloud relationships."


id: be200f49638c42bad7fea52c6d59ea2d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 27465
content:
"In AR5 the statistical relationship between cloud microphysical \r\nproperties and aerosol index (AI; AOD multiplied by Angstrom \r\nexponent) was used to make inferences about IRFaci were assessed \r\nalongside other studies which related cloud quantities to AOD. \r\nHowever, it is now well-documented that the latter approach leads to \r\nlow estimates of IRFaci since AOD is a poor proxy for cloud-base CCN \r\n(Penner et al., 2011; Stier, 2016). Gryspeerdt et al. (2017) demonstrated \r\nthat the statistical relationship between droplet concentration and \r\nAOD leads to an inferred IRFaci that is underestimated by at least \r\n30%, while the use of AI leads to estimates of IRFaci to within +-20%, \r\nif the anthropogenic perturbation of AI is known."


id: 14794807801ab8456b601eac0ff315d6
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 729
content:
"Since AR5, several studies assessed the global IRFaci from satellite \r\nobservations using different methods (Table 7.7). All studies relied \r\non statistical relationships between aerosol and cloud quantities \r\nto infer sensitivities. Four studies inferred IRFaci by estimating the \r\nanthropogenic perturbation of Nd (cloud drop number concentration). \r\nFor this, Bellouin et  al. (2013b) and Remy et  al. (2018) made use \r\nof regional-seasonal regressions between satellite-derived Nd and \r\nAOD following Quaas et  al. (2008), while Gryspeerdt et  al. (2017) "


id: bb33f6289ef7c2ee1deacf9da4ada44e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 26996
content:
"Summarizing the above findings related to statistical relationships and \r\ncausal aerosol effects on cloud properties, there is high confidence\r\nthat anthropogenic aerosols lead to an increase in cloud droplet \r\nconcentrations. Taking the average across the studies providing IRFaci \r\nestimates discussed above and considering the general agreement \r\namong estimates (Table 7.7), IRFaci is assessed to be -0.7 +- 0.5 W m-2 \r\n(medium confidence)."


id: 3edeecf40b36b1196ce7ba431e3dfc16
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 7273
content:
"Apart from adjustments involving LWP and Cf, several studies \r\nhave also documented a  negative relationship between cloud-top \r\ntemperature and AOD/AI in satellite observations (e.g., Koren et al., \r\n2005). Wilcox et al. (2016) proposed that this could be explained by \r\nblack-carbon (BC) absorption reducing boundary-layer turbulence, \r\nwhich in turn could lead to taller clouds. However, it has been \r\ndemonstrated that the satellite-derived relationships are affected \r\nby spurious co-variation (Gryspeerdt et al., 2014a), and it therefore \r\nremains unclear whether a systematic causal effect exists."


id: 4313d87eb724e1de8f9d4dd1ac354223
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 7949
content:
"Identifying relationships between INP concentrations and cloud \r\nproperties from satellites is intractable because the INPs generally \r\nrepresent a  very small subset of the overall aerosol population \r\nat any given time or location. For ice clouds, only a  few satellite \r\nstudies have so far investigated responses to aerosol perturbations. \r\nGryspeerdt et al. (2018) find a positive relationship between aerosol \r\nand ice crystal number for cold cirrus under strong dynamical forcing, \r\nwhich could be explained by an overall larger number of solution \r\ndroplets available for homogeneous freezing in polluted regions. "


id: 65032b8948c24ee88857e4dde340e3e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 13461
content:
"effects, and with land-surface cooling effect applied), based on \r\nnine CMIP5 models (Table 7.6). The corresponding ERFaci estimate \r\nbased on 17 RFMIP models from CMIP6 is slightly less negative \r\nat -0.86  +-  0.57 W m-2 (Table  7.6). Other post-AR5 estimates of \r\nERFaci based on single-model studies are either in agreement with \r\nor slightly larger in magnitude than the CMIP6 estimate (Gordon \r\net al., 2016; Fiedler et al., 2017, 2019; Neubauer et al., 2017; Karset \r\net al., 2018; Regayre et al., 2018; Zhou et al., 2018b; Golaz et al., \r\n2019; Diamond et al., 2020)."


id: 20dd96dfc9b7b92046cfe63a3e4e9c67
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 2603
content:
"Zhao et al. (2018) conclude that the sign of the relationship between \r\nice crystal size and aerosol depends on humidity. While these \r\nstudies support modelling results finding that ice clouds do respond \r\nto anthropogenic aerosols (Section  7.3.3.2.2), no quantitative \r\nconclusions about IRFaci or ERFaci for ice clouds can be drawn based \r\non satellite observations."


id: bff9367d29f67466abeae8eea3d846e5
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 27758
content:
"The adjustment contribution to the CMIP6 ensemble mean ERFaci \r\nis -0.20 W m-2, though with considerable differences between \r\nthe models (Smith et  al., 2020b). Generally, this adjustment in \r\nESMs arises mainly from LWP changes (e.g.,  Ghan et  al., 2016), \r\nwhile satellite observations suggest that cloud cover adjustments \r\ndominate and that aerosol effects on LWP are overestimated in ESMs \r\n(Bender et  al., 2019). Large-eddy-simulations also tend to suggest \r\nan overestimated aerosol effect on cloud lifetime in ESMs, but some \r\nreport an aerosol-induced decrease in cloud cover that is at odds \r\nwith satellite observations (Seifert et al., 2015). Despite this potential \r\ndisagreement when it comes to the dominant adjustment mechanism, \r\na  substantial negative contribution to ERFaci from adjustments is \r\nsupported both by observational and modelling studies."


id: 3b36e0eddebd935f90c2131663b69841
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 6672
content:
"Contributions to ERFaci from anthropogenic aerosols acting as INPs \r\nare generally not included in CMIP6 models. Two global modelling \r\nstudies incorporating parametrizations based on recent laboratory \r\nstudies both found a negative contribution to ERFaci (Penner et al., 2018; \r\nMcGraw et al., 2020), with central estimates of -0.3 and -0.13 W m-2, \r\nrespectively. However, previous studies have produced model estimates \r\nof opposing signs (Storelvmo, 2017). There is thus limited evidence and \r\nmedium agreement for a small negative contribution to ERFaci from \r\nanthropogenic INP-induced cirrus modifications (low confidence)."


id: ec0530413f19aebc521a868d57f674c5
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 34632
content:
"Combining IRFaci and the associated adjustments in Cf and LWP \r\n(adding uncertainties in quadrature), considering only liquid-water \r\nclouds and evidence from satellite observations alone, the \r\ncentral estimate and very likely range for ERFaci is assessed to be \r\n-1.0  +-  0.7 W  m-2 (medium confidence). The confidence level and \r\nwider range for ERFaci compared to IRFaci reflect the relatively large \r\nuncertainties that remain in the adjustment contribution to ERFaci."


id: 588163dec2ab45ad51316590b1680eef
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 11423
content:
"Similarly, aerosol effects on deep convective clouds are typically not \r\nincorporated in ESMs. However, cloud-resolving modelling studies \r\nsupport non-negligible aerosol effects on the radiative properties of \r\nconvective clouds and associated detrained cloud anvils (Tao et al., \r\n2012). While global ERF estimates are currently not available for \r\nthese effects, the fact that they are missing in most ESMs adds to the \r\nuncertainty range for the model-based ERFaci."


id: e510de6a1352b3b695bee665d0cb88aa
created_at: 2023-03-16 14:00:44
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content:
"As in AR5, the representation of aerosol-cloud interactions in ESMs \r\nremains a challenge, due to the limited representation of important \r\nsub-gridscale processes, from the emissions of aerosols and their \r\nprecursors to precipitation formation. ESMs that simulate ERFaci \r\ntypically include aerosol-cloud interactions in liquid stratiform clouds \r\nonly, while very few include aerosol interactions with mixed-phase, \r\nconvective and ice clouds. Adding to the spread in model-derived \r\nestimates of ERFaci is the fact that model configurations and \r\nassumptions vary across studies, for example when it comes to the \r\ntreatment of oxidants, which influence aerosol formation, and their \r\nchanges through time (Karset et al., 2018)."


id: 9f06faf7930850c282751e6c340cce2c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 22564
content:
"From model-based evidence, ERFaci is assessed to -1.0 +- 0.8 W m-2\r\n(medium confidence). This assessment uses the mean ERFaci in \r\nTable 7.6 as a starting point, but further allows for a small negative \r\nERF contribution from cirrus clouds. The uncertainty range is based on \r\nthose reported in Table 7.6, but widened to account for uncertain but \r\nlikely non-negligible processes currently unaccounted for in ESMs."


id: e529162ada8e67e8e15a755a975f6af7
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 33548
content:
"In AR5, ERFaci was assessed as the residual of the total aerosol ERF \r\nand ERFari, as the total aerosol ERF was easier to calculate based \r\non available model simulations (Boucher et  al., 2013). The central \r\nestimates of total aerosol ERF and ERFari in AR5 were -0.9 W m-2 and \r\n-0.45 W m-2, respectively, yielding an ERFaci estimate of -0.45 W m-2. \r\nThis value is much less negative than the bottom-up estimate of \r\nERFaci from ESMs presented in AR5 (-1.4 W m-2) and efforts have \r\nbeen made since to reconcile this difference. Zelinka et  al. (2014) \r\nestimated ERFaci to be -0.96 +- 0.55 W m-2 (including semi-direct "


id: ee12a14fb3fbbb8bce416c7a9da65907
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 34933
content:
"The assessment of ERFaci based on observational evidence alone \r\n(-1.0 +- 0.7 W m-2) is very similar to the one based on model evidence \r\nalone (-1.0 +- 0.8 W m-2), in strong contrast to what was reported \r\nin AR5. This reconciliation of observation-based and model-based \r\nestimates is the result of considerable scientific progress and reflects \r\ncomparable revisions of both model-based and observation-based "


id: ff09eb06ab681a7c79b84b13fd61973f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 37675
content:
"estimates. The strong agreement between the two largely independent \r\nlines of evidence increases confidence in the overall assessment of the \r\ncentral estimate and very likely range for ERFaci of -1.0 +- 0.7 W m-2\r\n(medium confidence). The assessed range is consistent with but \r\nnarrower than that reported by the review of Bellouin et al. (2020) of \r\n-2.65 to -0.07 W m-2. The difference is primarily due to a wider range \r\nin the adjustment contribution to ERFaci in Bellouin et  al. (2020), \r\nhowever adjustments reported relative to IRFaci ranging from 40% \r\nto 150% in that study are fully consistent with the ERFaci assessment \r\npresented here."


id: aa399ce3df35ff9d585d97d13b3bbc0f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 24319
content:
"Energy balance models of reduced complexity have in recent years \r\nincreasingly been combined with Monte Carlo approaches to provide \r\nvaluable 'top-down' (also called inverse) observational constraints \r\non the total aerosol ERF. These top-down approaches report ranges \r\nof aerosol ERF that are found to be consistent with the global mean \r\ntemperature record and, in some cases, also observed ocean heat \r\nuptake. However, the total aerosol ERF is also used together with the \r\nhistorical temperature record in Section 7.5 to constrain equilibrium \r\nclimate sensitivity (ECS) and transient climate response (TCR). Using \r\ntop-down estimates as a separate line of evidence also for the total \r\naerosol ERF would therefore be circular. Nevertheless, it is useful \r\nto examine the development of these estimates since AR5, and the \r\ndegree to which these estimates are consistent with the upper and \r\nlower bounds of the assessments of total aerosol ERF (ERFari+aci)."


id: a1b0d4d71f2a8c4a5ed7a7ed4b11f38a
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 23001
content:
"The historical temperature record was also the key observational \r\nconstraint applied in two additional studies (Rotstayn et al., 2015; \r\nShindell et al., 2015) based on a subset of CMIP5 models. Rotstayn \r\net  al. (2015) found a  strong temporal correlation (>0.9) between \r\nthe total aerosol ERF and the global surface temperature. They used \r\nthis relationship to produce a best estimate for the total aerosol ERF \r\nof -0.97 W m-2, but with considerable unquantified uncertainty, in \r\npart due to uncertainties in the TCR. Shindell et al. (2015) came to \r\na similar best estimate for the total aerosol ERF of -1.0 W m-2 and \r\na 95% confidence interval of -1.4 to -0.6 W m-2 but based this on \r\nspatial temperature and ERF patterns in the models in comparison \r\nwith observed spatial temperature patterns."


id: 3126f805885e4816ab1bd9a065b42a71
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 5154
content:
"A separate observational constraint on the total ERF was proposed \r\nby Cherian et al. (2014), who compared trends in downward fluxes of \r\nsolar radiation observed at surface stations across Europe (described \r\nin Section 7.2.2.3) to those simulated by a subset of CMIP5 models. \r\nBased on the relationship between solar radiation trends and the \r\ntotal aerosol ERF in the models, they inferred a total aerosol ERF of \r\n-1.3 W m-2 and a standard deviation of +- 0.4 W m-2."


id: aaaae378eec726f5c7540082eb368286
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 24382
content:
"Based solely on energy balance considerations or other observational \r\nconstraints, it is extremely likely that the total aerosol ERF is negative \r\n(high confidence), but extremely unlikely that the total aerosol ERF is \r\nmore negative than -2.0 W m-2 (high confidence)."


id: 2ce0c0221a6c7ad8f9ecaef5e1a3bd99
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 4497
content:
"In AR5 (Boucher et al., 2013), the overall assessment of total aerosol \r\nERF (ERFari+aci) used the median of all ESM estimates published prior \r\nto AR5 of -1.5 [-2.4 to -0.6] W m-2 as a starting point, but placed more \r\nconfidence in a subset of models that were deemed more complete \r\nin their representation of aerosol-cloud interactions. These models, \r\nwhich included aerosol effects on mixed-phase, ice and/or convective \r\nclouds, produced a smaller estimate of -1.38 W m-2. Likewise, studies \r\nthat constrained models with satellite observations (five in total), \r\nwhich produced a median estimate of -0.85 W m-2, were given extra \r\nweight. Furthermore, a longwave ERFaci of 0.2 W m-2 was added to \r\nstudies that only reported shortwave ERFaci values. Finally, based on "


id: fa8a7cdd80a182dda4f95d78b1eabb67
created_at: 2023-03-16 14:00:44
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vector_id: 36939
content:
"Beyond the inverse estimates described above, other efforts \r\nhave been made since AR5 to constrain the total aerosol ERF. For \r\nexample, Stevens (2015) used a simple (one-dimensional) model to "


id: 6eedac34eddf53b95e1f60b8537327b9
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 15077
content:
"The  corresponding range from Bellouin et  al. (2019) is -3.15 to \r\n-0.35 W m-2, thus there is agreement for the upper bound while the \r\nlower bound assessed here is less negative. A  lower bound more \r\nnegative than -2.0 W m-2 is not supported by any of the assessed \r\nlines of evidence. There is high confidence that ERFaci contributes \r\nmost (75-80%) to the total aerosol effect (ERFari+aci). In contrast \r\nto AR5 (Boucher et al., 2013), it is now virtually certain that the total \r\naerosol ERF is negative. Figure 7.5 depicts the aerosol ERFs from the \r\ndifferent lines of evidence along with the overall assessments."


id: e31c3bdf429509c7fc103e1a59e69734
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 33225
content:
"higher resolution models, doubt was raised regarding the ability of \r\nESMs to represent the cloud-adjustment component of ERFaci with \r\nfidelity. The expert judgement was therefore that aerosol effects \r\non cloud lifetime were too strong in the ESMs, further reducing the \r\noverall ERF estimate. The above lines of argument resulted in a total \r\naerosol assessment of -0.9 [-1.9 to -0.1] W m-2 in AR5."


id: 215acb911f9c341fb79b5cd7edab3e5
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 2679
content:
"As most modelling and observational estimates of aerosol ERF have \r\nend points in 2014 or earlier, there is limited evidence available for \r\nthe assessment of how aerosol ERF has changed from 2014 to 2019. \r\nHowever, based on a general reduction in global mean AOD over this \r\nperiod (Section  2.2.6 and Figure  2.9), combined with a  reduction \r\nin emissions of aerosols and their precursors in updated emissions \r\ninventories (Hoesly et  al., 2018), the aerosol ERF is assessed to \r\nhave decreased in magnitude from about 2014 to 2019 (medium \r\nconfidence). Consistent with Figure 2.10, the change in aerosol ERF \r\nfrom about 2014 to 2019 is assessed to be +0.2 W m-2, but with \r\nlow confidence due to limited evidence. Aerosols are therefore \r\nassessed to have contributed an ERF of -1.1 [-1.7 to -0.4] W m-2\r\nover 1750-2019 (medium confidence)."


id: 8ca22399662519e363425be7fc819318
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 19645
content:
"Combining the lines of evidence and adding uncertainties in \r\nquadrature, the ERFari+aci estimated for 2014 relative to 1750 \r\nis assessed to be -1.3 [-2.0 to -0.6] W m-2 (medium confidence)."


id: ef47be872a0b766ca869a5c396503ee
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 35121
content:
"with a  reduction in transpiration. A  multi-model quantification of \r\nland-use forcing in CMIP6 models (excluding one outlier) (Smith \r\net  al., 2020b) found an IRF of -0.15  +- 0.12 W m-2 (1850-2014), \r\nand an ERF (correcting for land-surface temperature change) of \r\n-0.11 +- 0.09 W m-2. This shows a small positive adjustment term \r\n(mainly from a  reduction in cloud cover). CMIP5 models show an \r\nIRF of -0.11  [-0.16 to -0.04] W m-2 (1850-2000) after excluding \r\nunrealistic models (Lejeune et al., 2020)."


id: d6c6d9ede02acf8bee0780b8da0aaf73
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 31298
content:
"In addition to the large anthropogenic ERFs associated with \r\nWMGHGs and atmospheric aerosols assessed in Sections 7.3.2 \r\nand 7.3.3, land-use change, contrails and aviation-induced cirrus, \r\nand light-absorbing particles deposited on snow and ice have also \r\ncontributed to the overall anthropogenic ERF and are assessed in \r\nSections 7.3.4.1, 7.3.4.2 and 7.3.4.3. Changes in solar irradiance, \r\ngalactic cosmic rays, and volcanic eruptions since pre-industrial \r\ntimes combined represent the natural contribution to the total \r\n(anthropogenic + natural) ERF and are discussed in Sections 7.3.4.4, \r\n7.3.4.5 and 7.3.4.6."


id: 13b8541b2ff9de36f9b0647743559f25
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updated_at: 2023-03-16 14:00:44
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vector_id: 604
content:
"The contribution of land-use change to albedo changes has recently \r\nbeen investigated using MODIS and AVHRR to attribute surface albedo \r\nto geographically specific land-cover types (Ghimire et  al., 2014). \r\nWhen combined with a historical land-use map (Hurtt et al., 2011) \r\nthis gives a SARF of -0.15 +- 0.01 W m-2 for the period 1700-2005, of \r\nwhich approximately -0.12 W m-2 is from 1850. This study accounted \r\nfor correlations between vegetation type and snow cover, but not the \r\nadjustment in snow cover identified in T. Andrews et al. (2017)."


id: b5c8dacba07340ca4bb76a1b1c06a909
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 26131
content:
"The indirect contributions of land-use change through biogenic \r\nemissions is very uncertain. Decreases in BVOCs reduce ozone and \r\nmethane (Unger, 2014), but also reduce the formation of organic \r\naerosols and their effects on clouds (Scott et al., 2017). Adjustments \r\nthrough changes in aerosols and chemistry are model dependent \r\n(Zhu et al., 2019b; Zhu and Penner, 2020), and it is not yet possible to \r\nmake an assessment based on a limited number of studies."


id: 39c116885344f6bbb88608e3336b250f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 6460
content:
"The contribution of irrigation (mainly to low-cloud amount) is \r\nassessed as -0.05 [-0.1 to 0.05] W  m-2 for the historical period \r\n(Sherwood et al., 2018)."


id: d6a8e87b3e054211db9b267cf059b068
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 31277
content:
"Because the CMIP5 and CMIP6 modelling studies are in agreement \r\nwith Ghimire et  al. (2014), that study is used as the assessed \r\nalbedo ERF. Adding the irrigation effect to this gives an overall \r\nassessment of the ERF from land-use change of -0.20 +- 0.10 W m-2\r\n(medium confidence). Changes in ERF since 2014 are assumed to \r\nbe small compared to the uncertainty, so this ERF applies to the \r\nperiod 1750-2019. The uncertainty range includes uncertainties in \r\nthe adjustments."


id: 56da8fc88eef90fa21a9ba46e8fa1a0f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 11140
content:
"ERF from contrails and aviation-induced cirrus is taken from the \r\nassessment of Lee et  al. (2020), at 0.057 [0.019 to 0.098] W m-2\r\nin 2018 (see Section 6.6.2 for an assessment of the total effects of \r\naviation). This is rounded up to address its low confidence and the \r\nextra year of air traffic to give an assessed ERF over 1750-2019 of \r\n0.06 [0.02 to 0.10] W m-2. This assessment is given low confidence due \r\nto the potential that processes missing from the assessment would \r\naffect the magnitude of contrails and aviation-induced cirrus ERF."


id: 33f02361167234104e32b03208f44ad0
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 5590
content:
"In AR5, it was assessed that the effects of light-absorbing particles \r\n(LAPs) did probably not significantly contribute to recent reductions \r\nin Arctic ice and snow (Vaughan et  al., 2013). The SARF from \r\nLAPs on snow and ice was assessed to 0.04 [0.02 to 0.09] W m-2\r\n(Boucher et al., 2013), a range appreciably lower than the estimates "


id: 3c352d16bd5070968f6eef8d63a24f3f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 6835
content:
"given in AR4 (Forster et  al., 2007). This effect was assessed to be \r\nlow confidence (medium evidence, low agreement) (Table  8.5 in \r\nMyhre et al., 2013b)."


id: 58414c6ff7eac827cfe085666cdd5668
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 11391
content:
"Since AR5 there has been progress in the understanding of the \r\nphysical state and processes in snow that govern the albedo \r\nreduction by black carbon (BC). The SROCC (IPCC, 2019a) assessed \r\nthat there is high confidence that darkening of snow by deposition \r\nof BC and other light-absorbing aerosol species increases the rate of \r\nsnow melt (Section 2.2 in Hock et al., 2019; Section 3.4 in Meredith \r\net al., 2019). C. He et al. (2018) found that taking into account both \r\nthe non-spherical shape of snow grains and internal mixing of BC \r\nin snow significantly altered the effects of BC on snow albedo. The \r\nreductions of snow albedo by dust and BC have been measured and \r\ncharacterized in the Arctic, the Tibetan Plateau, and mid-latitude \r\nregions subject to seasonal snowfall, including North America and \r\nnorthern and eastern Asia (Qian et al., 2015)."


