# document

id: 6f796362a277dbc54f59de9baa39fb7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16875
content:
"[...] remain exposed to air pollution exceeding the WHO guidelines (high \r confidence). Additional policies envisaged to attain Sustainable \r Development Goals (SDGs; e.g.,  access to clean energy, waste \r management) bring complementary SLCF reduction. Only strategies \r integrating climate, air quality, and development goals are found to \r effectively achieve multiple benefits. {6.6.3, 6.7.3, Box 6.2}"


id: 7a854c43a667c5c921618c1ee46bf6bb
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 18748
content:
"Emissions reductions associated with COVID-19 containment \r led to a discernible temporary improvement of air quality in \r most regions, but changes to global and regional climate are \r undetectable above internal variability. Global anthropogenic \r NOx emissions decreased by a maximum of about 35% in April 2020 \r (medium confidence). There is high confidence that, with the \r exception of surface ozone, these emissions reductions have \r contributed to improved air quality in most regions of the world. \r Global fossil CO2 emissions decreased by 7% (with a  range of \r 5.8-13.0%) in 2020 relative to 2019, largely due to reduced emissions \r from the transportation sector (medium confidence). Overall, the net \r ERF, relative to ongoing trends, from COVID-19 restrictions was likely \r small and positive for 2020 (less than 0.2 W m-2), thus temporarily \r adding to the total anthropogenic climate influence, with positive \r forcing from aerosol changes dominating over negative forcings \r [...]"


id: 7283a1509db32c6a677e719a0215ee36
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 17387
content:
"[...] from CO2, NOx and contrail cirrus changes. Consistent with this small \r net radiative forcing, and against a  large component of internal \r variability, Earth system model simulations show no detectable effect \r on global or regional surface temperature or precipitation (high \r confidence). {Cross-Chapter Box 6.1}"


id: 4ad0d44195cc0efbf3fa3d0a825ed797
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
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vector_id: 10404
content:
"Figure 6.1 | Sources and processes leading to atmospheric short-lived climate forcer (SLCF) burden and their interactions with the climate system. Both \r direct and indirect SLCFs and the role of atmospheric processes for the lifetime of SLCFs are depicted. Anthropogenic emissions sectors illustrated are: fossil fuel exploration, \r distribution and use; biofuel production and use; waste; transport; industry; agricultural sources; and open biomass burning. Emissions from natural systems include those from \r open biomass burning, vegetation, soil, ocean, lightning and volcanoes. SLCFs interact with solar or terrestrial radiation, surface albedo, and cloud or precipitation systems. \r The radiative forcing due to individual SLCFs can be either positive or negative. Climate change induces changes in emissions from most natural systems as well as from some \r [...]"


id: b217c209d66bb3aab051d0736d0b1cb3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 28563
content:
"[...] anthropogenic emissions sectors (e.g., agriculture) leading to a climate feedback (purple arrows). Climate change also influences atmospheric chemistry processes, such as \r chemical reaction rates or via circulation changes, thus affecting atmospheric composition leading to a climate feedback. Air pollutants influence emissions from terrestrial \r vegetation, including agriculture (grey arrow). "


id: debdc45dcbe48261904469df190f054a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 36450
content:
"In the IPCC Special Report on Global Warming of 1.5degC (SR1.5; \r Allen et al., 2018a), Rogelj et al. (2018a) state that the evolution \r of methane and SO2 emissions strongly influences the chances \r of limiting warming to 1.5degC, and that, considering mitigation \r scenarios to limit warming to 1.5degC or 2degC, a weakening of aerosol \r cooling would add to future warming in the near term, but can be \r tempered by reductions in methane emissions (high confidence). In \r addition, as some SLCFs are co-emitted alongside CO2, especially \r in the energy and transport sectors, low CO2 scenarios, relying on \r decline of fossil fuel use, can result in strong abatement of some \r cooling and warming SLCFs (Rogelj et al., 2018a). On the other hand, \r specific reductions of the warming SLCFs (methane and BC) would, \r in the short term, contribute significantly to the efforts of limiting \r warming to 1.5degC. Reductions of BC and methane would have \r [...]"


id: 1a72e36fe083c2fa0e630426e7c20c77
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 1854
content:
"[...] substantial co-benefits, improving air quality and therefore limiting \r effects on human health and agricultural yields. This would, in \r turn, enhance the institutional and socio-cultural feasibility of such \r actions in line with the United Nations' Sustainable Development \r Goals (SDGs; Coninck et al., 2018)."


id: 6622774f346ea595dce7d41ecbf68cde
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15232
content:
"As depicted in Figure  6.1, emissions of SLCFs are governed \r by anthropogenic activities and sources from natural systems \r (see Section 6.2 for details). Atmospheric chemistry in this context \r is both a  source and a  sink of SLCFs. For instance, ozone and \r secondary aerosols are exclusively formed through atmospheric \r mechanisms (Sections 6.3.2 and 6.3.5 respectively). The hydroxyl \r (OH) radical, the most important oxidizing agent in the troposphere, \r acts as a  sink for SLCFs by reacting with them and thereby \r influencing their lifetime (Section  6.3.6). Through SLCF radiative \r forcing and feedbacks (Section 6.4), key climate parameters, such as \r temperature, hydrological cycle and weather patterns are perturbed. \r Climate change also influences air quality (Section 6.5). As depicted \r in Figure 6.1, SLCFs affect both climate and air quality, hence SLCF \r mitigation has linkages to both issues (Section 6.6). Socio-economic \r [...]"


id: 1011e33e1da356aad8156f665c10db7a
created_at: 2023-04-19 13:22:37
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"[...] narratives including air-quality policies determine future projections \r of SLCFs in the five core Shared Socio-economic Pathways (SSPs): \r SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (described \r in Chapter 1), and in addition, a subset of SSP3 scenarios make it \r possible to isolate the effect of various SLCF mitigation trajectories \r on climate and air quality (Section 6.7)."


id: 1cb95ca1d1b33dd1ad5931655a8826c2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
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vector_id: 2239
content:
"Following SR1.5, the IPCC Special Report on Climate Change and \r Land (SRCCL; IPCC, 2019a) took into consideration the emissions \r on land of three major SLCFs: mineral dust, carbonaceous aerosols \r (BC and OA) and biogenic volatile compounds (BVOCs) (Jia et al., \r 2019). The SRCCL concluded that: (i) there is no agreement about \r the direction of future changes in mineral dust emissions; (ii) fossil \r fuel and biomass burning, and secondary organic aerosols (SOA) \r from natural BVOC emissions are the main global sources of \r carbonaceous aerosols whose emissions are expected to increase in \r the near future due to possible increases in open biomass burning \r and increase in SOA from oxidation of BVOCs (medium confidence); \r and (iii) BVOCs are emitted in large amounts by forests and they are \r rapidly oxidized in the atmosphere to form less volatile compounds \r that can condense and form SOA, and in a warming planet, BVOC \r [...]"


id: ddc3196d4bb973beb375a62541660a3c
created_at: 2023-04-19 13:22:37
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content_type: text
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"[...] emissions are expected to increase but magnitude is unknown and \r will depend on future land-use change, in addition to climate (limited \r evidence, medium agreement)."


id: 2a71d0c8058dc278df0f10e408bfe130
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
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vector_id: 4693
content:
"This chapter discusses air quality from a global point of view with \r a  focus on surface ozone and particulate matter concentrations. \r Local and indoor air pollution, as well as the effect of air pollution \r on health, are beyond the scope of this chapter. This Assessment \r is mainly based on results and studies relying on global models or \r observation datasets operated through global networks or from \r satellites. Global chemistry-climate models enable the quantification \r of changes in background concentrations, such as changes in \r surface ozone due to large-scale changes in climate or methane, \r by considering comprehensively the physiochemical processes \r (Box 6.1). In addition, climate effects are often non-linear responses \r to concentrations which already respond non-linearly to emissions, \r with per-mass unit effects often larger in pristine than in polluted \r regions, justifying the relevance of global models. However, specific \r [...]"


id: 4626488e1bd07ec704af40184c59821e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
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"[...] aspects of urban air quality cannot be captured by global models \r and require high-resolution models that reproduce the temporal and \r spatial variability of emissions and abundances necessary to precisely \r account for the non-linearity of the chemistry and the sensitivity of \r local air pollution to its drivers. Consequently, the sectoral analysis \r in Section  6.6 and the mitigation effects in Section  6.7 cannot be \r directly applied for local air-quality planning. "


id: 823b84dfd9ecf202d95d6361c9ed25b9
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
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"combined different emissions datasets. However, for the period \r after 1990, the CMIP6 dataset shows for all species, except for \r SO2, CO, and (since 2011) for NOx, a  different trend than CMIP5 \r (i.e., continued strong growth of emissions driven primarily by \r developments in Asia (Figure 6.19)). The unprecedented growth of \r emissions from Eastern and Southern Asia since 2000 changed the \r global landscape of emissions, making Asia the dominant SLCF source \r region (Figures  6.3 and 6.19). The Representative Concentration \r Pathways (RCP) scenarios used in AR5 started from the year 2000 \r (van Vuuren et al., 2011) and did not capture the SLCF emissions \r which actually occurred until 2015. The CEDS inventory (Hoesly et al., \r 2018) includes improved representation of these trends and the \r estimate for 2014. These findings have been largely supported by \r several independent emissions inventory studies and remote-sensing \r [...]"


id: 77076ce9d134fefaa216c729c11d9e6b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 18142
content:
"[...] data analysis. However, for the last decade the decline of Asian \r emissions of SO2 and NOx appears underestimated while growth \r of BC and OC emissions in Asia and Africa seems overestimated in \r CMIP6, compared to most recent regional evaluations (Klimont et al., \r 2017a; Zheng et al., 2018b; Elguindi et al., 2020; Kanaya et al., 2020; \r McDuffie et al., 2020), which are largely considered in the updated \r release of the CEDS (Hoesly et al., 2019). Consequently, global CMIP6 \r anthropogenic emissions for 2014 are likely overestimated by about \r 10% for SO2 and NOx and by about 15% for BC and OC. "


id: 252d1ac27ef7c2108a09add29551cb1a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3732
content:
"Estimates of global anthropogenic (human-caused) SLCF emissions \r and their historical evolution that were used in AR5 (CMIP5; Lamarque \r et al., 2010) have been revised for use in CMIP6 (Hoesly et al., 2018). \r The update considered new data and assessment of the impact \r of environmental policies, primarily regarding air pollution control \r (R. Wang et al., 2014; S.X. Wang et al., 2014; Montzka et al., 2015; \r Crippa et al., 2016; Turnock et al., 2016; Klimont et al., 2017a; Zanatta \r et al., 2017; Prinn et al., 2018). Additionally, Hoesly et al. (2018) have \r extended estimates of anthropogenic emissions back to 1750 and \r developed an updated and new set of spatial proxies allowing for more \r differentiated (source sector-wise) gridding of emissions (Feng et al., \r 2020). The CMIP6 emissions inventory has been developed with the \r Community Emissions Data System (CEDS) that improves upon existing \r inventories with a  more consistent and reproducible methodology, \r [...]"


id: 24b62106c8662c4c7ee4bbecf3eca3f9
created_at: 2023-04-19 13:22:37
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content_type: text
index: climate_gpt_v2
vector_id: 3656
content:
"[...] similar to approaches used in, for example, the EDGAR database \r (Crippa et al., 2016) and the GAINS model (Amann et al., 2011; Klimont \r et al., 2017a; Hoglund-Isaksson et al., 2020) where emissions of all \r compounds are consistently estimated using the same emissions drivers \r and propagating individual components (activity data and emissions \r factors) separately to capture fuel and technology trends affecting \r emissions trajectories over time. This contrasts with the approach used \r to establish historical emissions for CMIP5 where different datasets \r available at the time were combined. The CMIP6 exercise is based on \r the first release of the CEDS emissions dataset (version 2017-05-18, \r sometimes referred to hereafter as CMIP6 emissions) whose main \r features regarding SLCFs are described hereafter. The CEDS has been \r and will be regularly updated and extended; the recent update of the \r CEDS (Hoesly et al., 2019) and consequences for this Assessment is \r [...]"


id: 94fab06f960cee3ded86a72b5ad77742
created_at: 2023-04-19 13:22:37
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"[...] discussed when necessary. Some details on how SLCF emissions \r have been represented in scenarios used by IPCC assessments can be \r found in Chapter 1 (Section 1.6.1 and Cross-Chapter Box 1.4 and in \r Section 6.7.1.1)."


id: 4fcb87d29c312f3c989314cda1a5ade7
created_at: 2023-04-19 13:22:37
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content:
"Global emissions of NOx have been growing in spite of the \r successful reduction of emissions in North America, Europe, Japan \r and Korea (Crippa et al., 2016; Turnock et al., 2016; Miyazaki \r et al., 2017; Jiang et al., 2018), partly driven by continuous efforts \r to strengthen the emissions standards for road vehicles in most \r countries (Figures 6.18 and 6.19). In many regions, an increase in \r vehicle fleet as well as non-compliance with emissions standards \r (Anenberg et al., 2017, 2019; Jonson et al., 2017; Jiang et al., \r 2018), growing aviation (Grewe et al., 2019; Lee et al., 2021) and \r demand for energy, and consequently a  large number of new \r fossil fuel power plants, have more than compensated for these \r reductions. Since about 2011, global NOx emissions appear to \r have stabilized or slightly declined (medium confidence) but the \r global rate of decline has been underestimated in the CEDS, as \r recent data suggest that emissions reductions in China were larger \r [...]"


id: 7d7e9d0b8812cc95f7f7988a5578acf
created_at: 2023-04-19 13:22:37
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content:
"[...] than included in the CEDS (Figure  6.19 and Hoesly et al., 2018). \r Recent bottom-up emissions estimates (Zheng et al., 2018b) largely \r confirm what has been shown in satellite data (F. Liu et al., 2016; \r Miyazaki et al., 2017; Silver et al., 2018): a strong decline of NO2\r column over eastern China (high confidence) (Section  6.3.3.1). "


id: 2656ed40becc247731ef9f896a034847
created_at: 2023-04-19 13:22:37
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content_type: text
index: climate_gpt_v2
vector_id: 3916
content:
"At a  global level, the estimated CEDS CO emissions trends are \r comparable to NOx, which has been confirmed by several inverse \r modelling studies (Section 6.3.3.2). The transport sector (including \r international shipping and aviation) was the largest anthropogenic \r source of NOx (about 50% of the total) and also contributed over \r 25% of CO emissions in 2014; Asia represented 50% and North \r America and Europe about 20% of global total NOx and CO \r emissions (Figure 6.3).\r Oil production-distribution and transport sectors have dominated \r anthropogenic NMVOC emissions for most of the 20th century \r (Hoesly et al., 2018) and still represent a large share (Figure 6.3). \r Efforts to control transport emissions (i.e., increasing stringency \r of vehicle emissions limits) were largely offset by the fast growth \r of emissions from chemical industries and solvent use, as well as \r from fossil fuel production and distribution, resulting in continued \r [...]"


id: c8a85e8c97ef28f7476b7b925a7e7778
created_at: 2023-04-19 13:22:37
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content:
"[...] growth of global anthropogenic NMVOC emissions since 1900 \r (high confidence) (Figure 6.18). Since AR5, there is high confidence\r that motor vehicle NMVOC emissions have sharply declined in \r North America and Europe in the last decades (Rossabi and Helmig, \r 2018), for example, by about an order of magnitude in major US \r cities since 1990 (Bishop and Haugen, 2018; McDonald et al., \r 2018). Increasing (since 2008) oil- and gas-extraction activities in \r North America lead to a strong growth of NMVOC emissions (high \r confidence) as shown by analysis of ethane column data (Franco \r et al., 2016), but absolute emission amounts remain uncertain \r (Petron et al., 2014; Tzompa-Sosa et al., 2019). In Eastern Asia, \r there is medium confidence in a decreasing trend of motor vehicle \r emissions, suggested by ambient measurements in Beijing since \r 2002 (Wang et al., 2015) and by bottom-up estimates (Zheng et al., \r 2018b), and a  decrease in residential heating emissions due to \r [...]"


id: 5c78777f26b720ec1b092ed45092f513
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
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content:
"[...] declining coal and biofuel use since 2005 (Zheng et al., 2018b; M. \r Li et al., 2019). However, total anthropogenic NMVOC emissions \r have increased steadily in China since the mid-20th century, \r largely due to the growing importance of the solvent-use and \r industrial sectors (medium evidence, high agreement) (Sun et al., \r 2018; Zheng et al., 2018b; M. Li et al., 2019). Resulting changes \r in the NMVOC speciated emissions might be underestimated in \r the current regional and global inventories. For  example, in the \r USA, a recent study suggested an emergent shift in urban NMVOC \r sources from transportation to chemical products (i.e., household \r chemicals, personal care products, solvents, etc.), which is not in \r accordance with emissions inventories currently used (McDonald \r et al., 2018). In many European regions and cities, wood burning has \r been increasingly used for residential heating, partly for economic \r [...]"


id: e2de45156a88702933201fe88ca580aa
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 37211
content:
"[...] reasons and because it is considered CO2-neutral (Athanasopoulou \r et al., 2017); in situ measurements in several cities, including Paris, \r suggest that wood burning explains up to half of the NMVOC \r emissions during winter (Kaltsonoudis et al., 2016; Languille et al., \r 2020). Due to the vast heterogeneity of sources and components \r of NMVOCs, uncertainty in regional emissions and trends is higher \r than for most other components.\r Emissions of carbonaceous aerosols (BC, OC) have been steadily \r increasing and their emissions have almost doubled since 1950 \r (medium confidence) (Hoesly et al., 2018). Before 1950, North \r America and Europe contributed about half of the global total "


id: 6563f55893952ed85b5dd628e1e7ec15
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15108
content:
"but successful introduction of diesel particulate filters on road \r vehicles (Fiebig et al., 2014; Robinson et al., 2015; Klimont et al., \r 2017a) and declining reliance on solid fuels for heating brought in \r large reductions (high confidence) (Figure  6.19). Currently, global \r carbonaceous aerosol emissions originate primarily from Asia and \r Africa (Bond et al., 2013; Hoesly et al., 2018; Elguindi et al., 2020; \r McDuffie et al., 2020), representing about 80% of the global total \r (high confidence) (Figure 6.3). Consideration, in CMIP6, of emissions \r from kerosene lamps and gas flaring, revised estimates for open \r burning of waste, regional coal consumption, and new estimates for \r Russia (Stohl et al., 2013; Huang et al., 2015; Huang and Fu, 2016; \r Kholod et al., 2016; Conrad and Johnson, 2017; Evans et al., 2017; \r Klimont et al., 2017a) resulted in over 15% higher global emissions \r of OC and BC than in the CMIP5 estimates for the first decade of \r [...]"


id: 23861fcc64d2d1b6654a8fae63aa44c7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3450
content:
"[...] the 21st century (Figure  6.18). However, the continued increase \r of BC emissions over Eastern Asia after 2005, estimated in CMIP6 \r (Figure 6.19), has been questioned recently as a steady decline of \r BC concentrations was measured in the air masses flowing out from \r the east coast of China (Kanaya et al., 2020), which has been also \r estimated in recent regional bottom-up and top-down inventories \r (Zheng et al., 2018a; Elguindi et al., 2020; McDuffie et al., 2020). \r Since AR5, confidence in emissions estimates and trends in North \r America and Europe has increased, but high uncertainties remain for \r Asia and Africa, despite their major contribution to global emissions. \r The size distribution of emitted species, of importance for climate \r and health impacts, remains uncertain and the CEDS inventory \r does not provide such information. Overall, a factor two uncertainty \r [...]"


id: 7a103a86651c4165d8c267bb0d7c4fa2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 18672
content:
"[...] in global estimates of BC and OC emissions remains, with post\u00022005 emissions overestimated in Asia (high confidence) and Africa \r (medium confidence). "


id: 9b8b7ab291840cf6523ba9f27410a213
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 24617
content:
"Agricultural production (livestock and mineral nitrogen fertilizer \r application) is the primary source of ammonia in the atmosphere \r with more than half of present-day emissions originating in Asia \r (Hoesly et al., 2018; Figure 6.3, EC-JRC/PBL, 2020; Vira et al., 2020). \r NH3 emissions are estimated to have grown strongly since 1850, \r especially since 1950, driven by continuously increasing livestock \r production, widespread application of mineral nitrogen fertilizers, \r and lack of action to control ammonia (high confidence) (Erisman \r et al., 2008; Riddick et al., 2016; Hoesly et al., 2018; Fowler et al., \r 2020). The trends estimated in CMIP5 and CMIP6 are similar, while \r in absolute terms, CMIP6 has somewhat higher emissions as it \r includes emissions from wastewater and human waste that were \r largely missing in CMIP5 (Hoesly et al., 2018). CMIP6 has improved \r spatial and temporal distribution of emissions (Lamarque et al., \r [...]"


id: 308eb2bbff076dc026d1be9bd4f57394
created_at: 2023-04-19 13:22:37
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content:
"Figure 6.3 | Relative regional and sectoral contributions to the present day (year 2014) anthropogenic emissions of short-lived climate forcers (SLCFs).\r Emissions data are from the Community Emissions Data System (CEDS; Hoesly et al., 2018). Emissions are aggregated into the following sectors: fossil fuel production and \r distribution (coal mining, oil and gas production, upstream gas flaring, gas distribution networks), fossil fuel combustion for energy (power plants), residential and commercial \r (fossil and biofuel use for cooking and heating), industry (combustion and production processes, solvent-use losses from production and end use), transport (road and off-road \r vehicles), shipping (including international shipping), aviation (including international aviation), agriculture (livestock and crop production), waste management (solid waste, \r [...]"


id: aeea0e6b8e83bad543e41f659f186223
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 28016
content:
"[...] including landfills and open trash burning, residential and industrial waste water), and other. Further details on data sources and processing are available in the chapter data \r table (Table 6.SM.3)."


id: 8c78d3ead18f0199ba92d62afad76243
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 21900
content:
"Since AR5, the quality and completeness of activity and emission\u0002factor data and applied methodology, including spatial allocation \r together with independent satellite-derived observations, have \r improved, raising confidence in methods used to derive emissions. \r There is high confidence in the sign of global trends of SLCF emissions \r until the year 2000. However, only medium confidence for the rate \r of change in the two last decades, owing primarily to uncertainties \r in the actual application of reduction technologies in fast-growing \r economies of Asia. At a  regional level, bottom-up derived SLCF \r emissions trends, and magnitudes in regions with strong economic \r growth and changing air-quality regulation, are highly uncertain but \r can be better constrained with top-down methods (Section 6.3). For \r most SLCF species, there is high confidence in trends and magnitudes \r [...]"


id: 3e0a5bb459d8c381b5e63cf8ffd32f8d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 8083
content:
"[...] for affluent countries from the Organisation for Economic Co\u0002operation and Development (OECD) regions where accurate and \r detailed information about drivers of emissions exists; medium \r confidence is assessed for regional emissions of NH3, methane and \r NMVOC. "


id: 2794609083bb2ce70fa83fd2df5cb1eb
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 4141
content:
"This section assesses our current understanding of SLCF emissions \r by natural systems. Many naturally occurring emission processes in \r the Earth system have been perturbed by the growing influence of \r human activities either directly (e.g., deforestation, agriculture) or \r via human-induced atmospheric CO2 increase and climate change, \r and therefore cannot be considered as purely natural emissions. \r The  temporal evolution and spatial distribution of natural SLCF \r emissions are highly variable and their estimates rely on models \r with rather uncertain parametrizations for production mechanisms. \r For  some SLCFs, the natural processes by which emissions occur \r are also not well understood. In the following sections, we assess \r the level of confidence in present-day SLCF emissions by natural \r systems, in their perturbation since the pre-industrial period and \r their sensitivity to future changes. When available, the assessment \r [...]"


id: c607cb60b8ec7854a4d184349446c361
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32150
content:
"[...] also includes estimates from the CMIP6 model ensemble. Note that \r volcanic SO2 emissions are discussed in Section 2.2.2 and natural \r sources of methane and N2O are assessed in Sections 5.2.2 and \r 5.2.3."


id: cce59a707041d56dfa00cad00d68114c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 33387
content:
"A wide range of BVOCs are emitted from vegetation with the \r dominant compounds being isoprene and monoterpenes but also \r including sesquiterpenes, alkenes, alcohols, aldehydes and ketones. \r The photooxidation of BVOC emissions plays a  fundamental role \r in atmospheric composition by controlling the regional and global \r budgets of ozone and organic aerosols, and impacting the lifetime \r of methane and other reactive components (Arneth et al., 2010b; \r Heald and Spracklen, 2015). Substantial uncertainty exists across \r different modelling frameworks for estimates of global total BVOC \r emissions and individual compound emissions (Messina et al., 2016). \r Global isoprene emissions estimates differ by a factor of two from \r 300-600 TgC yr-1 and global monoterpene emissions estimates by \r a factor of five from 30-150 TgC yr-1 (Messina et al., 2016). A main \r driver of the uncertainty ranges is the choice of basal emissions rates \r [...]"


id: d3ad2e2194e274a6aa283ea983de4af2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 34582
content:
"[...] assigned to different plant functional types in the model; however, \r the smaller uncertainty range for isoprene than for monoterpenes is \r not fully understood (Arneth et al., 2008). The evaluation of global \r BVOC emissions is challenging because of poor measurement data \r coverage in many regions and the lack of year-round measurements \r (Unger et al., 2013). Several observational approaches have been \r developed in the past few years to improve understanding of BVOC \r emissions, including indirect methods such as the measurement of \r the OH loss rate in forested environments (Yang et al., 2016) and \r application of the variability in satellite formaldehyde concentrations \r (Palmer et al., 2006; Barkley et al., 2013; Stavrakou et al., 2014). \r Direct space-borne isoprene retrievals using infrared radiance (IR) \r measurements have very recently become available (Fu et al., 2019; \r Wells et al., 2020). Collectively these approaches have identified \r [...]"


id: fe9887bc56b1c790ad637542b2de854e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 42166
content:
"[...] weaknesses in the ability of the parametrizations in global models \r to reproduce BVOC emissions hotspots (Wells et al., 2020). However, \r none of the current observational approaches have yet been able to \r reduce the uncertainty ranges in global emissions estimates."


id: 60f2cdbfd5f1cbaa7d8b387b1be8754c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 14349
content:
"Global BVOC emissions are highly sensitive to environmental changes \r including changes in climate, atmospheric CO2\r , and vegetation \r composition and cover changes in natural and managed lands. Recent \r global modelling studies agree that global isoprene emissions have \r declined since the pre-industrial period, driven predominantly by \r anthropogenic land-use and land-cover change (LULCC) with results \r converging on a 10-25% loss of isoprene emissions between 1850 \r and the present day (Lathiere et al., 2010; Unger, 2013, 2014; Acosta \r Navarro et al., 2014; Heald and Geddes, 2016; Hantson et al., 2017; \r Hollaway et al., 2017; Scott et al., 2017). The historical evolution of \r monoterpene and sesquiterpene emissions is less well studied and \r there is no robust consensus on even the sign of the change (Acosta \r Navarro et al., 2014; Hantson et al., 2017). Future global isoprene \r and monoterpene emissions depend strongly on the climate and \r [...]"


id: 134a13fcc46f40262d402df7bd617349
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 605
content:
"[...] land-use scenarios considered (Hantson et al., 2017; Szogs et al., \r 2017). BVOC emissions will be sensitive to future land-based climate \r change mitigation strategies including afforestation and bioenergy, \r with impacts of bioenergy depending on the choice of crops (Szogs \r et al., 2017). "


id: 56ef96574121b4e90a56be4faab569f4
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 12544
content:
"At the plant level, BVOC emissions rates and composition depend \r strongly on plant species with plants tending to emit either isoprene \r or monoterpenes but not both. Photosynthetic activity is a  main \r driver of isoprene and monoterpene production. Therefore, radiation \r and temperature, along with leaf-water status, phenological state \r and atmospheric CO2 mixing ratio, affect emissions directly (on the \r leaf scale) and indirectly (via plant productivity; Guenther et al., 2012; \r Loreto et al., 2014; Niinemets et al., 2014). CO2 directly influences \r the isoprene-synthesis process, with inhibition under increasing \r atmospheric CO2 (Rosenstiel et al., 2003; Possell et al., 2005; \r Wilkinson et al., 2009). Direct CO2 inhibition has been observed for \r some monoterpene compounds (Loreto et al., 2001; Llorens et al., \r 2009). Severe/long-term water stress may reduce emissions whilst \r mild/short-term water stress may temporarily amplify or maintain \r [...]"


id: 18daee602bd29bc913c6a4b886e223b3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 1588
content:
"[...] BVOC emissions to protect plants against ongoing stress (Penuelas \r and Staudt, 2010; Potosnak et al., 2014; Genard-Zielinski et al., 2018). \r Furthermore, observations in the Amazon indicate that the chemical \r composition of monoterpene emissions could also change under \r elevated temperature conditions (Jardine et al., 2016). In addition, all \r these processes are investigated over short time scales but the long\u0002term response of BVOC emissions depends on how the vegetation "


id: a95ed45628fed6fe3e53d131e3142d5a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 27023
content:
"An extremely limited number of studies have explored the evolution \r of global dust sources since pre-industrial times (Mahowald et al., \r 2010; Stanelle et al., 2014). A modelling study estimated a  25% \r increase in global dust emissions between the late 19th century and \r the present, due to agricultural land expansion and climate change \r (Stanelle et al., 2014). CMIP5 models were unable to capture the \r observed variability of annual and longer time scales in North African \r dust emissions (Evan et al., 2014), however, more recent ESMs with \r process-based dust emissions schemes that account for changes in \r vegetation and climate in a more consistent manner, better match \r the observations (Kok et al., 2014; Evans et al., 2016). Feedbacks \r between the global dust cycle and the climate system (Section 6.4.5) \r could account for a substantial fraction of the total aerosol feedbacks \r in the climate system with an order of magnitude enhancement \r [...]"


id: ca1406505b45d8d83a20e3d150c10645
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32885
content:
"[...] on a  regional scale (Kok et al., 2018). In  summary, there is high \r confidence that atmospheric dust source and loading are sensitive to \r changes in climate and land use, however, there is low confidence in \r quantitative estimates of dust emission response to climate change. "


id: b12cdbd20833041a26d092d08fe34c9e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 28397
content:
"Oceans are a  significant source of marine aerosols that influence \r climate directly by scattering and absorbing solar radiation or \r indirectly through the formation of cloud condensation nuclei \r (CCN) and ice nucleating particles (INPs). Marine aerosols consist \r of primary sea-spray particles and secondary aerosols produced by \r the oxidation of emitted precursors, such as dimethylsulphide (DMS) \r and numerous other BVOCs. Sea-spray particles, composed of sea \r salt and primary organic aerosols (POA), are produced by wind\u0002induced wave breaking as well as the direct mechanical disruption \r of waves. The understanding of sea-spray emissions has increased \r substantially over the last five years, however, the knowledge of \r formation pathways and factors influencing their emissions continue \r to have large uncertainties (Forestieri et al., 2018; Saliba et al., 2019). \r The emission rate of sea-spray particles is predominantly controlled \r [...]"


id: 1e5b91cfd9b3f79ab37cf40685dfbb39
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 2538
content:
"[...] by wind speed. Since AR5, the influence of other factors, including \r sea surface temperature, wave history and salinity is increasingly \r evident (Callaghan et al., 2014; Grythe et al., 2014; Ovadnevaite et al., \r 2014; Salter et al., 2014; Barthel et al., 2019). Marine POA, often the \r dominant submicron component of sea spray, are emitted as a result \r of oceanic biological activity, however the biological processes by \r which these particles are produced remain poorly characterized \r contributing to large uncertainties in global marine POA emissions \r estimates (Tsigaridis et al., 2014; Cravigan et al., 2020; Hodzic et al., \r 2020). Furthermore, the particle size and chemical composition of \r sea-spray particles, and how these evolve in response to changing \r climate factors and dynamic oceanic biology, continue to have \r large uncertainties. "


id: 494b3cc79c6b9fd1e372f68af49b52c2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10115
content:
"For the last two decades, model-based emissions estimates are \r constrained by remote-sensing capacity to detect active fires and \r area burned. In AR5, biomass burning emissions were derived from \r a satellite product (Lamarque et al., 2010). Since then, improvements \r in detection of small fires has enhanced the agreement with higher\u0002resolution and ground-based data on burned area in several regions \r (Randerson et al., 2012; Mangeon et al., 2015), especially for areas \r subjected to agricultural waste burning (Chuvieco et al., 2016, 2019). \r The updated emissions factors and the contribution of forest versus \r savanna fires lead to significantly higher global emissions of NOx\r and lower emissions of OC and CO in CMIP6, compared with CMIP5. \r A recent compilation and assessment of emissions factors (Andreae, \r 2019) indicates that the emissions factors from Akagi et al. (2011), \r primarily used to produce the CMIP6 datasets, differ by +-50% for \r [...]"


id: 13d2892bb2f0477ee300a84cc040c024
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 702
content:
"[...] CO, OC, BC and NOx, depending on the biome, and would imply, for \r example, up to 10-30% higher OC and BC emissions from tropical \r forest fires. "


id: ff46a09ee7effbd756e38da339562682
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 42317
content:
"This section assesses the evolution of atmospheric abundance1 of \r SLCFs since AR5 based on observations and modelling, our knowledge \r of SLCF burden and distribution, and our understanding of the trends \r over longer time scales. In addition to emissions (Section  6.2), \r atmospheric chemistry (gas and heterogeneous chemistry), deposition \r (including wet and dry removal), and transport processes play a major \r role in determining the atmospheric distribution, budget and lifetime \r of SLCFs. The distribution and lifetime of SLCFs are further influenced \r by the modulation of chemical and physical processes in response \r to a changing climate. Therefore, the time evolution of atmospheric \r abundance of SLCFs is characterized by many complex non-linear \r interactions occurring at varying temporal and spatial scales. For this \r Assessment, global-scale, long-term measurements are employed \r only for a  few gaseous SLCFs while for most short-lived species \r [...]"


id: b1942a201eec80e3b73784b05d16b66a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 28465
content:
"Process-level understanding of tropospheric gas and aerosol chemistry developed through laboratory and simulation chamber \r experiments, as well as quantum chemical theory, is used to generate chemical mechanisms. Atmospheric simulation chambers are \r designed to identify the chemical pathways and quantify reaction kinetics in isolation from atmospheric transport, deposition and \r emission processes. Ideally the chemical regimes studied are representative for ambient atmospheric complexity and concentrations \r (e.g., McFiggans et al., 2019). Recently, quantum chemical theory has advanced to a level that it can provide kinetic and product \r information in a parameter range not possible with laboratory experiments (Vereecken et al., 2015). Iterative and interlinked use of \r simulation chamber and quantum chemical theory has led to improved knowledge of chemical mechanisms (Peeters et al., 2009, \r [...]"


id: d361297fd1c798fb1ef52a105cb78b28
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 34532
content:
"[...] 2014; Nguyen et al., 2010; Fuchs et al., 2013). For application in chemistry-climate models (CCMs), the chemical mechanisms need \r to be computationally efficient, requiring simplifications. Such simplifications include reduced hydrocarbon representations, the \r application of lumping techniques (one compound or a chemical structure representing a family of compounds, for example, as done \r for parametrizing SOA formation) and/or the implementation of artificial operators representing key steps of the chemistry (Emmerson \r and Evans, 2009; Xia et al., 2009; Stockwell et al., 2020). Additionally, aerosol microphysical processes (nucleation, coagulation, \r condensation, evaporation and sedimentation) that determine the evolution of aerosol number concentrations and size particle \r distribution are represented in parametrized forms in global models with varying levels of complexity (Mann et al., 2014)."


id: df87cfd0df4ab8ceec726fe035ffe770
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 36571
content:
"Global three-dimensional CCMs (Box 6.1, Figure 1) represent the full coupling of chemistry with climate physics (e.g., Morgenstern \r et al., 2017) with different levels of complexity (e.g., interactive aerosols with or without tropospheric and/or stratospheric chemistry). \r Methane concentrations are typically prescribed or constrained to observations while emissions of other SLCFs (or their precursors) are \r either prescribed or calculated interactively in the current generation of CCMs (Collins et al., 2017). CCMs, now part of Earth system \r models (ESMs), are applied extensively to simulate the distribution and evolution of chemical compounds on a variety of spatial and \r temporal scales to improve current knowledge, make future projections and investigate global scale chemistry-climate interactions \r and feedbacks (Section  3.8.2.2). CCMs are also used to interpret observations to disentangle the processes that drive observed \r [...]"


id: fb6ff31f1e997681bf2ab2aa9cd62b9f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 41612
content:
"[...] variability and trends. Some aspects of air quality, such as diurnal peaks or local threshold violations, strong gradient in chemical \r regimes and coupling between processes cannot be captured by relatively coarse spatial resolution (>50 km) global CCMs (Markakis \r et al., 2014) and necessitate subsequent downscaling modelling exercises."


id: 9a83b4bee768724983dc39ffdaaeed3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 24426
content:
"model parametrizations (e.g.,  chemical mechanisms, photolysis schemes, parametrizations for mixing and convective transport, \r and deposition), model input parameters (e.g., reaction rate constants, emissions) and an incomplete understanding of the physical \r and chemical processes that determine SLCF distributions (Brasseur and Jacob, 2017; Young et al., 2018). CCMs can therefore not \r capture every aspect of atmospheric chemical composition, but are expected to represent, as faithfully as possible, the sensitivity \r of chemical compounds to their drivers (e.g.,  anthropogenic emissions). Models are evaluated in multiple ways to identify their \r strengths and weaknesses in explaining the evolution of SLCF abundances. For example, CCM simulations are performed in the \r nudged or offline meteorology mode, that is, driven by observed or reanalysed meteorology rather than in the free-running mode, \r [...]"


id: bff3e8e4a6df6e1dac33fdbcf20d0b7e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31060
content:
"[...] for consistent comparison of modelled chemical composition with observations for a specific time period (Dameris and Jockel, 2013). \r However, caution is exercised as nudging can alter the model climate resulting in unintentional impacts on the simulated atmospheric \r physics and/or chemistry (Orbe et al., 2018; Chrysanthou et al., 2019). Chemical mechanisms implemented in CCMs are evaluated \r and intercompared to assess their skill in capturing relevant chemistry features (e.g., Brown-Steiner et al., 2018). The multi-model \r ensemble approach, employed for evaluating climate models, has been particularly useful for characterizing errors in CCM simulations \r of SLCFs related to structural uncertainty and internal variability (Naik et al., 2013; Shindell et al., 2013; Young et al., 2013; Turnock \r et al., 2020). However, as discussed in Box 4.1, this approach is unable to capture the full uncertainty range. "


id: 24ca23bcfc5ee30eae7072c05c3c927
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3670
content:
"This assessment draws upon results from single-model studies and recent multi-model intercomparisons (e.g., AeroCom, CCMI), in \r particular those endorsed by CMIP6 (see Table 1.3), which then allows for the full consideration of robustness and uncertainty due \r to model structures and processes. Based on the collective information provided in this body of literature, the CMIP6 multi-model \r ensemble is largely fit-for-purpose of evaluating the influence of SLCFs on radiative forcing, climate and non-CO2 biogeochemical \r feedbacks. Additionally, CMIP6 models are fit for capturing the global air pollution response to changes in emissions and meteorology, \r but have difficulty in simulating the mean state (Turnock et al., 2020). The set of CMIP6 simulations has been used to update the \r relations between emissions and surface temperature at the heart of the emulators (Cross-Chapter Box 7.1) and update emissions \r [...]"


id: 7654067707d07e5c9a37888ec7351aab
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 18040
content:
"[...] metrics (Section 7.6). Emulators and emissions metrics are used in this chapter (Sections 6.6 and 6.7) to assess more specifically the \r effect of the individual SLCFs for each sector and region, which would be of prohibitive computing cost with CCMs. CCMs are also \r used to build global source-receptor models which use relations between surface concentrations and emissions. Such a model is used \r to assess the impact of various mitigation policies on air quality (Sections 6.5 and 6.7)."


id: c01abf74c561fbef86e3e5607725eb12
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31088
content:
"The global chemical methane sink, essentially due to tropospheric \r OH, required to calculate the chemical lifetime is estimated by either \r bottom-up global CCMs and ESMs (BU) or top-down observational \r inversion methods (TD). BU global models represent the coupled \r chemical processes and feedbacks that determine the chemical sinks \r but show large diversity in their estimates, particularly the tropospheric \r OH sink (Y. Zhao et al., 2019; Stevenson et al., 2020). TD inversion \r methods, on the contrary, provide independent observational \r constraints on the methane sink due to tropospheric OH over large \r spatio-temporal scales, but are prone to observational uncertainties \r and do not account for the chemical feedbacks on OH (Prather and \r Holmes, 2017; Naus et al., 2019). The central estimate of mean chemical \r methane loss over the period 2008-2017 varied from 602 [minimum \r and maximum range of 507-803] Tg yr-1 from BU chemistry-climate \r [...]"


