# document

id: ee05743016b149fd1b5477ca9a133ff1
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39252
content:
"if CO2 release equals removal, the atmospheric CO2 concentration would stabilize; and if CO2 removal exceeds \r\nrelease, the CO2 concentration would decline. This applies in the same way to net CO2 emissions - that is, the sum \r\nof human-caused releases and deliberate removals."


id: ac0d1a3745f7a0dcd529298280085c62
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27508
content:
"'Overshoot' scenarios are a  class of future scenarios that are receiving increasing attention, particularly in \r\nthe context of ambitious climate goals, such as the global warming limits of 1.5degC or 2degC included in the Paris \r\nAgreement. In these scenarios, a slow rate of reduction in emissions in the near term is compensated by net \r\nnegative CO2 emissions in the later part of this century, which results in a temporary breach or 'overshoot' of \r\na given warming level. Due to the delayed reaction of several climate system components, it follows that the \r\ntemporary overshoot would result in additional climate changes compared to a scenario that reaches the goal \r\nwithout overshoot. These changes would take decades to many centuries to reverse, with the reversal taking \r\nlonger for scenarios with larger overshoot."


id: 952a1c8a55e1fe17e53bc07727db0a2b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23447
content:
"Removing more CO2 from the atmosphere than is emitted into it would indeed begin to reverse some aspects \r\nof climate change, but some changes would still continue in their current direction for decades to millennia. \r\nApproaches capable of large-scale removal of CO2 are still in the state of research and development or unproven \r\nat the scales of deployment necessary to achieve a  net reduction in atmospheric CO2 levels. CO2 removal \r\napproaches, particularly those deployed on land, can have undesired side effects on water, food production \r\nand biodiversity."


id: 49cb41f3ca12ec1dcc7952ddbc258faf
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10216
content:
"There are several types of carbon budgets. Most often, the term refers to the total net amount of carbon dioxide \r\n(CO2) that can still be emitted by human activities while limiting global warming to a specified level (e.g., 1.5degC \r\nor 2degC above pre-industrial levels). This is referred to as the 'remaining carbon budget'. Several choices and value \r\njudgements have to be made before it can be unambiguously estimated. When the remaining carbon budget is \r\ncombined with all past CO2 emissions to date, a 'total carbon budget' compatible with a specific global warming \r\nlimit can also be defined. A third type of carbon budget is the 'historical carbon budget', which is a scientific way \r\nto describe all past and present sources and sinks of CO2."


id: 744d513a03656d41139b8cfbba4cfbfa
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17695
content:
"The term remaining carbon budget is used to describe the total net amount of CO2 that human activities can \r\nstill release into the atmosphere while keeping global warming to a specified level, like 1.5degC or 2degC relative \r\nto pre-industrial temperatures. Emissions of CO2 from human activities are the main cause of global warming. \r\nA remaining carbon budget can be defined because of the specific way CO2 behaves in the Earth system. That \r\nis, global warming is roughly linearly proportional to the total net amount of CO2 emissions that are released \r\ninto the atmosphere by human activities - also referred to as cumulative anthropogenic CO2 emissions. Other \r\ngreenhouse gases behave differently and have to be accounted for separately."


id: f94091c8d99e926e7742f4518d6b5f6
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 41214
content:
"In summary, determining a remaining carbon budget - that is, how much CO2 can be released into the atmosphere \r\nwhile stabilizing global temperature below a chosen level - is well understood but relies on a set of choices. \r\nHowever, it is clear that, for limiting warming below 1.5degC or 2degC, the remaining carbon budget from 2020 \r\nonwards is much smaller than the total CO2 emissions released to date."


id: a612e7de037abdf342aae83794075204
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26438
content:
"The term carbon budget is used in several ways. Most often the term refers to the total net amount of carbon dioxide \r\n(CO2) that can still be emitted by human activities while limiting global warming to a specified level. "


id: 7368b2ec58ba208b25660e9f9de498e3
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17560
content:
"FAQ 5.4, Figure 1 | Various types of carbon budgets. Historical cumulative carbon dioxide (CO2) emissions determine to a large degree how much the \r\nworld has warmed to date, while the remaining carbon budget indicates how much CO2 could still be emitted while keeping warming below specific temperature \r\nthresholds. Several factors limit the precision with which the remaining carbon budget can be estimated. Therefore, estimates need to specify the probability with \r\nwhich they aim at limiting warming to the intended target level (e.g., limiting warming to 1.5degC with a 67% probability)."


id: 70aa32f1027f946ce141c622f0a20daa
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17081
content:
"Bhupesh Adhikary (Nepal), Paulo Artaxo (Brazil), Terje Berntsen (Norway), William D. Collins \r\n(United States of America), Sandro Fuzzi (Italy), Laura Gallardo (Chile), Astrid Kiendler-Scharr \r\n(Germany/Austria), Zbigniew Klimont (Austria/Poland), Hong Liao (China), Nadine Unger \r\n(United Kingdom/United States of America), Prodromos Zanis (Greece)"


id: c385d809f91b410e1e143a8a2e95d60f
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31677
content:
"Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, \r\nH. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. \r\nContribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change \r\n[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, \r\nK. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge \r\nUniversity Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817-922, doi:10.1017/9781009157896.008."


id: 90c032e5ef93a114cf9c58a46392f0fe
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22672
content:
"OH cannot be measured directly and is inferred from Earth system and \r\nchemistry-climate models (ESMs, CCMs) constrained by emissions \r\nand from observationally constrained inversion methods. There is \r\nconflicting information from these methods for the 1980-2014 \r\nperiod. ESMs and CCMs concur on a positive trend since 1980 (about \r\na  9% increase over 1980-2014) and there is medium confidence\r\nthat this trend is mainly driven by increases in global anthropogenic \r\n(human-caused) nitrogen oxide (NOx) emissions and decreases in \r\nanthropogenic CO emissions. The observation-constrained methods \r\nsuggest either positive trends or the absence of trends based on \r\nlimited evidence and medium agreement. Future changes in global \r\nOH, in response to SLCF emissions and climate change, will depend \r\non the interplay between multiple offsetting drivers of OH. {6.3.6 and \r\nCross-Chapter Box 5.1}"


id: d4f473b7afa058c0f004b757223d56fa
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34786
content:
"Over the historical period, changes in aerosols and their \r\neffective radiative forcing (ERF) have primarily contributed to \r\na surface cooling, partly masking the greenhouse gas-driven \r\nwarming (high confidence). Radiative forcings induced by aerosol \r\nchanges lead to both local and remote temperature responses (high \r\nconfidence). The temperature response preserves the south to north \r\ngradient of the aerosol ERF - hemispherical asymmetry - but is more \r\nuniform with latitude and is strongly amplified towards the Arctic \r\n(medium confidence). {6.4.1, 6.4.3}"


id: 62ea9d9a2958a0c949c796311678b045
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14703
content:
"Since the mid-1970s, trends in aerosols and their precursor \r\nemissions have led to a shift from an increase to a decrease of \r\nthe magnitude of the negative globally averaged net aerosol \r\nERF (high confidence). However, the timing of this shift varies by \r\ncontinental-scale region and has not occured for some finer regional \r\nscales. The spatial and temporal distribution of the net aerosol ERF \r\nfrom 1850 to 2014 is highly heterogeneous, with stronger magnitudes \r\nin the Northern Hemisphere (high confidence). {6.4.1}"


id: 5fe8ed115e2348459d019a97384ce9d7
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14187
content:
"For forcers with short lifetimes (e.g.,  months) and not \r\nconsidering chemical adjustments, the response in surface \r\ntemperature occurs strongly as soon as a  sustained change \r\nin emissions is implemented, and that response continues to \r\ngrow for a few years, primarily due to thermal inertia in the \r\nclimate system (high confidence). Near its maximum, the response \r\nslows down but will then take centuries to reach equilibrium (high \r\nconfidence). For SLCFs with longer lifetimes (e.g., a decade), a delay \r\nequivalent to their lifetimes is appended to the delay due to thermal \r\ninertia (high confidence). {6.6.1}"


id: 4f4cc3c91834b3d66b0727b9b6fa5536
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 11235
content:
"Over the 1750-2019 period, changes in SLCF emissions, \r\nespecially of methane, NOx and SO2, have substantial effects \r\non effective radiative forcing (ERF) (high confidence). The \r\nnet global emissions-based ERF of NOx is negative and that of \r\nnon-methane volatile organic compounds (NMVOCs) is positive, in \r\nagreement with the AR5 Assessment (high confidence). For methane, \r\nthe emissions-based ERF is twice as high as the abundance-based ERF \r\n(high confidence) attributed to chemical adjustment mainly via ozone \r\nproduction. SO2 emissions changes make the dominant contribution "


id: e498a44294c45dedc3d028086c01d33c
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37503
content:
"There is no significant trend in the global mean tropospheric \r\nconcentration of hydroxyl (OH) radical - the main sink for many \r\nSLCFs, including methane (CH4) - from 1850 up to around 1980 \r\n(low confidence) but OH has remained stable or exhibited \r\na positive trend since the 1980s (medium confidence). Global "


id: d9cd903edad6f9a9b551718551296ff0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 35616
content:
"to the ERF from aerosol-cloud interactions (high confidence).\r\nOver the 1750-2019 period, the contributions from the emitted \r\ncompounds to changes in global surface air temperature (GSAT) \r\nbroadly match their contributions to the ERF (high confidence). Since \r\na peak in emissions-induced SO2 ERF has already occurred recently \r\nand since there is a delay in the full GSAT response, changes in SO2\r\nemissions have a slightly larger contribution to GSAT change than \r\nCO2 emissions, relative to their respective contributions to ERF. {6.4.2, \r\n6.6.1 and 7.3.5} "


id: b5934a6bb3bb4001e1c2938d48884021
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29174
content:
"organic compounds, wildfire-emitted precursors, land and marine \r\naerosols, and lightning NOx) to climate change. {6.3, 6.5}"


id: f3a92ee326376b18b6b145bd61357eae
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40241
content:
"The SSPs span a  wider range of SLCF emissions than the \r\nRepresentative Concentration Pathways (RCPs), representing \r\nbetter the diversity of future options in air pollution \r\nmanagement (high confidence). In the SSPs, the socio-economic \r\nassumptions and climate change mitigation levels primarily drive \r\nfuture emissions, but the SLCF emissions trajectories are also \r\nsteered by varying levels of air pollution control originating from \r\nthe SSP narratives, independently from climate change mitigation.\r\nConsequently, SSPs consider a  large variety of regional ambitions \r\nand effectiveness in implementing air pollution legislation and result \r\nin wider range of future air pollution levels and SLCF-induced climate \r\neffects. {6.7.1}"


id: 421b5ca35ed704c8ec3114c1cba3c617
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8827
content:
"Reactive nitrogen, ozone and aerosols affect terrestrial \r\nvegetation and the carbon cycle through deposition and \r\neffects on large-scale radiation (high confidence). However, \r\nthe magnitude of these effects on the land carbon sink, ecosystem \r\nproductivity and hence their indirect CO2 forcing remain uncertain \r\ndue to the difficulty in disentangling the complex interactions \r\nbetween the individual effects. As such, these effects are assessed \r\nto be of second order in comparison to the direct CO2 forcing (high \r\nconfidence), but effects of ozone on terrestrial vegetation could \r\nadd a substantial (positive) forcing compared with the direct ozone \r\nforcing (low confidence). {6.4.4}"


id: e264e99e41fba16f12cdb4a482907057
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37121
content:
"Air pollution projections range from strong reductions in global \r\nsurface ozone and PM (e.g., SSP1-2.6, with strong mitigation \r\nof both air pollution and climate change) to no improvement \r\nand even degradation (e.g., SSP3-7.0 without climate change \r\nmitigation and with only weak air pollution control) (high \r\nconfidence). Under the SSP3-7.0 scenario, PM levels are projected \r\nto increase until 2050 over large parts of Asia, and surface ozone \r\npollution is projected to worsen over all continental areas through \r\n2100 (high confidence). Without climate change mitigation but with \r\nstringent air pollution control (SSP5-8.5), PM levels decline through \r\n2100, but high methane levels hamper the decline in global surface \r\nozone at least until 2080 (high confidence). {6.7.1}"


id: 1365d3e2f3fdd5622487a2a5a85f7ecd
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 620
content:
"Climate feedbacks induced from changes in emissions, \r\nabundances or lifetimes of SLCFs mediated by natural \r\nprocesses or atmospheric chemistry are assessed to have an \r\noverall cooling effect (low confidence), that is, a total negative \r\nfeedback parameter of -0.20 [-0.41 to +0.01] W m-2 degC-1. These \r\nnon-CO2 biogeochemical feedbacks are estimated from ESMs, which \r\nhave advanced since AR5 to include a consistent representation of \r\nbiogeochemical cycles and atmospheric chemistry. However, process\u0002level understanding of many chemical and biogeochemical feedbacks \r\ninvolving SLCFs, particularly natural emissions, is still emerging, \r\nresulting in low confidence in the magnitude and sign of most SLCF \r\nclimate feedback parameters. {6.2.2, 6.4.5} "


id: a03d40e45737e94c82e9a9977729bc6e
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25403
content:
"At present, emissions from the residential and commercial \r\nsectors (fossil and biofuel use for cooking and heating) and \r\nthe energy sector (fossil fuel production, distribution and \r\ncombustion) contribute the most to the world population's \r\nexposure to anthropogenic fine PM (high confidence), \r\nwhereas emissions from the energy and land transportation \r\nsectors contribute the most to ozone exposure (medium to\r\nhigh confidence). The contribution of different sectors to PM varies \r\nacross regions, with the residential sector being the most important \r\nin Southern Asia and Africa, agricultural emissions dominating in \r\nEurope and North America, and industry and energy production \r\ndominating in Central and Eastern Asia, Latin America and the \r\nMiddle East. Energy and industry are important PM2.5 contributors in \r\nmost regions, except Africa (high confidence). Sector contributions to \r\nsurface ozone concentrations are similar for all regions. {6.6.2}"


id: 26782f984d44fe5a7c4e9197eb043cc2
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3935
content:
"and span a  range from very high to very low emissions. In \r\nthe scenarios without climate change mitigation (SSP3-7.0 and \r\nSSP5-8.5) the likely range of the estimated warming due to SLCFs \r\nin 2100 relative to 2019 is 0.4degC-0.9degC {6.7.3, 6.7.4}. In SSP3-7.0 \r\nthere is a near-linear warming due to SLCFs of 0.08degC per decade, \r\nwhile for SSP5-8.5 there is a  more rapid warming in the first half \r\nof the century. For the scenarios considering the most stringent \r\nclimate and air pollution mitigations (SSP1-1.9 and SSP1-2.6), the \r\nreduced warming from reductions in methane, ozone and HFCs partly \r\nbalances the warming from reduced aerosols, and the overall SLCF \r\neffect is a likely increase in GSAT of 0.0degC-0.3degC in 2100, relative \r\nto 2019. The SSP2-4.5 scenario (with moderate climate change and \r\nair pollution mitigations) results in a likely warming of 0.2degC-0.5degC \r\nin 2100 due to SLCFs, with the largest warming from reductions in \r\naerosols. {6.7.3}"


id: 8993b7f79c9f9d7de27f05f75f4474fa
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21379
content:
"Assuming implementation and efficient enforcement of both \r\nthe Kigali Amendment to the Montreal Protocol on Ozone \r\nDepleting Substances and current national plans to limit \r\nemissions (as in SSP1-2.6), the effects of HFCs on GSAT, relative \r\nto 2019, would remain below +0.02degC from 2050 onwards \r\nversus about +0.04degC to +0.08degC in 2050 and +0.1degC to +0.3degC \r\nin 2100 considering only national HFC regulations decided \r\nprior to the Kigali Amendment (as in SSP5-8.5) (medium \r\nconfidence). Further improvements in the efficiency of refrigeration \r\nand air-conditioning equipment during the transition to low-global\u0002warming-potential refrigerants would bring additional greenhouse \r\ngas reductions (medium confidence) resulting in benefits for climate \r\nchange mitigation and to a lesser extent for air quality due to reduced \r\nair pollutant emissions from power plants. {6.6.3, 6.7.3} "


id: 5609b443dc71e0f43a9de30d7c920603
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12408
content:
"ammonium (NH+\r\n4), carbonaceous aerosols (e.g.,  black carbon (BC), \r\norganic aerosols (OA)), mineral dust, and sea spray (see Table 6.1) \r\nand can be present as internal or external mixtures and at sizes from \r\nnano-meters to tens of micro-meters. SLCFs can be emitted directly \r\nfrom natural systems and anthropogenic sources (primary) or can be \r\nformed by reactions in the atmosphere (secondary; Figure 6.1). "