id: 31c78a57a7f03b0e583cf7c6a2f14a7d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 5242
content:
"Since AR5, two further studies of global IRF from black carbon on \r\nsnow deposition are available, with best estimates of 0.01 W m-2\r\n(Lin et  al., 2014) and 0.045 W m-2 (Namazi et  al., 2015). Organic \r\ncarbon deposition on snow and ice has been estimated to contribute \r\na  small positive IRF of 0.001 to 0.003 W m-2 (Lin et  al., 2014). \r\nNo  comprehensive global assessments of mineral dust deposition \r\non snow are available, although the effects are potentially large \r\nin relation to the total effect of LAPs on snow and ice forcing \r\n(Yasunari et al., 2015)."


id: 136fcb8451529a6089ec5a308231bc05
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 569
content:
"The AR5 (Myhre et  al., 2013b) assessed solar SARF from around \r\n1750 to 2011 to be 0.05 [0.00 to 0.10] W m-2 which was computed \r\nfrom the seven-year mean around the solar minima in 1745 (being \r\nclosest to 1750) and 2008 (being the most recent solar minimum). \r\nThe inclusion of tropospheric adjustments that reduce ERF (compared \r\nto SARF in AR5) has a negligible effect on the overall forcing. Prior to \r\nthe satellite era, proxy records are used to reconstruct historical solar \r\nactivity. In AR5, historical records were constructed using observations \r\nof solar magnetic features. In this assessment historical time series \r\nare constructed from radiogenic compounds in the biosphere and in \r\nice cores that are formed from cosmic rays (Steinhilber et al., 2012)."


id: d283b51888c19ebcc2a39f0ce9d69597
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 30646
content:
"Variations in the total solar irradiance (TSI) represent a  natural \r\nexternal forcing agent. The dominant cycle is the solar 11-year \r\nactivity cycle, which is superimposed on longer cycles (Section 2.2). \r\nOver the last three 11-year cycles, the peak-to-trough amplitude in \r\nTSI has differed by about 1 W m-2 between solar maxima and minima \r\n(Figure 2.2)."


id: 2fc6f60ebd37bd7de0f52b78c1af2559
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 4941
content:
"maximum possible ERF to be 0.26 +- 0.09 W m-2. Hence the Egorova \r\net  al. (2018) estimate is not explicitly taken into account in the \r\nassessment presented in this section."


id: a27f472e08ed0f353bc406cb30c0bd3
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23127
content:
"literature since AR5 robustly supports these conclusions with key \r\nlaboratory, theoretical and observational evidence. There is high \r\nconfidence that GCRs contribute a  negligible ERF over the period \r\n1750-2019."


id: 795c1534a8c87727d50476458d0d3dea
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 16666
content:
"In contrast to AR5, the solar ERF in this assessment uses full solar \r\ncycles rather than solar minima. The pre-industrial TSI is defined as \r\nthe mean from all complete solar cycles from the start of the 14C \r\nSATIRE-M proxy record in 6755 BCE to 1744 CE. The mean TSI from \r\nsolar cycle 24 (2009-2019) is adopted as the assessment period for \r\n2019. The best estimate solar ERF is assessed to be 0.01 W m-2, using \r\nthe 14C reconstruction from SATIRE-M, with a likely range of -0.06 to \r\n+0.08 W m-2 (medium confidence). The uncertainty range is adopted \r\nfrom the evaluation of Lockwood and Ball (2020) using a  Monte \r\nCarlo analysis of solar activity from the Maunder Minimum to 2019 \r\nfrom several datasets, leading to an ERF of -0.12 to +0.15 W m-2. \r\nThe Lockwood and Ball (2020) full uncertainty range is halved as the \r\nperiod of reduced solar activity in the Maunder Minimum had ended \r\nby 1750 (medium confidence)."


id: 13e38300b21abc1429aef4e59144d539
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 638
content:
"Variations in the flux of galactic cosmic rays (GCR) reaching the \r\natmosphere are modulated by solar activity and affect new particle \r\nformation in the atmosphere through their link to ionization of \r\nthe troposphere (Lee et  al., 2019). It has been suggested that \r\nperiods of high GCR flux correlate with increased aerosol and \r\nCCN concentrations and therefore also with cloud properties \r\n(e.g., Dickinson, 1975; Kirkby, 2007)."


id: 55f9a661ad10a4cff202e7d4bf5cc7f1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 11015
content:
"Since AR5, the link between GCR and new particle formation has been \r\nmore thoroughly studied, particularly by experiments in the CERN \r\nCLOUD chamber (Cosmics Leaving OUtdoor Droplets; Dunne et al., \r\n2016; Kirkby et al., 2016; Pierce, 2017). By linking the GCR-induced \r\nnew particle formation from CLOUD experiments to CCN, Gordon \r\net  al. (2017) found that the CCN concentration for low-clouds \r\ndiffered by 0.2-0.3% between solar maximum and solar minimum. \r\nCombined with relatively small variations in the atmospheric ion \r\nconcentration over centennial time scales (Usoskin et al., 2015), it \r\nis therefore unlikely that cosmic ray intensity affects present-day \r\nclimate via nucleation (Yu and Luo, 2014; Dunne et al., 2016; Pierce, \r\n2017; Lee et al., 2019)."


id: 8aa1f2380c5d3aa03763a62a220c693c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 19353
content:
"Non-explosive volcanic eruptions generally yield negligible global \r\nERFs due to the short atmospheric lifetimes (a few weeks) of volcanic \r\naerosols in the troposphere. However, as discussed in Section 7.3.3.2, \r\nthe massive fissure eruption in Holuhraun, Iceland persisted for \r\nmonths in 2014 and 2015 and did in fact result in a  marked and \r\npersistent reduction in cloud droplet radii and a  corresponding \r\nincrease in cloud albedo regionally (Malavelle et al., 2017). This shows \r\nthat non-explosive fissure eruptions can lead to strong regional and \r\neven global ERFs, but because the Holuhraun eruption occurred in \r\nNorthern Hemisphere winter, solar insolation was weak and the \r\nobserved albedo changes therefore did not result in an appreciable \r\nglobal ERF (Gettelman et al., 2015)."


id: c758498cbd3a26a97894cc8134cdc5bf
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 28950
content:
"Studies continue to seek a relationship between GCR and properties \r\nof the climate system based on correlations and theory. Svensmark \r\net  al. (2017) proposed a  new mechanism for ion-induced increase \r\nin aerosol growth rate and subsequent influence on the CCN \r\nconcentration. The study does not include an estimate of the \r\nresulting effect on atmospheric CCN concentration and cloud \r\nradiative properties. Furthermore, Svensmark et  al. (2009, 2016) \r\nfind correlations between GCRs and aerosol and cloud properties \r\nin satellite and ground-based data. Multiple studies investigating \r\nthis link have challenged such correlations (Kristjansson et al., 2008; \r\nCalogovic et al., 2010; Laken, 2016)."


id: ec4fac07eea2dc72be2d27ce81e6113
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34668
content:
"The ERF for volcanic stratospheric aerosols is assessed to be \r\n-20 +- 5 W m-2 per unit SAOD (medium confidence) based on the \r\nCMIP5 multi-model mean from the Larson and Portmann (2016) \r\nSAOD forcing efficiency calculations combined with the single-model \r\nresults of Gregory et al. (2016), Schmidt et al. (2018) and Marshall \r\net al. (2020). This is applied to the SAOD time series from Chapter 2 \r\n(Section  2.2.2) to generate a  time series of ERF and temperature \r\nresponse shown in Chapter 2 (Figure 2.2 and Figure 7.8, respectively). "


id: bf460eaf7329f156d4dc149410795a09
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 27645
content:
"AR5 concluded that the GCR effect on CCN is too weak to have any \r\ndetectable effect on climate and no robust association was found \r\nbetween GCR and cloudiness (Boucher et  al., 2013). Published "


id: 1c95c7d4946bdf1d0cd133c68c99c0e0
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 1947
content:
"As discussed in Section 7.3.2, the radiative efficiencies for CO2, CH4\r\nand N2O have been updated since AR5 (Etminan et al., 2016). There \r\nhas been a  small (1%) increase in the stratospheric-temperature\u0002adjusted CO2 radiative efficiency, and a +5% tropospheric adjustment \r\nhas been added. The stratospheric-temperature-adjusted radiative \r\nefficiency for CH4 is increased by approximately 25% (high confidence). \r\nThe tropospheric adjustment is tentatively assessed to be -14% (low \r\nconfidence). A  +7% tropospheric adjustment has been added to \r\nthe radiative efficiency for N2O and +12% to CFC-11 and CFC-12 \r\n(low confidence)."


id: c51f17a6d8cb8f364f7c6b3730175782
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 28594
content:
"The period from 500 BCE to 1749 CE, spanning back to the start of \r\nthe record of Toohey and Sigl (2017), is defined as the pre-industrial \r\nbaseline and the volcanic ERF is calculated using an SAOD anomaly \r\nfrom this long-term mean. As in AR5, a pre-industrial to present-day \r\nERF assessment is not provided due to the episodic nature of \r\nvolcanic eruptions."


id: 5979c51ecaaa34c528ed57c1bc0336a0
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 11572
content:
"For aerosols there has been a convergence of model and observational \r\nestimates of aerosol forcing, and the partitioning of the total aerosol \r\nERF has changed. Compared to AR5 a greater fraction of the ERF is \r\nassessed to come from ERFaci compared to the ERFari. It is now assessed \r\nas virtually certain that the total aerosol ERF (ERFari+aci) is negative."


id: 229650092264439fa53b04a7ee65a6a1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 2895
content:
"The AR5 introduced the concept of effective radiative forcing (ERF) \r\nand radiative adjustments, and made a preliminary assessment that \r\nthe tropospheric adjustments were zero for all species other than \r\nthe effects of aerosol-cloud interaction and black carbon. Since AR5, \r\nnew studies have allowed for a tentative assessment of values for \r\ntropospheric adjustments to CO2, CH4, N2O, some CFCs, solar forcing, \r\nand stratospheric aerosols, and to place a  tighter constraint on \r\nadjustments from aerosol-cloud interaction (Sections 7.3.2, 7.3.3 \r\nand 7.3.4). In AR6, the definition of ERF explicitly removes the land\u0002surface temperature change as part of the forcing, in contrast to AR5 \r\nwhere only sea surface temperatures were fixed. The ERF is assessed \r\nto be a better predictor of modelled equilibrium temperature change \r\n(i.e., less variation in feedback parameter) than SARF (Section 7.3.1)."


id: fc31f4301c620a28ade41f29d266c56e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 37183
content:
"Figure 7.6 shows the industrial-era ERF estimates for 1750 to 2019 \r\nfor the concentration change in different forcing agents. The assessed \r\nuncertainty distributions for each individual component are combined \r\nwith a  100,000-member Monte Carlo simulation that samples the \r\ndifferent distributions, assuming they are independent, to obtain \r\nthe overall assessment of total present-day ERF (Supplementary \r\nMaterial 7.SM.1). The corresponding emissions-based ERF figure is \r\nshown in Chapter 6 (Figure 6.12)."


id: bbba4e0629aea87dac52de65e3f107e9
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 27091
content:
"in  Supplementary Material 7.SM.1.3. The resulting GSAT changes \r\nover time are shown in Figure 7.8. The historical time series of ERFs \r\nfor the WMGHGs can be derived by applying the ERF calculations of \r\nSection 7.3.2 to the observed time series of WMGHG concentrations \r\nin Chapter 2 (Section 2.2)."


id: 5e325278edf761d17f304f7e1738c346
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 12345
content:
"The total human forced GSAT change from 1750 to 2019 is calculated \r\nto be 1.29 [1.00 to 1.65] degC (high confidence). Although the total \r\nemulated GSAT change has high confidence, the confidence of the \r\nindividual contributions matches those given for the ERF assessment in \r\nthe subsections of Section 7.3. The calculated GSAT change is comprised "


id: c8011e15bec8c4a9e5e51e59854ea1a
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 29047
content:
"of a WMGHG warming of 1.58 [1.17 to 2.17] degC (high confidence),\r\na  warming from ozone changes of 0.23  [0.11  to  0.39]  degC (high \r\nconfidence), and a cooling of -0.50 [-0.22 to -0.96] degC from aerosol \r\neffects (medium confidence). The aerosol cooling has considerable \r\nregional time dependence (Section 6.4.3) but has weakened slightly \r\nover the last 20 years in the global mean (Figures 2.10 and 7.8). \r\nThere is also a -0.06 [-0.15 to +0.01] degC contribution from surface \r\nreflectance changes which is dominated by land-use change (medium \r\nconfidence). Changes in solar and volcanic activity are assessed to \r\nhave together contributed a small change of -0.02 [-0.06 to +0.02] degC \r\nsince 1750 (medium confidence)."


id: 2f7f52c9543bce212991c0aac40fccbe
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 4893
content:
"has been occurring at a  faster rate than would be expected due \r\nto CO2 alone (high confidence) (see also Sections 2.2.6 and 2.2.8). \r\nThroughout the record, but especially prior to 1930, periods of \r\nvolcanic cooling dominate decadal variability. These estimates \r\nof the forced response are compared with model simulations and \r\nattributable warming estimates in Chapter 3 (Section 3.3.1)."


id: edf5e4eef2c803b5d33f0375f0be0b78
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34914
content:
"The total (anthropogenic + natural) emulated GSAT between \r\n1850-1900 and 2010-2019 is 1.14 [0.89 to 1.45] degC, compared \r\nto the assessed GSAT of 1.06 [0.88 to 1.21] degC (Section 2.3.1 and \r\nCross Chapter Box  2.3). The emulated response is slightly warmer \r\nthan the observations and has a  larger uncertainty range. As the \r\nemulated response attempts to constrain to multiple lines of \r\nevidence (Supplementary Material 7.SM.2), only one of which is \r\nGSAT, they should not necessarily be expected to exactly agree. \r\nThe larger uncertainty range in the emulated GSAT compared to the \r\nobservations is reflective of the uncertainties in ECS, TCR and ERF \r\n(particularly the aerosol ERF) that drive the emulator response."


id: a2c0ba9fef93caa693fb43cb5a598420
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23177
content:
"The emulator gives a range of GSAT response for the period 1750 \r\nto 1850-1900 of 0.09 [0.04 to 0.14] degC from anthropogenic ERFs. \r\nThese results are used as a line of evidence for the assessment of this \r\nchange in Chapter 1 (Cross-Chapter Box 1.2), which gives an overall \r\nassessment of 0.1degC [likely range -0.1 to +0.3] degC."


id: 9ddcf7b60c238fcd2f68f5868ec95a1f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 22357
content:
"Figure 7.8 presents the GSAT time series using ERF time series for \r\nindividual forcing agents rather than their aggregation. It shows that \r\nfor most of the historical period the long time scale total GSAT trend \r\nestimate from the emulator closely follows the CO2 contribution. \r\nThe GSAT estimate from non-CO2 greenhouse gas forcing (from other \r\nWMGHGs and ozone) has been approximately cancelled out in the \r\nglobal average by a cooling GSAT trend from aerosols. However, since \r\n1980 the aerosol cooling trend has stabilized and may have started \r\nto reverse, so that over the last few decades the long-term warming "


id: 51e128d0ef552b7196c6dacf6ea0d1bd
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 10343
content:
"Cross-Chapter Box 7.1 | Physical Emulation of Earth System Models for Scenario Classification \r\nand Knowledge Integration in AR6"


id: 7255e4e9adecc530f2cf5bd9d71688c3
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 15614
content:
"Contributors: Zebedee R.J. Nicholls (Australia), Malte Meinshausen (Australia/Germany), Piers Forster (United Kingdom), Kyle Armour \r\n(United States of America), Terje Berntsen (Norway), William Collins (United Kingdom), Christopher Jones (United Kingdom), Jared Lewis \r\n(Australia/New Zealand), Jochem Marotzke (Germany), Sebastian Milinski (Germany), Joeri Rogelj (United Kingdom/Belgium), \r\nChris Smith (United Kingdom)"


id: 5b1415cec29fd3529888fbcdf9e48745
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 11842
content:
"Climate model emulators are simple physically based models that are used to approximate large-scale climate responses of complex \r\nEarth system models (ESMs). Due to their low computational cost they can populate or span wide uncertainty ranges that ESMs \r\ncannot. They need to be calibrated to do this and, once calibrated, they can aid inter-ESM comparisons and act as ESM extrapolation \r\ntools to reflect and combine knowledge from ESMs and many other lines of evidence (Geoffroy et al., 2013a; Good et al., 2013; Smith \r\net al., 2018a). In AR6, the term 'climate model emulator' (or simply 'emulator') is preferred over 'simple' or 'reduced-complexity climate \r\nmodel' to reinforce their use as specifically calibrated tools (Cross-Chapter Box 7.1, Figure 1). Nonetheless, simple physically based"


id: e0ae4091df892dca9374d0b49ef99377
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 32853
content:
"Emissions-driven emulators for scenario classification in AR6 WGIII\r\nAs in AR5 and SR1.5, emissions-driven emulators are used to communicate outcomes of the physical climate science assessment and \r\nuncertainties to quantify the temperature outcome associated with different emissions scenarios. In particular, the computational \r\nefficiency of these emulators allows the analysis of a large number of multi-gas emissions scenarios in terms of multiple characteristics, \r\ne.g., year of peak temperature or 2030 emissions levels, in line with keeping global warming to below 1.5degC or 2.0degC."


id: 3ca6f7037e88176d38fad51b2d9d4ed9
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 6909
content:
"How emulators match the assessed ranges used for the evaluation framework is summarized here and in Table 2. The first is too-low \r\nprojections for 2081-2100 under SSP1-1.9 (8% or 15% too low for the central estimate and 15% or 25% too low for the lower end in "


id: 33a259bc2601bc75919fdc340372c4ce
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 5544
content:
"the case of MAGICC7 or FaIRv1.6.2, respectively). The second is the representation of the aerosol ERF (both MAGICC7 and FaIRv1.6.2 \r\nare greater than 8% less negative than the central assessed range and greater than 10% less negative for the lower assessed range), \r\nas energy balance models struggle to reproduce an aerosol ERF with a magnitude as strong as the assessed best estimate and still \r\nmatch historical warming estimates. Both emulators have medium to large differences compared to the TCRE and airborne fraction \r\nranges (see notes beneath Cross-Chapter Box 7.1, Table 2). Finally, there is also a slight overestimate of the low end of the assessed \r\nhistorical GSAT range."


id: ae3fb2660a79e7c768ab5e37941e4d8a
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 24946
content:
"Overall, there is high confidence that emulated historical and future ranges of GSAT change can be calibrated to be internally consistent \r\nwith the assessment of key physical-climate indicators in this Report: greenhouse gas ERFs, ECS and TCR. When calibrated to match \r\nthe assessed ranges of GSAT and multiple physical climate indicators, physically based emulators can reproduce the best estimate of \r\nGSAT change over 1850-1900 to 1995-2014 to within 5% and the very likely range of this GSAT change to within 10%. MAGICC7 \r\nand FaIRv1.6.2 match at least two-thirds of the Chapter 4 assessed projected GSAT changes to within these levels of precision."


id: 5f32fff536ee544754a7f5f2ee1151d1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 12542
content:
"Cross-Chapter Box 7.1, Table 2 | Percentage differences between the emulator value and the WGI assessed best estimate and range for key \r\nmetrics. Values are given for four emulators in their respective AR6-calibrated probabilistic setups. Absolute values of these indicators are shown in Supplementary \r\nMaterial, Table 7.SM.4."


id: 82da15f5e9f2410064b464bedbdfb689
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 18112
content:
"It has long been recognized that the magnitude of climate feedbacks \r\ncan change as the climate state evolves over time (Manabe and \r\nBryan, 1985; Murphy, 1995), but the implications for projected future \r\nwarming have been investigated only recently. Since AR5, progress \r\nhas been made in understanding the key mechanisms behind this \r\ntime- and state-dependence. Specifically, the state-dependence \r\nis assessed by comparing climate feedbacks between warmer and \r\ncolder climate states inferred from paleoclimate proxies and model \r\nsimulations (Section 7.4.3). The time-dependence of the feedbacks \r\nis evident between the historical period and future projections and \r\nis assessed to arise from the evolution of the surface warming pattern \r\nrelated to changes in zonal and meridional temperature gradients \r\n(Section 7.4.4)."


id: 6df4a52ba0fda59f8159f7183b4db032
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 14912
content:
"The global surface temperature changes of the climate system are \r\ngenerally analysed with the classical forcing-feedback framework as \r\ndescribed in Box  7.1 (Equation 7.1). In this equation a  is the net \r\nfeedback parameter (W m-2 degC-1). As surface temperature changes in \r\nresponse to the TOA energy imbalance, many other climate variables \r\nalso change, thus affecting the radiative flux at the TOA. The aggregate \r\nfeedback parameter can then be decomposed into an approximate \r\nsum of terms a = Sx ax, where x is a vector representing variables \r\nthat have a direct effect on the net TOA radiative flux N and "


id: ebbc3d283e38efbb7d394a984799c713
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34593
content:
"Following the conventional definition, the physical climate feedbacks \r\nare here decomposed into terms associated with a vertically uniform \r\ntemperature change (Planck response, P), changes in the water-vapour \r\nplus temperature lapse-rate (WV+LR), surface albedo (A) and \r\nclouds (C). The water-vapour plus temperature lapse rate feedback \r\nis further decomposed using two different approaches, one based \r\non changes in specific humidity, the other on changes in relative \r\nhumidity. Biogeochemical feedbacks arise due to changes in aerosols \r\nand atmospheric chemical composition in response to changes in \r\nsurface temperature, and Gregory et al. (2009) and Raes et al. (2010) \r\nshow that they can be analysed using the same framework as for "


id: a7922efa9f7fef237e7d1944db78fac4
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23871
content:
"Up until AR5, process understanding and quantification of feedback \r\nmechanisms were based primarily on global climate models. Since \r\nAR5, the scientific community has undertaken a wealth of alternative \r\napproaches, including observational and fine-scale modelling \r\napproaches. This has in some cases led to more constrained feedbacks \r\nand, on the other hand, uncovered shortcomings in global climate \r\nmodels, which are starting to be corrected. Consequently, AR6 achieves \r\na more robust assessment of feedbacks in the climate system that is \r\nless reliant on global climate models than in earlier assessment reports."


id: eddc2e5ff0d9dc3215b145fbf361ebe1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34889
content:
"the physical climate feedbacks (Sections 5.4 and 6.4.5). Similarly, \r\nfeedbacks associated with biogeophysical and ice-sheet changes can \r\nalso be incorporated."


id: 5aed5f0e47809aad7d84dcdd057eb3fa
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 11819
content:
"In summary, the classical forcing-feedback framework has been \r\nextended to include biogeophysical and non-CO2 biogeochemical \r\nfeedbacks in addition to the physical feedbacks. It has also been used \r\nto analyse seasonal and interannual-to-decadal climate variations in \r\nobservations and ESMs, in addition to long-term climate changes \r\nas seen in abrupt4xCO2 experiments. These developments allow an \r\nassessment of the feedbacks based on a  larger variety of lines of \r\nevidence compared to AR5."