id: 26ab5d10e045437f903e7d77df014297
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3963
content:
"[...] models in the Chemistry-Climate Modelling Initiative (CCMI) to \r 514 [474-529] Tg yr-1 from TD inverse modelling (Section 5.2.2 and \r Table 5.2). The smaller range in the TD estimate (11%) results from the \r use of a common climatological mean OH distribution (Saunois et al., \r 2020; Zhao et al., 2020a), while the larger range in the BU estimate (49%) \r reflects the diversity in OH concentrations from different chemical"


id: 59b3f2d7f28ab6fc6f13ee9699b5b501
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 13020
content:
"About 10% of the total atmospheric ozone column resides in the \r troposphere. The ozone forcing on climate strongly depends on its \r vertical and latitudinal distribution in the troposphere. The lifetime \r of ozone in the troposphere ranges from a  few hours in polluted \r urban regions to up to few months in the upper troposphere. \r Observed tropospheric ozone concentrations range from less than \r 10 ppb over the tropical Pacific Ocean to as much as 100 ppb in \r the upper troposphere and more than 100 ppb downwind of major \r ozone precursor emissions regions. An ensemble of five CMIP6 \r models including whole atmospheric chemistry and interactive \r ozone has been shown to simulate consistently the present-day \r ozone distribution (north to south and latitudinal gradients) and its \r seasonal variability when compared with observations from sondes, \r background surface stations and satellite products (Griffiths et al., \r [...]"


id: a32f0fe208f9d1e452b1db37cc325236
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25919
content:
"[...] 2021). The biases, whose magnitude is similar to AR5, are lower than \r 15% against climatological seasonal cycles from ozonesondes with \r an overestimate in the Northern Hemisphere and an underestimate \r in the Southern Hemisphere (Griffiths et al., 2021). The CMIP6 multi\u0002model ensemble estimate of the global mean lifetime of ozone for \r present-day conditions is 25.5 +- 2.2 days (Griffiths et al., 2021), which \r is within the range of previous multi-model estimates (Stevenson \r et al., 2006; Young et al., 2013), indicating a high level of confidence."


id: c5c2646d6649a3a7fc3ecaadf5d009fa
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32101
content:
"The methane perturbation lifetime (tpert) is defined as the e-folding \r time it takes for the methane burden to decay back to its initial value \r after being perturbed by a change in methane emissions. Perturbation \r lifetime is longer than the total atmospheric lifetime of methane, as an \r increase in methane emissions decreases tropospheric OH, which in \r turn increases the lifetime and therefore the methane burden (Prather, \r 1994; Fuglestvedt et al., 1996; Holmes et al., 2013; Holmes, 2018). \r Since perturbation lifetime relates changes in emissions to changes \r in burden, it is used to determine the emissions metrics assessed in \r Chapter  7 (Section  7.6). The perturbation lifetime is related to the \r atmospheric lifetime as tpert = f * ttotal where f is the feedback factor \r and is calculated as f = 1/(1-s), where s = d (ln ttotal)/d (ln[CH4]) (Prather \r et al., 2001). Since there are no observational constraints for either \r [...]"


id: 1289ce6f583fa483375fd1a14858c2fb
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 461
content:
"[...] tpert or f, these quantities are derived from CCMs or ESMs. AR5 used \r f = 1.34 +- 0.06 based on a combination of multi-model (mostly CTMs \r and a few CCMs) estimates (Holmes et al., 2013). A recent model study \r explored new aspects of methane feedbacks finding that the strength \r of the feedback, typically treated as a constant, varies in space and \r time but will in all likelihood remain within 10% over the 21st century \r (Holmes, 2018). For this Assessment, the value of f is assessed to be \r 1.30 +- 0.07 based on a six-member ensemble of AerChemMIP ESMs "


id: 74c79f6801c0182314b7c6d6f3c8ad83
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 17785
content:
"Ninety percent of the total column ozone (TCO) resides in the \r stratosphere. The chemical lifetime of ozone in the stratosphere \r ranges from less than a  day in the upper stratosphere to several \r months in the lower stratosphere (Bekki and Lefevre, 2009). Global \r stratospheric ozone trends based on observations are assessed in \r Chapter 2 (Section 2.2.5.2). The CMIP6 model ensemble shows that \r global TCO has slightly changed from 1850-1960 (Keeble et al., 2021). \r The rapid decline in the 1970s and 1980s due to halogenated ozone\u0002depleting substances (ODSs, as assessed in Section  2.2.5.2 from \r observations) until the end of the 1990s, followed by a slight increase \r since then, is captured by the models (Keeble et al., 2021). Overall, \r the observed climatology patterns and annual cycle amplitudes are \r well represented in the CMIP6 ensemble mean. The CMIP6 ensemble \r overestimates the observed TCO values by up to 6% (10-20 Dobson \r [...]"


id: e1dc3a9e8f2fec3197d43b4c66f835cb
created_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 37016
content:
"[...] Units (DU)) globally in the NH and SH mid-latitudes, and in the \r tropics, but the trend in these regions is well captured between 1960 \r and 2014. However, there is poor agreement between the individual \r CMIP6 models in the pre-industrial period and throughout the \r historical period, with model TCO values spread across a range of \r about 60 DU. The global stratospheric ozone column decreased by \r 14.3 +- 8.7 DU from 1850-2014 (Keeble et al., 2021). "


id: a5419094aec806f6d212d53b0c57e804
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 26305
content:
"The distribution of tropospheric NOx is highly variable in space and \r time owing to its short lifetime coupled with highly heterogeneous \r emission and sink patterns. NOx undergoes chemical processing, \r including the formation of nitric acid (HNO3), nitrate (NO- ), and 3\r organic nitrates (e.g., alkyl nitrate and peroxyacyl nitrate), atmospheric \r transport, and deposition. Despite challenges in retrieving quantitative \r information from satellite observations (Duncan et al., 2014; Lin et al., \r 2015; Lorente et al., 2017; Silvern et al., 2018), improved accuracy and \r resolution of satellite-derived tropospheric NO2 columns over the past \r two decades have advanced understanding of the global distribution, \r long-term trends and source attribution of NOx. Long-term average \r tropospheric NO2 column based on multiple satellite-borne instruments \r (Figure 6.6a) reveals the highest NO2 levels over the most populated, \r [...]"


id: b4542658ad6020fbeffb3e52e5ca00ec
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 28961
content:
"[...] urbanized and industrialized regions of the world corresponding to high \r NOx emissions source regions (Krotkov et al., 2016; Georgoulias et al., \r 2019). Enhanced but highly variable NO2 columns are also associated \r with biomass-burning regions as well as areas influenced by lightning \r activity (Miyazaki et al., 2014; Tanimoto et al., 2015)."


id: 5f94d53ccc15cd1070ff436449663eca
created_at: 2023-04-19 13:22:37
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content_type: text
index: climate_gpt_v2
vector_id: 14123
content:
"The AR5 reported NO2 decreases by 30-50% in Europe and North \r America, and increases by more than a  factor of two in Asia, over \r the 1996-2011 period based on satellite observations (Hartmann \r et al., 2013). Extension of this analysis covering the time period up to \r 2015 reveals that NO2 has continued to decline over the USA, Western \r Europe and Japan (Schneider et al., 2015; Duncan et al., 2016; Krotkov \r et al., 2016) because of effective fossil fuel NOx emissions controls \r (Section 6.2), although this rate of decline has slowed down post-2011 \r (Jiang et al., 2018). Satellite observations also reveal a 32% decline in \r NO2 column over China after peaking in 2011 (Figure 6.6b), consistent \r with declining NOx emissions (Section 6.2) due to the implementation \r of emissions-control strategies (de Foy et al., 2016; Irie et al., 2016; \r F. Liu et al., 2016). Over Southern Asia, tropospheric NO2 levels have \r grown rapidly with increases of 50% during 2005-2015, largely \r [...]"


id: f5f8408b475c9fe32efb453c3c2ff1e9
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 40631
content:
"[...] driven by hotspot areas in India experiencing rapid expansion of \r the power sector (Duncan et al., 2016; Krotkov et al., 2016). Further \r analysis indicates that many parts of India have also undergone \r a  reversal in NO2 trends since 2011 that has been attributed to \r a combination of factors, including a slowdown in economic growth, \r implementation of cleaner technologies, non-linear NOx chemistry, \r and meteorological variability (Georgoulias et al., 2019). Satellite data "


id: e4050346cddd7e2fa33782279894f6e1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 37416
content:
"The AR5 reported a  global CO decline of about 1% yr-1 based on \r satellite data from 2002-2010, but biases in instruments rendered \r low confidence in this trend. The AR5 also indicated a  small CO \r decrease from in situ networks but did not provide quantitative \r estimates. New analysis of CO trends performed since AR5 and based \r on different observational platforms and assimilation products show \r a decline globally and over most regions during the last one to two \r decades with varying amplitudes partly depending on the period of \r analysis (Table 6.4). Inversion-based analysis attributes the global CO \r decline during the past two decades to decreases in anthropogenic \r and biomass-burning CO emissions despite probable increase in \r atmospheric CO chemical production (Gaubert et al., 2017; Jiang \r et al., 2017; Zheng et al., 2019). Furthermore, Buchholz et al. (2021) \r report a  slowdown in global CO decline in 2010-2018 compared \r [...]"


id: 10828af9cc397350596b018157b23525
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 92
content:
"[...] to 2002-2010, although the magnitude and sign of this change in \r the trend varies regionally. Global models prescribed with emissions \r inventories developed prior to the CMIP6 inventory capture the \r declining observed CO trends over North America and Europe but \r not over Eastern Asia (Strode et al., 2016). CMIP6 models driven by \r CMIP6 emissions simulate a negative trend in global CO burden over \r the 1990-2020 period (Griffiths et al., 2021), however the simulated \r trends have not yet been evaluated against observations. "


id: 111447daad0310e078f1f6afdad2189c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 198
content:
"Since AR5, advances in satellite retrievals (e.g.,  Worden et al., \r 2013; Warner et al., 2014; Buchholz et al., 2021), ground-based \r column observations (e.g.,  Zeng et al., 2012; Te et al., 2016), \r airborne platforms (e.g.,  Cohen et al., 2018; Petetin et al., 2018), \r surface measurement networks (e.g., Andrews et al., 2014; Schultz \r et al., 2015; Prinn et al., 2018; Petron et al., 2019) and assimilation \r products (e.g.,  Deeter et al., 2017; Flemming et al., 2017; Zheng \r et al., 2019) have resulted in better characterization of the present\u0002day atmospheric CO  distribution. Typical annual mean surface \r CO concentrations range from around 120 ppb in the Northern \r Hemisphere to around 40 ppb in the Southern Hemisphere (Petron \r et al., 2019). The sub-regional patterns in CO reflect the distribution of \r emissions sources. Seasonal hotspots are linked to areas of biomass \r burning in tropical South America, equatorial Africa, South East Asia \r [...]"


id: 797e8c18c7f7483e13f6d5b5ea15a6ba
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 18576
content:
"[...] and Australia. A study using data assimilation techniques estimates \r a global mean CO burden of 356 +- 27 Tg over the 2002-2013 period \r (Gaubert et al., 2017). "


id: 23e38e24ec29d4e239fc8863957ad5f7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3501
content:
"Formaldehyde (HCHO) is a short-lived, high-yield product of NMVOC \r oxidation, and formaldehyde column data from satellite instruments \r can therefore inform on trends in anthropogenic NMVOC abundances \r over very industrialized regions. The AR5 reported significant positive \r trends in formaldehyde between 1997 and 2009 over northeastern \r China (4% yr-1) and negative trends over northeastern US cities. \r Since AR5, there is robust evidence and high agreement of an upward \r trend of HCHO over eastern China, though large regional disparities \r exist in the trends (De Smedt et al., 2015; Shen et al., 2019) with \r a possible negligible or decreasing trend over Beijing and the Pearl \r River Delta. In other world regions, in particular North America, there \r is limited to medium evidence for significant changes in the HCHO \r columns, except in regions where the trend is particularly strong \r (e.g., the Houston area: -2.2% yr-1 over 2005-2014) and the Alberta \r [...]"


id: 6079bcf3d0c829f97c9cb33773bd1d9d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 14279
content:
"[...] oil sands (+3.8% yr-1; Zhu et al., 2017). Over the northeastern USA, \r even the sign of the trend differs between studies (De Smedt et al., \r 2015; Zhu et al., 2017) for reasons that are unclear. "


id: de11df2cb2982f95fef9067e601b0fb
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 36333
content:
"Of the atmospheric VSLSs, brominated and iodinated species \r are predominantly of oceanic origin, while chlorinated species \r have significant additional anthropogenic sources (Carpenter \r et al., 2014; Hossaini et al., 2015). Global mean chlorine from the \r VSLSs has increased in the troposphere from about 91  ppt in \r 2012 to about 110  ppt in 2016 (Engel et al., 2018). This increase \r is mostly due to dichloromethane (CH2Cl2), a  species that has \r predominantly anthropogenic sources reflected by three-times higher \r concentrations in the Northern Hemisphere than in the Southern \r Hemisphere (Hossaini  et al., 2017). The upward dichloromethane \r trend is corroborated by upper-tropospheric aircraft data over \r the period 1998-2014 (Elvidge et al., 2015b; Oram et al., 2017). \r The observations from the surface networks show that the abundance \r of dichloromethane continued to increase until 2019 (Annex III), \r although the accuracy of global abundance of VSLSs is limited by the \r [...]"


id: ecc595f8ddd8003691480f5898a29d53
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 39020
content:
"[...] scarce coverage by networks. No long-term changes of the bromine\u0002containing VSLSs have been observed (Engel et al., 2018)."


id: 33fb4c0c262152736de395b6e77ca7c0
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6392
content:
"controls since the mid-1980s, and increases over Asia. From about \r 2000, global mean AOD stabilized in the models, driven by soaring \r emissions in Southern Asia and declining emissions in Eastern Asia \r (Section  6.2.1). Trends after around 2010 are difficult to assess \r from CMIP6 models because the historical simulations end at 2014. \r Nevertheless, the strong decline in anthropogenic SO2 emissions over \r Eastern Asia since 2011 is underestimated in the CMIP6 emissions \r database (Hoesly et al., 2018), indicating that the observed AOD \r change over Eastern Asia may not be captured accurately by CMIP6 \r models (Wang et al., 2021). While all CMIP6 models simulate the \r increase of AOD between 1850 and 2014 there is strong inter-model \r diversity in the simulated AOD change since 1850 ranging from \r 0.01 (15%) to 0.08 (53%) in 2014. Some models therefore lie outside \r the 68% confidence interval of 0.02 (15%) to 0.04 (or 30%) for \r [...]"


id: 2cd4139ea05ef8a8f790e052472d53f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 5153
content:
"[...] global AOD change in 2005-2015 compared to 1850, estimated by \r Bellouin et al. (2020) based on observational and model (excluding \r CMIP6) lines of evidence. In addition to the horizontal distribution \r of aerosols documented by AOD, their number size distribution, \r vertical distribution, optical properties, hygroscopicity, ability to act \r as CCN, chemical composition, mixing state and morphology are key \r elements to assess their climate effect (Section 6.4). "


id: 3c533d8f7e4c48f8a782642f365f66d1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7802
content:
"Based on long-term surface-based in situ observations, AR5 \r reported a  strong decline in sulphate aerosols in Europe and the \r USA over 1990-2009, with the largest decreases occurring before \r 2000 in Europe and post-2000 in the USA. Since AR5, atmospheric \r measurements in conjunction with model results have provided \r insights into the spatial and temporal distribution of sulphate and \r sulphur deposition (Vet  et al., 2014; Tan et al., 2018; Aas et al., \r 2019). The in situ observations in North America and Europe reveal \r substantial reduction since the measurements started around \r 1980, though the trends have not been linear through this period \r (Table 6.5). Several regional studies agree with these trend estimates \r for Europe (Banzhaf et al., 2015; Theobald et al., 2019) and North \r America (Sickles II and Shadwick, 2015; Paulot et al., 2016). Further, \r the concentrations of primary emitted SO2 (Section  6.3.3.5) show \r [...]"


id: 45ec13b61a06dbaae2a61178b3a6119c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 9525
content:
"[...] greater decreases than secondary sulphate aerosols over these \r regions due to a  combination of higher oxidation rate (hence \r more SO2 converted to SO4\r 2-) and increased dry deposition rate of \r SO2 (Fowler et al., 2009; Banzhaf et al., 2015). In situ observations \r over other parts of the world are scattered (Figure  6.7), and the \r lack of observations makes it too uncertain to quantify regional "


id: dce3a7a0466d29b4b5876df3f51dd2b7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 36126
content:
"Ammonium nitrate is semi-volatile, which results in complex spatial \r and temporal patterns in its concentrations (Putaud et al., 2010; \r Hand et al., 2012a; H. Zhang et al., 2012), reflecting variations in \r its precursors, NH3 and HNO3, as well as SO4\r 2-, non-volatile cations, \r temperature and relative humidity (Nenes et al., 2020). High relative \r humidity and low temperature as well as elevated fine particulate \r matter loading (Huang et al., 2014; Petit et al., 2015; H. Li et al., 2016; \r Sandrini et al., 2016) favour nitrate production. Measurements reveal \r the high contribution of NO3\r -\r  to surface PM2.5 (>30%) in regions with \r elevated regional NOx and NH3 emissions, such as the Paris area \r (Beekmann et al., 2015; Zhang et al., 2019), northern Italy (Masiol \r et al., 2015; Ricciardelli et al., 2017), Salt Lake City (Kuprov et al., \r 2014; Franchin et al., 2018), the North China Plains (Guo et al., \r 2014; Chen et al., 2016) and New Delhi (Pant et al., 2015). Recent \r [...]"


id: 986460a3d4e211c39598fc2d5e4fa2b8
created_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 24021
content:
"[...] observations also show that ammonium nitrate contributes to the \r Asian tropopause aerosol layer (Vernier et al., 2018; Hopfner et al., \r 2019). Model diversity in simulating the present-day global fine-mode \r NO3\r -\r  burden is large with two multi-model intercomparison studies \r reporting estimates in the range of 0.14-1.88 Tg and 0.08-0.93 Tg \r respectively (Bian et al., 2017; Gliss et al., 2021). Models differ in their \r estimates of the global tropospheric nitrate burden by up to a factor \r of 13 with differences remaining nearly the same across the CMIP5 \r and CMIP6 generation of models (Bian et al., 2017; Gliss et al., 2021). \r While regional patterns in the concentration of fine-mode NO3\r -\r  are \r qualitatively captured by models, the simulation of fine-mode NO3\r -\r is generally worse than that of NH4\r + or SO42- (Bian et al., 2017). This \r can be partly attributed to the semi-volatile nature of ammonium \r [...]"


id: f07e1f0424e2dc91b8010ae554cd7a55
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 39688
content:
"[...] nitrate and biases in the simulation of its precursors (Heald et al., \r 2014; Paulot et al., 2016), including the sub-grid scale heterogeneity \r in NOx and NH3 emissions (Zakoura and Pandis, 2018). "


id: 83c56d014fb2520da655fc3ad0c1a15
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 20330
content:
"An extensive review on BC (Bond et al., 2013) discussed limitations in \r inferring its atmospheric abundance and highlighted inconsistencies \r between different terminology and related measurement techniques \r (Petzold et al., 2013; Sharma et al., 2017). Due to a lack of global \r observations, AR5 only reported declining total carbonaceous aerosol \r trends from the USA and a declining BC trend from the Arctic based \r on data available up to 2008. Since AR5, the number of observation \r sites has grown worldwide (Figure  6.7) but datasets suitable for \r global trend analyses remain limited (Reddington et al., 2017; \r Laj et al., 2020). Locally, studies based on observations from rural and \r background sites have reported decreasing surface carbonaceous \r aerosol trends in the Arctic, Europe, the USA, Japan and India \r (Table 6.6). Increases in carbonaceous aerosol concentrations in some \r rural sites of the western USA have been associated with wildfires \r [...]"


id: 27bf6f52e2e02727cb83b11e4c14cf99
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 4182
content:
"[...] (Hand et al., 2013; Malm et al., 2017). Long-term OA observations \r are scarce, so their trends outside of the USA are difficult to assess. \r Ice-core analysis has provided insight into carbonaceous aerosol \r trends predating the satellite and observation era over the Northern \r Hemisphere (Section 2.2.6, Figure 2.9b). "


id: 145a92a2958bb3bcf27375a67b1f82f7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 799
content:
"Simulated OA burdens also show a  large spread among global \r models, with Gliss et al. (2021) reporting a  multi-model median of \r 1.91 +- 0.65 Tg for the year 2010. The large spread reflects the wide \r range in the complexity of the OA parametrizations, particularly for \r SOA formation, as well as in the primary OA emissions (Tsigaridis \r et al., 2014; Gliss et al., 2021). The uncertainties are particularly large \r in model estimates of SOA production rates, which vary between \r 10 and 143 Tg yr-1 (Tsigaridis et al., 2014; Hodzic et al., 2016; Tilmes \r et al., 2019). While the level of complexity in the representation of OA \r in global models has increased since AR5 (Shrivastava et al., 2017; \r Hodzic et al., 2020), limitations in process-level understanding of \r the formation, ageing and removal of organic compounds lead to \r uncertainties in the global model predictions of global OA burden \r and distribution as well as the relative contribution of POA and \r [...]"


id: cb6c0347f4719483a17c383bd0967b6c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 33118
content:
"[...] SOA to OA. Jo et al. (2016) estimated that BrC contributes about \r 20% of total OA burden. That would give BrC a burden similar to \r that of BC (low confidence), enhancing the overall forcing exerted by \r carbonaceous aerosol absorption (Zhang et al., 2020)."


id: e93be356b71a634ecc594ba6475900e2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 38390
content:
"BC and OA lifetimes are estimated to be 5.5 days +- 35% and 6.0 \r days +- 29% (median +- 1 standard deviation), respectively, based \r on an ensemble of 14 models (Gliss et al., 2021). Disagreement \r in  simulated lifetime leads to horizontal and vertical variations in \r predicted carbonaceous aerosol concentrations, with implications for \r radiative forcing (Samset et al., 2013; Lund et al., 2018b). Airborne \r campaigns have provided valuable vertical-profile measurements \r of carbonaceous aerosol concentrations (Schwarz et al., 2013; \r Freney et al., 2018; Hodgson et al., 2018; Schulz et al., 2019; D. Zhao \r et al., 2019; Morgan et al., 2020). Compared to those measurements, \r models tend to transport BC too high in the atmosphere, suggesting \r that lifetimes are not larger than 5.5 days (Samset et al., 2013; Lund \r et al., 2018b). Newly developed size-dependent wet-scavenging \r parametrization for BC (Taylor et al., 2014; Schroder et al., 2015; \r [...]"


id: 3a527a60a995ac70c004df2472f8678a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 7465
content:
"[...] Ohata et al., 2016; G. Zhang et al., 2017; Ding et al., 2019; Moteki \r et al., 2019; Motos et al., 2019) may lead to decreased BC lifetimes \r and improve agreement with observed vertical profiles. "


id: c8d163d73344324428e2bebe9b883d5d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32657
content:
"unclear what level of sophistication is required to properly quantify \r aerosol effects on climate (Boucher et al., 2013). Since the AR5, \r Ekman (2014) found that the CMIP5 models with the most complex \r representations of aerosol impacts on cloud microphysics had \r the largest reduction in biases in surface temperature trends. \r CMIP6-generation CCMs that simulate aerosol and cloud-size \r distributions better represent the effect of a  volcanic eruption \r on lower atmosphere clouds than a  model with aerosol-mass \r only (Malavelle et al., 2017). This highlights the need for skilful \r simulation of conditions underlying aerosol-cloud interactions, \r such as the distribution, transport and properties of aerosol \r species, in addition to the interactions themselves (Chapter  7). \r In advance of CMIP6, representations of aerosol processes and \r aerosol-cloud interactions in ESMs have generally become more \r comprehensive (Meehl et al., 2020; Gliss et al., 2021; Thornhill \r [...]"


id: 32812c726b4cf7cd0dcb4456d1a542da
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6139
content:
"[...] et al., 2021b; see also Section 1.5), with enhanced links to aerosol \r emissions and gas-phase chemistry. Many CMIP6 models (Annex \r II: Table AII.5) now simulate aerosol number size distribution, in \r addition to mass distribution, which is a prerequisite for accurately \r simulating number concentrations of CCN (Bellouin et al., 2013), \r while some CMIP6 models use prescribed aerosol optical properties \r to constrain aerosol forcing (e.g., Stevens et al., 2017). Hence, the \r range of complexity in aerosol modeling noted in CMIP5 is still \r present in the CMIP6 ensemble. Although simulated CCN have \r been compared to surface (Fanourgakis et al., 2019) and aircraft \r (Reddington et al., 2017) measurements, with mixed results, \r the lack of global coverage limits confidence in the evaluations. \r Evaluations of AOD have been more wide-ranging (Section 6.3.5; \r Gliss et al., 2021) but are less relevant to aerosol-cloud interactions \r [...]"


id: 4e4abeb6b1211c04a7812a75d3448743
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 11046
content:
"[...] as they do not allow the evaluation of vertical profiles, aerosol\u0002cloud overlap regions, aerosol type or number. Nevertheless, biases \r in simulated patterns and trends in AOD, alongside biases in cloud \r fractions (Vignesh et al., 2020), likely affect quantifications of the \r aerosol-cloud interactions. "


id: bbd0ff1d73d8c40062124996a82bbb2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 30275
content:
"In AR5, the confidence in the spatial patterns of aerosol and ozone \r forcing was lower than that for the global mean because of the large \r spread in the regional distribution simulated by global models, and \r was assessed as medium. The AR5 assessment was based on aerosol \r and ozone RFs, and aerosol ERFs (with fixed SSTs) from ACCMIP and \r a small sample of CMIP5 models (Myhre et al., 2013; Shindell et al., \r 2013). For this assessement, the spatial distribution of aerosol ERF \r due to human-induced changes in aerosol concentrations over 1850-\r 2014 is quantified based on results from a seven-member ensemble \r of CMIP6 ESMs including interactive gas and aerosol chemistry \r analysed in AerChemMIP. There is insufficient information to estimate \r the spatial patterns of ozone ERF from CMIP6, however, the spatial \r patterns in SLCF ERF are dominated by that from aerosol ERF over \r most regions (e.g.,  Shindell et al., 2015). The aerosol ERF includes \r [...]"


id: 1e282070dfe743b615df825c8a805e1f
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vector_id: 2503
content:
"[...] contributions from both direct aerosol-radiation (ERFari) and indirect \r aerosol-cloud interactions (ERFaci; Section 7.3.3), and is computed \r as the difference between radiative fluxes from simulations with \r time-evolving aerosol and their precuror emissions, and identical \r simulations but with these emissions held at their 1850 levels (Collins \r et al., 2017). Both simulations are driven by time-evolving sea surface \r temperatures (SSTs) and sea ice from the respective coupled model \r historical simulation, and therefore, differ from ERFs computed using \r fixed pre-industrial SST and sea ice fields (Section  7.3.1), but the \r effect of this difference is generally small (Forster et al., 2016). A \r correction for land surface temperature change (Section 7.3.1) is not \r available from these data to explicitly quantify the contribution from \r adjustments. The ESMs included here used the CMIP6 anthropogenic \r and biomass-burning emissions for ozone and aerosol precursors \r [...]"


id: 92e0bc92efa0ecfc8d5956a81a34a8e1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 23054
content:
"[...] but varied in their representation of the natural emissions, chemistry \r and climate characteristics contributing to spread in the simulated \r concentrations (Section 6.3) and resulting forcings, partly reflecting \r uncertainties in the successive processes (Thornhill et al., 2021b)."


id: 6a8ec694791e732a297e9a0120380d3d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16032
content:
"The geographical distribution of the ensemble-mean aerosol ERF \r over the 1850-2014 period is highly heterogeneous (Figure 6.10a) in \r agreement with AR5. Negative ERF is greatest over and downwind of \r most industrialized regions in the Northern Hemisphere and to some \r extent over tropical biomass-burning regions, with robust signals. \r The largest negative forcing occurs over Eastern Asia and Southern \r Asia, followed by Europe and North America, reflecting the changes \r in anthropogenic aerosol emissions in recent decades (Section 6.2). \r Positive ERFover high albedo areas, including cryosphere, deserts and \r clouds, also found in AR5 and attributed to absorbing aerosols, are \r not robust across the small CMIP6 ensemble applied here. Regionally \r aggregated shortwave (SW) and longwave (LW) components of \r the aerosol ERF exhibit similar large variability across regions \r (Figure 6.10b). The SW flux changes come from aerosol-radiation and \r [...]"


id: 5fe86beb227e5de48dc4e30f39e5ecb0
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 14186
content:
"[...] aerosol-cloud interactions while the small positive LW flux changes \r come from aerosol-cloud interactions (related to liquid-water path \r changes (Section 7.3.2.2). These spatial patterns in aerosol ERF are \r similar to the patterns reported in AR5. "


id: 2b7f6c9c73452923180c8f9252af1e3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 4908
content:
"The best estimate for the ERF due to emissions of BC is reduced from \r the AR5, and is now estimated to be 0.11 [-0.20 to 0.42] W  m-2\r with an uncertainty also including negative values. As discussed in \r Section  7.3.3.1.2, a  significant portion of the positive BC forcing \r from aerosol-radiation interactions is offset by negative atmospheric \r adjustments due to cloud changes, as well as lapse rate and \r atmospheric water vapour changes, resulting in a smaller positive net \r ERF for BC compared with AR5. The large range in the forcing estimate \r stems from variation in the magnitude and sign of atmospheric \r adjustments across models and is related to the differences in the \r model treatment of different processes affecting BC (e.g.,  ageing, \r mixing) and its interactions with clouds and cryosphere (Section 7.3.3; \r Thornhill et al., 2021b). The emissions-based ERF for organic carbon \r aerosols is -0.21 [-0.44 to +0.02] W m-2, a weaker estimate compared \r [...]"


id: b1619494ab74487822256cafe49f6c2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 28434
content:
"The emissions-based contributions to GSAT change (Figure  6.12b) \r were not assessed in AR5, but with the ERF from aerosol-cloud \r interactions attributed to the emitted compounds there is now \r a better foundation for this assessment. The contribution to emissions\u0002based ERF at 2019 (Figure 6.12a) is scaled by the historical emissions \r (over the period 1750-2019) of each compound to reconstruct \r the historical time series of ERF. An impulse response function (Cross\u0002Chapter Box 7.1, Supplementary Material 7.SM5.2) is then applied \r to obtain the contribution of SLCF emissions to the GSAT response. \r Due  to the non-linear chemical and physical processes described \r above relating emissions to ERF, and the additional non-linear \r relations between ERF and GSAT, these emissions-based estimates \r of GSAT responses strongly depend on the methodology applied to \r estimate ERF and GSAT (Supplementary Material 6.SM.2). Therefore, \r [...]"


id: aba1790e802faf2ff04818e76f534f28
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 27433
content:
"[...] the relative contribution of each compound through its primary \r emissions versus secondary formation or destruction (e.g.,  for \r methane emissions its ozone versus methane contributions), by \r construction (omitting the non-linear processes), will be equal for \r ERF and GSAT. Uncertainties in the GSAT response are estimated \r using the assessed range of the equilibrium climate sensitivity (ECS) \r from Chapter 7 of this report. For most of the emitted compounds the \r uncertainty in the GSAT response is dominated by the uncertainty in \r the relationship between emissions and the ERF."


id: 37b4f0886873a6b5e32f2a00bfbe7bb0
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7009
content:
"On the global scale, as assessed in Chapter 3, anthropogenic aerosols \r have likely cooled GSAT since 1850-1900 driven by the negative \r aerosol forcing, while it is extremely likely that human-induced \r stratospheric ozone depletion has primarily driven stratospheric cooling \r between 1979 and the mid-1990s. Multiple modelling studies support \r the understanding that present-day emissions of SO2, a  precursor \r for sulphate aerosols, are the dominant driver of near- surface air \r temperature responses in comparison to BC or OC even though, for \r some regions, BC forcing plays a key role (Baker et al., 2015; Samset \r et al., 2016; Stjern et al., 2017; Zanis et al., 2020). Further, there is \r high confidence that the aerosol-driven cooling has led to detectable \r large-scale water-cycle changes since at least the mid-20th century \r as assessed in Chapter 8. The overall effect of surface cooling from \r [...]"


id: c74e78d05ee8523abd2eb88b0da467a7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32383
content:
"[...] anthropogenic aerosols is to reduce global precipitation and alter large\u0002scale atmospheric circulation patterns (high confidence), primarily \r driven by the cooling effects of sulphate aerosols (Section  8.2.1).\r In addition, there is high confidence that darkening of snow through \r the deposition of black carbon and other light-absorbing particles \r enhances snowmelt (Section 7.3.4.3; SROCC Chapter 3)."


id: d55cb16a383ba9aa948398d2939fa5dd
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 34858
content:
"The Arctic region is warming considerably faster than the rest of \r the globe (Atlas 11.2.2) and, generally, studies indicate that this \r amplification of the temperature response toward the Arctic has \r an important contribution from local and remote aerosol forcing \r (Stjern et al., 2017; Westervelt et al., 2018). Several studies indicate \r that changes in long-range transport of sulphate and BC from \r northern mid-latitudes can potentially explain a significant fraction \r of Arctic warming since the 1980s (e.g., Navarro et al., 2016; Breider \r et al., 2017; Ren et al., 2020). Modelling studies show that changes \r in mid-latitude aerosols have influenced Arctic climate by changing \r the radiative balance through aerosol-radiation and aerosol-cloud \r interactions, and enhancing poleward heat transport (Navarro et al., \r 2016; Ren et al., 2020). Idealized aerosol-perturbation studies have \r shed further light on the sensitivity of Arctic temperature response to \r [...]"


id: fc74c1fd684ad0c2675937df63ab0b8c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 41784
content:
"[...] individual aerosol species. Studies show relatively large responses in \r the Arctic to BC perturbations and reveal the importance of remote \r BC forcing by rapid adjustments (Sand et al., 2013b; Stjern et al., \r 2017; L. Liu et al., 2018; Yang et al., 2019b). Perturbations in SO2\r emissions over major emitting regions in the Northern Hemisphere \r have been shown to produce the largest Arctic temperature responses \r (Kasoar et al., 2018; Lewinschal et al., 2019). "


id: 2ba9795737c5cff4f6a5514ccbd114c3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 4941
content:
"the temperature response is more evenly distributed in line with the \r results of CMIP5 models including the temperature response to ozone \r changes (Shindell et al., 2015). The ensemble-mean global mean \r surface temperature decreases by 0.66degC +- 0.51degC while decreasing \r by 0.97degC +- 0.54degC for the Northern Hemisphere and 0.34degC +- 0.2degC \r for the Southern Hemisphere. The zonal-mean temperature response \r is negative at all latitudes (high confidence) and becomes more \r negative with increasing latitude, with a maximum ensemble-mean \r decrease of around 2.7degC at northern polar latitudes. The zonal\u0002mean response is not directly proportional to the zonal-mean forcing, \r especially in the Arctic where the temperature response is cooling \r while the local ERF is positive (Figure 6.10). This is consistent with \r prior studies showing that the Arctic, in particular, is highly sensitive \r to forcing at NH mid-latitudes (e.g.,  Shindell and Faluvegi, 2009; \r [...]"


id: c6c0060f1a95373d0f138a0c541d84ac
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32272
content:
"[...] Sand et al., 2013a) and with results from CMIP5 models (more on the \r Arctic below; Shindell et al., 2015). Thus, there is high confidence that \r the temperature response to aerosols is more asymmetric than the \r response to WMGHGs and negative at all latitudes."


id: 60bc240077b785c2210f981bfd0f1034
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 14314
content:
"Tropospheric aerosols influence the land and ocean ecosystem \r productivity and the carbon cycle through changing physical climate \r and meteorology (Jones, 2003; Cox et al., 2008; Mahowald, 2011; \r Unger et al., 2017) and through changing deposition of nutrients \r including nitrogen, sulphur, iron and phosphorous (Mahowald \r et al., 2017; Kanakidou et al., 2018). There is robust evidence and \r high agreement from field (Oliveira et al., 2007; Cirino et al., 2014; \r Rap et al., 2015; X. Wang et al., 2018) and modelling (Mercado et al., \r 2009; Strada and Unger, 2016; Lu et al., 2017; Yue et al., 2017) studies \r that aerosols affect plant productivity through increasing the diffuse \r fraction of downward shortwave radiation, although the magnitude \r and importance to the global land carbon sink is controversial. At large \r scales the dominant effect of aerosols on the carbon cycle is likely\r a global cooling effect of the climate (medium confidence) (Jones, \r [...]"


id: ccde76fe3f5a85bcb9aa5fe796c4022d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 33380
content:
"[...] 2003; Mahowald, 2011; Unger et al., 2017). We  assess that these \r interactions between aerosols and the carbon cycle are currently too \r uncertain to constrain quantitatively the indirect CO2 forcing."


id: baf7792f26897930bd9683f8b58c71ce
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 30106
content:
"Ozone uptake itself damages photosynthesis and reduces plant \r growth with consequences for the carbon and water cycles \r (Ainsworth et al., 2012; Emberson et al., 2018). The AR5 concluded \r there was robust evidence of the effect of ozone on plant physiology \r and subsequent alteration of the carbon storage, but considered \r insufficient quantification of and a lack of systematic incorporation \r of the ozone effect in carbon-cycle models as a limitation to assess \r the terrestrial carbon balance (Ciais et al., 2013). Since AR5 several \r more ESMs have incorporated interactive ozone-vegetation damage \r resulting in an increase in evidence to support the influence of ozone \r on the land carbon cycle. The new modelling studies tend to focus \r on ozone effects on plant productivity rather than the land carbon \r storage and agree that ozone-induced gross-primary productivity \r (GPP) losses are largest today in the eastern USA, Europe and eastern \r [...]"


id: 9940e5da41cd68fd0fb88f259eb29af
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 24180
content:
"[...] China, ranging from 5-20% on the regional scale (low confidence) \r (Yue and Unger, 2014; Lombardozzi et al., 2015; Yue et al., 2017; \r Oliver et al., 2018). There is medium evidence and high agreement\r based on observational studies and models that ozone-vegetation \r interactions further influence the climate system, including water and \r carbon cycles by affecting stomatal control over plant transpiration of \r water vapour between the leaf surface and atmosphere (Wittig et al., \r 2007; Sun et al., 2012; VanLoocke et al., 2012; Lombardozzi et al., \r 2013; Hoshika et al., 2015; Arnold et al., 2018). While some modelling \r studies suggest that the unintended Nr deposition fertilization effect \r in forests may potentially offset the ozone-induced carbon losses \r (Felzer et al., 2007; de Vries et al., 2017), complex interactions have \r been observed between ozone and Nr deposition to ecosystems that \r have not yet been included in ESMs. For some plants, the effects \r [...]"


id: 4b1f52b8c209acaa131078f4b0a0e430
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10462
content:
"climate feedbacks are assessed in Section 7.4.2. Additionally, non-CO2\r biogeochemical feedbacks due to climate-driven changes in methane \r sources and N2O sources and sinks are assessed in Section  5.4.7. \r The goal of this section is to estimate the feedback parameter \r (a  as defined in section  7.4.1.1) from climate-induced changes in \r atmospheric abundances or lifetimes of SLCFs mediated by natural \r processes or atmospheric chemistry. These non-CO2 biogeochemical \r feedbacks act on time scales of years to decades and have important \r implications for climate sensitivity and emissions-abatement policies. \r The feedback parameter is quantified entirely from ESMs that \r expand the complexity of CCMs by coupling the physical climate and \r atmospheric chemistry to land and ocean biogeochemistry. In AR5, \r a for non-CO2 biogeochemical feedbacks was estimated from an \r extremely limited set of modelling studies with much less confidence \r [...]"


id: 5e9000c46a1368abdf88d57208b6fd0
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 13925
content:
"[...] associated with the estimate. Since AR5, ESMs have advanced to \r include more feedback processes, facilitating a relatively more robust \r assessment of a. CMIP6 ESMs participating in AerChemMIP performed \r coordinated sets of experiments (Collins et al., 2017) facilitating the \r consistent estimation of a (Thornhill et al., 2021a) and we rely on this \r multi-model analysis for the best estimates (Table 6.8). Considering \r the consistent methodology, the assessed central values and 5-95% \r ranges for a are based on the AerChemMIP estimates. The full range \r of model uncertainty is not captured in AerChemMIP because of the \r relatively small ensemble size, therefore estimates from studies using \r other models or with different protocols are discussed to reinforce or \r critique these values. "


id: 579a8a3812c9a067b6cb6cd976d6b78b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 12678
content:
"effects on DMS fluxes produce diverging estimates of changes in \r DMS emissions strength under climate change resulting in large \r uncertainties in the DMS-sulphate-cloud albedo feedback (Bopp et al., \r 2004; Kloster et al., 2007; Gabric et al., 2013). In AR5, the climate\u0002DMS feedback parameter was estimated to be -0.02 W m-2 degC-1 based \r on a single model. Since AR5, new modelling studies using empirical \r relationships between pH and total DMS production find that global \r DMS emissions decrease due to combined ocean acidification and \r climate change, leading to a  strong positive climate feedback (Six \r et al., 2013; Schwinger et al., 2017). However, another study argues \r for a much weaker positive feedback globally due to complex and \r compensating regional changes in marine ecosystems (S. Wang et al., \r 2018). The AerChemMIP multi-model analysis suggests small positive \r feedback (Table 6.8), consistent with these recent studies, but with \r [...]"


