# document

id: ca00da612462fdd42f7b949e59e220bc
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 29337
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\nWGI core set of Shared Socio-economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), \r\nare 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\nof 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\nof 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"


id: 9c02056e4175d6de3a36e55a30465ff4
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 22102
content:
" The scenario total (grey bar) \r\nincludes 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\nand their uncertainties. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)."


id: cbe86da2fabac81a8a4f4ca641295e10
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 29619
content:
"In summary, the warming induced by SLCF changes is stable after \r\n2040 in the WGI core set of SSP scenarios associated with lower global \r\nair pollution as long as methane emissions are also mitigated, but \r\nthe overall warming induced by SLCF changes is higher in scenarios \r\nin which air quality continues to deteriorate (caused by growing \r\nfossil fuel use and limited air pollution control) (high confidence). \r\nIn the SSP3-7.0 context, applying an additional strong air pollution \r\ncontrol resulting in reductions in anthropogenic aerosols and non\u0002methane ozone precursors would lead to an additional near-term \r\nglobal warming of 0.08degC with a very likely range of [-0.05 to 0.25] \r\ndegC (compared with SSP3-7.0 for the same period). A simultaneous \r\nmethane mitigation consistent with SSP1's stringent climate change \r\nmitigation policy implemented in the SSP3 world, could entirely \r\nalleviate this warming and even lead to a  cooling of 0.07degC with \r\na very likely range of [-0.08 to +0"


id: 511a8969d64fb9cf2dbf67ed87a0946b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 10212
content:
"18] degC (compared with SSP3-7.0 \r\nfor the same period). Across the SSPs, the reduction of methane, \r\nozone precursors and HFCs can make a 0.2 [0.1 to 0.4] degC difference \r\non GSAT in 2040 and a 0.8 [0.5 to 1.3] degC difference at the end of the \r\n21st century (Figure 6.24), which is substantial in the context of the \r\nParis Agreement. Sustained methane mitigation, wherever it occurs,\r\nstands out as an option that combines near- and long-term gains \r\non surface temperature (high confidence) and leads to air pollution \r\nbenefits by reducing surface ozone level globally (high confidence). "


id: c6311b43953876f0b159b19c1a7cdf80
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 28755
content:
"Since AR5, the complexity of ESMs has increased to include many \r\nchemical and biogeochemical processes. These processes are \r\nnecessary to quantify non-CO2 biogeochemical feedbacks on the \r\nEarth system resulting from climate-driven changes in atmospheric \r\nchemistry and SLCF emissions from natural systems and, in turn, the \r\nimpact of SLCFs on biogeochemical cycles. Enhanced understanding \r\nof the biological, chemical and physical processes based on \r\nexperimental and observational work has facilitated advances in the \r\nESMs. However, assessment of non-CO2 biogeochemical feedbacks \r\nand SLCF effects on land and aquatic ecosystem productivity still \r\nremains challenging due to the multiple complex processes involved \r\nand limitations in observational constraints to evaluate the skill of \r\nESMs in realistically simulating the processes. Advances will come \r\nfrom better understanding of the processes and mechanisms, in \r\nparticular at the interfaces between components"


id: 2a419e877b4ae06c255fa3e61443fe06
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 4073
content:
" The development \r\nof high-resolution ESMs will facilitate their evaluation against high\u0002resolution observations. "


id: 995876075f2791f337f27cfd93d7af4d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 21641
content:
"Kari Alterskjaer (Norway), Chris Smith (United Kingdom), Govindasamy Bala (India/United States \r\nof America), Nicolas Bellouin (United Kingdom/France), Terje Berntsen (Norway), Fabio Boeira Dias \r\n(Finland/Brazil), Sandrine Bony (France), Natalie J. Burls (United States of America/South Africa), \r\nMichelle  Cain (United Kingdom), Catia M. Domingues (Australia, United Kingdom/Brazil), \r\nAaron Donohoe (United States of America), Mark Flanner (United States of America), Jan S. Fuglestvedt \r\n(Norway), Lily C. Hahn (United States of America), Glen R. Harris (United Kingdom/New Zealand, \r\nUnited Kingdom), Christopher Jones (United Kingdom), Seiji Kato (United States of America), \r\nJared Lewis (Australia/New Zealand), Zhanqing Li (United States of America), Mike  Lockwood \r\n(United Kingdom), Norman Loeb (United States of America), Jochem  Marotzke (Germany), \r\nMalte  Meinshausen (Australia/Germany), Sebastian Milinski (Germany), Zebedee  R.J.  Nicholls \r\n(Australia), Ryan S"


id: c6e1dd583ed704112e79547fb173ff79
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 28875
content:
" Padron Flasher (Switzerland/Ecuador, United States of America), Anna Possner \r\n(Germany), Cristian Proistosescu (Romania), Johannes Quaas (Germany), Joeri Rogelj (United \r\nKingdom/Belgium), Daniel Rosenfeld (Israel), Bjorn H. Samset (Norway), Abhishek Savita (Australia/\r\nIndia), Jessica Vial (France), Karina von Schuckmann (France/Germany), Mark Zelinka (United States \r\nof America), Shuyun Zhao (China)"


id: 75b41e773085caab8b395153c9f54f72
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 16113
content:
"This chapter assesses the present state of knowledge of Earth's energy \r\nbudget: that is, the main flows of energy into and out of the Earth \r\nsystem, and how these energy flows govern the climate response to \r\na  radiative forcing. Changes in atmospheric composition and land \r\nuse, like those caused by anthropogenic greenhouse gas emissions \r\nand emissions of aerosols and their precursors, affect climate \r\nthrough perturbations to Earth's top-of-atmosphere energy budget. \r\nThe effective radiative forcings (ERFs) quantify these perturbations, \r\nincluding any consequent adjustment to the climate system \r\n(but  excluding surface temperature response). How  the climate \r\nsystem responds to a given forcing is determined by climate feedbacks \r\nassociated with physical, biogeophysical and biogeochemical \r\nprocesses"


id: fe2ff7940edf269622659c3a9b19fb7c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
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vector_id: 37520
content:
" These feedback processes are assessed, as are useful \r\nmeasures of global climate response, namely equilibrium climate \r\nsensitivity (ECS) and the transient climate response (TCR). This chapter \r\nalso assesses emissions metrics, which are used to quantify how the \r\nclimate response to the emissions of different greenhouse gases \r\ncompares to the response to the emissions of carbon dioxide (CO2). \r\nThis chapter builds on the assessment of carbon cycle and aerosol \r\nprocesses from Chapters 5 and 6, respectively, to quantify non-CO2\r\nbiogeochemical feedbacks and the ERF for aerosols. Other chapters in \r\nthis Report use this chapter's assessment of ERF, ECS and TCR to help \r\nunderstand historical and future temperature changes (Chapters  3 \r\nand 4, respectively), the response to cumulative emissions and the \r\nremaining carbon budget (Chapter  5), emissions-based radiative \r\nforcing (Chapter 6) and sea level rise (Chapter 9)"


id: 7714b069c928948b560b60775346e4bb
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
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vector_id: 16302
content:
" This chapter builds \r\non findings from the IPCC Fifth Assessment Report (AR5), the Special \r\nReport on Global Warming of 1.5degC (SR1.5), the Special Report on \r\nthe Ocean and Cryosphere in a Changing Climate (SROCC) and the \r\nSpecial Report on climate change, desertification, land degradation, \r\nsustainable land management, food security, and greenhouse \r\ngas luxes in terrestrial ecosystems (SRCCL). Very likely ranges are \r\npresented unless otherwise indicated."


id: 6ace469ddf1589ab8ee822fed1d4e436
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
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vector_id: 14415
content:
"Since AR5, the accumulation of energy in the Earth system, \r\nquantified by changes in the global energy inventory for all \r\ncomponents of the climate system, has become established \r\nas a robust measure of the rate of global climate change on \r\ninterannual-to-decadal time scales. Compared to changes in \r\nglobal surface air temperature (GSAT), the global energy inventory \r\nexhibits less variability, which can mask underlying climate trends.\r\nCompared to AR5, there is increased confidence in the quantification \r\nof changes in the global energy inventory due to improved \r\nobservational records and closure of the sea level budget. Energy \r\nwill continue to accumulate in the Earth system until at least the \r\nend of the 21st  century, even under strong mitigation scenarios, \r\nand will primarily be observed through ocean warming and \r\nassociated with continued sea level rise through thermal expansion \r\n(high confidence). {7.2.2, Box 7.2, Table 7.1, Cross-Chapter Box 9.1, \r\nTable 9.5, 9.2.2, 9"


id: ce2f101c6e757cbf7eb90d87778b8fdf
created_at: 2023-03-16 14:02:02
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content:
"The total anthropogenic ERF over the industrial era \r\n(1750-2019) was 2.72 [1.96 to 3.48] W m-2. This estimate \r\nhas increased by 0.43 W m-2 compared to AR5 estimates for \r\n1750-2011. This increase includes +0.34  W m-2 from increases \r\nin atmospheric concentrations of well-mixed greenhouse gases \r\n(including halogenated species) since 2011, +0.15 W m-2 from \r\nupwards revisions of their radiative efficiencies and +0.10 W m-2\r\nfrom re-evaluation of the ozone and stratospheric water vapour ERF. \r\nThe 0.59 W m-2 increase in ERF from greenhouse gases is partly offset \r\nby a better-constrained assessment of total aerosol ERF that is more \r\nstrongly negative than in AR5, based on multiple lines of evidence \r\n(high confidence). Changes in  surface reflectance from land-use \r\nchange, deposition of light-absorbing particles on ice and snow, and \r\ncontrails and aviation-induced cirrus have also contributed to the \r\ntotal anthropogenic ERF over the industrial era, with -0.20 [-0.30 to \r\n-0"


id: 33a22a49c5c359e3e53a012c13e04243
created_at: 2023-03-16 14:02:02
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content:
"10] W m-2 (medium confidence), +0.08  [0  to 0.18] W  m-2 (low \r\nconfidence) and +0.06 [0.02  to 0.10]  W  m-2 (low  confidence), \r\nrespectively. {7.3.2, 7.3.4, 7.3.5}"


id: ee4dd8d05b5b2c14342510a1dc87876b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 34962
content:
"The combined effect of all known radiative feedbacks (physical, \r\nbiogeophysical, and non-CO2 biogeochemical) is to amplify the \r\nbase climate response, also known as the Planck temperature \r\nresponse (virtually certain). Combining these feedbacks with the \r\nbase climate response, the net feedback parameter based on process \r\nunderstanding is assessed to be -1.16 [-1.81 to -0.51] W m-2 degC-1, \r\nwhich is slightly less negative than that inferred from the overall ECS \r\nassessment. The combined water-vapour and lapse-rate feedback \r\nmakes the largest single contribution to global warming, whereas \r\nthe cloud feedback remains the largest contribution to overall \r\nuncertainty. Due to the state-dependence of feedbacks, as evidenced \r\nfrom paleoclimate observations and from models, the net feedback \r\nparameter will increase (become less negative) as global temperature \r\nincreases"


id: 7fcf744815f0d923931f27068419142c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 17653
content:
" Furthermore, on long time scales the ice-sheet feedback \r\nparameter is very likely positive, promoting additional warming on \r\nmillennial time scales as ice sheets come into equilibrium with the \r\nforcing (high confidence). {7.4.2, 7.4.3, 7.5.7}"


id: 43a90d861e3cb888c78f17697f006a52
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 8049
content:
"Anthropogenic emissions of greenhouse gases and their \r\nprecursors contribute an ERF of 3.84 [3.46 to 4.22] W m-2\r\nover the  industrial era (1750-2019). Most of this total \r\nERF, 3.32  [3.03  to 3.61] W m-2, comes from the well-mixed \r\ngreenhouse gases, with changes in ozone and stratospheric \r\nwater vapour (from methane oxidation) contributing \r\nthe remainder. The ERF of greenhouse gases is composed of \r\n2.16  [1.90  to  2.41] W  m-2 from carbon dioxide, 0.54 [0.43  to \r\n0.65] W m-2 from methane, 0.41 [0.33 to 0.49] W m-2 from halogenated \r\nspecies, and 0.21 [0.18 to 0.24] W m-2 from nitrous oxide. The ERF for \r\nozone is 0.47 [0.24 to 0.71] W m-2. The estimate of ERF for ozone \r\nhas increased since AR5 due to revised estimates  of precursor \r\nemissions and better accounting for effects of tropospheric ozone \r\nprecursors in the stratosphere"