id: d88defad371f9611bb115e64ca42f004
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 31588
content:
"This section provides an overall assessment of individual feedback \r\nparameters, ax, by combining different lines of evidence from \r\nobservations, theory, process models and ESMs. To achieve this, \r\nwe review the understanding of the key processes governing the \r\nfeedbacks, why the feedback estimates differ among models, studies or \r\napproaches, and the extent to which these approaches yield consistent \r\nresults. The individual terms assessed are the Planck response \r\n(Section  7.4.2.1) and feedbacks associated with changes in water \r\nvapour and lapse rate (Section 7.4.2.2), surface albedo (Section 7.4.2.3), \r\nclouds (Section 7.4.2.4), biogeophysical and non-CO2 biogeochemical \r\nprocesses (Section 7.4.2.5), and ice sheets (Section 7.4.2.6). A synthesis \r\nis provided in Section 7.4.2.7. Climate feedbacks in CMIP6 models are \r\nthen evaluated in Section 7.4.2.8, with an explanation of how they \r\nhave been incorporated into the assessment."


id: 2a5cb3b2d959372ed3dca4a8ce2cd0bb
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 4095
content:
"The change of the TOA radiative flux N as a function of the change of \r\na climate variable x (such as water vapour) is commonly computed \r\nusing the 'radiative kernel' method (Soden et al., 2008). In this method, \r\nthe kernel N/x is evaluated by perturbing x within a radiation code. \r\nThen multiplying the kernel by dx/dT inferred from observations, \r\nmeteorological analysis or GCMs produces a value of ax."


id: 5a267f1f5472a213cc31ca0361bd7393
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 11692
content:
"The Planck temperature response DTP is the equilibrium temperature \r\nchange in response to a forcing DF when the net feedback parameter \r\nis equal to the Planck response parameter: DTP = -DF / aP."


id: ca00c18000666d293d1a38dc446f658e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 29336
content:
"The LR feedback has been estimated at interannual time scales \r\nusing meteorological reanalysis and satellite measurements of \r\nTOA fluxes (Dessler, 2013). These estimates from climate variability \r\nare consistent between observations and ESMs (Dessler, 2013; \r\nColman and Hanson, 2017). The mean and standard deviation of \r\nthis feedback under global warming based on the cited studies are \r\naLR = -0.50 +- 0.20 W m-2 degC-1 (Dessler, 2013; Caldwell et al., 2016; \r\nColman and Hanson, 2017; Zelinka et al., 2020)."


id: 238bab0abc5639a3824549462bb89f5d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 3040
content:
"Two decompositions are generally used to analyse the feedbacks \r\nassociated with a  change in the water-vapour and temperature \r\nlapse-rate in the troposphere. As in any system, many feedback \r\ndecompositions are possible, each of them highlighting a particular \r\nproperty or aspect of the system (Ingram, 2010; Held and Shell, 2012; \r\nDufresne and Saint-Lu, 2016). The first decomposition considers \r\nseparately the changes (and therefore feedbacks) in the lapse rate \r\n(LR) and specific humidity (WV). The second decomposition considers \r\nchanges in the lapse rate assuming constant relative humidity (LR*) \r\nseparately from changes in relative humidity (RH)."


id: 78568bf7a33dea6df44d141f12ee9403
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 16498
content:
"Both decompositions allow estimates of the sum of the lapse-rate and \r\nspecific humidity feedbacks aLR+WV. The multi-kernel and multi-model \r\nmean of aLR+WV is equal to 1.24 and 1.26 W m-2 degC-1 respectively \r\nfor CMIP5 and CMIP6 models, with a  standard deviation of \r\n0.10 W m-2 degC-1 (Zelinka et al., 2020). These values are larger than the \r\nrecently assessed value of 1.15 W m-2 degC-1 by Sherwood et al. (2020) as \r\na larger set of kernels, including those obtained from meteorological \r\nreanalysis, are used here."


id: a42d2c6931dad8eba0a6dea33f4ec6d9
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23385
content:
"Since AR5, the effect of the water vapour increase in the stratosphere \r\nas a  result of global warming has been investigated by different \r\nstudies. This increase produces a positive feedback between 0.1 and "


id: 8b061e46dc2a2eb570536cffe9be11b6
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 19405
content:
"0.3 W m-2 degC-1 if the stratospheric radiative response is computed \r\nassuming temperatures that are adjusted with fixed dynamical \r\nheating (Dessler et al., 2013; Banerjee et al., 2019). However, various \r\nfeedbacks reduce this temperature adjustment and the overall \r\nphysical (water vapour, temperature and dynamical) stratospheric \r\nfeedback becomes much smaller (0.0 to 0.1 W m-2 degC-1; Huang et al., \r\n2016, 2020; Li and Newman, 2020), with uncertainty arising from \r\nlimitations of current ESMs in simulating stratospheric processes. \r\nThe total stratospheric feedback is assessed at 0.05 +- 0.1 W m-2 degC-1\r\n(one standard deviation)."


id: 7b6ffa9bf49799df1b120bcb8c544ee9
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 17013
content:
"aA to be 0.31  +-  0.04  W m-2 degC-1 (one standard deviation) and \r\n0.31 +- 0.08 W m-2 degC-1 (one standard deviation), respectively, whereas \r\nDonohoe et al. (2020) estimated it to be only 0.16 +- 0.04 W m-2 degC-1\r\n(one standard deviation). Much of this discrepancy can be traced to \r\ndifferent techniques and data used for assessing the attenuation of \r\nsurface-albedo change by Arctic clouds. For the NH land snow, Chen \r\net  al. (2016) estimated that observed changes during 1982-2013 \r\ncontributed (after converting from NH temperature change to global \r\nmean temperature change) by 0.1 W m-2 degC-1 to global aA, smaller \r\nthan the estimate of 0.24  W  m-2  degC-1 from Flanner et  al. (2011). \r\nThe  contribution of the Southern Hemisphere (SH) to global aA is \r\nexpected to be small because seasonal snow cover extent in the SH \r\nis limited, and trends in SH sea ice extent are relatively flat over much \r\nof the satellite record (Section 2.3.2)."


id: 73daac06c25305caff00242f8cb72415
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 15842
content:
"The combined 'water-vapour plus lapse-rate' feedback is positive. \r\nThe main physical processes that drive this feedback are well understood \r\nand supported by multiple lines of evidence including models, theory \r\nand observations. The combined 'water-vapour plus lapse-rate' \r\nfeedback parameter is assessed to be aLR+WV = 1.30 W m-2 degC-1, with \r\na very likely range of 1.1 to 1.5 W m-2 degC-1 and a likely range of 1.2 to \r\n1.4 W m-2 degC-1 with high confidence."


id: fdea04207d74ec1e86038f620b5e2d3f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 37470
content:
"Several studies have attempted to derive surface-albedo feedback \r\nfrom observations of multi-decadal changes in climate, but only \r\nover limited spatial and inconsistent temporal domains, inhibiting \r\na purely observational synthesis of global surface-albedo feedback \r\n(aA). Flanner et al. (2011) applied satellite observations to determine \r\nthat the northern hemisphere (NH) cryosphere contribution \r\nto  global aA over the period 1979-2008 was 0.48 [likely range \r\n0.29  to 0.78]  W  m-2  degC-1, with roughly equal contributions from \r\nchanges in land snow cover and sea ice. Since AR5, and over \r\nsimilar periods of observation, Crook and Forster (2014) found an \r\nestimate of 0.8  +-  0.3 W  m-2 degC-1 (one standard deviation) for the \r\ntotal NH extratropical surface-albedo feedback, when averaged \r\nover global surface area. For Arctic sea ice alone, Pistone et  al. \r\n(2014) and Cao et  al. (2015) estimated the contribution to global "


id: 186bdf75a0061790d696fb431a4a0acb
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 1338
content:
"radiation and atmospheric circulation, resulting in a highly complex \r\nset of processes governing cloud formation and life cycles that \r\noperate across a wide range of spatial and temporal scales."


id: bc59a0d3b201391c6f77801f55d44a29
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 27215
content:
"Flato et al., 2013), though this may also be an expression of internal \r\nvariability, since the observed behaviour is captured within large \r\nensemble simulations (Notz, 2015). CMIP6 models better capture the \r\nobserved Arctic sea ice decline (Section 3.4.1). In the SH the opposite \r\nsituation is observed. Observations show relatively flat trends in SH \r\nsea ice over the satellite era (Section 2.3.2.1) whereas CMIP5 models \r\nsimulate a small decrease (Section 3.4.1). SH aA is presumably larger \r\nin models than observations but only contributes about one quarter \r\nof the global aA. Thus, we assess that aA estimates are consistent, at \r\nglobal scale, in CMIP5 and CMIP6 models and satellite observations, \r\nthough hemispheric differences and the role of internal variability \r\nneed to be further explored."


id: 33276d3773772a0a33f8b1759b540433
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 5467
content:
"Based on the multiple lines of evidence presented above that \r\ninclude observations, CMIP5 and CMIP6 models and theory, the \r\nglobal surface-albedo feedback is assessed to be positive with \r\nhigh confidence. The basic phenomena that drive this feedback are \r\nwell understood and the different studies cover a  large variety of \r\nhypotheses or behaviours, including how the evolution of clouds \r\naffects this feedback. The value of the global surface-albedo \r\nfeedback is assessed to be aA = 0.35 W m-2 degC-1, with a very likely\r\nrange from 0.10 to 0.60 W m-2 degC-1 and a likely range from 0.25 to \r\n0.45 W m-2 degC-1 with high confidence."


id: 7543f9c9f4437a1b63b05a5e0f800e67
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 16038
content:
"In the global energy budget at TOA, clouds affect shortwave (SW) \r\nradiation by reflecting sunlight due to their high albedo (cooling \r\nthe climate system) and also longwave (LW) radiation by absorbing \r\nthe  energy from the surface and emitting at a  lower temperature \r\nto space, that is, contributing to the greenhouse effect, warming \r\nthe climate system. In general, the greenhouse effect of clouds \r\nstrengthens with height whereas the SW reflection depends on \r\nthe cloud optical properties. The effects of clouds on Earth's energy \r\nbudget are measured by the cloud radiative effect (CRE), which \r\nis the difference in the TOA radiation between clear and all skies "


id: 90125b52a73a4a98e0cadfee25b53143
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 20199
content:
"Clouds can be formed almost anywhere in the atmosphere when \r\nmoist air parcels rise and cool, enabling the water vapour to \r\ncondense. Clouds consist of liquid water droplets and/or ice crystals, \r\nand these droplets and crystals can grow into larger particles of rain, \r\nsnow or drizzle. These microphysical processes interact with aerosols, "


id: ce47808b60d3ab9631c54317d16c837
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 29977
content:
"(see Section 7.2.1). In the present climate, the SW CRE tends to be \r\ncompensated by the LW CRE over the equatorial warm pool, leading \r\nto the net CRE pattern showing large negative values over the \r\neastern part of the subtropical ocean and the extratropical ocean \r\ndue to the dominant influence of highly reflective marine low-clouds."


id: e59d18a8403ef5b5dba5f31ef3e6c9d4
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 33632
content:
"Updrafts in convective plumes lead to detrainment of moisture at \r\na  level where the buoyancy diminishes, and thus deep convective \r\nclouds over high SSTs in the tropics are accompanied by anvil and \r\ncirrus clouds in the upper troposphere. These clouds, rather than \r\nthe convective plumes themselves, play a  substantial role in the \r\nglobal TOA radiation budget. In the present climate, the net CRE \r\nof these clouds is small due to a cancellation between the SW and \r\nLW components (Hartmann et  al., 2001). However, high-clouds \r\nwith different optical properties could respond to surface warming \r\ndifferently, potentially perturbing this radiative balance and therefore \r\nleading to a non-zero feedback."


id: 529104500a98f8e25200ff0d2efe955
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 10450
content:
"A thermodynamic mechanism referred to as the 'stability iris effect' \r\nhas been proposed to explain that the anvil cloud amount decreases \r\nwith surface warming (Bony et  al., 2016). In this mechanism, \r\na  temperature-mediated increase of static stability in the upper \r\ntroposphere, where convective detrainment occurs, acts to balance \r\na  weakened mass outflow from convective clouds, and thereby \r\nreduce anvil cloud areal coverage (Figure 7.9). The reduction of anvil \r\ncloud amount is accompanied by enhanced convective aggregation \r\nthat causes a  drying of the surrounding air and thereby increases \r\nthe LW emission to space that acts as a  negative feedback (Bony \r\net al., 2020). This phenomenon is found in many CRM simulations \r\n(Emanuel et al., 2014; Wing and Emanuel, 2014; Wing et al., 2020) \r\nand also identified in observed interannual variability (Stein et al., \r\n2017; Saint-Lu et al., 2020)."


id: 9036251b5a22fdb4c80734429070b591
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 20221
content:
"Despite the reduction of anvil cloud amount supported by several \r\nlines of evidence, estimates of radiative feedback due to high-cloud \r\namount changes is highly uncertain in models. The assessment \r\npresented here is guided by combined analyses of TOA radiation and \r\ncloud fluctuations at interannual time scale using multiple satellite \r\ndatasets. The observationally based local cloud amount feedback \r\nassociated with optically thick high-clouds is negative, leading to \r\nits global contribution (by multiplying the mean tropical anvil cloud \r\nfraction of about 8%) of -0.24 +- 0.05 W m-2 degC-1 (one standard \r\ndeviation) for LW (Vaillant de Guelis et  al., 2018). Also, there is \r\na  positive feedback due to increase of optically thin cirrus clouds \r\nin the tropopause layer, estimated to be 0.09 +- 0.09 W  m-2  degC-1 "


id: ac0f0345e480769979b67dbbf4aee0b8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 24592
content:
"It has long been argued that cloud-top altitude rises under global \r\nwarming, concurrent with the rising of the tropopause at all latitudes \r\n(Marvel et al., 2015; Thompson et al., 2017). This increasing altitude \r\nof high-clouds was identified in early generation GCMs and the \r\ntropical high-cloud altitude feedback was assessed to be positive \r\nwith high confidence in AR5 (Boucher et al., 2013). This assessment \r\nis supported by a  theoretical argument called the 'fixed anvil \r\ntemperature mechanism', which ensures that the temperature of the \r\nconvective detrainment layer does not change when the altitude of \r\nhigh-cloud tops increases with the rising tropopause (Hartmann and \r\nLarson, 2002). Because the cloud-top temperature does not change \r\nsignificantly with global warming, cloud LW emission does not "


id: a50e4bb5f774a46e3d9c9cc20ed39bcf
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23535
content:
"(Cesana et al., 2019; Radtke et al., 2021). The stratocumulus feedback \r\nmay be underestimated because explicit simulations using LES show \r\na  larger local feedback of up to 2.5 W  m-2  degC-1, corresponding to \r\nthe global contribution of 0.2 W m-2 degC-1 by multiplying the mean \r\ntropical stratocumulus fraction of about 8% (Bretherton, 2015). \r\nSupported by different lines of evidence, the subtropical marine \r\nlow-cloud feedback is assessed as positive with high confidence. \r\nBased on the combined estimate using LESs and the cloud \r\ncontrolling factor analysis, the global contribution of the feedback \r\ndue to marine low-clouds equatorward of 30deg is assessed to be \r\n0.2 +- 0.16 W m-2 degC-1 (one standard deviation), for which the range \r\nreflects methodological uncertainties."


id: 392303c5ece13031f8b7510cc405fc6a
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 6364
content:
"It has long been argued that the response of marine boundary-layer \r\nclouds over the subtropical ocean to surface warming was the largest \r\ncontributor to the spread among GCMs in the net cloud feedback \r\n(Boucher et al., 2013). However, uncertainty of the marine low-cloud \r\nfeedback has been reduced considerably since AR5 through combined \r\nknowledge from theoretical, modelling and observational studies \r\n(Klein et al., 2017). Processes that control the low-clouds are complex \r\nand involve coupling with atmospheric motions on multiple scales, \r\nfrom the boundary-layer turbulence to the large-scale subsidence, \r\nwhich may be represented by a combination of shallow and deep \r\nconvective mixing (Sherwood et al., 2014)."


id: 2d380ead6c08cf52b33fb736dd781c7d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 4561
content:
"(one standard deviation) by combining estimates based on observed \r\ninterannual variability and the cloud controlling factors."


id: f682db795fdfdd047c3aff23e826c83
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 36301
content:
"Mixed-phase clouds that consist of both liquid and ice are dominant \r\nover the Southern Ocean (50degS-80degS), which accounts for 20% of the \r\nnet CRE in the present climate (Matus and L'Ecuyer, 2017). It has been \r\nargued that the cloud optical depth (opacity) will increase over the \r\nSouthern Ocean as warming drives the replacement of ice-dominated \r\nclouds with liquid-dominated clouds (Tan et al., 2019). Liquid clouds \r\ngenerally consist of many small cloud droplets, while the crystals \r\nin ice clouds are orders of magnitude fewer in number and much \r\nlarger, causing the liquid clouds to be optically thicker and thereby \r\nresulting in a  negative feedback (Boucher et  al., 2013). However, \r\nthis phase-change feedback works effectively only below freezing \r\ntemperature (Lohmann and Neubauer, 2018; Terai et al., 2019) and \r\nother processes that increase or decrease liquid water path (LWP) \r\nmay also affect the optical depth feedback (McCoy et al., 2019)."


id: 687db0f9eb3c22276d670c4d3b5fc9b1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 14028
content:
"The understanding of the response of clouds to warming and \r\nassociated radiative feedback has deepened since AR5 (Figure  7.9 \r\nand FAQ 7.2). Particular progress has been made in the assessment of \r\nthe marine low-cloud feedback, which has historically been a major \r\ncontributor to the cloud feedback uncertainty but is no longer the \r\nlargest source of uncertainty. Multiple lines of evidence (theory, \r\nobservations, emergent constraints and process modelling) are now \r\navailable in addition to ESM simulations, and the positive low-cloud \r\nfeedback is consequently assessed with high confidence."


id: aa332dbbf2f0a56eab1116e3e4beef92
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 24314
content:
"The best estimate of net cloud feedback is obtained by summing \r\nfeedbacks associated with individual cloud regimes and assessed \r\nto be aC = 0.42 W  m-2 degC-1. By assuming that the uncertainties \r\nof individual cloud feedbacks are independent of each other, \r\ntheir standard deviations are added in quadrature, leading to the \r\nlikely range of 0.12 to 0.72 W  m-2 degC-1 and the very likely range "


id: a0d0e3c4ab4829c74a869ce208b4f899
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 22850
content:
"Table 7.9 | Assessed sign and confidence level of cloud feedbacks in different regimes in AR5 and AR6. For some cloud regimes, the feedback was not assessed \r\nin AR5, indicated by N/A."


id: ea50c02b0ec7c333137208099e3ce3ed
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 34372
content:
"In reality, different types of cloud feedback may occur simultaneously in \r\none cloud regime. For example, an upward shift of high-clouds associated \r\nwith the altitude feedback could be coupled to an increase/decrease of \r\ncirrus/anvil cloud fractions associated with the cloud amount feedback. \r\nAlternatively, slowdown of the tropical circulation with surface warming \r\n(Section 4.5.3 and Figure 7.9) could affect both high and low-clouds \r\nso that their feedbacks are co-dependent. Quantitative assessments \r\nof such covariances require further knowledge about cloud feedback \r\nmechanisms, which will further narrow the uncertainty range."


id: 4e6c42be542e04ff70dc3b9052b82ef2
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 9768
content:
"In summary, deepened understanding of feedback processes in \r\nindividual cloud regimes since AR5 leads to an assessment of the \r\npositive net cloud feedback with high confidence. A small probability \r\n(less than 10%) of a net negative cloud feedback cannot be ruled \r\nout, but this would require an extremely large negative feedback \r\ndue to decreases in the amount of tropical anvil clouds or increases \r\nin optical depth of extratropical clouds over the Southern Ocean; \r\nneither is supported by current evidence."


id: 6b458cbe7e9c1f4b6697a4de171fcebc
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 14501
content:
"The chemical composition of the atmosphere (beyond CO2 and water \r\nvapour changes) is expected to change in response to a  warming \r\nclimate. These changes in greenhouse gases (methane, nitrous oxide \r\nand ozone) and aerosol amount (including dust) have the potential \r\nto alter the TOA energy budget and are collectively referred to as \r\n'non-CO2 biogeochemical feedbacks'. Methane (CH4) and nitrous \r\noxide (N2O) feedbacks arise partly from changes in their emissions \r\nfrom natural sources in response to temperature change; these are \r\nassessed in Chapter  5 (Section  5.4.7; see also Figure  5.29c). Here \r\nwe exclude the permafrost CH4 feedback (Section 5.4.9.1.2) because, \r\nalthough associated emissions are projected to increase under \r\nwarming on multi-decadal to centennial time scales, on longer time "


id: 55ebff76b92cc66265c99a0a01f11bf2
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 11003
content:
"The feedbacks presented in the previous sections (Sections 7.4.2.1-\r\n7.4.2.4) are directly linked to physical climate variables (for example \r\ntemperature, water vapour, clouds, or sea ice). The central role of "


id: aab4fe6e68f381b92362cb1006ed30de
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 24385
content:
"biogeophysical feedback associated with surface-albedo change due \r\nto changes in phytoplankton (Frouin and Iacobellis, 2002; Park et al., \r\n2015), but there is not currently enough evidence to quantitatively \r\nassess this feedback."


id: f64cb35ffa23d7f61a0a9385888707f2
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 36259
content:
"Given the limited number of studies, we take the full range of estimates \r\ndiscussed above for the biogeophysical feedback parameter, and \r\nassess the very likely range to be from 0.0 to +0.3 W m-2 degC-1, with \r\na central estimate of +0.15 W m-2 degC-1 (low confidence). Although this \r\nassessment is based on evidence from both models and paleoclimate \r\nproxies, and the studies above agree on the sign of the change, there is \r\nnonetheless limited evidence. Higher confidence could be obtained if \r\nthere were more studies that allowed calculation of a biogeophysical \r\nfeedback parameter (particularly from paleoclimates), and if the \r\npartitioning between biogeophysical feedbacks and physiological \r\nforcing were clearer for all lines of evidence."


id: faa327e8d5dfab85d5d1d3aa6c1e8ff8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 36955
content:
"The non-CO2 biogeochemical feedbacks are assessed in \r\nSection  7.4.2.5.1 to be -0.16 [-0.37 to +0.05] W m-2 degC-1 and the \r\nbiogeophysical feedbacks are assessed in Section  7.4.2.5.2 to be \r\n+0.15  [0.0 to +0.3] W  m-2 degC-1. The sum of the biogeophysical \r\nand non-CO2 biogeochemical feedbacks is assessed to have \r\na  central value of -0.01 W m-2 degC-1 and a  very likely range from "


id: e460a6b6c224859ee7b7b093f8e2eb45
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 33410
content:
"-0.27  to  +0.25 W  m-2  degC-1 (Table  7.10). Given the relatively long \r\ntime scales associated with the biological processes that mediate the \r\nbiogeophysical and many of the non-CO2 biogeochemical feedbacks, \r\nin comparison with the relatively short time scale of many of the \r\nunderlying model simulations, combined with the small number of \r\nstudies for some of the feedbacks, and the relatively small signals, this \r\noverall assessment has low confidence."