id: ea91063665be8b1e41b9c7b7a9b4fb50
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 38629
content:
"Climate-dust feedback: Mineral dust is the most abundant aerosol \r type in the atmosphere, when considering aerosol mass, and affects \r the climate system by interacting with both longwave and shortwave \r radiation as well as contributing to the formation of CCN and \r INP. Because dust emissions are sensitive to climate variability \r (e.g.,  through changes in the extent of arid land; Section  6.2.2.4), \r it has been hypothesized that the climate-dust feedback could be \r an important feedback loop in the climate system. Since AR5, an \r improved understanding of the shortwave absorption properties \r of dust as well as a  consensus that dust particles are larger than \r previously thought has led to a  revised understanding that the \r magnitude of radiative forcing due to mineral dust is small (Kok et al., \r 2017; Ryder et al., 2018). A recent study notes that global models \r underestimate the amount of coarse dust in the atmosphere and \r [...]"


id: 681dcdb0996a4f956fe85aa5f7ded526
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15585
content:
"[...] accounting for this limitation raises the possibility that dust emissions \r warm the climate system (Adebiyi and Kok, 2020). Model predictions \r of dust emissions in response to future climate change range from an \r increase (Woodward et al., 2005) to a decrease (Mahowald and Luo, \r 2003), thus leading to high uncertainties on the sign of the climate\u0002dust feedback. Since AR5, Kok et al. (2018) estimated the direct \r dust-climate feedback parameter, from changes in the dust direct \r radiative effect only, to be in the range -0.04 to +0.02W m-2 degC-1. \r The assessed central value and the 5-95% range of the climate-dust \r feedback parameter based on AerChemMIP ensemble (Table  6.8) \r is within the range of the published estimate, however both the \r magnitude and sign of a are model-dependent. "


id: 13bb46ac1ef3bd305050c0d9023b6ffa
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 684
content:
"Climate-sea-spray feedback: Sea-spray emissions from ocean \r surfaces influence climate directly or indirectly through the formation \r of CCN as discused in Section 6.2.1.2. They are sensitive to SST and \r sea ice extent, as well as to wind speed, and are therefore expected \r to feedback on climate (Struthers et al., 2013). However, there are \r large uncertainties in the strength of climate feedback from sea\u0002spray aerosols because of the diversity in the model representation \r of emissions (many represent sea-salt emissions only) and their \r functional dependence on environmental factors noted above, in situ \r atmospheric chemical and physical processes affecting the sea-spray \r lifetime, and aerosol-cloud interactions (Struthers et al., 2013; Soares \r et al., 2016; Nazarenko et al., 2017). Additional work is needed to \r identify how sea-spray and POA emissions respond to shifts in ocean \r biology and chemistry in response to warming, ocean acidification \r [...]"


id: 146302098043d4ec7ae6f5966353d55f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 809
content:
"[...] and changes in circulation patterns (Cochran et al., 2017), and \r affect CCN and INP formation (DeMott et al., 2016). AerChemMIP \r models, representing only the sea-salt emissions, agree that the \r sea-salt-climate feedback is negative, however there is a large range \r in the feedback parameter indicating large uncertainties (Table 6.8)."


id: 83b6eb6f5bb36eaa22c55ac2a4828177
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 20317
content:
"climate will lead to decreases in the tropical lower troposphere \r owing to increased water vapour, and increases in the subtropical \r to mid-latitude upper troposphere due to increases in lightning \r and stratosphere-to-troposphere transport (Stevenson et al., 2013). \r A small positive feedback is estimated from climate-induced changes \r in global mean tropospheric ozone (Dietmuller et al., 2014) while \r a small negative feedback is estimated by Heinze et al. (2019) based \r on the model results of Stevenson et al. (2013). Additionally, these \r ozone feedbacks induce a  change in stratospheric water vapour \r amplifying the feedback due to stratospheric ozone (Stuber et al., \r 2001). Since AR5, several modelling studies have estimated the \r intensity of meteorology-driven ozone feedbacks on climate from \r either combined tropospheric and stratospheric ozone changes or \r separately with contrasting results. One study suggests no change \r [...]"


id: 3893b2a16a092713fdf74694c4046190
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7162
content:
"[...] (Marsh et al., 2016), while other studies report reductions of ECS \r ranging from 7-8% (Dietmuller et al., 2014; Muthers et al., 2014) \r to 20% (Nowack et al., 2015). The estimate of this climate-ozone \r feedback parameter is very strongly model-dependent with values \r ranging from -0.13 to -0.01 W m-2 degC-1 though there is agreement \r that it is negative. The assessed central value and the 5-95% range of \r climate-ozone feedback parameter based on AerChemMIP ensemble \r is within the range of these published estimates, but closer to the \r lower bound. This climate-ozone feedback factor does not include \r the feedback on ozone from lightning changes which is discussed \r separately below. "


id: c5a64a36b96078a9921c802ba697f089
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32081
content:
"Climate-fire feedback: Wildfires are a  major source of SLCF \r emissions (Section  6.2.2.6). Climate change has the potential to \r enhance fire activity (Sections 12.4 and 5.4.3.2) thereby enhancing \r SLCF emissions leading to feedbacks. Climate-driven increases in fire \r could potentially lead to offsetting feedback from increased ozone \r and decreased methane lifetime (due to increases in OH) leaving \r the feedback from aerosols to dominate with an uncertain net \r effect (e.g., Landry et al., 2015). The AR5 assessment of climate-fire \r feedbacks included a value of a due to fire aerosols to be in the range \r of -0.03 to + 0.06 W m-2 degC-1 based on Arneth et al. (2010a). A recent \r study estimates climate feedback due to fire aerosols to be greater \r than that due to BVOCs, with a value of a equal to -0.15 (-0.24 to \r -0.05) W m-2 degC-1 (Scott et al., 2018). Clearly, the assessment of fire\u0002related non-CO2 biogeochemical feedbacks is very uncertain because \r [...]"


id: a66a7248eb1cbda9e02812d99f6ef055
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 26499
content:
"[...] of limitations in the process understanding of the interactions \r between climate, vegetation and fire dynamics, and atmospheric \r chemistry and their representation in the current generation \r ESMs. Some AerChemMIP ESMs include the representation of fire \r dynamics but do not activate their interaction with atmospheric \r chemistry. Given the large uncertainty and lack of information from \r AerChemMIP ESMs, we do not include a quantitative assessment of \r climate-fire feedback for AR6. "


id: dc5e13c0513ec9692c494af53e9f1276
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 36041
content:
"Climate-BVOC feedback: BVOCs, such as isoprene and \r terpenes, are produced by land vegetation and marine plankton \r (Sections 6.2.2.3 and 6.2.2.5). Once in the atmosphere, BVOCs and \r their oxidation products lead to the formation of secondary organic \r aerosols (SOA) exerting a  negative forcing, and increased ozone \r concentrations and methane lifetime exerting a  positive forcing. \r BVOC emissions are suggested to lead to a climate feedback in part \r because of their strong temperature dependence observed under \r present-day conditions (Kulmala et al., 2004; Arneth et al., 2010a). \r Their response to future changes in climate and CO2 levels remains \r uncertain (Section 6.2.2.3). Estimates of the climate-BVOC feedback \r parameter are typically based on global models which vary in their \r level of complexity of emissions parametrization, BVOC speciation, \r the mechanism of SOA formation and the interaction with ozone \r [...]"


id: 950fbefb8c4a8578a9552fe71fcfccab
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 23430
content:
"[...] chemistry (Thornhill et al., 2021a). Since AR5, observational studies \r (Paasonen et al., 2013) and models (Scott et al., 2018) estimate the \r feedback due to biogenic SOA (via changes in BVOC emissions) to \r be in the range of about -0.06 to -0.01 W m-2 degC-1. The assessed \r central estimate of the climate-BVOC feedback parameter based on \r the AerChemMIP ensemble suggests that climate-induced increases \r in SOA from BVOCs will lead to a  strong cooling effect that will \r outweigh the warming from increased ozone and methane lifetime, \r however the uncertainty is large (Thornhill et al., 2021a). "


id: b8dc9a5b708950d6ef468be5eff47457
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29727
content:
"For marine cloud brightening (MCB), seeded aerosols may affect both \r cloud microphysical and macrophysical properties (Section 7.3.3.2). \r By principle, MCB relies on ERFaci through the so-called Twomey \r effect (Twomey, 1977), but ERFari may be of equal magnitude \r as shown in studies that consider spraying of sea salt outside \r tropical marine cloud areas (Jones and Haywood, 2012; Partanen \r et al., 2012; Alterskjaer and Kristjansson, 2013; Ahlm et al., 2017). \r The maximum negative ERF estimated from modelling is within the \r range of -5.4 to -0.8 W m-2 (Latham et al., 2008; Rasch et al., 2009; \r Jones et al., 2011; Partanen et al., 2012; Alterskjaer and Kristjansson, \r 2013). For dry sea salt, the ERF efficiency is estimated to be within \r the range of -3 to -10 W m-2/(Pg yr-1), when emitted over tropical \r oceans in  ESMs in the Geoengineering Intercomparison Project \r (GeoMIP; Ahlm et al., 2017). Cloud-resolving models reveal the \r [...]"


id: 268803328e0c33eb070f09b9fdac4680
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3942
content:
"[...] complex behaviour and response of stratocumulus clouds to seeding, \r in that the ERF efficiency depends on meteorological conditions, \r and the ambient aerosol composition, where lower background \r particle concentrations may increase the ERFaci efficiency (Wang \r et al., 2011). Seeding could suppress precipitation formation and \r drizzle, and hence increase the lifetime of clouds, preserving their \r cooling effect (Ferek et al., 2000). In contrast, cloud lifetime could \r be decreased by making the smaller droplets more susceptible to \r evaporation. Modelling studies have shown that a  positive ERFaci\r (warming) could also result from seeding clouds with too large \r aerosols (Pringle et al., 2012; Alterskjaer and Kristjansson, 2013). \r These individual and combined processes are not well understood, \r and may have a limited representation in models, or counteracting \r errors (Mulmenstadt and Feingold, 2018), lending low to medium \r confidence to the ERF estimates."


id: 54c52f1ab3441e85745ba23868509bdc
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 12184
content:
"Stratospheric aerosol injections (SAI) have the potential to achieve \r a  high negative global ERF, with maximum ERFs ranging from \r -5  to  -2  W m-2 (Niemeier and Timmreck, 2015; Weisenstein \r et al., 2015; Niemeier and Schmidt, 2017; Kleinschmitt et al., 2018). \r The  magnitude of the maximum achievable ERF depends on the \r chosen aerosol type and mixture, internal structure and size, or \r precursor gas (e.g., SO2), as well as the injection strategy (latitude, \r altitude, magnitude and season of injections), plume dispersal, \r model representation of aerosol microphysics, and ambient aerosol \r concentrations (Rasch et al., 2008; Robock et al., 2008; Pierce et al., \r 2010; Weisenstein et al., 2015; Laakso et al., 2017; MacMartin et al., \r 2017; Dai et al., 2018; Kleinschmitt et al., 2018; Vattioni et al., 2019; \r Visioni et al., 2019). For sulphur, the radiative forcing efficiency is \r of around -0.1 to -0.4 W  m-2/(TgS yr-1) (Niemeier and Timmreck, \r [...]"


id: c96a51d0124a870c56f6dfb7471fb388
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32762
content:
"[...] 2015; Weisenstein et al., 2015; Niemeier and Schmidt, 2017). \r Different manufactured aerosols, such as ZrO2, TiO2 and Al2O3, have \r different ERF efficiencies compared to sulphate (Ferraro et al., 2011; \r Weisenstein et al., 2015; Dykema et al., 2016; Jones et al., 2016). \r The aerosol size distribution influences the optical properties of an "


id: d11a79ac77b02569be6f8578e93b96fb
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 34140
content:
"Air pollutants can be impacted by climate change through physical \r changes affecting meterorological conditions, chemical changes \r affecting their lifetimes, and biological changes affecting their natural \r emissions (Kirtman et al., 2013). Changes in meteorology affect air \r quality directly through modifications of atmospheric transport \r patterns (e.g.,  occurrence and length of atmospheric blocking \r episodes, ventilation of the polluted boundary layer), extent of mixing \r layer and stratosphere-troposphere exchange (STE) for surface ozone \r (von Schneidemesser et al., 2015), and through modifications of the \r rate of reactions that generate secondary species in the atmosphere. \r Changing precipitation patterns in a  future climate also influence \r the wet removal efficiency, in particular for atmospheric aerosols \r (Hou  et al., 2018). Processes at play in non-CO2 biogeochemical \r feedbacks (Section  6.4.5) are also involved in the perturbation of \r [...]"


id: 3fbb19b416b20931361c3c5219c74cc9
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 8406
content:
"Since the AR5, several studies have investigated the net effect of \r climate change on surface ozone, based on either global or regional \r model projections. A systematic and quantitative comparison of the \r ozone change, however, is difficult due to the variety of models with \r different complexities in the representation of natural emissions, \r chemical mechanisms and physical processes, as well as the surface \r ozone metrics applied for analysis. Ozone response to climate change \r has been shown to be particularly sensitive to model representation \r of processes like BVOC emissions, deposition, and isoprene chemistry\r (Squire et al., 2015; Val Martin et al., 2015; Schnell et al., 2016; \r Pommier et al., 2018). More robust protocols are now used more \r commonly comprising, notably, longer simulations necessary to \r separate change from interannual variability (Barnes et al., 2016; \r Lacressonniere et al., 2016; Garcia-Menendez et al., 2017). However, \r [...]"


id: 2328f0117cabead40385c477e64450d5
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3384
content:
"[...] the amplitude of climate change penalty on ozone over polluted \r regions may be different in high-resolution (regional- and urban\u0002scale) models in comparison to coarse-resolution global models, \r because a number of controlling processes are resolution-dependent \r including for example, local emissions and sensitivity to the chemical "


id: 9a1f96a3a4810f981235d4fc1d778ac2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 24350
content:
"Figure 6.14 | Multi-model annual mean change in surface O3 (ppb) concentrations at different warming levels. Changes are shown for (a) 1.0degC, (b) 1.5degC, (c)\r 2.0degC and (d) 2.5degC increases in global mean surface air temperature. CMIP6 models include GFDL-ESM4, GISS-E2-1-G, MRI-ESM2-0 and UKESM1-0-LL. For each model, the \r change in surface O3 is calculated as the difference between two AerChemMIP experiments - one with evolving future emissions and sea surface temperatures (SSTs) under \r the SSP3-7.0 scenario and the other with the same setup but with fixed present-day SSTs. The difference is calculated as a 20-year mean in surface O3 around the year when \r the temperature threshold in each model is exceeded. The multi-model change in global annual mean surface O3 concentrations with +- 1 standard deviation are shown within \r [...]"


id: 6ace4ca8d680964ef1ff1026424a4479
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16071
content:
"[...] parentheses. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, three out of four models agree on sign of change; \r diagonal lines indicate regions with low model agreement, where three out of four models agree on sign of change. For more information on the simple approach, please refer \r to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)."


id: dd67eab05c254415936ea1829a546c4c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 36222
content:
"Considering anthropogenic emissions of precursors globally higher \r than the current emissions (SSP3-7.0 in 2050; Figure 6.20), the CMIP6 \r ensemble confirms the surface ozone penalty due to climate change\r over regions close to anthropogenic pollution sources or close to \r natural emissions sources of ozone precursors (e.g., biomass-burning \r areas), with a penalty of a few ppb for the annual mean, proportional \r to warming levels (Figure  6.14). This rate ranges regionally from \r 0.2-2 ppb degC-1 (Supplementary Material Figure  6.SM.1). The CMIP6 \r ESMs show this consistently for South East Asia (in line with Hong et al. \r (2019) and Schnell et al. (2016)) and for India (in line with Pommier \r et al., 2018) as well as in parts of Africa and South America, close to \r enhanced BVOC emissions (at least three out of four ESMs agree on \r the sign of change). The results are mixed in polluted regions of Europe \r and US because of lower anthropogenic precursor emissions which \r [...]"


id: 391d625ac164ce99ae67efe693578e44
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7255
content:
"[...] leads to a very low sensitivity of surface ozone to climate change (-0.5 \r ppb degC-1 to 0.5 ppb degC-1; Supplementary Material Figure 6.SM.1) and \r thus the ESMs can disagree on sign of changes for a given warming \r level. This heterogeneity in the results is also found in regional studies \r over North America (Gonzalez-Abraham et al., 2015; Val Martin et al., \r 2015; Schnell et al., 2016; He et al., 2018; Nolte et al., 2018; Rieder \r et al., 2018) or over Europe (Colette et al., 2015; Lacressonniere et al., \r 2016; Schnell et al., 2016; Fortems-Cheiney et al., 2017)."


id: 3f477c7969f65da871375ad2c1228f43
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 8324
content:
"Overall, warmer climate is expected to reduce surface ozone in \r unpolluted regions as a result of greater water vapour abundance \r accelerating ozone chemical loss (high confidence). Over regions \r with high anthropogenic and/or natural ozone precursor emissions, \r there is prevailing evidence that climate change will introduce \r a surface O3 penalty increasing with increasing warming levels (with \r a  magnitude ranging regionally from 0.2-2 ppb degC-1) (medium to\r high confidence). Yet, there are uncertainties in processes affected \r in a warmer climate which can impact and modify future baseline \r and regional/local surface ozone levels. The response of surface \r ozone to future climate change through stratosphere-troposphere \r exchange, soil NOx emissions and wildfires is positive (medium \r confidence). In addition, there is low confidence in the magnitude \r of the effect of climate change on surface ozone through biosphere \r [...]"


id: 2f1f3c9becc7731d13a59c2743ae6e90
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 5559
content:
"[...] interactions (natural methane, non-methane BVOC emissions and \r ozone deposition) and lightning NOx emissions. "


id: 35fd51b3afc5d67fcda060b4efdab478
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6747
content:
"or a high percentile) as the sensitivity of peak values to meteorological \r conditions can be different from sensitivity of the median or mean \r (Porter et al., 2015). The AR5 assessed with medium confidence\r that uniformly higher temperatures in polluted environments will \r trigger regional feedbacks in chemistry and local emissions that will \r increase peak ozone and PM pollution, but assessed low confidence\r in projecting changes in meteorological blocking associated with \r these extreme episodes. \r Meteorological conditions, such as heatwaves, temperature \r inversions and atmospheric stagnation episodes favour air quality \r extremes and are influenced by changing climate (Fiore et al., 2015). \r The body of literature on the connection between climate change and \r extreme anthropogenic pollution episodes is essentially based on \r correlation and regression applied to observation reanalysis but the \r metrics and methodologies differ making quantitative comparisons \r [...]"


id: 972dfce6c416499b21cda0405bea061b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 23792
content:
"[...] difficult. Many emission processes in the natural systems are sensitive \r to temperature, and bursts of emissions as a  reponse to extreme \r weather, as in the case of wildfires in dry conditions (Bondur et al., \r 2020; Xie et al., 2020) can occur, which would then add to the risk \r of extreme air pollution but are not sufficiently constrained to be \r quantitatively assessed.\r 6.6 Air Quality and Climate Response \r to SLCF Mitigation\r Long-lived greenhouse gas (LLGHG) emissions reductions are \r typically motivated by climate change mitigation policies, whereas \r SLCF reductions mostly result from air pollution control and climate \r policies (FAQ6.2), as well as policies focusing on achieving UN \r Sustainable Development Goals (SDGs; Box 6.2). The management \r of several SLCFs (BC, methane, tropospheric ozone and HFCs) is \r considered in the literature as a  fast-response, near-term measure \r to curb climate change, while reduction of emissions of LLGHGs \r [...]"


id: bf7c190b939d71f399293b4eb4f8ae4a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 30952
content:
"[...] is an essential measure for mitigating long-term climate warming \r (Shindell et al., 2012, 2017b; Shoemaker et al., 2013; Rogelj et al., \r 2014b; Lelieveld et al., 2019). Note that the term short-lived climate \r pollutants (SLCPs), referring only to warming SLCFs, has been used \r within the policy arena. The SR1.5 report states that limiting warming \r to 1.5degC to achieve Paris Agreement goals, implies net-zero CO2\r emissions around 2050 and concurrent deep reductions in emissions \r of non-CO2 forcers, particularly methane (Rogelj et al., 2018a). \r In addition, several SLCFs are key air pollutants or precursors of fine \r particulate matter (PM2.5) and tropospheric ozone, and therefore \r subject to control driven by air-quality targets. "


id: 9e283830d7a2b4af6cb486aaf790b630
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25057
content:
"Since AR5, published studies provide augmented evidence for \r the connections between extreme ozone and PM pollution events \r and high temperatures, especially long-lasting heatwaves, whose \r frequency is increasing due to a warming climate (Lelieveld et al., \r 2014; Porter et al., 2015; Hou and Wu, 2016; Jing et al., 2017; Schnell \r and Prather, 2017; Sun et al., 2017; H. Zhang et al., 2017). However, \r the relationship between air pollution and individual meteorological \r parameters is exaggerated because of covariation on synoptic time \r scales (Fiore et al., 2015). For example, heatwaves are often associated \r with clear skies and stagnation, making clear attribution to specific \r meteorological variables complicated. In Asia, future changes in \r winter conditions have also been shown to favour more particulate \r pollution (Cai et al., 2017; Zou et al., 2017). The relationship between \r the occurrence of stagnation episodes and high concentrations \r [...]"


id: 8a54732d95ff318eda236b742db4a9df
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 21510
content:
"[...] of ozone and PM2.5 has been shown to be regionally and metric \r dependant (Oswald et al., 2015; Sun et al., 2017; Kerr and Waugh, \r 2018; Schnell et al., 2018; Garrido-Perez et al., 2019)."


id: c4c550d95624d58bf8773fd159c00645
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31933
content:
"It is often perceived that the full climatic response following \r mitigation of SLCFs will occur almost immediately. However, the \r inertia of the climate system strongly modifies the short-term and \r long-term response. SLCFs with lifetimes shorter than the time scales \r for inter-hemisheric mixing (1-2 years) can cause a more spatially \r heterogeneous forcing than LLGHGs and thus different regional \r patterns of the climate response (Section  6.4.3). The temporal \r response in GSAT to a radiative forcing can be quantified using linear \r impulse response functions (Cross-Chapter Box 7.1; Geoffroy et al., \r 2013; Olivie and Peters, 2013; Smith et al., 2018). Figure 6.15 shows \r the GSAT response for sustained step reduction in emissions of \r idealised SLCFs with different lifetimes. The response is relative to \r a  baseline with constant emissions, so effects of emissions before \r the step reduction is not shown. For SLCFs with lifetimes shorter \r [...]"


id: 88cedfdfdb6c229aeb74a4e31b7c8780
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 21231
content:
"[...] than a  few years, the concentrations quickly reach a  new steady \r state and the response time is primarily governed by the thermal \r inertia and thus the time scales of the climate system. For compounds \r with lifetime on the order of 10 years (e.g., methane), there is about \r a 10-year delay in the response during the first decades, compared \r to compounds with lifetimes less than one year. However on longer \r time scales the response is determined solely by the time scales of \r the climate system itself. For CO2 (dashed line in Figure  6.15) the \r temporal response is very different due to the long time scale for "


id: b6d6d5db72e2734b546496f8c5260e9e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29366
content:
"sign of the net global radiative effects of carbonaceous aerosols from \r the residential sector and solid-fuel cookstove emissions (warming \r or cooling) is not well constrained based on evidence from recent \r global atmospheric modelling studies. Estimates of direct aerosol-\r radiation and aerosol-cloud effects from the global residential \r sector range from -20 to +60 mW m-2 (Kodros et al., 2015) and \r -66 to +21 mW m-2 (Butt et al., 2016) and from -20 to +10 mW m-2\r (Kodros et al., 2015) and -52 to -16 mW m-2 (Butt et al., 2016), \r respectively. Uncertainties are due to assumptions about the aerosol \r emissions masses, size distribution, aerosol optical properties \r and mixing states (Section  6.3.5.3). Allowing BC to act as an INP \r in a  global model leads to a  much larger global forcing estimate \r from -275 to +154 mW m-2 with a large uncertainty range due to \r uncertainty in the plausible range of maximum freezing efficiency \r [...]"


id: 7f34fb5f199ed991a91b74af2a8f2300
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 19533
content:
"[...] of BC (Huang et al., 2018). The residential biofuel sector is a major \r concern for indoor air quality (Bonjour et al., 2013). In addition, several \r atmospheric modelling studies find that this sector is also important \r for outdoor air quality and even a dominant source of population\u0002weighted outdoor PM2.5 in India and China (Lelieveld et al., 2015b; \r Silva et al., 2016; Spracklen et al., 2018; Reddington et al., 2019)."


id: 34842e64bf199be6599028f27c86ccff
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6477
content:
"Temperature effects of emissions can be quantified for the historical \r contribution to the present temperature impact (Section 6.4.2), for \r idealized one-year pulses of emissions or for continued (sustained) \r emissions at present levels and for changes during a specific time \r period, for emissions from future scenarios with various hypotheses, \r giving complementary information to feed mitigation strategies. \r The AR5 assessed the net global temperature impact of source \r emissions sectors from a  one-year pulse (a single year's worth) \r of year 2008 emissions and found that the largest contributors to \r warming on 50-100-year time scales are the energy, industrial and \r on-road transportation sectors. Sectors that emit large amounts of \r methane (agriculture and waste management) and black carbon \r (residential biofuel) are important contributors to warming over \r short time horizons up to 20 years. Below, we discuss the effect on \r [...]"


id: f13abc6778b36012ff3d931e8936d6cf
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 39828
content:
"[...] ERFs, temperature and air pollution of selected key sectors estimated \r to have large non-CO2 forcing, including agriculture, residential and \r commercial, and transport (aviation, shipping, land transportation)."


id: 52cd44921895c00b76992999abdc1c67
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 11838
content:
"According to the SRCCL assessment (Jia et al., 2019), agriculture, \r forestry and other land use (AFOLU) are a  significant net source \r of GHG emissions (high confidence), with more than half of these \r emissions attributed to non-CO2 GHGs from agriculture. With respect \r to SLCFs, agricultural activities are major global sources of methane \r and NH3 (Section  6.2.1). The agriculture sector exerts strong near\u0002term warming due to large methane emissions that is slightly \r offset by a small cooling from secondary inorganic aerosols formed \r notably from the NH3 emissions (Heald and Geddes, 2016; Lund \r et al., 2020). For present-day emissions, agriculture is the second \r largest contributor to warming on short time scales but with a small \r persisting effect on surface temperature (+0.0012degC +- 0.00028degC) \r after a pulse of current emissions (Figure 6.16, see detailed description \r in Section  6.6.2.3.4; Lund et al., 2020). Aerosols produced from \r [...]"


id: 6edc911faae9aa75822d501c14c3d68b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16776
content:
"[...] agricultural emissions, released after nitrogen fertilizer application \r and from animal husbandry, influence surface air quality and make \r an important contribution to surface PM2.5 in many densely populated \r areas ( Figure 6.17; Lelieveld et al., 2015b; Bauer et al., 2016)."


id: f9417852960db264989b112741c4af7c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 41216
content:
"Aviation is associated with a range of SLCFs, in particular emissions \r of NOx and aerosol particles, alongside emissions of water vapour \r and CO2. The largest SLCF effects are those from the formation \r of persistent condensation trails (contrails) and NOx emissions. \r Persistent contrails are ice-crystal clouds, formed around aircraft \r soot particles (and water vapour from the engine), injected in \r ambient cold and ice-supersaturated atmosphere, which can spread \r and form contrail cirrus clouds. The 'net NOx' effect arises from the \r formation of tropospheric ozone, counterbalanced by the destruction \r of ambient methane and associated cooling effects of reductions \r in stratospheric water vapour and background ozone. The AR5 \r assessed the radiative forcing from persistent linear contrails to be \r +0.01 [+0.005  to  +0.03] W m-2 for the year 2011, with medium \r confidence (Boucher et al., 2013). The combined linear contrail and \r [...]"


id: 6ba882faeb8d975f2b73337bd0a0ba39
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 16248
content:
"[...] their subsequent evolution to contrail cirrus radiative forcing from \r aviation was assessed to be +0.05 [+0.02 to +0.15] W m-2, with low \r confidence. An additional forcing of +0.003 W m-2 due to emissions \r of water vapour in the stratosphere by aviation was also reported \r (Boucher et al., 2013). The aviation sector was also estimated to lead "


id: 9d9d1d36dca613f46d30a78fd33ef04
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 24968
content:
"Our assessment is built upon Lee et al. (2021). Their study consists of \r an updated, comprehensive assessment of aviation climate forcing \r in terms of RF and ERF based on a large number of studies and the \r most recent air-traffic and fuel-use datasets available (for 2018), \r new calculations and the normalization of values from published \r modelling studies, and combining the resulting best estimates via \r a Monte-Carlo analysis. Lee et al. (2021) report a net aviation ERF \r for year-2018 emissions of +0.101 [0.055-0.145] W m-2 with major \r contributions from contrail cirrus (0.057 W m-2), CO2 (0.034 W m-2) \r and NOx (0.017 W m-2). Contrails and aviation-induced cirrus yield \r the largest individual positive ERF followed by CO2 and NOx emissions \r (Lee  et al. 2021). The confidence level in ERF due to contrails \r and aviation-induced cirrus is assessed to be low in Chapter  7 \r (Section 7.3.4.2) due to potential missing processes. The formation \r [...]"


id: a4bde51c65bcb6ef0895f9b9d0199b84
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 26203
content:
"[...] and emission of sulphate aerosols yield a negative (cooling) term. \r SLCF forcing terms contribute about eight times more than CO2 to \r the uncertainty in the aviation net ERF in 2018 (Lee et al., 2021). \r The largest uncertainty in assessing aviation climate effects is on the \r interactions of BC and sulphate aerosols on cirrus and mixed-phase \r clouds, for which no best estimates of the ERFs were provided (Lee \r et al., 2021)."


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vector_id: 30280
content:
"Quantifying the effects of shipping on climate is particularly \r challenging because (i) the sulphate cooling impact is dominated \r by aerosol-cloud interactions and (ii) ship emissions contain NOx, \r SOx and BC, which lead to mixed particles. Previous estimates \r of the sulphate radiative effects from present-day shipping \r span the range -47 to -8 mW m-2 (direct radiative effect) and \r -600 to -38 mW m-2 (indirect radiative effects) (Lauer et al., 2007; \r Balkanski et al., 2010; Eyring et al., 2010; Lund et al., 2012). Partanen \r et al. (2013) reported a  global mean ERF for year-2010 shipping \r aerosol emissions of -390 mW m-2. The temperature change has \r been shown to be highly sensitive to the choice of aerosol-cloud \r parametrization (Lund  et al., 2012). One year of global present\u0002day shipping emissions, not considering the impact of recent low \r sulphur fuel regulation (IMO, 2016), are estimated to cause net \r [...]"


id: bb40d5022b1c627f066f2a410614bc90
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 30160
content:
"[...] cooling in the near term (-0.0024degC +- 0.0025degC) and slight warming \r (+0.00033degC +- 0.00015degC) on a 100-year horizon (Lund et al., 2020). "


id: 3917a32b08ed2f29a400510034564a39
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7249
content:
"The best estimate ERFs from aviation (Lee et al., 2021) have been used \r to calculate aviation-specific Absolute Global Temperature change \r Potential (AGTP) using the method described in Lund et al. (2020) \r and subsequently compute the effect of a one-year pulse of aviation \r emissions on global mean surface temperature on a 10- and 100\u0002year time horizon (Section 6.6.2.3.4 and Figure 6.16). The effect of \r contrail-cirrus is most important for the estimated net-GSAT response \r after the first decade, followed by similar warming contributions \r from NOx and CO2 emissions. At a  20-year time horizon, the net \r contribution from aviation to GSAT has switched from a positive to \r a small negative effect (see Supplementary Material 6.SM.4). This is \r due to the combination of rapidly decaying contrail-cirrus warming \r and the complex time variation of the net temperature response to \r NOx emissions, which changes sign between 10 and 20 years due \r [...]"


id: 31bd70954c0450ab4b94bcfdf30acb1b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 5996
content:
"[...] to the balance between the positive short-lived ozone forcing and \r negative forcing from changes in methane and methane-induced \r changes in ozone and stratospheric water vapour. The net GSAT \r response to aviation emissions has previously been estimated to be "


id: 5070b0423025df63facc695657eadce8
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 11429
content:
"There is consensus that on-road transportation sector emissions, \r including gasoline and diesel, are important anthropogenic \r contributors to elevated surface ozone and PM2.5 concentrations \r (Chambliss et al., 2014; Lelieveld et al., 2015b; Silva et al., 2016; \r Anenberg et al., 2019). At a  global scale, land transportation has \r been estimated to be the dominant contributor to surface ozone \r concentrations in populated areas (Silva et al., 2016) and ozone\u0002induced vegetation damages (Section  6.4.4; Unger et al., 2020). \r Furthermore, it is now well established that real-world diesel NOx\r emissions rates are substantially higher, the so-called 'excess NOx', \r in all regional markets than in laboratory tests, worsening air quality \r (Anenberg et al., 2017; Jonson et al., 2017; Chossiere et al., 2018) \r and contributing to slightly larger warming on the scale of years and \r smaller warming at the decadal scale (Tanaka et al., 2018). Excess \r [...]"


id: 3d2cbbf2c900c71395d5fb094dc7dd0c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7945
content:
"[...] NOx emissions from key global diesel markets are estimated at \r 4.6 Tg yr-1 in 2015, with annual mean ozone and PM2.5 increases of \r 1 ppb and 1ug m-3 across large regions of Europe, India and China \r (Anenberg et al., 2017)."


id: 40c5071fe134ec5bf0c31f8e3038d871
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 8583
content:
"The global sectoral ranking for near- and long-term global \r temperature effects is similar to the AR5 assessment despite regional \r reductions in aerosol precursor emissions between 2008 and 2014. \r This report has applied updated climate policy metrics for SLCFs \r and treatment of aerosol-cloud interactions for SO2, BC and OC \r (Lund et al., 2020). By far the largest 10-year GSAT effects are from \r the energy production (fossil fuel mining and distribution), agriculture \r and waste management sectors (high confidence). Methane is the \r dominant contributor in the energy production, agriculture and \r waste management sectors. On the 10-year time horizon, other net \r warming sectors are residential fossil fuel and energy combustion \r (dominated by CO2) and aviation and residential biofuel (dominated \r by SLCFs and cloud) (medium confidence). The total residential and \r commercial sector, including biofuel and fossil fuels, is the fourth \r [...]"


id: dd5c02178e36e1f36002b7fe43bc6445
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 36210
content:
"[...] largest contributor to warming globally on short time horizons of \r 10-20 years. The energy combustion sector has considerable cooling \r from high emissions of SO2 that result in a relatively small net GSAT \r temperature effect on short time horizons, despite the high CO2\r emissions from this activity. On the 10-year time horizon, global \r emissions from industry and shipping cause a  net cooling effect \r despite a considerable warming from CO2 emissions. On the 100-year \r time horizon, the net effects of agriculture and waste management are \r small, while energy combustion is the largest individual contributor \r to warming due to its high CO2 emissions. The second largest driver \r of long-term temperature change is industry, demonstrating the \r importance of non-CO2 emissions for shaping relative weight over \r different time frames. Transport contributes a small net warming on \r the 10-year time horizon that increases by a factor of three on the \r [...]"


id: 9fc9a8ce8e51419a23a8a4f500b09d8e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25319
content:
"[...] 100-year time horizon. In contrast, the aviation sector contribution to \r warming shrinks by about a factor of three between the 10- and 100-"


id: c64aed8577e1e3a322599c4a847ecbe6
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32201
content:
"Natural sources contribute more than 50% to surface ozone in all \r regions except Southern Asia and South East Asia. Southern Asia, \r Eastern Asia and the Middle East experience the highest surface \r ozone levels of all regions. For ozone, the anthropogenic sectoral \r attribution is more uniform across regions than for PM2.5, except \r for Southern and South East Asia, where land transportation \r plays a  larger role, and Eastern Asia, where the most significant \r contribution is from energy and industry. Land transportation and \r energy are the most important contributors to ozone across many \r of the regions, with smaller contributions from agriculture, biomass \r burning, waste management and industry. Open biomass burning is \r not a major contributor to surface ozone, except for in Africa, Latin \r America and South East Asia where its contribution is estimated at \r about 5-10% of anthropogenic sources. The relative importance of \r [...]"


id: d11bbc63a0a6a180beec613d6e80238a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 34141
content:
"[...] natural and anthropogenic emissions sources on surface ozone has \r been assessed in several studies (Uherek et al., 2010; Zare et al., \r 2014; Mertens et al., 2020; Unger et al., 2020) and the results are \r comparable with the estimates of the TM5-FASST used here."


id: 5e22c6b3626191c1195b63a99086e999
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 13856
content:
"The attribution of present-day surface PM2.5 and ozone concentrations \r to sectors and regions (Figure  6.17) is based on 2014 CMIP6 \r emissions used in the TM5-FASST model (Van Dingenen et al., 2018) \r that has been widely applied to analyse air quality in regional and \r global scenarios (e.g., Van Dingenen et al., 2009; Rao et al., 2016, \r 2017; Vandyck et al., 2018; Harmsen et al., 2020b). Regions with the \r largest year-2014 population-weighted annual average surface PM2.5\r concentrations are Southern Asia, Eastern Asia and the Middle East. \r The dominant anthropogenic source of ambient PM2.5 in Southern \r Asia are the residential and commercial sectors (biomass and coal \r fuel-based cooking and heating) with secondary contributions from \r energy and industry. In Eastern Asia, the main anthropogenic sources \r of ambient PM2.5 are energy, industry and residential sources. Natural \r sources, predominantly dust, are the most important PM2.5 source in \r [...]"


id: 1101d05ca5cf1f93d1f8219009d2b4ad
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 185
content:
"[...] the Middle East, Africa and Eurasia, contributing about 40-70% of \r ambient annual average concentrations (Figure  6.17). Agriculture "


id: 7fcf11402304f1923eff1ef277d58c24
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 19624
content:
"affected by significant air pollutant burden changes due to air-quality \r policy in selected regions. Turnock et al. (2016) estimated that the \r strong decrease in NOx, SO2 and PM2.5 emissions in Europe, induced \r by air-quality policies resulting in implementation of abatement \r measures since the 1970s, have caused a  surface warming of \r +0.45degC +- 0.11degC and increase of precipitation +13 +- 0.8 mm yr-1 \r over Europe, compared to the scenario without such policies. While \r the temperature increase is likely overestimated since the impact of \r the increase in ammonium nitrate was not considered in this study, \r the simulated European all-sky TOA radiative effect of the European \r air pollutant mitigation over the period 1970-2009 is 2.5 times the \r change in global mean CO2 radiative forcing over the same period \r (Myhre et al., 2013). Other studies found that the recent measures to \r reduce pollution over China have induced a decrease of aerosols and \r [...]"


id: a1bdd361f565f2e6502a5278484ad40d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25678
content:
"[...] increase of ozone over east China (K. Li et al., 2019, 2020), resulting \r in an overall warming effect mainly due to the dominant effect of \r sulphate reductions in the period 2012-2017 (Dang and Liao, 2019). "


id: b723c5bdac88d672997ca866afea5358
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29431
content:
"International shipping emissions regulation: from January 2020, \r a  new global standard, proposed by the International Maritime \r Organisation, limits the sulphur content in marine fuels to \r 0.5%  against the previous 3.5% (IMO, 2016). This legislation is \r considered in the SSP5 and SSP2-4.5 and with a delay of few years \r in SSP3-lowSLCF, SSP1-1.9, and SSP1-2.6, and in other SSP-emissions \r scenarios achieved by the mid-21st century. This global measure \r aims to reduce the formation of sulphate (and consequently PM2.5) \r and largely reduce the health exposure to PM2.5, especially over \r India, east China and coastal areas of Africa, and the Middle East \r (Sofiev  et al., 2018). Sofiev et al. (2018) used a  high spatial-and\u0002temporal resolution chemistry climate model and estimated a  net \r total ERF of +71 mW m-2 associated with this measure and due to \r lower direct aerosol cooling (+3.9 mW m-2) and lower cloud albedo \r [...]"