id: 648bde096761d907dc1372f168717989
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
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vector_id: 13386
content:
" The estimated ERF for methane has \r\nslightly increased due to a combination of increases from improved \r\nspectroscopic treatments being somewhat offset by accounting for \r\nadjustments (high confidence). {7.3.2, 7.3.5}"


id: ea84f490375482e3feb98819ab5671ab
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 34407
content:
"Aerosols contribute an ERF of -1.3 [-2.0 to -0.6] W m-2 over \r\nthe industrial era (1750-2014) (medium confidence). The ERF \r\ndue to aerosol-cloud interactions (ERFaci) contributes most \r\nto the magnitude of the total aerosol ERF (high confidence) \r\nand is assessed to be -1.0 [-1.7 to -0.3] W m-2 (medium \r\nconfidence), with the remainder due to aerosol-radiation \r\ninteractions (ERFari), assessed to be -0.3 [-0.6 to 0.0] W m-2 \r\n(medium confidence). There has been an increase in the estimated \r\nmagnitude but a  reduction in the uncertainty of the total aerosol \r\nERF relative to AR5, supported by a  combination of increased \r\nprocess-understanding and progress in modelling and observational \r\nanalyses. ERF estimates from these separate lines of evidence are \r\nnow consistent with each other, in contrast to AR5, and support the \r\nassessment that it is virtually certain that the total aerosol ERF is \r\nnegative"


id: ce81b912affc7d27bc60bd7f3d1983ba
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 30028
content:
" Compared to AR5, the assessed magnitude of ERFaci has \r\nincreased, while the magnitude of ERFari has decreased. The total \r\naerosol ERF over the period 1750-2019 is less certain than the \r\nheadline statement assessment. It is also assessed to be smaller \r\nin magnitude at -1.1 [-1.7 to -0.4] W m-2, primarily due to recent \r\nemissions changes (medium confidence). {7.3.3, 7.3.5, 2.2.6}"


id: 12fe64014fe5d96e952b4c933a1646b5
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 492
content:
"On average, Coupled Model Intercomparison Project Phase 6 \r\n(CMIP6) models have higher mean ECS and TCR values than \r\nthe Phase 5 (CMIP5) generation of models. They also have \r\nhigher mean values and wider spreads than the assessed best \r\nestimates and very likely ranges within this Report. These higher \r\nECS and TCR values can, in some models, be traced to changes in \r\nextra-tropical cloud feedbacks that have emerged from efforts to \r\nreduce biases in these clouds compared to satellite observations \r\n(medium confidence). The broader ECS and TCR ranges from CMIP6 \r\nalso lead the models to project a range of future warming that is wider \r\nthan the assessed warming range, which is based on multiple lines of \r\nevidence. However, some of the high-sensitivity CMIP6 models are less \r\nconsistent with observed recent changes in global warming and with \r\npaleoclimate proxy data than models with ECS within the very likely\r\nrange"


id: ef61c221b87dc8bad1d5d04ea31c0b64
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 35140
content:
" Similarly, some of the low-sensitivity models are less consistent \r\nwith the paleoclimate data. The CMIP models with the highest ECS \r\nand TCR values provide insights into low-likelihood, high-impact \r\noutcomes, which cannot be excluded based on currently available \r\nevidence (high confidence). {4.3.1, 4.3.4, 7.4.2, 7.5.6}"


id: e5d5f5dcc656d06c4e29d0b5afe1b2b0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 33661
content:
"It is now well understood that the Arctic warms more quickly \r\nthan the Antarctic due to differences in radiative feedbacks \r\nand ocean heat uptake between the poles, but that surface \r\nwarming will eventually be amplified in both the Arctic and \r\nAntarctic (high confidence). The causes of this polar amplification \r\nare well understood, and the evidence is stronger than at the \r\ntime of AR5, supported by better agreement between modelled \r\nand observed polar amplification during warm paleo time periods \r\n(high confidence). The Antarctic warms more slowly than the Arctic \r\nowing primarily to upwelling in the Southern Ocean, and even at \r\nequilibrium is expected to warm less than the Arctic. The rate of Arctic \r\nsurface warming will continue to exceed the global average over this \r\ncentury (high confidence)"


id: fc7ff093f3e673913eaf178eb30dc659
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 37237
content:
" There is also high confidence that Antarctic \r\namplification will emerge as the Southern Ocean surface warms on \r\ncentennial time scales, although only low confidence regarding \r\nwhether this feature will emerge during the 21st century. {7.4.4}"


id: 56a97840e6eb2dfc3bd5fa56cb1b011b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 11114
content:
"Throughout this chapter, global surface air temperature (GSAT) \r\nis used to quantify surface temperature change (Cross-Chapter \r\nBox  2.3 and Section  4.3.4). The total energy accumulation in \r\nthe Earth system represents a  metric of global change that is \r\ncomplementary to GSAT but shows considerably less variability on \r\ninterannual-to-decadal time scales (Section  7.2.2). Research and \r\nnew observations since AR5 have improved scientific confidence in \r\nthe quantification of changes in the global energy inventory and \r\ncorresponding estimates of Earth's energy imbalance (Section 7.2). \r\nImproved understanding of adjustments to radiative forcing and of \r\naerosol-cloud interactions have led to revisions of forcing estimates \r\n(Section 7.3). New approaches to the quantification and treatment \r\nof feedbacks (Section 7"


id: fc2bebd293db2e76fbc80c1e8a79674
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 37178
content:
"4) have improved the understanding of their \r\nnature and time-evolution, leading to a better understanding of how \r\nthese feedbacks relate to equilibrium climate sensitivity (ECS). This \r\nhas helped to reconcile disparate estimates of ECS from different \r\nlines of evidence (Section 7.5). Innovations in the use of emissions \r\nmetrics have clarified the relationships between metric choice \r\nand temperature policy goals (Section  7.6), linking this chapter to \r\nWGIII which provides further information on metrics, their use, and \r\npolicy goals beyond temperature. Very likely (5-95%) ranges are \r\npresented unless otherwise indicated. In particular, the addition of \r\n'(one standard deviation)' indicates that the range represents one \r\nstandard deviation."


id: 74fd9bc3624996bc055ce1445cd37e47
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 16001
content:
"Given that TCR and ECS are metrics of GSAT response to \r\na  theoretical doubling of atmospheric CO2 (Box  7.1), they do not \r\ndirectly correspond to the warming that would occur under realistic \r\nforcing scenarios that include time-varying CO2 concentrations and \r\nnon-CO2 forcing agents (such as aerosols and land-use changes). \r\nIt has been argued that TCR, as a metric of transient warming, is \r\nmore policy-relevant than ECS (Frame et al., 2006; Schwartz, 2018). \r\nHowever, as detailed in Chapter  4, both established and recent \r\nresults (Forster et  al., 2013; Gregory et  al., 2015; Marotzke and \r\nForster, 2015; Grose et al., 2018; Marotzke, 2019) indicate that TCR \r\nand ECS help explain variation across climate models both over the \r\nhistorical period and across a range of concentration-driven future \r\nscenarios. In emission-driven scenarios the carbon cycle response \r\nis also important (Smith et  al., 2019)"


id: baa1e2e8dcc59cf71bc62b58d09a00ff
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 26901
content:
" The proportion of variation \r\nexplained by ECS and TCR varies with scenario and the time period \r\nconsidered, but both past and future surface warming depend on \r\nthese metrics (Section 7.5.7)."


id: 5211153e400e820d46ff65eb2aa54d6
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 10367
content:
"A simple way to characterize the behaviour of multiple aspects of \r\nthe climate system at once is to summarize them using global-scale \r\nmetrics. This Report distinguishes between 'climate metrics' (e.g., ECS, \r\nTCR) and 'emissions metrics' (e.g., global warming potential, GWP, or \r\nglobal temperature-change potential, GTP), but this distinction is not \r\ndefinitive. Climate metrics are generally used to summarize aspects \r\nof the surface temperature response (Box  7.1). Emissions metrics \r\nare generally used to summarize the relative effects of emissions of \r\ndifferent forcing agents, usually greenhouse gases (GHGs; Section 7.6). \r\nThe climate metrics used in this report typically evaluate how the \r\nEarth system response varies with atmospheric gas concentration or \r\nchange in radiative forcing. Emissions metrics evaluate how radiative \r\nforcing or a key climate variable (such as GSAT) is affected by the \r\nemissions of a certain amount of gas"


id: 314e5a95614f057e962599edb8f19fcf
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 5177
content:
" Emissions-related metrics are \r\nsometimes used in mitigation policy decisions such as trading GHG \r\nreduction measures and life cycle analysis. Climate metrics are useful \r\nto gauge the range of future climate impacts for adaptation decisions \r\nunder a  given emissions pathway. Metrics such as the transient \r\nclimate response to cumulative emissions of carbon dioxide (TCRE) \r\nare used in both adaptation and mitigation contexts: for gauging \r\nfuture global surface temperature change under specific emissions \r\nscenarios, and to estimate remaining carbon budgets that are used \r\nto inform mitigation policies (Section 5.5)."


id: c5cf75f9c419c94a9cfd29be5a2bb40a
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 28697
content:
"any GSAT change (Smith et al., 2018b). These adjustments affect the TOA energy balance and hence the ERF. They are generally assumed \r\nto 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\nchange response associated with forcing agents than stratospheric-temperature-adjusted radiative forcing (SARF) or the instantaneous \r\nradiative forcing (IRF; Section  7.3.1). Adjustments are processes that are independent of GSAT change, whereas feedbacks  refer to \r\nprocesses caused by GSAT change. Although adjustments generally occur on time scales of hours to several months, and feedbacks \r\nrespond to ocean surface temperature changes on time scales of a year or more, time scale is not used to separate the definitions"


id: 59f16d867fe6acdda62ac0dcbdd3be0b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 11660
content:
" ERF has \r\noften been approximated as the TOA energy balance change due to an imposed perturbation in climate model simulations with sea surface \r\ntemperature and sea-ice concentrations set to their pre-industrial climatological values (e.g., Forster et al., 2016). However, to match \r\nthe adopted forcing-feedback framework, the small effects of any GSAT change from changes in land surface temperatures need to be \r\nremoved 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: 26cf8f27af10f63128fc5aecadc91939
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 3499
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\n(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\nhave been associated with a distinct set of physical processes (Planck response and changes in water vapour, lapse rate, surface albedo, \r\nand clouds; Charney et al., 1979). In this assessment, a more general definition of a and ECS is adopted such that they include additional \r\nEarth system processes that act across many time scales (e.g., changes in natural aerosol emissions or vegetation). Because, in our \r\nassessment, these additional processes sum to a near-zero value, including these additional processes does not change the assessed \r\ncentral value of ECS but does affect its assessed uncertainty range (Section 7.4.2)"


id: fc0b43dfc288ce2ef68f8a7d25f4e033
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 37149
content:
" Note that there is no standardized notation or sign \r\nconvention for the feedback parameter in the literature. Here the convention is used that the sum of all feedback terms (the net feedback \r\nparameter, a) is negative for a stable climate that radiates additional energy to space with a GSAT increase, with a more negative value \r\nof a corresponding to a stronger radiative response and thus a smaller GSAT change required to balance a change in ERF (Equation 7.1). "


id: 4cd643180af4ace4423f35493d44b9d0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 9515
content:
"The equilibrium climate sensitivity, ECS (units: degC), is defined as the equilibrium value of DT in response to a sustained doubling \r\nof atmospheric CO2 concentration from a pre-industrial reference state. The value of ERF for this scenario is denoted by DF2xCO2, giving \r\nECS = -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\nwhere DN averages to zero over a multi-century period. ECS is representative of the multi-century to millennial DT response to DF2xCO2, \r\nand is based on a CO2 concentration change so any feedbacks that affect the atmospheric concentration of CO2 do not influence \r\nits 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\nmillennia to reach equilibrium, but includes all other feedbacks. Due to a number of factors, studies rarely estimate ECS or a at \r\nequilibrium or under CO2 forcing alone"


id: 336e3eb6cb8854b3a91ad2f8667d4d94
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 5511
content:
" Rather, they give an 'effective feedback parameter' (Section 7.4.1 and Box 7.1, Figure 1b) or \r\nan '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\nECS' represents the equilibrium value of DT in response to a sustained doubling of atmospheric CO2 concentration that would occur \r\nassuming the 'effective feedback parameter' applied at that equilibrium state. For example, a feedback parameter can be estimated \r\nfrom the linear slope of DN against DT over a set number of years within ESM simulations of an abrupt doubling or quadrupling \r\nof atmospheric CO2 (2xCO2 or 4xCO2, respectively), and the ECS can be estimated from the intersect of this regression line with \r\nDN = 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\nvaries with CO2 concentration (Section 7.3"


id: e12b3ae31efdebc217836203da151c49
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 32924
content:
"2) and how the slope of DN against DT relates to the slope of the straight line from ERF to \r\nECS (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\ntheir estimates of the effective ECS into the ECS definition used here (Section 7.5.5)."