id: ed4bdf0079916de5bb5469e2523cbfbe
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34811
content:
"and as a  result, long-term simulations are often carried out with \r\nlower complexity models, and/or are asynchronously coupled."


id: fc18aa2ed9bf6543f24e708a0c4b1821
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 37163
content:
"In AR5, it was described that both the Greenland and Antarctic ice \r\nsheets would continue to lose mass in a warming world (M. Collins \r\net  al., 2013), with a  continuation in sea level rise beyond the year \r\n2500 assessed as virtually certain. However, there was low confidence\r\nin the associated radiative feedback mechanisms, and as such, there \r\nwas no assessment of the magnitude of long-term radiative feedbacks \r\nassociated with ice sheets. That assessment is consistent with SROCC, \r\nwherein it was stated that 'with limited published studies to draw \r\nfrom and no simulations run beyond 2100, firm conclusions regarding \r\nthe net importance of atmospheric versus ocean melt feedbacks on \r\nthe long-term future of Antarctica cannot be made.'"


id: a4bf314126a7e71450c4adb8e595b2e7
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23493
content:
"Although long-term radiative feedbacks associated with ice sheets are \r\nnot included in our definition of ECS (Box 7.1), the relevant feedback \r\nparameter is assessed here because the time scales on which these \r\nfeedbacks act are relatively uncertain, and the long-term temperature \r\nresponse to CO2 forcing of the entire Earth system may be of interest."


id: 3d87db3b4f2b94d66f4916f55fa4e098
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 7040
content:
"Table  7.10 summarizes the estimates and the assessment of the \r\nindividual and the net feedbacks presented in the above sections. \r\nThe uncertainty range of the net climate feedback was obtained by \r\nadding standard deviations of individual feedbacks in quadrature, \r\nassuming that they are independent and follow the Gaussian "


id: ae6a42ac99cd281f3f2ff1c8d1261b91
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 24967
content:
"The assessment of the net climate feedback presented above is \r\nbased on a single approach (i.e., process understanding) and directly \r\nresults in a value for ECS given in Section 7.5.1; this is in contrast to \r\nthe synthesis assessment of ECS in Section  7.5.5 which combines \r\nmultiple approaches. The total (net) feedback parameter consistent \r\nwith the final synthesis assessment of the ECS and Equation 7.1 \r\n(Box 7.1) is provided there."


id: 1c62159e2d558fedbf3851f35b9b62e3
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 1920
content:
"Since AR5, many modelling groups have newly participated in CMIP \r\nexperiments, leading to an increase in the number of models in \r\nCMIP6 (Section 1.5.4). Other modelling groups that contributed to \r\nCMIP5 also updated their ESMs for carrying out CMIP6 experiments. \r\nWhile some of the CMIP6 models share components and are \r\ntherefore not independent, they are analysed independently when \r\ncalculating climate feedbacks. This, and more subtle forms of model \r\ninter-dependence, creates challenges when determining appropriate \r\nmodel weighting schemes (Section 1.5.4). Additionally, it must be kept \r\nin mind that the ensemble sizes of the CMIP5 and CMIP6 models are \r\nnot sufficiently large to sample the full range of model uncertainty."


id: c1fc2277c90a8c24c3a56b793f60e2a8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 28100
content:
"The multi-model mean values of all physical climate feedbacks are \r\ncalculated using the radiative kernel method (Section  7.4.1) and \r\ncompared with the assessment in the previous sections (Figure 7.10). \r\nFor CMIP models, there is a  discrepancy between the net climate \r\nfeedback calculated directly using the time evolutions of DT and DN\r\nin each model and the accumulation of individual feedbacks, but it \r\nis negligibly small (Supplementary Material 7.SM.4). Feedbacks \r\ndue to biogeophysical and non-CO2 biogeochemical processes are \r\nincluded in some models but neglected in the kernel analysis. In AR6, \r\nbiogeophysical and non-CO2 biogeochemical feedbacks are explicitly \r\nassessed (Section 7.4.2.5)."


id: d37b3d4239843401f6af4090bc60c314
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 30799
content:
"All the physical climate feedbacks apart from clouds are very similar \r\nin the CMIP5 and CMIP6 model ensembles (see also Table 7.10). These \r\nvalues, where possible supported by other lines of evidence, are used \r\nfor assessing feedbacks in Sections 7.4.2.1-7.4.2.3. A  difference \r\nfound between CMIP5 and CMIP6 models is the net cloud feedback, "


id: e146ed77ae35091b7f4cf1e4a2b8da04
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 32936
content:
"Table 7.10 | Synthesis assessment of climate feedbacks (central estimate shown in bold). The mean values and their 90% ranges in CMIP5/6 models, derived using \r\nmultiple radiative kernels (Zelinka et al., 2020) are also presented for comparison."


id: 174a86c3ec87f171fe247c62a8e6fe89
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 1174
content:
"which is larger in CMIP6 by about 20%. This change is the major \r\ncause of less-negative values of the net climate feedback in CMIP6 \r\nthan in CMIP5 and hence an increase in modelled ECS (Section 7.5.1)."


id: e689f2660f86eb3f0109e618bc60d3d9
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 33754
content:
"including those reconstructed from the past (encompassing a range \r\nof states warmer and colder than today, with varying continental \r\ngeographies) or projected for the future. If this approximation holds, \r\nthen the equilibrium global surface temperature response to a fixed \r\nradiative forcing will be constant, regardless of the climate state \r\nto which that forcing is applied."


id: cd8847c5b7424f995fffc8950577ca9d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 29870
content:
"This approximation will break down if climate feedbacks are not \r\nconstant, but instead vary as a function of, for example, background \r\ntemperature (Roe and Baker, 2007; Zaliapin and Ghil, 2010; Roe and \r\nArmour, 2011; Bloch-Johnson et al., 2015), continental configuration \r\n(Farnsworth et al., 2019), or configuration of ice sheets (Yoshimori \r\net al., 2009). If the real climate system exhibits this state-dependence, \r\nthen the future equilibrium temperature change in response to \r\nlarge forcing may be different from that inferred using the standard \r\nframework, and/or different to that inferred from paleoclimates. Such \r\nconsiderations are important for the assessment of ECS (Section 7.5). \r\nClimate models generally include representations of feedbacks that \r\nallow state-dependent behaviour, and so model results may also \r\ndiffer from the predictions from the standard framework."


id: a15d9c9099ddd5c27102dbf60e67cfae
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 22949
content:
"In AR5 (Boucher et  al., 2013), there was a  recognition that climate \r\nfeedbacks could be state-dependent (Colman and McAvaney, 2009), \r\nbut modelling studies that explored this (e.g., Manabe and Bryan, 1985; \r\nVoss and Mikolajewicz, 2001; Stouffer and Manabe, 2003; Hansen et al., \r\n2005b) were not assessed in detail. Also in AR5 (Masson-Delmotte \r\net  al.,  2013), it was assessed that some models exhibited weaker \r\nsensitivity to Last Glacial Maximum (LGM; Cross-Chapter Box  2.1) \r\nforcing than to 4xCO2 forcing, due to state-dependence in shortwave \r\ncloud feedbacks."


id: acf2a9893797e2e8363bf2bd0803f88a
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 24757
content:
"In the standard framework of forcings and feedbacks (Section 7.4.1 \r\nand Box 7.1), the approximation is made that the strength of climate \r\nfeedbacks is independent of the background global mean surface \r\ntemperature. More generally, the individual feedback parameters, \r\nax, are often assumed to be constant over a range of climate states, "


id: c58ad8ed71e89125f87a9909d6f10111
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 28660
content:
"Here, recent evidence for state-dependence in feedbacks from \r\nmodelling studies (Section  7.4.3.1) and from the paleoclimate \r\nrecord (Section 7.4.3.2) are assessed, with an overall assessment in \r\nSection 7.4.3.3. The focus is on temperature-dependence of feedbacks \r\nwhen the system is in equilibrium with the forcing; evidence for transient \r\nchanges in the net feedback parameter associated with evolving spatial \r\npatterns of warming is assessed separately in Section 7.4.4."


id: 732f86b96131108741a967d100d33a3c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 15739
content:
"LGM, provide conflicting evidence of either decreased (Yoshimori \r\net al., 2011) or increased (Kutzbach et al., 2013; Stolpe et al., 2019) \r\ntemperature response per unit forcing during cold climates compared \r\nto the modern era."


id: 7dc51376904641cfdad010edd36d0bae
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 17360
content:
"In contrast to most ESMs, the majority of Earth system models of \r\nintermediate complexity (EMICs) do not exhibit state-dependence, \r\nor have a  net feedback parameter that decreases with increasing \r\ntemperature (Pfister and Stocker, 2017). This is unsurprising since \r\nEMICs usually do not include process-based representations of \r\nwater-vapour and cloud feedbacks. Although this shows that care \r\nmust be taken when interpreting results from current generation \r\nEMICs, Pfister and Stocker (2017) also suggest that non-linearities \r\nin feedbacks can take a long time to emerge in model simulations \r\ndue to slow adjustment time scales associated with the ocean; \r\nlonger simulations also allow better estimates of equilibrium \r\nwarming (Bloch-Johnson et al., 2020). This implies that multi-century \r\nsimulations (Rugenstein et  al., 2020) could increase confidence in \r\nESM studies examining state-dependence."


id: 7cce2f037eb67fda6e3df64100c44e3d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 17233
content:
"The possibility of more substantial changes in climate feedbacks, \r\nsometimes accompanied by hysteresis and/or irreversibility, has been \r\nsuggested from some theoretical and modelling studies. It has \r\nbeen postulated that such changes could occur on a global scale and \r\nacross relatively narrow temperature changes (Popp et al., 2016; von \r\nder Heydt and Ashwin, 2016; Steffen et al., 2018; Schneider et al., \r\n2019; Ashwin and von der Heydt, 2020; Bjordal et al., 2020). However, \r\nthe associated mechanisms are highly uncertain, and as such there is \r\nlow confidence as to whether such behaviour exists at all, and in the \r\ntemperature thresholds at which it might occur."


id: fe805dfe6ff4c5314e97005304af5147
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 37580
content:
"Overall, the modelling evidence indicates that there is medium \r\nconfidence that the net feedback parameter, a, increases (i.e., becomes \r\nless negative) with increasing temperature (i.e., that sensitivity \r\nto forcing increases with increasing temperature), under global \r\nsurface background temperatures at least up to 40degC (Meraner \r\net  al., 2013; Seeley and Jeevanjee, 2021), and medium confidence\r\nthat this temperature dependence primarily derives from increases \r\nin the water-vapour and shortwave cloud feedbacks. This assessment \r\nis further supported by recent analysis of CMIP6 model simulations \r\n(Bloch-Johnson et al., 2020) in the framework of nonlinMIP (Good \r\net  al., 2016), which showed that out of 10 CMIP6 models, seven \r\nof them showed an increase of the net feedback parameter with \r\ntemperature, primarily due to the water-vapour feedback."


id: 575181a4b0f5faada22f50961f0919d6
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vector_id: 11226
content:
"Several studies have estimated ECS from observations of the \r\nglacial-interglacial cycles of the last approximately 2 million years, \r\nand found a  state-dependence, with more-negative a (i.e.,  lower \r\nsensitivity to forcing) during colder periods of the cycles and \r\nless-negative a during warmer periods (von der Heydt et al., 2014; \r\nKohler et al., 2015, 2017; Friedrich et al., 2016; Royer, 2016; Snyder, \r\n2019); see summaries in Skinner (2012) and von der Heydt et  al. \r\n(2016). However, the nature of the state-dependence derived from \r\nthese observations is dependent on the assumed ice-sheet forcing "


id: 9871b153b018d9fd52d25994fb1b4393
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 21496
content:
"Paleoclimate proxy evidence from past high-CO2 time periods much \r\nwarmer than present (the early Eocene and Paleocene-Eocene \r\nThermal Maximum, PETM; Cross-Chapter Box  2.1) show that the \r\nfeedback parameter increases as temperature increases (Anagnostou \r\net  al., 2016, 2020; Shaffer et  al., 2016). However, such \r\ntemperature-dependence of feedbacks was not found in the warm \r\nPliocene relative to the cooler Pleistocene (Martinez-Boti et al., 2015), \r\nalthough the temperature changes are relatively small at this time, \r\nmaking temperature-dependence challenging to detect given the \r\nuncertainties in reconstructing global mean temperature and forcing. \r\nOverall, the paleoclimate proxy record provides medium confidence\r\nthat the net feedback parameter, a, was less negative in these past \r\nwarm periods than in the present day."


id: edaabccaeffb91834b7f8469e66ebc1c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34858
content:
"lines of evidence would be obtained from stronger constraints on \r\natmospheric CO2 concentrations, ice-sheet forcing, and temperatures, \r\nduring past warm climates."


id: 5d46a43d8cc5220fb2905bae387f936c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 12188
content:
"The large-scale patterns of surface warming in observations since \r\nthe 19th  century (Section  2.3.1) and climate model simulations \r\n(Section  4.3.1 and Figure  7.12a) share several common features. \r\nIn particular, surface warming in the Arctic is greater than for the \r\nglobal average and greater than in the Southern Hemisphere (SH) \r\nhigh latitudes; and surface warming is generally greater over land \r\nthan over the ocean. Observations and climate model simulations \r\nalso show some notable differences. ESMs generally simulate \r\na weakening of the equatorial Pacific Ocean zonal (east-west) SST \r\ngradient on multi-decadal to centennial time scales, with greater \r\nwarming in the east than the west, but this trend has not been seen \r\nin observations (Section 9.2.1 and Figure 2.11b)."


id: e1b75755ab8ffab5e9c1027bedd8819d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 33007
content:
"Chapter 4 (Section 4.5.1) discusses patterns of surface warming for \r\n21st-century climate projections under the Shared Socio-economic \r\nPathways (SSP) scenarios. Chapter  9 (Section  9.2.1) assesses \r\nhistorical SST trends and the ability of coupled ESMs to replicate the \r\nobserved changes. Chapter 4 (Section 4.5.1) discusses the processes \r\nthat cause the land to warm more than the ocean (land-ocean \r\nwarming contrast). This section assesses process understanding of \r\nthe large-scale patterns of surface temperature response from the "


id: 65e1374e9ec152e9d75b9eb30924adf1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 13615
content:
"perspective of a regional energy budget. It then assesses evidence \r\nfrom the paleoclimate proxy record for patterns of surface warming \r\nduring past time periods associated with changes in atmospheric CO2\r\nconcentrations. Finally, it assesses how radiative feedbacks depend \r\non the spatial pattern of surface temperature, and thus how they \r\ncan change in magnitude as that pattern evolves over time, with \r\nimplications for the assessment of ECS based on historical warming \r\n(Sections 7.4.4.3 and 7.5.2.1)."


id: 770f26184a4fb96cdc537d1c34ec7ae1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 16300
content:
"circulations (Alexeev et  al., 2005; Held and Soden, 2006; Hwang \r\nand Frierson, 2010; Hwang et al., 2011; Kay et al., 2012; Huang and \r\nZhang, 2014; Feldl et al., 2017a; Donohoe et al., 2020). ESMs project \r\nthat within the mid-latitudes, where eddies dominate the heat \r\ntransport, an increase in poleward latent energy transport arises from \r\nan increase in the equator-to-pole gradient in atmospheric moisture \r\nwith global warming, with moisture in the tropics increasing more \r\nthan at the poles as described by the Clausius-Clapeyron relation \r\n(Section  8.2). This change is partially compensated by a  decrease \r\nin dry-static energy transport arising from a  weakening of the \r\nequator-to-pole temperature gradient as the polar regions warm \r\nmore than the tropics."


id: 35a44fe77eb3005bd897631f5c773d12
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 5845
content:
"Several processes contribute to polar amplification under greenhouse \r\ngas forcing, including the loss of sea ice and snow (an amplifying \r\nsurface-albedo feedback), the confinement of warming to near the \r\nsurface in the polar atmosphere (an amplifying lapse-rate feedback), \r\nand increases in poleward atmospheric and oceanic heat transport \r\n(Pithan and Mauritsen, 2014; Goosse et al., 2018; Dai et al., 2019; \r\nFeldl et  al., 2020). Modelling and process studies since AR5 have \r\nled to an improved understanding of the combined effect of these \r\ndifferent processes in driving polar amplification and how they differ \r\nbetween the hemispheres."


id: 5a7b73ce3636563dac9beec2d6d8418e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 11748
content:
"Regional energy budget analyses are commonly used to diagnose the \r\nrelative contributions of radiative feedbacks and energy fluxes to polar \r\namplification as projected by ESMs under increased CO2 concentrations \r\n(Figure  7.12; Feldl and Roe, 2013; Pithan and Mauritsen, 2014; \r\nGoosse et al., 2018; Stuecker et al., 2018). These analyses suggest that \r\na primary cause of amplified Arctic warming in ESMs is the latitudinal \r\nstructure of radiative feedbacks, which warm the Arctic more than \r\nthe tropics (Figure  7.12b), and enhanced latent energy transport \r\ninto the Arctic. That net atmospheric heat transport into the Arctic \r\ndoes not change substantially within ESMs, on average, under CO2\r\nforcing (Figure 7.12b) reflects a compensating decrease in poleward \r\ndry-static energy transport as a response to polar amplified warming \r\n(Hwang et al., 2011; Armour et al., 2019; Donohoe et al., 2020). The \r\nlatitudinal structure of radiative feedbacks primarily reflects that of "


id: 4a04717c04858374c988b0f1f052a288
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 9052
content:
"Idealized modelling studies suggest that polar amplification would \r\noccur even in the absence of any amplifying polar surface-albedo \r\nor lapse-rate feedbacks owing to changes in poleward atmospheric \r\nheat transport under global warming (Hall, 2004; Alexeev et al., 2005; \r\nGraversen and Wang, 2009; Alexeev and Jackson, 2013; Graversen \r\net al., 2014; Roe et al., 2015; Merlis and Henry, 2018; Armour et al., \r\n2019). Poleward heat transport changes reflect compensating \r\nchanges in the transport of latent energy (moisture) and dry-static \r\nenergy (sum of sensible and potential energy) by atmospheric "


id: c8a1e1adcb8d1c7ea44db0ae70f4232a
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 29124
content:
"Figure 7.12 | Contributions of effective radiative forcing, ocean heat uptake, atmospheric heat transport, and radiative feedbacks to regional surface \r\ntemperature changes at year 100 of abrupt4xCO2 simulations of CMIP6 Earth system models (ESMs). "


id: 34826fd44174eecb2ff737c03001a89d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 5671
content:
"The magnitude of polar amplification also depends on the type of \r\nradiative forcing applied (Section  4.5.1.1; Stjern et  al., 2019), with \r\nChapter  6 (Section  6.4.3) discussing changes in sulphate aerosol \r\nemissions and the deposition of black carbon aerosols on ice and \r\nsnow as potential drivers of amplified Arctic warming. The timing of "


id: 411d0c1527bfb35a9e15a5b999613c09
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 7633
content:
"The energy budget analyses (Figure 7.12) suggest that greater surface \r\nwarming in the Arctic than the Antarctic under greenhouse gas forcing \r\narises from two main processes. The first is large surface heat uptake \r\nin the Southern Ocean (Figure 7.12c) driven by the upwelling of deep "


id: b9dc4e2a299c6afa4cb8a688ba609b57
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 26781
content:
"long-term feedbacks associated with ice sheets (Section 7.4.2.6), and \r\nin particular for the Early Eocene there is a forcing associated with \r\npaleogeographic change (Farnsworth et al., 2019). However, because \r\nthese non-CO2 effects can all be included as boundary conditions in \r\nmodel simulations, these time periods allow an assessment of the \r\npatterns of modelled response to known forcing (although uncertainty \r\nin the forcing increases further back in time). Because these changes \r\nto boundary conditions can be complex to implement in models, \r\nand because long simulations (typically longer than 500 years) are \r\nrequired to approach equilibrium, these simulations have been carried \r\nout mostly by pre-CMIP6 models, with relatively few (or none for the \r\nEarly Eocene) fully coupled CMIP6 models in the ensembles."


id: 122ac554e5e0f0becf3f6943d08865eb
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 361
content:
"At the time of AR5, polar amplification was evident in proxy \r\nreconstructions of paleoclimate sea surface temperature (SST) \r\nand surface air temperature (SAT) from the LGM, MPWP and the \r\nEarly Eocene, but uncertainties associated with proxy calibrations \r\n(Waelbroeck et  al., 2009; Dowsett et  al., 2012; Lunt et  al., 2012a) \r\nand the role of orbital forcing (for the MPWP; Lisiecki and Raymo, \r\n2005) meant that the degree of polar amplification during these \r\ntime periods was not accurately known. Furthermore, although some \r\nmodels (CCSM3; Winguth et al., 2010; Huber and Caballero, 2011) \r\nat that time were able to reproduce the strong polar amplification \r\nimplied by temperature proxies of the Early Eocene, this was achieved \r\nat higher CO2 concentrations (>2000 ppm) than those indicated by \r\nCO2 proxies (<1500 ppm; Beerling and Royer, 2011)."


id: 892250cf7e418e2da99d3c856527146a
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 19114
content:
"Since AR5 there has been progress in improving the accuracy of proxy \r\ntemperature reconstructions of the LGM (Cleator et al., 2020; Tierney \r\net  al., 2020b), the MPWP (McClymont et  al., 2020), and the Early \r\nEocene (Hollis et al., 2019) time periods. In addition, reconstructions \r\nof the MPWP have been focused on a short time slice with an orbit \r\nsimilar to modern-day (isotopic stage KM5C; Haywood et al., 2013, \r\n2016b). Furthermore, there are more robust constraints on CO2\r\nconcentrations from the MPWP (Martinez-Boti et  al., 2015; de la \r\nVega et  al., 2020) and the Early Eocene (Anagnostou et  al., 2016, \r\n2020). As such, polar amplification during the LGM, MPWP, and Early \r\nEocene time periods can now be better quantified than at the time \r\nof AR5, and the ability of climate models to reproduce this pattern \r\ncan be better assessed; model-data comparisons for SAT and SST for \r\nthese three time periods are shown in Figure 7.13."


id: 33cab2adaa90db432d90a4dcd51d642f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 5562
content:
"7.4.4.1.2 Polar amplification from proxies and models during \r\npast climates associated with CO2 change"


id: 42f78e840efc9942eb72148210c4f112
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 7930
content:
"with the most data and the time period with the least uncertainty in \r\nmodel boundary conditions, polar amplification in the PMIP4 ensemble \r\nmean is in good agreement with the proxies, especially for SAT (medium \r\nconfidence). Overall, the confidence in the ability of models to accurately \r\nsimulate polar amplification is higher than at the time of AR5, but \r\na more complete model evaluation could be carried out if there were \r\nmore CMIP6 paleoclimate simulations included in the assessment."


id: 3005d59288c742ed1942efb3ec8e3ec8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 4984
content:
"proxies and DeepMIP models show greater terrestrial warming \r\nin the high latitudes than the mid-latitudes in both hemispheres \r\n(Figure 7.13a,d). SST proxies also exhibit polar amplification in both \r\nhemispheres, but the magnitude of this polar amplification is too low \r\nin the models, in particular in the south-west Pacific (Figure 7.13g,j)."