id: beb9565e406d08fe731de205abeb663
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 30820
content:
"[...] (+67 mW m-2). This value, which correponds to an 80% decrease of \r the cooling effect of shipping induced by about 8 Tg of SO2 of avoided \r emissions, is consistent with older estimates which considered \r similar reduction of emitted sulphur. However, there is considerable \r uncertainty in the indirect forcing since small changes in aerosols, \r acting as CCNs in a clean environment, can have disproportionally \r large effects on the radiative balance. Since sulphate is by far the \r largest component of the radiative forcing (Fuglestvedt et al., 2008) \r and of surface temperature effect (Figure 6.16) due to ship emissions \r over a short time scale, limiting the co-emitted SLCFs can not offset \r the warming by sulphur reductions. The reduction of sulphur emissions \r from shipping is assessed to lead to a slight warming mainly due to \r aerosol-cloud interactions (medium evidence, medium agreement)."


id: 5b1b4f74d2af2c4fcba3632e31f325d5
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 13279
content:
"In the following subsections, we assess the SLCF mitigation and its \r effects as identified in regional and global studies evaluating past \r and current air quality and other SLCF regulations (Sections 6.6.3.1, \r 6.6.3.2 and 6.6.3.3). Development policies, independent from the \r CMIP6 assessment framework, including peer-reviewed studies and \r initiatives like the United Nations Environment Programme (UNEP), \r analysing win-win climate, air-quality and SDG-motivated strategies \r are discussed in Section 6.6.3.4. Note that sensitivity studies where \r impacts of complete removal of particular species are analysed \r (e.g., Samset et al., 2018) are used sparingly in this assessment. While \r such analysis can be useful for assessing the effect of a zero-emissions \r commitment (Chapter 4.7.1.1), they do not correspond to a realistic \r SLCF-mitigation strategy with plausible pace of implementation and \r removal of co-emitted species (Shindell and Smith, 2019). Discussion \r [...]"


id: c91f4f3df3f2c555d7d137c5ea140b76
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32706
content:
"[...] of climate and air-quality implications of SLCF reductions in SSP \r scenarios is provided in Section 6.7. "


id: 85f061bd04c713ca4779b121f07f49d7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 20708
content:
"Air-quality policies emerged several decades ago focusing on \r emissions mitigation, first driven by local- then by regional-scale \r air-quality and ecosystem-damage concerns, that is, health impacts, \r acidification and eutrophication. They have made it possible to reduce \r or limit pollution exposure in many megacities or highly populated \r regions, for example, in Los Angeles, Mexico City and Houston in North \r America (Parrish et al., 2011), Santiago in Chile (Gallardo et al., 2018), \r Sao Paulo in Brazil (Andrade et al., 2017), Europe (Reis et al., 2012; \r Crippa et al., 2016; Serrano et al., 2019), and over Eastern Asia during \r the last decade (Silver et al., 2018; Zheng et al., 2018b). However, very \r few studies have quantified the impact of these policies on climate. \r The AR5 concluded that air-quality control will have consequences on \r climate including strong regional variability, however, no estimates \r [...]"


id: c4f480e25260bba49e9ed743f77dd8a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31959
content:
"[...] of impacts of specific air-quality policy were available. Since AR5, \r few studies have provided estimates of climate-relevant indicators "


id: 5cc4c5118becf71565382a769e0f5ddf
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 13583
content:
"Efficient implementation of the Kigali Amendment and national and \r regional regulations has been projected to reduce global average \r warming in 2050 by 0.05degC-0.07degC (Klimont et al., 2017b; WMO, \r 2018) and by 0.2degC-0.4degC in 2100 compared with the baseline \r (see Figure  2.20 of WMO, 2018). Analysis of SSP scenarios based \r on an emulator (Section  6.7.3) shows a  comparable mitigation \r potential of about 0.02degC-0.07degC in 2050 and about 0.1degC-0.3degC \r in 2100 (Figure  6.22, SSP5-8.5 versus SSP1-2.6). Furthermore, the \r energy efficiency improvements of cooling equipment alongside the \r transition to low-global-warming potential alternative refrigerants \r for refrigeration and air-conditioning equipment could potentially \r increase the climate benefits from the HFC phasedown under the \r Kigali Amendment (Shah et al., 2015; Hoglund-Isaksson et al., \r 2017; Purohit and Hoglund-Isaksson, 2017; WMO, 2018). Purohit \r [...]"


id: 799f9343f5b43036e52447be4611bc45
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 18603
content:
"[...] et al. (2020) estimated that depending on the expected rate of \r technological development, improving the energy efficiency of \r stationary cooling technologies and compliance with the Kigali \r Amendment could bring future global electricity savings of more than \r 20% of the world's expected electricity consumption beyond 2050 \r or cumulative reduction of about 75-275 Gt CO2-eq over the period \r 2018-2100 (medium confidence). This could potentially double the \r climate benefits of the HFC phase-down of the Kigali Amendment as \r well as result in small air-quality improvements due to reduced air \r pollutant emissions from the power sector (i.e., 8-16% reduction of \r PM2.5, SO2 and NOx; Purohit et al., 2020)."


id: 81539af4f9f02784e438cd7a32a1f390
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 19884
content:
"A multi-model study (four ESMs and six CTMs) found a consistent \r response to the removal of SO2 emissions that resulted in a global \r mean surface temperature increase of 0.69degC (0.4degC-0.84degC). \r However, results are mixed for a  global BC-focused deep SLCF \r reduction without SO2 and methane mitigation which remain as in the \r baseline (see ECLIPSE in Figure 6.18). BC contributed about -0.022degC \r temperature reduction for the decade 2041-2050 based on the \r assumption that mitigation of the non-methane species contributed \r only about 10% of the global temperature reduction for the strategy \r where methane mitigation was also included (-0.22degC  +-  0.07degC; \r Stohl et al., 2015). These results are consistent with studies analysing \r similar strategies using emulators (e.g.,  Smith and Mizrahi, 2013; \r Rogelj et al., 2014b). Stohl et al. (2015) also analysed the impact of \r BC-focused mitigation on air quality, estimating large-scale regional \r [...]"


id: dc47a25901dbf82642e46f4d8d2ddeaf
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 36025
content:
"[...] reduction in PM2.5 mean concentration from about 2% in Europe to \r 20% over India for the decade 2041-2050. "


id: 3548f9d1bbad9614d940167a8b4d9482
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6621
content:
"Local response to global reduction can be higher than the global \r temperature response, particularly for regions subjected to rapid \r changes. Hence, mitigation of rapid warming in the Arctic has been \r subject to an increasing number of studies (Sand et al., 2013b, 2016; \r Jiao et al., 2014; AMAP, 2015a, b; Mahmood et al., 2016; Christensen \r et al., 2019). Considering maximum technically feasible reductions \r (MTFR) for methane globally and an idealized strategy reducing key \r global anthropogenic sources of BC (about 80% reduction by 2030 \r and sustained thereafter) and precursors of ozone was estimated to \r jointly bring a reduction of Arctic warming, averaged over the 2041-\r 2050 period, between 0.2degC and 0.6degC (AMAP, 2015a; Sand et al., \r 2016). Stohl et al. (2015) have estimated that a global SLCF mitigation \r strategy (excluding further reduction of SO2) would lead to about \r twice as high a temperature reduction (-0.44 (-0.39 to -0.49) degC) in \r [...]"


id: 3662076095b2a9dc71f99d8882cb74dd
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6804
content:
"There is robust evidence that reducing atmospheric methane will \r benefit climate and improve air quality through near-surface ozone \r reduction (Fiore et al., 2015; Shindell et al., 2017a) and wide agreement \r that strategies reducing methane offer larger (and less uncertain) \r climate benefits than policies addressing BC (e.g., Smith and Mizrahi, \r 2013; Rogelj et al., 2014b, 2018b; Stohl et al., 2015; Christensen \r et al., 2019; Shindell and Smith, 2019). SR1.5 (Rogelj et al., 2018b) \r highlighted the importance of methane mitigation in limiting warming \r to 1.5oC in addition to net zero CO2 emissions by 2050. Implementation \r of the identified maximum technically feasible reductions (MTFR) \r potential for methane globally, estimated at nearly 50% reduction \r (or 205 Tg CH4 in 2050) of anthropogenic emissions from the baseline, \r would lead to a reduction in warming, calculated as the differences \r between the baseline and MTFR scenario, for the 2036-2050 period \r [...]"


id: 41c4a5e8c337342b316f18672fb44430
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 8766
content:
"[...] of about 0.20degC +- 0.02degC globally (AMAP, 2015b). Plausible levels of \r methane mitigation, achieved with proven technologies, can increase \r the feasibility of achieving the Paris Agreement goal through slightly \r slowing down the pace of CO2 reductions (but not changing the final \r CO2 reduction goal) while this benefit is enhanced by the indirect \r effects of methane mitigation on ozone levels (Collins et al., 2018). \r Adressing methane mitigation appears even more important in view of \r recently observed growth in atmospheric concentrations that is linked \r to increasing anthropogenic emissions (Section 5.2.2). "


id: ea5f9c9c8d1fb6b343b4000a55c7aa9d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 38594
content:
"Decarbonization of energy supply and end-use sectors is among key \r pillars of any ambitious climate change mitigation strategy and it \r would result in improved air quality owing to associated reduction of \r co-emitted SLCF emissions (e.g., McCollum et al., 2013; Rogelj et al., \r 2014b; Braspenning Radu et al., 2016; Rao et al., 2016; Stechow \r et al., 2016; Lelieveld et al., 2019; Shindell and Smith, 2019). Regional \r studies (Lee et al., 2016; Shindell et al., 2016; Chen et al., 2018; Li et al., \r 2018), where significant CO2 reductions were assumed for 2030 and \r 2050, show consistently reduced of PM2.5 and ozone concentrations \r resulting in important health benefits. However, these improvements \r are not sufficient to bring PM2.5 levels in agreement with the WHO air\u0002quality guidelines in several regions. Amann et al. (2020) and UNEP \r (2019) highlight that only the combination of strong air-quality, \r development and climate policies, including societal transformations, \r [...]"


id: 4286fe5364d4851c8f785a9925aab6e5
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 8900
content:
"[...] could pave the way towards the achievement of such a  target at \r a regional and global level. "


id: 7099ac1d50f74f679f06538e03abd600
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 17068
content:
"At a  global level, Rao et al. (2016) showed that climate policies, \r compatible with Copenhagen pledges and a long-term CO2 target of \r 450 ppm, result in important air-quality benefits, reducing the share \r of the global population exposed to PM2.5 levels above the WHO \r Tier 1 standard (35 ug m-3) in 2030 from 21% to 5%. The impacts \r are similar to a  strong air-quality policy but still leave large parts \r of population, especially in Asia and Africa, exposed to levels well \r above the WHO air quality guideline level of 10 ug m-3. The latter can \r be partly alleviated by combining such climate policy with strong air\u0002quality policy. Shindell et al. (2018) analysed more ambitious climate \r change mitigation scenarios than Rao et al. (2016) and highlighted \r the opportunities to improve air quality and avert societal effects \r associated with warmer climate by accelerated decarbonization \r strategies. Most climate change mitigation strategies compatible with \r [...]"


id: 67b306811cbe32071d807550949e0dec
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15512
content:
"[...] limiting global warming to well below 2degC rely on future negative CO2\r emissions postponing immediate reduction. Alternatively, a  faster \r decarbonization could allow the achievement of a 2degC goal without "


id: 9157bfac5b3e08f947f9c53f97576d36
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 22775
content:
"between species, such as through atmospheric chemistry, can weaken the benefit of emissions reduction efforts. An illustration lies \r in the recent (2013-2017) reduction of aerosols over China (Silver et al., 2018; Zheng et al., 2018b) resulting from the strategy to \r improve air quality ('Clean Air Action'); this has successfully reduced the level of PM2.5 but has led to a concurrent increase in surface \r ozone, partly due to declining heterogeneous interactions of ozone precursors with aerosols (K. Li et al., 2019; Yu et al., 2019). This side \r effect on ozone has been addressed since then by amending the legislation to target NMVOC sources, especially solvent use. Complex \r interactions between anthropogenic and biogenic volatile compounds are also at play and reduction of certain SLCFs could possibly \r promote new particle formation from organic vapours (e.g., Lehtipalo et al., 2018). Finally, a recent example of this complexity is the \r [...]"


id: 4f647ecec68119a7f239a833dfebeec
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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"[...] mixed effects on ozone pollution induced by NOx decrease during the COVID-19 pandemic (Cross-Chapter Box 6.1). Thus, the climate \r and air pollution effects of policies depend strongly on the choice of regulated compounds and the degree of reduction. Such policies \r have to be informed by strong science support, including for example multi-model analyses such as HTAP (UNECE, 2010) and AMAP \r (AMAP, 2015a, b), based on global and regional CCMs. This is essential to capture the complexity and inform the policy development \r process."


id: 2d5369f3eaf4cd1bfd8a06b81c79cf41
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 5259
content:
"In addition, pursuing SDG objectives, apparently decoupled from air pollution, such as improved waste management, access to clean \r energy, or improved agricultural practices, would also stimulate and lead to mitigation of SLCFs. Amann et al. (2020) show that a global \r strategy to achieve the WHO air quality guidelines, cannot only rely on air pollution control but also on a combination of SDG-aligned \r policies. Such actions would include energy efficiency improvements, increased use of renewables, reduction of methane from waste \r management and agriculture, and CO2 and methane due to lower fossil fuel consumption, resulting in climate co-benefits. Consideration \r of SDGs including local air-quality co-benefits, creates an opportunity to support and gain acceptance for ambitious climate change \r mitigation (Jakob and Steckel, 2016; Stechow et al., 2016; Vandyck et al., 2018). Such near-term policies targeting SDGs and air quality \r [...]"


id: f517973aca07bd322bd77483f0ffde95
created_at: 2023-04-19 13:22:37
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content:
"[...] would enable longer-term transformations necessary to achieve climate goals (Chapter 17, WGIII)."


id: e149c32ccab5bdecf1788fce28b8e8d7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 36898
content:
"Cross-Chapter Box 6.1, Figure 1 | Emissions reductions and their effect on aerosols and climate in response to COVID-19. Estimated reductions in \r emissions of CO2, SO2 and NOx are shown in panel (a) based on reconstructions using activity data (updated from Forster et al., 2020). Eight Earth system models \r (ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until \r August 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. \r Panel (b) shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel (c) shows ESM-simulated GSAT anomalies during 2020; curves \r denote the ensemble mean result for each model with shading used for +-1 standard deviation for each model. ESM data from these simulations ('ssp245-covid') \r [...]"


id: 463d185cb5f440006793048c22318861
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 9577
content:
"[...] is archived on the Earth System Grid CMIP6 database. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, \r where >=80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. \r For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1."


id: adc8f276df904cd8599fe7dfbc11f6ab
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 27817
content:
"Model simulations of the response to COVID-19 emissions reductions indicate a small warming of global surface air temperature \r (GSAT) due to a decrease in sulphate aerosols (Forster et al., 2020; Fyfe et al., 2021), balanced by cooling due to an ozone decrease \r (Forster et al., 2020; Weber et al., 2020), black carbon decrease (Weber et al., 2020) and CO2 decrease. It is noted that observational \r studies report little SO2 change, at least locally near the surface (Shi et al., 2021), and do not correlate with emissions inventory-based \r changes (Gkatzelis et al., 2021). One study suggests a small net warming while another using idealized simulations suggests a small \r cooling (Weber et al., 2020). Simulated GSAT and rainfall changes are unlikely to be detectable in observations (high confidence) \r (Samset et al., 2020; Fyfe et al., 2021). Multi-model ESM simulations based on a realistic COVID-19 containment forcing scenario \r [...]"


id: 9aeaec905af35d751563f6d885482b7d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 24498
content:
"[...] (Forster et al., 2020) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross-Chapter \r Box 6.1)."


id: 3d003415a5f9bb63d0cec4d88e2f948a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7909
content:
"Growing urbanization worldwide has strongly modified the spatial \r distribution and intensity of SLCF emissions. The effect and extent \r of urbanization on air pollution and other emissions species are \r captured within Integrated Assessment Models (IAMs) at varying \r levels of complexity. In most cases, models use a  combination of \r proxies and assumptions of end-use efficiency and technological \r improvement assumptions to estimate emissions arising from rural\u0002to-urban migration and population growth within cities, utilizing \r quantifications of urbanization for the SSPs (Jiang and O'Neill, 2017). \r In addition, spatial patterns of future rural and urban population \r growth, migration, and decline have been quantified for the SSPs \r using a  gravity model (Jiang and O'Neill, 2017). However, linking \r these spatial patterns with IAM regional emissions pathways is still \r an ongoing area of study and has not yet been represented in spatial \r [...]"


id: d322321c4e4e5debb80f8ec90f4a6c42
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 34490
content:
"[...] emissions estimates provided by IAMs (Riahi et al., 2017; Gidden \r et al., 2019; Feng et al., 2020). As described in Feng et al. (2020), \r spatial emissions estimates derived for CMIP6 are largely a product of \r existing spatial patterns of population, but do not vary dynamically in \r future patterns. To the extent urbanization is accounted for in gridded \r emissions, IAM native region resolution (varying, for example, from \r 11 world regions to more than 30, depending on the model) provides \r urbanization-based dynamics. Despite the interest of studying the \r effect of well-planned, densely populated urban centres, which \r can help to maximize the benefits of agglomeration, by providing \r proximity to infrastructure and services, the opportunity for energy \r saving, and providing a frame for air-quality control, IAM realizations \r of SSPs are not sufficient to assess this effect. The  opportunities \r and risks associated with this rapid urbanization for SLCF emissions \r [...]"


id: 72a26a52f89f1afb2db640255c5199f5
created_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 17409
content:
"[...] and air quality are analysed in the Chapter 6 of the WGII report and \r Chapter 8 of the WGIII report."


id: 772d447749b9f910bf39af5d997eee45
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
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vector_id: 18171
content:
"This section assesses the 21st-century projections of SLCF emissions, \r abundances and responses in terms of climate and air quality \r following the SSPs (Chapter  1, Section  1.6.1.3 and Cross-Chapter \r Box 1.5; Riahi et al., 2017; Gidden et al., 2019). The future evolution \r of atmospheric abundances and the resulting climate and AQ \r responses is driven mainly by anthropogenic emissions and by natural \r emissions modulated by chemical, physical and biological processes \r as discussed in Sections 6.2 and 6.3. Like the RCP scenarios used in \r AR5, the SSP emissions scenarios consider only direct anthropogenic \r (including biomass burning) emissions and do not project natural \r emissions changes due to climate or land-use changes; ESMs \r intrinsically consider these biogeochemical feedbacks to varying \r degrees (Section  6.4.5). We rely on future projections based on \r CMIP6 ESMs with comprehensive representation of chemistry, \r [...]"


id: c2b47a0d669e6d1fe0d4c08290ee46d5
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 31538
content:
"[...] aerosol microphysics and biospheric processes that participated \r in the ScenarioMIP (O'Neill et al., 2016) and AerChemMIP (Collins \r et al., 2017). However, due to the high computational costs of \r running coupled ESMs, they cannot be used for quantifying the \r contributions from individual species, regions and sectors, and across \r the scenarios. Therefore, reduced complexity models (Box  1.3 and \r Cross-Chapter Box 7.1), which represent chemistry and complex ESM \r interactions in parametrized forms updated since the AR5, are also \r applied here. "


id: 4aeeb0a919d7e5fb103313c8c7bc5752
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10421
content:
"estimates for the post-2000 period (Hoesly et al., 2018) and those \r used in RCPs (Figure 6.18). Since several SLCFs are also air pollutants, \r the narrow range of the RCP emissions trajectories in the future \r allowed for only limited analysis of near-future air quality (Amann \r et al., 2013; Chuwah et al., 2013; von Schneidemesser et al., 2015). \r However, the range of storylines in the SSPs lead to a wider range \r of assumed pollution-control policies in the SSPs (Rao et al., 2017; \r Riahi et al., 2017). In SSP1 and SSP5, strong air-quality policies are \r assumed to minimize the adverse effects of pollution on population \r and ecosystems. In SSP2, a medium pollution control, with lower than \r current policy targets, is considered. Only weak, regionally varied, air \r pollution policies are applied in the SSP3 and SSP4. Additional climate \r policies introduced to reach defined radiative forcing targets will also \r [...]"


id: 4bdfd8bb836ce0e1a8a87e071449fa9
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 10607
content:
"[...] affect SLCF emissions. The SSP SLCF-emissions trajectories (Rao et al., \r 2017; Gidden et al., 2019) assume a long-term coupling of economic \r growth and specific emissions indicators, such as sectoral emission "


id: 356187028a46f81512d3f6936cb79b1c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6638
content:
"For global methane emissions, the range is similar for SSPs and RCPs \r over the entire century (Figure  6.18), with highest projections in \r SSP3-7.0 (slightly below RCP8.5) estimating doubling of the current \r emissions and a reduction of about 75% by 2100 in scenarios consistent \r with 1.5degC to 2degC targets; similar to RCP2.6. At the regional level, the \r evolution of methane emissions in climate change mitigation scenarios \r is comparable to RCPs but there are significant differences for some \r regions with respect to high CO2 emissions scenarios. In  particular, \r projections for Eastern Asia differ significantly, the highest SSP3-7.0 \r is about half of the highest RCP by 2100 (Figure 6.19), which is due to \r much lower projections of coal use in China driven largely by efforts \r during the last decade to combat poor air quality. At the same time, \r the SSP scenarios without climate change mitigation project faster \r [...]"


id: 9098bbbcd8b6b3553b1bc3e68eb7ed8a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 22647
content:
"[...] growth in methane emissions in Africa, the Middle East and Latin \r America (Figure 6.19) driven by developments in agriculture, the oil \r and gas sectors, and, especially in Africa, waste management. There \r are significant differences in the assessment and feasibility of rapid \r methane mitigation. Hoglund-Isaksson et al. (2020) review most \r recent studies and assess feasibility of rapid widespread mitigation, \r concluding that significant (over 50%) reductions are attainable but \r the feasibility of such reductions could be constrained in the short \r term due to locked capital. This might have implications for near-term \r evolution assumed in, for example, SSP1-1.9 or SSP3-lowSLCF-lowCH4, \r where emissions drop very quickly due to fast decarbonization and \r reductions in agriculture. Such high reduction potential in agriculture \r has been also assumed in other studies (Lucas et al., 2007; Harmsen \r et al., 2020a) but is questioned by Hoglund-Isaksson et al. (2020) \r [...]"


id: 650992409a176575b0955deef091141a
created_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 15044
content:
"[...] who indicate that widespread implementation (within decades) of \r policies bringing about institutional and behavioural changes would \r be important for transition towards very low methane emissions from \r livestock production."


id: c8446ed3fa527f4876a78afa5c141842
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32545
content:
"SSP1 and SSP2, accounting for the expected growth in food demand \r and a general lack of effective policies targeting agricultural emissions. \r Additionally, mitigation potential for NH3 is generally smaller than \r for other species owing to fugitive and widely distributed sources \r (Pinder et al., 2007; Klimont and Winiwarter, 2015; Mohankumar \r Sajeev et al., 2018; Sajeev et al., 2018). Most significant changes of \r SLCF emissions in the near and long-term compared to the present \r day are expected for SO2. This is due to ever more stringent (and \r enforced) legislation in China's power sector, extended recently to \r industrial sources (Zheng et al., 2018b; Tong et al., 2020), declining \r coal use in most SSPs, recently announced stricter emissions limits \r for the power sector in India, and reduction of the sulphur content of \r oil fuel used in international shipping from 2020 (IMO, 2016). For the \r lower forcing targets (e.g., SSP1-2.6), the SO2 trajectories are similar \r [...]"


id: a2d7ad19bcc34ae566563e8ae6b08517
created_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 25867
content:
"[...] to the RCPs resulting in over 50-90% decline by 2050 and 2100, \r respectively, while for the scenarios with no climate policies, the SSPs \r show a large spread even at the end of the century."


id: bb02bb5a97e8755b9272dfa304b1c67c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 30118
content:
"Until the mid-21st century, SSP3-7.0 and SSP5-8.5 scenarios project \r no reduction in NOx emissions at the global level with decline in \r most OECD countries and Eastern Asia, driven by existing legislation \r in power, industry and transportation (e.g.,  Tong et al., 2020), \r and continued increase in the rest of the world (Figures  6.18 and \r 6.19). Towards the end of the century, similar trends continue \r in SSP3-7.0 while emissions in SSP5 decline strongly owing to \r faster technological progress and stronger air-quality action (Rao \r et al., 2017; Riahi et al., 2017). By 2100, the 'Regional Rivalry' (SSP3) \r scenario emissions of NOx (and most other SLCFs, except ammonia) \r are typically twice as high as the next highest SSP projection, both at \r the global (Figure 6.18) and regional levels (Figure 6.19). In emissions \r pathways consistent with Paris Agreement goals (SSP1-1.9 or SSP1\u00022.6; Section 1.6.1), NOx drops, compared to 2015, by 50% in SSP1-2.6 \r [...]"


id: ad1be4fbb149aca00a59476a58d91ecc
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vector_id: 27696
content:
"[...] and by 65% in SSP1-1.9 by 2050, is reduced by about 70% by 2100, \r resulting in global emissions levels comparable to the 1950s and \r below the RCP range. In these pathways considering strong climate \r change mitigation, similar reductions are projected at the regional \r level, except in Africa (less than 50% decline) due to its high share of \r biomass emissions as well as strong growth in population and fossil \r fuel use. The trends in anthropogenic and biomass-burning emissions \r for other ozone precursors (NMVOC, CO) are similar to that of NOx. "


id: a4a90d119ab75f541aa9806c7af15f47
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 26185
content:
"Global emissions of carbonaceous aerosols are projected to decline \r in all SSP scenarios (Figure 6.18) except SSP3-7.0. In that scenario, \r which also has much higher emissions than any of the RCPs, about \r half of the anthropogenic BC originates from cooking and heating on \r solid fuels, mostly in Asia and Africa (Figure 6.19), where only limited \r progress in access to clean energy is achieved. Slow progress in \r improving waste management, high coal use in energy and industry, \r and no further progress in controlling diesel engines in Asia, Africa \r and Latin America contributes most of the remaining emissions, \r resulting in about 90% of anthropogenic BC emitted in the non\u0002OECD world by 2100 in SSP3-7.0. A similar picture emerges for OC \r but with greater contribution of the waste management sector and \r biomass burning, and lower impact of transportation and industry \r developments. Since scenarios compliant with Paris Agreement goals \r [...]"


id: 9d3f310b3f56c6a48cde40f0e7500dc7
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content:
"[...] (SSP1-1.9 or SSP1-2.6; Section 1.6.1) include widespread access to \r clean energy already by 2050, the global and regional emissions of \r BC decline by 70-75% by 2050 and 80% by 2100 relayive to 2015. \r The decline in the residential sector (about 90% by 2050 and over \r 95% by 2100) is accompanied by a  strong reduction in transport \r (over 98%) and the decarbonization of the industry and energy sector. "


id: e8c47087769d35c7173d9cff744a5b83
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 38302
content:
"The SSP scenarios draw on the HFC projections developed by Velders \r et al. (2015) considering, in climate change mitigation scenarios, the \r provisions of the Kigali Amendment (2016) to the Montreal Protocol \r leading to phase-down of HFCs (Section  6.6.3.2; Papanastasiou \r et al., 2018). The SSP scenarios without climate change mitigation \r (e.g, SSP3-7.0, SSP5-8.5) show a range in HFC emissions of 3.2-5.3 \r GtCO2-eq yr-1 in 2050 and about 4-7.2 GtCO2-eq yr-1 by 2100 while in \r deep climate change mitigation scenarios (SSP1-1.9 and SSP1-2.6), \r consistent with the 1.5-2degC targets, they are expected to drop to \r 0.1-0.35 GtCO2-eq yr-1 (Figure  6.18). In SSP1-1.9, the extent of \r reduction and its pace is more ambitious than current estimates of \r the effect of the fully implemented and enforced Kigali Amendment \r (Figure 6.18; Hoglund-Isaksson et al., 2017; Purohit and Hoglund\u0002Isaksson, 2017). The best representation of the HFC emissions \r [...]"


id: 4ca5f8aa8b0bed2e7c0e9cadbb253767
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10750
content:
"[...] trajectories in the SSP framework compliant with the Kigali \r Amendment is the SSP1-2.6 and the baseline (including only pre\u0002Kigali national legislation; Section  6.6.3) is best represented by \r SSP5-8.5 (Figure 6.18). However, since HFC emissions in SSPs were \r developed shortly after the Kigali Amendment had been agreed, \r none of these projections represents accurately the HFC emissions \r trajectory corresponding to the phase-out emissions levels agreed \r to in the Kigali Amendment (Meinshausen et al., 2020), leading to \r medium confidence in the assessment of the benefits of the Kigali \r Amendment when using SSP projections for HFCs. "


id: 83edd605d72cf7a9b8b4374357f37ce6
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 20361
content:
"Surface ozone increases continuously until 2050 across most regions \r in SSP3-7.0 and SSP5-8.5, (Turnock et al., 2020), particularly over \r Eastern Asia, Southern Asia, the Middle East, Africa, South East Asia \r and Developing Pacific, where this increase can reach and even exceed \r 5 ppb for annual mean averaged over land areas (Figure 6.20). After \r 2050, surface ozone concentrations decrease in SSP5-8.5, reaching \r levels below their 2005-2014 mean levels in most regions, but level \r off or continue to increase under SSP3-7.0. The increase in surface \r ozone in the SSP5-8.5 scenario occurs despite an emissions decrease \r of several ozone precursors because the methane emissions increase \r until about 2080 in the absence of climate change mitigation. Ozone \r decreases over all regions in response to strong emissions mitigation \r in SSP1-1.9 and SSP1-2.6 (Turnock et al., 2020), with decreases of \r 5  to 10 ppb as soon as 2030 in North America, Europe, Eurasia, \r [...]"


id: 8e0abecf96799973ab5270114d880c13
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 22185
content:
"[...] Eastern Asia, the Middle East and Southern Asia in their annual \r means over land areas. In most regions surface ozone is reduced \r slightly or remains near present day values in the middle of the road \r scenario, SSP2-4.5. In 2100, the largest differences in surface ozone \r changes across the scenarios occur for the Middle East, Southern \r Asia and Eastern Asia with differences ranging up to 40 ppb between \r SSP3-7.0 and SSP1-1.9 at the end of the century. In each scenario, \r despite discrepancies in the magnitude of changes, especially over \r North America, Europe, Eurasia, Eastern Asia and Southern Asia, the \r models are in high agreement regarding the signs of the changes and \r are thus assessed as of high confidence."


id: c49b67c6c53b292e8978bc5f3b174332
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31898
content:
"After about 2040, it is likely that, across the scenarios, the net effect \r of the removal of aerosols is a further increase in GSAT. However, \r their contribution to the rate of change decreases towards the end \r of the century (from up to 0.2degC per decade before 2040 to about \r 0.03degC per decade after 2040). After 2040, the changes in methane, \r HFCs and tropospheric ozone become equally important as the \r changes in the aerosols for the GSAT trends. In the low-emissions \r scenarios (SSP1-1.9 and SSP1-2.6), the contribution to warming from \r the SLCFs peaks around 2040 with a very likely range of 0.04degC to \r 0.34degC. After the peak, the reduced warming from reductions in \r methane and ozone dominates, giving a best total estimate warming \r induced by SLCF and HFC changes of 0.12degC and 0.14degC respectively, \r in 2100, with a very likely range of -0.07degC to +0.45degC (Figure 6.22). \r However, in the longer term towards the end of the century there \r [...]"


id: 7560564f5b526905391abc3d57deb3c4
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
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vector_id: 17878
content:
"[...] are very significant differences between the scenarios. In SSP3-7.0 \r there is a near-linear warming due to SLCFs of 0.08degC per decade, \r while for SSP5-8.5 there is a more rapid early warming. In SSP3-7.0, \r the limited reductions in aerosols, but a steady increase in methane, \r HFCs and ozone lead to a nearly linear contribution to GSAT reaching \r a best estimate of 0.5degC in 2100. Contributions from methane and \r ozone decrease towards 2100 in SSP5-8.5, however the warming \r from HFCs still increase and the SSP5-8.5 has the largest SLCF and \r HFC warming in 2100 with a best estimate of 0.6degC. In the SSP2-4.5 \r scenario, a reduction in aerosols contributes to about 0.3degC warming \r in 2100, while contributions from ozone and methane in this scenario \r are small."


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content:
"The simplified approach used to estimate the contributions to GSAT in \r Figure 6.22 has been supplemented with ESM simulations driven by \r the two versions of the SSP3-7.0-lowSLCF scenario (Section 6.7.1.1). \r Results from five CMIP6 ESMs with fully interactive atmospheric \r chemistry and aerosols for the high-methane scenario show (Allen \r et al., 2020, 2021) that reductions in emissions of air pollutants \r would lead to an additional increase in GSAT by 2055 relative to 2015 \r compared to the standard SSP3-7.0 scenario, with a best estimate \r of 0.23degC +- 0.05degC, and a corresponding increase in global mean \r precipitation of 1.3 +- 0.17% (note that uncertainties from the work \r of Allen et al. here and elsewhere are reported as twice standard \r deviation). Including methane mitigation (SSP3-7.0-lowSLCF-lowCH4) \r would lead to a small increase in global precipitation (0.7 +- 0.1%) \r by mid-century despite a decrease in GSAT (Section 6.7.3), which is \r [...]"


id: 9869d53c6d057652b656644050a48b8e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 24025
content:
"[...] related to the higher sensitivity of precipitation to sulphate aerosols \r than greenhouse gases (Section 8.2.1; Allen et al., 2021). "


id: aed3bcefb855e957e746aa29ee87c618
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 28002
content:
"While the emulators used for GSAT projections shown in Figure 6.22 do \r not take the regional perspective into account, the set of simulations \r performed within the Hemispheric Transport of Air Pollutants Phase \r 2 (HTAP2) project (Galmarini et al., 2017) allows for this perspective. \r The results from the chemistry-transport model OsloCTM3 taking \r part in the HTAP2 have been used by Lund et al. (2020) to derive \r region-specific absolute global warming potentials (AGTPs; cf. \r Aamaas et al., 2016) for each emitted SLCF and each HTAP2 region. \r With this set of AGTPs, Lund et al. (2020) estimate the transient \r response in GSAT to the regional anthropogenic emissions. There are \r important differences in the contributions to GSAT in 2040 and 2100 \r (relative to 2020) between the regions and scenarios, mainly due to \r the differences in the mixture of emitted SLCFs (Figure 6.23). There is \r overall good agreement between the total net contribution from all \r [...]"


id: c1c37d091252a2e06a265b91f3917fb6
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31392
content:
"[...] regions to GSAT and the estimate based on global ERF and the two\u0002layer emulator (Figure 6.22). "


id: 1a76843ef070ae4eb7ec687446c45341
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 1860
content:
"The role of the different SLCFs, and also the net of all the SLCFs \r relative to the total warming in the scenarios, is quite different across \r the SSP scenarios varying with the summed levels of climate change \r mitigation and air pollution control (Figure  6.24). In the scenario \r without climate change mitigation but with strong air pollution \r control (SSP5-8.5) all the SLCFs (methane, aerosols and tropospheric \r ozone) and the HFCs (with lifetimes less than 50 years) add to \r the warming, while in the strong climate change and air pollution \r mitigation scenarios (SSP1-1.9 and SSP1-2.6), the emissions controls \r act to reduce methane, ozone and BC, and these reductions thus \r contribute to cooling. In  all scenarios, except SSP3-7.0, emissions \r controls lead to a  reduction of the aerosols relative to 2019, \r causing a warming. However, the warming from aerosol reductions \r is stronger in the SSP1 scenarios (with best estimates of 0.21degC in \r [...]"


id: 65025d1118ba7fd495dc7def6eeefdb1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15040
content:
"[...] 2040 and 0.4degC in 2100 in SSP1-2.6) because of higher emissions \r reductions from stronger decrease of fossil fuel use in these \r scenarios than in SSP5-8.5 (0.13degC in 2040 and 0.22degC in 2100). The \r changes in methane abundance contribute a warming of 0.14degC in \r SSP5-8.5, but a cooling of 0.14degC in SSP1-2.6 by the end of the 21st \r century relative to 2019. Furthermore, under SSP5-8.5, HFCs induce \r a warming of 0.06degC with a very likely range of [0.04 to 0.08] degC in \r 2050 and 0.2 [0.1 to 0.3] degC by the end of the 21st century, relative \r to 2019, while under SSP1-2.6, warming due to HFCs is negligible \r (below 0.02degC) (high confidence). This assessment relies on these \r estimates, which are based on updated ERFs and HFC lifetimes. It is in \r accordance with previous estimates (Section 6.6.3.2) of the efficiency \r of the implementation of the Kigali Amendment and national \r regulations. It  is very likely that under a  stringent climate and air \r [...]"


id: 4a76b8654abfad3170c9e5408cafa1ab
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10343
content:
"[...] pollution mitigation scenario (SSP1-2.6), the warming induced by \r changes in methane, ozone, aerosols and HFCs towards the end of \r the 21st century, will be very low compared with the warming they \r would cause under the SSP5-8.5 scenario (0.14degC in SSP1-2.6 versus \r 0.62degC in SSP5-8.5). "


id: fbb1fe570003b6049d70b70eaea2186f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 41651
content:
"In the low- and medium-emissions scenarios (SSP1-2.6 and SSP2-4.5), \r the warming effects induced by changes in SLCFs on GSAT are \r dominated by emissions in North America, Europe and Eastern Asia \r (Figure 6.23). In SSP1-2.6 the emissions of all SLCFs in all regions \r decrease and the net effect of the changes in SLCFs from all of these \r three regions is an increase in GSAT of about 0.02degC (per region) \r in 2040 and about 0.04degC in 2100. For SSP2-4.5, emissions of most \r SLCFs continue to increase in Southern Asia (Figure  6.19), leading \r to a net cooling in the near term (-0.03degC in 2040), while in 2100, \r North America, Europe, Eastern and Southern Asia all contribute \r to a warming, most pronounced from Eastern Asia (0.05degC). In the \r SSP3-7.0 scenario, the net effect induced by changes in SLCFs in all \r regions is an enhanced warming towards the end of the century, \r driven predominantly by change in methane. Africa is the region \r [...]"


id: 68a60ceb97cbde52de5b945f056e7570
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15680
content:
"[...] contributing the most to predicted global warming due to SLCF \r changes in 2100 (0.24degC). In SSP5-8.5, methane emissions increase \r in North America, Europe and Africa, while there is a  decrease \r in the Asian regions. For North America and Europe, the methane \r increase combined with a reduction in aerosol leads to highest net \r contribution to GSAT in this scenario (0.06degC and 0.04degC in 2100, \r respectively). The high growth in methane makes Africa the region \r with the largest contribution to future warming by SLCFs (0.18degC in \r 2100 versus 2020) in this scenario. "


id: 48fda056c0716264d6ee9a971fdc8e1f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10062
content:
"There is robust evidence and high agreement that non-methane \r SLCF mitigation measures, through reductions in aerosols and \r non-methane ozone precursors to improve air quality (SSP3-7.0\u0002lowSLCF-highCH4 vs SSP3-7.0), would lead to additional near-term \r warming with a range of 0.1degC-0.3degC. Methane mitigation that also \r reduces tropospheric ozone, stands out as an option that combines \r near- and long-term gains on surface temperature (high confidence). \r With stringent but realistic methane mitigation (SSP3-7.0-lowSLCF\u0002lowCH4), it is very likely that warming (relative to SSP3-7.0) from non\u0002methane SLCFs can be offset (Figure 6.24; Allen et al., 2021). Due to \r the slower response to the methane mitigation, this offset becomes \r more robust over time and it is very likely to be an offset after \r 2050. However, when comparing to the present day, it is unlikely\r that methane mitigation can fully cancel out the warming over the \r [...]"


id: cb396583803d7d59335d051fc594e0fd
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 33090
content:
"To assess the effect of dedicated air-quality versus climate policy \r on air quality, PM2.5 and ozone indicators were estimated for three \r SSP3 scenarios by applying a widely used approach for the analysis \r of air-quality implications for given emissions scenarios (Rao \r et al., 2017; Van Dingenen et al., 2018; Vandyck et al., 2018) and \r whose sensitivity of surface concentrations to emissions changes is \r comparable to that in the ESM ensemble (Supplementary Material \r 6.SM.5). The assessment shows that both strong air pollution control \r and strong climate change mitigation, implemented independently, \r lead to a large reduction of exposure to PM2.5 and ozone by the end \r of the century (high confidence) (Figures 6.25 and 6.26). However, \r implementation of air pollution control, relying on the deployment \r of existing technologies, leads to benefits more rapidly than climate \r change mitigation (high confidence), which requires systemic changes \r [...]"