id: 47f0ce7e961faee12081554bf832c9a0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 8692
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\nfrom a pre-industrial reference state to the time of a doubling of atmospheric CO2 concentration (year 70; Section 7.5). TCR is based \r\non a CO2 concentration change, so any feedbacks that affect the atmospheric concentration of CO2 do not influence its value. It is \r\na measure of transient warming accounting for the strength of climate feedbacks and ocean heat uptake. The transient climate \r\nresponse to cumulative emissions of carbon dioxide (TCRE) is defined as the transient DT per 1000 Gt C of cumulative CO2\r\nemissions increase since the pre-industrial period. TCRE combines information on the airborne fraction of cumulative CO2 emissions \r\n(the fraction of the total CO2 emitted that remains in the atmosphere at the time of doubling, which is determined by carbon cycle \r\nprocesses) with information on the TCR"


id: a1e1d5f0041f992f8f23b7b58334532e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 23032
content:
"Earth's energy budget encompasses the major energy flows of relevance \r\nfor the climate system (Figure 7.2). Virtually all the energy that enters or \r\nleaves the climate system does so in the form of radiation at the TOA. \r\nThe TOA energy budget is determined by the amount of incoming solar \r\n(shortwave) radiation and the outgoing radiation that is composed of \r\nreflected solar radiation and outgoing thermal (longwave) radiation \r\nemitted by the climate system. In a steady-state climate, the outgoing \r\nand incoming radiative components are essentially in balance in the \r\nlong-term global mean, although there are still fluctuations around this \r\nbalanced state that arise through internal climate variability (Brown \r\net  al., 2014; Palmer and McNeall, 2014). However, anthropogenic \r\nforcing has given rise to a persistent imbalance in the global mean \r\nTOA radiation budget that is often referred to as Earth's energy \r\nimbalance (e.g., Trenberth et al., 2014; von Schuckmann et al"


id: d8a2e57c6d3ede546a2c2a2c52bad2df
created_at: 2023-03-16 14:02:02
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vector_id: 31596
content:
", 2016), \r\nwhich is a key element of the energy budget framework (N; Box 7.1, \r\nEquation 7.1) and an important metric of the rate of global climate \r\nchange (Hansen et al., 2005a; von Schuckmann et al., 2020). In addition "


id: 4f78ada359519bd368498f1118f07800
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 9938
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\neffects (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\nranges 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\nlower 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\nthe 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\nthe way clear-sky fluxes are calculated in climate models"


id: a8aa23baa2792d64794822d9a66832cf
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 24066
content:
" Thus, the cloud-free energy budget is not closed and therefore the sensible and latent heat fluxes are not quantified \r\nin the lower panel. Figure adapted from Wild et al. (2015, 2019)."


id: 3fa3f58a99d22db977dd7a57fabfaa99
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 7399
content:
"ranges of their individual components (Wild et al., 2013, 2015; L'Ecuyer \r\net al., 2015). However, on regional scales, the closure of the surface \r\nenergy budgets remains a  challenge with satellite-derived datasets \r\n(Loeb et al., 2014; L'Ecuyer et al., 2015; Kato et al., 2016). Nevertheless, \r\nattempts have been made to derive surface energy budgets over land \r\nand ocean (Wild et al., 2015), over the Arctic (Christensen et al., 2016b), \r\nand over individual continents and ocean basins (L'Ecuyer et al., 2015; \r\nThomas et al., 2020). Since AR5, the quantification of the uncertainties \r\nin surface energy flux datasets has improved. Uncertainties in global \r\nmonthly mean downward solar and thermal fluxes in the CERES-EBAF \r\nsurface dataset are, respectively, 10 W m-2 and 8 W m-2 (converted to \r\n5-95% ranges; Kato et al., 2018). The uncertainty in the surface fluxes \r\nfor polar regions is larger than in other regions (Kato et al"


id: 306a9f93cf53de2db629e4b91c81df0f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 5047
content:
", 2018) due \r\nto the limited number of surface sites and larger uncertainty in surface \r\nobservations (Previdi et  al., 2015). The uncertainties in ocean mean \r\nlatent and sensible heat fluxes are approximately 11 W m-2 and 5 W m-2\r\n(converted to 5-95% ranges), respectively (L'Ecuyer et al., 2015). A recent \r\nreview of the latent and sensible heat flux accuracies over the period \r\n2000-2007 highlights significant differences between several gridded \r\nproducts over ocean, where root-mean-squared differences between \r\nthe multi-product ensemble and data at more than 200  moorings \r\nreached up to 25 W m-2 for latent heat and 5 W m-2 for sensible heat \r\n(Bentamy et  al., 2017). This uncertainty stems from the retrieval of \r\nflux-relevant meteorological variables, as well as from differences in the \r\nflux parametrizations (Yu, 2019). Estimating the uncertainty in sensible \r\nand latent heat fluxes over land is difficult because of the large temporal \r\nand spatial variability"


id: 96ac69eee6e652972c9762ee11dfd3fe
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 21228
content:
" The flux values over land computed with three \r\nglobal datasets vary by 10-20% (L'Ecuyer et al., 2015)."


id: c13d1e9f024cb97fd514b4d286cf86fc
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 27985
content:
"The AR5 (Church et al., 2013; Hartmann et al., 2013; Myhre et al., 2013b) \r\nhighlighted the progress that had been made in quantifying the TOA \r\nradiation budget following new satellite observations that became \r\navailable in the early 21st century (Clouds and the Earth's Radiant \r\nEnergy System, CERES; Solar Radiation and Climate Experiment, \r\nSORCE). Progress in the quantification of changes in incoming solar \r\nradiation at the TOA is discussed in Chapter  2 (Section  2.2). Since \r\nAR5, the CERES Energy Balance EBAF Ed4.0 product was released, \r\nwhich includes algorithm improvements and consistent input datasets \r\nthroughout the record (Loeb et  al., 2018b). However, the overall \r\nprecision of these fluxes (uncertainty in global mean TOA flux of \r\n1.7% (1.7 W m-2) for reflected solar and 1.3% (3.0 W m-2) for outgoing \r\nthermal radiation at the 90% confidence level) is not sufficient to \r\nquantify the Earth's energy imbalance in absolute terms"


id: 5b7a1117c7805a6b22633e64d83e5bbb
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 11893
content:
" Therefore, \r\nthe CERES EBAF reflected solar and emitted thermal TOA fluxes were \r\nadjusted, within the estimated uncertainties, to ensure that the net \r\nTOA flux for July 2005 to June 2015 was consistent with the estimated \r\nEarth's energy imbalance for the same period based on ocean heat \r\ncontent (OHC) measurements and energy uptake estimates for the \r\nland, cryosphere and atmosphere (Section  7.2.2.2; Johnson et  al., \r\n2016; Riser et al., 2016). ESMs typically show good agreement with \r\nglobal mean TOA fluxes from CERES-EBAF. However, as some ESMs are \r\nknown to calibrate their TOA fluxes to CERES or similar data (Hourdin \r\net al., 2017), this is not necessarily an indication of model accuracy, \r\nespecially as ESMs show significant discrepancies on regional scales, \r\noften related to their representation of clouds (Trenberth and Fasullo, \r\n2010; Donohoe and Battisti, 2012; Hwang and Frierson, 2013; J.-L.F. Li \r\net al., 2013; Dolinar et al., 2015; Wild et al., 2015)."


id: 9d25470b47e489a76ca0e66081ad368e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 22259
content:
"The radiation components of the surface energy budget are associated \r\nwith substantially larger uncertainties than at the TOA, since they \r\nare less directly measured by passive satellite sensors and require \r\nretrieval algorithms and ancillary data for their estimation (Raschke \r\net al., 2016; Kato et al., 2018; Huang et al., 2019). Confidence in the \r\nquantification of the global mean surface radiation components has \r\nincreased recently, as independent estimates now converge to within \r\na few W m-2 (Wild, 2017). Current best estimates for downward solar \r\nand thermal radiation at Earth's surface are approximately 185 W m-2\r\nand 342 W m-2, respectively (Figure 7.2). These estimates are based on \r\ncomplementary approaches that make use of satellite products from \r\nactive and passive sensors (L'Ecuyer et  al., 2015; Kato et  al., 2018) \r\nand information from surface observations and Earth system models \r\n(ESMs; Wild et al., 2015)"


id: 7939468468248641b75b4b4f58c3c85e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 16640
content:
" Inconsistencies in the quantification of the \r\nglobal mean energy and water budgets discussed in AR5 (Hartmann \r\net al., 2013) have been reconciled within the (considerable) uncertainty "


id: 25ee4c35354951d0d3193c9477672899
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 3390
content:
"The assessment of changes in the global energy inventory for \r\nthe  periods 1971-2018, 1993-2018 and 2006-2018 draws upon \r\nthe latest observational time series and the assessments presented \r\nin other chapters of this report. The estimates of OHC change come \r\ndirectly from the assessment presented in Chapter 2 (Section 2.3.3.1). \r\nThe assessment of land and atmospheric heating comes from von \r\nSchuckmann et al. (2020), based on the estimates of Cuesta-Valero \r\net al. (2021) and Steiner et al. (2020), respectively. Heating of inland \r\nwaters, including lakes, reservoirs and rivers, is estimated to account \r\nfor <0.1% of the total energy change, and is therefore omitted from \r\nthis assessment (Vanderkelen et al., 2020). The cryosphere contribution \r\nfrom the melting of grounded ice is based on the mass-loss \r\nassessments presented in Chapter  9, Section  9.4.1 (Greenland \r\nIce Sheet), Section  9.4.2 (Antarctic Ice Sheet) and Section  9.5.1 \r\n(glaciers)"


id: 37e573365b2954ee3e8ee1b6e7c4399d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 6168
content:
" Following AR5, the estimate of heating associated with \r\nloss of Arctic sea ice is based on a reanalysis (Schweiger et al., 2011), \r\nfollowing the methods described by Slater et  al. (2021). Chapter  9 \r\n(Section 9.3.2) finds no significant trend in Antarctic sea ice area over \r\nthe observational record, so a zero contribution is assumed. Ice melt \r\nassociated with the calving and thinning of floating ice shelves is \r\nbased on the decadal rates presented in Slater et al. (2021). For all \r\ncryospheric components, mass loss is converted to heat input using \r\na latent heat of fusion of 3.34 x 105 J Kg-1 degC-1 with the second-order \r\ncontributions from variations associated with ice type and warming \r\nof ice from sub-freezing temperatures disregarded, as in AR5. The net \r\nchange in energy, quantified in Zettajoules (1 ZJ = 1021  Joules), \r\nis computed for each component as the difference between the \r\nfirst and last year of each period (Table  7.1)"


id: 2ea3f27fc1deb974a43fd13f06ba76c6
created_at: 2023-03-16 14:02:02
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content:
" The uncertainties in \r\nthe depth-interval contributions to OHC are summed to get the \r\nuncertainty in global OHC change. All other uncertainties are assumed \r\nto be independent and added in quadrature. "


id: 38ea1952f73f24a2c5ae6d410814fd51
created_at: 2023-03-16 14:02:02
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content:
"For the period 1971-2010, AR5 (Rhein et al., 2013) found an increase \r\nin the global energy inventory of 274 [196 to 351] ZJ with a 93% \r\ncontribution from total OHC change, approximately 3% for both ice \r\nmelt and land heating, and approximately 1% for warming of the \r\natmosphere. For the same period, this Report finds an upwards revision \r\nof OHC change for the upper (<700 m depth) and deep (>700 m depth) \r\nocean of approximately 8% and 20%, respectively, compared to AR5 \r\nand a modest increase in the estimated uncertainties associated with \r\nthe ensemble approach of Palmer et al. (2021). The other substantive \r\nchange compared to AR5 is the updated assessment of land heating, \r\nwith values approximately double those assessed previously, based \r\non a  more comprehensive analysis of the available observations \r\n(von Schuckmann et al., 2020; Cuesta-Valero et al., 2021)"


id: 432b5c26af2f557e92d9cb400d4075b2
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 7967
content:
" The result \r\nof these changes is an assessed energy gain of 329 [224 to 434] ZJ \r\nfor the period 1971-2010, which is consistent with AR5 within the \r\nestimated uncertainties, despite the systematic increase."