id: 67f5ca869585ab5377b1828d020cc4d
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 15559
content:
"Short-lived climate forcers (SLCFs) are a  set of chemically and \r\nphysically reactive compounds with atmospheric lifetimes typically \r\nshorter than two decades but differing in terms of physiochemical \r\nproperties and environmental effects. SLCFs can be classified as \r\ndirect or indirect, with direct SLCFs exerting climate effects through \r\ntheir radiative forcing and indirect SLCFs being precursors of direct \r\nclimate forcers. Direct SLCFs include methane (CH4), ozone (O3), short\u0002lived halogenated compounds, such as hydrofluorocarbons (HFCs), \r\nhydrochlorofluorocarbons (HCFCs), and aerosols. Indirect SLCFs \r\ninclude nitrogen oxides (NOx), carbon monoxide (CO), non-methane \r\nvolatile organic compounds (NMVOCs), sulphur dioxide (SO2), and \r\nammonia (NH3). Aerosols consist of sulphate (SO2\r\n4\r\n-\r\n), nitrate (NO- ), 3"


id: 9562afa3195ca33edae8151e1669ef1
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23483
content:
"greenhouse gases (GHGs) in Chapters 2, 5, and 7). In contrast to well\u0002mixed GHGs, such as CO2, methane and some HFCs, the radiative \r\nforcing effects of most SLCFs are largest at regional scales and \r\nclimate effects predominantly occur in the first two decades after "


id: b39393aabf93c2e64c8b100f300d95d7
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 28834
content:
"their emissions or formation. However, changes in their emissions \r\ncan also induce long-term climate effects, for instance by altering \r\nsome biogeochemical cycles. Therefore, the temporal evolution of \r\nradiative effects of SLCFs follows that of emissions, that is, when SLCF "


id: 6dd64a36d6f65d60e1eecbc85f9df6f8
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16587
content:
"Table 6.1 | Overview of SLCFs of interest for Chapter 6. For each SLCF, its source types, lifetime in the atmosphere, and associated radiatively active agent is given. Source \r\ntype can be primary (emitted) and/or secondary (formed through multiple atmospheric mechanisms). Unless otherwise noted, the stated lifetime refers to tropospheric lifetime.* \r\nClimate effect of increased SLCFs is indicated as '+' for warming and '-' for cooling. 'Direct' is used for SLCFs exerting climate effects through their radiative forcing and \r\n'Indirect' for SLCFs which are precursors affecting the atmospheric burden of other climatically active compounds. Other processes through which SLCFs affect climate are listed \r\nwhere applicable. The World Health Organization (WHO) guidelines for air quality (AQ) are given, where applicable, to show which SLCFs are regulated for air-quality purposes. "


id: d9bf66f040a831728b2e747dbae5f602
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 35607
content:
"* For lifetimes reported in this table, it is assumed that the compounds are uniformly mixed throughout the troposphere, however, this assumption is unlikely for compounds \r\nwith lifetimes <1 year and, therefore, the reported values should be viewed as approximations (Prather et al., 2001). "


id: 99e6f99906573890f65349f12fc2d9b4
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24306
content:
" a Clouds: effect on clouds through aerosol-cloud interactions, Ecosystems: effect on ecosystems through changes in radiation and deposition, Cryo: effect on planetary albedo \r\nthrough deposition on snow and ice; b Krzyzanowski and Cohen (2008); c regulated through Kyoto/Montreal protocols; d For Particulate Matter with diameter <2.5 um (PM2.5): \r\n10 ug m-3 annual mean or 25 ug m-3 24-hour mean (99th percentile) and for Particulate Matter with diameter <10 um (PM10): 20 ug m-3 annual mean or 50 ug m-3 24-hour \r\nmean (99th percentile)."


id: 8cb54bbf63272bcc779596b968df64ae
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21941
content:
"emissions decline to zero their atmospheric abundance and radiative \r\neffects decline towards zero. The total influence of individual SLCF \r\nemissions on radiative forcing and climate includes their effects \r\non the abundances of other forcers through chemistry (chemical \r\nadjustments)."


id: 15566276cb6316955b71dd1be9c8d9c0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 968
content:
"remained of very low to low confidence. The AR5 also reported that \r\nforcing agents such as aerosols and ozone are highly heterogeneous \r\nspatially and temporally, and these patterns affect global and \r\nregional temperature responses as well as other aspects of climate \r\nresponse such as the hydrologic cycle (Myhre et al., 2013)."


id: bcfc1b2b25fd8524b97a69db5299c347
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30493
content:
"SLCFs can affect climate by interacting with radiation or by \r\nperturbing other components of the climate system (e.g.,  the \r\ncryosphere and carbon cycle through deposition, or the water cycle \r\nthrough modifications of cloud properties via cloud condensation \r\nnuclei or ice nuclei). SLCFs can have either net warming or net \r\ncooling effects on climate. In addition to altering the Earth's radiative \r\nbalance, many SLCFs are also air pollutants with adverse effects \r\non human health and ecosystems. SLCFs are of interest for climate \r\npolicies (e.g.,  methane, HFCs), and are regulated as air pollutants \r\n(e.g., aerosols, ozone) or because of their deleterious influence on \r\nstratospheric ozone (e.g., HCFCs). The list of SLCFs assessed in this \r\nchapter and their effects are provided in Table 6.1. "


id: 59c691f77302171e765ef3578169ad12
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 13034
content:
"The AR5 WGI also evaluated the air quality-climate interaction \r\nthrough the projected trends of surface ozone and PM2.5. Kirtman \r\net al. (2013) concluded with high confidence that the response of air \r\nquality to climate-driven changes is more uncertain than the response \r\nto emissions-driven changes, and also that locally higher surface \r\ntemperatures in polluted regions will trigger regional feedbacks in \r\nchemistry and local emissions that will increase peak levels of ozone \r\nand PM2.5 (medium confidence)."


id: 794127433842a47f3219b0e47daf263a
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18528
content:
"Although ozone, aerosols and their precursors have been considered \r\nin previous IPCC assessment reports, AR5 considered SLCFs as \r\na  specific category of climate-relevant compounds but referred \r\nto them as near-term climate forcers (NTCFs; Myhre et al., 2013). \r\nIn AR5, the linkages between air quality and climate change were \r\nalso considered in a  more detailed and quantitative way than in \r\nprevious reports (Kirtman et al., 2013; Myhre et al., 2013)."


id: 54460a147b19551da44b9a9e19833c59
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12102
content:
"The AR5 WGI assessed radiative forcings for short-lived gases, \r\naerosols, aerosol precursors and aerosol-cloud interactions as well \r\nas the evolution of confidence levels in the forcing mechanisms from \r\nSAR to AR5. Whereas the forcing mechanisms for ozone and aerosol-\r\nradiation interactions were estimated to be characterized with high \r\nconfidence, the ones induced by aerosols through other processes "


id: d0fcd1bb02a941cbf6fd34c3c977622d
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34117
content:
"and other light-absorbing particles enhances snowmelt in the Arctic \r\n(Meredith et al., 2019), but that there is limited evidence and low \r\nagreement that long-term changes in glacier mass of high mountain \r\nareas are linked to light-absorbing particles (Hock et al., 2019)."


id: 631930a9134a7a2abc44b92049376aa2
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14728
content:
"Specific aspects of SLCFs can also be found in other chapters of this \r\nreport: the evolution of ozone, HFCs and aerosols, as well as the long\u0002term evolution of methane, dust and volcanic aerosols are discussed\r\nin Chapter  2; near-term climate projections and SLCFs are \r\ndiscussed in Chapter 4; the global budget of methane is addressed in \r\nChapter 5; aerosol-cloud and aerosol-precipitation interactions are \r\ntreated in Chapters 7 and 8, respectively; the global radiative forcing \r\nof SLCFs is assessed in Chapter  7; some aspects of downscaling \r\nmethodology in climate modelling concerning SLCFs are discussed \r\nin Chapter 10. The WGII report assesses how climate change affects \r\nair pollution and its impacts on human health and the WGIII report \r\nassesses the role of SLCFs in abatement strategies and their cost\u0002effectiveness, the implications of mitigation efforts on air pollution \r\nas well as the articulation between air pollution policies and \r\nGHG mitigation. "


id: 9fdc0f6d6e78f1a3f0b9034458afcc66
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25334
content:
"Due to their short lifetimes, SLCF trends and effects are strongly \r\nrelated to the localization and evolution of the emissions sources. \r\nTo better link the drivers of emissions evolution and SLCFs, Chapter 6 \r\nmakes use of regions defined by the WGIII in most of the analysis. \r\nAn exception is made for the effect of SLCFs on the climate, for which \r\nanalysis relies on WGI Atlas regions."


id: 37dcb43d097ef2e62e1d2979df8e827f
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7037
content:
"SLCF emissions originate from a  variety of sources driven by \r\nanthropogenic activities and natural processes. The natural sources \r\ninclude vegetation, soil, fire, lightning, volcanoes and oceans. \r\nChanges in SLCF emissions from natural systems occur either due \r\nto human activities, such as land-use change, or due to global \r\nchanges. Their sensitivity to climate change thus induces climate \r\nfeedbacks (see Section 6.4.5 for a quantification of these feedbacks). \r\nThis section reviews the current understanding of historical emissions \r\nfor anthropogenic, natural, and open biomass burning sources. \r\nA detailed discussion of methane sources, sinks, trends are provided \r\nin Chapter 5, Section 5.2.2. "


id: 35255226b9750c5a93b1299011d96fb5
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6591
content:
"For SO2, independent emissions inventories and observational \r\nevidence show that on a  global scale strong growth of Asian \r\nemissions has been countered by reduction in North America and \r\nEurope (Reis et al., 2012; Amann et al., 2013; Crippa et al., 2016; \r\nAas et al., 2019). However, Chinese emissions declined by nearly \r\n70% between about 2006 and 2017 (high confidence) (Silver et al., \r\n2018; Zheng et al., 2018b; Mortier et al., 2020; Tong et al., 2020). \r\nThe estimated reduction in China contrasts with continuing strong \r\ngrowth of SO2 emissions in Southern Asia (Figure 6.19). In 2014, over \r\n80% of anthropogenic SO2 emissions originated from power plants \r\nand industry, with Asian sources contributing more than 50% of the \r\ntotal (Figure 6.3)."


id: 9712d771d7620609a3b3a137395a2dac
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23772
content:
"For most of the SLCF species, the global and regional anthropogenic \r\nemissions trends developed for CMIP6 for the period 1850-2000 are \r\nnot substantially different from those used in CMIP5 (Figures 6.18 \r\nand 6.19) despite the different method used to derive them. Hoesly \r\net al. (2018, CEDS) developed independent time series capturing \r\ntrends in fuel use, technology and level of control, whereas CMIP5 "


id: 2ae87b38748d96e1140062449e52cb8b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 4782
content:
"Bottom-up global emissions estimates of methane (Lamarque \r\net al., 2010; Hoesly et al., 2018; Janssens-Maenhout et al., 2019; \r\nHoglund-Isaksson et al., 2020) for the last two decades are higher \r\nthan top-down assessments (e.g.,  Saunois et al., 2016, 2020) but \r\ntrends from the two methods are similar and indicate continued \r\ngrowth (high confidence). Larger discrepancies exist at the sectoral \r\nand regional levels, notably for coal mining (Peng et al., 2016; \r\nMiller et al., 2019) and the oil and gas sector due to the growth of \r\nunconventional production and higher loss estimates (Section 5.2.2; \r\nFranco et al., 2016; Alvarez et al., 2018; Dalsoren et al., 2018). "


id: 4cf14dfd0072486cdcf64a6d96ae479a
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10807
content:
"To summarize, there are significant differences in spatial and temporal \r\npatterns of SLCF emissions across global regions (Figure 6.18). Until \r\nthe 1950s, the majority of SLCF emissions were associated with \r\nfossil fuel use (SO2, NOx, NMVOCs, CO) and about half of BC and OC \r\noriginated from North America and Europe (Lamarque et al., 2010; \r\nHoesly et al., 2018). Since the 1990s a large redistribution of emissions \r\nwas associated with strong economic growth in Asia and declining \r\nemissions in North America and Europe due to air-quality legislation "


id: e87ddda00ef2c13e684dd6e3164f8077
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38239
content:
"but important uncertainties remain for regionally specific temporal \r\npatterns (Riddick et al., 2016; Liu et al., 2019; Feng et al., 2020; \r\nVira et al., 2020). The continuing increase in global NH3 emissions \r\nis driven primarily by growing livestock and crop production in Asia \r\nwhile emissions in the USA and Europe remain about constant or \r\nhave slightly declined in the last decade (Hoesly et al., 2018). Recent \r\nsatellite and ground observations support trends estimated in CMIP6 \r\ndataset (Section 6.3.3.4). "


id: e97b69ca9e6e07cdd868d48871b26452
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38434
content:
"and the declining capacity of energy-intensive industry; currently more \r\nthan 50% of anthropogenic emissions of each SLCF species (including \r\nmethane and NH3) originates from Asia (Figure  6.3; Amann et al., \r\n2013; Bond et al., 2013; Fiore et al., 2015; Crippa et al., 2016, 2018; \r\nKlimont et al., 2017a; Hoesly et al., 2018). The dominance of Asia for \r\nSLCF emissions is corroborated by growing remote-sensing capacity \r\nthat has been providing an independent evaluation of estimated \r\npollution trends in the last decade (Duncan et al., 2013; Lamsal et al., \r\n2015; Luo et al., 2015; Fioletov et al., 2016; Geddes et al., 2016; Irie \r\net al., 2016; Krotkov et al., 2016; Wen et al., 2018)."


id: 1cdc0992d31036bf596d7146d351056
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2258
content:
"ozone and OH, and on the lifetime of methane compared with surface \r\nNOx emissions. Whereas the global spatial and temporal distribution \r\nof lightning flashes can be characterized thanks to satellite-borne \r\nand ground sensors (Virts et al., 2013; Cecil et al., 2014), constraining \r\nthe amount of NOx produced per flash (Miyazaki et al., 2014; Medici \r\net al., 2017; Nault et al., 2017; Marais et al., 2018; D.J. Allen et al., \r\n2019; Bucsela et al., 2019) and its vertical allocation (Koshak et al., \r\n2014; Medici et al., 2017) has been more elusive. "


id: 35bc1e0a79e3f782f4b571c49a98aba3
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6707
content:
"Atmospheric Chemistry and Climate Model Intercomparison \r\nProject (ACCMIP) models in CMIP5 used a range of LNOx between \r\n1.2-9.7 TgN y-1 (Lamarque et al., 2013b). In CMIP6, the corresponding \r\nLNOx range is between 3.2-7.6 TgN y-1 (Griffiths et al., 2021). \r\nAll CMIP6 models (as well as most models included in CMIP5, Young \r\net al., 2013) apply a parametrization that relates cloud-top height \r\nto lightning intensity (Price and Rind, 1992), projecting an increase \r\nin LNOx in a warmer world in the range of 0.27-0.61 TgN yr-1 degC-1\r\n(Thornhill et al., 2021a). However, models using parametrizations \r\nbased on convection (Grewe et al., 2001), updraft mass flux (Allen \r\nand Pickering, 2002) or ice flux (Finney et al., 2016a) show either \r\nmuch less sensitivity or a  negative response (Finney et al., 2016b, \r\n2018; Clark et al., 2017). "


id: 4ab639779cf90b568028f1c5cd7f1968
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10382
content:
"In summary, the total present-day global lightning NOx emissions are \r\nstill estimated to be within a factor of two. There is high confidence\r\nthat LNOx are perturbed by climate change; however, there is low \r\nconfidence in the sign of the change due to fundamental uncertainties \r\nin parametrizations."


id: 580998be9860349c193f9b0f129daa7f
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12748
content:
"Soil NOx (SNOx) emissions occur in connection with complex \r\nbiogenic/microbial nitrification and denitrification processes (Ciais \r\net al., 2013), which in turn are sensitive - in a non-linear manner - \r\nto temperature, precipitation, soil moisture, carbon and nutrient \r\ncontent, and the biome itself (e.g., Hudman et al., 2012). Global SNOx\r\nestimates, based on observationally constrained chemistry-transport \r\nmodel and vegetation model studies, show a broad range between \r\n4.7-16.8 TgN yr-1 (Young et al., 2018). This estimate is generally \r\nlarger than the current source strength used in CMIP6 simulations, \r\nwhich is prescribed using an early empirical estimate, typically scaled \r\nto about 5 TgN yr-1 (Yienger and Levy, 1995). "


id: c2d469afa6674b00c3a6a4beaa80a017
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31553
content:
"By the end of the 21st century, the overall nitrogen fixation in non\u0002agricultural ecosystems could be 40% larger than in 2000, due to \r\nincreased enzyme activity with growing temperatures, but the \r\nemission rates of NO (and N2O) could be dominated by changes in \r\nprecipitation patterns and evapotranspiration fluxes (Fowler et al., \r\n2015). Current Earth system models (ESMs) incorporate biophysical \r\nand biogeochemical processes only to a  limited extent (Jia et al., \r\n2019), precluding adequate climate sensitivity studies for SNOx. \r\nHence, while the current strength source of soil NOx has been \r\nbetter constrained over the last decade, adequate representations \r\nof SNOx and how it escapes from the canopy, which could provide \r\nquantitative estimates of climate-driven changes in SNOx, are still \r\nmissing in ESMs."