id: 1f6bcda84e96f287e377612d48488edb
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 2729
content:
"[...] and is thus implemented later in this scenario. Notably, under the \r underlying SSP3 context, significant parts of the population remain \r exposed to air quality exceeding the WHO guidelines for PM2.5 over \r the whole century (high confidence), in particular in Africa, Eastern \r and Southern Asia and the Middle East, and for ozone only a small \r improvement in population exposure is expected in Africa and Asia. "


id: e93b680137b36922a82747e408b9e11
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 38388
content:
"Figure 6.24 | Effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the \r WGI core set of Shared Socio-economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), \r are compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. The GSAT changes are based on the assessed historic and future evolution \r of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity \r of 3.0degC for a doubling of atmospheric CO2 (feedback parameter of -1.31 W m-2 degC-1; Cross-Chapter Box 7.1). Uncertainties are 5-95% ranges. The scenario total (grey bar) \r [...]"


id: fc5a74b392f0f3b26baf4b19028e3a68
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41763
content:
"[...] includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs \r and their uncertainties. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)."


id: 61b637237ff7993dc1ff5b0e96f48758
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 14481
content:
"In summary, the warming induced by SLCF changes is stable after \r 2040 in the WGI core set of SSP scenarios associated with lower global \r air pollution as long as methane emissions are also mitigated, but \r the overall warming induced by SLCF changes is higher in scenarios \r in which air quality continues to deteriorate (caused by growing \r fossil fuel use and limited air pollution control) (high confidence). \r In the SSP3-7.0 context, applying an additional strong air pollution \r control resulting in reductions in anthropogenic aerosols and non\u0002methane ozone precursors would lead to an additional near-term \r global warming of 0.08degC with a very likely range of [-0.05 to 0.25] \r degC (compared with SSP3-7.0 for the same period). A simultaneous \r methane mitigation consistent with SSP1's stringent climate change \r mitigation policy implemented in the SSP3 world, could entirely \r alleviate this warming and even lead to a  cooling of 0.07degC with \r [...]"


id: 8f858309f2aca8c930a916bb6cf05198
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 22463
content:
"[...] a very likely range of [-0.08 to +0.18] degC (compared with SSP3-7.0 \r for the same period). Across the SSPs, the reduction of methane, \r ozone precursors and HFCs can make a 0.2 [0.1 to 0.4] degC difference \r on GSAT in 2040 and a 0.8 [0.5 to 1.3] degC difference at the end of the \r 21st century (Figure 6.24), which is substantial in the context of the \r Paris Agreement. Sustained methane mitigation, wherever it occurs,\r stands out as an option that combines near- and long-term gains \r on surface temperature (high confidence) and leads to air pollution \r benefits by reducing surface ozone level globally (high confidence). "


id: 740d429a87946a7fdb8fc1da7d1e5bab
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17658
content:
"Since AR5, the complexity of ESMs has increased to include many \r chemical and biogeochemical processes. These processes are \r necessary to quantify non-CO2 biogeochemical feedbacks on the \r Earth system resulting from climate-driven changes in atmospheric \r chemistry and SLCF emissions from natural systems and, in turn, the \r impact of SLCFs on biogeochemical cycles. Enhanced understanding \r of the biological, chemical and physical processes based on \r experimental and observational work has facilitated advances in the \r ESMs. However, assessment of non-CO2 biogeochemical feedbacks \r and SLCF effects on land and aquatic ecosystem productivity still \r remains challenging due to the multiple complex processes involved \r and limitations in observational constraints to evaluate the skill of \r ESMs in realistically simulating the processes. Advances will come \r from better understanding of the processes and mechanisms, in \r particular at the interfaces between components. The development \r [...]"


id: c569e6e4e9f2abce7a4f9e676f9a8b48
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 32040
content:
"[...] of high-resolution ESMs will facilitate their evaluation against high\u0002resolution observations. "


id: cc6e3ee80e6e1ac92e43ab78ee30e800
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 33285
content:
"Kari Alterskjaer (Norway), Chris Smith (United Kingdom), Govindasamy Bala (India/United States \r of America), Nicolas Bellouin (United Kingdom/France), Terje Berntsen (Norway), Fabio Boeira Dias \r (Finland/Brazil), Sandrine Bony (France), Natalie J. Burls (United States of America/South Africa), \r Michelle  Cain (United Kingdom), Catia M. Domingues (Australia, United Kingdom/Brazil), \r Aaron Donohoe (United States of America), Mark Flanner (United States of America), Jan S. Fuglestvedt \r (Norway), Lily C. Hahn (United States of America), Glen R. Harris (United Kingdom/New Zealand, \r United Kingdom), Christopher Jones (United Kingdom), Seiji Kato (United States of America), \r Jared Lewis (Australia/New Zealand), Zhanqing Li (United States of America), Mike  Lockwood \r (United Kingdom), Norman Loeb (United States of America), Jochem  Marotzke (Germany), \r Malte  Meinshausen (Australia/Germany), Sebastian Milinski (Germany), Zebedee  R.J.  Nicholls \r [...]"


id: 6b006259ca352dc3022ebdae81c63fa0
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 16122
content:
"[...] (Australia), Ryan S. Padron Flasher (Switzerland/Ecuador, United States of America), Anna Possner \r (Germany), Cristian Proistosescu (Romania), Johannes Quaas (Germany), Joeri Rogelj (United \r Kingdom/Belgium), Daniel Rosenfeld (Israel), Bjorn H. Samset (Norway), Abhishek Savita (Australia/\r India), Jessica Vial (France), Karina von Schuckmann (France/Germany), Mark Zelinka (United States \r of America), Shuyun Zhao (China)"


id: 59cef2c0eb2e68a34ba5b97ac1af574a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 13042
content:
"This chapter assesses the present state of knowledge of Earth's energy \r budget: that is, the main flows of energy into and out of the Earth \r system, and how these energy flows govern the climate response to \r a  radiative forcing. Changes in atmospheric composition and land \r use, like those caused by anthropogenic greenhouse gas emissions \r and emissions of aerosols and their precursors, affect climate \r through perturbations to Earth's top-of-atmosphere energy budget. \r The effective radiative forcings (ERFs) quantify these perturbations, \r including any consequent adjustment to the climate system \r (but  excluding surface temperature response). How  the climate \r system responds to a given forcing is determined by climate feedbacks \r associated with physical, biogeophysical and biogeochemical \r processes. These feedback processes are assessed, as are useful \r measures of global climate response, namely equilibrium climate \r [...]"


id: 7a19688800c81048b619f8c6be4c2d20
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 18680
content:
"[...] sensitivity (ECS) and the transient climate response (TCR). This chapter \r also assesses emissions metrics, which are used to quantify how the \r climate response to the emissions of different greenhouse gases \r compares to the response to the emissions of carbon dioxide (CO2). \r This chapter builds on the assessment of carbon cycle and aerosol \r processes from Chapters 5 and 6, respectively, to quantify non-CO2\r biogeochemical feedbacks and the ERF for aerosols. Other chapters in \r this Report use this chapter's assessment of ERF, ECS and TCR to help \r understand historical and future temperature changes (Chapters  3 \r and 4, respectively), the response to cumulative emissions and the \r remaining carbon budget (Chapter  5), emissions-based radiative \r forcing (Chapter 6) and sea level rise (Chapter 9). This chapter builds \r on findings from the IPCC Fifth Assessment Report (AR5), the Special \r Report on Global Warming of 1.5degC (SR1.5), the Special Report on \r [...]"


id: 9cc4d1cbac5db498f5ae173511dae105
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 24816
content:
"[...] the Ocean and Cryosphere in a Changing Climate (SROCC) and the \r Special Report on climate change, desertification, land degradation, \r sustainable land management, food security, and greenhouse \r gas luxes in terrestrial ecosystems (SRCCL). Very likely ranges are \r presented unless otherwise indicated."


id: 3ad30424c150c721cb73cf0397d2982a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7545
content:
"Since AR5, the accumulation of energy in the Earth system, \r quantified by changes in the global energy inventory for all \r components of the climate system, has become established \r as a robust measure of the rate of global climate change on \r interannual-to-decadal time scales. Compared to changes in \r global surface air temperature (GSAT), the global energy inventory \r exhibits less variability, which can mask underlying climate trends.\r Compared to AR5, there is increased confidence in the quantification \r of changes in the global energy inventory due to improved \r observational records and closure of the sea level budget. Energy \r will continue to accumulate in the Earth system until at least the \r end of the 21st  century, even under strong mitigation scenarios, \r and will primarily be observed through ocean warming and \r associated with continued sea level rise through thermal expansion \r (high confidence). {7.2.2, Box 7.2, Table 7.1, Cross-Chapter Box 9.1, \r [...]"


id: e0d848bb541d48008e03e4830624bfd8
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36822
content:
"The total anthropogenic ERF over the industrial era \r (1750-2019) was 2.72 [1.96 to 3.48] W m-2. This estimate \r has increased by 0.43 W m-2 compared to AR5 estimates for \r 1750-2011. This increase includes +0.34  W m-2 from increases \r in atmospheric concentrations of well-mixed greenhouse gases \r (including halogenated species) since 2011, +0.15 W m-2 from \r upwards revisions of their radiative efficiencies and +0.10 W m-2\r from re-evaluation of the ozone and stratospheric water vapour ERF. \r The 0.59 W m-2 increase in ERF from greenhouse gases is partly offset \r by a better-constrained assessment of total aerosol ERF that is more \r strongly negative than in AR5, based on multiple lines of evidence \r (high confidence). Changes in  surface reflectance from land-use \r change, deposition of light-absorbing particles on ice and snow, and \r contrails and aviation-induced cirrus have also contributed to the \r total anthropogenic ERF over the industrial era, with -0.20 [-0.30 to \r [...]"


id: ae3020a00b7b4530909bccf74e43fc6
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 27882
content:
"[...] -0.10] W m-2 (medium confidence), +0.08  [0  to 0.18] W  m-2 (low \r confidence) and +0.06 [0.02  to 0.10]  W  m-2 (low  confidence), \r respectively. {7.3.2, 7.3.4, 7.3.5}"


id: 10a6a4a503c6725ace1bf60ea4a840dd
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 114
content:
"The combined effect of all known radiative feedbacks (physical, \r biogeophysical, and non-CO2 biogeochemical) is to amplify the \r base climate response, also known as the Planck temperature \r response (virtually certain). Combining these feedbacks with the \r base climate response, the net feedback parameter based on process \r understanding is assessed to be -1.16 [-1.81 to -0.51] W m-2 degC-1, \r which is slightly less negative than that inferred from the overall ECS \r assessment. The combined water-vapour and lapse-rate feedback \r makes the largest single contribution to global warming, whereas \r the cloud feedback remains the largest contribution to overall \r uncertainty. Due to the state-dependence of feedbacks, as evidenced \r from paleoclimate observations and from models, the net feedback \r parameter will increase (become less negative) as global temperature \r increases. Furthermore, on long time scales the ice-sheet feedback \r [...]"


id: d326ab03897110d33c97afbc5cd2bd8c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34493
content:
"[...] parameter is very likely positive, promoting additional warming on \r millennial time scales as ice sheets come into equilibrium with the \r forcing (high confidence). {7.4.2, 7.4.3, 7.5.7}"


id: 55212f8fce9fb75742e2d7bf3e71cda3
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 12251
content:
"Anthropogenic emissions of greenhouse gases and their \r precursors contribute an ERF of 3.84 [3.46 to 4.22] W m-2\r over the  industrial era (1750-2019). Most of this total \r ERF, 3.32  [3.03  to 3.61] W m-2, comes from the well-mixed \r greenhouse gases, with changes in ozone and stratospheric \r water vapour (from methane oxidation) contributing \r the remainder. The ERF of greenhouse gases is composed of \r 2.16  [1.90  to  2.41] W  m-2 from carbon dioxide, 0.54 [0.43  to \r 0.65] W m-2 from methane, 0.41 [0.33 to 0.49] W m-2 from halogenated \r species, and 0.21 [0.18 to 0.24] W m-2 from nitrous oxide. The ERF for \r ozone is 0.47 [0.24 to 0.71] W m-2. The estimate of ERF for ozone \r has increased since AR5 due to revised estimates  of precursor \r emissions and better accounting for effects of tropospheric ozone \r precursors in the stratosphere. The estimated ERF for methane has \r slightly increased due to a combination of increases from improved \r [...]"


id: 3bc98052e2319b940fd424e52b5dc6a5
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7705
content:
"[...] spectroscopic treatments being somewhat offset by accounting for \r adjustments (high confidence). {7.3.2, 7.3.5}"


id: f6b10e12a334d69328b6c94c941f6636
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updated_at: 2023-04-19 13:22:38
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content:
"Aerosols contribute an ERF of -1.3 [-2.0 to -0.6] W m-2 over \r the industrial era (1750-2014) (medium confidence). The ERF \r due to aerosol-cloud interactions (ERFaci) contributes most \r to the magnitude of the total aerosol ERF (high confidence) \r and is assessed to be -1.0 [-1.7 to -0.3] W m-2 (medium \r confidence), with the remainder due to aerosol-radiation \r interactions (ERFari), assessed to be -0.3 [-0.6 to 0.0] W m-2 \r (medium confidence). There has been an increase in the estimated \r magnitude but a  reduction in the uncertainty of the total aerosol \r ERF relative to AR5, supported by a  combination of increased \r process-understanding and progress in modelling and observational \r analyses. ERF estimates from these separate lines of evidence are \r now consistent with each other, in contrast to AR5, and support the \r assessment that it is virtually certain that the total aerosol ERF is \r negative. Compared to AR5, the assessed magnitude of ERFaci has \r [...]"


id: 872a61c84b7ea6dde48c9ea066a10ea8
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 20944
content:
"[...] increased, while the magnitude of ERFari has decreased. The total \r aerosol ERF over the period 1750-2019 is less certain than the \r headline statement assessment. It is also assessed to be smaller \r in magnitude at -1.1 [-1.7 to -0.4] W m-2, primarily due to recent \r emissions changes (medium confidence). {7.3.3, 7.3.5, 2.2.6}"


id: 7a084cc492549b5fc16162af9053d293
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 18665
content:
"On average, Coupled Model Intercomparison Project Phase 6 \r (CMIP6) models have higher mean ECS and TCR values than \r the Phase 5 (CMIP5) generation of models. They also have \r higher mean values and wider spreads than the assessed best \r estimates and very likely ranges within this Report. These higher \r ECS and TCR values can, in some models, be traced to changes in \r extra-tropical cloud feedbacks that have emerged from efforts to \r reduce biases in these clouds compared to satellite observations \r (medium confidence). The broader ECS and TCR ranges from CMIP6 \r also lead the models to project a range of future warming that is wider \r than the assessed warming range, which is based on multiple lines of \r evidence. However, some of the high-sensitivity CMIP6 models are less \r consistent with observed recent changes in global warming and with \r paleoclimate proxy data than models with ECS within the very likely\r [...]"


id: 2c12fa56ef2272c2677ab03146cb0bb6
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 5013
content:
"[...] range. Similarly, some of the low-sensitivity models are less consistent \r with the paleoclimate data. The CMIP models with the highest ECS \r and TCR values provide insights into low-likelihood, high-impact \r outcomes, which cannot be excluded based on currently available \r evidence (high confidence). {4.3.1, 4.3.4, 7.4.2, 7.5.6}"


id: e9f656e6d0505f7e364c7ff846df9106
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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vector_id: 38510
content:
"It is now well understood that the Arctic warms more quickly \r than the Antarctic due to differences in radiative feedbacks \r and ocean heat uptake between the poles, but that surface \r warming will eventually be amplified in both the Arctic and \r Antarctic (high confidence). The causes of this polar amplification \r are well understood, and the evidence is stronger than at the \r time of AR5, supported by better agreement between modelled \r and observed polar amplification during warm paleo time periods \r (high confidence). The Antarctic warms more slowly than the Arctic \r owing primarily to upwelling in the Southern Ocean, and even at \r equilibrium is expected to warm less than the Arctic. The rate of Arctic \r surface warming will continue to exceed the global average over this \r century (high confidence). There is also high confidence that Antarctic \r amplification will emerge as the Southern Ocean surface warms on \r centennial time scales, although only low confidence regarding \r [...]"


id: f55974532a95299620dd30d17cf75167
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 40534
content:
"Throughout this chapter, global surface air temperature (GSAT) \r is used to quantify surface temperature change (Cross-Chapter \r Box  2.3 and Section  4.3.4). The total energy accumulation in \r the Earth system represents a  metric of global change that is \r complementary to GSAT but shows considerably less variability on \r interannual-to-decadal time scales (Section  7.2.2). Research and \r new observations since AR5 have improved scientific confidence in \r the quantification of changes in the global energy inventory and \r corresponding estimates of Earth's energy imbalance (Section 7.2). \r Improved understanding of adjustments to radiative forcing and of \r aerosol-cloud interactions have led to revisions of forcing estimates \r (Section 7.3). New approaches to the quantification and treatment \r of feedbacks (Section 7.4) have improved the understanding of their \r nature and time-evolution, leading to a better understanding of how \r [...]"


id: b6f0d9f3bb8c387f22b0d8d81204bf63
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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content:
"[...] these feedbacks relate to equilibrium climate sensitivity (ECS). This \r has helped to reconcile disparate estimates of ECS from different \r lines of evidence (Section 7.5). Innovations in the use of emissions \r metrics have clarified the relationships between metric choice \r and temperature policy goals (Section  7.6), linking this chapter to \r WGIII which provides further information on metrics, their use, and \r policy goals beyond temperature. Very likely (5-95%) ranges are \r presented unless otherwise indicated. In particular, the addition of \r '(one standard deviation)' indicates that the range represents one \r standard deviation."


id: 649a7f43fb2631daa533c5181846bcc3
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 14976
content:
"Given that TCR and ECS are metrics of GSAT response to \r a  theoretical doubling of atmospheric CO2 (Box  7.1), they do not \r directly correspond to the warming that would occur under realistic \r forcing scenarios that include time-varying CO2 concentrations and \r non-CO2 forcing agents (such as aerosols and land-use changes). \r It has been argued that TCR, as a metric of transient warming, is \r more policy-relevant than ECS (Frame et al., 2006; Schwartz, 2018). \r However, as detailed in Chapter  4, both established and recent \r results (Forster et  al., 2013; Gregory et  al., 2015; Marotzke and \r Forster, 2015; Grose et al., 2018; Marotzke, 2019) indicate that TCR \r and ECS help explain variation across climate models both over the \r historical period and across a range of concentration-driven future \r scenarios. In emission-driven scenarios the carbon cycle response \r is also important (Smith et  al., 2019). The proportion of variation \r [...]"


id: a8ed0f2b4d6389207384eb2552ce6808
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 26930
content:
"[...] explained by ECS and TCR varies with scenario and the time period \r considered, but both past and future surface warming depend on \r these metrics (Section 7.5.7)."


id: 39f61c14c06589487095ffc3bec4e0d5
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7409
content:
"A simple way to characterize the behaviour of multiple aspects of \r the climate system at once is to summarize them using global-scale \r metrics. This Report distinguishes between 'climate metrics' (e.g., ECS, \r TCR) and 'emissions metrics' (e.g., global warming potential, GWP, or \r global temperature-change potential, GTP), but this distinction is not \r definitive. Climate metrics are generally used to summarize aspects \r of the surface temperature response (Box  7.1). Emissions metrics \r are generally used to summarize the relative effects of emissions of \r different forcing agents, usually greenhouse gases (GHGs; Section 7.6). \r The climate metrics used in this report typically evaluate how the \r Earth system response varies with atmospheric gas concentration or \r change in radiative forcing. Emissions metrics evaluate how radiative \r forcing or a key climate variable (such as GSAT) is affected by the \r emissions of a certain amount of gas. Emissions-related metrics are \r [...]"


id: f9d5eb282a3d760f42612061f7e283bc
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41316
content:
"[...] sometimes used in mitigation policy decisions such as trading GHG \r reduction measures and life cycle analysis. Climate metrics are useful \r to gauge the range of future climate impacts for adaptation decisions \r under a  given emissions pathway. Metrics such as the transient \r climate response to cumulative emissions of carbon dioxide (TCRE) \r are used in both adaptation and mitigation contexts: for gauging \r future global surface temperature change under specific emissions \r scenarios, and to estimate remaining carbon budgets that are used \r to inform mitigation policies (Section 5.5)."


id: 67c6bd7b85e4e3fb425e379873488fbb
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 15530
content:
"any GSAT change (Smith et al., 2018b). These adjustments affect the TOA energy balance and hence the ERF. They are generally assumed \r to be linear and additive (Section 7.3.1). Accounting for such processes gives an estimate of ERF that is more representative of the climate \r change response associated with forcing agents than stratospheric-temperature-adjusted radiative forcing (SARF) or the instantaneous \r radiative forcing (IRF; Section  7.3.1). Adjustments are processes that are independent of GSAT change, whereas feedbacks  refer to \r processes caused by GSAT change. Although adjustments generally occur on time scales of hours to several months, and feedbacks \r respond to ocean surface temperature changes on time scales of a year or more, time scale is not used to separate the definitions. ERF has \r often been approximated as the TOA energy balance change due to an imposed perturbation in climate model simulations with sea surface \r [...]"


id: fddb1617a8b883af4f283dbb6146a272
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 42024
content:
"[...] temperature and sea-ice concentrations set to their pre-industrial climatological values (e.g., Forster et al., 2016). However, to match \r the adopted forcing-feedback framework, the small effects of any GSAT change from changes in land surface temperatures need to be \r removed from the TOA energy balance in such simulations to give an approximate measure of ERF (Box 7.1, Figure 1b and Section 7.3.1)."


id: 7a40adcee0f2d962c9adc588a50b3fd2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 18702
content:
"where x represents a variable of the Earth system that has a direct effect on the energy budget at the TOA. The sum of the feedback terms \r (i.e., a in Equation 7.1) governs Earth's equilibrium GSAT response to an imposed ERF. In previous assessments, a and the related ECS \r have been associated with a distinct set of physical processes (Planck response and changes in water vapour, lapse rate, surface albedo, \r and clouds; Charney et al., 1979). In this assessment, a more general definition of a and ECS is adopted such that they include additional \r Earth system processes that act across many time scales (e.g., changes in natural aerosol emissions or vegetation). Because, in our \r assessment, these additional processes sum to a near-zero value, including these additional processes does not change the assessed \r central value of ECS but does affect its assessed uncertainty range (Section 7.4.2). Note that there is no standardized notation or sign \r [...]"


id: 8019f8e1f86c9fe0d4201f117ff44da1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 19670
content:
"[...] convention for the feedback parameter in the literature. Here the convention is used that the sum of all feedback terms (the net feedback \r parameter, a) is negative for a stable climate that radiates additional energy to space with a GSAT increase, with a more negative value \r of a corresponding to a stronger radiative response and thus a smaller GSAT change required to balance a change in ERF (Equation 7.1). "


id: d2100768c3f42ed2fe392a34e24b0c40
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 34312
content:
"The equilibrium climate sensitivity, ECS (units: degC), is defined as the equilibrium value of DT in response to a sustained doubling \r of atmospheric CO2 concentration from a pre-industrial reference state. The value of ERF for this scenario is denoted by DF2xCO2, giving \r ECS = -DF2xCO2/a from Equation 7.1 applied at equilibrium (Box 7.1, Figure 1a and Section 7.5). 'Equilibrium' refers to a steady state \r where DN averages to zero over a multi-century period. ECS is representative of the multi-century to millennial DT response to DF2xCO2, \r and is based on a CO2 concentration change so any feedbacks that affect the atmospheric concentration of CO2 do not influence \r its value. As employed here, ECS also excludes the long-term response of the ice sheets (Section 7.4.2.6) which may take multiple \r millennia to reach equilibrium, but includes all other feedbacks. Due to a number of factors, studies rarely estimate ECS or a at \r [...]"


id: 9924e6886c3c00dfd749e14fbb28a3e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 24155
content:
"[...] equilibrium or under CO2 forcing alone. Rather, they give an 'effective feedback parameter' (Section 7.4.1 and Box 7.1, Figure 1b) or \r an 'effective ECS' (Section 7.5.1 and Box 7.1, Figure 1b), which represent approximations to the true values of a or ECS. The 'effective \r ECS' represents the equilibrium value of DT in response to a sustained doubling of atmospheric CO2 concentration that would occur \r assuming the 'effective feedback parameter' applied at that equilibrium state. For example, a feedback parameter can be estimated \r from the linear slope of DN against DT over a set number of years within ESM simulations of an abrupt doubling or quadrupling \r of atmospheric CO2 (2xCO2 or 4xCO2, respectively), and the ECS can be estimated from the intersect of this regression line with \r DN = 0 (Box 7.1, Figure 1b). To infer ECS from a given estimate of effective ECS necessitates that assumptions are made for how ERF \r [...]"


id: f4215dedebcd54ea82bf1dcf75ad523f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 40325
content:
"[...] varies with CO2 concentration (Section 7.3.2) and how the slope of DN against DT relates to the slope of the straight line from ERF to \r ECS (Section 7.5 and Box 7.1, Figure 1b). Care has to be taken when comparing results across different lines of evidence to translate \r their estimates of the effective ECS into the ECS definition used here (Section 7.5.5)."


id: 6a9d54c9e61b64d9ceb493217b576601
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 16040
content:
"The transient climate response, TCR (units: degC), is defined as the DT for the hypothetical scenario in which CO2 increases at 1% yr-1\r from a pre-industrial reference state to the time of a doubling of atmospheric CO2 concentration (year 70; Section 7.5). TCR is based \r on a CO2 concentration change, so any feedbacks that affect the atmospheric concentration of CO2 do not influence its value. It is \r a measure of transient warming accounting for the strength of climate feedbacks and ocean heat uptake. The transient climate \r response to cumulative emissions of carbon dioxide (TCRE) is defined as the transient DT per 1000 Gt C of cumulative CO2\r emissions increase since the pre-industrial period. TCRE combines information on the airborne fraction of cumulative CO2 emissions \r (the fraction of the total CO2 emitted that remains in the atmosphere at the time of doubling, which is determined by carbon cycle \r [...]"


id: 7b7cf104293dce8a9337cef78cbfe52
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 18932
content:
"[...] processes) with information on the TCR. TCR is assessed in this chapter, whereas TCRE is assessed in Chapter 5 (Section 5.5)."


id: df9188c9e15aa4692dabf826b96d14ca
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36583
content:
"Earth's energy budget encompasses the major energy flows of relevance \r for the climate system (Figure 7.2). Virtually all the energy that enters or \r leaves the climate system does so in the form of radiation at the TOA. \r The TOA energy budget is determined by the amount of incoming solar \r (shortwave) radiation and the outgoing radiation that is composed of \r reflected solar radiation and outgoing thermal (longwave) radiation \r emitted by the climate system. In a steady-state climate, the outgoing \r and incoming radiative components are essentially in balance in the \r long-term global mean, although there are still fluctuations around this \r balanced state that arise through internal climate variability (Brown \r et  al., 2014; Palmer and McNeall, 2014). However, anthropogenic \r forcing has given rise to a persistent imbalance in the global mean \r TOA radiation budget that is often referred to as Earth's energy \r [...]"


id: bc85a354a582d4e34333fe39429c8acb
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 30413
content:
"[...] imbalance (e.g., Trenberth et al., 2014; von Schuckmann et al., 2016), \r which is a key element of the energy budget framework (N; Box 7.1, \r Equation 7.1) and an important metric of the rate of global climate \r change (Hansen et al., 2005a; von Schuckmann et al., 2020). In addition "


id: 26a19c9747a4df23eb67f6c78c56bf9e
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vector_id: 3956
content:
"Figure 7.2 | Schematic representation of the global mean energy budget of the Earth (upper panel), and its equivalent without considerations of cloud \r effects (lower panel). Numbers indicate best estimates for the magnitudes of the globally averaged energy balance components in W m-2 together with their uncertainty \r ranges in parentheses (5-95% confidence range), representing climate conditions at the beginning of the 21st century. Note that the cloud-free energy budget shown in the \r lower panel is not the one that Earth would achieve in equilibrium when no clouds could form. It rather represents the global mean fluxes as determined solely by removing \r the clouds but otherwise retaining the entire atmospheric structure. This enables the quantification of the effects of clouds on the Earth energy budget and corresponds to \r [...]"


id: b9fb948b42d3dbb4588de67ca1c65ac9
created_at: 2023-04-19 13:22:38
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content_type: text
index: climate_gpt_v2
vector_id: 29945
content:
"[...] the way clear-sky fluxes are calculated in climate models. Thus, the cloud-free energy budget is not closed and therefore the sensible and latent heat fluxes are not quantified \r in the lower panel. Figure adapted from Wild et al. (2015, 2019)."


id: c6bacc24bb55669cfb2b999d5441d6f6
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 32263
content:
"ranges of their individual components (Wild et al., 2013, 2015; L'Ecuyer \r et al., 2015). However, on regional scales, the closure of the surface \r energy budgets remains a  challenge with satellite-derived datasets \r (Loeb et al., 2014; L'Ecuyer et al., 2015; Kato et al., 2016). Nevertheless, \r attempts have been made to derive surface energy budgets over land \r and ocean (Wild et al., 2015), over the Arctic (Christensen et al., 2016b), \r and over individual continents and ocean basins (L'Ecuyer et al., 2015; \r Thomas et al., 2020). Since AR5, the quantification of the uncertainties \r in surface energy flux datasets has improved. Uncertainties in global \r monthly mean downward solar and thermal fluxes in the CERES-EBAF \r surface dataset are, respectively, 10 W m-2 and 8 W m-2 (converted to \r 5-95% ranges; Kato et al., 2018). The uncertainty in the surface fluxes \r for polar regions is larger than in other regions (Kato et al., 2018) due \r [...]"


id: a1dcd8f73521ab2b0ff9cdab856fa45a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 25701
content:
"[...] to the limited number of surface sites and larger uncertainty in surface \r observations (Previdi et  al., 2015). The uncertainties in ocean mean \r latent and sensible heat fluxes are approximately 11 W m-2 and 5 W m-2\r (converted to 5-95% ranges), respectively (L'Ecuyer et al., 2015). A recent \r review of the latent and sensible heat flux accuracies over the period \r 2000-2007 highlights significant differences between several gridded \r products over ocean, where root-mean-squared differences between \r the multi-product ensemble and data at more than 200  moorings \r reached up to 25 W m-2 for latent heat and 5 W m-2 for sensible heat \r (Bentamy et  al., 2017). This uncertainty stems from the retrieval of \r flux-relevant meteorological variables, as well as from differences in the \r flux parametrizations (Yu, 2019). Estimating the uncertainty in sensible \r and latent heat fluxes over land is difficult because of the large temporal \r [...]"


id: 793c53f9a707102d6d4f65b5753d4d5
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updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 18522
content:
"[...] and spatial variability. The flux values over land computed with three \r global datasets vary by 10-20% (L'Ecuyer et al., 2015)."


id: 4da06add33b82b74ecb65b6f1f964619
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 10937
content:
"The AR5 (Church et al., 2013; Hartmann et al., 2013; Myhre et al., 2013b) \r highlighted the progress that had been made in quantifying the TOA \r radiation budget following new satellite observations that became \r available in the early 21st century (Clouds and the Earth's Radiant \r Energy System, CERES; Solar Radiation and Climate Experiment, \r SORCE). Progress in the quantification of changes in incoming solar \r radiation at the TOA is discussed in Chapter  2 (Section  2.2). Since \r AR5, the CERES Energy Balance EBAF Ed4.0 product was released, \r which includes algorithm improvements and consistent input datasets \r throughout the record (Loeb et  al., 2018b). However, the overall \r precision of these fluxes (uncertainty in global mean TOA flux of \r 1.7% (1.7 W m-2) for reflected solar and 1.3% (3.0 W m-2) for outgoing \r thermal radiation at the 90% confidence level) is not sufficient to \r quantify the Earth's energy imbalance in absolute terms. Therefore, \r [...]"


id: 4abbcdb6563245a46cb22928e44e236d
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 10387
content:
"[...] the CERES EBAF reflected solar and emitted thermal TOA fluxes were \r adjusted, within the estimated uncertainties, to ensure that the net \r TOA flux for July 2005 to June 2015 was consistent with the estimated \r Earth's energy imbalance for the same period based on ocean heat \r content (OHC) measurements and energy uptake estimates for the \r land, cryosphere and atmosphere (Section  7.2.2.2; Johnson et  al., \r 2016; Riser et al., 2016). ESMs typically show good agreement with \r global mean TOA fluxes from CERES-EBAF. However, as some ESMs are \r known to calibrate their TOA fluxes to CERES or similar data (Hourdin \r et al., 2017), this is not necessarily an indication of model accuracy, \r especially as ESMs show significant discrepancies on regional scales, \r often related to their representation of clouds (Trenberth and Fasullo, \r 2010; Donohoe and Battisti, 2012; Hwang and Frierson, 2013; J.-L.F. Li \r et al., 2013; Dolinar et al., 2015; Wild et al., 2015)."


id: d07d97afce420c60bf7777554c05ee2
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 34031
content:
"The radiation components of the surface energy budget are associated \r with substantially larger uncertainties than at the TOA, since they \r are less directly measured by passive satellite sensors and require \r retrieval algorithms and ancillary data for their estimation (Raschke \r et al., 2016; Kato et al., 2018; Huang et al., 2019). Confidence in the \r quantification of the global mean surface radiation components has \r increased recently, as independent estimates now converge to within \r a few W m-2 (Wild, 2017). Current best estimates for downward solar \r and thermal radiation at Earth's surface are approximately 185 W m-2\r and 342 W m-2, respectively (Figure 7.2). These estimates are based on \r complementary approaches that make use of satellite products from \r active and passive sensors (L'Ecuyer et  al., 2015; Kato et  al., 2018) \r and information from surface observations and Earth system models \r (ESMs; Wild et al., 2015). Inconsistencies in the quantification of the \r [...]"


id: dd62f33068c186ac4fe53311e13864af
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36217
content:
"[...] global mean energy and water budgets discussed in AR5 (Hartmann \r et al., 2013) have been reconciled within the (considerable) uncertainty "


id: a3481ce25752c430ec347e71cfb8f75b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 25935
content:
"The assessment of changes in the global energy inventory for \r the  periods 1971-2018, 1993-2018 and 2006-2018 draws upon \r the latest observational time series and the assessments presented \r in other chapters of this report. The estimates of OHC change come \r directly from the assessment presented in Chapter 2 (Section 2.3.3.1). \r The assessment of land and atmospheric heating comes from von \r Schuckmann et al. (2020), based on the estimates of Cuesta-Valero \r et al. (2021) and Steiner et al. (2020), respectively. Heating of inland \r waters, including lakes, reservoirs and rivers, is estimated to account \r for <0.1% of the total energy change, and is therefore omitted from \r this assessment (Vanderkelen et al., 2020). The cryosphere contribution \r from the melting of grounded ice is based on the mass-loss \r assessments presented in Chapter  9, Section  9.4.1 (Greenland \r Ice Sheet), Section  9.4.2 (Antarctic Ice Sheet) and Section  9.5.1 \r [...]"


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updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 1144
content:
"[...] (glaciers). Following AR5, the estimate of heating associated with \r loss of Arctic sea ice is based on a reanalysis (Schweiger et al., 2011), \r following the methods described by Slater et  al. (2021). Chapter  9 \r (Section 9.3.2) finds no significant trend in Antarctic sea ice area over \r the observational record, so a zero contribution is assumed. Ice melt \r associated with the calving and thinning of floating ice shelves is \r based on the decadal rates presented in Slater et al. (2021). For all \r cryospheric components, mass loss is converted to heat input using \r a latent heat of fusion of 3.34 x 105 J Kg-1 degC-1 with the second-order \r contributions from variations associated with ice type and warming \r of ice from sub-freezing temperatures disregarded, as in AR5. The net \r change in energy, quantified in Zettajoules (1 ZJ = 1021  Joules), \r is computed for each component as the difference between the \r [...]"


id: 988f9559e4dae77cdda979923a92d2ab
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 24054
content:
"[...] first and last year of each period (Table  7.1). The uncertainties in \r the depth-interval contributions to OHC are summed to get the \r uncertainty in global OHC change. All other uncertainties are assumed \r to be independent and added in quadrature. "


id: 474feca8ff7c26151fbce167bda57c5c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 9753
content:
"For the period 1971-2010, AR5 (Rhein et al., 2013) found an increase \r in the global energy inventory of 274 [196 to 351] ZJ with a 93% \r contribution from total OHC change, approximately 3% for both ice \r melt and land heating, and approximately 1% for warming of the \r atmosphere. For the same period, this Report finds an upwards revision \r of OHC change for the upper (<700 m depth) and deep (>700 m depth) \r ocean of approximately 8% and 20%, respectively, compared to AR5 \r and a modest increase in the estimated uncertainties associated with \r the ensemble approach of Palmer et al. (2021). The other substantive \r change compared to AR5 is the updated assessment of land heating, \r with values approximately double those assessed previously, based \r on a  more comprehensive analysis of the available observations \r (von Schuckmann et al., 2020; Cuesta-Valero et al., 2021). The result \r of these changes is an assessed energy gain of 329 [224 to 434] ZJ \r [...]"


id: 1c3c8ff1f4340f179d3e76a21521a73f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 2161
content:
"[...] for the period 1971-2010, which is consistent with AR5 within the \r estimated uncertainties, despite the systematic increase."


id: 4eb6b5ef9b48517642a0a32efdda1fbd
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 11124
content:
"Since AR5, both modelling and observation-based studies have \r established Earth's energy imbalance (characterized by OHC change) \r as a more robust metric of the rate of global climate change than \r GSAT on interannual-to-decadal time scales (Palmer and McNeall, \r 2014; von Schuckmann et al., 2016; Wijffels et al., 2016; Cheng et al., \r 2018; Allison et al., 2020). This is because GSAT is influenced by large \r unforced variations, for example linked to ENSO and Pacific Decadal \r Variability (Roberts et al., 2015; Yan et al., 2016; Cheng et al., 2018). \r Measuring OHC change more comprehensively over the full ocean \r depth results in a higher signal-to-noise ratio and a time series that \r increases steadily over time (Box 7.2, Figure 1; Allison et al., 2020). \r In addition, understanding of the potential effects of historical ocean \r sampling on estimated global ocean heating rates has improved \r (Durack et al., 2014; Good, 2017; Allison et al., 2019) and there are \r [...]"


id: 868dc31eda7fc3ba19bb174ea2dafcda
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 20841
content:
"[...] now more estimates of OHC change available that aim to mitigate the \r effect of limited observational sampling in the Southern Hemisphere \r (Lyman and Johnson, 2008; Cheng et al., 2017; Ishii et al., 2017)."


id: fdb4b40482eb17010a7e153ffa3ac6da
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41997
content:
"of the globe, such as Iran, Bahrain, Tenerife, Hawaii, the Taklaman \r Desert and the Tibetan Plateau (Elagib and Alvi, 2013; You et al., 2013; \r Garcia et al., 2014; Longman et al., 2014; Rahimzadeh et al., 2015). \r Strong decadal trends in surface solar radiation remain evident after \r careful data quality assessment and homogenization of long-term \r records (Sanchez-Lorenzo et  al., 2013, 2015; Manara et  al., 2015, \r 2016; Wang et al., 2015; Z. Li et al., 2016; Wang and Wild, 2016; Y. He \r et al., 2018; Yang et al., 2018). Since AR5, new studies on the potential \r effects of urbanization on solar radiation trends indicate that these \r effects are generally small, with the exception of some specific sites \r in Russia and China (Wang et al., 2014; Imamovic et al., 2016; Tanaka \r et al., 2016). Also, surface-based solar radiation observations have \r been shown to be representative over large spatial domains of up \r to several degrees latitude/longitude on monthly and longer time \r [...]"


id: dfd837653e397fe0d8dc6ee7a57a91b5
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36625
content:
"[...] scales (Hakuba et al., 2014; Schwarz et al., 2018). Thus, there is high \r confidence that the observed dimming between the 1950s and 1980s \r and the subsequent brightening are robust and do not arise from \r measurement artefacts or localized phenomena."


id: ab5ccf72680dc7ddcbfeabfbaabfa18c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 27380
content:
"As noted in AR5 (Hartmann et al., 2013) and supported by recent studies, \r the trends in surface solar radiation are less spatially coherent since the \r beginning of the 21st century, with evidence for continued brightening \r in parts of Europe and the USA, some stabilization in China and India, \r and dimming in other areas (Augustine and Dutton, 2013; Sanchez\u0002Lorenzo et al., 2015; Manara et al., 2016; Soni et al., 2016; Wang and \r Wild, 2016; Jahani et al., 2018; Pfeifroth et al., 2018; Yang et al., 2018; \r Schwarz et  al., 2020). The CERES-EBAF satellite-derived dataset of \r surface solar radiation (Kato et al., 2018) does not indicate a globally \r significant trend over the short period 2001-2012 (Zhang et al., 2015), \r whereas a statistically significant increase in surface solar radiation of \r +3.4 W m-2 per decade over the period 1996-2010 has been found \r in the Satellite Application Facility on Climate Monitoring (CM SAF) \r [...]"


id: b6350ed88a7102419e8a178c8116f255
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 29282
content:
"[...] record of the geostationary satellite Meteosat, which views Europe, \r Africa and adjacent ocean (Posselt et al., 2014)."