id: ae81f4006d32f1e5c5ddd5a45c8a9701
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 24986
content:
"Since AR5, both modelling and observation-based studies have \r\nestablished Earth's energy imbalance (characterized by OHC change) \r\nas a more robust metric of the rate of global climate change than \r\nGSAT on interannual-to-decadal time scales (Palmer and McNeall, \r\n2014; von Schuckmann et al., 2016; Wijffels et al., 2016; Cheng et al., \r\n2018; Allison et al., 2020). This is because GSAT is influenced by large \r\nunforced variations, for example linked to ENSO and Pacific Decadal \r\nVariability (Roberts et al., 2015; Yan et al., 2016; Cheng et al., 2018). \r\nMeasuring OHC change more comprehensively over the full ocean \r\ndepth results in a higher signal-to-noise ratio and a time series that \r\nincreases steadily over time (Box 7.2, Figure 1; Allison et al., 2020). \r\nIn addition, understanding of the potential effects of historical ocean \r\nsampling on estimated global ocean heating rates has improved \r\n(Durack et al., 2014; Good, 2017; Allison et al"


id: f653869cad314a72b87379ea29c497c1
created_at: 2023-03-16 14:02:02
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vector_id: 36263
content:
", 2019) and there are \r\nnow more estimates of OHC change available that aim to mitigate the \r\neffect of limited observational sampling in the Southern Hemisphere \r\n(Lyman and Johnson, 2008; Cheng et al., 2017; Ishii et al., 2017)."


id: 268266eebcb9317945a0c20e06eaa5c5
created_at: 2023-03-16 14:02:02
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vector_id: 3466
content:
"of the globe, such as Iran, Bahrain, Tenerife, Hawaii, the Taklaman \r\nDesert and the Tibetan Plateau (Elagib and Alvi, 2013; You et al., 2013; \r\nGarcia et al., 2014; Longman et al., 2014; Rahimzadeh et al., 2015). \r\nStrong decadal trends in surface solar radiation remain evident after \r\ncareful data quality assessment and homogenization of long-term \r\nrecords (Sanchez-Lorenzo et  al., 2013, 2015; Manara et  al., 2015, \r\n2016; Wang et al., 2015; Z. Li et al., 2016; Wang and Wild, 2016; Y. He \r\net al., 2018; Yang et al., 2018). Since AR5, new studies on the potential \r\neffects of urbanization on solar radiation trends indicate that these \r\neffects are generally small, with the exception of some specific sites \r\nin Russia and China (Wang et al., 2014; Imamovic et al., 2016; Tanaka \r\net al., 2016)"


id: 94cbb43c9880ed4708f408f1f6b7e1e0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 20923
content:
" Also, surface-based solar radiation observations have \r\nbeen shown to be representative over large spatial domains of up \r\nto several degrees latitude/longitude on monthly and longer time \r\nscales (Hakuba et al., 2014; Schwarz et al., 2018). Thus, there is high \r\nconfidence that the observed dimming between the 1950s and 1980s \r\nand the subsequent brightening are robust and do not arise from \r\nmeasurement artefacts or localized phenomena."


id: a58d44af810fbccaf3bf65922433a385
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 23618
content:
"As noted in AR5 (Hartmann et al., 2013) and supported by recent studies, \r\nthe trends in surface solar radiation are less spatially coherent since the \r\nbeginning of the 21st century, with evidence for continued brightening \r\nin parts of Europe and the USA, some stabilization in China and India, \r\nand dimming in other areas (Augustine and Dutton, 2013; Sanchez\u0002Lorenzo et al., 2015; Manara et al., 2016; Soni et al., 2016; Wang and \r\nWild, 2016; Jahani et al., 2018; Pfeifroth et al., 2018; Yang et al., 2018; \r\nSchwarz et  al., 2020). The CERES-EBAF satellite-derived dataset of \r\nsurface solar radiation (Kato et al., 2018) does not indicate a globally \r\nsignificant trend over the short period 2001-2012 (Zhang et al., 2015), \r\nwhereas a statistically significant increase in surface solar radiation of \r\n+3"


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created_at: 2023-03-16 14:02:02
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vector_id: 35866
content:
"4 W m-2 per decade over the period 1996-2010 has been found \r\nin the Satellite Application Facility on Climate Monitoring (CM SAF) \r\nrecord of the geostationary satellite Meteosat, which views Europe, \r\nAfrica and adjacent ocean (Posselt et al., 2014)."


id: b1e9d7bbf7fc473bb94aaaabc970830e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 25494
content:
"Soni et al., 2016; Tanaka et al., 2016; Kazadzis et al., 2018; J. Li et al., \r\n2018; Yang et al., 2019; Wild et al., 2021). This suggests that changes \r\nin the composition of the cloud-free atmosphere, primarily in aerosols, \r\ncontributed to these variations, particularly since the second half of \r\nthe 20th  century (Wild, 2016). Water vapour and other radiatively \r\nactive gases seem to have played a minor role (Wild, 2009; Mateos \r\net al., 2013; Posselt et al., 2014; Yang et al., 2019). For Europe and East \r\nAsia, modelling studies also point to aerosols as an important factor \r\nfor dimming and brightening by comparing simulations that include \r\nor exclude variations in anthropogenic aerosol and aerosol-precursor \r\nemissions (Golaz et al., 2013; Nabat et al., 2014; Persad et al., 2014; \r\nFolini and Wild, 2015; Turnock et  al., 2015; Moseid et  al., 2020)"


id: 66a7fb886f91392459d888b79dbd4983
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 13742
content:
" \r\nMoreover, decadal changes in surface solar radiation have often \r\noccurred in line with changes in anthropogenic aerosol emissions \r\nand associated aerosol optical depth (Streets et al., 2006; Wang and \r\nYang, 2014; Storelvmo et al., 2016; Wild, 2016; Kinne, 2019). However, \r\nfurther evidence for the influence of changes in cloudiness on dimming \r\nand brightening is emphasized in some studies (Augustine and \r\nDutton, 2013; Parding et al., 2014; Stanhill et al., 2014; Pfeifroth et al., \r\n2018; Antuna-Marrero et al., 2019). Thus, the contribution of aerosol \r\nand clouds to dimming and brightening is still debated. The relative \r\ninfluence of cloud-mediated aerosol effects versus direct aerosol \r\nradiative effects on dimming and brightening in a specific region may \r\ndepend on the prevailing pollution levels (Section 7.3.3; Wild, 2016)."


id: 319589fefc134bfb5948cbf79d91b536
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 5210
content:
"Turbulent fluxes of latent and sensible heat are also an important \r\npart of the surface energy budget (Figure 7.2). Large uncertainties \r\nin measurements of surface turbulent fluxes continue to prevent the \r\ndetermination of their decadal changes. Nevertheless, over the ocean, \r\nreanalysis-based estimates of linear trends from 1948-2008 indicate \r\nhigh spatial variability and seasonality. Increases in magnitudes of \r\n4 to 7 W m-2 per decade for latent heat and 2 to 3 W m-2 per decade \r\nfor sensible heat in the western boundary current regions are mostly \r\nbalanced by decreasing trends in other regions (Gulev and Belyaev, \r\n2012). Over land, the terrestrial latent heat flux is estimated to have \r\nincreased in magnitude by 0.09 W m-2 per decade from 1989-1997, \r\nand subsequently decreased by 0.13 W m-2 per  decade from \r\n1998-2005 due to soil-moisture limitation mainly in the Southern \r\nHemisphere (derived from Mueller et al., 2013)"


id: dded221d4be2af2160c3df840725fdd8
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 32414
content:
" These trends are small \r\nin comparison to the uncertainty associated with satellite-derived \r\nand in situ observations, as well as from land-surface models forced \r\nby observations and atmospheric reanalyses. Ongoing advances \r\nin remote sensing of evapotranspiration from space (Mallick \r\net  al., 2016; Fisher et  al., 2017; McCabe et  al., 2017a, b), as well \r\nas terrestrial water storage (Rodell et al., 2018) may contribute to \r\nfuture constraints on changes in latent heat flux."


id: 694a33c14c9eda399d6a070adbaccc1c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 14157
content:
"Box 7.2, Figure 1 | Estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971-2018 associated with: (a) observations of \r\nchanges in the global energy inventory; (b) integrated radiative forcing; and (c) integrated radiative response. Black dotted lines indicate the central \r\nestimate with likely and very likely ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial \r\nEarth energy imbalance of 0.2 W m-2 (a), and an illustration of an assumed pattern effect of -0.5 W m-2 degC-1 (c). Background grey lines indicate equivalent heating \r\nrates in W m-2 per unit area of Earth's surface. Panels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and \r\nintegrated radiative forcing, respectively"


id: dd4b0f1f5b0cb357729222e49480e137
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 32307
content:
" Panel (f) shows the global energy budget assessed for the period 1971-2018, that is, the consistency between the change \r\nin the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under \r\na number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response. Shading \r\nrepresents the very likely range for observed energy change relative to pre-industrial levels and likely range for all other quantities. Forcing and response time series \r\nare expressed relative to a baseline period of 1850-1900. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)."


id: 154f83b8040a953a9d60d9501ef700b7
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 870
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\n(high confidence) (Box 7.2, Figure 1d; Table 7.1). Estimating the total uncertainty associated with radiative forcing and radiative \r\nresponse remains a scientific challenge and depends on the degree of correlation between the two (Box 7.2, Figure 1f). However, the \r\ncentral estimate of observed energy change falls well with the estimated likely range, assuming either correlated or uncorrelated \r\nuncertainties. Furthermore, the energy budget assessment would accommodate a  substantial pattern effect (Section  7.4.4.3) \r\nduring 1971-2018 associated with systematically larger values of radiative response (Box 7.2, Figure 1c), and potentially improved \r\nclosure of the global energy budget"


id: e822fc353d032307962f5999a3a694f8
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 34028
content:
" For the period 1970-2011, AR5 reported that the global energy budget was closed within \r\nuncertainties (high confidence) and consistent with the likely range of assessed climate sensitivity (Church et  al., 2013). This \r\nReport provides a  more robust quantitative assessment based on additional evidence and improved scientific understanding."


id: 30543a21fa2708609f8db8e66de76c80
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 5033
content:
"rather than defining a  specific time scale. The AR5 used the term \r\n'rapid adjustment', but in this assessment the definition is based on \r\nthe independence from surface temperature rather than the rapidity. \r\nThe definition of ERF in Box 7.1 aims to create a clean separation \r\nbetween forcing (energy budget changes that are not mediated by \r\nsurface temperature) and feedbacks (energy budget changes that \r\nare mediated by surface temperature). This means that changes in \r\nland or ocean surface temperature patterns (for instance as identified \r\nby Rugenstein et al., 2016b) are not included as adjustments. As in \r\nprevious assessments (Forster et al., 2007; Myhre et al., 2013b) ERFs \r\ncan be attributed simply to changes in the forcing agent itself or \r\nattributed to components of emitted gases (Figure  6.12)"


id: c97d59bcad700ec440e6d5437de67f73
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 29260
content:
" Because \r\nERFs can include chemical and biospheric responses to emitted \r\ngases, they can be attributed to precursor gases, even if those gases \r\ndo not have a direct radiative effect themselves. Similar chemical and \r\nbiospheric responses to forcing agents can also be included in the \r\nERF in addition to their direct effects."