id: 7c2a43b7eeb40dbb88f270b3b4472777
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 17136
content:
"Southern Ocean SSTs have been slow to warm over the instrumental \r\nperiod, with cooling since about 1980 owing to a  combination of \r\nupper-ocean freshening from ice-shelf melt, intensification of surface \r\nwesterly winds from ozone depletion, and variability in ocean \r\nconvection (Section 9.2.1). This stands in contrast to the equilibrium \r\nwarming pattern either inferred from the proxy record or simulated \r\nby ESMs under CO2 forcing. There is high confidence that the SH high \r\nlatitudes will warm more than the tropics on centennial time scales \r\nas the climate equilibrates with radiative forcing and Southern Ocean \r\nheat uptake is reduced. However, there is only low confidence that \r\nthis feature will emerge this century."


id: 8e0ffaa3e30c51ad0572b419e62fdc2f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 19882
content:
"The Last Glacial Maximum (LGM) also gives an opportunity to \r\nevaluate model simulation of polar amplification under CO2 forcing, \r\nalbeit under colder conditions than today (Kageyama et al., 2021). \r\nTerrestrial SAT and marine SST proxies exhibit clear polar amplification \r\nin the Northern Hemisphere, and the PMIP4 models capture this well \r\n(Figure  7.13c,f,i,l), particularly for SAT. There is less proxy data in \r\nthe mid- to high latitudes of the Southern Hemisphere, but here the \r\nmodels exhibit polar amplification of both SST and SAT. LGM regional \r\nmodel-data agreement is also assessed in Chapter 3 (Section 3.8.2)."


id: 7cb55ce4d0d095a573f5e8c4e506e315
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 17218
content:
"Overall, the proxy reconstructions give high confidence that there was \r\npolar amplification in the LGM, MPWP and EECO, and this is further \r\nsupported by model simulations of these time periods (Figure  7.13; \r\nZhu et al., 2019a; Haywood et al., 2020; Kageyama et al., 2021; Lunt \r\net al., 2021). For both the MPWP and EECO, models are more consistent \r\nwith the temperature and CO2 proxies than at the time of AR5 (high \r\nconfidence). For the LGM Northern Hemisphere, which is the region "


id: ce9ec8632eeda21ee0671518b0befb04
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 30051
content:
"Research published since AR5 identifies changes in the tropical \r\nPacific Ocean zonal SST gradient over time as a key factor affecting \r\nhow radiative feedbacks may evolve in the future (Section 7.4.4.3). "


id: eee13a99bca12da46045e9f07b18f498
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 35056
content:
"There is now a much-improved understanding of the processes that \r\ngovern the tropical Pacific SST gradient (Section 7.4.4.2.1) and the \r\npaleoclimate record provides evidence for its equilibrium changes \r\nfrom time periods associated with changes in CO2 (Section 7.4.4.2.2)."


id: 716e80d20996d03aec60379f19b8a7a1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 15483
content:
"Y. Luo  et  al., 2017) which will occur as the subducting waters in \r\nmid-latitudes warm by more than the tropics on average as polar \r\namplification emerges. An individual CMIP5 ESM (GFDL's ESM2M) \r\nhas been found to exhibit a La Nina-like pattern of Pacific temperature \r\nchange through the 21st  century, similar to the SST trends seen \r\nover the historical record (Section  9.2.1 and Figure  7.14a), owing \r\nto a  weakening asymmetry between El Nino and La Nina events \r\n(Kohyama et al., 2017), but this pattern of warming may not persist \r\nto equilibrium (Paynter et al., 2018)."


id: b374404863a00783d5089eb353e4feb3
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 25741
content:
"In the coupled climate system, changes in atmospheric and oceanic \r\ncirculations will influence the east-west temperature gradient \r\nas well. It is expected that as global temperature increases and \r\nas  the east-west temperature gradient weakens, east-west sea \r\nlevel pressure gradients and easterly trade winds (characterizing \r\nthe Walker circulation) will weaken as well (Sections 4.5.3, 8.2.2.2 \r\nand 8.4.2.3, and Figure 7.14b; Vecchi et al., 2006, 2008). This would, \r\nin turn, weaken the east-west temperature gradient through \r\na reduction of equatorial upwelling of cold water in the east Pacific \r\nand a  reduction in the transport of warmer water to the western \r\nequatorial Pacific and Indian Ocean (England et al., 2014; Dong and \r\nMcPhaden, 2017; Li et al., 2017; Maher et al., 2018)."


id: 469fc89a052e90c90c6d7f9cdc8aecc0
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 8494
content:
"Research published since AR5 (Burls and Fedorov, 2014b; Fedorov \r\net  al., 2015; Erfani and Burls, 2019) has built on an earlier theory \r\n(Liu and Huang, 1997; Barreiro and Philander, 2008) linking the \r\neast-west temperature gradient to the north-south temperature \r\ngradient. In particular, model simulations suggest that a reduction in the \r\nequator-to-pole temperature gradient (polar amplification) increases \r\nthe temperature of water subducted in the extra-tropics, which in turn \r\nis upwelled in the eastern Pacific. Thus, polar amplified warming, with \r\ngreater warming in the mid-latitudes and subtropics than in the deep \r\ntropics, is expected to contribute to the weakening of the east-west \r\nequatorial Pacific SST gradient on decadal to centennial time scales."


id: 4f5a0122dd272890ccc8f114c0e3586c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 9924
content:
"The transient adjustment of the equatorial Pacific SST gradient is \r\ninfluenced by upwelling waters which delay surface warming in the \r\neast since they have not been at the surface for years-to-decades to \r\nexperience the greenhouse gas forcing. This 'thermostat mechanism' \r\n(Clement et al., 1996; Cane et al., 1997) is not thought to persist to \r\nequilibrium since it does not account for the eventual increase in \r\ntemperatures of upwelled waters (Liu et al., 2005; Xie et al., 2010; "


id: 3d26ea9f182df831817d20b14be95692
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 6985
content:
"The AR5 stated that paleoclimate proxies indicate a  reduction in \r\nthe longitudinal SST gradient across the equatorial Pacific during the \r\nMid-Pliocene Warm Period (MPWP; Masson-Delmotte et al., 2013; "


id: bf8b498800e22f8d9c2a5a4cfbe4242b
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 27692
content:
"and enhanced warming in coastal upwelling regions, suggesting \r\nthat the tropical thermocline was deeper and/or less stratified during \r\nthe Pliocene. The Pliocene data therefore suggest that the observed \r\ncooling trend over the last 60 years in parts of the eastern equatorial \r\nPacific (Section 9.2.1.1 and Figure 9.3; Seager et al., 2019), whether \r\nforced or due to internal variability, involves transient processes \r\nthat are probably distinct from the longer-time scale process (Burls \r\nand Fedorov, 2014a, b; Luo et  al., 2015; Heede et  al., 2020) that \r\nmaintained warmer eastern Pacific SST during the Pliocene."


id: bd5aa8d78f32b4c8761f57b7338d1445
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 27356
content:
"see  Cross-Chapter Box  2.1 and Cross-Chapter Box  2.4 in this \r\nReport). This assessment was based on SST reconstructions \r\nbetween two sites situated very close to the equator in the heart \r\nof the western Pacific warm pool and eastern Pacific cold tongue, \r\nrespectively. Multiple SST reconstructions based on independent \r\npaleoclimate proxies generally agreed that during the Pliocene the \r\nSST gradient between these two sites was reduced compared with \r\nthe modern long-term mean (Wara et al., 2005; Dekens et al., 2008; \r\nFedorov et al., 2013)."


id: 2234bb6550dc85338ae4a8b85f62d599
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 2828
content:
"7.4.4.2.3 Overall assessment of tropical Pacific sea surface \r\ntemperature gradients under CO2 forcing"


id: 7fc3f69ae1a4fad9a5f2be87e3dd9ce1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 17646
content:
"The paleoclimate proxy record and ESM simulations of the MPWP, \r\nprocess understanding, and ESM projections of climate response to \r\nCO2 forcing provide medium evidence and a medium agreement and \r\nthus medium confidence that equilibrium warming in response to \r\nelevated CO2 will be characterized by a weakening of the east-west \r\ntropical Pacific SST gradient."


id: 1bbdbdb26044556fed45ac537f8944e6
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 1825
content:
"Overall the observed pattern of warming over the instrumental \r\nperiod, with a  warming minimum in the eastern tropical Pacific \r\nOcean (Figure 7.14a), stands in contrast to the equilibrium warming \r\npattern either inferred from the MPWP proxy record or simulated \r\nby ESMs under CO2 forcing. There is medium confidence that the \r\nobserved strengthening of the east-west SST gradient is temporary \r\nand will transition to a weakening of the SST gradient on centennial \r\ntime scales. However, there is only low confidence that this transition \r\nwill emerge this century owing to a low degree of agreement across \r\nstudies about the factors driving the observed strengthening of \r\nthe east-west SST gradient and how those factors will evolve in \r\nthe  future. These trends in tropical Pacific SST gradients reflect \r\nchanges in the climatology, rather than changes in ENSO amplitude \r\nor variability, which are assessed in Chapter 4 (Section 4.3.3)."


id: f24b5f5fc965905e93eea9f5f149ed3e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 35649
content:
"While more western Pacific warm pool temperature reconstructions \r\nare needed to refine estimates of the longitudinal gradient, several \r\nPliocene SST reconstructions from the east Pacific indicate enhanced \r\nwarming in the centre of the eastern equatorial cold tongue \r\nupwelling region (Liu et  al., 2019). This enhanced warming in the \r\neast Pacific cold tongue appears to be dynamically consistent with \r\nreconstruction of enhanced subsurface warming (Ford et al., 2015) "


id: 3d06bc69e0b0ae0091c239b127edd273
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 6963
content:
"resulting in an observed increase in low-cloud cover over the tropical \r\neastern Pacific (Figure 7.14a; Zhou et al., 2016; Ceppi and Gregory, \r\n2017; Fueglistaler and Silvers, 2021). Thus, tropical low-cloud cover \r\nincreased over recent decades even as global surface temperature \r\nincreased, resulting in a  negative low-cloud feedback which is \r\nat odds with the positive low-cloud feedback expected for the \r\npattern of equilibrium warming under CO2 forcing (Section 7.4.2.4 \r\nand Figure 7.14b)."


id: 517e7b6a74224255de4877f423ee8a23
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 10274
content:
"The spatial pattern of SST changes since 1870 shows relatively \r\nlittle warming in key regions of less-negative radiative feedbacks, \r\nincluding the eastern tropical Pacific Ocean and Southern Ocean \r\n(Sections 7.4.4.1 and 7.4.4.2, and Figures 2.11b and 7.14a). Cooling \r\nin these regions since 1980 has occurred along with an increase in \r\nthe strength of the capping inversion in tropical descent regions, "


id: bc154aa40276b46e2e484b602dd7e377
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 27156
content:
"While there are not yet direct observational constraints on the \r\nmagnitude of the pattern effect, satellite measurements of variations \r\nin TOA radiative fluxes show strong co-variation with changing \r\npatterns of SSTs, with a  strong dependence on SST changes in \r\nregions of deep convective ascent (e.g., in the western Pacific warm \r\npool; Loeb et al., 2018a; Fueglistaler, 2019). Cloud and TOA radiation \r\nresponses to observed warming patterns in atmospheric models have \r\nbeen found to compare favourably with those observed by satellite \r\n(Section 7.2.2.1 and Figure 7.3; Zhou et al., 2016; Loeb et al., 2020). \r\nThis observational and modelling evidence indicates the potential for \r\na  strong pattern effect in nature that will only be negligible if the \r\nobserved pattern of warming since pre-industrial levels persists to "


id: b4554be73a5bad99b885b0ee6e65ac3b
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 25887
content:
"more distinct from the equilibrium warming pattern expected under \r\nCO2 forcing (high confidence) (similar to CMIP6 projections shown in \r\nFigure 7.12a; Andrews et al., 2018)."


id: 70237e25ff9566718c964aa802da201c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 15259
content:
"Equilibrium climate sensitivity (ECS) and transient climate response \r\n(TCR) are metrics of the global surface air temperature (GSAT) \r\nresponse to forcing, as defined in Box  7.1. ECS is the magnitude \r\nof the long-term GSAT increase in response to a  doubling of \r\natmospheric CO2 concentration after the planetary energy budget is \r\nbalanced, though leaving out feedbacks associated with ice sheets; \r\nwhereas the TCR is the magnitude of GSAT increase at year 70 when \r\nCO2 concentration is doubled in a 1% yr-1 increase scenario. Both \r\nare idealized quantities, but can be inferred from paleoclimate or \r\nobservational records or estimated directly using climate simulations, \r\nand are strongly correlated with the climate response in realistic \r\nfuture projections (Sections 4.3.4 and 7.5.7; Grose et al., 2018)."


id: 9cf22c0b709b33f1aed389592406aead
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 22227
content:
"TCR is always smaller than ECS because ocean heat uptake acts to \r\nreduce the rate of surface warming. Yet, TCR is related to ECS across \r\nCMIP5 and CMIP6 models (Grose et al., 2018; Flynn and Mauritsen, \r\n2020) as expected since TCR and ECS are inherently measures \r\nof climate response to forcing; both depend on effective radiative \r\nforcing (ERF) and the net feedback parameter, a. The relationship \r\nbetween TCR and ECS is, however, non-linear and becomes more so "


id: 485d808968b52cf2855e3833827469b8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 8757
content:
"by aggregating estimates of individual climate feedbacks based not \r\nonly on ESMs but also on theory, observations, and high-resolution \r\nprocess modelling (Section 7.4.2)."


id: d33e8a8ec74951151a60e93d2c0779f7
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 30761
content:
"for higher ECS values (Hansen et al., 1985; Knutti et al., 2005; Millar \r\net  al., 2015; Flynn and Mauritsen, 2020; Tsutsui, 2020) owing to \r\nocean heat uptake processes and surface temperature pattern effects \r\ntemporarily reducing the rate of surface warming. When a is small \r\nin magnitude, and correspondingly ECS is large (recall that ECS is \r\ninversely proportional to a), these temporary effects are increasingly \r\nimportant in reducing the ratio of TCR to ECS."


id: ec6866a3f9fca44b978df28dae5ea108
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34675
content:
"The effective radiative forcing of CO2 doubling is assessed to be \r\nDF2xCO2 = 3.93 +- 0.47 W m-2 (Section 7.3.2.1), while the net feedback \r\nparameter is assessed to be a = -1.16 +- 0.40 W m-2 degC-1 (Table 7.10), \r\nwhere the ranges indicate one standard deviation. These values are \r\nslightly different from those directly calculated from ESMs because \r\nmore information is used to assess them, as explained above. Assuming \r\nDF2xCO2 and a each follow an independent normal distribution, the \r\nuncertainty range of ECS can be obtained by substituting the respective \r\nprobability density function into the expression of ECS (red curved bar \r\nin Figure 7.16). Since a is in the denominator, the normal distribution \r\nleads to a long tail in ECS towards high values, indicating the large \r\neffect of uncertainty in a in estimating the likelihood of a high ECS \r\n(Roe and Baker, 2007; Knutti and Hegerl, 2008)."


id: 11dab17097fa3afc30f9ef5cfefbabc6
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 307
content:
"This section assesses the estimates of ECS and TCR based on process \r\nunderstanding of the ERF due to a  doubling of CO2 concentration \r\nand the net climate feedback (Sections 7.3.2 and 7.4.2). This \r\nprocess-based assessment is made in Section 7.5.1.1 and applied to \r\nTCR in Section 7.5.1.2."


id: 4f21e3e24970fc4d7e4dddf27aa6be21
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 9892
content:
"The process-based assessment is based on the global energy budget \r\nequation (Box 7.1, Equation 7.1), where the ERF (DF) is set equal to \r\nthe effective radiative forcing due to a doubling of CO2 concentration \r\n(denoted as DF2xCO2) and the climate state reaches a new equilibrium, \r\nthat is, Earth's energy imbalance averages to zero (DN = 0). ECS is \r\ncalculated as the ratio between the ERF and the net feedback \r\nparameter: ECS = -DF2xCO2/a. Estimates of DF2xCO2 and a are obtained \r\nseparately based on understanding of the key processes that \r\ndetermine each of these quantities. Specifically, DF2xCO2 is estimated \r\nbased on instantaneous radiative forcing that can be accurately \r\nobtained using line-by-line calculations, to which uncertainty due \r\nto adjustments are added (Section 7.3.2). The range of a is derived "


id: 67d9e4497fd748208a2a8362447a1a8c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 13928
content:
"dependence of radiative feedbacks and heat uptake on the evolving SST \r\npattern under CO2 forcing alone (Section 7.4.4). In the real world, natural \r\ninternal variability and aerosol radiative forcing also affect the efficacy \r\nparameter, but these effects are excluded for the current discussion."


id: e9fa2cc96b3f02355aa6bc9dcaa03578
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34310
content:
"In summary, the ECS based on the assessed values of DF2xCO2 and a is \r\nassessed to have a median value of 3.4degC with a likely range of 2.5 to \r\n5.1 degC and very likely range of 2.1 to 7.7 degC. To this assessed range of \r\nECS, the contribution of uncertainty in a is approximately three times \r\nas large as the contribution of uncertainty in DF2xCO2."


id: be09616c202bbbd62300f17dade755a1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 27454
content:
"ECS estimated using the ERF due to a doubling of CO2 concentration \r\nand the net feedback parameter (ECS = -DF2xCO2/a) can be translated \r\ninto the TCR so that both climate sensitivity metrics provide consistent \r\ninformation about the climate response to forcing. Here a two-layer \r\nenergy budget emulator is used to transfer the process-based \r\nassessment of forcing, feedback, efficacy and heat uptake to TCR \r\n(Supplementary Material 7.SM.2.1 and Cross-Chapter Box 7.1). The \r\nemulator can reproduce the transient surface temperature evolution in \r\nESMs under 1pctCO2 simulations and other climate change scenarios, \r\ndespite the very low number of degrees of freedom (Held et al., 2010; \r\nGeoffroy et al., 2012, 2013a; Palmer et al., 2018). Using this model \r\nwith parameters given from assessments in Sections 7.2, 7.3, and 7.4, \r\nTCR is assessed based on the process-based understanding."


id: e4fd0e1e5645bf65d7ffc2520bb3490d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 33519
content:
"temperature record (Section 7.5.2.2), and internal variability in global \r\ntemperature and TOA radiation (Section  7.5.2.3). Section  7.5.2.4 \r\nprovides an overall assessment of TCR and ECS based on these lines \r\nof evidence from the instrumental record."


id: 6bf4c5f73f9877d6d2b2fe09dbebc990
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 14606
content:
"k = 0.84 +- 0.38 W m-2 degC-1 (one standard deviation) by ignoring their \r\ncovariance (the mean value is very similar to that used for Box 4.1, \r\nFigure  1; see Supplementary Material 7.SM.2.1). By incorporating \r\nthis inter-model spread in k, the range of TCR is widened by about \r\n10% (blue bar in Figure 7.17b). Yet, the dominant contribution to the \r\nuncertainty range of TCR arises from the net feedback parameter a, \r\nconsistent with analyses of CMIP6 models (Williams et  al., 2020), \r\nand this assessment remains unchanged from AR5 stating that \r\nuncertainty in ocean heat uptake is of secondary importance."


id: aa67c5070f4c6520431255bb92a916e6
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 24346
content:
"In summary, the process-based estimate of TCR is assessed to have \r\nthe central value of 2.0degC with the likely range from 1.6 to 2.7 degC and \r\nthe very likely range from 1.3 to 3.1 degC (high confidence). The upper \r\nbound of the assessed range was slightly reduced from AR5 but can \r\nbe further constrained using multiple lines of evidence (Section 7.5.5)."


id: ff6bf15da2e7e464b15d4969df6b360
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 37734
content:
"This section assesses the estimates of ECS and TCR based on the \r\ninstrumental record of climate change and variability with an \r\nemphasis on new evidence since AR5. Several lines of evidence \r\nare assessed including the global energy budget (Section  7.5.2.1), \r\nthe use of simple climate models evaluated against the historical "


id: 4cdc1695eada03145a381bc749c1b540
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 9520
content:
"Energy budget estimates of TCR and ECS assessed here are based \r\non improved observations and understanding of global surface \r\ntemperature trends extended to the year 2020 (Section  2.3.1), \r\nrevised estimates of Earth's energy imbalance (Section  7.2), and \r\nrevised estimates of ERF (Section  7.3). Accurate, in situ-based \r\nestimates of Earth's energy imbalance can be made from around \r\n2006 based on near-global ocean temperature observations \r\nfrom the ARGO array of autonomous profiling floats (Sections 2.3 \r\nand 7.2). Over the period 2006-2018 the Earth's energy imbalance \r\nis estimated to be 0.79 +- 0.27 W m-2 (Section 7.2) and it is assumed \r\nthat this value is also representative for the period 2006-2019. \r\nAnomalies are taken with respect to the baseline period 1850-1900, \r\nalthough other baselines could be chosen to avoid major volcanic \r\nactivity (Otto et al., 2013; Lewis and Curry, 2018). Several lines of \r\nevidence, including ESM simulations (Lewis and Curry, 2015), energy "


id: 3a77f1b8a7b42a09a3bc2fb9d59ad0f3
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 6564
content:
"The traditional energy budget framework has been evaluated within \r\nESM simulations by comparing the effective ECS estimated under \r\nhistorical forcing with the ECS estimated using regression methods \r\n(Box 7.1) under abrupt4xCO2 (Andrews et al., 2019; Winton et al., \r\n2020). For one CMIP6 model (GFDL-CM4.0), the value of effective \r\nECS derived from historical energy budget constraints is 1.8degC while \r\nECS is estimated to be 5.0degC (Winton et al., 2020). For another model "


id: 9bfcfc577fb4d45b0f021b79b012070d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 22098
content:
"The values of ECS obtained from the techniques outlined above \r\nare all higher than those estimated from both AR5 and recently \r\npublished estimates (M. Collins et al., 2013; Otto et al., 2013; Lewis \r\nand Curry, 2015, 2018; Forster, 2016). Four revisions made in this \r\nReport are responsible for this increase: (i) an upwards revision of \r\nhistoric global surface temperature trends from newly published \r\ntrend estimates (Section  2.3.1); (ii) an 8% increase in the ERF for \r\nDF2xCO2 (Section  7.3.2); (iii) a  more negative central estimate of \r\naerosol ERF, which acts to reduce estimates of historical ERF trends; \r\nand (iv) accounting for the pattern effect in ECS estimates. Values of \r\nECS provided here are similar to those based on the historical energy \r\nbudget found in Sherwood et al. (2020), with small differences owing \r\nto methodological differences and the use of different estimates of \r\nobserved warming, Earth's energy imbalance, and ERF."