id: 44b21d4778020fe23c4772ba4f8e1433
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9312
content:
"Lightning contributes approximately 10% of the total NOx emissions \r\n(Murray, 2016). Since lightning NOx (LNOx) is predominantly released \r\nin the upper troposphere, it has a disproportionately large impact on "


id: 6c2d714ec700e0d184d12220df9b6d30
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16329
content:
"Most CMIP6 models use overly simplistic parametrizations and \r\nproject an increase in global BVOC emissions in response to warming \r\ntemperatures (Turnock et al., 2020). This good agreement actually \r\nreflects the lack of diversity in BVOC-emissions parametrizations in \r\nglobal models that do not fully account for the complex processes \r\ninfluencing emissions that are discussed above."


id: 9b62dc84adc5ffb498c0552b42fdf3d4
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24590
content:
"Overall, we assess that historical global isoprene emissions declined \r\nbetween the pre-industrial period and the present day by 10-25% \r\n(low confidence) but historical changes in global monoterpenes and \r\nsesquiterpenes are too uncertain to provide an assessment. Future \r\nchanges in BVOCs depend strongly on the evolution of climate and \r\nland use and are strongly sensitive to land-based climate change \r\nmitigation strategies. However, the net response of BVOC emissions \r\nis uncertain due to the complexity of processes that are hard to \r\nconstrain observationally and are considered with various degrees \r\nof details in models."


id: d4175ee723101b28f1d71bf729e8b652
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34647
content:
"The emission of dust particles into the atmosphere results from \r\na natural process, namely saltation bombardment of the soil by large \r\nwind-blown particles, such as sand grains, and from disintegration \r\nof saltating particle clusters (Kok et al., 2012). The occurrence \r\nand intensity of dust emissions are controlled by soil properties, \r\nvegetation and near-surface wind, making dust emissions sensitive \r\nto climate change and LULCC (Jia et al., 2019). In addition, dust can \r\nbe directly emitted through human activities, such as agriculture, \r\noff-road vehicles, building construction and mining, and indirectly \r\nemitted through hydrological changes due to human actions such as \r\nwater diversion for irrigation (e.g., Ginoux et al., 2012). Estimates of \r\nthe anthropogenic fraction of global dust vary from less than 10% to \r\nover 60% suggesting that the human contribution to the global dust "


id: 12dcd2c747a90c96d2ab2b5ce396bc5b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 523
content:
"budget is quite uncertain (Ginoux et al., 2012; Stanelle et al., 2014; \r\nXi and Sokolik, 2016). Reconstruction of global dust (deposition) \r\nfrom paleo records indicate factor of two to four changes between \r\nthe different climate regimes in the glacial and interglacial periods \r\n(Section 2.2.6). "


id: 2fb51bd073e668d9db77d910e1774a3b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 5652
content:
"water to the atmosphere due to wind-induced mixing of surface \r\nwater. DMS oxidizes to produce sulphate aerosols and contributes \r\nto the formation of CCN. Since AR5, the range in global DMS flux \r\nestimates reduced from 10-40 TgS yr-1 to 9-34 TgS yr-1 with a very \r\nlikely range of 18-24 TgS yr-1 based on sea-surface measurements \r\nand satellite observations (Lana et al., 2011). DMS production, \r\nand consequently emissions, have been shown to respond to \r\nmultiple stressors, including climate warming, eutrophication, and \r\nocean acidification. However, large uncertainties in process-based \r\nunderstanding of the mechanisms controlling DMS emissions, from \r\nphysiological to ecological, limit our knowledge of past variations \r\nand our capacity to project future changes."


id: 41f56861009d4389c797ac39bf81e4ac
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8797
content:
"Overall, there is low confidence in the magnitude and changes in \r\nmarine aerosol emissions in response to shifts in climate and marine \r\necosystem processes."


id: bc3b108390f2f7d86f3ebe145be5eea2
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30361
content:
"Emissions from open biomass burning (including forest, grassland, \r\npeat fires and agricultural waste burning) represent about 30%, 10%, \r\n15% and 40% of present-day global emissions of CO, NOx, BC and \r\nOC, respectively (van Marle et al., 2017; Hoesly et al., 2018). Wildfires \r\nalso play an important role in several atmospheric chemistry-climate \r\nfeedback mechanisms (Bowman et al., 2009; Fiore et al., 2012) and \r\nfire events occurring near populated areas induce severe air pollution \r\nepisodes (Marlier et al., 2020; Rooney et al., 2020; Yu et al., 2020)."


id: c9b19231b0c8134fb5796091fc2f0bc7
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32807
content:
"The historical (pre-satellite era) dataset for CMIP6 considers advances \r\nin knowledge of past fire dynamics (new fire proxy datasets, such as \r\ncharcoal in sediments and levoglucosan in ice cores) and visibility \r\nrecords from weather stations (Marlon et al., 2016; van Marle et al., \r\n2017). At a  global level, CMIP5 and CMIP6 emissions trends are \r\nsimilar, however, there are substantial differences at the regional \r\nlevel, especially for the USA, South America (south of Amazonia) and \r\nSouthern Hemisphere Africa (van Marle et al., 2017)."


id: 82ed658f1b439c0f14c173957b0c1fc2
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 20163
content:
"Globally, the CMIP5 estimates (Lamarque et al., 2010), indicated \r\na  gradual decline of open biomass burning emissions from 1920 "


id: 439075a2926e305a79c17ab1a0273afe
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9110
content:
"1 The word 'concentration' is used to denote abundances in terms of mixing ratio for most species unless specified."


id: 971ea1186ec51517f0bafc7d3e6fee88
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23780
content:
"satellite era is assessed. There is low to medium confidence in SLCF \r\nemissions from biomass burning from the pre-industrial period to the \r\n1980s, which rely on the incorporation of several proxy data, with \r\nlimited spatial representativeness. Nevertheless, uncertainties in \r\nthe absolute value of pre-industrial emissions remain high, limiting \r\nconfidence in radiative forcing estimates. "


id: e33065a262141a80ed705f3b7e4785d2
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37269
content:
"to about 1950 and then steady, and stronger than CMIP6, increase \r\ntowards 2000. In contrast, CMIP6 biomass burning emissions \r\n(van Marle et al., 2017) increase only slightly over 1750-2015 - they \r\npeak during the 1990s after which they decrease gradually, which is \r\nconsistent with the assessment of fire trends in Chapter 5. Therefore, \r\nthe CMIP6 evolution has a smaller difference between pre-industrial \r\nand present-day emissions than CMIP5, resulting in a lower radiative \r\nforcing of biomass burning SLCFs, possibly leading to a lower effect \r\non climate (van Marle et al., 2017). "


id: 9317a127efac8ec8960b005b5deca317
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23084
content:
"Climate warming, especially through change in temperature and \r\nprecipitation, will generally increase the risk of fire (Jia et al., 2019, \r\nsee also Chapter 12) and can also affect the fire injection and plume \r\nheight (Veira et al., 2016), but occurrence of fires and their emissions \r\nin the future strongly depends on anthropogenic factors, such as \r\npopulation density, land use and fire management (Veira et al., \r\n2016). Consequently, future emissions vary widely with increases \r\nand decreases amongst the SSP scenarios due to different land-use \r\nchange scenarios. "


id: f27674dcef02ea96e5763456bcb14f1a
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40049
content:
"In summary, there has been an improvement in the knowledge of \r\nbiomass burning emissions by reducing key uncertainties highlighted \r\nin AR5. However, systematic assessment of remaining uncertainties \r\nis limited, with a lower limit of uncertainties due to emissions factors \r\nof 30%, and larger uncertainties due to burning-activity estimates, \r\nespecially at regional level. Overall, a medium confidence in current \r\nglobal biomass burning SLCF emissions and their evolution over the "


id: 90090087df72cff5a356e61799934508
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22546
content:
"Changes in the atmospheric distribution of SLCFs determine their radiative forcing, and climate and air-quality impacts. This box \r\nprovides an overview of how process-level understanding of the distribution and evolution of chemical compounds is derived and \r\nwhere uncertainties come from. "


id: ac21afd28db14da7518c18a83615d70f
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27529
content:
"A wide range of in situ and remotely sensed observations are used to characterize atmospheric chemical composition. Measurements \r\nmade routinely as part of long-term monitoring programmes are particularly useful for assessing long-term trends and variability, and \r\nspatial distributions (Sections 2.2, 6.3 and 7.3.3), while intensive field campaigns provide a more comprehensive view of atmospheric "


id: 9ad172ba44c1bbee7239640678358506
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24481
content:
"composition at a specific location for a limited time, facilitating an improved process-level understanding. Retrieval of atmospheric \r\nconcentrations from satellites, in particular, has been tremendously useful for providing global continuous coverage, although the \r\nretrievals themselves depend on prior information of atmospheric composition usually derived from models. Over the last decade \r\nor so, observations of atmospheric concentrations have been combined with information from global chemistry-climate models to \r\nproduce global assimilation and forecasting systems with the purpose of producing chemical reanalysis or improving model inputs \r\n(i.e., emissions or boundary conditions) and forecasts (Miyazaki et al., 2015; Randles et al., 2017; Inness et al., 2019). "


id: 29febd1852171d87c454edc5a1c31096
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 4608
content:
"The skill of CCMs is typically assessed by their ability to reproduce observed abundance, trends and variability of chemical compounds. \r\nHowever, uncertainty remains large because of observation limitations (errors and uncertainties, spatial and temporal coverage), "


id: 50aa6e8d74b4c550371c484c14916e1a
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 11465
content:
"Box 6.1, Figure 1 | Knowledge exchange between laboratory/theoretical studies, observations and global climate-chemistry models (CCMs) to \r\ninform our understanding of short-lived climate forcers (SLCFs)."


id: eff2d600da41ab1804c9e28493c45036
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39596
content:
"The global mean surface mixing ratio of methane has increased by \r\n156% since 1750 (Section  2.2.3.4 and Annex III). Since AR5, the \r\nmethane mixing ratio has increased by about 3.5% from 1803 +- 2 ppb \r\nin 2011 to 1866 +- 3 ppb in 2019 (Section 2.2.3.3.2) largely driven by \r\nanthropogenic activities as assessed in Chapter 5 (Section 5.2.2 and \r\nCross-Chapter Box 5.2). "


id: 9b223551a59410305f75386718944ff0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24544
content:
"An assessment of the global methane budget is provided in Chapter 5, \r\nwhile this section assesses methane atmospheric lifetime and \r\nperturbation time (Prather et al., 2001). The AR5 based its assessment \r\nof methane lifetime on Prather et al. (2012). The methane chemical \r\nlifetime due to tropospheric OH, the primary sink of methane, was \r\nassessed to be 11.2 +- 1.3 years constrained by surface observations \r\nof methyl chloroform (MCF), and lifetimes due to stratospheric loss,2\r\ntropospheric halogen loss and soil uptake were assessed to be \r\n150 +- 50 years, 200 +- 100 years, and 120 +- 24 years, respectively \r\n(Myhre et al., 2013). Considering the full range of individual lifetimes, \r\nthe total methane lifetime was assessed in AR5 to be 9.25 +- 0.6 years."


id: 38c7dc09403e09d9f5aef431563342da
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7194
content:
"Table 6.2 | Methane lifetime due to chemical losses, soil uptake and total atmospheric lifetime based on CMIP6 multi-model analysis, and bottom-up \r\nand top-down methane budget estimates in Table 5.2. Bottom-up and top-down methane lifetimes are calculated using the central estimates of the respective sinks for \r\nthe mean 2008-2017 period in Table 5.2 together with the mean 2008-2017 global methane concentration of 1815 ppb (see Annex III) converted to methane burden using \r\na fill-factor of 2.75 Tg/ppb from Prather et al. (2012). Values in parentheses show the minimum and maximum range."


id: ab7a4ded04f518d9dc1cc4992041e409
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27399
content:
"mechanisms implemented in the global models (Y. Zhao et al., 2019). \r\nSee Section 6.3.6 for further discussion on the conflicting information \r\non OH from CCMs/ESMs and TD inversion approaches. Further work is \r\nrequired to reconcile differences between BU and TD estimates of the \r\nchemical methane sink. "


id: 4d1cad5d28e1ce181625347cbac4d4e5
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10839
content:
"(Thornhill et al., 2021b). This f value is slightly smaller but within the \r\nrange of the AR5 value. This results in an overall perturbation methane \r\nlifetime of 11.8 +- 1.8 years, within the range of the AR5 value of \r\n12.4 +- 1.4 years. The methane perturbation lifetime assessed here is \r\nused in the calculation of emissions metrics in Section 7.6. "


id: 911bb203841f73cf1dc9012f38bd60a7
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22733
content:
"The present-day BU methane chemical lifetime shows a larger spread \r\nthan that in the TD estimates (Table 6.2) in line with the spread seen \r\nin the sink estimates. The spread in the methane lifetime calculated \r\nby three CMIP6 ESMs is narrower and is enclosed within the spread \r\nof the BU CCMI model ensemble. Based on the consideration that the \r\nsmall imbalance in total methane sources versus sinks derived from \r\nTD estimates is close to the observed atmospheric methane growth \r\nrate (Table 5.2), the TD values are assessed to be the best estimates \r\nfor this assessment. The relative uncertainty (+- 1 standard deviation) \r\nis taken to be the same as that in AR5, that is, 11.8%, 33% and 10% \r\nfor chemical, soil and total lifetime, respectively. The central estimate \r\nof the total atmospheric methane lifetime assessed here is the same \r\nas that in AR5. "


id: b5a0ad55adf56c3b7438d17ae880e53
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29181
content:
"The AR5 assessed the tropospheric ozone burden to be 337 +- 23 Tg for \r\nthe year 2000 based on the ACCMIP ensemble of model simulations \r\n(Myhre et al., 2013). Multiple satellite products, ozonesondes and \r\nCCMs are used to estimate tropospheric ozone burden (Table 6.3). \r\nSatellite products provide lower-bound values as they exclude regions \r\nunder polar night conditions (Gaudel et al., 2018). The tropospheric \r\nozone burden values from multi-model exercises are within the range "


id: d9cc41911c4f7e923455eb5e57ba567
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 35614
content:
"Table 6.3 | Global tropospheric ozone budget terms and burden based on multi-model estimates and observations for present conditions. All uncertainties \r\nquoted as +- 1 standard deviation. Values of tropospheric ozone burden with asterisk indicate average over the latitudinal zone 60degN-60degS. STE = stratospheric-tropospheric \r\nexchange."


id: 19ca3778743759d93ec6c4efd242d965
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1734
content:
"of the observational estimates despite different definitions of the \r\ntropopause for multi-model estimates which can lead to differences \r\nof about 10% on the ozone-burden model estimates (Griffiths et al., \r\n2021). Weighted by their number of members, CMIP6 and CCMI \r\nmulti-model estimates and observational estimates of tropospheric \r\nozone burden in about the year 2010, lead to an assessment of the \r\ntropospheric ozone burden of 347 +- 28 Tg for 2010. "


id: 8d8de00daac68e96e2c9c93c911bd50b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22093
content:
"between 1850 and the present (Myhre et al., 2013). Based on limited \r\nisotopic evidence, Chapter  2 assesses that the global tropospheric \r\nozone increased by less than 40% between 1850 and 2005 (low "


id: 6d09384f7c77e8d2a9172953210ea577
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16463
content:
"The tropospheric ozone budget is controlled by chemical production and \r\nloss, by stratospheric-tropospheric exchange (STE), and by deposition \r\nat the Earth's surface, whose magnitude are calculated by CCMs \r\n(Table 6.3). Despite the high agreement of the model ensemble mean \r\nwith observational estimates in the present-day tropospheric ozone \r\nburden, the values of individual budget terms can vary widely across \r\nmodels in CMIP6, consistent with previous model intercomparison \r\nexperiments (Young et al., 2018). Furthermore, single-model studies \r\nhave shown that the halogen chemistry, which is typically neglected \r\nfrom model chemistry schemes in CCMs, may have a notable impact on \r\nthe ozone budget, as halogens, particularly of marine origin, take part \r\nin efficient ozone-loss catalytic cycles in the troposphere (Saiz-Lopez \r\net al., 2012; Sarwar et al., 2015; Sherwen et al., 2016)."