id: c51826f975294ac44d639cb4c136f6ac
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 28588
content:
"the ultimate temperature response. This work is assessed to have led \r\nto a much-improved understanding and increased confidence in the \r\nquantification of radiative forcing across the Report. These same \r\ntechniques allow for an evaluation of radiative forcing within Earth \r\nsystem models (ESMs) as a key test of their ability to represent both \r\nhistorical and future temperature changes (Sections 3.3.1 and 4.3.4).\r\nThe ERF for a  particular forcing agent is the sum of the IRF and \r\nthe contribution from the adjustments, so in principle this could \r\nbe constructed bottom-up by calculating the IRF and adding in the \r\nadjustment contributions one-by-one or together. However, there \r\nis no simple way to derive the global tropospheric adjustment \r\nterms or adjustments related to circulation changes without using \r\na comprehensive climate model (e.g., CMIP5 or CMIP6). There have \r\nbeen two main modelling approaches used to approximate the ERF \r\ndefinition in Box 7.1"


id: 773ee4f4d82dea33e3b4db8884a76e8f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
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content:
" The first approach is to use the assumed linearity \r\n(Box 7.1, Equation 7.1) to regress the net change in the TOA radiation \r\nbudget (DN) against change in global mean surface temperature \r\n(DT) following a step change in the forcing agent (Box 7.1, Figure 1; \r\nGregory et al., 2004). The ERF (DF) is then derived from DN when \r\nDT = 0. Regression-based estimates of ERF depend on the temporal \r\nresolution of the data used (Modak et al., 2016, 2018). For the first \r\nfew months of a  simulation both surface temperature change and \r\nstratospheric-temperature adjustment occur at the same time, leading \r\nto misattribution of the stratospheric-temperature adjustment to the \r\nsurface temperature feedback. Patterns of sea surface temperature \r\n(SST) change also affect estimates of the forcing obtained by regression \r\nmethods (Andrews et  al., 2015)"


id: 6a64ccb84a398b53dbf107ad0055b936
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 14341
content:
" At multi-decadal time scales the \r\ncurvature of the relationship between net TOA radiation and surface \r\ntemperature can also lead to biases in the ERF estimated from the \r\nregression method (Section 7.4; Armour et al., 2013; Andrews et al., \r\n2015; Knutti et al., 2017). The second modelling approach to estimate \r\nERF is to set the DT term in Box 7.1 (Box 7.1, Equation 7.1) to zero. It is \r\ntechnically difficult to constrain land surface temperatures in ESMs \r\n(Shine et al., 2003; Ackerley and Dommenget, 2016; Andrews et al., \r\n2021), so most studies reduce the DT term by prescribing the SSTs \r\nand sea ice concentrations in a pair of 'fixed-SST' (fSST) simulations \r\nwith and without the change in forcing agent (Hansen et al., 2005b). \r\nAn approximation to ERF (DFfsst) is then given by the difference in \r\nDNfsst between the simulations. The fSST method has less noise due \r\nto internal variability than the regression method"


id: 5d0b54b27681c761404d8533ab11e6de
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 12155
content:
" Nevertheless \r\na 30-year fSST integration or 10 x 20-year regression ensemble needs \r\nto be conducted in order to reduce the 5-95% confidence range to \r\n0.1 W m-2 (Forster et al., 2016).Neither the regression or fSST methods \r\nare practical for quantifying the ERF of agents with forcing magnitudes \r\nof the order of 0.1 W m-2 or smaller. The internal variability in the \r\nfSST method can be further constrained by nudging winds towards \r\na  prescribed climatology (Kooperman et  al., 2012). This allows the \r\ndetermination of the ERF of forcing agents with smaller magnitudes \r\nbut excludes adjustments associated with circulation responses \r\n(Schmidt et al., 2018). There are insufficient studies to assess whether \r\nthese circulation adjustments are significant.\r\nSince the near-surface temperature change over land, DTland, is not \r\nconstrained in the fSST method, this response needs to be removed \r\nfor consistency with the Section 7.1 definition"


id: 3b3925affb86e7017862c9355d626925
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 6673
content:
"the tropospheric temperature and water vapour that should also be \r\nremoved to conform with the physical definition of ERF. The radiative \r\nresponse to DTland can be estimated through radiative transfer \r\nmodelling in which a kernel, k, representing the change in net TOA \r\nradiative flux per unit of change in near-surface temperature change \r\nover land (or an approximation using land surface temperature), is \r\nprecomputed (Smith et  al., 2018b, 2020b; Richardson et  al., 2019; \r\nTang et al., 2019). Thus ERF  DFfsst - k DTland. Since k is negative \r\nthis means that DFfsst underestimates the ERF. For 2xCO2, this \r\nunderestimate is around 0.2 W m-2 (Smith et  al., 2018b, 2020b). \r\nThere have been estimates of the corrections due to tropospheric \r\ntemperature and water vapour (Tang et al., 2019; Smith et al., 2020b) \r\nshowing additional radiative responses of comparable magnitude to \r\nthose directly from DTland"


id: d8ed3554b30913a3292152149dd8779e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 31640
content:
" An alternative to computing the response \r\nterms directly is to use the feedback parameter, a (Hansen et al., 2005b; \r\nSherwood et al., 2015; Tang et al., 2019). This gives approximately \r\ndouble the correction compared to the kernel approach (Tang et al., \r\n2019). The response to land surface temperature change varies with \r\nlocation and even for GSAT change k is not expected to be the same \r\nas a (Section 7.4). One study where land surface temperatures are \r\nconstrained in a model (Andrews et al., 2021) finds this constraint \r\nadds +1.0 W m-2 to DFfsst for 4xCO2, thus confirming the need for \r\na  correction in calculations where this constraint is not applied. \r\nFor this assessment the correction is conservatively based only on \r\nthe direct radiative response kernel to DTland as this has a  strong \r\ntheoretical basis to support it"


id: e305d00ce19906e00ecc753109377512
created_at: 2023-03-16 14:02:02
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vector_id: 33212
content:
" While there is currently insufficient \r\ncorroborating evidence to recommend including tropospheric \r\ntemperature and water-vapour corrections in this assessment, it is \r\nnoted that the science is progressing rapidly on this topic."


id: 475c93413ab4b051ec4a70000c68ec86
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 8613
content:
"TOA radiative flux changes due to the individual adjustments can be \r\ncalculated by perturbing the meteorological fields in a climate model's \r\nradiative transfer scheme (partial radiative perturbation approach) \r\n(Colman, 2015; Mulmenstadt et al., 2019) or by using precomputed \r\nradiative kernels of sensitivities of the TOA radiation fluxes to \r\nchanges in these fields (as done for near-surface temperature change \r\nabove; Vial et al., 2013; Zelinka et al., 2014; Zhang and Huang, 2014; \r\nSmith et al., 2018b, 2020b). The radiative kernel approach is easier to \r\nimplement through post-processing of output from multiple ESMs, \r\nwhereas it is recognized that the partial radiation perturbation \r\napproach gives a more accurate estimate of the adjustments within \r\nthe setup of a  single model and its own radiative transfer code"


id: d414344319f4bdb0df6bd10bef4c7071
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 30885
content:
" \r\nThere is little difference between using a radiative kernel from the \r\nsame or a different model when calculating the adjustment terms, \r\nexcept for stratospheric temperature adjustments where it is \r\nimportant to have sufficient vertical resolution in the stratosphere \r\nin the model used to derive the kernel (Smith et al., 2018b, 2020a)."


id: fc993304f31d0aaf68a50c06b36bed96
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 37258
content:
"kernels are shown for seven different climate drivers (using a fSST \r\napproach) in Figure  7.4. Table  7.2 shows the estimates of SARF, \r\nDFfsst and ERF (corrected for land surface temperature change) for \r\n2xCO2 from the nine climate models analysed in Smith et al. (2018b). \r\nThe  SARF shows a  smaller spread over previous studies (Pincus \r\net  al., 2016; Soden et  al., 2018) and most estimates are within \r\n10% of the multi-model mean and the assessment of 2xCO2 SARF \r\nin Section 7.3.2 (3.75 W m-2). It is not possible from these studies to \r\ndetermine how much of this reduction in spread is due to convergence \r\nin the model radiation schemes or the meteorological conditions \r\nof the model base states; nevertheless the level of agreement in \r\nthis and earlier intercomparisons gives medium confidence in the \r\nability of ESMs to represent radiative forcing from CO2. The 4xCO2\r\nCMIP6 fSST experiments (Smith et  al., 2020b) in Table  7"


id: b04244e010a2a0c45eee53ece69a2d40
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 25255
content:
"2 include \r\nESMs with varying levels of complexity in aerosols and reactive \r\ngas chemistry. The CMIP6 experimental setup allows for further \r\nclimate effects of CO2 (including on aerosols and ozone) depending \r\non model complexity. The chemical effects are adjustments to CO2\r\nbut are not separable from the SARF in the diagnosis in Table 7.2. \r\nIn these particular models, this leads to higher SARF than when only \r\nCO2 varies, however there are insufficient studies to make a formal \r\nassessment of composition adjustments to CO2."


id: 4dbf92dbfcbbde8fe08ec4a5cd77775c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 9659
content:
"Table 7.2 | SARF, DFfsst, and ERF diagnosed from Earth system models for \r\nfixed-SST (fSST) CO2 experiments. 2xCO2 data taken from fixed atmospheric \r\ncomposition experiments (Smith et  al., 2018b). 4xCO2 data taken from CMIP6 \r\nexperiments with interactive aerosols (and interactive gas phase chemistry in some; \r\nSmith et al., 2020b). The radiative forcings from the 4xCO2 experiments are scaled by \r\n0.476 for comparison with 2xCO2 (Meinshausen et al., 2020). SARF is approximated \r\nby removing the (non-stratospheric-temperature) adjustment terms from the ERF. \r\nIn Smith et al. (2018b), separation of temperature adjustments into tropospheric and \r\nstratospheric contributions is approximate based on a fixed tropopause of 100 hPa at \r\nthe equator, varying linearly in latitude to 300 hPa at the poles. In Smith et al. (2020b), \r\nthis separation is based on the model-diagnosed tropopause. ERF is approximated by \r\nremoving the response to land surface temperature change from DFfsst"


id: 4b0bc5a8059a3b181e0bccf1384f406d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 9226
content:
"Figure 7.4 | Radiative adjustments at top of atmosphere for seven different \r\nclimate drivers as a proportion of forcing. Tropospheric temperature (orange), \r\nstratospheric temperature (yellow), water vapour (blue), surface albedo (green), \r\nclouds (grey) and the total adjustment (black) is shown. For the greenhouse gases \r\n(carbon dioxide, methane, nitrous oxide and CFC-12) the adjustments are expressed \r\nas a  percentage of stratospheric-temperature-adjusted radiative forcing (SARF), \r\nwhereas for aerosol, solar and volcanic forcing they are expressed as a percentage \r\nof instantaneous radiative forcing (IRF). Land surface temperature response (outline \r\nred bar) is shown, but included in the definition of forcing. Data from Smith et al. \r\n(2018b) for carbon dioxide and methane; Smith et al. (2018b) and Gray et al. (2009) \r\nfor solar; Hodnebrog et al. (2020b) for nitrous oxide and CFC-12; Smith et al. (2020b) \r\nfor aerosol, and Marshall et al. (2020) for volcanic"


id: 6a9d1959af83c1cafd3d147f4c1687bd
created_at: 2023-03-16 14:02:02
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vector_id: 14389
content:
" Further details on data sources and \r\nprocessing are available in the chapter data table (Table 7.SM.14)."


id: 2522cfbdc385bb8badf6d5efd022385b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 3280
content:
"ERFs have been found to yield more consistent values of GSAT change \r\nper unit forcing than SARF, that is, a shows less variation across different \r\nforcing agents (Rotstayn and Penner, 2001; Shine et al., 2003; Hansen \r\net  al., 2005b; Marvel et  al., 2016; Richardson et  al., 2019). Having \r\na consistent relationship between forcing and response is advantageous \r\nwhen making climate projections using simple models (Cross-Chapter \r\nBox 7.1) or emissions metrics (Section 7.6). The definition of ERF used in \r\nthis assessment, which excludes the radiative response to land surface \r\ntemperature changes, brings the a values into closer agreement than \r\nwhen SARF is used (Richardson et al., 2019), although for individual \r\nmodels there are still variations, particularly for more geographically \r\nlocalized forcing agents. However, even for ERF, studies find that \r\na  is not identical across all forcing agents (Shindell, 2014; Shindell \r\net al., 2015; Modak et al"


id: 5f0a8fbcc042f36d7c4ca6b32af06f6e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 12497
content:
", 2018; Modak and Bala, 2019; Richardson \r\net al., 2019). Section 7.4.4 discusses the effect of different SST response \r\npatterns on a. Analysis of the climate feedbacks (Kang and Xie, 2014; \r\nGregory et  al., 2016, 2020; Marvel et  al., 2016; Duan et  al., 2018; \r\nPersad and Caldeira, 2018; Stuecker et al., 2018; Krishnamohan et al., \r\n2019) suggests a  weaker feedback (i.e., less-negative a) and hence \r\nlarger sensitivity for forcing of the higher latitudes (particularly the \r\nNorthern Hemisphere). Nonetheless, as none of these variations are \r\nrobust across models, the ratio of 1/a from non-CO2 forcing agents \r\n(with approximately global distributions) to that from doubling CO2 is \r\nwithin 10% of unity."