id: 9e78a07dd9de4d80faddc63bac026cdf
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 25103
content:
"Soni et al., 2016; Tanaka et al., 2016; Kazadzis et al., 2018; J. Li et al., \r 2018; Yang et al., 2019; Wild et al., 2021). This suggests that changes \r in the composition of the cloud-free atmosphere, primarily in aerosols, \r contributed to these variations, particularly since the second half of \r the 20th  century (Wild, 2016). Water vapour and other radiatively \r active gases seem to have played a minor role (Wild, 2009; Mateos \r et al., 2013; Posselt et al., 2014; Yang et al., 2019). For Europe and East \r Asia, modelling studies also point to aerosols as an important factor \r for dimming and brightening by comparing simulations that include \r or exclude variations in anthropogenic aerosol and aerosol-precursor \r emissions (Golaz et al., 2013; Nabat et al., 2014; Persad et al., 2014; \r Folini and Wild, 2015; Turnock et  al., 2015; Moseid et  al., 2020). \r Moreover, decadal changes in surface solar radiation have often \r [...]"


id: 89869106ae2ec372c7edc130ec50db1e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 21373
content:
"[...] occurred in line with changes in anthropogenic aerosol emissions \r and associated aerosol optical depth (Streets et al., 2006; Wang and \r Yang, 2014; Storelvmo et al., 2016; Wild, 2016; Kinne, 2019). However, \r further evidence for the influence of changes in cloudiness on dimming \r and brightening is emphasized in some studies (Augustine and \r Dutton, 2013; Parding et al., 2014; Stanhill et al., 2014; Pfeifroth et al., \r 2018; Antuna-Marrero et al., 2019). Thus, the contribution of aerosol \r and clouds to dimming and brightening is still debated. The relative \r influence of cloud-mediated aerosol effects versus direct aerosol \r radiative effects on dimming and brightening in a specific region may \r depend on the prevailing pollution levels (Section 7.3.3; Wild, 2016)."


id: 8d9902723bf95a2c89dc94b531f8aa54
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 22102
content:
"Turbulent fluxes of latent and sensible heat are also an important \r part of the surface energy budget (Figure 7.2). Large uncertainties \r in measurements of surface turbulent fluxes continue to prevent the \r determination of their decadal changes. Nevertheless, over the ocean, \r reanalysis-based estimates of linear trends from 1948-2008 indicate \r high spatial variability and seasonality. Increases in magnitudes of \r 4 to 7 W m-2 per decade for latent heat and 2 to 3 W m-2 per decade \r for sensible heat in the western boundary current regions are mostly \r balanced by decreasing trends in other regions (Gulev and Belyaev, \r 2012). Over land, the terrestrial latent heat flux is estimated to have \r increased in magnitude by 0.09 W m-2 per decade from 1989-1997, \r and subsequently decreased by 0.13 W m-2 per  decade from \r 1998-2005 due to soil-moisture limitation mainly in the Southern \r Hemisphere (derived from Mueller et al., 2013). These trends are small \r [...]"


id: 8b0c83fe37540c8638f94a63e8b5461b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 21632
content:
"[...] in comparison to the uncertainty associated with satellite-derived \r and in situ observations, as well as from land-surface models forced \r by observations and atmospheric reanalyses. Ongoing advances \r in remote sensing of evapotranspiration from space (Mallick \r et  al., 2016; Fisher et  al., 2017; McCabe et  al., 2017a, b), as well \r as terrestrial water storage (Rodell et al., 2018) may contribute to \r future constraints on changes in latent heat flux."


id: 6da9c01588ac46bb108d2893ed2991a4
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 16550
content:
"Earth energy imbalance (Section 7.5.2 and Box 7.2, Figure 1a) with a central estimate and very likely range of 284 [96 to 471] ZJ \r (high confidence) (Box 7.2, Figure 1d; Table 7.1). Estimating the total uncertainty associated with radiative forcing and radiative \r response remains a scientific challenge and depends on the degree of correlation between the two (Box 7.2, Figure 1f). However, the \r central estimate of observed energy change falls well with the estimated likely range, assuming either correlated or uncorrelated \r uncertainties. Furthermore, the energy budget assessment would accommodate a  substantial pattern effect (Section  7.4.4.3) \r during 1971-2018 associated with systematically larger values of radiative response (Box 7.2, Figure 1c), and potentially improved \r closure of the global energy budget. For the period 1970-2011, AR5 reported that the global energy budget was closed within \r [...]"


id: 765490f755eeef0c72f1abfebfc70239
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 18041
content:
"[...] uncertainties (high confidence) and consistent with the likely range of assessed climate sensitivity (Church et  al., 2013). This \r Report provides a  more robust quantitative assessment based on additional evidence and improved scientific understanding."


id: e5e3b836ad5eeb654cd1f205e9cdfd2e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 37745
content:
"rather than defining a  specific time scale. The AR5 used the term \r 'rapid adjustment', but in this assessment the definition is based on \r the independence from surface temperature rather than the rapidity. \r The definition of ERF in Box 7.1 aims to create a clean separation \r between forcing (energy budget changes that are not mediated by \r surface temperature) and feedbacks (energy budget changes that \r are mediated by surface temperature). This means that changes in \r land or ocean surface temperature patterns (for instance as identified \r by Rugenstein et al., 2016b) are not included as adjustments. As in \r previous assessments (Forster et al., 2007; Myhre et al., 2013b) ERFs \r can be attributed simply to changes in the forcing agent itself or \r attributed to components of emitted gases (Figure  6.12). Because \r ERFs can include chemical and biospheric responses to emitted \r gases, they can be attributed to precursor gases, even if those gases \r [...]"


id: 9c8f6b7de632385c853e3f3db59b63e5
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 24794
content:
"[...] do not have a direct radiative effect themselves. Similar chemical and \r biospheric responses to forcing agents can also be included in the \r ERF in addition to their direct effects."


id: c0b038565d2a1f1dceb1065cfed06243
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 31198
content:
"the ultimate temperature response. This work is assessed to have led \r to a much-improved understanding and increased confidence in the \r quantification of radiative forcing across the Report. These same \r techniques allow for an evaluation of radiative forcing within Earth \r system models (ESMs) as a key test of their ability to represent both \r historical and future temperature changes (Sections 3.3.1 and 4.3.4).\r The ERF for a  particular forcing agent is the sum of the IRF and \r the contribution from the adjustments, so in principle this could \r be constructed bottom-up by calculating the IRF and adding in the \r adjustment contributions one-by-one or together. However, there \r is no simple way to derive the global tropospheric adjustment \r terms or adjustments related to circulation changes without using \r a comprehensive climate model (e.g., CMIP5 or CMIP6). There have \r been two main modelling approaches used to approximate the ERF \r [...]"


id: 9584b0d4437499f300f36fc03ec9b0a9
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 23506
content:
"[...] definition in Box 7.1. The first approach is to use the assumed linearity \r (Box 7.1, Equation 7.1) to regress the net change in the TOA radiation \r budget (DN) against change in global mean surface temperature \r (DT) following a step change in the forcing agent (Box 7.1, Figure 1; \r Gregory et al., 2004). The ERF (DF) is then derived from DN when \r DT = 0. Regression-based estimates of ERF depend on the temporal \r resolution of the data used (Modak et al., 2016, 2018). For the first \r few months of a  simulation both surface temperature change and \r stratospheric-temperature adjustment occur at the same time, leading \r to misattribution of the stratospheric-temperature adjustment to the \r surface temperature feedback. Patterns of sea surface temperature \r (SST) change also affect estimates of the forcing obtained by regression \r methods (Andrews et  al., 2015). At multi-decadal time scales the \r curvature of the relationship between net TOA radiation and surface \r [...]"


id: d1f77b2a6227e05522f0ec517d8bc3ac
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34289
content:
"[...] temperature can also lead to biases in the ERF estimated from the \r regression method (Section 7.4; Armour et al., 2013; Andrews et al., \r 2015; Knutti et al., 2017). The second modelling approach to estimate \r ERF is to set the DT term in Box 7.1 (Box 7.1, Equation 7.1) to zero. It is \r technically difficult to constrain land surface temperatures in ESMs \r (Shine et al., 2003; Ackerley and Dommenget, 2016; Andrews et al., \r 2021), so most studies reduce the DT term by prescribing the SSTs \r and sea ice concentrations in a pair of 'fixed-SST' (fSST) simulations \r with and without the change in forcing agent (Hansen et al., 2005b). \r An approximation to ERF (DFfsst) is then given by the difference in \r DNfsst between the simulations. The fSST method has less noise due \r to internal variability than the regression method. Nevertheless \r a 30-year fSST integration or 10 x 20-year regression ensemble needs \r to be conducted in order to reduce the 5-95% confidence range to \r [...]"


id: d3341b08eb8ba6c3fc3376ba2445ec12
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34503
content:
"[...] 0.1 W m-2 (Forster et al., 2016).Neither the regression or fSST methods \r are practical for quantifying the ERF of agents with forcing magnitudes \r of the order of 0.1 W m-2 or smaller. The internal variability in the \r fSST method can be further constrained by nudging winds towards \r a  prescribed climatology (Kooperman et  al., 2012). This allows the \r determination of the ERF of forcing agents with smaller magnitudes \r but excludes adjustments associated with circulation responses \r (Schmidt et al., 2018). There are insufficient studies to assess whether \r these circulation adjustments are significant.\r Since the near-surface temperature change over land, DTland, is not \r constrained in the fSST method, this response needs to be removed \r for consistency with the Section 7.1 definition. These changes in the \r near-surface temperature will also induce further responses in "


id: b9f7e4bf5fb998d4ae21d8ecc42080c2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 29942
content:
"the tropospheric temperature and water vapour that should also be \r removed to conform with the physical definition of ERF. The radiative \r response to DTland can be estimated through radiative transfer \r modelling in which a kernel, k, representing the change in net TOA \r radiative flux per unit of change in near-surface temperature change \r over land (or an approximation using land surface temperature), is \r precomputed (Smith et  al., 2018b, 2020b; Richardson et  al., 2019; \r Tang et al., 2019). Thus ERF  DFfsst - k DTland. Since k is negative \r this means that DFfsst underestimates the ERF. For 2xCO2, this \r underestimate is around 0.2 W m-2 (Smith et  al., 2018b, 2020b). \r There have been estimates of the corrections due to tropospheric \r temperature and water vapour (Tang et al., 2019; Smith et al., 2020b) \r showing additional radiative responses of comparable magnitude to \r those directly from DTland. An alternative to computing the response \r [...]"


id: 4204606075062dffbbf45ba20bd70fb4
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 8815
content:
"[...] terms directly is to use the feedback parameter, a (Hansen et al., 2005b; \r Sherwood et al., 2015; Tang et al., 2019). This gives approximately \r double the correction compared to the kernel approach (Tang et al., \r 2019). The response to land surface temperature change varies with \r location and even for GSAT change k is not expected to be the same \r as a (Section 7.4). One study where land surface temperatures are \r constrained in a model (Andrews et al., 2021) finds this constraint \r adds +1.0 W m-2 to DFfsst for 4xCO2, thus confirming the need for \r a  correction in calculations where this constraint is not applied. \r For this assessment the correction is conservatively based only on \r the direct radiative response kernel to DTland as this has a  strong \r theoretical basis to support it. While there is currently insufficient \r corroborating evidence to recommend including tropospheric \r temperature and water-vapour corrections in this assessment, it is \r [...]"


id: c9a4288c179077a6209cbf4447efb2d2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 32801
content:
"TOA radiative flux changes due to the individual adjustments can be \r calculated by perturbing the meteorological fields in a climate model's \r radiative transfer scheme (partial radiative perturbation approach) \r (Colman, 2015; Mulmenstadt et al., 2019) or by using precomputed \r radiative kernels of sensitivities of the TOA radiation fluxes to \r changes in these fields (as done for near-surface temperature change \r above; Vial et al., 2013; Zelinka et al., 2014; Zhang and Huang, 2014; \r Smith et al., 2018b, 2020b). The radiative kernel approach is easier to \r implement through post-processing of output from multiple ESMs, \r whereas it is recognized that the partial radiation perturbation \r approach gives a more accurate estimate of the adjustments within \r the setup of a  single model and its own radiative transfer code. \r There is little difference between using a radiative kernel from the \r same or a different model when calculating the adjustment terms, \r [...]"


id: 1e14fc16b089323fa9b21d23bfc3b815
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 2487
content:
"[...] except for stratospheric temperature adjustments where it is \r important to have sufficient vertical resolution in the stratosphere \r in the model used to derive the kernel (Smith et al., 2018b, 2020a)."


id: 6b5b730bb72ef77b7339a0262e443b1e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 16187
content:
"kernels are shown for seven different climate drivers (using a fSST \r approach) in Figure  7.4. Table  7.2 shows the estimates of SARF, \r DFfsst and ERF (corrected for land surface temperature change) for \r 2xCO2 from the nine climate models analysed in Smith et al. (2018b). \r The  SARF shows a  smaller spread over previous studies (Pincus \r et  al., 2016; Soden et  al., 2018) and most estimates are within \r 10% of the multi-model mean and the assessment of 2xCO2 SARF \r in Section 7.3.2 (3.75 W m-2). It is not possible from these studies to \r determine how much of this reduction in spread is due to convergence \r in the model radiation schemes or the meteorological conditions \r of the model base states; nevertheless the level of agreement in \r this and earlier intercomparisons gives medium confidence in the \r ability of ESMs to represent radiative forcing from CO2. The 4xCO2\r CMIP6 fSST experiments (Smith et  al., 2020b) in Table  7.2 include \r [...]"


id: 22da55af0344a97f968924b83d080696
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 3339
content:
"[...] ESMs with varying levels of complexity in aerosols and reactive \r gas chemistry. The CMIP6 experimental setup allows for further \r climate effects of CO2 (including on aerosols and ozone) depending \r on model complexity. The chemical effects are adjustments to CO2\r but are not separable from the SARF in the diagnosis in Table 7.2. \r In these particular models, this leads to higher SARF than when only \r CO2 varies, however there are insufficient studies to make a formal \r assessment of composition adjustments to CO2."


id: 968f278bf321b86c8561ce8bea0b88a4
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 23690
content:
"Table 7.2 | SARF, DFfsst, and ERF diagnosed from Earth system models for \r fixed-SST (fSST) CO2 experiments. 2xCO2 data taken from fixed atmospheric \r composition experiments (Smith et  al., 2018b). 4xCO2 data taken from CMIP6 \r experiments with interactive aerosols (and interactive gas phase chemistry in some; \r Smith et al., 2020b). The radiative forcings from the 4xCO2 experiments are scaled by \r 0.476 for comparison with 2xCO2 (Meinshausen et al., 2020). SARF is approximated \r by removing the (non-stratospheric-temperature) adjustment terms from the ERF. \r In Smith et al. (2018b), separation of temperature adjustments into tropospheric and \r stratospheric contributions is approximate based on a fixed tropopause of 100 hPa at \r the equator, varying linearly in latitude to 300 hPa at the poles. In Smith et al. (2020b), \r this separation is based on the model-diagnosed tropopause. ERF is approximated by \r [...]"


id: 7f13a510fc69c575a2274b987cd8dd4a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 19506
content:
"[...] removing the response to land surface temperature change from DFfsst. The confidence \r range is based on the inter-model standard deviation."


id: 74f2e9bf94d7742a93ffcbfcc05b1f1a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17811
content:
"Figure 7.4 | Radiative adjustments at top of atmosphere for seven different \r climate drivers as a proportion of forcing. Tropospheric temperature (orange), \r stratospheric temperature (yellow), water vapour (blue), surface albedo (green), \r clouds (grey) and the total adjustment (black) is shown. For the greenhouse gases \r (carbon dioxide, methane, nitrous oxide and CFC-12) the adjustments are expressed \r as a  percentage of stratospheric-temperature-adjusted radiative forcing (SARF), \r whereas for aerosol, solar and volcanic forcing they are expressed as a percentage \r of instantaneous radiative forcing (IRF). Land surface temperature response (outline \r red bar) is shown, but included in the definition of forcing. Data from Smith et al. \r (2018b) for carbon dioxide and methane; Smith et al. (2018b) and Gray et al. (2009) \r for solar; Hodnebrog et al. (2020b) for nitrous oxide and CFC-12; Smith et al. (2020b) \r [...]"


id: a1d1dede6fdfbeb175d73af114491c9c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 25689
content:
"[...] for aerosol, and Marshall et al. (2020) for volcanic. Further details on data sources and \r processing are available in the chapter data table (Table 7.SM.14)."


id: 900907522dda4e6993a738188c5daa4f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 22547
content:
"ERFs have been found to yield more consistent values of GSAT change \r per unit forcing than SARF, that is, a shows less variation across different \r forcing agents (Rotstayn and Penner, 2001; Shine et al., 2003; Hansen \r et  al., 2005b; Marvel et  al., 2016; Richardson et  al., 2019). Having \r a consistent relationship between forcing and response is advantageous \r when making climate projections using simple models (Cross-Chapter \r Box 7.1) or emissions metrics (Section 7.6). The definition of ERF used in \r this assessment, which excludes the radiative response to land surface \r temperature changes, brings the a values into closer agreement than \r when SARF is used (Richardson et al., 2019), although for individual \r models there are still variations, particularly for more geographically \r localized forcing agents. However, even for ERF, studies find that \r a  is not identical across all forcing agents (Shindell, 2014; Shindell \r [...]"


id: 694e04101743d459d7c8040f12bd6903
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 15793
content:
"[...] et al., 2015; Modak et al., 2018; Modak and Bala, 2019; Richardson \r et al., 2019). Section 7.4.4 discusses the effect of different SST response \r patterns on a. Analysis of the climate feedbacks (Kang and Xie, 2014; \r Gregory et  al., 2016, 2020; Marvel et  al., 2016; Duan et  al., 2018; \r Persad and Caldeira, 2018; Stuecker et al., 2018; Krishnamohan et al., \r 2019) suggests a  weaker feedback (i.e., less-negative a) and hence \r larger sensitivity for forcing of the higher latitudes (particularly the \r Northern Hemisphere). Nonetheless, as none of these variations are \r robust across models, the ratio of 1/a from non-CO2 forcing agents \r (with approximately global distributions) to that from doubling CO2 is \r within 10% of unity."


id: b94202abc5c47eab72e0b2685d0f6867
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 29799
content:
"In summary, this Report adopts an estimate of ERF based on the \r change in TOA radiative fluxes in the absence of GSAT changes. \r This allows for a  theoretically cleaner separation between forcing \r and feedbacks in terms of factors respectively unrelated and related \r to GSAT change (Box  7.1). ERF can be computed from prescribed \r SST and sea ice experiments after removing the TOA energy budget \r change associated with the land surface temperature response. \r In  this assessment this is removed using a  kernel accounting only \r for the direct radiative effect of the land surface temperature \r response. To compare these results with sophisticated high spectral \r resolution radiative transfer models the individual tropospheric \r adjustment terms can be removed to leave the SARF. SARFs for \r 2xCO2 calculated by ESMs from this method agree within 10% with \r the more sophisticated models. The new studies highlighted above \r suggest that physical feedback parameters computed within this \r [...]"


id: d56833188af1cd45a3331ac236d036af
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34864
content:
"[...] framework have less variation across forcing agents. There is high \r confidence that an a based on ERF as defined here varies by less \r (less than variation 10% across a range of forcing agents with global \r distributions), than a based on SARF. For geographically localized \r forcing agents there are fewer studies and less agreement between \r them, resulting in low confidence that ERF is a suitable estimator of \r the resulting global mean near-surface temperature response."


id: 7429d1fb6601a43ed84f8a09648ecc76
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17672
content:
"Zhang  and  Huang, 2014; Smith et  al., 2018b, 2020b). The DFfsst\r includes any effects represented within the ESMs on tropospheric \r adjustments due to changes in evapotranspiration or leaf area \r (mainly affecting surface and boundary-layer temperature, low-cloud \r amount, and albedo) from the CO2-physiological effects (Doutriaux\u0002Boucher et al., 2009; Cao et al., 2010; T.B. Richardson et al., 2018). \r The effect on surface temperature (negative longwave response) is \r consistent with the expected physiological responses and needs to \r be removed for consistency with the ERF definition. The split between \r surface and tropospheric temperature responses was not reported in \r Vial et al. (2013) or Zhang and Huang (2014) but the total of surface \r and tropospheric temperature response agrees with Smith et  al. \r (2018b, 2020b), giving medium confidence in this decomposition. \r Doutriaux-Boucher et al. (2009) and Andrews et al. (2021) (using the \r [...]"


id: 31809569ee2fdae2e1cdab4f810a7453
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 5954
content:
"[...] same land surface model) find a 13% and 10% increase respectively \r in ERF due to the physiological responses to CO2. The physiological \r adjustments are therefore assessed to make a substantial contribution \r to the overall tropospheric adjustment for CO2 (high confidence), \r but there is insufficient evidence to provide a quantification of the \r split between physiological and thermodynamic adjustments. These \r forcing adjustments due to the effects of CO2 on plant physiology \r differ from the biogeophysical feedbacks due to the effects of \r temperature changes on vegetation discussed in Section  7.4.2.5. \r The adjustment is assumed to scale with the SARF in the absence of \r evidence for non-linearity. The tropospheric adjustment is assessed \r from Table 7.3 to be +5% of the SARF with an uncertainty of 5%, \r which is added to the Meinshausen et al. (2020) formula for SARF. \r Due to the agreement between the studies and the understanding \r [...]"


id: f8397cc64a7d3af9d479165c198d1e23
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 41045
content:
"[...] of the physical mechanisms there is medium confidence in the \r mechanisms underpinning the tropospheric adjustment, but low \r confidence in its magnitude."


id: eb3b7f44085f3bf3ea7818ff53e9192f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 38742
content:
"The ERF from doubling CO2 (2xCO2) from the 1750 level (278 ppm; \r Section 2.2.3.3) is assessed to be 3.93 +- 0.47 W m-2 (high confidence). \r Its assessed components are given in Table  7.4. The combined \r spectroscopic and radiative transfer modelling uncertainties give an \r uncertainty in the CO2 SARF of around 10% or less (Etminan et al., \r 2016; Mlynczak et al., 2016). The overall uncertainty in CO2 ERF is \r assessed as +-12%, as the more uncertain adjustments only account \r for a small fraction of the ERF (Table 7.3). The 2xCO2 ERF estimate \r is 0.2 W m-2 larger than using the AR5 formula (Myhre et al., 2013b) \r due to the combined effects of tropospheric adjustments which were \r assumed to be zero in AR5. CO2 concentrations have increased from \r 278 ppm in 1750 to 410 ppm in 2019 (Section 2.2.3.3). The historical \r ERF estimate from CO2 is revised upwards from the AR5 value of \r 1.82 +- 0.38 W m-2 (1750-2011) to 2.16 +- 0.26 W m-2 (1750-2019) in \r [...]"


id: d73468797bd7b75a8d97c2c313cb73a0
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 35170
content:
"[...] this assessment, from a combination of the revisions described above \r (0.06 W m-2) and the 19 ppm rise in atmospheric concentrations \r between 2011 and 2019 (0.27 W m-2). The ESM estimates of 2xCO2\r ERF (Table 7.2) lie within +-12% of the assessed value (apart from \r CESM2). The definition of ERF can also include further physiological \r effects - for instance on dust, natural fires and biogenic emissions \r from the land and ocean - but these are not typically included in the \r modelling setup for 2xCO2 ERF."


id: 2755b9c306d69980976c80554b51c669
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 4095
content:
"For these models  the adjustments act to reduce the ERF because \r the shortwave absorption leads to tropospheric heating and \r reductions in upper tropospheric cloud amounts. The adjustment \r is -14% +- 15%, which counteracts much of the increase in SARF \r identified by Etminan et al. (2016). Modak et al. (2018) also found \r negative forcing adjustments from a methane perturbation including \r shortwave absorption in the NCAR CAM5 model, in agreement with \r the above assessment. The uncertainty in the shortwave component \r leads to a higher radiative modelling uncertainty (14%) than for CO2 \r (Etminan et al., 2016). When combined with the uncertainty in the \r adjustment, this gives an overall uncertainty of +-20%. There is high \r confidence in the spectroscopic revision but only medium confidence\r in the adjustment modification. CH4 concentrations have increased \r from 729 ppb in 1750 to 1866 ppb in 2019 (Section  2.2.3.3). \r The  historical ERF estimate from AR5 of 0.48 +- 0.10  W m-2\r [...]"


id: 9e8dc8663c99f495cf38c4ff344215d9
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 25121
content:
"[...] (1750-2011) is revised to 0.54 +- 0.11 W m-2 (1750 to 2019) in this \r assessment from a combination of spectroscopic radiative efficiency \r revisions (+0.12 W m-2), adjustments (-0.08 W m-2) and the 63 ppb \r rise in atmospheric CH4 concentrations between 2011 and 2019 \r (+0.03 W m-2). As the adjustments are assessed to be small, there \r is high confidence in the overall assessment of ERF from methane. \r Increased methane leads to tropospheric ozone production and \r increased stratospheric water vapour, so that an attribution of forcing \r to methane emissions gives a larger effect than that directly from the \r methane concentration itself. This is discussed in detail in Chapter 6 \r (Section 6.4.2) and shown in Figure 6.12."


id: 7003f67fe8fd5b5dfb0413c6558f602f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 16965
content:
"The tropospheric adjustments to chlorofluorocarbons (CFCs), \r specifically CFC-11 and CFC-12, have been quantified as 13% +- 10% \r and 12% +- 14% of the SARF, respectively (Hodnebrog et al., 2020b). \r The assessed adjustment to CFCs is therefore 12% +- 13% with \r low confidence due to the lack of corroborating studies. There have \r been no calculations for other halogenated species so for these the \r tropospheric adjustments are therefore assumed to be 0 +-  13% \r with low confidence. The radiative modelling uncertainties are \r 14% and 24% for compounds with lifetimes greater than and less \r than five years, respectively (Hodnebrog et al., 2020a). The overall \r uncertainty in the ERFs of halogenated compounds is therefore \r assessed to be 19% and 26% depending on the lifetime. The ERF \r from CFCs is slowly decreasing, but this is compensated for by the \r increased forcing from the replacement species (HCFCs and HFCs). \r [...]"


id: ab92afbd2071c193fa6e51ca24bd1f9b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 27420
content:
"[...] The ERF from HFCs has increased by 0.028 +- 0.05 W m-2. Thus, the \r concentration changes mean that the total ERF from halogenated \r compounds has increased since AR5 from 0.360 +- 0.036 W m-2\r to 0.408 +- 0.078 W m-2 (Table 7.5). Of this, 0.034 W m-2 is due to \r increased radiative efficiencies and tropospheric adjustments, and \r 0.014 W m-2 is due to increases in concentrations. As the adjustments \r are assessed to be small there remains high confidence in the \r overall assessment."


id: 8ea4d33cc4ba59e75f9c9f3b14bf2f52
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 22282
content:
"Estimates of the pre-industrial to present-day tropospheric ozone \r radiative forcing are based entirely on models. The lack of pre-industrial \r ozone measurements prevents an observational determination. There \r have been limited studies of ozone ERFs (MacIntosh et  al., 2016; \r Xie et  al., 2016; Skeie et  al., 2020). Skeie et  al. (2020) found little \r net contribution to the ERF from tropospheric adjustment terms for \r 1850-2000 change in ozone (tropospheric and stratospheric ozone \r combined), although MacIntosh et al. (2016) suggested that increases \r in stratospheric or upper tropospheric ozone reduces high-cloud \r and increases low-cloud, whereas an increase in lower tropospheric \r ozone reduces low-cloud. Further studies suggest that changes in \r circulation due to decreases in stratospheric ozone affect Southern \r Hemisphere clouds and the atmospheric levels of sea salt aerosol \r that would contribute additional adjustments, possibly of comparable \r [...]"


id: 2b021d994ba6307eae18796466b63b6e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 4799
content:
"[...] magnitude to the SARF from stratospheric ozone depletion (Grise \r et al., 2013, 2014; Xia et al., 2016, 2020). ESM responses to changes \r in ozone-depleting substances (ODS) in CMIP6 show a much more \r negative ERF than would be expected from offline calculations \r of SARF (Morgenstern et  al., 2020; Thornhill et  al., 2021b) again \r suggesting a negative contribution from adjustments. However there \r is insufficient evidence available to quantify this effect."


id: 81e7b3b28ea1b39d6f5387b61c2003b7
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 19981
content:
"This section considers direct anthropogenic effects on stratospheric \r water vapour by oxidation of methane. Since AR5 the SARF from \r methane-induced stratospheric water vapour changes has been \r calculated in Winterstein et al., 2019, corresponding to 0.09 W m-2\r when scaling to 1850 to 2014 methane changes. This is marginally \r larger than the AR5 assessed value of 0.07 +- 0.05 W m-2 (Myhre \r et  al., 2013b). Wang and Huang (2020) quantified the adjustment \r terms to a stratospheric water vapour change equivalent to a forcing \r from a 2xCO2 warming (which has a different vertical profile). They \r found that the ERF was less than 50% of the SARF due to high-cloud \r decrease and upper tropospheric warming. The assessed ERF is \r therefore 0.05 +- 0.05 W m-2 with a lower limit reduced to zero and \r the central value and upper limit reduced to allow for adjustment \r terms. This still encompasses the two recent SARF studies. There is \r [...]"


id: f50d347097583a7e8fa4b8e6c12b58c3
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 40478
content:
"[...] medium confidence in the SARF from agreement with the recent \r studies and AR5. There is low confidence in the adjustment terms."


id: f91a550b91d0a1683c103d4f93b0c485
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41188
content:
"As there is insufficient evidence to quantify adjustments, for total \r ozone the assessed central estimate for ERF is assumed to be equal \r to SARF (low confidence) and follows Skeie et al. (2020), since that \r study uses the most recent emissions data. The dataset is extended \r over the entire historical period following Skeie et  al. (2020), \r with a SARF for 1750-1850 of 0.03 W m-2 and for 2010-2018 of \r 0.03 W m-2, to give 0.47 [0.24 to 0.70] W m-2 for 1750-2019. This \r maintains the 50% uncertainty (5-95% range) from AR5 which is \r largely due to the uncertainty in pre-industrial emissions (Rowlinson \r et al., 2020). There is also high confidence that this range includes \r uncertainty due to the adjustments. The CMIP6 SARF is more positive \r than the AR5 value of 0.31 W m-2 for the period 1850-2011 (Myhre \r et al., 2013b) which was based on the Atmospheric Chemistry and \r Climate Intercomparison Project (ACCMIP; Shindell et al., 2013). The \r [...]"


id: 24f800c74c76cd121d7052b3140a8cf8
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 3701
content:
"[...] assessment is sensitive to the assumptions on precursor emissions \r used to drive the models, which are larger in CMIP6 than ACCMIP."


id: 8e1d0d7907cb504257253a728fd15aab
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 22198
content:
"The ERF of GHGs (excluding ozone and stratospheric water vapour) \r over 1750-2019 is assessed to be 3.32 +- 0.29 W m-2. It has \r increased by 0.49 W m-2 compared to AR5 (reference year 2011) \r (high confidence). Most of this has been due to an increase in CO2\r concentration since 2011 [0.27 +- 0.03] W m-2, with concentration \r increases in CH4, N2O and halogenated compounds adding 0.02, \r 0.02 and 0.01 W m-2 respectively (Table  7.5). Changes in the \r radiative efficiencies (including adjustments) of CO2, CH4, N2O and \r halogenated compounds have increased the ERF by an additional \r 0.15 W m-2 compared to the AR5 values (high confidence). Note \r that the ERFs in this section do not include chemical effects of \r GHGs on production or destruction of ozone or aerosol formation \r (Section 6.2.2). The ERF for ozone is considerably increased compared \r to AR5 due to an increase in the assumed ozone precursor emissions \r in CMIP6 compared to CMIP5, and better accounting for the effects \r [...]"


id: a8ff4c7a4ca1c68f3509beee68bfb202
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 26942
content:
"[...] of both ozone precursors and ODSs in the stratosphere. The ERF for \r stratospheric water vapour is slightly reduced. The combined ERF \r from ozone and stratospheric water vapour has increased since AR5 \r by 0.10 +- 0.50 W m-2 (high confidence), although the uncertainty \r ranges still include the AR5 values."


id: 703ef51e588c809c59ae6269b362ca98
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17004
content:
"In AR5, the assessment of the black carbon (BC) contribution to IRFari \r was markedly strengthened in confidence by the review by Bond et al. \r (2013), where a key finding was a perceived model underestimate \r of atmospheric absorption when compared to Aeronet observations \r (Boucher et  al., 2013). This assessment has since been revised \r considering: new knowledge on the effect of the temporal resolution \r of emissions inventories (Wang et al., 2016); the representativeness \r of Aeronet sites (Wang et  al., 2018); issues with comparing \r absorption retrieval to models (E. Andrews et  al., 2017); and the \r ageing (Peng et al., 2016), lifetime (Lund et al., 2018b) and average \r optical parameters (Zanatta et al., 2016) of BC. Consistent with these \r updates, Lund et al. (2018a) estimated the net IRFari in 2014 (relative \r to 1750) to be -0.17 W m-2, using CEDS emissions (Hoesly et al., 2018) \r as input to a chemical transport model. They attributed the weaker \r [...]"


id: 2095c9a6892a8b48eaf9f66c84aafc17
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 2958
content:
"[...] estimate relative to AR5 (-0.35 +- 0.5 W m-2; Myhre et al., 2013a) \r to stronger absorption by organic aerosol, updated parametrization \r of BC absorption, and slightly reduced sulphate cooling. Broadly \r consistent with Lund et al. (2018a), another single-model study by \r Petersik et al. (2018) estimated an IRFari of -0.19 W m-2. Another \r single-model study by Lurton et al. (2020) reported a more negative \r estimate at -0.38 W m-2, but is given less weight here because \r the model lacked interactive aerosols and instead used prescribed \r climatological aerosol concentrations."


id: e81a95f94c52273062ed9a4d0586c868
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 38156
content:
"Estimating IRFari requires an estimate of industrial-era changes in \r aerosol optical depth (AOD) and absorption AOD, which are often \r taken from global aerosol model simulations. Since AR5, updates \r to methods of estimating IRFari based on aerosol remote sensing \r or data-assimilated reanalyses of atmospheric composition have \r been published. Ma et  al. (2014) applied the method of Quaas \r et  al. (2008) to updated broadband radiative flux measurements \r from CERES, MODIS-retrieved AODs, and modelled anthropogenic \r aerosol fractions to find a clear-sky IRFari of -0.6 W m-2. This would \r translate into an all-sky estimate of about -0.3 W m-2 based on the \r clear-sky to all-sky ratio implied by Kinne (2019). Remy et al. (2018) \r applied the methods of Bellouin et al. (2013a) to the reanalysis by \r the Copernicus Atmosphere Monitoring Service, which assimilates \r MODIS total AOD. Their estimate of IRFari varies between -0.5 W m-2 \r [...]"


id: b5fa9b3538c71746ca69565374a1b8e3
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 29238
content:
"[...] and  -0.6  W  m-2  over the period 2003-2018, and they attribute \r those relatively small variations to  variability in biomass-burning \r activity.  Kinne (2019) provided updated monthly total AOD and \r absorption AOD climatologies, obtained by blending multi-model \r averages with ground-based sun-photometer retrievals, to find \r a best estimate of IRFari of -0.4 W m-2. The updated IRFari estimates \r above are all scattered around the midpoint of the IRFari range of \r -0.35 +- 0.5 W m-2 assessed by AR5 (Boucher et al., 2013)."


id: f66471ed677cd5743e218e7cb635477
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 40713
content:
"Since AR5 considerable progress has been made in the understanding \r of adjustments in response to a wide range of climate forcings, as \r discussed in Section 7.3.1. The adjustments in ERFari are principally \r caused by cloud changes, but also by lapse rate and atmospheric \r water vapour changes, all mainly associated with absorbing aerosols \r like BC. Stjern et  al. (2017) found that for BC, about 30% of the \r (positive) IRFari is offset by adjustments of clouds (specifically, an \r increase in low-clouds and decrease in high-clouds) and lapse rate, \r by analysing simulations by five Precipitation Driver Response Model \r Intercomparison Project (PDRMIP) models. Smith et  al. (2018b) \r considered more models participating in PDRMIP and suggested \r that about half the IRFari was offset by adjustments for BC, a finding \r generally supported by single-model studies (Takemura and Suzuki, \r 2019; Zhao and Suzuki, 2019). Thornhill et al. (2021b) also reported \r [...]"


id: deca97966dd0f9b065af1577701ae888
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36463
content:
"[...] a negative adjustment for BC based on AerChemMIP (Collins et al., \r 2017) but found it to be somewhat smaller in magnitude than those \r reported in Smith et al. (2018b) and Stjern et al. (2017). In contrast, \r Allen et al. (2019) found a positive adjustment for BC and suggested \r that most models simulate negative adjustment for BC because of \r a misrepresentation of aerosol atmospheric heating profiles."


id: 809a3b738afb35e2ccf43b93812c93a2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 19754
content:
"Zelinka et  al. (2014) used the approximate partial radiation \r perturbation technique to quantify the ERFari in 2000 relative \r to 1860 in nine CMIP5 models; they estimated the ERFari \r (accounting for a small contribution from longwave radiation) to be \r -0.27 +- 0.35 W m-2. However, it should be noted that in Zelinka et al. \r (2014) adjustments of clouds caused by absorbing aerosols through \r changes in the thermal structure of the atmosphere (termed the \r semidirect effect of aerosols in AR5) are not included in ERFari but \r in ERFaci. The corresponding estimate emerging from the Radiative \r Forcing Model Intercomparison Project (RFMIP, Pincus et al., 2016) \r is -0.25 +- 0.40 W m-2 (Smith et  al., 2020b), which is generally \r supported by single-model studies published since AR5 (Zhang et al., \r 2016; Fiedler et al., 2017; Nazarenko et al., 2017; Zhou et al., 2017c, \r 2018b; Grandey et al., 2018). A 5% inflation is applied to the CMIP5 \r [...]"


id: 47b65281fa2ec982d71456e1fcffd236
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 9831
content:
"[...] and CMIP6 fixed-SST derived estimates of ERFari from Zelinka et al. \r (2014) and Smith et al. (2020b) to account for land surface cooling \r (Table 7.6). Based on the above, ERFari from model-based evidence is \r assessed to be -0.25 +- 0.25 W m-2."


id: cbcaa0260eb59aabd4d8228ba9da0960
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 33179
content:
"Further, studies assessed in AR5 mostly investigated linear relationships \r between cloud droplet concentration and aerosol (Boucher et  al., \r 2013). Since in most cases the relationships are not linear, this leads \r to a  bias (Gryspeerdt et  al., 2016). Several studies did not relate \r cloud droplet concentration, but cloud droplet effective radius, to the \r aerosol (Brenguier et al., 2000). This is problematic because in order to \r infer IRFaci, stratification by cloud LWP is required (McComiskey and \r Feingold, 2012). Where LWP positively co-varies with aerosol retrievals \r (which is often the case), IRFaci inferred from such relationships is \r biased towards low values. Also, it is increasingly evident that different \r cloud regimes show different sensitivities to aerosols (Stevens and \r Feingold, 2009). Averaging statistics over regimes thus biases the \r inferred IRFaci (Gryspeerdt et  al., 2014b). The AR5 concluded that \r [...]"


id: c90f8ad03beae901542c69e34557b186
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 32693
content:
"[...] IRFaci estimates tied to satellite studies generally show weak IRFaci \r (Boucher et al., 2013), but when correcting for the biases discussed \r above, this is no longer the case."


id: 5c53b4a2a1338ddf6458d3cf970c1f3c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 13495
content:
"used AI instead of AOD in the regression to infer IRFaci. McCoy \r et al. (2017b) instead used the sulphate-specific mass derived in the \r MERRA aerosol reanalysis that assimilated MODIS AOD (Rienecker \r et al., 2011). All approaches have in common the need to identify \r the anthropogenic perturbation of the aerosol to assess IRFaci. \r Gryspeerdt et  al. (2017) and Remy et  al. (2018) used the same \r approach as Bellouin et  al. (2013b), while McCoy et  al. (2017b) \r used an anthropogenic fraction from the AEROCOM multi-model \r ensemble (Schulz et al., 2006). Chen et al. (2014), Christensen et al. \r (2016a) and Christensen et  al. (2017) derived the combination of \r IRFaci and the LWP adjustment to IRFaci ('intrinsic forcing' in their \r terminology). They relate AI and cloud albedo statistically and use the \r anthropogenic aerosol fraction from Bellouin et al. (2013b). This was \r further refined by Hasekamp et  al. (2019) who used additional \r [...]"