id: 14d8ddf0c93166e0ad9a37d9999e9ffc
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 897
content:
"Because of the heterogeneous distribution of ozone, limited \r\nobservations or proxies do not provide accurate information about \r\nthe global pre-industrial abundance, posing a  challenge to the \r\nestimation of the historical evolution of tropospheric ozone. Therefore, \r\nglobal CCMs complemented by observations are relied upon for \r\nestimating the long-term changes in tropospheric ozone. The AR5 \r\nconcluded that anthropogenic changes in ozone precursor emissions \r\nare unequivocally responsible for the increase in tropospheric ozone "


id: 62c071bfaa74c1065c93f3e9f8553e01
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14669
content:
"confidence) (Section  2.2.5.3). The CMIP6 models are in line with \r\nthis increase of tropospheric ozone with an ensemble-mean value \r\nof 109  +- 25 Tg (model range) from 1850-1859 to 2005-2014 \r\n(Figure 6.4). This increase is higher than the AR5 value of 100 +- 25 Tg \r\nfrom 1850-2010 due to higher ozone precursor emissions in CMIP6. \r\nHowever, the AR5 and CMIP6 values are close when considering the \r\nreported uncertainties. The uncertainties are equivalent in CMIP6 and \r\nAR5 despite enhanced inclusion of coupled processes in the CMIP6 \r\nESMs (e.g., biogenic NMVOC emissions or interactive stratospheric \r\nozone chemistry). "


id: 3dd046d4bac5f8f3799315e5079b0f36
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8042
content:
"In summary, there is high confidence in the estimated present\u0002day (about 2010) global tropospheric ozone burden based on an \r\nensemble of models and observational estimates (347 +- 28 Tg), but \r\nthere is medium confidence among the individual models for their \r\nestimates of the tropospheric ozone-related budget terms. "


id: 85ba90f84bdb7a5f64c8c5350ead72f6
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 20671
content:
"Evidence from successive multi-model intercomparisons and the \r\nlimited isotopic evidence agree on the magnitude of the increase \r\nof the tropospheric ozone burden from 1850 to the present day in \r\nresponse to anthropogenic changes in ozone precursor emissions \r\ncorroborating AR5 findings. This increase is assessed to be 109 +- 25 Tg \r\n(medium confidence). The CMIP6 model ensemble shows a constant \r\nglobal increase since the mid-20th century whose rate is consistent \r\nwith that derived from observations since the mid-1990s. "


id: e3494c71eb261452f3e1f73b47e0fedd
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37287
content:
"Since the mid-20th century, the CMIP6 model ensemble shows \r\na higher global trend (Figure 6.4). Since the mid-1990s, the trends \r\nare better documented by observations (Section  2.2.5.3) and \r\nindicate spatial heterogeneity. In particular, in situ observations \r\nat remote surface sites and in the lower free troposphere indicate \r\npositive trends that are far more common than negative trends, \r\nespecially in the northern tropics and across Southern and Eastern \r\nAsia (Figure  6.5). The CMIP6 ensemble and observations largely \r\nagree on the magnitude of the global positive trend since 1997 \r\n(0.82 +- 0.13 Tg yr-1 in the model ensemble; 0.70 +- 0.15 Tg yr-1 in the \r\nozonesonde dataset; 0.83 +- 0.85 Tg yr-1 in the satellite ensemble) and \r\nqualitatively reproduce positive trends in the Southern Hemisphere \r\n(Griffiths et al., 2021). More analyses are needed for evaluation in \r\nother parts of the world to assess the skill of the recent ensemble \r\nbased on CMIP6 emissions. "


id: a8475104a4e4d532a53986dec04d82e8
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26827
content:
"Model simulations attribute about half of the observed upper\u0002stratospheric ozone increase after 2000 to the decline of ODS since \r\nthe late 1990s while the other half of the ozone increase is attributed \r\nto the slowing of gas-phase ozone destruction cycles due to cooling \r\nof the upper stratosphere by increasing GHGs (Aschmann et al., \r\n2014; Oberlander-Hayn et al., 2015). "


id: 5e99f11d87f8e5b5dfb5cc4cbd50623d
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 13935
content:
"Figure 6.5 | Decadal tropospheric ozone trends since 1994. Trends are shown \r\nat 28 remote and regionally representative surface sites (Cooper et al., 2020) and in \r\n11 regions of the lower free troposphere (650 hPa, about 3.5 km) as measured by \r\nIn-Service Aircraft for a Global Observing System (IAGOS) above Europe, north-eastern \r\nUSA, south-eastern USA, western North America, north-east China, South East Asia, \r\nsouthern India, the Persian Gulf, Malaysia/Indonesia, the Gulf of Guinea and northern \r\nSouth America (Gaudel et al., 2020). High-elevation surface sites are >1500 m above \r\nsea level. All trends end with the most recently available year but begin in 1995 \r\nor 1994. The sites and datasets are the same as those used in Figure 2.8, further \r\ndetails on data sources and processing are available in the Chapter  2 data table \r\n(Table 2.SM.1). "


id: cd188269d774e79c01963ba775a7c0c6
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 33421
content:
"In summary, global stratospheric ozone column has decreased from \r\npre-industrial period to present day in response to the ODS-induced \r\nozone rapid decline in the 1970s and 1980s, followed by slow, and \r\nstill incomplete, recovery. There is medium confidence that global \r\nstratospheric ozone column has changed by 14.3 +- 8.7 DU between \r\n1850 and 2014. "


id: f6f760992889ff24e5e7f92561eafcc0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40817
content:
"Observational constraints derived from the isotopic composition \r\nof atmospheric nitrate inferred from ice cores provide evidence of \r\nincreasing anthropogenic NOx sources since pre-industrial times \r\n(Hastings et al., 2009; Geng et al., 2014). Global NOx emissions trends \r\nin bottom-up inventories (Section 6.2.1) as well as model simulations \r\nof nitrogen deposition (Lamarque et al., 2013a) are in qualitative \r\nagreement with these observational constraints. CMIP6 ESMs exhibit \r\nstable NOx burden over the first half of the 20th century and then \r\na sharp increase driven by a factor of three increase in emissions, \r\nhowever, the magnitude of this increase remains uncertain due to \r\npoor observational constraints on pre-industrial concentrations of \r\nNOx (Griffiths et al., 2021)."


id: 8dbb99f3f085cbf713017048f32ce1ce
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22127
content:
"reveals spatially heterogeneous NO2 trends over the Middle East with \r\nan overall increase over 2005-2010 and a decrease over large parts \r\nof the region after 2011-2012. The reasons for trend reversal within \r\nindividual areas are diverse, including warfare, imposed sanctions, \r\nand air-quality controls (Lelieveld et al., 2015a; Georgoulias et al., \r\n2019). Satellite-derived tropospheric NO2 levels over Africa and Latin \r\nAmerica do not show a clear trend; both increasing and decreasing \r\ntrends are observed over large agglomerations in these regions since \r\nthe early 2000s (Schneider et al., 2015; Duncan et al., 2016). "


id: 92882df24543e6eb35db0494ba32a291
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23004
content:
"In summary, global tropospheric NOx abundance has increased from \r\n1850-2015 (high confidence). Satellite observations of tropospheric \r\nNOx indicate strong regional variations in trends over 2005-2015. \r\nThere is high confidence that NO2 has declined over the USA and \r\nWestern Europe since the mid-1990s and increased over China until \r\n2011. NO2 trends have reversed (declining) over China beginning in \r\n2012 and NO2 has increased over Southern Asia by 50% since 2005 \r\n(medium confidence). "


id: 6423fd1c884f0e407e237975eb604dfb
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14899
content:
"An increase of global CO burden of about 50% for the year 2000 \r\nrelative to 1850 is found in CMIP6 (Griffiths et al., 2021). "


id: bfa08bafa1b44f47f265fc6b3a11e867
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30984
content:
"About half of the atmospheric CO burden is due to its direct emissions \r\nand the remainder is due to the atmospheric oxidation of methane \r\nand NMVOCs. Reaction with OH is the primary sink of CO with \r\na smaller contribution from dry deposition."


id: 39ce1ae9909e2dfaf44927755b420d53
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7384
content:
"Global models generally capture the global spatial distribution \r\nof the observed CO concentrations but have regional biases of \r\nup to 50% (e.g.,  Emmons et al., 2020; Horowitz et al., 2020). \r\nDespite updated emissions datasets, the global multi-model and \r\nsingle-model simulations persistently underestimate observed CO \r\nconcentrations at northern high and mid-latitudes as well as in the \r\nSouthern Hemisphere, but with smaller biases compared with that \r\nin the Northern Hemisphere (Naik et al., 2013; Stein et al., 2014; \r\nMonks et al., 2015; Strode et al., 2015). Models are biased high in the \r\ntropics, particularly over highly polluted areas in India and Eastern \r\nAsia (Strode et al., 2016; Yarragunta et al., 2017)."


id: 430bcd9e50fe0787818d865dc60707b8
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8999
content:
"In summary, our understanding of present-day global CO distribution \r\nhas increased since AR5 with newer and improved observations and \r\nreanalysis. There is high confidence that global CO burden is declining \r\nsince 2000. Evidence from observational CO reanalysis suggests \r\nthis decline is driven by reductions in anthropogenic CO emissions, \r\nhowever this is yet to be corroborated by global ESM studies with the \r\nmost recent emissions inventories. "


id: ce546745f56d620956ccf3a2187b0f40
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 33639
content:
"NMVOCs encompass thousands of compounds with lifetimes from \r\nhours to days to months, and abundances and chemical composition \r\nhighly variable with respect to space and time. Although the biogenic \r\nsource (Section  6.2.2) dominates the global NMVOC budget, \r\nanthropogenic activities are the main driver of long-term trends in \r\nthe abundance of many compounds."


id: 1db9946d18f70ca383ac441db75cdce0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2416
content:
"Estimates of global CO burden simulated by global models generally \r\nfall within the range of that derived from data assimilation \r\ntechniques, though the spread across the models is large (Naik et al., \r\n2013; Stein et al., 2014; Zeng et al., 2015; Myriokefalitakis et al., \r\n2016). There is a large diversity in model-simulated CO budget driven \r\nby uncertainties in CO sources and sinks, particularly those related to \r\nin situ production from NMVOCs and loss due to reaction with OH \r\n(Stein et al., 2014; Zeng et al., 2015; Myriokefalitakis et al., 2016). \r\nGlobal CO budget analysis from a multi-model ensemble for more \r\nrecent years, including results from the CMIP6 model runs, are not \r\nyet available. "


id: d4582f880f67ee9fef6d23435ecadf6c
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34681
content:
"Information on the global distribution of individual NMVOCs is scarce, \r\nexcept for the less reactive compounds having lifetimes of several \r\ndays to months. Based on measurements from polar firn air samples \r\nand ground-based networks, AR5 reported that the abundances of \r\nthe predominantly anthropogenic light alkanes (C2-C5) increased \r\nuntil 1980 and declined afterwards. The decline was attributed to \r\nair-quality emissions controls and to fugitive emissions decreases \r\nfollowing the collapse of the Soviet Union (Simpson et al., 2012). \r\nSince AR5, scarce ground-based measurements have shown that the \r\ndecline in C2-C3 alkanes ended around 2008 and their abundances \r\nare since growing again, which is primarily attributed to increasing \r\nNorth American emissions (Section  6.2.1). Furthermore, since AR5 \r\nthe evolution of ethane levels during the past millennium was made \r\naccessible by analysis of ice-core samples (Nicewonger et al., 2016). "


id: 17a38098805cf8efe64c6101c2e2fb31
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1367
content:
"Reconstructions of CO concentrations based on limited ice-core \r\nsamples in the Northern Hemisphere high latitudes suggest CO mole \r\nfractions of about 145 ppb in the 1950s, which rose by 10-15 ppb \r\nin the mid- 1970s, and then declined by about 30-130 ppb by 2008 \r\n(Petrenko et al., 2013). The negative trends since the 1990s are often \r\nattributed to emissions regulations from road transportation in North \r\nAmerica and Europe. Due to limited observations prior to the satellite \r\nera, long-term global CO trends are based on estimates from models. "


id: 23802104235e6a1f4dc03c36740bdcb2
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3448
content:
"The large observed interpolar ratio of ethane in pre-industrial times \r\n(3.9) corroborates a  large geologic source of ethane previously \r\nput forward by (Etiope and Ciccioli, 2009), and narrows down its \r\nlikely global magnitude (Nicewonger et al., 2018) (low to medium \r\nconfidence). The incorporation of geologic emissions in CCMs is \r\nnot yet systematic though a one-model study has shown improved \r\nagreement of the results with observations (Dalsoren et al., 2018). "


id: 56221148e509b0dd323bee224c90a638
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
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index: climate_gpt_v2
vector_id: 12427
content:
"emissions inventories (Van Damme et al., 2018). NH3 exhibits a strong \r\nvertical gradient, with a  maximum in the boundary layer (Schiferl \r\net al., 2016), and can be transported into the upper troposphere and \r\nlower stratosphere (UTLS), particularly in the Asian Monsoon region, \r\nas indicated by observations (Froyd et al., 2009; Hopfner et al., 2016, \r\n2019) and theoretical considerations (Ge et al., 2018). There is a large \r\nrange in the present-day NH3 burden (from 0.04-0.7 TgN) simulated \r\nby CCMs, highlighting deficiencies in the process-level representation \r\nof NH3 in current global models (Bian et al., 2017). The underestimate \r\nof surface NH3 concentrations (Bian et al., 2017) further highlights such \r\ndeficiencies and the limitations in comparing site-specific observations \r\nwith relatively coarse-resolution models."


id: a4910398634b6d42c4ca847f98b112a2
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26167
content:
"Observations show that NH3 concentration has been increasing \r\nin recent decades in the USA (Butler et al., 2016; Warner et al., 2016; \r\nYu et al., 2018), Western Europe (van Zanten et al., 2017; Warner et al., \r\n2017; Wichink Kruit et al., 2017; Tang et al., 2018), and China (Warner \r\net al., 2017; M. Liu et al., 2018). This trend has been attributed to \r\na combination of increasing ammonia emissions (Sutton et al., 2013; \r\nFowler et al., 2015) and decreases in the chemical reaction of NH3\r\nwith nitric and sulphuric acids associated with reductions in  SO2\r\nand NOx emissions whose rate depends on the region (Warner et al., \r\n2017; Yao and Zhang, 2019). Over longer time scales, CCMs simulate \r\nan increase of the NH3 burden by a factor of two to seven since pre\u0002industrial conditions (Xu and Penner, 2012; Hauglustaine et al., 2014). "


id: 1dee06d1aa690f325923215f8f3ffc1
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2455
content:
"In summary, progress has been made in the understanding of the \r\nspatio-temporal distribution of ammonia, though representation of \r\nNH3 remains rather unsatisfactory due to process-level uncertainties. \r\nEvidence from observations and models suggests that ammonia \r\nconcentrations have been increasing over recent decades due to \r\nemissions and chemistry. There is high confidence that the global NH3\r\nburden has increased considerably from the pre-industrial period to the \r\npresent day, although the magnitude of the increase remains uncertain."


id: 4894460f8b883d58a03c5c7ec10f5563
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9984
content:
"In summary, after a decline between 1980 and 2008, abundances of \r\nlight NMVOCs have increased again over the Northern Hemisphere \r\ndue to the extraction of oil and gas in North America (high \r\nconfidence). Trends in satellite HCHO observations, used as a proxy of \r\nanthropogenic NMVOC over industrialized areas, show a significant \r\npositive trend over eastern China (high confidence) but also indicate \r\nlarge regional disparities in the magnitude of the trends over China \r\nand even in their signs over North America."


id: 20e6051d4aae5f7fc42bec6f9d581f77
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3009
content:
"The AR5 did not assess trends in SO2 concentrations. Trends in SO2\r\nabundances are consistent with the overall anthropogenic emissions \r\nchanges as presented in Section  6.2 and Figure  6.18. Long-term \r\nsurface-based in situ observations in North America and Europe \r\nshow reductions of more than 80% since the measurements began \r\naround 1980 (Table 6.5). Europe had the largest reductions in the first \r\npart of the period while the highest reduction came later in North \r\nAmerica. Observed trends are qualitatively reproduced by global \r\nand regional models over North America and Europe during the \r\nperiod 1990-2015 for which emissions changes are well quantified \r\n(Table 6.5; Aas et al., 2019)."