id: afaa234ba10275e01fb6aa142bd63431
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 25152
content:
"In summary, this Report adopts an estimate of ERF based on the \r\nchange in TOA radiative fluxes in the absence of GSAT changes. \r\nThis allows for a  theoretically cleaner separation between forcing \r\nand feedbacks in terms of factors respectively unrelated and related \r\nto GSAT change (Box  7.1). ERF can be computed from prescribed \r\nSST and sea ice experiments after removing the TOA energy budget \r\nchange associated with the land surface temperature response. \r\nIn  this assessment this is removed using a  kernel accounting only \r\nfor the direct radiative effect of the land surface temperature \r\nresponse. To compare these results with sophisticated high spectral \r\nresolution radiative transfer models the individual tropospheric \r\nadjustment terms can be removed to leave the SARF. SARFs for \r\n2xCO2 calculated by ESMs from this method agree within 10% with \r\nthe more sophisticated models"


id: 639142ab70c2e53fcd0426830915a395
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 13237
content:
" The new studies highlighted above \r\nsuggest that physical feedback parameters computed within this \r\nframework have less variation across forcing agents. There is high \r\nconfidence that an a based on ERF as defined here varies by less \r\n(less than variation 10% across a range of forcing agents with global \r\ndistributions), than a based on SARF. For geographically localized \r\nforcing agents there are fewer studies and less agreement between \r\nthem, resulting in low confidence that ERF is a suitable estimator of \r\nthe resulting global mean near-surface temperature response."


id: 4099ebdf827c479b31c53aa7468f6ecc
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
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vector_id: 7542
content:
"Zhang  and  Huang, 2014; Smith et  al., 2018b, 2020b). The DFfsst\r\nincludes any effects represented within the ESMs on tropospheric \r\nadjustments due to changes in evapotranspiration or leaf area \r\n(mainly affecting surface and boundary-layer temperature, low-cloud \r\namount, and albedo) from the CO2-physiological effects (Doutriaux\u0002Boucher et al., 2009; Cao et al., 2010; T.B. Richardson et al., 2018). \r\nThe effect on surface temperature (negative longwave response) is \r\nconsistent with the expected physiological responses and needs to \r\nbe removed for consistency with the ERF definition. The split between \r\nsurface and tropospheric temperature responses was not reported in \r\nVial et al. (2013) or Zhang and Huang (2014) but the total of surface \r\nand tropospheric temperature response agrees with Smith et  al. \r\n(2018b, 2020b), giving medium confidence in this decomposition. \r\nDoutriaux-Boucher et al. (2009) and Andrews et al"


id: 1ba75d6e51866ea351e5f0409b283ca0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 1811
content:
" (2021) (using the \r\nsame land surface model) find a 13% and 10% increase respectively \r\nin ERF due to the physiological responses to CO2. The physiological \r\nadjustments are therefore assessed to make a substantial contribution \r\nto the overall tropospheric adjustment for CO2 (high confidence), \r\nbut there is insufficient evidence to provide a quantification of the \r\nsplit between physiological and thermodynamic adjustments. These \r\nforcing adjustments due to the effects of CO2 on plant physiology \r\ndiffer from the biogeophysical feedbacks due to the effects of \r\ntemperature changes on vegetation discussed in Section  7.4.2.5. \r\nThe adjustment is assumed to scale with the SARF in the absence of \r\nevidence for non-linearity. The tropospheric adjustment is assessed \r\nfrom Table 7.3 to be +5% of the SARF with an uncertainty of 5%, \r\nwhich is added to the Meinshausen et al. (2020) formula for SARF"


id: f7ed47443bad29e81eb589e46e3d7895
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 36521
content:
" \r\nDue to the agreement between the studies and the understanding \r\nof the physical mechanisms there is medium confidence in the \r\nmechanisms underpinning the tropospheric adjustment, but low \r\nconfidence in its magnitude."


id: 5ba0f4e853213e53d2f93cf72817de14
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 11923
content:
"The ERF from doubling CO2 (2xCO2) from the 1750 level (278 ppm; \r\nSection 2.2.3.3) is assessed to be 3.93 +- 0.47 W m-2 (high confidence). \r\nIts assessed components are given in Table  7.4. The combined \r\nspectroscopic and radiative transfer modelling uncertainties give an \r\nuncertainty in the CO2 SARF of around 10% or less (Etminan et al., \r\n2016; Mlynczak et al., 2016). The overall uncertainty in CO2 ERF is \r\nassessed as +-12%, as the more uncertain adjustments only account \r\nfor a small fraction of the ERF (Table 7.3). The 2xCO2 ERF estimate \r\nis 0.2 W m-2 larger than using the AR5 formula (Myhre et al., 2013b) \r\ndue to the combined effects of tropospheric adjustments which were \r\nassumed to be zero in AR5. CO2 concentrations have increased from \r\n278 ppm in 1750 to 410 ppm in 2019 (Section 2.2.3.3). The historical \r\nERF estimate from CO2 is revised upwards from the AR5 value of \r\n1.82 +- 0.38 W m-2 (1750-2011) to 2.16 +- 0"


id: 208ee30e3b017289e5ee3583e64959fd
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 2569
content:
"26 W m-2 (1750-2019) in \r\nthis assessment, from a combination of the revisions described above \r\n(0.06 W m-2) and the 19 ppm rise in atmospheric concentrations \r\nbetween 2011 and 2019 (0.27 W m-2). The ESM estimates of 2xCO2\r\nERF (Table 7.2) lie within +-12% of the assessed value (apart from \r\nCESM2). The definition of ERF can also include further physiological \r\neffects - for instance on dust, natural fires and biogenic emissions \r\nfrom the land and ocean - but these are not typically included in the \r\nmodelling setup for 2xCO2 ERF."


id: 758f2e04008993831a39ac427b597ba5
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 16086
content:
"For these models  the adjustments act to reduce the ERF because \r\nthe shortwave absorption leads to tropospheric heating and \r\nreductions in upper tropospheric cloud amounts. The adjustment \r\nis -14% +- 15%, which counteracts much of the increase in SARF \r\nidentified by Etminan et al. (2016). Modak et al. (2018) also found \r\nnegative forcing adjustments from a methane perturbation including \r\nshortwave absorption in the NCAR CAM5 model, in agreement with \r\nthe above assessment. The uncertainty in the shortwave component \r\nleads to a higher radiative modelling uncertainty (14%) than for CO2 \r\n(Etminan et al., 2016). When combined with the uncertainty in the \r\nadjustment, this gives an overall uncertainty of +-20%. There is high \r\nconfidence in the spectroscopic revision but only medium confidence\r\nin the adjustment modification. CH4 concentrations have increased \r\nfrom 729 ppb in 1750 to 1866 ppb in 2019 (Section  2.2.3.3). \r\nThe  historical ERF estimate from AR5 of 0.48 +- 0"


id: 20e4645bf611e53bfb758d1f802633bd
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 2606
content:
"10  W m-2\r\n(1750-2011) is revised to 0.54 +- 0.11 W m-2 (1750 to 2019) in this \r\nassessment from a combination of spectroscopic radiative efficiency \r\nrevisions (+0.12 W m-2), adjustments (-0.08 W m-2) and the 63 ppb \r\nrise in atmospheric CH4 concentrations between 2011 and 2019 \r\n(+0.03 W m-2). As the adjustments are assessed to be small, there \r\nis high confidence in the overall assessment of ERF from methane. \r\nIncreased methane leads to tropospheric ozone production and \r\nincreased stratospheric water vapour, so that an attribution of forcing \r\nto methane emissions gives a larger effect than that directly from the \r\nmethane concentration itself. This is discussed in detail in Chapter 6 \r\n(Section 6.4.2) and shown in Figure 6.12."


id: 42a2b647f6d1ea2b7c5ad5523c1c9d7
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 7876
content:
"The tropospheric adjustments to chlorofluorocarbons (CFCs), \r\nspecifically CFC-11 and CFC-12, have been quantified as 13% +- 10% \r\nand 12% +- 14% of the SARF, respectively (Hodnebrog et al., 2020b). \r\nThe assessed adjustment to CFCs is therefore 12% +- 13% with \r\nlow confidence due to the lack of corroborating studies. There have \r\nbeen no calculations for other halogenated species so for these the \r\ntropospheric adjustments are therefore assumed to be 0 +-  13% \r\nwith low confidence. The radiative modelling uncertainties are \r\n14% and 24% for compounds with lifetimes greater than and less \r\nthan five years, respectively (Hodnebrog et al., 2020a). The overall \r\nuncertainty in the ERFs of halogenated compounds is therefore \r\nassessed to be 19% and 26% depending on the lifetime. The ERF \r\nfrom CFCs is slowly decreasing, but this is compensated for by the \r\nincreased forcing from the replacement species (HCFCs and HFCs). \r\nThe ERF from HFCs has increased by 0.028 +- 0.05 W m-2"


id: ed383bc68d3b8ea9a0d81b562999063b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 34799
content:
" Thus, the \r\nconcentration changes mean that the total ERF from halogenated \r\ncompounds has increased since AR5 from 0.360 +- 0.036 W m-2\r\nto 0.408 +- 0.078 W m-2 (Table 7.5). Of this, 0.034 W m-2 is due to \r\nincreased radiative efficiencies and tropospheric adjustments, and \r\n0.014 W m-2 is due to increases in concentrations. As the adjustments \r\nare assessed to be small there remains high confidence in the \r\noverall assessment."


id: 7b141a0d65c874ab673411cf823b46a7
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 16961
content:
"Estimates of the pre-industrial to present-day tropospheric ozone \r\nradiative forcing are based entirely on models. The lack of pre-industrial \r\nozone measurements prevents an observational determination. There \r\nhave been limited studies of ozone ERFs (MacIntosh et  al., 2016; \r\nXie et  al., 2016; Skeie et  al., 2020). Skeie et  al. (2020) found little \r\nnet contribution to the ERF from tropospheric adjustment terms for \r\n1850-2000 change in ozone (tropospheric and stratospheric ozone \r\ncombined), although MacIntosh et al. (2016) suggested that increases \r\nin stratospheric or upper tropospheric ozone reduces high-cloud \r\nand increases low-cloud, whereas an increase in lower tropospheric \r\nozone reduces low-cloud"


id: 46afa302fc19f6483adf88217d378b30
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 8504
content:
" Further studies suggest that changes in \r\ncirculation due to decreases in stratospheric ozone affect Southern \r\nHemisphere clouds and the atmospheric levels of sea salt aerosol \r\nthat would contribute additional adjustments, possibly of comparable \r\nmagnitude to the SARF from stratospheric ozone depletion (Grise \r\net al., 2013, 2014; Xia et al., 2016, 2020). ESM responses to changes \r\nin ozone-depleting substances (ODS) in CMIP6 show a much more \r\nnegative ERF than would be expected from offline calculations \r\nof SARF (Morgenstern et  al., 2020; Thornhill et  al., 2021b) again \r\nsuggesting a negative contribution from adjustments. However there \r\nis insufficient evidence available to quantify this effect."


id: c7f57f6efa6c58ef57857edc15a3c0c4
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 29039
content:
"This section considers direct anthropogenic effects on stratospheric \r\nwater vapour by oxidation of methane. Since AR5 the SARF from \r\nmethane-induced stratospheric water vapour changes has been \r\ncalculated in Winterstein et al., 2019, corresponding to 0.09 W m-2\r\nwhen scaling to 1850 to 2014 methane changes. This is marginally \r\nlarger than the AR5 assessed value of 0.07 +- 0.05 W m-2 (Myhre \r\net  al., 2013b). Wang and Huang (2020) quantified the adjustment \r\nterms to a stratospheric water vapour change equivalent to a forcing \r\nfrom a 2xCO2 warming (which has a different vertical profile). They \r\nfound that the ERF was less than 50% of the SARF due to high-cloud \r\ndecrease and upper tropospheric warming. The assessed ERF is \r\ntherefore 0.05 +- 0.05 W m-2 with a lower limit reduced to zero and \r\nthe central value and upper limit reduced to allow for adjustment \r\nterms. This still encompasses the two recent SARF studies"


id: 395997c49cbd74b36fdb9ad5ea16f5d5
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 6400
content:
" There is \r\nmedium confidence in the SARF from agreement with the recent \r\nstudies and AR5. There is low confidence in the adjustment terms."


id: 8a6331e42a4ec1d22152d8e55a85db1f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 19310
content:
"As there is insufficient evidence to quantify adjustments, for total \r\nozone the assessed central estimate for ERF is assumed to be equal \r\nto SARF (low confidence) and follows Skeie et al. (2020), since that \r\nstudy uses the most recent emissions data. The dataset is extended \r\nover the entire historical period following Skeie et  al. (2020), \r\nwith a SARF for 1750-1850 of 0.03 W m-2 and for 2010-2018 of \r\n0.03 W m-2, to give 0.47 [0.24 to 0.70] W m-2 for 1750-2019. This \r\nmaintains the 50% uncertainty (5-95% range) from AR5 which is \r\nlargely due to the uncertainty in pre-industrial emissions (Rowlinson \r\net al., 2020). There is also high confidence that this range includes \r\nuncertainty due to the adjustments. The CMIP6 SARF is more positive \r\nthan the AR5 value of 0.31 W m-2 for the period 1850-2011 (Myhre \r\net al., 2013b) which was based on the Atmospheric Chemistry and \r\nClimate Intercomparison Project (ACCMIP; Shindell et al., 2013)"


id: 589ecb23807563ed9fd3e11e4c1c0621
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 11438
content:
" The \r\nassessment is sensitive to the assumptions on precursor emissions \r\nused to drive the models, which are larger in CMIP6 than ACCMIP."