id: ff6e3237f59f3231e19de79036c9649f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 42343
content:
"[...] polarimetric satellite information over ocean to obtain a better proxy \r for CCN. They derived an IRFaci of -1.14 [-1.72 to -0.84] W m-2. \r The variant by Christensen et al. (2017) is an update compared to \r the Chen et al. (2014) and Christensen et al. (2016a) studies in that it \r better accounts for ancillary influences on the aerosol retrievals such \r as aerosol swelling and three-dimensional radiative effects. McCoy \r et  al. (2020) used the satellite-observed hemispheric difference in \r Nd as an emergent constraint on IRFaci as simulated by GCMs to \r obtain a  range of -1.2 to -0.6 W m-2 (95% confidence interval). \r Diamond et al. (2020) analysed the difference in clouds affected by \r ship emissions with unperturbed clouds and based on this inferred \r a global IRFaci of -0.69 [-0.99 to -0.44] W m-2."


id: a9c17c20e06d87c5677bd4c6ae0e3f66
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 27092
content:
"on the basis of which Rosenfeld et al. (2019) still obtained a positive \r relationship between cloud fraction and Nd relationship.\r The relationship between LWP and cloud droplet number is debated. \r Most recent studies (primarily based on MODIS data) find negative \r statistical relationships (Michibata et al., 2016; Toll et al., 2017; Sato \r et al., 2018; Gryspeerdt et al., 2019), while Rosenfeld et al. (2019) \r obtained a  modest positive relationship. To increase confidence \r that observed relationships between aerosol emissions and cloud \r adjustments are causal, known emissions of aerosols and aerosol \r precursor gases into otherwise pristine conditions have been \r exploited. Ship exhaust is one such source. Goren and Rosenfeld \r (2014) suggested that both LWP and Cf increase in response \r to ship emissions, contributing approximately 75% to the total \r ERFaci in mid-latitude stratocumulus. Christensen and Stephens \r (2011) found that such strong adjustments occur for open-cell \r [...]"


id: a8b79fbc04438251d857ed1285f98cfd
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 26889
content:
"[...] stratocumulus regimes, while adjustments are comparatively small \r in closed-cell regimes. Volcanic emissions have been identified \r as another important source of information (Gasso, 2008). From \r satellite observations, Yuan et  al. (2011) documented substantially \r larger Cf, higher cloud tops, reduced precipitation likelihood, and \r increased albedo in cumulus clouds in the plume of the Kilauea \r volcano in Hawaii. Ebmeier et al. (2014) confirmed the increased LWP \r and albedo for other volcanoes. In contrast, for the large Holuhraun \r eruption in Iceland, Malavelle et al. (2017) did not find any large-scale \r change in LWP in satellite observations. However, when accounting \r for meteorological conditions, McCoy et al. (2018) concluded that for \r cyclonic conditions, the extra Holuhraun aerosol did enhance LWP. \r Toll et al. (2017) examined a large sample of volcanoes and found \r a distinct albedo effect, but only modest LWP changes, on average. \r [...]"


id: 81693f639a5c77ad06f4a47aea773201
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 19896
content:
"[...] Gryspeerdt et  al. (2019) demonstrated that the negative LWP-Nd\r relationship becomes very small when conditioned on a  volcanic \r eruption, and therefore concluded that LWP adjustments are small \r in most regions. Similarly, Toll et al. (2019) studied clouds downwind \r of various anthropogenic aerosol sources using satellite observations \r and inferred an IRFaci of -0.52 W m-2 that was partly offset by 29% \r due to aerosol-induced LWP decreases."


id: 2073ee7e71226d448776e7065bd81a2e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 2929
content:
"Multiple studies have found a positive relationship between cloud \r fraction and/or cloud LWP and aerosols (e.g.,  Nakajima et  al., \r 2001; Kaufman and Koren, 2006; Quaas et  al., 2009). Since AR5, \r however, it has been documented that factors independent of \r causal aerosol-cloud interactions heavily influence such statistical \r relationships. These include the swelling of aerosols in the high \r relative humidity in the vicinity of clouds (Grandey et al., 2013) and the \r contamination of aerosol retrievals next to clouds by cloud remnants \r and cloud-side scattering (Varnai and Marshak, 2015; Christensen \r et al., 2017). Stratifying relationships by possible influencing factors \r such as relative humidity (Koren et al., 2010) does not yield satisfying \r results since observations of the relevant quantities are not available \r at the resolution and quality required. Another approach to tackle \r this problem was to assess the relationship of cloud fraction with \r [...]"


id: 788300d27e3624b7bd725482984edb2d
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 18402
content:
"[...] droplet concentration (Gryspeerdt et  al., 2016; Michibata et  al., \r 2016; Sato et al., 2018). The relationship between satellite-retrieved \r cloud fraction and Nd was found to be positive (Christensen et al., \r 2016a, 2017; Gryspeerdt et al., 2016), implying an overall adjustment \r that leads to a more negative ERFaci. However, since retrieved Nd is \r biased low for broken clouds this result has been called into question \r (Grosvenor et al., 2018). Zhu et al. (2018) proposed to circumvent \r this problem by considering Nd of only continuous thick cloud covers, "


id: 3b84a8eee3914a9bb9358bfad75e72c2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7654
content:
"Only a handful of studies have estimated the LWP and Cf adjustments \r that are needed for satellite-based estimates of ERFaci. Chen et al. \r (2014) and Christensen et al. (2017) used the relationship between \r cloud fraction and AI to infer the cloud fraction adjustment. \r Gryspeerdt et al. (2017) used a similar approach but tried to account \r for non-causal coorelations between aerosols and cloud fraction by \r using Nd as a mediating factor. These three studies together suggest \r a global Cf adjustment that augments ERFaci relative to IRFaci by \r -0.5 +- 0.4 W m-2 (medium confidence). For global estimates of the \r LWP adjustment, evidence is even scarcer. Gryspeerdt et al. (2019) \r derived an estimate of the LWP adjustment using a method similar \r to Gryspeerdt et al. (2016). They estimated that the LWP adjustment \r offsets 0-60% of the (negative) IRFaci (0.0 to +0.3 W m-2). Supporting \r an offsetting LWP adjustment, Toll et al. (2019) estimated a moderate \r [...]"


id: 354f38bf0bfd7c925827940616e7b1f1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 6629
content:
"[...] LWP adjustment of 29% (+0.15 W m-2). The adjustment due to LWP is \r assessed to be small, with a central estimate and very likely range of \r 0.2 +- 0.2 W m-2, but with low confidence due to the limited number \r of studies available."


id: 357356d8aff0839da014fe8ff1a321a2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 6649
content:
"simulate the historical total aerosol ERF evolution consistent with \r the observed temperature record. Given the lack of temporally \r extensive cooling trends in the 20th-century record and the fact that \r the historical evolution of GHG forcing is relatively well constrained, \r the study concluded that a  more negative total aerosol ERF than \r -1.0 W m-2 was incompatible with the historical temperature record. \r This was countered by Kretzschmar et al. (2017), who argued that \r the model employed in Stevens (2015) was too simplistic to account \r for the effect of geographical redistributions of aerosol emissions \r over time. Following the logic of Stevens (2015), but basing their \r estimates on a subset of CMIP5 models as opposed to a simplified \r modelling framework, Kretzschmar et al. argued that a total aerosol \r ERF as negative as -1.6 W  m-2 was consistent with the observed \r temperature record. Similar arguments were put forward by Booth \r [...]"


id: 2fc6117a92fd4f81a0c3059d94e7a0b4
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 5660
content:
"[...] et al. (2018), who emphasized that the degree of non-linearity of the \r total aerosol ERF with aerosol emissions is a central assumption in \r Stevens (2015)."


id: 389b70b6d43f81035ae6d5ccc5104072
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7164
content:
"When the first top-down estimates emerged (e.g., Knutti et al., 2002), \r it became clear that some of the early ('bottom-up') ESM estimates \r of total aerosol ERF were inconsistent with the plausible top-down \r range. However, as more inverse estimates have been published, it \r has increasingly become clear that they too are model-dependent \r and span a wide range of ERF estimates, with confidence intervals \r that in some cases do not overlap (Forest, 2018). It has also become \r evident that these methods are sensitive to revised estimates of other \r forcings and/or updates to observational datasets. A recent review \r of 19 such estimates reported a mean of -0.77 W m-2 for the total \r aerosol ERF, and a 95% confidence interval of [-1.15 to -0.31] W m-2 \r (Forest, 2018). Adding to that review, a  more recent study using \r the same approach reported an estimate of total aerosol ERF of \r -0.89 [-1.82 to -0.01] W m-2 (Skeie et al., 2018). However, in the \r [...]"


id: eaca70cfc2db01b1c821758a31d26ae
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 38666
content:
"[...] same study, an alternative way of incorporating ocean heat content in \r the analysis produced a total aerosol ERF estimate of -1.34 [-2.20 to \r -0.46] W m-2, illustrating the sensitivity to the manner in which \r observations are included. A new approach to inverse estimates took \r advantage of independent climate radiative response estimates from \r eight prescribed SST and sea ice-concentration simulations over the \r historical period to estimate the total anthropogenic ERF. From this \r a total aerosol ERF of -0.8 [-1.6 to +0.1] W m-2 was derived (valid \r for near-present relative to the late 19th  century). This range was \r found to be more invariant to parameter choices than earlier inverse \r approaches (Andrews and Forster, 2020)."


id: 55d1a39c34e2076d8e72428a66f4ef5a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 12370
content:
"Here, the best estimate and range is revised relative to AR5 (Boucher \r et al., 2013), partly based on updates to the above lines of argument. \r Firstly, the studies that included aerosol effects on mixed-phase \r clouds in AR5 relied on the assumption that anthropogenic black \r carbon (BC) could act as INPs in these clouds, which has since \r been challenged by laboratory experiments (Kanji et  al., 2017; \r Vergara-Temprado et al., 2018). There is no observational evidence of \r appreciable ERFs associated with aerosol effects on mixed-phase and \r ice clouds (Section 7.3.3.2.1), and modelling studies disagree when it \r comes to both their magnitude and sign (Section 7.3.3.2.2). Likewise, \r very few ESMs incorporate aerosol effects on deep convective \r clouds, and cloud-resolving modelling studies report different \r effects on cloud radiative properties depending on environmental \r conditions (Tao et al., 2012). Thus, it is not clear whether omitting \r [...]"


id: df63c9ed7a14ede4e44efab68b90cab8
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36548
content:
"[...] such effects from ESMs would lead to any appreciable ERF biases, \r or if so, what the sign of such biases would be. As a result, all ESMs \r are given equal weight in this assessment. Furthermore, there is now \r a considerably expanded body of literature which suggests that early \r modelling studies that incorporated satellite observations may have \r resulted in overly conservative estimates of the magnitude of ERFaci \r (Section 7.3.3.2.1). Finally, based on an assessment of the longwave \r ERFaci in the CMIP5 models, the offset of +0.2 W m-2 applied in AR5 \r appears to be too large (Heyn et al., 2017). As in AR5, there is still \r reason to question the ability of ESMs to simulate adjustments in \r LWP and cloud cover in response to aerosol perturbation, but it is \r not clear that this will result in biases that exclusively increase the \r magnitude of the total aerosol ERF (Section 7.3.3.2.2)."


id: f384608978e4d5bbcb137dbe9ccc4647
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 40220
content:
"The assessment of total aerosol ERF here uses the following lines of \r evidence: satellite-based evidence for IRFari; model-based evidence \r for IRFari and ERFari; satellite-based evidence of IRFaci and ERFaci; \r and finally model-based evidence for ERFaci. Based on this, ERFari and \r ERFaci for 2014 relative to 1750 are assessed to be -0.3 +- 0.3 W m-2\r and -1.0 +- 0.7 W m-2, respectively. There is thus strong evidence \r for a substantive negative total aerosol ERF, which is supported by \r the broad agreement between observation-based and model-based \r lines of evidence for both ERFari and ERFaci that has emerged since \r AR5 (Gryspeerdt et  al., 2020). However, considerable uncertainty \r remains, particularly with regards to the adjustment contribution \r to ERFaci, as well as missing processes in current ESMs, notably \r aerosol effects on mixed-phase, ice and convective clouds. This leads \r to a medium confidence in the estimate of ERFari+aci and a slight \r [...]"


id: a465ac3e9aa2dd73c835d6765ae4ba15
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 26129
content:
"[...] narrowing of the uncertainty range. Because the estimates informing \r the different lines of evidence are generally valid for approximately \r 2014 conditions, the total aerosol ERF assessment is considered valid \r for 2014 relative to 1750."


id: 2640a862dd45aa03f205b151e46c2ac3
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 3900
content:
"Land-use forcing is defined as those changes in land-surface \r properties directly caused by human activity rather than by climate \r processes (see also Section  2.2.7). Land-use change affects the \r surface albedo. For example, deforestation typically replaces darker \r forested areas with brighter cropland, and thus imposes a negative \r radiative forcing on climate, while afforestation and reforestation \r can have the opposite effect. Precise changes depend on the nature \r of the forest, crops and underlying soil. Land-use change also affects \r the amount of water transpired by vegetation (Devaraju et al., 2015). \r Irrigation of land directly affects evaporation (Sherwood et  al., \r 2018), causing a  global increase of 32,500 m3 s-1 due to human \r activity. Changes in evaporation and transpiration affect the latent \r heat budget, but do not directly affect the top-of-atmosphere (TOA) \r radiative fluxes. The lifetime of water vapour is so short that the \r [...]"


id: a61d097e937949c416ff06a9fc9ce049
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 26445
content:
"[...] effect of changes in evaporation on the greenhouse contribution \r of water vapour are negligible (Sherwood et  al., 2018). However, \r evaporation can affect the ERF through adjustments, particularly \r through changes in low-cloud amounts. Land management affects \r the emissions or removal of GHGs from the atmosphere (such as \r CO2, CH4, N2O). These emissions changes have the greatest effect on \r climate (Ward et al., 2014), however they are already included in GHG \r inventories. Land-use change also affects the emissions of dust and \r biogenic volatile organic compounds (BVOCs), which form aerosols \r and affect the atmospheric concentrations of ozone and methane \r (Section 6.2.2). The effects of land use on surface temperature and \r hydrology were recently assessed in SRCCL (Jia et al., 2019)."


id: d4f866ade6a55386ac7bd434017754bf
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34793
content:
"Using the definition of ERF from Section  7.1, the adjustment in \r land-surface temperature is excluded from the definition of ERF, \r but changes in vegetation and snow cover (resulting from land-use \r change) are included (Boisier et al., 2013). Land-use change in the \r mid-latitudes induces a substantial amplifying adjustment in snow \r cover. Few climate model studies have attempted to quantify the \r ERF of land-use change. T. Andrews et al. (2017) calculated a very \r large surface albedo ERF (-0.47 W m-2) from 1860 to 2005 in the \r HadGEM2-ES model, although they did not separate out the surface \r albedo change from snow cover change. HadGEM2-ES is known \r to overestimate the amount of boreal trees and shrubs in the \r unperturbed state (Collins et al., 2011) so will tend to overestimate \r the ERF associated with land-use change. The increases in dust in \r HadGEM2-ES contributed an extra -0.25 W m-2, whereas cloud cover \r [...]"


id: ea6cb6bdcef9163eed78df13e1b91756
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 38607
content:
"in the 200-400 nanometre (nm) band than for the broad visible/\r infrared band that dominates TSI (Krivova et al., 2006). The IRF can be \r derived simply by DTSI x (1 - albedo)/4 irrespective of wavelength, \r where the best estimate of the planetary albedo is usually taken to \r be 0.29 and DTSI represents the change in total solar irradiance \r (Stephens et al., 2015). (The factor 4 arises because TSI is per unit \r area of Earth  cross section presented to the Sun and IRF is per \r unit area of Earth's surface). The adjustments are expected to be \r wavelength dependent. Gray et al. (2009) determined a stratospheric \r temperature adjustment of -22% to spectrally resolved changes in \r the solar radiance over one solar cycle. This negative adjustment is \r due to stratospheric heating from increased absorption by ozone at \r the short wavelengths, increasing the outgoing longwave radiation \r to space. A multi-model comparison (Smith et al., 2018b) calculated \r [...]"


id: a02d89958b30b20ea1f61f88fcf23eb8
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 25393
content:
"[...] adjustments of -4% due to stratospheric temperatures and -6% due \r to tropospheric processes (mostly clouds), for a change in TSI across \r the spectrum (Figure 7.4). The smaller magnitude of the stratospheric \r temperature adjustment is consistent with the broad spectral change \r rather than the shorter wavelengths characteristic of solar variation. \r A single-model study also found an adjustment that acts to reduce \r the forcing (Modak et  al., 2016). While there has not yet been \r a  calculation based on the appropriate spectral change, the -6% \r tropospheric adjustment from Smith et al. (2018b) is adopted along \r with the Gray et  al. (2009) stratospheric temperature adjustment. \r The ERF due to solar variability over the historical period is therefore \r represented by 0.72 x DTSI x (1 - albedo)/4 using the TSI timeseries \r from Chapter 2 (Section 2.2.1)."


id: d507b54e24b7f3d451de46d5c04bcc9c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34803
content:
"Most radiative forcing estimates have a  regional emphasis. \r The regional focus makes estimating a global mean radiative forcing \r from aggregating different studies challenging, and the relative \r importance of each region is expected to change if the global pattern \r of emissions sources changes (Bauer et al., 2013). The lower bound \r of the assessed range of BC on snow and ice is extended to zero \r to encompass Lin et al. (2014), with the best estimate unchanged, \r resulting in 0.04 [0.00 to 0.09] W m-2. The efficacy of BC on snow \r forcing was estimated to be 2 to 4 times as large as for an equivalent \r CO2 forcing as the effects are concentrated at high latitudes in the \r cryosphere (Bond et al., 2013). However, it is unclear how much of \r this effect is due to radiative adjustments leading to a higher ERF, \r and how much comes from a less negative feedback a due to the \r high-latitude nature of the forcing. To estimate the overall ERF, \r [...]"


id: 7c9e1b423f36cf9e3e8044d66a673b60
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 19122
content:
"[...] the IRF is doubled assuming that part of the increased efficacy is due \r to adjustments. This gives an overall assessed ERF of +0.08 [0.00 to \r 0.18] W m-2, with low confidence."


id: 583f30b807e9ae4285ad1f17488e0f1e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 12780
content:
"In this assessment the TSI from the Paleoclimate Model \r Intercomparison Project Phase 4 (PMIP4) reconstruction is used \r (Section 2.2.1; Jungclaus et al., 2017). Proxies constructed from the \r 14C and 10Be radiogenic records for the SATIRE-M model (Vieira et al., \r 2011) and 14C record for the PMOD model (Shapiro et  al., 2011) \r for the 1745 solar minimum provide ERFs for 1745-2008 of -0.01, \r -0.02 and 0.00 W m-2 respectively. An independent dataset from the \r National Oceanic and Atmospheric Administration's Climate Data \r Record (Coddington et  al., 2016; Lean, 2018) provides an ERF for \r 1745-2008 of +0.03 W m-2. One substantially higher ERF estimate of \r +0.35 W m-2 derived from TSI reconstructions is provided by Egorova \r et  al. (2018). However, the estimate from Egorova et  al. (2018) \r hinges on assumptions about long-term changes in the quiet Sun \r for which there is no observed evidence. Lockwood and Ball (2020) \r [...]"


id: 848645c1297e1cd318be625cb889be1d
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 20460
content:
"[...] analysed the relationship between observed changes in cosmic ray \r fluxes and recent, more accurate, TSI data and derived ERF between \r -0.01 and +0.02 W m-2, and Yeo et al. (2020) modelling showed the "


id: b214941bcf7c092e042a06520b1af7d7
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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vector_id: 28560
content:
"There is large episodic negative radiative forcing associated with \r sulphur dioxide (SO2) being ejected into the stratosphere from \r explosive volcanic eruptions, accompanied by more frequent smaller \r eruptions (Figure  2.2 and Cross-Chapter Box  4.1). From SO2 gas, \r reflective sulphate aerosol is formed in the stratosphere where it may \r persist for months to years, reducing the incoming solar radiation. \r The volcanic SARF in AR5 (Myhre et  al., 2013b) was derived by \r scaling the stratospheric aerosol optical depth (SAOD) by a factor of \r -25 W m-2 per unit SAOD from Hansen et al. (2005b). Quantification \r of the adjustments to SAOD perturbations from climate model \r simulations have determined a significant positive adjustment driven \r by a reduction in cloud amount (Figure 7.4; Marshall et al., 2020). \r Analysis of CMIP5 models provides a mean ERF of -20 W m-2 per \r unit SAOD (Larson and Portmann, 2016). Single-model studies with \r [...]"


id: 7fbd274549c2c2587db8e67e66e3bee3
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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content:
"[...] successive generations of Hadley Centre climate models produce \r estimates between -17 and -19 W m-2 per unit SAOD (Gregory et al., \r 2016; Marshall et al., 2020), with some evidence that ERF may be non\u0002linear with SAOD for large eruptions (Marshall et al., 2020). Analysis \r of the volcanically active periods of 1982-1985 and 1990-1994 using \r the CESM1(WACCM) aerosol-climate model provided an SAOD\u0002to-ERF relationship of -21.5 (+- 1.1) W m-2 per unit SAOD (Schmidt \r et al., 2018). Volcanic SO2 emissions may contribute a positive forcing \r through effects on upper tropospheric ice clouds, due to additional \r ice nucleation on volcanic sulphate particles (Friberg et  al., 2015; \r Schmidt et  al., 2018), although one observational study found no \r significant effect (Meyer et  al., 2015). Due to low agreement, the \r contribution of sulphate aerosol effects on ice clouds to volcanic ERF \r is not included in the overall assessment."


id: 4f084e2f10db3d73cd4aef62fa40f974
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 11193
content:
"Material 7.SM.2). The TCR has an ensemble median value of 1.81degC, \r in good agreement with Section  7.5.5. Two error bars are shown \r in Figure  7.7. The dashed error bar shows the contribution of ERF \r uncertainty (as assessed in the subsections of Section 7.3) employing \r the best estimate of climate response with an ECS of 3.0degC. The solid \r bar is the total response uncertainty using the Section 7.5.5 assessment \r of ECS. The uncertainty in the historical temperature contributions \r ofthe different forcing agents is mostly due to uncertainties in ERF, \r yet for the WMGHG the uncertainty is dominated by the climate \r response as its ERF is relatively well known (Figure 7.7). From the \r assessment of emulator responses in Cross-Chapter Box 7.1, there is \r high confidence that calibrated emulators such as the one employed \r here can represent the historical GSAT change between 1850-1900 \r and 1995-2014 to within 5% for the best estimate and 10% for the \r [...]"


id: 56fa62a4d103d60e98fc94c8e3349e37
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 12551
content:
"[...] very likely range (Supplementary Material, Table 7.SM.4). This gives \r high confidence in the overall assessment of GSAT change for the \r response to ERFs over 1750-2019 derived from the emulator."


id: cc69e6b5530700940380bb0261b7b7de
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 33281
content:
"These ERF timeseries are combined with a  two-layer emulator \r (Cross-Chapter Box 7.1 and Supplementary Material 7.SM.2) using \r a  2237-member constrained Monte Carlo sample of both forcing \r uncertainty (by sampling ERF ranges) and climate response (by \r sampling ECS, TCR and ocean heat capacity ranges). The net model \r warming over the historical period is matched to the assessment \r of historical GSAT warming from 1850-1900 to 1995-2014 of \r 0.85 [0.67 to 0.98] degC (Cross-Chapter Box 2.3) and ocean heat content \r change from 1971 to 2018 (Section  7.2.2.2). Therefore the model \r gives the breakdown of the GSAT trend associated with different \r forcing mechanisms that are consistent with the overall GSAT \r change. The model assumes that there is no variation in feedback \r parameter across forcing mechanisms (Section 7.3.1) and variations \r in the effective feedback parameter over the historical record \r (Section 7.4.4). The distribution of ECS was informed by Section 7.5.5 \r [...]"


id: 1bf6a6490f67624747e9ca9afc895094
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 2106
content:
"[...] and chosen to approximately maintain the best estimate and likely/\r very likely ranges assessed in that section (see also Supplementary "


id: eab10e89496f13c5c1b94f766a767e6f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 38650
content:
"climate models have a long history of use in previous IPCC reports (Section 1.5.3.4). Climate model emulators can include carbon and \r other gas cycles and can combine uncertainties along the cause-effect chain, from emissions to temperature response. AR5 (M. Collins \r et al., 2013) used the MAGICC6 emulator (Meinshausen et al., 2011a) in a probabilistic setup (Meinshausen et al., 2009) to explore \r the uncertainty in future projections. A simple impulse response emulator (Good et al., 2011) was also used to ensure a consistent \r set of ESM projections could be shown across a range of scenarios. Chapter 8 in AR5 WGI (Myhre et al., 2013b) employed a two-layer \r emulator for quantifying global temperature-change potentials (GTP). In AR5 WGIII (Clarke et al., 2014), MAGICC6 was also used \r for the classification of scenarios, and in AR5 Synthesis Report (IPCC, 2014) this information was used to estimate carbon budgets. \r [...]"


id: 2cf45449985d1be04d5a65ec9105b060
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 5180
content:
"[...] In SR1.5, two emulators were used to provide temperature projections of scenarios: the MAGICC6 model, which was used for the \r scenario classification, and the FaIR1.3 model (Millar et al., 2017; Smith et al., 2018a)."


id: c14deef0ac235088e2d41447066fbec4
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 31309
content:
"The SR1.5 found that the physically based emulators produced different projected non-CO2 forcing and identified the largely \r unexplained differences between the two emulators used as a  key knowledge gap (Forster et  al., 2018). This led to a  renewed \r effort to test the skill of various emulators. The Reduced Complexity Model Intercomparison Project (RCMIP; Nicholls et al., 2020) \r found that the latest generation of the emulators can reproduce key characteristics of the observed changes in global surface air \r temperature (GSAT) together with other key responses of ESMs (Cross-Chapter Box 7.1, Figure 1a). In particular, despite their reduced \r structural complexity, some emulators are able to replicate the non-linear aspects of ESM GSAT response over a range of scenarios. \r GSAT emulation has been more thoroughly explored in the literature than other types of emulation. Structural differences between \r [...]"


id: 867f80e5d4a62936da716ebdbbba8c1f
created_at: 2023-04-19 13:22:38
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vector_id: 20833
content:
"[...] emulation approaches lead to different outcomes and there are problems with emulating particular ESMs. In conclusion, there is \r medium confidence that emulators calibrated to single ESM runs can reproduce ESM projections of the forced GSAT response to \r other similar emissions scenarios to within natural variability (Meinshausen et al., 2011b; Geoffroy et al., 2013a; Dorheim et al., 2020; \r Nicholls et al., 2020; Tsutsui, 2020), although larger differences can remain for scenarios with very different forcing characteristics. \r For variables other than GSAT there has not yet been a comprehensive effort to evaluate the performance of emulators."


id: 99ae10999ee5fb17fb358f7e69333a51
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 24269
content:
"Cross-Chapter Box 7.1 Table 1 shows the use of emulators within the WGI Report. The main use of emulation in the Report is to estimate \r GSAT change from effective radiative forcing (ERF) or concentration changes, where various versions of a two-layer energy budget \r emulator are used. The two-layer emulator is equivalent to a two-timescale impulse-response model (Supplementary Material 7.SM.2; \r Geoffroy et al., 2013b). Both a single configuration version and probabilistic forms are used. The emulator is an extension of the energy \r budget equation (Box 7.1, Equation 7.1) and allows for heat exchange between the upper- and deep-ocean layers, mimicking the \r ocean heat uptake that reduces the rate of surface warming under radiative forcing (Gregory, 2000; Held et al., 2010; Winton et al., \r 2010; Armour, 2017; Mauritsen and Pincus, 2017; Rohrschneider et al., 2019). Although the same energy budget emulator approach \r [...]"


id: 16e37f6695d04875004a363b5a031b54
created_at: 2023-04-19 13:22:38
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content:
"[...] is used, different calibrations are employed in various sections, to serve different purposes and keep lines of evidence as independent \r as possible. Chapter 9 additionally employs projections of ocean heat content from the Chapter 7 two-layer emulator to estimate the \r thermostatic component of future sea level rise (Section 9.6.3 and Supplementary Material 7.SM.2)."


id: c1ae0392808204a37c1487259d3fbc91
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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content:
"Emissions-driven emulators (as opposed to ERF-driven or concentration-driven emulators) are also used in the Report. In Chapter 4 \r (Section 4.6) MAGICC7 is used to emulate GSAT beyond 2100 since its long-term response has been assessed to be fit-for-purpose \r to represent the behaviour of ESMs. In Chapter  5 (Section  5.5) MAGICC7 is used to explore the non-CO2 GSAT contribution in \r emissions scenarios. In Chapter 6 and Chapter 7 (Section 7.6), two-layer model configurations are tuned to match the probabilistic \r GSAT responses of FaIRv1.6.2 and MAGICC7 emissions-driven emulators. For Chapter 6 the two median values from FaIRv1.6.2 and \r MAGICC7 emulators are averaged and then matched to the best-estimate ECS of 3degC and TCR of 1.8degC (Tables 7.13 and 7.14) under \r the best-estimate ERF due to a doubling of CO2 of 3.93 W m-2 (Table 7.4). For Section 7.6 a distribution of responses is used from the \r [...]"


id: fffc24a6972333fe86ad0a34ea6a3492
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 42429
content:
"Four emissions-driven emulators have been considered as tools for WGIII to explore the range of GSAT response to multiple scenarios \r beyond those assessed in WGI. The four emulators are CICERO-SCM (Skeie et al., 2017, 2021), FaIRv1.6.2 (Millar et al., 2017; Smith \r et al., 2018a), MAGICC7 (Meinshausen et al., 2009) and OSCARv3.1.1 (Gasser et al., 2017a, 2020). Each emulator's probabilistic \r distribution has been calibrated to capture the relationship between emissions and GSAT change. The calibration is informed by \r the WGI assessed ranges of ECS, TCR, historical GSAT change, ERF, carbon cycle metrics and future warming projections under the \r (concentration-driven) SSP scenarios. The emulators are then provided as a tool for WGIII to perform a GSAT-based classification of \r mitigation scenarios consistent with the physical understanding assessed in WGI. The calibration step reduced the emulator differences \r [...]"


id: 1252952ddf344d6d7c07d2f7e7cf5fb6
created_at: 2023-04-19 13:22:38
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vector_id: 427
content:
"[...] identified in SR1.5. Note that evaluation of both central and range estimates of each emulator's probabilistic projections is important \r to assess the fitness-for-purpose for the classification of scenarios in WGIII, based on information beyond the central estimate of \r GSAT warming."


id: fc37e982a21afc499c07648f066000
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41741
content:
"MAGICC7 and FaIRv1.6.2 emissions-based emulators are able to represent the WGI assessment to within small differences (defined \r here as within typical rounding precisions of +-5% for central estimates and +-10% for ranges) across more than 80% of metric ranges \r (Cross-Chapter Box 7.1, Table 2). Both calibrated emulators are consistent with assessed ranges of ECS, historical GSAT, historical \r ocean heat uptake, total greenhouse gas ERF, methane ERF and the majority of the assessed SSP warming ranges. FaIRv1.6.2 also \r matches the assessed central value of TCRE and airborne fraction. Whereas, MAGICC7 matches the assessed TCR ranges as well as \r providing a closer fit to the SSP warming ranges for the lower-emissions scenarios. In the evaluation framework considered here, \r CICERO-SCM represents historical warming to within 2% of the assessed ranges and also represents future temperature ranges \r [...]"


id: 8069d54eff5474dfb8594b437ee099a2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 19722
content:
"[...] across the majority of the assessment, although it lacks the representation of the carbon cycle. In this framework, OSCARv3.1.1 is less \r able to represent the assessed projected GSAT ranges although it matches the range of airborne fraction estimates closely and the \r assessed historical GSAT likely range to within 0.5%. Despite these identified limitations, both CICERO-SCM and OSCARv3.1.1 provide \r additional information for evaluating the sensitivity of scenario classification to model choice."


id: 8351a54d01c2cf26cfcf406e182d518e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 20235
content:
"Cross-Chapter Box 7.1, Figure 1 | A comparison between the global surface air temperature (GSAT) response of various calibrated simple \r climate models, assessed ranges and Earth system models (ESMs). (a) and (b) compare the assessed historical GSAT time series (Section 2.3.1) with four \r multi-gas emulators calibrated to replicate numerous assessed ranges (panel (a); Cross-Chapter Box 7.1, Table 2) and also compares idealized CO2-only concentration \r scenario response for one ESM (IPSL CM6A-LR) and multiple emulators which participated in RCMIP Phase 1 (Nicholls et al., 2020) calibrated to that single ESM \r (panel (b)). (c) and (d) compare this Report's assessed ranges for GSAT warming (Box 4.1) under the multi-gas scenario SSP1-2.6 with the same calibrated emulators \r as in (a). For context, a range of CMIP6 ESM results are also shown (thin lines in (c) and open circles in (d)). Panel (b) adapted from Nicholls et al. (2020). Further \r [...]"


id: 627126cd0237e8327918888de7e7b50c
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 14598
content:
"[...] details on data sources and processing are available in the chapter data table (Table 7.SM.14)."


id: 1f73b8682b072afcd8687f2e753dcea1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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vector_id: 2739
content:
"Notes. Metrics calibrated against are equilibrium climate sensitivity, ECS (Section 7.5); transient climate response to cumulative CO2 emissions, TCRE (Section 5.5); \r transient climate response, TCR (Section  7.5), historical GSAT change (Section  2.3); ocean heat uptake (Sections 7.2 and 2.3); effective radiative forcing, ERF \r (Section 7.3); carbon cycle metrics, namely airborne fractions of idealized CO2 scenarios (taking the likely range as twice the standard deviation across the models \r analysed in Arora et al. (2020; see also Table 5.7, 'cross-AR6 lines of evidence' row); and GSAT projections under the concentration-driven SSP scenarios for the near \r term (2021-2040), mid-term (2041-2060) and long term (2081-2100) relative to 1995-2014 (Table 4.2). See Supplementary Material, Table 7.SM.4 for a version \r of this table with the absolute values rather than percentage differences. The columns labelled 'upper' and 'lower' indicate 5-95% ranges, except for the variables \r [...]"


id: 5ef8e725802ce5c6f1cef66f152c125c
created_at: 2023-04-19 13:22:38
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content:
"[...] demarcated with an asterisk or double asterisk (* or **), where they denote likely ranges from 17-83%. Note that the TCRE assessed range (**) is wider than the \r combination of the TCR and airborne fraction to account for uncertainties related to model limitations (Table 5.7) hence it is expected that the emulators are too \r narrow on this particular metric and/or too wide on TCR and airborne fraction. For illustrative purposes, the cells are coloured as follows: white cells indicate small \r differences (up to +-5% for the central value and +10% for the ranges), light blue and light yellow cells indicate medium differences (up to +10% and -10% for light \r blue and light yellow for central values, respectively; up to +-20% for the ranges) and darker cells indicate larger positive (blue) or negative (yellow) differences. Note \r that values are rounded after the colours are applied."


id: eeb561dc90ba48e839b3e26d81274f30
created_at: 2023-04-19 13:22:38
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content_type: text
index: climate_gpt_v2
vector_id: 39380
content:
"The magnitude of global surface temperature change primarily \r depends on the strength of the radiative forcings and feedbacks, \r the latter defined as the changes of the net energy budget at the \r top-of-atmosphere (TOA) in response to a change in the GSAT (Box 7.1, \r Equation 7.1). Feedbacks in the Earth system are numerous, and it can \r be helpful to categorize them into three groups: (i) physical feedbacks; \r (ii) biogeophysical and biogeochemical feedbacks; and (iii) long-term \r feedbacks associated with ice sheets. The physical feedbacks (e.g., those \r associated with changes in lapse rate, water vapour, surface albedo, or \r clouds; Sections 7.4.2.1-7.4.2.4) and biogeophysical/biogeochemical \r feedbacks (e.g., those associated with changes in methane, aerosols, \r ozone, or vegetation; Section 7.4.2.5) act both on time scales that are \r used to estimate the equilibrium climate sensitivity (ECS) in models \r (typically 150 years, see Box 7.1) and on longer time scales required \r [...]"


id: 5ce5b19100944b7a188b0bee6c0e1291
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 13610
content:
"[...] to reach equilibrium. Long-term feedbacks associated with ice sheets \r (Section  7.4.2.6) are relevant primarily after several centuries or \r more. The feedbacks associated with biogeophysical/biogeochemical \r processes and ice sheets, often collectively referred to as Earth system \r feedbacks, had not been included in conventional estimates of the \r climate feedback (e.g., Hansen et al., 1984), but the former can now \r be quantified and included in the assessment of the total (net) climate \r feedback. Feedback analysis represents a formal framework for the \r quantification of the coupled interactions occurring within a complex \r Earth system in which everything influences everything else (e.g., Roe, \r 2009). As  used here (as presented in Section  7.4.1), the primary \r objective of feedback analysis is to identify and understand the key \r processes that determine the magnitude of the surface temperature \r response to an external forcing. For each feedback, the basic underlying \r [...]"


id: 8808f166868dfa062d01a599f70b549b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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vector_id: 21083
content:
"In global climate models, the feedback parameters ax in global \r warming conditions are often estimated as the mean differences in \r the radiative fluxes between atmosphere-only simulations in which \r the change in SST is prescribed (Cess et al., 1990), or as the regression \r slope of change in radiation flux against change in GSAT using \r atmosphere-ocean coupled simulations with abrupt CO2 changes \r (abrupt4xCO2) for 150 years (Box 7.1; Gregory et al., 2004; Andrews \r et  al., 2012; Caldwell et  al., 2016). Neither method is perfect, but \r both are useful and yield consistent results (Ringer et al., 2014). In \r the regression method, the radiative effects of land warming are \r excluded from the ERF due to doubling of CO2 (Section 7.3.2), which \r may overestimate feedback values by about 15%. At the same time, \r the feedback calculated using the regression over years 1-150 ignores \r its state-dependence on multi-centennial time scales (Section 7.4.3), \r [...]"


id: 4486a8c032afcb469c1464866760c88d
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vector_id: 9287
content:
"[...] probably giving an underestimate of a  by about 10% (Rugenstein \r et al., 2019). These effects are both small and approximately cancel \r each other in the ensemble mean, justifying the use of regression over \r 150 years as an approximation to feedbacks in ESMs."


id: d01a0a41743e34f6711d90dd8d927bd5
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 33967
content:
"The Planck response represents the additional thermal or longwave (LW) \r emission to space arising from vertically uniform warming of the surface \r and the atmosphere. The Planck response aP, often called the Planck \r feedback, plays a fundamental stabilizing role in Earth's climate and has \r a value that is strongly negative: a warmer planet radiates more energy \r to space. A  crude estimate of aP can be made using the normalized \r greenhouse effect g, defined as the ratio between the greenhouse effect \r G and the upwelling LW flux at the surface (Raval and Ramanathan, \r 1989). Current estimates (Section 7.2, Figure 7.2) give G = 159 W m-2 and \r g 0.4. Assuming gis constant, one obtains for a surface temperature \r Ts = 288 K, aP = (g - 1) 4 s T 3\r s  -3.3 W m-2 degC-1, where s is the Stefan-\r Boltzmann constant. This parameter aP is estimated more accurately \r using kernels obtained from meteorological reanalysis or climate \r [...]"


id: a617b5f2c2451bb37da52ab0b741e563
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content:
"[...] simulations (Soden and Held, 2006; Dessler, 2013; Vial et  al., 2013; \r Caldwell et al., 2016; Colman and Hanson, 2017; Zelinka et al., 2020). \r Discrepancies among estimates primarily arise because differences \r in cloud distributions make the radiative kernels differ (Kramer et al., \r 2019). Using six different kernels, Zelinka et  al. (2020) obtained \r a  spread of +-0.1 W m-2 degC-1 (one standard deviation). Discrepancies \r among estimates secondarily arise from differences in the pattern of \r equilibrium surface temperature changes among ESMs. For the CMIP5 \r and CMIP6 models this introduces a  spread of +-0.04  W  m-2  degC-1\r (one  standard deviation). The multi-kernel and multi-model mean of \r aP is equal to -3.20 W m-2 degC-1 for the CMIP5 and -3.22 W m-2 degC-1\r for the CMIP6 models (Supplementary Material, Table 7.SM.5). Overall, \r there is high confidence in the estimate of the Planck response, which is \r [...]"


id: 9ef3b1c195b1062209c24d23162928da
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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vector_id: 25194
content:
"[...] assessed to be aP = -3.22 W m-2 degC-1 with a very likely range of -3.4 to \r -3.0 W m-2 degC-1 and a likely range of -3.3 to -3.1 W m-2 degC-1."


id: 10c9ff54aebd27cfab0e698f16e155db
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 140
content:
"Feedback parameters from lines of evidence other than global \r models are estimated in various ways. For example, observational \r data combined with GCM simulations could produce an emergent \r constraint on a  particular feedback (Hall and Qu, 2006; Klein and \r Hall, 2015), or the observed interannual fluctuations in the global \r mean TOA radiation and the surface air temperature, to which the \r linear regression analysis is applied, could generate a direct estimate \r of the climate feedback, assuming that the feedback associated with \r internal climate variability at short time scales can be a surrogate \r of the feedback to CO2-induced warming (Dessler, 2013; Loeb et al., \r 2016). The assumption is not trivial, but can be justified given that the \r climate feedbacks are fast enough to occur at the interannual time \r scale. Indeed, a  broad agreement has been obtained in estimates \r of individual physical climate feedbacks based on interannual \r [...]"


id: 2a45112bd718196195968c1bd68b6bf1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 4656
content:
"[...] variability and longer climate change time scales in GCMs (Zhou \r et al., 2015; Colman and Hanson, 2017). This means that the climate \r feedbacks estimated from the observed interannual fluctuations are \r representative of the longer-term feedbacks (decades to centuries). \r Care must be taken for these observational estimates because they \r can be sensitive to details of the calculation such as data sets and \r periods used (Dessler, 2013; Proistosescu et al., 2018). In particular, \r there would be a dependence of physical feedbacks on the surface \r warming pattern at the interannual time scale due, for example, to \r El Nino-Southern Oscillation. However, this effect both amplifies and \r suppresses the feedback when data include the positive and negative \r phases of the interannual fluctuation, and therefore the net bias will \r be small."