id: a65a035e1b097b1e0e57216d90a470a5
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26483
content:
"Ammonia is the most abundant alkaline gas in the atmosphere. \r\nIts present-day source is dominated by livestock and crop production \r\n(Section  6.2). Ammonia reacts with nitric acid and sulphuric acid \r\nto produce ammonium sulphate and ammonium nitrate, which \r\ncontribute to the aerosol burden (Section 6.3.5.2), promotes aerosol \r\nnucleation by stabilizing sulphuric acid clusters (Kirkby et al., 2011), \r\nand contributes to nitrogen deposition (Section 6.4.4; Sheppard et al., \r\n2011; Flechard et al., 2020). Trends in NH3 were not assessed in AR5."


id: 55c123053eeb35a0b9b69e6ce3d59dce
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12360
content:
"Considerable expansion of satellite (Clarisse et al., 2009; Shephard \r\nand Cady-Pereira, 2015; Warner et al., 2016) and ground-based \r\nobservations (Miller et al., 2014; Y. Li et al., 2016; Pan et al., 2018) has \r\nimproved our understanding of the spatial distribution and seasonal to \r\ninterannual variability of ammonia, and advanced its representations \r\nin models (e.g., Zhu et al., 2015). Regionally, peak NH3 concentrations \r\nare observed over large agricultural (e.g.,  northern India, the USA \r\nMidwest and Central Valley) and biomass-burning regions, in good \r\nqualitative agreement with emissions inventories (Van Damme \r\net al., 2015, 2018). However, several large agricultural and industrial \r\nhotspots have been found to be missing or greatly underestimated in "


id: 2fdf5bac009a408ca279474d0c725bc0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 5676
content:
"In situ observations over other parts of the world are scattered. \r\nHowever, the limited in situ observations in Eastern Asia indicate an \r\nincrease in atmospheric SO2 up to around 2005 and then a decline \r\n(Aas et al., 2019). This is confirmed by satellite observations (Krotkov \r\net al., 2016), which further reveal a rapid decline in SO2 since around \r\n2012 or 2013 (Krotkov et al., 2016; Zheng et al., 2018b). In India, on \r\nthe other hand, SO2 levels have doubled between 2005 and 2015 \r\n(Krotkov et al., 2016). "


id: c93dfe434cf250d2e0ae27d4664ac0cc
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32734
content:
"Table 6.5 | Summary of changes or trends in atmospheric abundance of sulphur dioxide (SO2) and sulphate (SO4\r\n2-) aerosols based on in situ and satellite \r\nobservations."


id: a0d5cfffe417829f37bf2bc7001662e4
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25513
content:
"VSLSs are halogenated substances with atmospheric lifetimes less \r\nthan half a  year. While longer-lived ODSs account for most of the \r\npresent-day stratospheric halogen loading, there is robust evidence\r\nthat VSLSs contribute to stratospheric bromine and chlorine \r\n(Carpenter et al., 2014; Elvidge et al., 2015a; Hossaini et al., 2015), \r\nthus also contributing to stratospheric ozone depletion. "


id: bc70f8946371612d2d1064587d6bf4e6
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30396
content:
"In summary, surface and satellite observations indicate strong \r\nregional variations in trends of atmospheric SO2 abundance. The SO2\r\nconcentrations in North America and Europe have declined over \r\n1980-2015 with slightly stronger reductions in North America \r\n(70 +- 20%) than in Europe (58 +- 32%) over 2000-2015, though \r\nEurope had larger reductions than the US in the prior decade (1990-\r\n2000). In Asia, the SO2 trends are more scattered, though there is \r\nmedium confidence that there was a strong increase up to around \r\n2005, followed by a  steep decline in China, while over India, the \r\nconcentrations are increasing steadily."


id: d345c0a34ac175b687aa8ddb16f4ad13
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34513
content:
"Halogenated species are emitted in the atmosphere in the form \r\nof the synthetically produced chlorofluorocarbons (CFCs), halons, \r\nhydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and \r\nothers. Their historical global abundances are provided in Annex III \r\nand discussed in Chapter 2 (Section 2.2.4 and Table 2.3). In summary, \r\nfor the period 2011-2019, the abundance of total chlorine from \r\nHCFCs has continued to increase in the atmosphere with decreased \r\ngrowth rates; total tropospheric bromine from halons and methyl \r\nbromide continued to decrease while abundances of most currently \r\nmeasured HFCs increased significantly, consistent with expectations \r\nbased on the ongoing transition away from the use of ODSs. Here, \r\nemphasis is given on the very short-lived halogenated species \r\n(VSLSs). The trends for these species were not discussed in IPCC AR5. "


id: 3c68644eb28a0aa85cbbb0059ae61dd5
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7819
content:
"Models participating in Phase III of the AeroCom intercomparison \r\nproject were found to underestimate present-day AOD by about \r\n20% (Gliss et al., 2021), although different remote-sensing estimates \r\nobtain different estimates of global mean AOD. Gliss et al. (2021) also \r\nhighlight the considerable diversity in the simulated contribution of \r\nvarious aerosol types to total AOD. However, models simulate regional \r\ntrends in AODs that agree well, when expressed as percentage change, \r\nwith ground- (Mortier et al., 2020; Gliss et al., 2021) and satellite\u0002based (Cherian and Quaas, 2020; Gliss et al., 2021) observations. \r\nAOD  trends simulated by CMIP6 models are more consistent with \r\nsatellite-derived trends than CMIP5 models for several sub-regions, \r\nthanks to improved emissions estimates (Cherian and Quaas, 2020). "


id: b5699cbe1cb208f6f7b014af09bd9c3c
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29146
content:
"This section assesses trends in the atmospheric distribution of \r\naerosols and improvements in relevant physical and chemical \r\nprocesses. The observed large-scale temporal evolution of aerosols \r\nis assessed in Section 2.2.6. Since AR5, long-term measurements of \r\naerosol mass concentrations from regional global surface networks \r\nhave continued to expand and provide information on the distribution \r\nand trends in aerosols (Figure 6.7). There is large spatial variability in \r\naerosol mass concentration, expressed as PM2.5, dominant aerosol \r\ntype and aerosol composition, consistent with the findings in AR5. "


id: d2fde82343ce4dd6f18b8e5739867d40
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34465
content:
"Remote-sensing instruments provide a larger-scale view of aerosol \r\ndistributions and trends than ground-based monitoring networks by \r\nretrieving the aerosol optical depth (AOD), which is indirectly related \r\nto aerosol mass concentrations. AOD is the column-integrated \r\nmeasure of extinction of the solar intensity due to aerosols at a given \r\nwavelength, and is therefore relevant to the estimation of the \r\nradiative forcing of aerosol-radiation interactions (Section 7.3.3.1). "


id: b90f92da1d8684ab97dbf59456f18520
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29764
content:
"All CMIP6 models simulate a positive trend in global mean AOD from \r\n1850, with a  strong increase after the 1950s coinciding with the \r\nmassive increase in anthropogenic SO2 emissions (Figure 6.8). Global \r\nmean AOD increases have slowed since 1980, or even reversed in \r\nsome models, as a result of a compensation between SO2 emissions \r\ndecreases over the USA and Europe in response to air-quality "


id: 124abe7482951a7250df2d85d7cc1b7f
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 416
content:
"layer (Kremser et al., 2016). SO2 emissions from volcanic eruptions are \r\na significant source of stratospheric sulphate loading (see Chapter 2 \r\nfor reconstruction of stratospheric aerosol optical depth and \r\nChapter  7 for radiative forcing of volcanic aerosols). Furthermore, \r\nstudies suggest sulphate contributions from anthropogenic SO2\r\nemissions transported into the stratosphere could have a consequent \r\nimpact on radiative forcing (Myhre et al., 2004; Yu et al., 2016). \r\nHowever, there is significant uncertainty in the relative importance of \r\nthis stratospheric sulphate source (Kremser et al., 2016)."


id: a6eee2aa27f93c503b04564aa8013cd8
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26579
content:
"Process understanding of sulphate production pathways from SO2\r\nemissions has seen some progress since AR5. More specifically, \r\nmany global climate models now have a more complete description \r\nof chemical reactions such that oxidant levels (including ozone) \r\nare better described, include a  pH-dependence of SO2 oxidation \r\n(e.g., Kirkevag et al., 2018; Bauer et al., 2020), and implement explicit \r\ndescriptions of ammonium and nitrate aerosol components, which \r\nmay influence the partitioning of sulphate (Bian et al., 2017; Lund \r\net al., 2018a). The pH influences the heterogeneous chemistry as well \r\nas the physical properties of the aerosols, and this topic has been \r\na subject of growing interest since AR5 (Cheng et al., 2016; Freedman \r\net al., 2019; Nenes et al., 2020). Increases in cloudwater pH have \r\nbeen shown to significantly increase the radiative forcing due to \r\nsulphate aerosols (Turnock et al., 2019). "


id: b36c17856de93ef6ca12da3711632574
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 28807
content:
"Sulphate is removed from the atmosphere by dry deposition and \r\nwet scavenging, and these processes depend on the characteristics \r\nof the Earth's surface, and the intensity, frequency and amount of \r\nprecipitation (Boucher et al., 2013). Even though there have been \r\nsome improvements since AR5, representation of atmospheric \r\ntransport and of wet scavenging and related cloud processes remains \r\na key source of uncertainty in the simulated aerosol distribution and \r\nlifetime, with further consequences for the sulphate forcing estimates \r\n(Kristiansen et al., 2016; Lund et al., 2018a). There are also still \r\nrelatively large uncertainties in the emission height used in models \r\naffecting the simulated aerosol distribution (Yang et al., 2019a)."


id: 16d27b06d0bfcd0fd514aab83ee5671e
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1230
content:
"Sulphate aerosols (or sulphate-containing aerosols) are emitted \r\ndirectly or formed in the atmosphere by gas- and aqueous-phase \r\noxidation of precursor sulphur gases, including SO2, DMS and carbonyl \r\nsulphide (OCS), emitted from anthropogenic and natural sources \r\n(Section 6.2). Sulphate aerosols influence climate forcing directly by \r\neither scattering solar radiation or absorbing longwave radiation, \r\nand indirectly by influencing cloud micro- and macrophysical \r\nproperties and precipitation (Boucher et al., 2013; Myhre et al., 2013). \r\nAdditionally, sulphate aerosols and sulphate deposition have a large \r\nimpact on air quality and ecosystems (Reis et al., 2012). The majority \r\nof sulphate particles are formed in the troposphere, however, SO2 and \r\nother longer-lived natural precursors, such as OCS, transported into \r\nthe stratosphere, contribute to the background stratospheric aerosol "


id: 630c84190d031c7433475a6897516a41
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14718
content:
"representative trends (Hammer  et al., 2018). However, limited in \r\nsitu observations in Eastern Asia indicate an increase in atmospheric \r\nsulphate up to around 2005 and then a decline (Aas et al., 2019), \r\nwhich is confirmed by satellite observations of SO2 (Section 6.3.3.5). \r\nIn India, on the other hand, satellite observations indicate a rapid \r\nincrease in the SO2 levels (Krotkov et al., 2016), and long-term \r\nmeasurements of sulphate in precipitation in India further provide \r\nevidence of an increasing trend from 1980-2010 (Bhaskar and \r\nRao, 2017; Aas et al., 2019). Further improvements in global trend \r\nassessments are expected with new integrated reanalysis products \r\nfrom the Earth-system data assimilation projects (Randles et al., \r\n2017; Inness et al., 2019). "


id: de52a51bc6cfb32eefa2298ee87652ba
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36375
content:
"produced in the atmosphere by the oxidation of NOx and SO2\r\nrespectively. Ammonium nitrate is formed only after H2SO4 is fully \r\nneutralized. NH4\r\n+ and NO3-\r\n aerosols produced via these gas-to-particle \r\nreactions are a major fraction of fine-mode particles (with diameter \r\n<1um) affecting air quality and climate. Coarse-mode nitrate, \r\nformed by the heterogeneous reaction of nitric acid with dust and \r\nsea salt, dominates the overall global nitrate burden, but has little \r\nradiative effect (Hauglustaine et al., 2014; Bian et al., 2017). Trends in \r\nammonium (NH4\r\n+) and nitrate (NO3-\r\n) were not assessed in AR5."


id: 483d65978796380f6cc1f04db99f8c58
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9922
content:
"Global model present-day estimates of the global NH4\r\n+ burden range \r\nfrom 0.1-0.6 TgN (Bian et al., 2017). Models generally simulate \r\nsurface NH4\r\n+ concentrations better than surface NH3 concentrations \r\n(Bian et al., 2017), which reflects its thermodynamic control by SO4\r\n2-\r\nrather than NH3 (Shi et al., 2017). The concomitant increases of NH3, \r\nSO2, and NOx emissions (see Section  6.2) have led to a  factor of \r\nthree to nine increase in the simulated NH4\r\n+ burden from 1850-2000 \r\n(Hauglustaine et al., 2014; Lund et al., 2018a), driven primarily by \r\nammonium sulphate (70-90%). The increases in the NH3 and NH4\r\n+\r\nburdens are indirectly supported by the observed increase of NH4\r\n+\r\nconcentration in ice cores in mid- to high latitudes (Kang et al., 2002; \r\nKekonen et al., 2005; Lamarque et al., 2013; Iizuka et al., 2018). "


id: 3c3b707243c2022735e3cd35e213732b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7790
content:
"Indirect evidence of decadal trends in the atmospheric loading of \r\nsulphur are provided by Alpine ice cores, mainly influenced by \r\nEuropean sources (Engardt et al., 2017), and ice cores from Svalbard \r\n(Samyn et al., 2012) and Greenland (Patris et al., 2002; Iizuka et al., \r\n2018) influenced by sources in Europe and North America. These \r\nshow similar patterns with a  weak increase from the end of the \r\n19th  century up to around 1950, followed by a  steep increase up \r\nto around 1980, and then a significant decrease over the next two \r\ndecades (see also Section 2.2.6). This general trend is consistent with \r\nthe emissions of SO2 in North America and Europe (Figures 6.18 and \r\n6.19; Hoesly et al., 2018)."


id: f6310f617cd830f1d19b5bc25e6198a6
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40674
content:
"Global and regional models qualitatively reproduce observed trends \r\nover North America and Europe for the period 1990-2015 for which \r\nemissions changes are generally well quantified (Aas et al., 2019; \r\nMortier et al., 2020), building confidence in the relationship between \r\nemissions, concentration, deposition and radiative forcing derived \r\nfrom these models. However, the models seem to systematically \r\nunderestimate sulphate (Bian et al., 2017; Lund et al., 2018a) and \r\nAOD (Lund et al., 2018a; Gliss et al., 2021), and there are quite \r\nlarge differences in the models' distribution of the concentration \r\nfields of sulphate driven by differences in the representation of \r\nphotochemical production and sinks of aerosols. One global model \r\nstudy also highlighted biases in simulated sulphate trends over the \r\n2001-2015 period over eastern China due to uncertainties in the \r\nCEDS anthropogenic SO2 emissions trends (Paulot et al., 2018a)."


id: f6c2ba0f997ba7e72a0c9a5323b88d69
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40779
content:
"In summary, there is high confidence that the global tropospheric \r\nsulphate burden increased from 1850 to around 2005, but there \r\nare large regional differences in the magnitude. Sulphate aerosol \r\nconcentrations in North America and Europe have declined over 1980-\r\n2015 with slightly stronger reductions in North America (47 +- 20%) \r\nthan in Europe (40 +- 30%) over 2000-2015, though Europe had \r\nlarger reductions in the prior decade (1990-2000; 52 +- 21% and \r\n21 +- 14% respectively for Europe and North America). In Asia, the \r\ntrends are more scattered, though there is medium confidence\r\nthat there was a  strong increase up to around 2005, followed by \r\na  steep decline in China, while over India, the concentrations are \r\nincreasing steadily."