id: 615308445cd13da28bdcadd48fcc7fec
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 12885
content:
"The ERF of GHGs (excluding ozone and stratospheric water vapour) \r\nover 1750-2019 is assessed to be 3.32 +- 0.29 W m-2. It has \r\nincreased by 0.49 W m-2 compared to AR5 (reference year 2011) \r\n(high confidence). Most of this has been due to an increase in CO2\r\nconcentration since 2011 [0.27 +- 0.03] W m-2, with concentration \r\nincreases in CH4, N2O and halogenated compounds adding 0.02, \r\n0.02 and 0.01 W m-2 respectively (Table  7.5). Changes in the \r\nradiative efficiencies (including adjustments) of CO2, CH4, N2O and \r\nhalogenated compounds have increased the ERF by an additional \r\n0.15 W m-2 compared to the AR5 values (high confidence). Note \r\nthat the ERFs in this section do not include chemical effects of \r\nGHGs on production or destruction of ozone or aerosol formation \r\n(Section 6.2.2)"


id: 4c339fabd255f571771d96fcf29e7afc
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 9411
content:
" The ERF for ozone is considerably increased compared \r\nto AR5 due to an increase in the assumed ozone precursor emissions \r\nin CMIP6 compared to CMIP5, and better accounting for the effects \r\nof both ozone precursors and ODSs in the stratosphere. The ERF for \r\nstratospheric water vapour is slightly reduced. The combined ERF \r\nfrom ozone and stratospheric water vapour has increased since AR5 \r\nby 0.10 +- 0.50 W m-2 (high confidence), although the uncertainty \r\nranges still include the AR5 values."


id: 9f669cfd5475b2d7bd462a389ceb7a15
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 22622
content:
"In AR5, the assessment of the black carbon (BC) contribution to IRFari \r\nwas markedly strengthened in confidence by the review by Bond et al. \r\n(2013), where a key finding was a perceived model underestimate \r\nof atmospheric absorption when compared to Aeronet observations \r\n(Boucher et  al., 2013). This assessment has since been revised \r\nconsidering: new knowledge on the effect of the temporal resolution \r\nof emissions inventories (Wang et al., 2016); the representativeness \r\nof Aeronet sites (Wang et  al., 2018); issues with comparing \r\nabsorption retrieval to models (E. Andrews et  al., 2017); and the \r\nageing (Peng et al., 2016), lifetime (Lund et al., 2018b) and average \r\noptical parameters (Zanatta et al., 2016) of BC. Consistent with these \r\nupdates, Lund et al. (2018a) estimated the net IRFari in 2014 (relative \r\nto 1750) to be -0.17 W m-2, using CEDS emissions (Hoesly et al., 2018) \r\nas input to a chemical transport model"


id: 47f966cd66d5833b95bb8378bca4b92a
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 8702
content:
" They attributed the weaker \r\nestimate relative to AR5 (-0.35 +- 0.5 W m-2; Myhre et al., 2013a) \r\nto stronger absorption by organic aerosol, updated parametrization \r\nof BC absorption, and slightly reduced sulphate cooling. Broadly \r\nconsistent with Lund et al. (2018a), another single-model study by \r\nPetersik et al. (2018) estimated an IRFari of -0.19 W m-2. Another \r\nsingle-model study by Lurton et al. (2020) reported a more negative \r\nestimate at -0.38 W m-2, but is given less weight here because \r\nthe model lacked interactive aerosols and instead used prescribed \r\nclimatological aerosol concentrations."


id: 595fc966f2cf15351d88addf80e76594
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 11553
content:
"Estimating IRFari requires an estimate of industrial-era changes in \r\naerosol optical depth (AOD) and absorption AOD, which are often \r\ntaken from global aerosol model simulations. Since AR5, updates \r\nto methods of estimating IRFari based on aerosol remote sensing \r\nor data-assimilated reanalyses of atmospheric composition have \r\nbeen published. Ma et  al. (2014) applied the method of Quaas \r\net  al. (2008) to updated broadband radiative flux measurements \r\nfrom CERES, MODIS-retrieved AODs, and modelled anthropogenic \r\naerosol fractions to find a clear-sky IRFari of -0.6 W m-2. This would \r\ntranslate into an all-sky estimate of about -0.3 W m-2 based on the \r\nclear-sky to all-sky ratio implied by Kinne (2019). Remy et al. (2018) \r\napplied the methods of Bellouin et al. (2013a) to the reanalysis by \r\nthe Copernicus Atmosphere Monitoring Service, which assimilates \r\nMODIS total AOD. Their estimate of IRFari varies between -0.5 W m-2 \r\nand  -0"


id: c78e5d300789d4a957c03daf28968c7
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 28982
content:
"6  W  m-2  over the period 2003-2018, and they attribute \r\nthose relatively small variations to  variability in biomass-burning \r\nactivity.  Kinne (2019) provided updated monthly total AOD and \r\nabsorption AOD climatologies, obtained by blending multi-model \r\naverages with ground-based sun-photometer retrievals, to find \r\na best estimate of IRFari of -0.4 W m-2. The updated IRFari estimates \r\nabove are all scattered around the midpoint of the IRFari range of \r\n-0.35 +- 0.5 W m-2 assessed by AR5 (Boucher et al., 2013)."


id: 130666fc0914ccc17b08bfea9f55da3c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 504
content:
"Since AR5 considerable progress has been made in the understanding \r\nof adjustments in response to a wide range of climate forcings, as \r\ndiscussed in Section 7.3.1. The adjustments in ERFari are principally \r\ncaused by cloud changes, but also by lapse rate and atmospheric \r\nwater vapour changes, all mainly associated with absorbing aerosols \r\nlike BC. Stjern et  al. (2017) found that for BC, about 30% of the \r\n(positive) IRFari is offset by adjustments of clouds (specifically, an \r\nincrease in low-clouds and decrease in high-clouds) and lapse rate, \r\nby analysing simulations by five Precipitation Driver Response Model \r\nIntercomparison Project (PDRMIP) models. Smith et  al. (2018b) \r\nconsidered more models participating in PDRMIP and suggested \r\nthat about half the IRFari was offset by adjustments for BC, a finding \r\ngenerally supported by single-model studies (Takemura and Suzuki, \r\n2019; Zhao and Suzuki, 2019). Thornhill et al"


id: c4e339ce8165d7789c6076c8388a5fc0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 28550
content:
" (2021b) also reported \r\na negative adjustment for BC based on AerChemMIP (Collins et al., \r\n2017) but found it to be somewhat smaller in magnitude than those \r\nreported in Smith et al. (2018b) and Stjern et al. (2017). In contrast, \r\nAllen et al. (2019) found a positive adjustment for BC and suggested \r\nthat most models simulate negative adjustment for BC because of \r\na misrepresentation of aerosol atmospheric heating profiles."


id: 41d2b545285950b9d75e61745857fc43
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 7759
content:
"Zelinka et  al. (2014) used the approximate partial radiation \r\nperturbation technique to quantify the ERFari in 2000 relative \r\nto 1860 in nine CMIP5 models; they estimated the ERFari \r\n(accounting for a small contribution from longwave radiation) to be \r\n-0.27 +- 0.35 W m-2. However, it should be noted that in Zelinka et al. \r\n(2014) adjustments of clouds caused by absorbing aerosols through \r\nchanges in the thermal structure of the atmosphere (termed the \r\nsemidirect effect of aerosols in AR5) are not included in ERFari but \r\nin ERFaci. The corresponding estimate emerging from the Radiative \r\nForcing Model Intercomparison Project (RFMIP, Pincus et al., 2016) \r\nis -0.25 +- 0.40 W m-2 (Smith et  al., 2020b), which is generally \r\nsupported by single-model studies published since AR5 (Zhang et al., \r\n2016; Fiedler et al., 2017; Nazarenko et al., 2017; Zhou et al., 2017c, \r\n2018b; Grandey et al., 2018)"


id: 250eb0eb46fe5310676074afc791cf45
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 3273
content:
" A 5% inflation is applied to the CMIP5 \r\nand CMIP6 fixed-SST derived estimates of ERFari from Zelinka et al. \r\n(2014) and Smith et al. (2020b) to account for land surface cooling \r\n(Table 7.6). Based on the above, ERFari from model-based evidence is \r\nassessed to be -0.25 +- 0.25 W m-2."


id: fcd48ffa1ffc8c4fe44b32c6ffdd67dd
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 37290
content:
"Further, studies assessed in AR5 mostly investigated linear relationships \r\nbetween cloud droplet concentration and aerosol (Boucher et  al., \r\n2013). Since in most cases the relationships are not linear, this leads \r\nto a  bias (Gryspeerdt et  al., 2016). Several studies did not relate \r\ncloud droplet concentration, but cloud droplet effective radius, to the \r\naerosol (Brenguier et al., 2000). This is problematic because in order to \r\ninfer IRFaci, stratification by cloud LWP is required (McComiskey and \r\nFeingold, 2012). Where LWP positively co-varies with aerosol retrievals \r\n(which is often the case), IRFaci inferred from such relationships is \r\nbiased towards low values. Also, it is increasingly evident that different \r\ncloud regimes show different sensitivities to aerosols (Stevens and \r\nFeingold, 2009). Averaging statistics over regimes thus biases the \r\ninferred IRFaci (Gryspeerdt et  al., 2014b)"


id: 986f0960de16000e882c65a765f28aef
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 21492
content:
" The AR5 concluded that \r\nIRFaci estimates tied to satellite studies generally show weak IRFaci \r\n(Boucher et al., 2013), but when correcting for the biases discussed \r\nabove, this is no longer the case."


id: 65c4a3e4ec73dc1ca3cefd2a4dfa79b8
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vector_id: 13592
content:
"used AI instead of AOD in the regression to infer IRFaci. McCoy \r\net al. (2017b) instead used the sulphate-specific mass derived in the \r\nMERRA aerosol reanalysis that assimilated MODIS AOD (Rienecker \r\net al., 2011). All approaches have in common the need to identify \r\nthe anthropogenic perturbation of the aerosol to assess IRFaci. \r\nGryspeerdt et  al. (2017) and Remy et  al. (2018) used the same \r\napproach as Bellouin et  al. (2013b), while McCoy et  al. (2017b) \r\nused an anthropogenic fraction from the AEROCOM multi-model \r\nensemble (Schulz et al., 2006). Chen et al. (2014), Christensen et al. \r\n(2016a) and Christensen et  al. (2017) derived the combination of \r\nIRFaci and the LWP adjustment to IRFaci ('intrinsic forcing' in their \r\nterminology). They relate AI and cloud albedo statistically and use the \r\nanthropogenic aerosol fraction from Bellouin et al. (2013b). This was \r\nfurther refined by Hasekamp et  al"


id: dc2d0b28cf8b6667392429bbb06ddeb8
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 32150
content:
" (2019) who used additional \r\npolarimetric satellite information over ocean to obtain a better proxy \r\nfor CCN. They derived an IRFaci of -1.14 [-1.72 to -0.84] W m-2. \r\nThe variant by Christensen et al. (2017) is an update compared to \r\nthe Chen et al. (2014) and Christensen et al. (2016a) studies in that it \r\nbetter accounts for ancillary influences on the aerosol retrievals such \r\nas aerosol swelling and three-dimensional radiative effects. McCoy \r\net  al. (2020) used the satellite-observed hemispheric difference in \r\nNd as an emergent constraint on IRFaci as simulated by GCMs to \r\nobtain a  range of -1.2 to -0.6 W m-2 (95% confidence interval). \r\nDiamond et al. (2020) analysed the difference in clouds affected by \r\nship emissions with unperturbed clouds and based on this inferred \r\na global IRFaci of -0.69 [-0.99 to -0.44] W m-2."