id: f1f0fab99df8c1a609e9ad13ac58e465
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 35586
content:
"ERF, ECS and TCR from across the report. Therefore, the fact that the \r\nemulator effective ECS values estimated from previous studies tend to \r\nlie at the lower end of the range inferred from historical global energy \r\nbudget constraints may reflect that the energy budget constraints in \r\nSection 7.5.2.1 use updated estimates of Earth's energy imbalance, \r\nGSAT trends and ERF, rather than any methodological differences \r\nbetween the lines of evidence. The 'emergent constraints' on ECS \r\nbased on observations of climate variability used in conjunction with \r\ncomprehensive ESMs are assessed in Section 7.5.4.1."


id: 1a908df193b59c278068dbd2a2628ed0
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 1659
content:
"The median estimates of TCR and effective ECS inferred from emulator \r\nstudies generally lie within the 5-95% ranges of those inferred from \r\nhistorical global energy budget constraints (1.3 to 2.7 degC for TCR and \r\n1.6 to 4.8 degC for effective ECS). Their estimates would be consistent \r\nwith still-higher values of ECS when accounting for changes in \r\nradiative feedbacks as the spatial pattern of global warming evolves \r\nin the future (Section 7.5.2.1). Cross-Chapter Box 7.1 and references \r\ntherein show that four very different physically based emulators can \r\nbe calibrated to match the assessed ranges of historical GSAT change, "


id: d9d516320363de7cb8e3981ffa107e63
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 31791
content:
"constraints on the upper bound of ECS, while the studies assessed in \r\nSection 7.5.2.3 based on climate variability provide low confidence\r\nin its value owing to limited evidence."


id: 73875e9aa58f38054a0265a50bb9f473
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 15798
content:
"Finding such correlations within models requires simulations that \r\nspan multiple centuries, suggesting that the satellite record may \r\nnot be of sufficient length to produce robust feedback estimates. \r\nHowever, correlations between regression-based feedbacks and \r\nlong-term feedbacks have been found to be higher when focused on \r\nspecific processes or regions, such as for the cloud- or water-vapour \r\nfeedbacks (Section 7.4.2; Dessler, 2013; Zhou et al., 2015). Assessing \r\nthe global radiative feedback in terms of the more stable relationship \r\nbetween tropospheric temperature and TOA radiation offers another \r\npotential avenue for constraining ECS. The 'emergent constraints' \r\non ECS based on variability in the TOA energy budget are assessed \r\nin Section 7.5.4.1."


id: c572835a869d41cbacf9ca90b6bfd6e8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 28647
content:
"Global energy budget constraints indicate a central estimate (median) \r\nTCR value of 1.9degC and that TCR is likely in the range 1.5 to 2.3 degC and \r\nvery likely in the range 1.3 to 2.7 degC (high confidence). Studies that \r\nconstrain TCR based on the instrumental temperature record used in \r\nconjunction with ESM simulations are summarized in Section 7.5.4.3."


id: 8561b0a157839adf4758ea5da65561cc
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 18513
content:
"AR5 stated that data and modelling of the Last Glacial Maximum \r\n(LGM; Cross-Chapter Box 2.1) indicated that it was very unlikely that \r\nECS lay outside the range 1degC-6degC (Masson-Delmotte et al., 2013). \r\nFurthermore, AR5 reported that climate records of the last 65 million \r\nyears indicated an ECS 95% confidence interval of 1.1 to 7.0 degC."


id: 2f44609c20da60ccc93b39611a857b13
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 4856
content:
"Compared with AR5, there are now improved constraints on estimates \r\nof ECS from paleoclimate evidence. The strengthened understanding \r\nand improved lines of evidence come in part from the use of \r\nhigh-resolution paleoclimate data across multiple glacial-interglacial \r\ncycles, taking into account state-dependence (Section  7.4.3; von \r\nder Heydt et  al., 2014; Kohler et  al., 2015, 2017, 2018; Friedrich \r\net al., 2016; Snyder, 2019; Stap et al., 2019) and better constrained \r\npre-ice-core estimates of atmospheric CO2 concentrations (Martinez\u0002Boti et al., 2015; Anagnostou et al., 2016, 2020; de la Vega et al., \r\n2020) and surface temperature (Hollis et al., 2019; Inglis et al., 2020; \r\nMcClymont et al., 2020)."


id: a5c3e7dab05450252f117510a99570f4
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 23644
content:
"and emergent constraints on paleoclimate model simulations. This \r\nsection focuses on the first type only; the second type (emergent \r\nconstraints) are discussed in Section 7.5.4."


id: 89e40a266c681b7dc6b34e476ea69e1
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 19223
content:
"In order to provide estimates of ECS, evidence from the paleoclimate \r\nrecord can be used to estimate forcing (DF) and global surface \r\ntemperature response (DT) in Equation 7.1, Box  7.1, under the \r\nassumption that the system is in equilibrium (i.e., DN = 0). However, \r\nthere are complicating factors when using the paleoclimate record \r\nin this way, and these challenges and uncertainties are somewhat \r\nspecific to the time period being considered."


id: 488cc681cb5c5380ce2e830fc9bcf7a4
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 8786
content:
"Since AR5, several studies have extended the Rohling et  al. \r\n(2012) approach (described above for the LGM) to the glacial-\r\ninterglacial cycles of the last approximately 1 to 2 million years "


id: ba6c035baaedfe0941c2747f1b583d8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 26868
content:
"Further back in time, in the Early Eocene (Cross-Chapter Box  2.1), \r\nuncertainties in forcing and temperature change become larger, but \r\nthe signals are generally larger too (Anagnostou et al., 2016, 2020; "


id: e3a07cfc7c7c791181f9e45e4f8727bf
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 33292
content:
"Shaffer et al., 2016; Inglis et al., 2020). Caution must be applied when \r\nestimating ECS from these time periods, due to differing continental \r\nposition and topography/bathymetry (Farnsworth et  al., 2019), \r\nand due to temperature-dependence of feedbacks (Section  7.4.3). \r\nOn even longer time scales of the last 500 million years (Royer, 2016) \r\nthe temperature and CO2 measurements are generally asynchronous, \r\npresenting challenges in using this information for assessments \r\nof ECS."


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content:
"The lines of evidence directly constraining ECS from paleoclimates \r\nare summarized in Table  7.11. Although some of the estimates \r\nin Table  7.11 are not independent because they use similar proxy \r\nrecords to each other (e.g., von der Heydt et al., 2014; Kohler et al., \r\n2015, 2017; Stap et al., 2019), there are still multiple independent "


id: 3a4dd91e350dc7fca261c908d92c4532
created_at: 2023-03-16 14:00:44
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vector_id: 6542
content:
"Table 7.11 | Estimates of equilibrium climate sensitivity (ECS) derived from paleoclimates; from AR5 (above double lines) and from post-AR5 studies \r\n(below double lines). Many studies provide an estimate of ECS that includes only CO2 and the ice-sheet feedback as forcings, providing an estimate of S[CO2, LI] using the \r\nnotation of Rohling et al. (2012), which is equivalent to our definition of ECS (Box 7.1). However, some studies provide estimates of other types of sensitivity (column 4). Different \r\nstudies (column 1) focus on different time periods (column 2) and use a variety of different paleoclimate proxies and models (column 3) to give a best estimate (column 5) and/or \r\na range (column 5). The published ranges given account for varying sources of uncertainty (column 6). See Cross-Chapter Box 2.1 for definition of time periods. All temperature \r\nvalues in column 5 are shown to a precision of 1 decimal place."


id: c4c6ef9ffa4afa7f81d12c491a4eef56
created_at: 2023-03-16 14:00:44
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content:
"a Sa\r\n in this table denotes a classification of climate sensitivity following Rohling et al. (2012). \r\nb\r\n Although our assessed value of ERF due to CO2 doubling is 3.93 W m-2 (Section 7.3.2.1), for these studies the best estimate and range of temperature is calculated from the \r\npublished estimate of sensitivity in units of degC (W m-2)\r\n-1 using an ERF of 3.7 W m-2, for consistency with the typical value used in the studies to estimate the paleo CO2 forcing."


id: d0dd86f45210eb47f2937fccb36cee42
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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content:
"None of the post-AR5 studies in Table 7.11 have an estimated lower \r\nrange for ECS below 1.6degC. As such, based solely on the paleoclimate \r\nrecord, it is assessed to be very likely that ECS is greater than 1.5degC \r\n(high confidence)."


id: 92f5bc16d773f070758bbcbdb4baef77
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
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vector_id: 20639
content:
"from warmer paleoclimates (Section  7.4.3.2). A  central estimate \r\nof  ECS, derived from the LGM (Section  7.5.3.1) and warm periods \r\nof the pre-Quaternary (Section  7.5.3.3), that takes into account \r\nsome of the interdependencies between the different studies, can be \r\nobtained by averaging across studies within each of these two time \r\nperiods, and then averaging across the two time periods; this results \r\nin a central estimate of 3.4degC. This approach of focussing on the LGM \r\nand warm climates was also taken by Sherwood et al. (2020) in their \r\nassessment of ECS from paleoclimates. An alternative method is to \r\naverage across all studies, from all periods, that have considered \r\nmultiple sources of uncertainty (Table  7.11); this approach leads \r\nto a  similar central estimate of 3.3degC. Overall, we assess medium \r\nconfidence for a central estimate of 3.3degC to 3.4degC."


id: 2090febc649431ee3f00a607444ee173
created_at: 2023-03-16 14:00:44
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content:
"In general, it is the studies based on the warm periods of the \r\nglacial-interglacial cycles (Section 7.5.3.2) that give the largest values \r\nof ECS. Given the large uncertainties associated with estimating \r\nthe magnitude of the ice-sheet forcing during these intervals (Stap \r\net  al., 2019), and other uncertainties discussed in Section  7.5.3.2, \r\nin particular the direct effect of orbital forcing on estimates of ECS, \r\nthere is only low confidence in estimates from the studies based on \r\nglacial-interglacial periods. This low confidence also results from the \r\ntemperature-dependence of the net feedback parameter, a, resulting \r\nfrom several of these studies (Figure 7.10), that is hard to reconcile \r\nwith the other lines of evidence for a, including proxy estimates "


id: 27010f313ce8139056eb025800b1195b
created_at: 2023-03-16 14:00:44
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content:
"There is more variation in the upper bounds of ECS than in the lower \r\nbounds. Estimates of ECS from pre-Quaternary warm periods have an "


id: 7b661def1fa5aa0ba8635f9d8dc8766c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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content:
"average upper range of 4.9degC, and from the LGM of 4.4degC; taking \r\ninto account the independence of the estimates from these two time \r\nperiods, and accounting for state-dependence (Section  7.4.3) and \r\nother uncertainties discussed in Section 7.5.3, the paleoclimate record \r\non its own indicates that ECS is likely less than 4.5degC. Given the higher \r\nvalues from many glacial-interglacial studies, this value has only \r\nmedium confidence. Despite the large variation in individual studies \r\nat the extreme upper end, all except two studies (both of which are \r\nfrom glacial-interglacial time periods associated with low confidence) \r\nhave central estimates that are below 6degC; overall we assess that it is \r\nextremely likely that ECS is below 8degC (high confidence)."


id: c7084a65fbda21df4520a23c3762e1f1
created_at: 2023-03-16 14:00:44
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vector_id: 28895
content:
"ESMs exhibit substantial spread in ECS and TCR (Section  7.5.7). \r\nNumerous studies have leveraged this spread in order to narrow \r\nestimates of Earth's climate sensitivity by employing methods known \r\nas 'emergent constraints' (Section  1.5.4). These methods establish \r\na relationship between an observable and either ECS or TCR based \r\non an ensemble of models, and combine this information with \r\nobservations to constrain the probability distribution of ECS or TCR. \r\nMost studies of this kind have clearly benefitted from the international \r\nefforts to coordinate the CMIP and other multi-model ensembles."


id: aad0d32c8e89cbe8c03fc32e2bff9126
created_at: 2023-03-16 14:00:44
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content:
"Perhaps the simplest class of emergent constraints regress past \r\nequilibrium paleoclimate temperature change against modelled ECS \r\nto obtain a relationship that can be used to translate a past climate \r\nchange to ECS. The advantage is that these are constraints on "


id: e8f7c0d3ffa404b334373fbec0303abe
created_at: 2023-03-16 14:00:44
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vector_id: 34171
content:
"changed less over the 1970-2005 period, potentially leading to \r\nhigh-biased results (Jimenez-de-la-Cuesta and Mauritsen, 2019), \r\nwhereas during the later period 1975-2019 these anomalies roughly \r\ncancel (Nijsse et al., 2020). Pattern effects may have been substantial \r\nover these periods (Andrews et  al., 2018), however the extent to \r\nwhich TOA radiation anomalies influenced surface temperature may \r\nhave been dampened by the deep ocean (Hedemann et  al., 2017; \r\nNewsom et al., 2020). It is therefore deemed more likely than not that \r\nthese estimates based on post-1970s global warming are biased low \r\nby internal variability."


id: ef3efac23b76ebdaa354e298253d9984
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 35113
content:
"a  range of models consistent with observations (those with ECS \r\nvalues between 2.0degC and 3.9degC; Dessler et al., 2018) or as a formal \r\nemergent constraint by deriving further model-based relationships \r\nto yield a median of 3.3 [2.4 to 4.5] degC (Dessler and Forster, 2018). \r\nThere are major challenges associated with short-term variability \r\nin the energy budget, in particular how it relates to the long-term \r\nforced response of clouds (Colman and Hanson, 2017; Lutsko and \r\nTakahashi, 2018). Variations in the surface temperature that are not \r\ndirectly affecting the radiation balance lead to an overestimated \r\nECS when using linear regression techniques where it appears as \r\nnoise in the independent variable (Proistosescu et al., 2018; Gregory \r\net  al., 2020). The latter issue is largely overcome when using the \r\ntropospheric mean or mid-tropospheric temperature (Trenberth et al., \r\n2015; Dessler et al., 2018)."


id: 2c7fd35d9f2ae878999640ab33c52558
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 4432
content:
"A study that developed an emergent constraint based on the response \r\nto the Mount Pinatubo 1991 eruption yielded a  best estimate of \r\n2.4 [likely range 1.7 to 4.1] degC (Bender et al., 2010). When accounting \r\nfor ENSO variations they found a somewhat higher best estimate of \r\n2.7degC, which is in line with results of later studies that suggest ECS \r\ninferred from periods with substantial volcanic activity are low-biased \r\ndue to strong pattern effects (Gregory et  al., 2020) and that the \r\nshort-term nature of volcanic forcing could exacerbate possible \r\nunderestimates of modelled pattern effects."


id: 1137b1efc3bad443729a66be623b098c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 186
content:
"The fidelity of models in reproducing aspects of temperature variability \r\nor the radiation budget has also been proposed as emergent constraints \r\non ECS (Covey et al., 2000; Knutti et al., 2006; Huber et al., 2010; Bender \r\net al., 2012; Brown and Caldeira, 2017; Siler et al., 2018a). Here indices \r\nbased on spatial or seasonal variability are linked to modelled ECS, \r\nand overall the group of emergent constraints yields best estimates \r\nof 3.3degC-3.7degC. Nevertheless, the physical relevance of present-day \r\nbiases to the sum of long-term climate change feedbacks is unclear \r\nand therefore these constraints on ECS are not considered reliable."


id: 79e36c8f04e363fe90bcfdbf402203f5
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 16751
content:
"present-day climate biases. The former class is arguably superior in \r\nrepresenting ECS, since it is a global surface temperature or energy \r\nbudget change, whereas the latter class is perhaps best thought of as \r\nproviding constraints on individual climate feedbacks, for example, the \r\ndetermination that low-level cloud feedbacks are positive. The latter \r\nresult is consistent with and confirms process-based estimates of \r\nlow-cloud feedbacks (Section 7.4.2.4), but are potentially biased as \r\na group by missing or biased feedbacks in ESMs and is accordingly \r\nnot taken into account here. A  limiting case here is Dessler and \r\nForster (2018) which is focused on monthly co-variability in the \r\nglobal TOA energy budget with mid-tropospheric temperature, at \r\nwhich time scale the surface-albedo feedback is unlikely to operate, \r\nthus implicitly assuming it is unbiased in the model ensemble."


id: bd4c5294cbea280d7e298dcd92e96c28
created_at: 2023-03-16 14:00:44
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content:
"Substantial quantitative progress has been made in interpreting \r\nevidence of Earth's climate sensitivity since AR5, through innovation, \r\nscrutiny, theoretical advances and a rapidly evolving data base from \r\ncurrent, recent and paleo climates. It should be noted that, unlike AR5 \r\nand earlier reports, our assessment of ECS is not directly informed by \r\nESM simulations (Section 7.5.6). The assessments of ECS and TCR are \r\nfocussed on the following lines of evidence: process-understanding; \r\nthe instrumental record of warming; paleoclimate evidence; and \r\nemergent constraints. ESMs remain essential tools for establishing \r\nthese lines of evidence, for instance, in estimating part of the feedback \r\nparameters and radiative forcings, and emergent constraints rely on \r\nsubstantial model spread in ECS and TCR (Section 7.5.6)."


id: fbb798dc563fa9acdc2d6560f855f3f3
created_at: 2023-03-16 14:00:44
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vector_id: 37105
content:
"Emergent constraints on TCR with a  focus on the instrumental \r\ntemperature record, though less abundant, have also been proposed. \r\nThese can be influenced by internal variability and pattern effects, as \r\ndiscussed in Section 7.5.4.1, although the influence is smaller because \r\nuncertainty in forced pattern effects correlates between transient \r\nhistorical warming and TCR. In the simplest form Gillett et al. (2012) \r\nregressed the response of one model to individual historical forcing \r\ncomponents to obtain a tight range of 1.3degC-1.8degC, but later when an \r\nensemble of models was used the range was widened to 0.9degC-2.3degC "


id: 65d9952391edc0dd50429cb14147a7a9
created_at: 2023-03-16 14:00:44
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vector_id: 13609
content:
"A key advance over the AR5 assessment is the broad agreement across \r\nmultiple lines of evidence. These support a  central estimate of ECS \r\nclose to, or at least not inconsistent with, 3degC. This advance is foremost \r\nfollowing improvements in the understanding and quantification of \r\nEarth's energy imbalance, the instrumental record of global temperature \r\nchange, and the strength of anthropogenic radiative forcing. Further "


id: c0fc6587080ced5ed38e1b8f5c9df913
created_at: 2023-03-16 14:00:44
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content:
"Table 7.12 | Emergent constraint studies used in the assessment of equilibrium climate sensitivity (ECS). These are studies that rely on global or near-global \r\ntemperature change as the observable."


id: 83dad824fd36c481f8589cf33479df4d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 18274
content:
"advances include increased understanding of how the pattern effect \r\ninfluences ECS inferred from historical global warming (Sections 7.4.4 \r\nand 7.5.3), improved quantification of paleo climatechange from proxy \r\nevidence and a deepened understanding of how feedback mechanisms \r\nincrease ECS in warmer climate states (Sections 7.4.3, 7.4.4 and 7.5.4), \r\nand also an improved quantification of individual cloud feedbacks \r\n(Sections 7.4.2 and 7.5.4.2). The assessment findings for ECS and TCR \r\nare summarized in Table 7.13 and Table 7.14, respectively, and also \r\nvisualized in Figure 7.18."


id: 598d804b4fb0a40c11ddf238604d0f53
created_at: 2023-03-16 14:00:44
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content:
"There has long been a consensus (Charney et al., 1979) supporting an \r\nECS estimate of 1.5degC-4.5degC. In this regard it is worth remembering \r\nthe many debates challenging an ECS of this magnitude. These started \r\nas early as Angstrom (1900) criticizing the results of Arrhenius (1896) "


id: 9a68ce62d1012cd86f7e8a0d94512e49
created_at: 2023-03-16 14:00:44
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content:
"The evidence for TCR is less abundant than for ECS, and focuses \r\non the instrumental temperature record (Sections 7.5.2 and 7.5.6), \r\nemergent constraints (Section  7.5.4.3) and process understanding \r\n(Section  7.5.1). The AR5 assessed a  likely range for TCR of 1.0  to \r\n2.5  degC. TCR and ECS are related, though, and in any case TCR is "


id: 3d5409fde2b55055755caa396436abd8
created_at: 2023-03-16 14:00:44
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content:
"The primary consideration that led to excluding ECS and TCR \r\ndirectly derived from ESMs is that information from these models \r\nis incorporated in the lines of evidence used in the assessment: \r\nESMs are partly used to estimate historical and paleoclimate ERFs \r\n(Sections 7.5.2 and 7.5.3); to convert from local to global mean paleo \r\ntemperatures (Section 7.5.3); to estimate how feedbacks change with \r\nSST patterns (Section 7.4.4.3); and to establish emergent constraints \r\non ECS (Section 7.5.4). They are also used as important evidence in the \r\nprocess understanding estimates of the temperature, water vapour, \r\nalbedo, biogeophysical, and non-CO2 biogeochemical feedbacks, \r\nwhereas other evidence is primarily used for cloud feedbacks where \r\nthe climate model evidence is weak (Section 7.4.2). One perspective \r\non this is that the process understanding line of evidence builds on \r\nand replaces ESM estimates."


id: 74dd49d22824b00a9d56c7ee9529aa4
created_at: 2023-03-16 14:00:44
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content:
"As a  result of the above considerations, in this Report projections \r\nof global surface temperature are produced using climate model \r\nemulators that are constrained by the assessments of ECS, TCR and \r\nERF. In reports up to and including AR5, ESM values of ECS did not fully \r\nencompass the assessed very likely range of ECS, raising the possibility \r\nthat past multi-model ensembles underestimated the  uncertainty in \r\nclimate change projections that existed at the times of those reports \r\n(e.g., Knutti, 2010). However, due to an increase in the modelled ECS "


id: edd4acb0b41cf3abb5b7beecdb39d205
created_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 34881
content:
"spread and a decrease in the assessed ECS spread based on improved \r\nknowledge in multiple lines of evidence, the CMIP6 ensemble \r\nencompasses the very likely range of ECS [2 to 5]  degC assessed in \r\nSection 7.5.5. Models outside of this range are useful for establishing \r\nemergent constraints on ECS and TCR and provide useful examples of \r\n'tail risk' (Sutton, 2018), producing dynamically consistent realizations \r\nof future climate change to inform impact studies and risk assessments."


id: d450be3c35b5bfdc46811fe5aad4af23
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 30926
content:
"both CMIP5 and that assessed based on multiple lines of evidence \r\n(Sections 4.3.1 and 4.3.4, and FAQ 7.3)."