id: 926d3172be7c387fe13f859576b587e4
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22979
content:
"Models indicate that the burden of fine-mode NO3\r\n-\r\n has increased \r\nby a factor of two to five from 1850-2000 (Xu and Penner, 2012; \r\nHauglustaine et al., 2014; Lund et al., 2018a), an increase that has "


id: ab7099ee01cafe19c835fe0e80d90209
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27391
content:
"accelerated between 2001 and 2015 (Lund et al., 2018a; Paulot et al., \r\n2018b). The sensitivity of NO3\r\n-\r\n to changes in NH3, SO4\r\n2-, and HNO3\r\nis determined primarily by aerosol pH, temperature, and aerosol \r\nliquid water (Guo et al., 2016, 2018; Weber et al., 2016; Nenes et al., \r\n2020). In regions where aerosol pH is high, changes in NO3\r\n-\r\n follow \r\nchanges in NOx emissions, consistent with the observed increase of \r\nammonium nitrate in northern China from 2000-2015 (Wen et al., \r\n2018) and its decrease in the US Central Valley (Pusede et al., 2016). \r\nIn contrast, the decrease in SO2 emissions in the south-east USA has \r\ncaused little change in NO3\r\n- from 1998-2014 as nitric acid largely \r\nremains in the gas phase due to highly acidic aerosols (Weber et al., \r\n2016; Guo et al., 2018). "


id: 60f97d831a74e7ba20eee7a061c6ca22
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14355
content:
"Carbonaceous aerosols are black carbon (BC)3, which is soot \r\nmade almost purely of carbon, and organic aerosols4 (OA), which \r\nalso contain hydrogen and oxygen and can be of both primary \r\n(POA) or secondary (SOA) origin. BC and a  fraction of OA called \r\nbrown carbon (BrC) absorb solar radiation. The various components \r\nof carbonaceous aerosols have different optical properties, so the \r\nknowledge of their partition, mixing, coating and ageing is essential \r\nto assess their climate effect (Section 7.3.3.1.2). "


id: ad49c915fb8cfcbaeca6e0751476745f
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27730
content:
"Carbonaceous aerosols receive attention in the scientific and policy \r\narena due to their radiative forcing, and their sizeable contribution \r\nto PM in an air-quality context (Rogelj et al., 2014b; Harmsen et al., \r\n2015; Shindell et al., 2016; Haines et al., 2017; Myhre et al., 2017). \r\nBC  exerts a  positive forcing, but the forcing from carbonaceous \r\naerosol as a  whole is negative (Bond et al., 2013; Thornhill et al., \r\n2021b). On average, carbonaceous aerosols account for 50-70% \r\nof PM with a  diameter lower than 1 um in polluted and pristine \r\nareas (Zhang et al., 2007; Carslaw et al., 2010; Andreae et al., 2015; \r\nMonteiro dos Santos et al., 2016; Chen et al., 2017)."


id: f844c7b31ee1fbe08f458953fd103c7e
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 41053
content:
"In summary, there is high confidence that the NH4\r\n+ and NO3-\r\n burdens \r\nhave increased from the pre-industrial period to the present day, \r\nalthough the magnitude of the increase is uncertain especially \r\nfor NO3\r\n-\r\n. The sensitivity of NH4\r\n+ and NO3-\r\n to changes in NH3, H2SO4\r\nand HNO3 is well understood theoretically. However, it remains \r\nchallenging to represent in models, in part because of uncertainties \r\nin the simulation of aerosol pH, and only a minority of ESMs consider \r\nnitrate aerosols in CMIP6."


id: dc39ea58bc696148d847d1a7f6265b99
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36018
content:
"et al. (2021), based on 14 AeroCom models, is 0.131 +- 0.047 Tg \r\n(median  +- 1 standard deviation). The range encompasses values \r\nreported by independent single-model estimates (Huang et al., 2013; \r\nLee et al., 2013; Sharma et al., 2013; Q. Wang et al., 2014; Tilmes \r\net al., 2019). "


id: b70d9987e95a57b4d7909b04d648410e
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29415
content:
"Knowledge of carbonaceous aerosol atmospheric abundance \r\ncontinues to rely on global models due to a  lack of global-scale \r\nobservations. For BC, models agree within a  factor of two with \r\nmeasured surface mass concentrations in Europe and North America, \r\nbut underestimate concentrations at the Arctic surface by one to two \r\norders of magnitude, especially in winter and spring (Lee et al., 2013; \r\nLund et al., 2018a). For OA, AeroCom models underestimate surface \r\nmass concentrations by a factor of two over urban areas, as their low \r\nhorizontal resolution prevents them from resolving local pollution \r\npeaks (Tsigaridis et al., 2014; Lund et al., 2018a). Models agree \r\nwithin a  factor of two with OA surface concentrations measured \r\nat remote sites, where surface concentrations are more spatially \r\nuniform (Tsigaridis et al., 2014). "


id: b2c1a5f44f46923700fa8ece5e8a3143
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 28691
content:
"In summary, the lack of global-scale observations of carbonaceous \r\naerosols, the complexity of processes influencing them, and the large \r\nspread in their simulated global budget and burdens means that \r\nthere is only low confidence in the quantification of the present-day \r\natmospheric distribution of individual components of carbonaceous \r\naerosols. Global trends in carbonaceous aerosols cannot be \r\ncharacterized due to limited observations, but sites representative of \r\nbackground conditions have reported multi-year declines in BC over \r\nseveral regions of the Northern Hemisphere."


id: dcc9103614b425e203266238d1444f3c
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36108
content:
"The atmospheric oxidising capacity is determined primarily by \r\ntropospheric hydroxyl (OH) radical and to a smaller extent by NO3\r\nradical, ozone, hydrogen peroxide (H2O2) and halogen radicals. OH \r\nis the main sink for many SLCFs, including methane, halogenated \r\ncompounds (HCFCs and HFCs), CO and NMVOCs, controlling their \r\nlifetimes and consequently their abundance and climate influence. \r\nOH-initiated oxidation of methane, CO and NMVOCs in the presence \r\nof NOx leads to the production of tropospheric ozone. OH also \r\ncontributes to the formation of aerosols from oxidation of SO2 to \r\nsulphate and NMVOCs to secondary organic aerosols. The evolution \r\nof the atmospheric oxidising capacity of the Earth driven by human \r\nactivities and natural processes is, therefore, of significance for \r\nclimate and air-quality concerns. "


id: 52217e44f9865bbd4ca27b6379812f5c
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 11721
content:
"Simulated BC burdens show a  large spread among models \r\n(Gliss  et al., 2021), despite using harmonised primary emissions, \r\nbecause of differences in BC removal efficiency linked to different \r\ntreatment of ageing and mixing, particularly in strong source regions. \r\nThe multi-model median BC burden for the year 2010 from Gliss "


id: 45358136822caca96f6789ac40dc1fd7
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9404
content:
"The main source of tropospheric OH is the photoexcitation of \r\ntropospheric ozone that creates an electronically excited oxygen atom \r\nwhich reacts with water vapour producing OH. A secondary source of \r\nimportance for global OH is the recycling of peroxy radicals formed by "


id: bf5e7aef7fa4dec564f4e3c26294d4d4
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30937
content:
"Oscillation, also contribute to OH variability via changes in lightning \r\nNOx emissions and deep convection (Turner et al., 2018), and fire \r\nemissions (Rowlinson et al., 2019). "


id: 172637a9b4ae64228081dc265ca572e1
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1288
content:
"the reaction of OH with reduced and partly oxidized species, including \r\nmethane, CO and NMVOCs. In polluted air, NOx emissions control the \r\nsecondary OH production, while in pristine air it occurs via other \r\nmechanisms involving, in particular, isoprene (Lelieveld et al., 2016; \r\nWennberg et al., 2018). Knowledge of the effect of isoprene oxidation \r\non OH recycling has evolved tremendously over the past decade, \r\nfacilitating mechanistic explanation of elevated OH concentrations \r\nobserved in locations characterised by low NOx levels (Hofzumahaus \r\net al., 2009; Paulot et al., 2009; Peeters et al., 2009, 2014; Fuchs et al., \r\n2013). Since AR5, the inclusion of improved chemical mechanisms \r\nin some CTMs suggest advances in understanding of the global OH \r\nbudget, however, these improvements have yet to be incorporated in \r\nCMIP6-generation ESMs. "


id: 11cf7a59ce20d724aaf9c284223e9e44
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 327
content:
"Global-scale OH observations are non-existent because of its \r\nextremely short lifetime (around 1 second) and therefore global \r\nOH abundance and its time variations are either inferred from \r\natmospheric measurements of methyl chloroform (MCF; Prinn et al., \r\n2018 and references therein) or derived from global atmospheric \r\nchemistry models (Lelieveld et al., 2016). The AR5 reported small \r\ninterannual OH variations in the 2000s based on atmospheric \r\ninversions of MCF observations (within +-5%) and global CCMs and \r\nCTMs (within +-3%) (Ciais et al., 2013). "


id: 39c63179406d1d3d5b21f48c5974ab0c
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7379
content:
"Since AR5, there is much closer agreement in the estimates of \r\ninterannual variations in global mean OH derived from atmospheric \r\ninversions, empirical reconstruction, and global CCMs and ESMs, \r\nwith an estimate of 2-3% over the 1980-2015 period (Table 6.7). \r\nWhile the different methodologies agree on the occurrence of small \r\ninterannual variations, there is much debate over the longer-term \r\nglobal OH trend. Two studies using multi-box model inversions of \r\nMCF and methane observations suggest large positive and negative \r\ntrends since the 1990s in global mean OH (Rigby et al., 2017; \r\nTurner et al., 2017), however, both find that observational "


id: 985420d7124e57c79e259d56fc55ddac
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24009
content:
"As a  result of the complex photochemistry, tropospheric OH \r\nabundance is sensitive to changes in SLCF emissions as well as \r\nclimate. Increases in methane, CO and NMVOCs reduce OH while \r\nincreases in water vapour and temperature, incoming solar radiation, \r\nNOx and tropospheric ozone enhance OH. The OH level thus responds \r\nto climate change and climate variability via its sensitivity to \r\ntemperature and water vapour, as well as the influence of climate \r\non natural emissions (e.g.,  wetland methane emissions, lightning \r\nNOx, BVOCs, fire emissions) with consequent feedbacks on climate \r\n(Section 6.4.5). Climate modes of variability, like El Nino-Southern "


id: 434001472a4bfe2e5f306b29387a0683
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9044
content:
"CO over the 2002-2013 period (with CO declining trends) into \r\na CCM (Section 6.3.4; Gaubert et al., 2017). Multi-model sensitivity \r\nanalysis suggests that increasing OH since 1980 is predominantly \r\ndriven by changes in anthropogenic SLCF emissions with the \r\ncomplementary influence of increasing NOx and decreasing CO \r\nemissions (Stevenson et al., 2020). "


id: a76e17e4e7e1ef41be577748848fc147
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26669
content:
"constraints are weak, such that a wide range of multi-annual OH \r\nvariations are possible. Indeed, Naus et al. (2019) find an overall \r\npositive global OH trend over the past two decades (Table  6.7) \r\nafter accounting for uncertainties and biases in atmospheric MCF \r\nand methane inversions, confirming the weakness in observational \r\nconstraints for deriving OH trends. Global ESMs, CCMs and CTMs \r\nexhibit increasing global OH after 1980 contrary to the lack of \r\ntrend derived from some atmospheric inversions and empirical \r\nreconstructions (Table 6.7). In particular, a three-member ensemble \r\nof ESMs participating in the AerChemMIP/CMIP6 agrees that global \r\nOH has increased since 1980 by around 9% (Figure 6.9) with an \r\nassociated reduction in methane lifetime (Stevenson et al., 2020). \r\nThis positive OH trend is in agreement with the OH increase of about \r\n7% derived by assimilating global-scale satellite observations of "


id: ce0c9e93d34ee472d4a3a788c74775db
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 33581
content:
"Over paleo time scales, proxy-based observational constraints from \r\nmethane and formaldehyde suggest tropospheric OH to be a factor \r\nof two to four lower in the Last Glacial Maximum (LGM) relative \r\nto pre-industrial levels, though these estimates are highly uncertain \r\n(Alexander and Mickley, 2015). Global models, in contrast, exhibit no \r\nchange in tropospheric OH (and consequently in methane lifetime) \r\nat the LGM relative to the pre-industrial period (Murray et al., 2014; "


id: 443b8021b20350c368ee91e9d0efe04
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9242
content:
"Quiquet et al., 2015), however, the sign and magnitude of OH changes \r\nare sensitive to model predictions of changes in natural emissions, \r\nincluding lightning NOx and BVOCs, and model representation of \r\nisoprene oxidation chemistry (Achakulwisut et al., 2015; Hopcroft \r\net al., 2017). "


id: 766486dfa227dacec81b7184d9a7a507
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18054
content:
"Regarding change since the pre-industrial era, at the time of the \r\nAR5, the ensemble mean of 17 global models participating in \r\nACCMIP indicated little change in tropospheric OH from 1850-2000. \r\nThis was due to the competing and finally offsetting changes in \r\nfactors enhancing or reducing OH with a consequent small decline \r\nin methane lifetime (Naik et al., 2013; Voulgarakis et al., 2013). \r\nHowever, there was large diversity in both the sign and magnitude \r\nof past OH changes across the individual models attributed to the \r\ndisparate implementation of chemical and physical processes (Nicely \r\net al., 2017; Wild et al., 2020). Analysis of historical simulations from \r\nthree CMIP6 ESMs indicates little change in global mean OH from \r\n1850 to about 1980 (Stevenson et al., 2020). However, there is no \r\nobservational evidence of changes in global OH since 1850 up to the \r\nearly 1980s to evaluate the ESMs. "


id: 3a400f940d968b7b307a7086e2067b2b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7454
content:
"In summary, global mean tropospheric OH does not show a significant \r\ntrend from 1850 up to around 1980 (low confidence). There is \r\nconflicting information from global models constrained by emissions \r\nversus observationally constrained inversion methods over the 1980-\r\n2014 period. A positive trend since 1980 (about 9% increase over \r\n1980-2014) is a  robust feature among ESMs and CCMs and there \r\nis medium confidence that this trend is mainly driven by increases in \r\nglobal anthropogenic NOx emissions and decreases in CO emissions. \r\nThere is limited evidence and medium agreement for positive trends \r\nor absence of trends inferred from observation-constrained methods. \r\nOverall, there is medium confidence that global mean OH has remained \r\nstable or exhibited a positive trend since the 1980s. "


id: c53000093ff6a8497c7f64d3bba1cbf3
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31999
content:
"In summary, CMIP6 models generally represent more processes \r\nthat drive aerosol-cloud interactions than the previous generation \r\nof climate models, but there is only medium confidence that those \r\nenhancements improve their fitness for the purpose of simulating \r\nradiative forcing due to aerosol-cloud interactions because only a few \r\nstudies have identified the level of sophistication required to do so. \r\nIn addition, the challenge of representing the small-scale processes \r\ninvolved in aerosol-cloud interactions, and a lack of relevant model\u0002data comparisons, does not allow a  quantitative assessment of \r\nthe progress of the models from CMIP5 to CMIP6 in simulating the \r\nunderlying conditions relevant for aerosol-cloud interactions at this \r\ntime. "


id: 828497674c046c19d8d00da6090e19e8
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 20088
content:
"The radiative forcing on the climate system introduced by SLCFs is \r\ndistinguished from that of long-lived greenhouse gases (LLGHGs) by \r\nthe diversity of forcing mechanisms for SLCFs, and the challenges \r\nof constraining these mechanisms via observations and of inferring \r\ntheir global forcings from available data. Chapter  7 assesses the \r\nglobal estimates of effective radiative forcing (ERF) due to SLCF \r\nabundance changes. This section assesses the characteristics \r\n(e.g.,  spatial patterns, temporal evolution) of forcings, emissions\u0002based SLCF forcings, climate response and feedbacks due to SLCFs \r\nrelying primarily on results from CMIP6 models. Additionally, the \r\nERFs for several aerosol-based forms of solar -radiation modification \r\n(SRM) are discussed in Section 6.4.6. "


id: fbff7bc2e1c6b3a8a97cb4ccf9848502
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 41693
content:
"Forcing and climate response due to changes in SLCFs are typically \r\nestimated from global models that vary in their representation of \r\nthe various chemical, physical and radiative processes (Box  6.1) \r\naffecting the causal chain from SLCF emissions to climate response \r\n(Figure  6.1). The AR5 noted that the representation of aerosol \r\nprocesses varied greatly in CMIP5 models and that it remained "


id: b88e568676103530ea67600138764aa0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29678
content:
"The highly heterogeneous distribution of SLCF abundances \r\n(Section 6.3) translates to strong heterogeneity in the spatial pattern \r\nand temporal evolution of forcing and climate responses due to \r\nSLCFs. This section assesses the spatial patterns of the current forcing \r\ndue to aerosols and their historical evolution by region. "


id: bf5ad36a959177d99078cfeb14f8c08d
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30933
content:
"of ERF trends is consistent with the regional long-term trends in \r\naerosol precursor emissions (Section 6.2; Figures 6.18 and 6.19) and \r\ntheir abundances (Section  6.3). However, at finer regional scales, \r\nthere are regions where sulphate aerosols are still following an \r\nupward trend (e.g., Southern Asia; Section 6.3.5) implying that the \r\ntrends in ERF may not have shifted for these regions. The continental\u0002scale ERF trends are also in line with the satellite-observed AOD \r\ntrends assessed in Section 2.2.6. Global mean ERF reaches maximum "


id: c986ef617a3cb61de82b7be2290ea393
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32781
content:
"Time evolution of 20-year means of regional net aerosol ERF shows \r\nthat the regions are divided into two groups depending on whether \r\nthe mean ERF attains its negative peak value in the 1970s-1980s \r\n(e.g., Europe, North America) or in the late 1990s-2000s (e.g., Asia, \r\nSouth America; Figure 6.11). Qualitatively, this shift in the distribution "


id: 496a4088f6670027a31c6f5901f4ecca
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10141
content:
"formation and oxidation capacity with feedbacks on the methane \r\nlifetime, depend non-linearly on the concentrations of NOx, CO and \r\nNMVOCs. This means that the results from the model simulations \r\ndepend to some extent on the chosen methodology. In AR5 (based \r\non Shindell et al., 2009; Stevenson et al., 2013) the attribution was \r\ndone by removing the anthropogenic emissions of individual species \r\none  by one from a  control simulation for present-day conditions. \r\nFurther, only the radiative forcings, and not the ERF (mainly including \r\nthe effect of aerosol-cloud interactions) were attributed to the \r\nemitted compounds."