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vector_id: 8618
content:
"The second decomposition was proposed by Held and Shell (2012) to \r separate the response that would occur under the assumption that \r relative humidity remains constant from that due to the change in \r relative humidity. The feedback is decomposed into three: (i) change \r in  water vapour due to an identical temperature increase at the \r surface and throughout the troposphere assuming constant relative \r humidity, which will be called the Clausius-Clapeyron (CC) feedback \r here; (ii) change in LR assuming constant relative humidity (LR*); \r (iii) change in relative humidity (RH). Since AR5 it has been clarified \r that by construction, the sum of the temperature lapse rate and specific \r humidity (LR + WV) feedbacks is equal to the sum of the Clausius-\r Clapeyron feedback, the lapse rate feedback assuming constant \r relative humidity, and the feedback from changes in relative humidity \r (that is, CC + LR* + RH). Therefore, each of these two sums may \r [...]"


id: e8c02fb883d9233e89a8c4f778da216d
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 38296
content:
"The specific humidity (WV) feedback, also known as the water-vapour \r feedback, quantifies the change in radiative flux at the TOA due to \r changes in atmospheric water vapour concentration associated with \r a change in global mean surface air temperature. According to theory, \r observations and models, the water vapour increase approximately \r follows the Clausius-Clapeyron relationship at the global scale \r with regional differences dominated by dynamical processes \r (Section 8.2.1; Sherwood et al., 2010a; Chung et al., 2014; Romps, \r 2014; R. Liu et al., 2018; Schroder et al., 2019). Greater atmospheric \r water vapour content, particularly in the upper troposphere, results in \r enhanced absorption of LW and SW radiation and reduced outgoing \r radiation. This is a  positive feedback. Atmospheric moistening \r has been detected in satellite records (Section  2.3.1.3.3), it is \r simulated by climate models (Section  3.3.2.2), and the estimates \r [...]"


id: f88b6617b47676265b0d5b5012119bf2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41087
content:
"[...] agree within model and observational uncertainty (Soden et  al., \r 2005; Dessler, 2013; Gordon et  al., 2013; Chung et  al., 2014). \r The estimate of this feedback inferred from satellite observations is \r aWV = 1.85 +- 0.32 W m-2 degC-1 (R. Liu et al., 2018). This is consistent \r with the value aWV= 1.77 +- 0.20 W m-2 degC-1 (one standard deviation) \r obtained with CMIP5 and CMIP6 models (Zelinka et al., 2020)."


id: 486142695d95e43fe74011b4f1f3469e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 9947
content:
"The CC feedback has a large positive value due to well understood \r thermodynamic and radiative processes: aCC= 1.36 +- 0.04 W m-2 degC-1\r (one standard deviation; Held and Shell, 2012; Zelinka et al., 2020). \r The lapse-rate feedback assuming a constant relative humidity (LR*) in \r CMIP6 models has small absolute values (aLR* = -0.10 +- 0.07 W m-2 degC-1\r (one standard deviation)), as expected from theoretical arguments \r (Ingram, 2010, 2013). It includes the pattern effect of surface warming \r that modulates the lapse rate and associated specific humidity \r changes (Po-Chedley et al., 2018b). The relative humidity feedback is \r close to zero (aRH = 0.00 +- 0.06 W m-2 degC-1 (one standard deviation)) \r and the spread among models is confined to the tropics (Sherwood \r et  al.,  2010b; Vial et  al., 2013; Takahashi et  al., 2016; Po-Chedley \r et al., 2018b). The change in upper tropospheric RH is closely related \r to model representation of current climate (Sherwood et al., 2010b; \r [...]"


id: 256eff3ff32068ec1043a4fe2165f4ee
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 3771
content:
"[...] Po-Chedley et  al., 2019), and a  reduction in model RH biases is \r expected to reduce the uncertainty of the RH feedback. At interannual \r time scales, it has been shown that the change in RH in the tropics is \r related to the change of the spatial organization of deep convection \r (Holloway et al., 2017; Bony et al., 2020)."


id: 8e0472ead5150209a622b2db47b6b437
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 22180
content:
"The lapse-rate (LR) feedback quantifies the change in radiative flux \r at the TOA due to a nonuniform change in the vertical temperature \r profile. In the tropics, the vertical temperature profile is mainly driven \r by moist convection and is close to a moist adiabat. The warming is \r larger in the upper troposphere than in the lower troposphere (Manabe \r and Wetherald, 1975; Santer et al., 2005; Bony et al., 2006), leading \r to a  larger radiative emission to space and therefore a  negative \r feedback. This larger warming in the upper troposphere than at \r the surface has been observed over the last 20 years thanks to the \r availability of sufficiently accurate observations (Section 2.3.1.2.2). \r In the extratropics, the vertical temperature profile is mainly driven \r by a balance between radiation, meridional heat transport and ocean \r heat uptake (Rose et al., 2014). Strong winter temperature inversions \r lead to warming that is larger in the lower troposphere (Payne et al., \r [...]"


id: dc9cee927b68b3a8289d2ead1ee71f4f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36079
content:
"[...] 2015; Feldl et al., 2017a) and a positive LR feedback in polar regions \r (Section 7.4.4.1; Manabe and Wetherald, 1975; Bintanja et al., 2012; \r Pithan and Mauritsen, 2014). However, the tropical contribution \r dominates, leading to a negative global mean LR feedback (Soden \r and Held, 2006; Dessler, 2013; Vial et al., 2013; Caldwell et al., 2016). "


id: 2b9f02fe412af8c7ab84e5c9df14efa1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 4927
content:
"CMIP5 and CMIP6 models show moderate spread in global aA, \r determined from century time scale changes (Qu and Hall, 2014; \r Schneider et  al., 2018; Thackeray and Hall, 2019; Zelinka et  al., \r 2020), owing to variations in modelled sea ice loss and snow cover \r response in boreal forest regions. The multi-model mean global-scale \r aA (from all contributions) over the 21st century in CMIP5 models \r under the RCP8.5 scenario was derived by Schneider et  al. (2018) \r to be 0.40 +- 0.10 W m-2 degC-1 (one standard deviation). Moreover, \r they found that modelled aA does not decline over the 21st century, \r despite large losses of snow and sea ice, though a weakened feedback \r is apparent after 2100. Using the idealized abrupt4xCO2, as for the \r other feedbacks, the estimate of the global-scale albedo feedback in \r the CMIP5 models is 0.35 +- 0.08 W m-2 degC-1 (one standard deviation; \r Vial et  al., 2013; Caldwell et  al., 2016). The CMIP6 multi-model \r [...]"


id: c513821d79397574245b7a83ac747b07
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 31984
content:
"[...] mean varies from 0.3 to 0.5 W m-2 degC-1 depending on the kernel \r used (Zelinka et al., 2020). Donohoe et al. (2020) derived a multi\u0002model mean aA and its inter-model spread of 0.37 +- 0.19 W m-2 degC-1\r from the CMIP5 abrupt4xCO2 ensemble, employing model-specific \r estimates of atmospheric attenuation and thereby avoiding bias \r associated with use of a single radiative kernel."


id: d187527b4e644581f6a3b81d98115c48
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34215
content:
"Surface albedo is determined primarily by reflectance at Earth's surface, \r but also by the spectral and angular distribution of incident solar \r radiation. Changes in surface albedo result in changes in planetary \r albedo that are roughly reduced by two-thirds, owing to atmospheric \r absorption and scattering, with variability and uncertainty arising \r primarily from clouds (Bender, 2011; Donohoe and Battisti, 2011; Block \r and Mauritsen, 2013). Temperature change induces surface-albedo \r change through several direct and indirect means. In the present climate \r and at multi-decadal time scales, the largest contributions by far are \r changes in the extent of sea ice and seasonal snow cover, as these \r media are highly reflective and are located in regions that are close \r to the melting temperature (Sections 2.3.2.1 and 2.3.2.2). Reduced \r snow cover on sea ice may contribute as much to albedo feedback as \r reduced extent of sea ice (Zhang et al., 2019). Changes in the snow \r [...]"


id: 88b95d3366272d31d79a14d9e2118d2a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 21215
content:
"[...] metamorphic rate, which generally reduces snow albedo with warmer \r temperature, and warming-induced consolidation of light-absorbing \r impurities near the surface, also contribute secondarily to the albedo \r feedback (Flanner and Zender, 2006; Qu and Hall, 2007; Doherty et al., \r 2013; Tuzet et al., 2017). Other contributors to albedo change include \r vegetation state (assessed separately in Section 7.4.2.5), soil wetness \r and ocean roughness."


id: bd78ed88bdb63767f2ed4ad4842869e8
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 30592
content:
"The surface-albedo feedback estimates using centennial changes \r have been shown to be highly correlated to those using seasonal \r regional changes for NH land snow (Qu and Hall, 2014) and Arctic \r sea ice (Thackeray and Hall, 2019). For the NH land snow, because \r the physics underpinning this relationship are credible, this opens the \r possibility to use it as an emergent constraint (Qu and Hall, 2014). \r Considering only the eight models whose seasonal cycle of albedo \r feedback falls within the observational range does not change \r the multi-model mean contribution to global aA (0.08 W m-2 degC-1) \r but decreases the inter-model spread by a  factor of two (from \r +-0.03 to  +-0.015 W m-2 degC-1; Qu and Hall, 2014). For Arctic sea \r ice, Thackeray and Hall (2019) show that the seasonal cycle also \r provides an emergent constraint, at least until mid-century when the \r relationship degrades. They find that the CMIP5 multi-model mean \r [...]"


id: 3a0a42a88265787463f0a7a13e1633ac
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7425
content:
"[...] of the Arctic sea ice contribution to aA is 0.13 W m-2 degC-1 and that \r the inter-model spread is reduced by a factor of two (from +-0.04 to \r +-0.02 W m-2  degC-1) when the emergent constraint is used. This model \r estimate is smaller than observational estimates (Pistone et al., 2014; \r Cao et al., 2015) except those of Donohoe et al. (2020). This can be \r traced to CMIP5 models generally underestimating the rate of Arctic \r sea ice loss during recent decades (Section 9.3.1; Stroeve et al., 2012; "


id: 725727526e978b83a9180d397fb6009b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17360
content:
"Clouds have various types, from optically thick convective clouds \r to thin stratus and cirrus clouds, depending upon thermodynamic \r conditions and large-scale circulation (Figure 7.9). Over the equatorial \r warm pool and inter-tropical convergence zone (ITCZ) regions, high \r SSTs stimulate the development of deep convective cloud systems, \r which are accompanied by anvil and cirrus clouds near the tropopause \r where the convective air outflows. The large-scale circulation \r associated with these convective clouds leads to subsidence over \r the subtropical cool ocean, where deep convection is suppressed by \r a  lower tropospheric inversion layer maintained by the subsidence \r and promoting the formation of shallow cumulus and stratocumulus \r clouds. In the extratropics, mid-latitude storm tracks control cloud \r formation, which occurs primarily in the frontal bands of extratropical \r cyclones. Since liquid droplets do not freeze spontaneously at \r [...]"


id: be76422a7206e74c5c2a1734e84fcfd1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 30772
content:
"[...] temperatures warmer than approximately -40degC and ice nucleating \r particles that can aid freezing at warmer temperatures are scarce \r (see Section 7.3.3), extratropical clouds often consist both of super\u0002cooled liquid and ice crystals, resulting in mixed-phase clouds."


id: d71a16f3674b161c3fac3a48800ba720
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 35151
content:
"increase even though the surface warms, resulting in an enhancement \r of the high-cloud greenhouse effect (a positive feedback; Yoshimori \r et al. (2020)). The upward shift of high-clouds with surface warming \r is detected in observed interannual variability and trends in satellite \r records for recent decades (Chepfer et al., 2014; Norris et al., 2016; \r Saint-Lu et  al., 2020). The observational detection is not always \r successful (Davies et al., 2017), but the cloud altitude shifts similarly in \r many CRM experiments (Khairoutdinov and Emanuel, 2013; Tsushima \r et al., 2014; Narenpitak et al., 2017). The high-cloud altitude feedback \r was estimated to be 0.5 W m-2 degC-1 based on GCMs in AR5, but is \r revised, using a recent re-evaluation that excludes aliasing effects by \r reduced low-cloud amounts, downward to 0.22 +- 0.12 W m-2 degC-1\r (one standard deviation; Zhou et  al., 2014; Zelinka et  al., 2020). \r In conclusion, there is high confidence in the positive high-cloud \r [...]"


id: be5a538c3b21f06a8a8d4ad05498184e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 30751
content:
"[...] altitude feedback simulated in ESMs as it is supported by theoretical, \r observational, and process modelling studies."


id: f7c12a9c162f35fda191538f71596bad
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 40959
content:
"In a  first attempt to systematically evaluate equilibrium climate \r sensitivity (ECS) based on fully coupled general circulation models \r (GCMs) in AR4, diverging cloud feedbacks were recognized as \r a dominant source of uncertainty. An advance in understanding the \r cloud feedback was to assess feedbacks separately for different cloud \r regimes (Gettelman and Sherwood, 2016). A  thorough assessment \r of cloud feedbacks in different cloud regimes was carried out in AR5 \r (Boucher et al., 2013), which assigned high or medium confidence for \r some cloud feedbacks but low or no confidence for others (Table 7.9). \r Many studies that estimate the net cloud feedback using CMIP5 \r simulations (Vial et  al., 2013; Caldwell et  al., 2016; Zelinka et  al., \r 2016; Colman and Hanson, 2017) show different values depending on \r the methodology and the set of models used, but often report a large \r inter-model spread of the feedback, with the 90% confidence interval \r [...]"


id: 36c9a556379f389deb18faf3262d5bf9
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 6864
content:
"[...] spanning both weak negative and strong positive net feedbacks. \r Part of this diversity arises from the dependence of the model cloud \r feedbacks on the parametrization of clouds and their coupling to \r other sub-grid-scale processes (Zhao et al., 2015)."


id: a804550e42229e4bd656be358f7135
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 26769
content:
"Since AR5, community efforts have been undertaken to understand \r and quantify the cloud feedbacks in various cloud regimes coupled \r with large-scale atmospheric circulation (Bony et al., 2015). For some \r cloud regimes, alternative tools to ESMs, such as observations, theory, \r high-resolution cloud resolving models (CRMs), and large eddy \r simulations (LES), help quantify the feedbacks. Consequently, the net \r cloud feedback derived from ESMs has been revised by assessing \r the regional cloud feedbacks separately and summing them with \r weighting by the ratio of fractional coverage of those clouds over the \r globe to give the global feedback, following an approach adopted \r in Sherwood et al. (2020). This 'bottom-up' assessment is explained \r below with a  summary of updated confidence of individual cloud \r feedback components (Table 7.9). Dependence of cloud feedbacks on \r evolving patterns of surface warming will be discussed in Section 7.4.4 \r [...]"


id: ac2c2f3c9b448ced7eef1f53d91f35ce
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 27537
content:
"(one  standard deviation; Zhou et  al., 2014). The negative LW \r feedback due to reduced amount of thick high-clouds is partly \r compensated by the positive SW feedback (due to less reflection of \r solar radiation), so that the tropical high-cloud amount feedback \r is assessed to be equal to or smaller than their sum. Consistently, \r the net high-cloud feedback in the tropical convective regime, \r including a  part of the altitude feedback, is estimated to have \r the global contribution of -0.13 +- 0.06 W m-2 degC-1 (one standard \r deviation; Williams and Pierrehumbert, 2017). The negative cloud \r LW feedback is considerably biased in CMIP5 GCMs (Mauritsen and \r Stevens, 2015; Su et al., 2017; Li et al., 2019) and highly uncertain, \r primarily due to differences in the convective parametrization (Webb \r et al., 2015). Furthermore, high-resolution CRM simulations cannot \r alone be used to constrain uncertainty because the results depend \r [...]"


id: ed72cd3444ca4d891fc72d5843cb33e6
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 39141
content:
"[...] on parametrized cloud microphysics and turbulence (Bretherton \r et  al., 2014; Ohno et  al., 2019). Therefore, the tropical high-cloud \r amount feedback is assessed as negative but with low confidence \r given the lack of modelling evidence. Taking observational estimates \r altogether and methodological uncertainty into account, the global \r contribution of the high-cloud amount feedback is assessed to be \r -0.15 +- 0.2 W m-2 degC-1 (one standard deviation)."


id: e607e5ce1bc8315e88cc87deb81c9c44
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 37766
content:
"Intensification of the global hydrological cycle is a  robust feature \r of global warming, but at the same time, many land areas in \r the subtropics will experience drying at the surface and in the \r atmosphere (Section  8.2.2). This occurs due to limited water \r availability in these regions, where the cloudiness is consequently \r expected to decrease. Reduction in clouds over land is consistently \r identified in the CMIP5 models and also in a  GCM with explicit \r convection (Bretherton et al., 2014; Kamae et al., 2016a). Because \r low-clouds make up the majority of subtropical land clouds, this \r reduced amount of low-clouds reflects less solar radiation and leads \r to a positive feedback similar to the marine low-clouds. The mean \r estimate of the global land cloud  feedback in CMIP5 models is \r smaller than the marine low-cloud feedback, 0.08 +- 0.08 W m-2 degC-1 \r (Zelinka et al., 2016). These values are nearly unchanged in CMIP6 \r [...]"


id: b44cdef6c190001369cb13d0d069a828
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 28954
content:
"[...] (Zelinka et al., 2020). However, ESMs still have considerable biases \r in the climatological temperature and cloud fraction over land, \r and the magnitude of this feedback has not yet been supported by \r observational evidence. Therefore, the feedback due to decreasing \r land clouds is assessed to be 0.08 +- 0.08 W m-2 degC-1 (one standard \r deviation) with low confidence."


id: 94ba7115fa624ae1663634a7ce69d941
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 23373
content:
"In order to disentangle the large-scale processes that cause the cloud \r amount either to increase or decrease in response to the surface \r warming, the cloud feedback has been expressed in terms of several \r 'cloud controlling factors' (Qu et al., 2014, 2015; Zhai et al., 2015; \r Brient and Schneider, 2016; Myers and Norris, 2016; McCoy et al., \r 2017a). The advantage of this approach over conventional calculation \r of cloud feedbacks is that the temperature-mediated cloud response \r can be estimated without using information of the simulated cloud \r responses that are less well-constrained than the changes in the \r environmental conditions. Two dominant factors are identified for \r the subtropical low-clouds: a thermodynamic effect due to rising SST \r that acts to reduce low-cloud by enhancing cloud-top entrainment \r of dry air, and a  stability effect accompanied by an enhanced \r inversion strength that acts to increase low-cloud (Qu et al., 2014, \r [...]"


id: e75b4762aaffe0c6af5c4e6d84a12bf2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 38000
content:
"[...] 2015; Kawai et  al., 2017). These controlling factors compensate \r with a  varying degree in different ESMs, but can be constrained \r by referring to the observed seasonal or interannual relationship \r between the low-cloud amount and the controlling factors in the \r environment as a  surrogate. The analysis leads to a  positive local \r feedback that has the global contribution of 0.14 to 0.36 W m-2 degC-1\r (Klein et  al., 2017), to which the feedback in the stratocumulus \r regime dominates over the feedback in the trade cumulus regime "


id: fa8465509e7fb4c0e135cadee8896930
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41444
content:
"Poleward shifts in the mid-latitude jets are evident since the 1980s \r (Section  2.3.1.4.3) and are a  feature of the large-scale circulation \r change in future projections (Section 4.5.1.6). Because mid-latitude \r clouds over the North Pacific, North Atlantic and Southern Ocean \r are induced mainly by extratropical cyclones in the storm tracks \r along the jets, it has been suggested that the jet shifts should be \r accompanied by poleward shifts in the mid-latitude clouds, which \r would result in a positive feedback through the reduced reflection \r of insolation (Boucher et al., 2013). However, studies since AR5 have \r revealed that this proposed mechanism does not apply in practice \r (Ceppi and Hartmann, 2015). While a poleward shift of mid-latitude \r cloud maxima in the free troposphere has been identified in satellite \r and ground-based observations (Bender et al., 2012; Eastman and \r Warren, 2013), associated changes in net CRE are small because \r [...]"


id: f8cd32cfdf3bdb8e3ba069bc6e3d3842
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41135
content:
"[...] the responses in high and low-clouds to the jet shift act to cancel \r each other (Grise and Medeiros, 2016; Tselioudis et al., 2016; Zelinka \r et  al., 2018). This cancellation is not well captured in ESMs (Lipat \r et al., 2017), but the above findings show that the mid-latitude cloud \r feedback is not dynamically driven by the poleward jet shifts, which \r are rather suggested to occur partly in response to changes in high \r clouds (Y. Li et al., 2018)."


id: c1abc0c1536baafc72c6ba045ee8225e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 31373
content:
"Thermodynamics play an important role in controlling extratropical \r cloud amount equatorward of about 50deg latitude. Recent studies \r showed, using observed cloud controlling factors, that the mid-latitude \r low-cloud fractions decrease with rising SST, which also acts to \r weaken stability of the atmosphere unlike in the subtropics (McCoy \r et al., 2017a). ESMs consistently show a decrease of cloud amounts \r and a resultant positive SW feedback in the 30deg-40deg latitude bands, \r which can be constrained using observations of seasonal migration \r of cloud amount (Zhai et  al., 2015). Based on the qualitative \r agreement between observations and ESMs, the mid-latitude cloud \r amount feedback is assessed as positive with medium confidence. \r Following these emergent constraint studies using observations \r and CMIP5/6 models, the global contribution of net cloud amount \r feedback over 30deg-60deg ocean areas, covering 27% of the globe, \r [...]"


id: 6b78f3e3a1d17bfcb6c625aa44841a86
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 16209
content:
"[...] is assessed at 0.09  +- 0.1 W m-2 degC-1 (one standard deviation), in \r which the uncertainty reflects potential errors in models' low-cloud \r response to changes in thermodynamic conditions."


id: fd3cb9b06b87362dbb58cc3043c23dfe
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41903
content:
"Arctic cloud feedback \r Clouds in polar regions, especially over the Arctic, form at low altitude \r above or within a stable to neutral boundary layer and are known to \r co-vary with sea ice variability beneath. Because the clouds reflect \r sunlight during summer but trap LW radiation throughout the year, \r seasonality plays an important role in cloud effects on Arctic climate \r (Kay et al., 2016b). AR5 assessed that Arctic low-cloud amount will \r increase in boreal autumn and winter in response to declining sea \r ice in a  warming climate, due primarily to an enhanced upward \r moisture flux over open water. The cloudier conditions during these \r seasons result in more downwelling LW radiation, acting as a positive \r feedback on surface warming (Kay and Gettelman, 2009). Over \r recent years, further evidence of the cloud contribution to the Arctic \r amplification has been obtained (Section 7.4.4.1; Goosse et al., 2018). \r [...]"


id: b8abf545010369a7b246b061fd1b1570
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 29698
content:
"[...] Space-borne lidar (light detection and ranging) observations show \r that the cloud response to summer sea ice loss is small and cannot \r overcome the cloud effect in autumn (Taylor et al., 2015; Morrison \r et al., 2019). The seasonality of the cloud response to sea ice variability \r is reproduced in GCM simulations (Laine et al., 2016; Yoshimori et al., \r 2017). The agreement between observations and models indicates \r that the Arctic cloud feedback is positive at the surface. This leads \r to an Arctic cloud feedback at TOA that is likely positive, but very \r small in magnitude, as found in some climate models (Pithan and \r Mauritsen, 2014; Morrison et al., 2019). The observational estimates \r are sensitive to the analysis period and the choice of reanalysis data, \r and a recent estimate of the TOA cloud feedback over 60degN-90degN \r using atmospheric reanalysis data and CERES satellite observations \r suggests a regional value ranging from -0.3 to +0.5 W m-2 degC-1, which \r [...]"


id: 9660f4589e2b211798fb594688e4e979
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 23659
content:
"[...] corresponds to a global contribution of -0.02 to +0.03 W m-2 degC-1\r (R. Zhang et al., 2018). Based on the overall agreement between ESMs \r and observations, the Arctic cloud feedback is assessed to be small \r positive and has the value of 0.01 +- 0.05 W m-2 degC-1 (one standard \r deviation). The assessed range indicates that a negative feedback is \r almost as probable as a positive feedback, and the assessment that \r the Arctic cloud feedback is positive is therefore given low confidence."


id: 9aa8002f98cce36d47c36681cc86a69c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 24448
content:
"Due to insufficient amounts of super-cooled liquid water in the \r simulated atmospheric mean state, many CMIP5 models overestimated \r the conversion from ice to liquid clouds with climate warming and \r the resultant negative phase-change feedback (Kay et  al., 2016a; \r Tan et al., 2016; Lohmann and Neubauer, 2018). This feedback can \r be constrained using satellite-derived LWP observations over the \r past 20 years that enable estimates of both long-term trends and \r the interannual relationship with SST variability (Gordon and Klein, \r 2014; Ceppi et al., 2016; Manaster et al., 2017). The observationally\u0002constrained SW feedback ranges from -0.91 to -0.46 W m-2 degC-1 over \r 40degS-70degS depending on the methodology (Ceppi et al., 2016; Terai \r et al., 2016). In some CMIP6 models, representation of super-cooled \r liquid water content has been improved, leading to weaker \r negative optical depth feedback over the Southern Ocean closer \r [...]"


id: 3fc6b6e151e471b152de196a30e26ca4
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 8414
content:
"[...] to observational estimates (Bodas-Salcedo et  al., 2019; Gettelman \r et al., 2019). This improvement at the same time results in a positive \r optical depth feedback over other extratropical ocean where LWP \r decreased in response to reduced stability in those CMIP6 models \r (Zelinka et al., 2020). Given the accumulated observational estimates \r and an improved agreement between ESMs and observations, the \r extratropical optical depth feedback is assessed to be small negative \r with medium confidence. Quantitatively, the global contribution of \r this feedback is assessed to have a value of -0.03 +- 0.05 W m-2 degC-1 "


id: 14749d50ed0ea9b91b6e4e6ab68fd67b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 821
content:
"climate feedbacks associated with these variables has been recognized \r since early studies of climate change. However, in addition to these \r physical climate feedbacks, the Earth system includes feedbacks for \r which the effect of global mean surface temperature change on the \r TOA energy budget is mediated through other mechanisms, such \r as the chemical composition of the atmosphere, or by vegetation \r changes. Among these additional feedbacks, the most important is \r the CO2 feedback that describes how a change of the global surface \r temperature affects the atmospheric CO2 concentration. In  ESM \r simulations in which CO2 emissions are prescribed, changes in surface \r carbon fluxes affect the CO2 concentration in the atmosphere, the \r TOA radiative energy budget, and eventually the global mean surface \r temperature. In ESM simulations in which the CO2 concentration \r is prescribed, changes in the carbon cycle allow compatible CO2\r [...]"


id: a0cd8eb33c10a9b9361fc0096cf46f17
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 25504
content:
"[...] emissions to be calculated, that is, the CO2 emissions that are \r compatible with both the prescribed CO2 concentration and the \r representation of the carbon cycle in the ESM. The CO2 feedback \r is assessed in Chapter 5 (Section 5.4). The framework presented in \r this chapter assumes that the CO2 concentration is prescribed, and \r our assessment of the net feedback parameter, a, does not include \r carbon cycle feedbacks on the atmospheric CO2 concentration \r (Section  7.1 and Box  7.1).  However, our assessment of a does \r include non-CO2 biogeochemical feedbacks (including effects due to \r changes in atmospheric methane concentration; Section  7.4.2.5.1) \r and biogeophysical feedbacks (Section  7.4.2.5.2). A  synthesis of \r the combination of biogeophysical and non-CO2 biogeochemical \r feedbacks is given in Section 7.4.2.5.3."


id: 1bb0385f0d9182d7bcdfddd77739e818
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 2057
content:
"of -0.10 to +0.94 W m-2 degC-1 (Table 7.10). This approach potentially \r misses feedbacks from cloud regimes that are not assessed, but almost \r all the major cloud regimes were taken into consideration (Gettelman \r and Sherwood, 2016) and therefore additional uncertainty will be \r small. This argument is also supported by an agreement between \r the net cloud feedback assessed here and the net cloud feedback \r directly estimated using observations. The observational estimate, \r which is sensitive to the period considered and is based on two \r atmospheric reanalyses (ERA-Interim and MERRA) and TOA radiation \r budgets derived from the CERES satellite observations for the years \r 2000-2010, is 0.54 +- 0.7 W m-2 degC-1 (one standard deviation; Dessler, \r 2013). The observational estimate overlaps with the assessed range \r of the net cloud feedback. The assessed very likely range is reduced \r by about 50% compared to AR5, but is still wide compared to those \r [...]"


id: 3b6470858e594ead41ad11b733983fc5
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7639
content:
"[...] of other climate feedbacks (Table 7.10). The largest contribution to \r this uncertainty range is the estimate of tropical high-cloud amount \r feedback which is not yet well quantified using models."


id: 4c86e9303a983a9243e7ba55cca6b5b7
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 10724
content:
"scales these emissions would eventually substantially decline as the \r permafrost carbon pools were depleted (Schneider von Deimling \r et  al., 2012, 2015). This leaves the wetland CH4, land N2O, and \r ocean N2O feedbacks, the assessed mean values of which sum to \r a  positive feedback parameter of +0.04 [0.02 to 0.06] W m-2 degC-1\r (Section  5.4.7). Other non-CO2 biogeochemical feedbacks that are \r relevant to the net feedback parameter are assessed in Chapter 6 \r (Section 6.4.5 and Table 6.8). These feedbacks are associated with \r sea salt, dimethyl sulphide, dust, ozone, biogenic volatile organic \r compounds, lightning, and CH4 lifetime, and sum to a  negative \r feedback parameter of -0.20 [-0.41 to +0.01] W m-2 degC-1. The \r overall feedback parameter for non-CO2 biogeochemical feedbacks \r is obtained by summing the Chapter 5 and Chapter 6 assessments, \r which gives -0.16 [-0.37 to +0.05] W m-2 degC-1. However, there is \r [...]"


id: e15e344d496a8a938dda178a6fd66a3b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36913
content:
"[...] low confidence in the estimates of both the individual non-CO2\r biogeochemical feedbacks as well as their total effect, as evident \r from the large range in the magnitudes of a from different studies, \r which can be attributed to diversity in how models account for these \r feedbacks and limited process-level understanding."


id: 18b42bf8e51e18dad626b922ecf304d3
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 1565
content:
"Since AR5, several studies have confirmed that a shift from tundra to \r boreal forests and the associated albedo change leads to increased \r warming in Northern Hemisphere high latitudes (high confidence) \r (Willeit et al., 2014; W. Zhang et al., 2018; Armstrong et al., 2019). \r However, regional modelling indicates that vegetation feedbacks may \r act to cool climate in the Mediterranean (Alo and Anagnostou, 2017), \r and in the tropics and subtropics the regional response is in general \r not consistent across models. On a global scale, several modelling \r studies have either carried out a feedback analysis (Stocker et al., 2013; \r Willeit et al., 2014) or presented simulations that allow a feedback \r parameter to be estimated (O'ishi et al., 2009; Armstrong et al., 2019), \r in such a way that the physiological response can be accounted for \r as a  forcing rather than a  feedback. The central estimates of the \r biogeophysical feedback parameter from these studies range from \r [...]"


id: d520ba7a46b375eadd709a537c8c41d5
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34819
content:
"[...] close to zero (Willeit et al., 2014) to +0.13 W m-2 degC-1 (Stocker et al., \r 2013). An additional line of evidence comes from the mid-Pliocene \r warm period (MPWP, Chapter 2, Cross-Chapter Box 2.1), for which \r paleoclimate proxies provide evidence of vegetation distribution \r and CO2 concentrations. Model simulations that include various \r combinations of modern versus MPWP vegetation and CO2 allow an \r associated feedback parameter to be estimated, as long as account \r is also taken of the orographic forcing (Lunt et al., 2010, 2012b). This \r approach has the advantage over pure modelling studies in that the \r reconstructed vegetation is based on (paleoclimate) observations, \r and is in equilibrium with the CO2 forcing. However, there are \r uncertainties in the vegetation reconstruction in regions with little \r or no proxy data, and it is uncertain how much of the vegetation \r change is associated with the physiological response to CO2. This \r [...]"


id: 4bfe3bf3c6559c30bd076e69662226a7
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 10635
content:
"[...] paleoclimate approach gives an estimate for the biogeophysical \r feedback parameter of +0.3 W m-2 degC-1."


id: 92c8cc24703f531d0fa50a3b4a5ce845
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 23041
content:
"Biogeophysical feedbacks are associated with changes in the spatial \r distribution and/or biophysical properties of vegetation, induced \r by surface temperature change and attendant hydrological cycle \r change. These vegetation changes can alter radiative fluxes directly \r via albedo changes, or via surface momentum or moisture flux \r changes and hence changes in cloud properties. However, the direct \r physiological response of vegetation to changes in CO2, including \r changes in stomatal conductance, is considered part of the CO2\r effective radiative forcing rather than a feedback (Section 7.3.2.1). \r The time scale on which vegetation responds to climate change is \r relatively uncertain but can be from decades to hundreds of years \r (Willeit et al., 2014), and could occur abruptly or as a tipping point \r (Sections 5.4.9.1.1, 8.6.2.1 and 8.6.2.2); equilibrium only occurs \r when the soil system and associated nutrient and carbon pools \r [...]"


id: 43e0d63f3d168bacece98214793ff709
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 9174
content:
"[...] equilibrate, which can take millennia (Brantley, 2008; Sitch et  al., \r 2008). The overall effects of climate-induced vegetation changes may \r be comparable in magnitude to those from anthropogenic land-use \r and land-cover change (Davies-Barnard et al., 2015). Climate models \r that include a  dynamical representation of vegetation (e.g.,  Reick \r et al., 2013; Harper et al., 2018) are used to explore the importance \r of biogeophysical feedbacks (Notaro et  al., 2007; Brovkin et  al., \r 2009; O'ishi et al., 2009; Port et al., 2012; Willeit et al., 2014; Alo and \r Anagnostou, 2017; W. Zhang et al., 2018; Armstrong et al., 2019). \r In AR5, it was discussed that such model experiments predicted \r that expansion of vegetation in the high latitudes of the Northern \r Hemisphere would enhance warming due to the associated surface\u0002albedo change, and that reduction of tropical forests in response \r to climate change would lead to regional surface warming, due to \r [...]"


id: ee63aa48e9415b27982f5e33e3c77277
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 39316
content:
"[...] reduced evapotranspiration (M. Collins et al., 2013), but there was no \r assessment of the associated feedback parameter. The SRCCL stated \r that regional climate change can be dampened or enhanced by \r changes in local land cover, but that this depends on the location and \r the season; however, in general the focus was on anthropogenic land\u0002cover change, and no assessment of the biogeophysical feedback \r parameter was carried out. There are also indications of a  marine "


id: 2d3210c599ed2e12606d3b34ae86b27a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 5235
content:
"Some supporting evidence for this overall assessment can be \r obtained from the CMIP6 ensemble, which provides some pairs of \r instantaneous 4xCO2 simulations carried out using related models, \r with and without biogeophysical and non-CO2 biogeochemical \r feedbacks. This is not a  direct comparison because these pairs of \r simulations may differ by more than just their inclusion of these \r additional feedbacks; furthermore, not all biogeophysical and \r non-CO2 biogeochemical feedbacks are fully represented. However, \r a comparison of the pairs of simulations does provide a first-order \r estimate of the magnitude of these additional feedbacks. Seferian \r et  al. (2019) find a  slightly more negative feedback parameter in \r CNRM-ESM2-1 (with additional feedbacks) then in CNRM-CM6-1 \r (a decrease of 0.02 W m-2 degC-1, using the linear regression method \r from years 10-150). Andrews et al. (2019) also find a slightly more \r negative feedback parameter when these additional feedbacks are \r [...]"


id: 422687d7726e342bc23880185cd398e8
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 8841
content:
"[...] included (a decrease of 0.04 W m-2 degC-1 in UKESM1 compared with \r HadGEM3-GC3.1). Both of these studies suggest a small but slightly \r negative feedback parameter for the combination of biogeophysical \r and non-CO2 biogeochemical feedbacks, but with relatively large \r uncertainty given (i) interannual variability and (ii) that feedbacks \r associated with natural terrestrial emissions of CH4 and N2O were not \r represented in either pair."


id: fbe7a6341573df525d21fbd062ca1588
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41682
content:
"The magnitude of the radiative feedback associated with changes to \r ice sheets can be quantified by comparing the global mean long-term \r equilibrium temperature response to increased CO2 concentrations in \r simulations that include interactive ice sheets with that of simulations \r that do not include the associated ice sheet-climate interactions \r (Swingedouw et al., 2008; Vizcaino et al., 2010; Goelzer et al., 2011; \r Bronselaer et  al., 2018; Golledge et  al., 2019). These simulations \r indicate that on multi-centennial time scales, ice-sheet mass loss leads \r to freshwater fluxes that can modify ocean circulation (Swingedouw \r et  al., 2008; Goelzer et  al., 2011; Bronselaer et  al., 2018; Golledge \r et al., 2019). This leads to reduced surface warming (by about 0.2degC \r in the global mean after 1000 years; Section 7.4.4.1.1; Goelzer et al., \r 2011), although other work suggests no net global temperature \r effect of ice-sheet mass loss (Vizcaino et  al., 2010). However, \r [...]"


id: a8406436e1f9032b2ffad2e0a6c5d313
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 26819
content:
"[...] model simulations in which the Antarctic Ice Sheet is removed \r completely in a  paleoclimate context indicate a  positive global \r mean feedback on multi-millennial time scales due primarily to the \r surface-albedo change (Goldner et al., 2014a; Kennedy-Asser et al., \r 2019); in Chapter 9 (Section 9.6.3) it is assessed that such ice-free \r conditions could eventually occur given 7degC-13degC of warming. This \r net positive feedback from ice-sheet mass loss on long time scales \r is also supported by model simulations of the mid-Pliocene Warm \r Period (MPWP; Cross-chapter Box  2.1) in which the volume and \r area of the Greenland and West Antarctic ice sheets are reduced in \r model simulations in agreement with geological data (Chandan and \r Peltier, 2018), leading to surface warming. As such, overall, on multi\u0002centennial time scales the feedback parameter associated with ice \r sheets is likely negative (medium confidence), but on multi-millennial \r [...]"


id: d5c1f048680b5aa850b54e05655ad82f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34920
content:
"[...] time scales by the time the ice sheets reach equilibrium, the feedback \r parameter is very likely positive (high confidence) (Table  7.10). \r However, a relative lack of models carrying out simulations with and \r without interactive ice sheets over centennial to millennial time scales \r means that there is currently not enough evidence to quantify the \r magnitude of these feedbacks, or the time scales on which they act."


id: 9c3c0e548a41c030cb7768c5302a0ed2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 24741
content:
"Earth's ice sheets (Greenland and Antarctica) are sensitive to climate \r change (Section  9.4; Pattyn et  al., 2018). Their time evolution is \r determined by both their surface mass balance and ice dynamic \r processes, with the latter being particularly important for the West \r Antarctic Ice Sheet. Surface mass balance depends on the net energy \r and hydrological fluxes at their surface, and there are mechanisms of \r ice-sheet instability that depend on ocean temperatures and basal \r melt rates (Section 9.4.1.1). The presence of ice sheets affects Earth's \r radiative budget, hydrology, and atmospheric circulation due to their \r characteristic high albedo, low roughness length, and high altitude, \r and they influence ocean circulation through freshwater input from \r calving and melt (e.g.,  Fyke et  al., 2018). Ice-sheet changes also \r modify surface albedo through the attendant change in sea level \r and therefore land area (Abe-Ouchi et al., 2015). The time scale for \r [...]"


id: 7140be956d31af4bd0b8a4371daf4492
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17185
content:
"[...] ice sheets to reach equilibrium is of the order of thousands of years \r (Clark et al., 2016). Due to the long time scales involved, it is a major \r challenge to run coupled climate-ice sheet models to equilibrium, "