id: 86d3041099f1c8219b7d01b353cc7dc8
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 18748
content:
"While the magnitude of global warming by the end of the 21st century \r\nis dominated by future GHG emissions, the uncertainty in warming for \r\na given ERF change is dominated by the uncertainty in ECS and TCR \r\n(Section  4.3.4). The proportion of variation explained by ECS and \r\nTCR varies with scenario and the time period considered, but within \r\nCMIP5 models around 60-90% of the globally averaged projected \r\nsurface warming range in 2100 can be explained by the model range \r\nof these metrics (Grose et  al., 2018). Uncertainty in the long-term \r\nglobal surface temperature change can further be understood in \r\nterms of the processes affecting the global TOA energy budget, \r\nnamely the ERF, the radiative feedbacks which govern the efficiency of \r\nradiative energy loss to space with surface warming, and the increase \r\nin the global energy inventory (dominated by ocean heat uptake) \r\nwhich reduces the transient surface warming. A  variety of studies "


id: e1734519de1d6d7883a00592f77ca099
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 32963
content:
"In summary, the distribution of CMIP6 models have higher average \r\nECS and TCR values than the CMIP5 generation of models and \r\nthe assessed values of ECS and TCR in Section  7.5.5. The high \r\nECS and TCR values can in some CMIP6 models be traced to \r\nimproved representation of extratropical cloud feedbacks (medium \r\nconfidence). The ranges of ECS and TCR from the CMIP6 models are \r\nnot considered  robust samples of possible values and the models \r\nare not considered a  separate line of evidence for ECS and TCR. \r\nSolely based on its ECS or TCR values an individual ESM cannot be \r\nruled out as implausible, though some models with high (greater \r\nthan 5degC) and low (less than 2degC) ECS are less consistent with past \r\nclimate change (high confidence). High climate sensitivity in models \r\nleads to generally higher projected warming in CMIP6 compared to "


id: 2e17768bb372e20d6044b6c19f7fe4ae
created_at: 2023-03-16 14:00:44
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content:
"at depth away from the ocean surface.  Section  7.4.4.1 shows the \r\nwarming contributions from these factors at the regional scale."


id: c96a733f5b5a8a82f22240e919d69711
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 29251
content:
"Differences in projected transient global warming across ESMs are \r\ndominated by differences in their radiative feedbacks, while differences \r\nin ocean heat uptake and radiative forcing play secondary roles \r\n(Figure 7.20b; Vial et al., 2013). The uncertainty in projected global \r\nsurface temperature change associated with inter-model differences \r\nin cloud feedbacks is the largest source of uncertainty in CMIP5 and \r\nCMIP6 models (Figure  7.20b), just as they were for CMIP3 models \r\n(Dufresne and Bony, 2008). Extending this energy budget analysis to \r\nequilibrium surface warming suggests that about 70% of the inter\u0002model differences in ECS arises from uncertainty in cloud feedbacks, \r\nwith the largest contribution to that spread coming from shortwave \r\nlow-cloud feedbacks (Vial et al., 2013; Zelinka et al., 2020)."


id: 69a4a7d498f3f323de6704b891b319f4
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 14219
content:
"vertical coordinate systems, among other differences); TCR and ECS \r\nchanged by 0.4degC and 1.4degC, respectively, when the atmospheric \r\nmodel component was changed, while TCR and ECS changed by \r\n0.3degC and less than 0.05degC, respectively, when the oceanic model \r\ncomponent was changed. By perturbing ocean vertical diffusivities \r\nover a wide range, Watanabe et al. (2020) found that TCR changed by \r\n0.16degC within the model MIROC5.2 while Krasting et al. (2018) found \r\nthat ECS changed by about 0.6degC within the model GFDL-ESM2G, \r\nwith this difference linked to different radiative feedbacks associated \r\nwith different spatial patterns of sea surface warming (Section 7.4.4.3). \r\nBy comparing simulations of CMIP6 models with and without the \r\neffects of CO2 on vegetation, Zarakas et al. (2020) find a physiological \r\ncontribution to TCR of 0.12degC (range 0.02degC-0.29degC across models) \r\nowing to physiological adjustments to the CO2 ERF (Section 7.3.2.1)."


id: c175f2ac72eb6916dce5e3954690c690
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 28025
content:
"There is robust evidence and high agreement across a  diverse \r\nrange of modelling approaches and thus high confidence that \r\nradiative feedbacks are the largest source of uncertainty in projected \r\nglobal warming out to 2100 under increasing or stable emissions \r\nscenarios, and that cloud feedbacks in particular are the dominant \r\nsource of that uncertainty. Uncertainty in radiative forcing plays an \r\nimportant but generally secondary role. Uncertainty in global ocean \r\nheat uptake plays a lesser role in global warming uncertainty, but \r\nocean circulation could play an important role through its effect on \r\nsea surface warming patterns which in turn project onto radiative \r\nfeedbacks through the pattern effect (Section 7.4.4.3)."


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vector_id: 20152
content:
"The spread in historical surface warming across CMIP5 ESMs \r\nshows a weak correlation with inter-model differences in radiative \r\nfeedback or ocean heat uptake processes but a high correlation with \r\ninter-model differences in radiative forcing owing to large variations \r\nin aerosol forcing across models (Forster et al., 2013). Likewise, the \r\nspread in projected 21st-century warming across ESMs depends \r\nstrongly on which emissions scenario is employed (Section  4.3.1; \r\nHawkins and Sutton, 2012). Strong emissions reductions would \r\nremove aerosol forcing (Section 6.7.2) and this could dominate the \r\nuncertainty in near-term warming projections (Armour and Roe, 2011; \r\nMauritsen and Pincus, 2017; Schwartz, 2018; Smith et al., 2019). On \r\npost-2100 time scales carbon cycle uncertainty such as that related to \r\npermafrost thawing could become increasingly important, especially \r\nunder high-emissions scenarios (Figure 5.30)."


id: 491b722d6b50521f6ec6732a2b6682e9
created_at: 2023-03-16 14:00:44
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vector_id: 8890
content:
"In summary, there is high confidence that cloud feedbacks are the \r\ndominant source of uncertainty for late 21st-century projections \r\nof transient global warming under increasing or stable emissions \r\nscenarios, whereas uncertainty is dominated by aerosol ERF in strong \r\nmitigation scenarios. Global ocean heat uptake is a smaller source of \r\nuncertainty in long-term surface warming (high confidence)."


id: 195dabcfa8074f9ed091c43a8bf2bf2e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 1475
content:
"Emissions metrics are used to compare the relative effect of emissions \r\nof different gases over time in terms of radiative forcing, global surface \r\ntemperature or other climate effects. They are introduced in Chapter 1 \r\n(Box 1.3). Chapter 8 of AR5 (Myhre et al., 2013b) comprehensively \r\ndiscussed different emissions metrics so this section focuses on "


id: 62c69bd62d2110680567f0fab4e9966b
created_at: 2023-03-16 14:00:44
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content:
"updates since that report. Section  7.6.1 updates the physical \r\nassessment. Section 7.6.2 assesses developments in the comparison \r\nof emissions of short- and long-lived gases. Box 7.3 assesses physical \r\naspects of emissions metric use within climate policy."


id: c2e131eb488dd25a02106955c56a7820
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 28240
content:
"The radiative properties and lifetimes of compounds are the \r\nfundamental component of all emissions metrics. Since AR5, there \r\nhave been advances in the understanding of the radiative properties \r\nof various compounds (see Sections 7.3.1, 7.3.2 and 7.3.3), and \r\nhence their effective radiative efficiencies (ERFs per unit change \r\nin concentration). For CO2, CH4 and N2O, better accounting of the \r\nspectral properties of these gases has led to re-evaluation of their \r\nstratospheric-temperature-adjusted radiative forcing (SARF) \r\nradiative efficiencies and their dependence on the background \r\ngas concentrations (Section  7.3.2). For CO2, CH4, N2O, CFC-11 and \r\nCFC-12 the tropospheric adjustments (Sections 7.3.1 and 7.3.2) are \r\nassessed to make a non-zero contribution to ERF. There is insufficient \r\nevidence to include tropospheric adjustments for other halogenated \r\ncompounds. The re-evaluated effective radiative efficiency for CO2\r\nwill affect all emissions metrics relative to CO2."


id: c72b0f859074824e0a3e4d4ea8b04184
created_at: 2023-03-16 14:00:44
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vector_id: 28922
content:
"This section discusses metrics that relate emissions to physical \r\nchanges in the climate system. Other metrics, for instance relating \r\nto economic costs or 'damage' are discussed in WGIII, Chapter  2. \r\nThe same Chapter also assesses literature examining the extent \r\nto which different physical metrics are linked to cost-benefit and \r\ncost-effectiveness metrics. One metric, the 100-year global warming \r\npotentials (GWP-100), has extensively been employed in climate \r\npolicy to report emissions of different GHGs on the same scale. Other \r\nphysical metrics exist, and these are discussed in this section."


id: 63abb4f1a725f8eb664d4b0f06b05d1e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 13251
content:
"Emissions metrics can be quantified as the magnitude of the effect \r\na unit mass of emission of a species has on a key measure of climate \r\nchange. This section focuses on physical measures such as the radiative \r\nforcing, GSAT change, global average precipitation change, and global \r\nmean sea level rise (Myhre et al., 2013b; Sterner et al., 2014; Shine \r\net al., 2015). When used to represent a climate effect, the metrics are \r\nreferred to as absolute metrics and expressed in units of 'effect per kg' \r\n(e.g., absolute global warming potentials, AGWP or absolute global \r\ntemperature-change potentials, AGTP). More commonly, these are \r\ncompared with a reference species (almost always CO2 in kg (CO2)), to \r\ngive a dimensionless factor (written as e.g., global warming potentials \r\n(GWP) or global temperature-change potential (GTP)). The unit mass \r\nis usually taken as a 1 kg instantaneous 'pulse' (Myhre et al., 2013b), \r\nbut can also refer to a 'step' in emissions rate of 1 kg yr-1."


id: c18059e0721875b40f1dd85a3c1a1227
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 28029
content:
"The effective radiative efficiencies (including adjustments from \r\nSection 7.3.2) for 2019 background concentrations for CO2, CH4 and N2O \r\nare assessed to be 1.33x10-5, 3.89x10-4 and 3.19x10-3 W m-2 ppb-1\r\nrespectively (see Table  7.15 for uncertainties), compared to AR5 \r\nassessments of 1.37x10-5, 3.63x10-4 and 3.00x10-3 W  m-2 ppb-1. \r\nFor CO2, increases due to the adjustments do not quite balance the \r\ndecreases due to the increasing background concentration. For CH4, \r\nincreases due to the re-evaluated radiative properties more than offset \r\nthe decreases due to the increasing background concentration. For \r\nN2O the addition of tropospheric adjustments increases the effective \r\nradiative efficiency. Radiative efficiencies of halogenated species have \r\nbeen revised slightly (Section 7.3.2.4) and for CFCs include tropospheric \r\nadjustments."


id: 391b53cc5781de0ce6bd8853f135537f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 6358
content:
"The perturbation lifetimes of CH4 (Section 6.3.1). and N2O (Section 5.2.3.1) \r\nhave been slightly revised since AR5 to be 11.8  +-  1.8  years and \r\n109 +- 10 years, respectively (Table 7.15). The lifetimes of halogenated \r\ncompounds have also been slightly revised (Hodnebrog et al., 2020a)."


id: 3654c6f3e2ea83802cba305aee95e152
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 5939
content:
"There is a cause-effect chain that links human activity to emissions, \r\nthen from emissions to radiative forcing, climate response and climate \r\nimpacts (Fuglestvedt et  al., 2003). Each step in the causal  chain \r\nrequires an inference or modelling framework that maps causes to \r\neffects. Emissions metrics map from emissions of some compound \r\nto somewhere further down the cause-and-effect chain, radiative \r\nforcing (e.g., GWP) or temperature (e.g., GTP) or other effects (such \r\nas sea level rise or socio-economic impacts). While variables later \r\nin the chain have greater policy or societal relevance, they are also \r\nsubject to greater uncertainty because each step in the chain includes \r\nmore modelling systems, each of which brings its own uncertainty \r\n(Figure 1.15; Balcombe et al., 2018)."


id: 941508ad3c7b46c52fcaec415113bfec
created_at: 2023-03-16 14:00:44
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vector_id: 20803
content:
"Although there has been greater understanding since AR5 of the \r\ncarbon cycle responses to CO2 emissions (Sections 5.4 and 5.5), there \r\nhas been no new quantification of the response of the carbon cycle \r\nto an instantaneous pulse of CO2 emission since Joos et al. (2013)."


id: d374533349ccf157e6f22bb61f5447d9
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 30790
content:
"The basis of all the emissions metrics is the time profile of effective \r\nradiative forcing (ERF) following the emission of a  particular \r\ncompound. The emissions metrics are then built up by relating the \r\nforcing to the desired physical indicators. These forcing-response \r\nrelationships can either be generated from emulators (Cross-Chapter \r\nBox 7.1; Tanaka et al., 2013; Gasser et al., 2017b), or from analytical \r\nexpressions based on parametric equations (response functions) \r\nderived from more complex models (Myhre et al., 2013b)."


id: 34b8c578adf6882c1838b24d3da4b203
created_at: 2023-03-16 14:00:44
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vector_id: 5697
content:
"Since AR5, understanding of the radiative effects of emitted \r\ncompounds has continued to evolve and these changes are assessed \r\nin Section 7.6.1.1. Metrics relating to precipitation and sea level have \r\nalso been quantified (Section 7.6.1.2). Understanding of how emissions \r\nmetrics are affected by the carbon cycle response to temperature has \r\nimproved. This allows the carbon cycle response to temperature \r\nto be more fully included in the emissions metrics presented here \r\n(Section 7.6.1.3). There have also been developments in approaches \r\nfor comparing short-lived GHGs to CO2 in the context of mitigation and \r\nglobal surface temperature change (Section 7.6.1.4). Emissions metrics \r\nfor selected key compounds are presented in Section 7.6.1.5."


id: 64bf437b3adfa98e71c0ea8ac30ae712
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 13421
content:
"To illustrate the analytical approach, the ERF time evolution following \r\na  pulse of emission can be considered an absolute global forcing \r\npotential (AGFP; similar to the 'Instantaneous Climate Impact' of \r\nEdwards and Trancik, 2014). This can be transformed into an absolute "


id: b8c67baf7697d2443a926a451012f7ee
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 26592
content:
"derived by replacing the global surface temperature response \r\nfunction with a  regional response matrix relating forcing changes \r\nin one region to temperature changes in another (W.J. Collins et al., \r\n2013; Aamaas et al., 2017; Lund et al., 2017)."


id: d59dda1e882d390b44bd616df2d9b732
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 31127
content:
"global temperature-change potential (AGTP) by combining the \r\nradiative forcing with a global surface temperature response function. \r\nThis temperature response is typically derived from a two-layer energy \r\nbalance emulator (Supplementary Material 7.SM.5; Myhre et  al., \r\n2013b). For further physical indicators further response functions are \r\nneeded based on the radiative forcing or temperature, for instance. \r\nSterner et  al. (2014) used an upwelling-diffusiveenergy balance \r\nmodel to derive the thermosteric component of sea level rise as \r\nresponse functions to radiative forcing or global surface temperature. \r\nA metric for precipitation combines both the radiative forcing (AGFP) \r\nand temperature (AGTP) responses to derive an absolute global \r\nprecipitation potential (AGPP; Shine et  al., 2015). The equations \r\nrelating these metrics are given in Supplementary Material 7.SM.5."


id: a5bc77290f385267c6205a261ed07f31
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 23641
content:
"For the research discussed above, metrics for several physical \r\nvariables can be constructed that are linear functions of radiative \r\nforcing. Similar metrics could be devised for other climate variables \r\nprovided they can be related by response functions to radiative \r\nforcing or global surface temperature change. The radiative forcing \r\ndoes not increase linearly with emissions for any species, but the \r\nnon-linearities (for instance changes in CO2 radiative efficiency) are \r\nsmall compared to other uncertainties."


id: ee53ec88f6383eeeb92662425dc55775
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
content_type: text
index: climate_gpt_only_giec
vector_id: 34966
content:
"The effect of a  compound on climate is not limited to its direct \r\nradiative forcing. Compounds can perturb the carbon cycle affecting \r\natmospheric CO2 concentrations. Chemical reactions from emitted \r\ncompounds can produce or destroy other GHGs or aerosols."


id: 356d93cb7e6476a3d10b3fce48cdfe8e
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 5817
content:
"Each step from radiative forcing to global surface temperature to \r\nsea level rise introduces longer time scales and therefore prolongs \r\nfurther the contributions to climate change of short-lived GHGs \r\n(Myhre et al., 2013b). Thus, short-lived GHGs become more important \r\n(relative to CO2) for sea level rise than for temperature or radiative \r\nforcing (Zickfeld et al., 2017). Integrated metrics include the effects \r\nof a pulse emission from the time of emission up to the time horizon, \r\nwhereas endpoint metrics only include the effects that persist out \r\nto the time horizon. Because the largest effects of short-lived GHGs \r\noccur shortly after their emission and decline towards the end of \r\nthe time period, short-lived GHGs have relatively higher integrated \r\nmetrics than their corresponding endpoint metrics (Peters et  al., \r\n2011; Levasseur et al., 2016)."


id: 43aaf3f3dec82cdb95e0c789f81e93c5
created_at: 2023-03-16 14:00:44
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content:
"For species perturbations that lead to a strong regional variation in \r\nforcing pattern, the regional temperature response can be different \r\nto that for CO2. Regional equivalents to the global metrics can be "


id: 5901d2085de4555e0a07a5bed03a731d
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 11496
content:
"Methane can also affect the oxidation pathways of aerosol formation \r\n(Shindell et  al., 2009) but the available literature is insufficient to \r\nmake a  robust assessment of this. Hydrocarbon and molecular \r\nhydrogen oxidation also leads to tropospheric ozone production \r\nand change in methane lifetime (Collins et  al., 2002; Hodnebrog \r\net al., 2018). For reactive species the emissions metrics can depend \r\non where the emissions occur, and the season of emission (Aamaas \r\net al., 2016; Lund et al., 2017; Persad and Caldeira, 2018). The AR5 \r\nincluded a contribution to the emissions metrics for ozone-depleting \r\nsubstances (ODSs) from the loss of stratospheric ozone. The \r\nassessment of ERFs from ODSs in Chapter 6 (Section 6.4.2) suggests \r\nthe quantification of these terms may be more uncertain than the \r\nformulation in AR5 so these are not included here."


id: 1c66ec857936a51545a489ef95a9f1cf
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 1925
content:
"Emissions of non-CO2 species can affect the carbon cycle in other \r\nways: emissions of ozone precursors can reduce the carbon uptake \r\nby plants (W.J. Collins et al., 2013); emissions of reactive nitrogen \r\nspecies can fertilize plants and hence increase the carbon uptake \r\n(Zaehle et al., 2015); and emissions of aerosols or their precursors can \r\naffect the utilisation of light by plants (Cohan et al., 2002; Mercado \r\net  al., 2009; Mahowald et  al., 2017; see Section  6.4.4 for further \r\ndiscussion). There is robust evidence that these processes occur and \r\nare important, but insufficient evidence to determine the magnitude \r\nof their contributions to emissions metrics. Ideally, emissions metrics \r\nshould include all indirect effects to be consistent, but limits to our \r\nknowledge restrict how much can be included in practice."


id: 9dadf532016d3c2b6e4cdfb570062190
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 22336
content:
"a ratio AGTP S\r\nX /AGTPCO2 (Collins et al., 2020). This is referred to as \r\na  combined GTP (CGTP) in Collins et  al.  (2020), and has units of \r\nyears (the standard GTP is dimensionless). This CGTP shows less \r\nvariation with time than the standard GTP (comparing Figure 7.21c \r\nwith Figure 7.21d) and provides a scaling for comparing a change \r\nin emissions rate (in kg yr -1) of short-lived GHGs with a  pulse \r\nemission or change in cumulative CO2 emissions (in kg). Cumulative \r\nCO2 equivalent emissions are given by CGTP x emissions rate of \r\nshort-lived GHGs. The CGTP can be calculated for any species, but \r\nit is least dependent on the chosen time horizon for species with \r\nlifetimes less than half the time horizon of the metric (Collins \r\net al., 2020). Pulse-step metrics can therefore be useful where time \r\ndependence of pulse metrics, like GWP or GTP, complicates their use \r\n(see Box 7.3)."


id: 7001ed4527f19f145e6b0fc2534ddc4f
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 15238
content:
"accounted for exactly with the CO2 forcing equivalent metric (Wigley, \r\n1998; Allen et al., 2018b; Jenkins et al., 2018) that produces a CO2\r\nemissions time profile such that the radiative forcing matches the \r\ntime evolution of that from the non-CO2 emissions. But other metric \r\napproaches can approximate this exact approach."


id: f4bef92b9c09f847921b5878c008abdc
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 36011
content:
"For a  stable global warming from non-CO2 climate agents (gas or \r\naerosol) their effective radiative forcing needs to gradually decrease \r\n(Tanaka and O'Neill, 2018). Cain et al. (2019) find this decrease to \r\nbe around 0.3% yr-1 for the climate response function in AR5 \r\n(Myhre et  al., 2013b). To account for this, a  quantity referred to \r\nas GWP* has been defined that combines emissions (pulse) and \r\nchanges in emissions levels (step) approaches (Cain et  al., 2019; "


id: df1c3fb052cce0c869d77b77b6951db3
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 32596
content:
"Smith  et  al.,  2021).2 The emissions component accounts for the \r\nneed for emissions to decrease to deliver a stable warming. The step \r\n(sometimes referred to as flow or rate) term in GWP* accounts for \r\nthe change in global surface temperature that arises from a change \r\nin short-lived GHG emissions rate, as in CGTP, but here approximated \r\nby the change in emissions over the previous 20 years."


id: c2b8170f799cfc411f1b9247d0860112
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 28217
content:
"Cumulative CO2 emissions and GWP*-based cumulative CO2\r\nequivalent GHG emissions multiplied by TCRE closely approximate \r\nthe global warming associated with emissions time series (of CO2 and \r\nGHG, respectively) from the start of the time series (Lynch et al., 2020). \r\nBoth the CGTP and GWP* convert short-lived GHG emissions rate \r\nchanges into cumulative CO2 equivalent emissions, hence scaling \r\nthese by TCRE gives a  direct conversion from short-lived GHG \r\nemissions to global surface temperature change. By comparison \r\nexpressing methane emissions as CO2 equivalent emissions using \r\nGWP-100 overstates the effect of constant methane emissions on \r\nglobal surface temperature by a  factor of 3-4 (Lynch et  al., 2020, \r\ntheir Figure 5), while understating the effect of any new methane \r\nemission source by a factor of 4-5 over the 20 years following the \r\nintroduction of the new source (Lynch et al., 2020, their Figure 4)."