id: ee74123ed4e7d71910adf7d7d4c88da0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39328
content:
"Since AR5, the emissions estimates have been revised and extended \r\nfor CMIP6 (Hoesly et al., 2018), the models have been further \r\ndeveloped, the period has been extended (1750-2019, versus 1750-\r\n2011 in AR5) and the experimental setup for the model simulations \r\nhas changed (Collins et al., 2017), making a  direct comparison of \r\nresults difficult. Figure  6.12a shows the global and annual mean \r\nERF attributed to emitted compounds over the period 1750-2019 \r\nbased on AerChemMIP simulations (Thornhill et al., 2021b) where \r\nanthropogenic emissions or concentrations of individual species \r\nwere perturbed from 1850 to 2014 levels (methodology described in \r\nSupplementary Material 6.SM.1). "


id: 1797bdf1d2b5a1ce4949d2454127d45f
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1357
content:
"The ERF based on primary CO2 emissions is slightly lower than the \r\nabundance-based estimate (Section 7.3.2.1) because the abundance\u0002based ERF combines the effect of primary CO2 emissions and a small \r\nadditional secondary contribution from atmospheric oxidation of \r\nmethane, CO, and NMVOCs (4%) of fossil origin, consistent with \r\nAR5 findings."


id: 42ddff67a9b54bd624e6be6d70e42d50
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8956
content:
"negative values in the mid-1970s and its magnitude gradually \r\ndecreases thereafter. This weakening of the negative forcing since \r\n1990 agrees with findings that attribute this to a  reduction in \r\nglobal mean SO2 emissions combined with an increase in global \r\nBC (Myhre et al., 2017). Uncertainties in model-simulated aerosol \r\nERF distribution and trends can result from inter-model variations \r\nin the representation of aerosol-cloud interactions and aerosol \r\nmicrophysical processes as also demonstrated by Bauer et al. (2020). "


id: 33fb104e9bac1f0923abf208efd38c46
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6390
content:
"Ozone-depleting substances, such as N2O and halocarbons, cause \r\na  reduction in stratospheric ozone, which affects ozone and OH \r\nproduction in the troposphere through ultraviolet radiation changes \r\n(and thus affect methane). They also have indirect effects on aerosols \r\nand clouds (Karset et al., 2018), since changes in oxidants induce \r\nchanges in the oxidation of aerosol precursors. "


id: 7f3004f868feea9815da73520eaeede0
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 19530
content:
"In summary, the spatial and temporal distribution of the net aerosol \r\nERF from 1850-2014 is highly heterogeneous (high confidence). \r\nGlobally, there has been a  shift from increase to decrease of the \r\nnegative net aerosol ERF driven by trends in aerosol and their \r\nprecursor emissions (high confidence). However, the timing of this \r\nshift varies by continental-scale region and has not occurred for some \r\nfiner regional scales. "


id: db41addd604ea89be6680cd02ac3eb92
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 35848
content:
"The net ERF from N2O emissions is estimated to be 0.24 [0.13 to 0.34] \r\nW m-2, which is very close to the abundance-based estimate of \r\n0.21 W m-2 (Section  7.3.2.3). The indirect contributions from N2O \r\nare relatively minor with negative (methane-lifetime) and positive \r\n(ozone-and-cloud) effects nearly compensating each other. Emissions \r\nof halogenated compounds, including CFCs and HCFCs, were assessed \r\nas very likely causing a net-positive ERF in the AR5. However, recent \r\nstudies (Morgenstern et al., 2020; O'Connor et al., 2021; Thornhill \r\net al., 2021b) find strong adjustments in Southern Hemisphere \r\naerosols and clouds, such that the very likely range in the emission\u0002based ERF for CFC + HCFCs + HFCs now also include negative values. "


id: 1a36802469c133f14447b66337eb0465
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1807
content:
"The ERFs attributable to emissions versus concentrations for several \r\nSLCFs including ozone and methane are different. A concentration \r\nchange, used to assess the abundance-based ERF, results from the\r\nchanges in emissions of multiple species and subsequent chemical \r\nreactions. The corollary is that the perturbation of a single emitted \r\ncompound can induce subsequent chemical reactions and affect the \r\nconcentrations of several climate forcers (chemical adjustments); \r\nthis  is what is accounted for in emissions-based ERF. Due to non\u0002linear chemistry (Section  6.3) and non-linear aerosol-cloud \r\ninteractions (Section  7.3.3.2), the ERF attributed to the individual \r\nspecies cannot be precisely defined and can only be estimated \r\nthrough model simulations. For example, the ERF attributed to \r\nmethane emissions, which includes indirect effects through ozone "


id: 89f83051db27012d050bd0e301ed7a67
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21441
content:
"For methane emissions, in addition to their direct effect, there are \r\nindirect positive ERFs from methane enhancing its own lifetime, \r\ncausing ozone production, enhancing stratospheric water vapour, \r\nand influencing aerosols and the lifetimes of HCFCs and HFCs (Myhre \r\net al., 2013; O'Connor et al., 2021). The ERF from methane emissions \r\nis considerably higher than the ERF estimate resulting from its \r\nabundance change. The central estimate with the very likely range is \r\n1.19 [0.81 to 1.58] W m-2 for the emissions-based estimate compared "


id: ef092eb62331acdc2f55f8b1341a3dd
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39423
content:
"Anthropogenic emissions of SO2 lead to the formation of sulphate \r\naerosols and a negative ERF through aerosol-radiation and aerosol-\r\ncloud interactions. The emissions-based ERFaci, which was not \r\npreviously considered in AR5, is now included. The estimated ERF is \r\nthus considerably more negative than the AR5 estimate with a radiative \r\nforcing of -0.4 W m-2, despite the decline of ERF due to aerosols since \r\n2011 (Section 7.3.3.1.3, Figure 6.12a). SO2 emissions are estimated \r\nto contribute to a negative ERF of -0.94 [-1.63 to -0.25] W m-2, with \r\n-0.23 W m-2 from aerosol-radiation interactions and -0.70 W m-2 \r\nfrom aerosol-cloud interactions. Emissions of NH3 lead to formation \r\nof ammonium-nitrate aerosols with an estimated ERF of -0.03 W m-2. "


id: 11d559591a02d28fb835d7cd67d13a35
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 331
content:
"with 0.54 W m-2 for the abundance-based estimate (Section 7.3.5). \r\nThe abundance-based ERF estimate for methane results from \r\ncontributions of its own emissions and the effects of several other \r\ncompounds, some decreasing methane lifetime, notably NOx, which \r\nimportantly reduce the methane abundance-based ERF. Emissions of \r\nCO and NMVOCs both indirectly contribute to a positive ERF through \r\nenhancing ozone production in the troposphere and increasing \r\nthe methane lifetime. For CO and NMVOCs of fossil origin there is \r\nalso a 0.07 W m-2 contribution to CO2 from their oxidation. The very \r\nlikely total ERF of CO and NMVOCs emissions is estimated to be \r\n0.44 [0.22 to 0.67] W m-2. "


id: fe0ec0d58edf030dde00715c4463134b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 42065
content:
"NOx causes a  positive ERF through enhanced tropospheric ozone \r\nproduction and a negative ERF through enhanced OH concentrations \r\nthat reduce the methane lifetime. There is also a small negative ERF \r\ncontribution through the formation of nitrate aerosols, although \r\nonly three of the AerChemMIP models include nitrate aerosols. The \r\nbest estimate of the net ERF from changes in anthropogenic NOx\r\nemissions is -0.27 [-0.55 to 0.01] W m-2. The magnitude is somewhat \r\ngreater than the AR5 estimate (-0.15 [-0.34 to +0.02] W m-2) but \r\nwith a similar level of uncertainty. The difference between AR6 and \r\nAR5 estimates is possibly due to the different modeling protocols \r\n(see Supplementary Material: 6.SM.1)."


id: d9b12d5d9a84f34bcb9d6d5da8928039
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 35595
content:
"This section briefly discusses the climate response to SLCFs, in \r\nparticular to changes in aerosols, and gathers complementary \r\ninformation and assessments from Chapters 3, 7, 8 and 10. "


id: f7a3f728ad75db3d5242f73010c5cef
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40942
content:
"Warming or cooling atmospheric aerosols, such as BC and sulphate, \r\ncan affect temperature and precipitation in distinct ways by \r\nmodifying the shortwave and longwave radiation, the lapse rate \r\nof the troposphere, and influencing cloud microphysical properties \r\n(Section  10.1.4.1.4, Box  8.1). An important distinction between \r\nscattering and absorbing aerosols is the opposing nature of their \r\ninfluences on circulation, clouds and precipitation, besides surface \r\ntemperature as evident from the contrasting regional climate \r\nresponses to regional aerosol emissions (e.g., Lewinschal et al., 2019; \r\nSand et al., 2020; also see Chapters 8 and 10). "


id: 4d093566a6113de3bf22c01e5166215
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10824
content:
"The contributions from the emitted compounds to GSAT broadly follow \r\ntheir contributions to the ERF, mainly because their evolution over the \r\npast decades have been relatively similar and slow enough compared \r\nto their lifetimes to be reflected similarly in their ERF and GSAT despite \r\nthe delay of the GSAT response to ERF changes (Section  6.6.1). \r\nHowever, for some SLCFs (e.g., SO2) that have been reduced globally, \r\ntheir contribution to GSAT change is slightly higher compared with that \r\nof CO2 than their relative contribution to ERF because the peak in their \r\nERF change has already occurred (Section 6.4.1) whereas the peak of \r\ntheir GSAT effect started to decline recently (Figure 7.9). This is due \r\nto the inertia of the climate system delaying the full response of GSAT \r\nto a change in forcing (Figure 6.15). "


id: 34d328e0e60d6a046aa4a3a3866795d9
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6535
content:
"In summary, emissions of SLCFs, especially methane, NOx and SO2, \r\nhave substantial effects on effective radiative forcing (ERF) (high \r\nconfidence). The net global emissions-based ERF of NOx is negative \r\nand that of NMVOCs is positive, in agreement with the AR5 assessment \r\n(high confidence). For methane, the emissions-based ERF is twice as \r\nhigh as the abundance-based ERF (high confidence). SO2 emissions \r\nmake the dominant contribution to the ERF associated with the \r\naerosol-cloud interaction (high confidence). The contributions from \r\nthe emitted compounds to GSAT broadly follow their contributions to \r\nthe ERF (high confidence). However, due to the inertia of the climate \r\nsystem delaying the full GSAT response to a  change in forcing, \r\nthe contribution to GSAT change due to SO2 emissions is slightly \r\nhigher compared with that due to CO2 emissions (than their relative \r\ncontributions to ERF) because the peak in emission-induced SO2 ERF \r\nhas already occurred."


id: 9e0d3d2b10c33a9ab8cef069ddc5fed5
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25044
content:
"In AR5, there was low confidence in the overall understanding of \r\nclimate response to spatially varying patterns of forcing, though \r\nthere was medium to high confidence in some regional climate \r\nresponses, such as the damped warming of the NH and shifting of \r\nthe ITCZ from aerosols, and positive feedbacks enhancing the local \r\nresponse from high-latitude snow and ice albedo changes. Since \r\nAR5, the relationship between inhomogeneous forcing and climate \r\nresponse is better understood, providing further evidence of the \r\nclimate influence of SLCFs (aerosols and ozone in particular) on \r\nglobal to regional scales (Collins et al., 2013; Shindell et al., 2015; \r\nAamaas et al., 2017; Kasoar et al., 2018; Persad and Caldeira, 2018; \r\nWilcox et al., 2019) which differ from the relatively homogeneous \r\nspatial influence from LLGHGs. "


id: ceadddffea6615246e5ca9badf56276
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 33700
content:
"Large geographical variations in aerosol ERFs (Section 6.4.1) affect \r\nglobal and regional temperature responses (Myhre et al., 2013; \r\nShindell et al., 2015). Multi-model CMIP6 ensemble-mean results \r\n(Figure  6.13) show cooling over almost all areas of the globe in \r\nresponse to increases of aerosol and their precursor emissions from \r\n1850 to the recent past (1995-2014). While the ERF has hotspots, "


id: 47410cedfd89bcf3a9a5ea348b638841
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9738
content:
"The asymmetric aerosol and greenhouse gas forcing on regional\u0002scale climate responses have also been assessed to lead to \r\ncontrasting effects on precipitation in Chapter  8. The asymmetric \r\nhistorical radiative forcing due to aerosols led to a southward shift in \r\nthe tropical rain belt (high confidence) and contributed to the Sahel \r\ndrought from the 1970s to the 1980s (high confidence). Furthermore, \r\nthe asymmetry of the forcing led to contrasting effects in monsoon \r\nprecipitation changes over West Africa, Southern Asia and Eastern \r\nAsia over much of the mid-20th century due to GHG-induced \r\nprecipitation increases counteracted by anthropogenic aerosol\u0002induced decreases (high confidence) (see Section 8.3 and Box 8.1). "


id: 8c381e2fdd468c59935349d118d852af
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21842
content:
"The effects of changes in aerosols on local and remote changes in \r\ntemperature, circulation and precipitation are sensitive to a number \r\nof model uncertainties affecting aerosol sources, transformation \r\nand resulting radiative efficacy. Therefore, regional climate effects \r\nin global model studies must be interpreted with caution. When \r\ninvestigating the climate response to regional aerosol emissions, \r\nsuch uncertainties are likely to be confounded even further by \r\nthe variability between models in regional climate and circulation "


id: 8b7cc7036601a6e09d8ce12911acbb3e
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21706
content:
"patterns, leading to greater inter-model spread at regional scales \r\nthan at a global scale (Baker et al., 2015; Kasoar et al., 2016). "


id: 57d4ff60c26ea4c9affc9d0ad0ffd671
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12714
content:
"Nr deposition increased, and the beneficial effects of Nr on root \r\ndevelopment were lost at higher ozone treatments (Mills et al., \r\n2016). Reducing uncertainties in ozone vegetation damage effects \r\non the carbon cycle requires improved information on the sensitivity \r\nof different plant species to ozone, and measurements of ozone \r\ndose-response relationships for tropical plants, which are currently \r\nlacking. Surface ozone effect on the land carbon sink and indirect CO2\r\nforcing, therefore, remains uncertain. Collins et al. (2010) showed that \r\nadding in the effects of surface ozone on vegetation damage and \r\nreduced uptake of CO2 added about 10% to the methane emissions \r\nmetrics and could change the sign of the NOx metrics. However, this \r\nestimate has to be considered as an upper limit due to limitation of \r\nthe paramaterization used by Sitch et al. (2007) considering more \r\nrecent knowledge and is thus not included in the current metrics \r\n(Section 7.6.1.3)."


id: e81b4f150ee66f32a2710008af3e9056
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38158
content:
"In summary, over the historical period, changes in aerosols and \r\ntheir ERF have primarily contributed to cooling, partly masking \r\nthe human-induced warming (high confidence). Radiative forcings \r\ninduced by aerosol changes lead to both local and remote changes in \r\ntemperature (high confidence). The temperature response preserves \r\nhemispheric asymmetry of the ERF but is more latitudinally uniform \r\nwith strong amplification of the temperature response towards the \r\nArctic (medium confidence)."


id: e9f96f249995e9e32e463dccd5a0fcf
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38515
content:
"Deposition of reactive nitrogen (Nr; i.e., NH3 and NOx) increases the \r\nplant productivity and carbon sequestration in N-limited forests and \r\ngrasslands, and also in open and coastal waters and open ocean. Such \r\ninadvertent fertilization of the biosphere can lead to eutrophication \r\nand reduction in biodiversity in terrestrial and aquatic ecosystems. \r\nThe AR5 assessed that it is likely that Nr deposition over land currently \r\nincreases natural CO2 sinks, in particular in forests, but the magnitude \r\nof this effect varies between regions (Ciais et al., 2013). Increasing \r\nNr deposition or the synergy between increasing Nr deposition \r\nand atmospheric CO2 concentration could have contributed to the \r\nincreasing global-net land CO2 sink (Section 5.2.1.4.1). "


id: a7a78d16a065596313121949f279b6c6
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26707
content:
"In summary, reactive nitrogen, ozone and aerosols affect terrestrial \r\nvegetation and the carbon cycle through deposition and effects on \r\nlarge-scale radiation (high confidence) but the magnitude of these \r\neffects on the land carbon sink, ecosystem productivity and indirect \r\nCO2 forcing remain uncertain due to the difficulty in disentangling \r\nthe complex interactions between the effects. As such, we assess the \r\neffects to be of second order in comparison to the direct CO2 forcing \r\n(high confidence) but, at least for ozone, it could add a substantial \r\n(positive) forcing compared with its direct forcing (low confidence). "


id: 283f826713a7727a200e4185076a8a40
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 4280
content:
"Climate change-induced changes in atmospheric composition and \r\nforcing due to perturbations in natural processes constitute an \r\nEarth system feedback amplifying (positive feedback) or diminishing \r\n(negative feedback) the initial climate perturbation (Ciais et al., 2013; \r\nHeinze et al., 2019). Quantification of these biogeochemical feedbacks \r\nis important to allow for a better estimate of the expected effects \r\nof emissions reduction policies for mitigating climate change and \r\nthe effect on the allowable global carbon budget (Lowe and Bernie, \r\n2018). Biogeochemical feedbacks due to changes in the carbon cycle \r\nare assessed in Section  5.4.5, while physical and biogeophysical "


id: 44fac1b9718a83dc6f80dde4dfd4f462
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9366
content:
"Climate-ozone feedback: Changes in ozone concentrations in \r\nresponse to projected climate change have been shown to lead to \r\na  potential climate-atmospheric chemistry feedback. Chemistry-\r\nclimate models consistently project a  decrease in lower tropical \r\nstratospheric ozone levels due to enhanced upwelling of ozone\u0002poor tropospheric air associated with surface warming-driven \r\nstrengthening of the Brewer-Dobson circulation (Bunzel and \r\nSchmidt, 2013). Further, models project an increase in middle and \r\nextratropical stratospheric ozone due to increased downwelling \r\nthrough the strengthened Brewer-Dobson circulation (Bekki et al., \r\n2013; Dietmuller et al., 2014). These stratospheric ozone changes \r\ninduce a  net-negative global mean ozone radiative feedback \r\n(Dietmuller et al., 2014). Tropospheric ozone shows a  range of \r\nresponses to climate with models generally agreeing that warmer "


id: 7e323311016903ccfee68ac601d036aa
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 19371
content:
"Climate-DMS feedback: Dimethyl sulphide (DMS) is produced \r\nby marine phytoplankton and is emitted to the atmosphere where \r\nit can lead to the subsequent formation of sulphate aerosol and \r\nCCN (Section  6.2.2.5). Changes in DMS emissions from ocean \r\ncould feedback on climate through their response to changes in \r\ntemperature, solar radiation, ocean mixed-layer depth, sea ice extent, \r\nwind speed, nutrient recycling or shifts in marine ecosystems due to \r\nocean acidification and climate change, or atmospheric processing \r\nof DMS into CCN (Heinze et al., 2019). Models with varying degrees \r\nof representation of the relevant biogeochemical processes and "


id: bc53c634c0a0003712e2a25f8539385a
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30380
content:
"parametrization of lightning in ESMs (Section 6.2.1.2; Finney et al., \r\n2016b; Clark et al., 2017). AerChemMIP multi-model ensemble mean \r\nestimate a net-negative climate feedback from increases in lightning \r\nNOx in a warming world (Thornhill et al., 2021a). All AerChemMIP \r\nmodels use a  cloud-top height lightning parametrization that \r\npredicts increases in lightning with warming. However, a  positive \r\nclimate-lightning NOx feedback cannot be ruled out because of \r\nthe dependence of the response to lightning parametrizations as \r\ndiscussed in Section 6.2.2.1. "


id: a0a5bd2fe15523c86db307fa0468ee59
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25472
content:
"Climate-methane lifetime feedback: Warmer and wetter climate \r\nwill lead to increases in OH and oxidation rates leading to reduced \r\natmospheric methane lifetime  - a  negative feedback (Naik et al., \r\n2013; Voulgarakis et al., 2013). Furthermore, since OH is in turn \r\nremoved by methane, the climate-methane lifetime feedback will be \r\namplified (Section 6.3.1; Prather, 1996). Based on the multi-model \r\nresults of Voulgarakis et al. (2013), a for climate-methane lifetime \r\nis estimated to be -0.030 +- 0.01 W m-2 degC-1 by Heinze et al. (2019). \r\nThe assessed central value of a based on the AerChemMIP ensemble \r\nis within the range of this estimate but with greater uncertainty \r\n(Thornhill et al., 2021a). "


id: 7908673a364a144693181dcfa9297b47
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18483
content:
"In summary, climate-driven changes in emissions, atmospheric \r\nabundances or lifetimes of SLCFs are assessed to have an\r\noverall cooling effect, that is, a  negative feedback parameter of \r\n-0.20  [-0.41  to + 0.01] W m-2 degC-1, thereby reducing climate \r\nsensitivity (Section 7.4.2.5.1). This net feedback parameter is obtained \r\nby summing the assessed estimates for the individual feedback given \r\nin Table 6.8. Confidence in the magnitude and the sign of most of the \r\nindividual as well as the total non-CO2 biogeochemical feedbacks \r\nremains low as evident from the large range in the value of a. This \r\nlarge uncertainty is attributed to the diversity in model representation \r\nof the relevant chemical and biogeochemical processes based on \r\nlimited process-level understanding. "


id: 246d5af9f3756be14a1f5132ec1358bc
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3602
content:
"Climate-lightning NOx feedback: As discussed in Section 6.2.2.1, \r\nclimate change influences lightning NOx emissions. Increases in \r\nlightning NOx emissions will not only increase tropospheric ozone \r\nand decrease methane lifetime but also increase the formation \r\nof sulphate and nitrate aerosols, via oxidant changes, offsetting \r\nthe positive forcing from ozone. The response of lightning NOx to \r\nclimate change remains uncertain and is highly dependent on the "


id: ac1c37aafc62b7da4ed0152bcf86bf15
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27519
content:
"Table 6.8 | Assessed central estimates and the very likely ranges (5-95%) of non-CO2 biogeochemical feedback parameter (ax) based on the AerChemMIP \r\nensemble estimates (Thornhill et al., 2021a). As in Section 7.4.1.1, ax (W m-2 degC-1) for a feedback variable x is defined as ax = dN\r\ndx\r\ndx\r\ndT where dNdx is the change in TOA energy \r\nbalance in response to a change in x induced by a change in surface temperature (T). The 5-95% range is calculated as mean +- standard deviation x 1.645 for each feedback. \r\nThe level of confidence in these estimates is low owing to the large intermodel spread. Published estimates of ax are also shown for comparison. "


id: 99a11522983a9a397a00456a3d458566
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24263
content:
"aerosol layer, and hence the ERF efficiency, which also depends \r\non the dispersion, transport, and residence time of the aerosols. "


id: d14e18849da2fe698e90781c2f228628
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34175
content:
"Solar radiation modification (SRM; Sections 4.6.3.3 and 8.6.3) has \r\nthe potential to exert a  significant ERF on the climate, mainly by \r\naffecting the SW component of the radiation budget (e.g., Caldeira \r\net al., 2013; NRC, 2015; Lawrence et al., 2018). The possible ways \r\nand the extent to which the most commonly discussed options may \r\naffect radiative forcing is addressed in this section. Side effects of \r\nSRM on stratospheric ozone and changes in atmospheric transport \r\ndue to radiative heating of the lower stratosphere are discussed in \r\nSection 4.6.3.3. "


id: 156a47174f7e5b90d0a598b0c11a00aa
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 982
content:
"5 Baseline ozone is defined as the observed ozone at a site when it is not influenced by recent, locally emitted or anthropogenically produced pollution (Jaffe et al., 2018)."


id: 166b3f2e031cf08809ff5db9d1698c28
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1163
content:
"need for long simulations that include complex chemistry-natural \r\nsystem interactions with high computational cost, in addition \r\nto the difficulty related to the regionalization of climate change \r\n(Section  10.3.1.2). Changes in the occurrence of weather patterns \r\ninfluencing air pollution (e.g.,  anticyclonic stagnation conditions, \r\ntransport pathways from pollution sources, convection) due to \r\nclimate change are assessed in Chapters 4 and 11. "


id: 9bb53403294091fb18f09144597f80c8
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24652
content:
"Modelled ERFaci associated with cirrus cloud thinning (CCT) cover \r\na wide range in the literature, and the maximum are of the order of \r\n-0.8 to -3.5 W m-2, though they are of low confidence, with some \r\nstudies using more simplified representations (Mitchell and Finnegan, \r\n2009; Storelvmo et al., 2013; Kristjansson et al., 2015; Jackson et al., \r\n2016; Muri et al., 2018; Gasparini et al., 2020). ERFaci for CCT is mainly \r\naffected by particle seeding concentrations, with an optimum around \r\n20 L-1, according to limited evidence from models (Storelvmo et al., \r\n2013). Seeding leading to higher particle concentrations could lead \r\nto a warming (Storelvmo et al., 2013; Penner et al., 2015; Gasparini \r\nand Lohmann, 2016). The lack of representation of processes related \r\nto, for example, heterogeneous and homogeneous freezing and their \r\nprevalence, is a dominant source of uncertainty in ERF estimates, in \r\naddition to less research activity. "


id: 23466e5fa1fda4f1d7ab8491af607055
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3410
content:
"The AR5 assessed with high confidence that in unpolluted regions, \r\nhigher water vapour abundances and temperatures in a  warmer \r\nclimate would enhance ozone chemical destruction, leading to lower \r\nbaseline5 surface ozone levels (Kirtman et al., 2013). In polluted \r\nregions, AR5 assessed with medium confidence that higher surface \r\ntemperatures will trigger regional feedbacks in chemistry and local \r\nemissions that will increase surface ozone and the intensity of surface \r\nozone peaks. "


id: 4b7425526901c0220f87251b445d0f30
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10525
content:
"In summary, the aerosol and cloud microphysics involved with SRM \r\nare not well understood, notably due to insufficient (and varying \r\ndegrees of) representation of relevant processes in models. ERF of up \r\nto several W m-2 is reported in the literature, with SAI at the higher \r\nend and CCT with lower potentials, though it remains a challenge to \r\nestablish ERF potentials and efficacies with confidence. Modelling \r\nstudies have been published with more sophisticated treatment of \r\nSRM since AR5, but the uncertainties, such as cloud-aerosol radiation \r\ninteractions, remain large (high confidence)."


id: a6d70d7ff498242a179d8d328dfba4f3
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26564
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 41809
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12323
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36179
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22489
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12604
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31493
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23423
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10283
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29160
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 13599
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38662
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38863
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21708
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10131
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16016
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32702
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34386
content:
"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
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37837
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26701
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1027
content:
"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
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 15953
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31743
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6457
content:
"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
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27635
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36630
content:
"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
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32487
content:
"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
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14604
content:
"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 "


id: 7121b8b6622da195ca406cbb85a685d7
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17165
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14557
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2341
content:
"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).    "


id: 91bf0380e4b78ce3e9bf89e0fea518ba
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22864
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16466
content:
"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)."


id: 8a803db9954b9b82b7b70c39040ab667
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21540
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
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index: climate_gpt_v2
vector_id: 10447
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34046
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3487
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 5613
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14556
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8671
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36856
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18752
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32890
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23806
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39968
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 41460
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25900
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26905
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36060
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38879
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8658
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37635
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24104
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30877
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8280
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1864
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34356
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 33579
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2585
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2143
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 5765
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21864
content:
"Cross-Chapter Box 6.1 | Implications of COVID-19 Restrictions for Emissions, Air Quality \r\nand Climate"


id: e53c2714662318353bfef7a0f76427c3
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37631
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40746
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6882
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40744
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
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index: climate_gpt_v2
vector_id: 16108
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
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index: climate_gpt_v2
vector_id: 11501
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8159
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 11440
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36920
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21831
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6601
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21862
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40795
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34240
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23626
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17082
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38982
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 23564
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12283
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16278
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36074
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22419
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 36124
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12196
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2520
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24339
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2710
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32409
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25868
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30172
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6235
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 28940
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37116
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18803
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10215
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)."


id: e250ce2c558bc8ed8bce2683d5bb9adc
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37105
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
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index: climate_gpt_v2
vector_id: 18231
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 677
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29334
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24251
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 19026
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 20736
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 14377
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25844
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 42326
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37823
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34675
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25511
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1783
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21185
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29055
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 746
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17895
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9650
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 1139
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24855
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32189
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27666
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3208
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 28620
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10666
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21888
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18900
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37002
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25539
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
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2788
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37373
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31829
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
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 4625
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18786
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
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vector_id: 30763
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24400
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 13753
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27520
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: 6609ff75a780bc9a117408ee98d51dad
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 15211
content:
"Robert Colman (Australia), H. Damon Matthews (Canada), Venkatachalam Ramaswamy (United \r\nStates of America)"


id: f0407b7f5437461c9ad6b506d95bc211
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39656
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25669
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29843
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22822
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2508
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38055
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34255
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26371
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24964
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10992
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40464
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 20562
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31968
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3348
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31612
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26628
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25387
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 11325
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24502
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34213
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18787
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 19907
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16265
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21296
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30068
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29232
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 11552
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9639
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16779
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 11753
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 28673
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 5548
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21498
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32022
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17898
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 42356
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40319
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22060
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 474
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12192
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39842
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26529
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16487
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37087
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40224
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21525
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17997
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 24674
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18517
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12760
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 42226
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 13885
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 612
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 20430
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 15681
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 33540
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40483
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 19454
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)."


id: 8441476a07dff0838f0683ba90648d5b
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 20422
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26667
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
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index: climate_gpt_v2
vector_id: 5418
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21917
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 22067
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25460
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27573
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21887
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12595
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10701
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 15557
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9445
content:
"(WMO, 2018) updated the lifetimes of many halogenated compounds \r\nand these were used in Hodnebrog et al. (2020a)."


id: b158a07aaca22a8cc86500978356ade
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 28430
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 32779
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 10281
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31347
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30070
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17168
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17255
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 4100
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 20881
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 15167
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31802
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 19174
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 26098
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 2119
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 40408
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 28674
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12601
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 17052
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3810
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30421
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27287
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."


id: be6e4cdd616a2096cf64c8cf2553f6cd
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30766
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 19310
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9518
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9795
content:
"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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30716
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 827
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 30151
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 8246
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 9004
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 15041
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3002
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 31065
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7605
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 38892
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12814
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37598
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25205
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37618
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39260
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 42273
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27179
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25668
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 5901
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 27249
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 5165
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 41451
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 16786
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 37253
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 3081
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21928
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 39468
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 35095
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
created_at: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 681
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 29209
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7375
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
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index: climate_gpt_v2
vector_id: 35070
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12528
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6381
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 7783
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12783
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 6003
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 626
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 34390
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 5662
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 25806
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 18548
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 718
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 12401
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: 2023-04-19 13:21:36
updated_at: 2023-04-19 13:21:36
content_type: text
index: climate_gpt_v2
vector_id: 21563
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 32388
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 38940
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 30921
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 2209
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 31991
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 12977
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 3302
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 41737
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 30256
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 13860
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 32495
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 5617
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 39238
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 25852
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 25009
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: 7255e4e9adecc530f2cf5bd9d71688c3
created_at: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 17358
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 13277
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 36786
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 7858
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 6332
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
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index: climate_gpt_v2
vector_id: 27893
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 14058
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 20149
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 16609
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 38835
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 26693
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 39212
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 13248
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 35392
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 4677
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 13108
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 32869
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 3455
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 18380
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 26076
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 21627
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 18917
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 17635
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 42039
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 1508
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 30384
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 6255
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 17863
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 22555
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 33624
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 37707
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 11809
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 22576
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 27510
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 26263
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: 2023-04-19 13:21:37
updated_at: 2023-04-19 13:21:37
content_type: text
index: climate_gpt_v2
vector_id: 7261
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)."