id: fe7825f313d5a654b902bb46fc2b1f68
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 37575
content:
"In summary, new emissions metric approaches such as GWP* and \r\nCGTP are designed to relate emissions changes in short-lived GHGs \r\nto emissions of CO2 as they better account for the different physical \r\nbehaviours of short- and long-lived gases. Through scaling the \r\ncorresponding cumulative CO2 equivalent emissions by the TCRE, \r\nthe GSAT response from emissions over time of an aggregated set of "


id: c47b9352d62a6f334f3016143012b486
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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index: climate_gpt_only_giec
vector_id: 28487
content:
"Figure  7.22 explores how cumulative CO2 equivalent emissions \r\nestimated for methane vary under different emissions metric choices \r\nand how estimates of the global surface air temperature (GSAT) \r\nchange deduced from these cumulative emissions compare to the "


id: a45b8767fb76f0509356fb234467a28c
created_at: 2023-03-16 14:00:44
updated_at: 2023-03-16 14:00:44
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vector_id: 23421
content:
"gases can be estimated. Using either these new approaches, or treating \r\nshort- and long-lived GHG emissions pathways separately, can improve \r\nthe quantification of the contribution of emissions to global warming \r\nwithin a  cumulative emissions framework, compared to approaches \r\nthat aggregate emissions of GHGs using standard CO2 equivalent \r\nemissions metrics. As discussed in Box 7.3, there is high confidence \r\nthat multi-gas emissions pathways with the same time-dependence \r\nof aggregated CO2 equivalent emissions estimated from standard \r\napproaches, such as weighting emissions by their GWP-100 values, \r\nrarely lead to the same estimated temperature outcomes."


id: 349d7facb6adc580da208f9f9e9fa26c
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
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index: climate_gpt_only_giec
vector_id: 5684
content:
"The lifetimes and radiative efficiencies for halogenated compounds \r\nare taken from Hodnebrog et al. (2020a). Combined metrics (CGTPs) \r\nare presented for compounds with lifetimes less than 20 years. Note \r\nthat CGTP has units of years and is applied to a change in emissions rate \r\nrather than a change in emissions amount. Changes since AR5 are due \r\nto changes in radiative properties and lifetimes (Section 7.6.1.1), and \r\nindirect contributions (Section 7.6.1.3). Table 7.15 also gives overall \r\nemissions uncertainties in the emissions metrics due to uncertainties \r\nin radiative efficiencies, lifetimes and the climate response function \r\n(Supplementary Material, Tables 7.SM.8 to 7.SM.13)."


id: bf6cb38ada066194b060528f19b705c8
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
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index: climate_gpt_only_giec
vector_id: 27672
content:
"Following their introduction in AR5 the assessed metrics now routinely \r\ninclude the carbon cycle response for non-CO2 gases (Section 7.6.1.3). \r\nAs assessed in this earlier section, the carbon cycle contribution is \r\nlower than in AR5. Contributions to CO2 formation are included for \r\nmethane depending on whether or not the source originates from \r\nfossil carbon, thus methane from fossil fuel sources has slightly higher \r\nemissions metric values than that from non-fossil sources."


id: ccbf32713ab9dd93f791ecbaaa4d62c6
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
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index: climate_gpt_only_giec
vector_id: 29753
content:
"Emissions metrics for selected compounds are presented in Table 7.15, \r\nwith further compounds presented in the Supplementary Material, \r\nTable 7.SM.7. The evolution of the CO2 concentrations in response to \r\na pulse emission is as in AR5 (Joos et al., 2013; Myhre et al., 2013b), \r\nthe perturbation lifetimes for CH4 and N2O are from Section 7.6.1.1. "


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content:
"Table 7.15 | Emissions metrics for selected species: global warming potential (GWP), global temperature-change potential (GTP). All values include carbon \r\ncycle responses as described in Section 7.6.1.3. Combined GTPs (CGTPs) are shown only for species with a lifetime less than 20 years (Section 7.6.1.4). Note CGTP has units of \r\nyears and is applied to a change in emissions rate rather than a change in emissions amount. The radiative efficiencies are as described in Section 7.3.2 and include tropospheric \r\nadjustments where assessed to be non-zero in Section 7.6.1.1. The climate response function is from Supplementary Material 7.SM.5.2. Uncertainty calculations are presented \r\nin Supplementary Tables 7.SM.8 to 7.SM.13. Chemical effects of CH4 and N2O are included (Section 7.6.1.3). Contributions from stratospheric ozone depletion to halogenated \r\nspecies metrics are not included. Supplementary Table 7.SM.7 presents the full table."


id: cf760f50bf3916d7cb329e78196c697a
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
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content:
"Following AR5, this Report does not recommend an emissions metric because the appropriateness of the choice depends on the \r\npurposes for which gases or forcing agents are being compared. Emissions metrics can facilitate the comparison of effects of emissions \r\nin support of policy goals. They do not define policy goals or targets but can support the evaluation and implementation of choices within \r\nmulti-component policies (e.g., they can help prioritize which emissions to abate). The choice of metric will depend on which aspects of \r\nclimate change are most important to a particular application or stakeholder and over which time horizons. Different international and \r\nnational climate policy goals may lead to different conclusions about what is the most suitable emissions metric (Myhre et al., 2013b)."


id: e61989dcfb3722a1940116adbd5b3e5a
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 33690
content:
"Global warming potentials (GWP) and global temperature-change potentials (GTP) give the relative effect of pulse emissions, that is, how \r\nmuch more energy is trapped (GWP) or how much warmer (GTP) the climate would be when unit emissions of different compounds are \r\ncompared (Section 7.6.1.2). Consequently, these metrics provide information on how much energy accumulation (GWP) or how much \r\nglobal warming (GTP) could be avoided (over a given time period, or at a given future point in time) by avoiding the emission of a unit of \r\na short-lived greenhouse gas compared to avoiding a unit of CO2. By contrast, the new metric approaches of combined GTP (CGTP) and \r\nGWP* closely approximate the additional effect on climate from a time series of short-lived GHG emissions, and can be used to compare \r\nthis to the effect on temperature from the emission or removal of a unit of CO2 (Section 7.6.1.4; Allen et al., 2018b; Collins et al., 2020)."


id: 4050e647e3ae75d9e331fed40483ce2
created_at: 2023-03-16 14:00:45
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content:
"If global surface temperature stabilization goals are considered, cumulative CO2 equivalent emissions computed with the GWP-100 \r\nemissions metric would continue to rise when short-lived GHG emissions are reduced but remain above zero (Figure 7.22b). Such \r\na  rise would not match the expected global surface temperature stabilization or potential decline in warming that comes from \r\na reduction in emissions of short-lived greenhouse gases (Pierrehumbert, 2014; Allen et al., 2018b; Cain et al., 2019; Collins et al., \r\n2020; Lynch et al., 2020, 2021). This is relevant to net zero GHG emissions goals (Section 7.6.2 and Box 1.4)."


id: 4d5f57bfc1c1dc3e1b0174e1e0a98cf
created_at: 2023-03-16 14:00:45
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vector_id: 9605
content:
"Although there is significant history of using single-basket approaches, supported by emissions metrics such as GWP-100, in climate \r\npolicies such as the Kyoto Protocol, multi-basket approaches also have many precedents in environmental management, including the \r\nMontreal Protocol (Daniel et al., 2012). Further assessment of the performance of physical and economics-based metrics in the context \r\nof climate change mitigation is provided in the contribution of Working Group III to AR6."


id: 8753ca1330df46dd107e06fa4aca9b39
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
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index: climate_gpt_only_giec
vector_id: 18833
content:
"The Earth's energy budget describes the flow of energy within the climate system. Since at least 1970 there \r\nhas been a  persistent imbalance in the energy flows that has led to excess energy being absorbed by the \r\nclimate system. By measuring and understanding these energy flows and the role that human activities play in \r\nchanging them, we are better able to understand the causes of climate change and project future climate change \r\nmore accurately."


id: 422095a6a3e72001fb18e74f17bcd8b6
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
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content:
"Our planet receives vast amounts of energy every day in the form of sunlight. Around a third of the sunlight is \r\nreflected back to space by clouds, by tiny particles called aerosols, and by bright surfaces such as snow and ice. \r\nThe rest is absorbed by the ocean, land, ice and atmosphere. The planet then emits energy back out to space in \r\nthe form of thermal radiation. In a world that was not warming or cooling, these energy flows would balance. \r\nHuman activity has caused an imbalance in these energy flows."


id: c38e18e3a6ab1ecdb39ffd9e92cb03f0
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
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vector_id: 28339
content:
"Human activities have unbalanced these energy flows in two main ways. First, increases in greenhouse gas levels \r\nhave led to more of the emitted thermal radiation being absorbed by the atmosphere, instead of being released to \r\nspace. Second, increases in pollutants have increased the amount of aerosols such as sulphates in the atmosphere \r\n(see FAQ 6.1). This has led to more incoming sunlight being reflected away, by the aerosols themselves and \r\nthrough the formation of more cloud drops, which increases the reflectivity of clouds (see FAQ 7.2)."


id: 869c9948074581f71851702a499e3560
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
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vector_id: 18715
content:
"Altogether, the global energy flow imbalance since the 1970s has been just over half a watt per square metre \r\nof the Earth's surface. This sounds small, but because the imbalance is persistent and because Earth's surface is \r\nlarge, this adds up to about 25 times the total amount of primary energy consumed by human society, compared \r\nover 1971 to 2018. Compared to the IPCC Fifth Assessment Report (AR5), we are now better able to quantify \r\nand track these energy flows from multiple lines of evidence, including satellite data, direct measurements of \r\nocean temperatures, and a wide variety of other Earth system observations (see FAQ 1.1). We also have a better \r\nunderstanding of the processes contributing to this imbalance, including the complex interactions between \r\naerosols, clouds and radiation."


id: d1c50ba9668fd1fd74c9c7c490ba014e
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
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vector_id: 30533
content:
"Research has shown that the excess energy since the 1970s has mainly gone into warming the ocean (91%), \r\nfollowed by the warming of land (5%) and the melting of ice sheets and glaciers (3%). The atmosphere has \r\nwarmed substantially since 1970, but because it is comprised of thin gases it has absorbed only 1% of the excess \r\nenergy (FAQ 7.1, Figure 1). As the ocean has absorbed the vast majority of the excess energy, especially within \r\nits top two kilometres, the deep ocean is expected to continue to warm and expand for centuries to millennia, \r\nleading to long-term sea level rise - even if atmospheric greenhouse gas levels were to decline (see FAQ 5.3). \r\nThis is in addition to the sea level rise expected from melting ice sheets and glaciers."


id: 6035f96b90e382edf7c10fbecf58606
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 12708
content:
"Understanding the Earth's energy budget  al.o helps to narrow uncertainty in future projections of climate. \r\nBy testing climate models against what we know about the Earth's energy budget, we can make more confident \r\nprojections of surface temperature changes we might expect this century and beyond."


id: eb604db4a21bb6800b96e3547e7c855a
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 34527
content:
"Since at least 1970, there has been a persistent imbalance in the energy flows that has \r\nled to excess energy being absorbed by different components of the climate system."


id: 3b994a6d5d6af1524a1d2a83797c1119
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 6730
content:
"FAQ 7.1, Figure 1 | The Earth's energy budget compares the flows of incoming and outgoing energy that are relevant for the climate \r\nsystem. Since at least the 1970s, less energy is flowing out than is flowing in, which leads to excess energy being absorbed by the ocean, land, ice and \r\natmosphere, with the ocean absorbing 91%."


id: a662e9a07a6bf3fcd0a8605ea5759b76
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 23710
content:
"One of the biggest challenges in climate science has been to predict how clouds will change in a warming world \r\nand whether those changes will amplify or partially offset the warming caused by increasing concentrations of \r\ngreenhouse gases and other human activities. Scientists have made significant progress over the past decade and \r\nare now more confident that changes in clouds will amplify, rather than offset, global warming in the future."


id: c7fc7a8d51074b1cd750df380a0a38fe
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 29041
content:
"The concentration of aerosols in the atmosphere has markedly increased since the pre-industrial era, and this \r\nhas had two important effects on clouds. First, clouds now reflect more incoming energy because cloud droplets \r\nhave become more numerous and smaller. Second, smaller droplets may delay rain formation, thereby making \r\nthe clouds last longer, although this effect remains uncertain. Hence, aerosols released by human activities have \r\nhad a cooling effect, counteracting a considerable portion of the warming caused by increases in greenhouse \r\ngases over the last century (see FAQ 3.1). Nevertheless, this cooling effect is expected to diminish in the future, as \r\nair pollution policies progress worldwide, reducing the amount of aerosols released into the atmosphere."


id: 931f1f0d04e0a0fa8cf3a413836d9f0a
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 20663
content:
"Since the pre-industrial period, the Earth's surface and atmosphere have warmed, altering the properties of \r\nclouds, such as their altitude, amount and composition (water or ice), thereby affecting the Earth's energy budget \r\nand, in turn, changing temperature. This cascading effect of clouds, known as the cloud feedback, could either \r\namplify or offset some of the future warming and has long been the biggest source of uncertainty in climate \r\nprojections. The problem stems from the fact that clouds can change in many ways and that their processes occur \r\non much smaller scales than global climate models can explicitly represent. As a result, global climate models \r\nhave disagreed on how clouds, particularly over the subtropical ocean, will change in the future and whether the \r\nchange will amplify or suppress the global warming."


id: bac5851355bbd0972ce9716a9514f38c
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 26926
content:
"In summary, clouds will amplify rather than suppress the warming of the climate system in the future, as more \r\ngreenhouse gases and fewer aerosols are released to the atmosphere by human activities."


id: 6c5c28dede993e5b0c65bed72429f39
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 14672
content:
"FAQ 7.2, Figure 1 | Interactions between clouds and the climate, today and in a warmer future. Global warming is expected to alter the altitude \r\n(left) and the amount (centre) of clouds, which will amplify warming. On the other hand, cloud composition will change (right), offsetting some of the \r\nwarming. Overall, clouds are expected to amplify future warming."


id: b34b2c4758f5229dd2a6c89c8aebfd74
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 25722
content:
"For a given future scenario, climate models project a range of changes in global surface temperature. This range \r\nis closely related to equilibrium climate sensitivity, or ECS, which measures how climate models respond to \r\na doubling of carbon dioxide in the atmosphere. Models with high climate sensitivity project stronger future \r\nwarming. Some climate models of the new generation are more sensitive than the range assessed in the IPCC \r\nSixth Assessment Report. This leads to end-of-century global warming in some simulations of up to 2degC-3degC \r\nabove the current IPCC best estimate. Although these higher warming levels are not expected to occur, high-ECS \r\nmodels are useful for exploring low-likelihood, high-impact futures."


id: d758857793a86348eb770bb71f6c0e5a
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 31398
content:
"The equilibrium climate sensitivity (ECS) is defined as the long-term global warming caused by a doubling of \r\ncarbon dioxide above its pre-industrial concentration. For a given emissions scenario, much of the uncertainty in \r\nprojections of future warming can be explained by the uncertainty in ECS (FAQ 7.3, Figure 1). The significance \r\nof equilibrium climate sensitivity has long been recognized, and the first estimate was presented by Swedish \r\nscientist Svante Arrhenius in 1896."


id: 5d27e1ce5e853be4bafbd190effe0ce0
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
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vector_id: 12173
content:
"This Sixth Assessment Report concludes that there is a 90% or more chance (very likely) that the ECS is between \r\n2degC and 5degC. This represents a significant reduction in uncertainty compared to the Fifth Assessment Report, \r\nwhich gave a 66% chance (likely) of ECS being between 1.5degC and 4.5degC. This reduction in uncertainty has been \r\npossible not through a single breakthrough or discovery but instead by combining evidence from many different \r\nsources and by better understanding their strengths and weaknesses."


id: b8931012a692dbcbf2cadfee0ef55bf
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 26565
content:
"ce from ancient climates that had reached equilibrium with greenhouse gas concentrations, such as the \r\n period of the last ice age around 20,000 years ago, or warmer periods further back in time, provide \r\ndata on the ECS of the climate system (see FAQ 1.3)."


id: a73a14e78eb4e3f2a47aeb726b064d49
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 23821
content:
"All four lines of evidence rely, to some extent, on climate models, and interpreting the evidence often benefits \r\nfrom model diversity and spread in modelled climate sensitivity. Furthermore, high-sensitivity models can provide \r\nimportant insights into futures that have a low likelihood of occurring but that could result in large impacts. \r\nBut, unlike in previous assessments, climate models are not considered a line of evidence in their own right in the \r\nIPCC Sixth Assessment Report."


id: c76f92f217d17d71a7a5435280d7bd0c
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 28964
content:
"The ECS of the latest climate models is, on average, higher than that of the previous generation of models \r\nand also higher than this Report's best estimate of 3.0degC. Furthermore, the ECS values in some of the new \r\nmodels are both above and below the 2degC to 5degC very likely range, and although such models cannot be ruled \r\nout as implausible solely based on their ECS, some simulations display climate change that is inconsistent with \r\nthe observed changes when tested with ancient climates. A slight mismatch between models and this Report's \r\nassessment is only natural because this Report's assessment is largely based on observations and an improved \r\nunderstanding of the climate system."


id: 49d8a161c4ba27dc4e67baa1b3b5def0
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 9019
content:
"FAQ 7.3, Figure 1 | Equilibrium climate sensitivity and future warming. (left) Equilibrium climate sensitivities for the current generation (Coupled \r\nModel Intercomparison Project Phase 6, CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. \r\n(right) Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. The thick horizontal lines \r\nrepresent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 \r\nand CMIP6 and the range assessed in AR6."


id: a7b0e77990a60057f2c271338c28a7b4
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 23889
content:
"Richard P. Allan (United Kingdom), Paola A. Arias (Colombia), Mathew Barlow (United States \r\nof America), Ruth Cerezo-Mota (Mexico), Annalisa Cherchi (Italy), ThianY. Gan (Canada/Malaysia), \r\nJoelle Gergis (Australia), Dabang Jiang (China), Asif Khan (Pakistan), Wilfried Pokam Mba \r\n(Cameroon), Daniel Rosenfeld (Israel), Jessica Tierney (United States of America), Olga Zolina \r\n(The Russian Federation/France, The Russian Federation)"


id: 387896e5b3d10c085fb1db31998edd9f
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 6261
content:
"Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, \r\nD.  Jiang, A.  Khan, W.  Pokam Mba, D.  Rosenfeld, J. Tierney, and O.  Zolina, 2021: Water Cycle Changes. In Climate \r\nChange 2021: The Physical Science Basis. Contribution of Working Group I  to the Sixth Assessment Report of the \r\nIntergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, \r\nN. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, \r\nO. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, \r\npp. 1055-1210, doi:10.1017/9781009157896.010."


id: f4f67fdee37363cf79bdb6304d6e02f7
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 36050
content:
"increase plant growth and water-use efficiency, but there is low \r\nconfidence in how these factors drive regional water cycle changes. \r\n{8.2.2, 8.2.3}"


id: 41773332e94ccba89afb9795c6734e59
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 7700
content:
"This chapter assesses multiple lines of evidence to evaluate past, \r\npresent and future changes in the global water cycle. It complements \r\nmaterial in Chapters 2, 3 and 4 on observed and projected changes \r\nin the water cycle, and Chapters 10 and 11 on regional climate \r\nchange and extreme events. The assessment includes the physical \r\nbasis for water cycle changes, observed changes in the water cycle \r\nand attribution of their causes, future projections and related key \r\nuncertainties, and the potential for abrupt change. Paleoclimate \r\nevidence, observations, reanalyses and global and regional model \r\nsimulations are considered. The assessment shows widespread, non\u0002uniform human-caused alterations of the water cycle, which have \r\nbeen obscured by a competition between different drivers across the \r\n20th century and that will be increasingly dominated by greenhouse \r\ngas forcing at the global scale."


id: bb388c37ee2dfa43905ad36c53736c5e
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 26999
content:
"Human-caused climate change has driven detectable changes \r\nin the global water cycle since the mid-20th century (high \r\nconfidence). Global warming has contributed to an overall increase \r\nin atmospheric moisture and precipitation intensity (high confidence), \r\nincreased terrestrial evapotranspiration (medium confidence), \r\ninfluenced global patterns in aridity (very likely), and enhanced \r\ncontrasts in surface salinity and precipitation minus evaporation \r\npatterns over the oceans (high confidence). {3.4.2, 3.4.3, 3.5.2, \r\n8.3.1, 9.2.2}"


id: 1186dbe4c1723019c1ac528e354490a6
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 245
content:
"Greenhouse gas forcing has driven increased contrasts \r\nin precipitation amounts between wet and dry seasons \r\nand weather regimes over tropical land areas (medium \r\nconfidence) and a  detectable precipitation increase in the \r\nnorthern high latitudes (high confidence). Greenhouse gas \r\nforcing has also contributed to drying in dry summer climates, \r\nincluding the Mediterranean, south-western Australia, south-western \r\nSouth America, South Africa, and western North America (medium \r\nto high confidence). Earlier onset of spring snowmelt and increased \r\nmelting of glaciers have already contributed to seasonal changes in \r\nstreamflow in high-latitude and low-elevation mountain catchments \r\n(high confidence). {Box 8.2, 8.2.2.1, 8.3.1, 3.3.2, 3.3.3, 3.5.2}"


id: 912475875a7237f4c7f32acdef52c819
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 20372
content:
"A warmer climate increases moisture transport into weather \r\nsystems, which, on average, makes wet seasons and events \r\nwetter (high confidence). An increase in near-surface atmospheric \r\nwater holding capacity of about 7%  per  1degC of warming explains \r\na  similar magnitude of intensification of heavy precipitation events \r\n(from sub-daily up to seasonal time scales) that increases the severity \r\nof flood hazards when these extremes occur (high confidence). \r\nThe severity of very wet and very dry events increases in a warming \r\nclimate (high confidence), but changes in atmospheric circulation \r\npatterns alter where and how often these extremes occur, with \r\nsubstantial regional differences and seasonal contrasts. A slowdown \r\nof tropical circulation with global warming partly offsets the warming\u0002induced strengthening of precipitation in monsoon regions (high \r\nconfidence). {8.2.2, 8.2.3, 8.3.1.7, 8.4.1, 8.5.1}"


id: 8ecec104a9863fd535e6fb9bf144767
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 19993
content:
"Land-use change and water extraction for irrigation have \r\ninfluenced local and regional responses in the water cycle \r\n(high confidence). Large-scale deforestation has likely decreased \r\nevapotranspiration and precipitation and increased runoff over the \r\ndeforested regions. Urbanization has increased local precipitation \r\n(medium confidence) and resulting runoff intensity (high confidence). \r\nIncreased precipitation intensities have enhanced groundwater \r\nrecharge, most notably in tropical regions (medium confidence). \r\nThere is high confidence that groundwater depletion has occurred \r\nsince at least the start of the 21st century as a  consequence of "


id: dfb6a0fc69bbed28b83bd6ca6667d71b
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 32696
content:
"Warming over land drives an increase in atmospheric \r\nevaporative demand and the severity of droughts (high \r\nconfidence). Greater warming over land than over the ocean alters \r\natmospheric circulation patterns and, on average, reduces continental \r\nnear-surface relative humidity, which contributes to regional drying \r\n(high confidence). Increasing atmospheric CO2 concentrations "


id: ae8ad8b9507c5d3e22b0b8c31bf814
created_at: 2023-03-16 14:00:45
updated_at: 2023-03-16 14:00:45
content_type: text
index: climate_gpt_only_giec
vector_id: 24992
content:
"groundwater withdrawals for irrigation in agricultural areas in \r\ndrylands (e.g., the southern High Plains and California Central Valley \r\nof the USA, North China Plain, and north-west India). {8.2.3.4, \r\n8.3.1.7, Box 10.3, FAQ 8.1}"