id: 3e684d653ac46f979a06442ecc3b1980
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 7193
content:
"on the basis of which Rosenfeld et al. (2019) still obtained a positive \r\nrelationship between cloud fraction and Nd relationship.\r\nThe relationship between LWP and cloud droplet number is debated. \r\nMost recent studies (primarily based on MODIS data) find negative \r\nstatistical relationships (Michibata et al., 2016; Toll et al., 2017; Sato \r\net al., 2018; Gryspeerdt et al., 2019), while Rosenfeld et al. (2019) \r\nobtained a  modest positive relationship. To increase confidence \r\nthat observed relationships between aerosol emissions and cloud \r\nadjustments are causal, known emissions of aerosols and aerosol \r\nprecursor gases into otherwise pristine conditions have been \r\nexploited. Ship exhaust is one such source. Goren and Rosenfeld \r\n(2014) suggested that both LWP and Cf increase in response \r\nto ship emissions, contributing approximately 75% to the total \r\nERFaci in mid-latitude stratocumulus"


id: a3d5c4c01d5f6b250be533ac5cb6e62a
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 23338
content:
" Christensen and Stephens \r\n(2011) found that such strong adjustments occur for open-cell \r\nstratocumulus regimes, while adjustments are comparatively small \r\nin closed-cell regimes. Volcanic emissions have been identified \r\nas another important source of information (Gasso, 2008). From \r\nsatellite observations, Yuan et  al. (2011) documented substantially \r\nlarger Cf, higher cloud tops, reduced precipitation likelihood, and \r\nincreased albedo in cumulus clouds in the plume of the Kilauea \r\nvolcano in Hawaii. Ebmeier et al. (2014) confirmed the increased LWP \r\nand albedo for other volcanoes. In contrast, for the large Holuhraun \r\neruption in Iceland, Malavelle et al. (2017) did not find any large-scale \r\nchange in LWP in satellite observations. However, when accounting \r\nfor meteorological conditions, McCoy et al. (2018) concluded that for \r\ncyclonic conditions, the extra Holuhraun aerosol did enhance LWP. \r\nToll et al"


id: 4d5b9fa2584ce664fead37342977b16c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 9602
content:
" (2017) examined a large sample of volcanoes and found \r\na distinct albedo effect, but only modest LWP changes, on average. \r\nGryspeerdt et  al. (2019) demonstrated that the negative LWP-Nd\r\nrelationship becomes very small when conditioned on a  volcanic \r\neruption, and therefore concluded that LWP adjustments are small \r\nin most regions. Similarly, Toll et al. (2019) studied clouds downwind \r\nof various anthropogenic aerosol sources using satellite observations \r\nand inferred an IRFaci of -0.52 W m-2 that was partly offset by 29% \r\ndue to aerosol-induced LWP decreases."


id: f352a04b92e9b61ffbfec6a55cc42648
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 35791
content:
"Multiple studies have found a positive relationship between cloud \r\nfraction and/or cloud LWP and aerosols (e.g.,  Nakajima et  al., \r\n2001; Kaufman and Koren, 2006; Quaas et  al., 2009). Since AR5, \r\nhowever, it has been documented that factors independent of \r\ncausal aerosol-cloud interactions heavily influence such statistical \r\nrelationships. These include the swelling of aerosols in the high \r\nrelative humidity in the vicinity of clouds (Grandey et al., 2013) and the \r\ncontamination of aerosol retrievals next to clouds by cloud remnants \r\nand cloud-side scattering (Varnai and Marshak, 2015; Christensen \r\net al., 2017). Stratifying relationships by possible influencing factors \r\nsuch as relative humidity (Koren et al., 2010) does not yield satisfying \r\nresults since observations of the relevant quantities are not available \r\nat the resolution and quality required"


id: 41e12295482365287b3978d63f89738a
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 7767
content:
" Another approach to tackle \r\nthis problem was to assess the relationship of cloud fraction with \r\ndroplet concentration (Gryspeerdt et  al., 2016; Michibata et  al., \r\n2016; Sato et al., 2018). The relationship between satellite-retrieved \r\ncloud fraction and Nd was found to be positive (Christensen et al., \r\n2016a, 2017; Gryspeerdt et al., 2016), implying an overall adjustment \r\nthat leads to a more negative ERFaci. However, since retrieved Nd is \r\nbiased low for broken clouds this result has been called into question \r\n(Grosvenor et al., 2018). Zhu et al. (2018) proposed to circumvent \r\nthis problem by considering Nd of only continuous thick cloud covers, "


id: 328d80954dff0c996afe5e4412117f42
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 5372
content:
"Only a handful of studies have estimated the LWP and Cf adjustments \r\nthat are needed for satellite-based estimates of ERFaci. Chen et al. \r\n(2014) and Christensen et al. (2017) used the relationship between \r\ncloud fraction and AI to infer the cloud fraction adjustment. \r\nGryspeerdt et al. (2017) used a similar approach but tried to account \r\nfor non-causal coorelations between aerosols and cloud fraction by \r\nusing Nd as a mediating factor. These three studies together suggest \r\na global Cf adjustment that augments ERFaci relative to IRFaci by \r\n-0.5 +- 0.4 W m-2 (medium confidence). For global estimates of the \r\nLWP adjustment, evidence is even scarcer. Gryspeerdt et al. (2019) \r\nderived an estimate of the LWP adjustment using a method similar \r\nto Gryspeerdt et al. (2016). They estimated that the LWP adjustment \r\noffsets 0-60% of the (negative) IRFaci (0.0 to +0.3 W m-2). Supporting \r\nan offsetting LWP adjustment, Toll et al. (2019) estimated a moderate \r\nLWP adjustment of 29% (+0"


id: 5e8e954a800b98d5a4a714262fff0eb6
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 12411
content:
"15 W m-2). The adjustment due to LWP is \r\nassessed to be small, with a central estimate and very likely range of \r\n0.2 +- 0.2 W m-2, but with low confidence due to the limited number \r\nof studies available."


id: 23d9e28b50bb841ddf4edd485a2567aa
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 3080
content:
"simulate the historical total aerosol ERF evolution consistent with \r\nthe observed temperature record. Given the lack of temporally \r\nextensive cooling trends in the 20th-century record and the fact that \r\nthe historical evolution of GHG forcing is relatively well constrained, \r\nthe study concluded that a  more negative total aerosol ERF than \r\n-1.0 W m-2 was incompatible with the historical temperature record. \r\nThis was countered by Kretzschmar et al. (2017), who argued that \r\nthe model employed in Stevens (2015) was too simplistic to account \r\nfor the effect of geographical redistributions of aerosol emissions \r\nover time. Following the logic of Stevens (2015), but basing their \r\nestimates on a subset of CMIP5 models as opposed to a simplified \r\nmodelling framework, Kretzschmar et al. argued that a total aerosol \r\nERF as negative as -1.6 W  m-2 was consistent with the observed \r\ntemperature record. Similar arguments were put forward by Booth \r\net al"


id: f68e14e5b287958b116c3d3aa71de16b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 36308
content:
" (2018), who emphasized that the degree of non-linearity of the \r\ntotal aerosol ERF with aerosol emissions is a central assumption in \r\nStevens (2015)."


id: fff339d1e2aa84ed48506250c44d7944
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 37805
content:
"When the first top-down estimates emerged (e.g., Knutti et al., 2002), \r\nit became clear that some of the early ('bottom-up') ESM estimates \r\nof total aerosol ERF were inconsistent with the plausible top-down \r\nrange. However, as more inverse estimates have been published, it \r\nhas increasingly become clear that they too are model-dependent \r\nand span a wide range of ERF estimates, with confidence intervals \r\nthat in some cases do not overlap (Forest, 2018). It has also become \r\nevident that these methods are sensitive to revised estimates of other \r\nforcings and/or updates to observational datasets. A recent review \r\nof 19 such estimates reported a mean of -0.77 W m-2 for the total \r\naerosol ERF, and a 95% confidence interval of [-1.15 to -0.31] W m-2 \r\n(Forest, 2018). Adding to that review, a  more recent study using \r\nthe same approach reported an estimate of total aerosol ERF of \r\n-0.89 [-1.82 to -0.01] W m-2 (Skeie et al., 2018)"


id: be520a777fc9b11ee1192f63dffc0357
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 27493
content:
" However, in the \r\nsame study, an alternative way of incorporating ocean heat content in \r\nthe analysis produced a total aerosol ERF estimate of -1.34 [-2.20 to \r\n-0.46] W m-2, illustrating the sensitivity to the manner in which \r\nobservations are included. A new approach to inverse estimates took \r\nadvantage of independent climate radiative response estimates from \r\neight prescribed SST and sea ice-concentration simulations over the \r\nhistorical period to estimate the total anthropogenic ERF. From this \r\na total aerosol ERF of -0.8 [-1.6 to +0.1] W m-2 was derived (valid \r\nfor near-present relative to the late 19th  century). This range was \r\nfound to be more invariant to parameter choices than earlier inverse \r\napproaches (Andrews and Forster, 2020)."


id: 7ac30d44f96a75cb24a1a25041dc724d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 16893
content:
"Here, the best estimate and range is revised relative to AR5 (Boucher \r\net al., 2013), partly based on updates to the above lines of argument. \r\nFirstly, the studies that included aerosol effects on mixed-phase \r\nclouds in AR5 relied on the assumption that anthropogenic black \r\ncarbon (BC) could act as INPs in these clouds, which has since \r\nbeen challenged by laboratory experiments (Kanji et  al., 2017; \r\nVergara-Temprado et al., 2018). There is no observational evidence of \r\nappreciable ERFs associated with aerosol effects on mixed-phase and \r\nice clouds (Section 7.3.3.2.1), and modelling studies disagree when it \r\ncomes to both their magnitude and sign (Section 7.3.3.2.2). Likewise, \r\nvery few ESMs incorporate aerosol effects on deep convective \r\nclouds, and cloud-resolving modelling studies report different \r\neffects on cloud radiative properties depending on environmental \r\nconditions (Tao et al., 2012)"


id: 51024b5fa008309bdef1fca311ac6013
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 10195
content:
" Thus, it is not clear whether omitting \r\nsuch effects from ESMs would lead to any appreciable ERF biases, \r\nor if so, what the sign of such biases would be. As a result, all ESMs \r\nare given equal weight in this assessment. Furthermore, there is now \r\na considerably expanded body of literature which suggests that early \r\nmodelling studies that incorporated satellite observations may have \r\nresulted in overly conservative estimates of the magnitude of ERFaci \r\n(Section 7.3.3.2.1). Finally, based on an assessment of the longwave \r\nERFaci in the CMIP5 models, the offset of +0.2 W m-2 applied in AR5 \r\nappears to be too large (Heyn et al., 2017). As in AR5, there is still \r\nreason to question the ability of ESMs to simulate adjustments in \r\nLWP and cloud cover in response to aerosol perturbation, but it is \r\nnot clear that this will result in biases that exclusively increase the \r\nmagnitude of the total aerosol ERF (Section 7.3.3.2.2)."


id: 94bff5e21760b423aff924df13bf367d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 20915
content:
"The assessment of total aerosol ERF here uses the following lines of \r\nevidence: satellite-based evidence for IRFari; model-based evidence \r\nfor IRFari and ERFari; satellite-based evidence of IRFaci and ERFaci; \r\nand finally model-based evidence for ERFaci. Based on this, ERFari and \r\nERFaci for 2014 relative to 1750 are assessed to be -0.3 +- 0.3 W m-2\r\nand -1.0 +- 0.7 W m-2, respectively. There is thus strong evidence \r\nfor a substantive negative total aerosol ERF, which is supported by \r\nthe broad agreement between observation-based and model-based \r\nlines of evidence for both ERFari and ERFaci that has emerged since \r\nAR5 (Gryspeerdt et  al., 2020). However, considerable uncertainty \r\nremains, particularly with regards to the adjustment contribution \r\nto ERFaci, as well as missing processes in current ESMs, notably \r\naerosol effects on mixed-phase, ice and convective clouds"


id: ac952e2246545b1afa069c6d6c07bd3c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 24698
content:
" This leads \r\nto a medium confidence in the estimate of ERFari+aci and a slight \r\nnarrowing of the uncertainty range. Because the estimates informing \r\nthe different lines of evidence are generally valid for approximately \r\n2014 conditions, the total aerosol ERF assessment is considered valid \r\nfor 2014 relative to 1750."


id: 633f196dd424b8e5c76497754ea11179
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 13179
content:
"Land-use forcing is defined as those changes in land-surface \r\nproperties directly caused by human activity rather than by climate \r\nprocesses (see also Section  2.2.7). Land-use change affects the \r\nsurface albedo. For example, deforestation typically replaces darker \r\nforested areas with brighter cropland, and thus imposes a negative \r\nradiative forcing on climate, while afforestation and reforestation \r\ncan have the opposite effect. Precise changes depend on the nature \r\nof the forest, crops and underlying soil. Land-use change also affects \r\nthe amount of water transpired by vegetation (Devaraju et al., 2015). \r\nIrrigation of land directly affects evaporation (Sherwood et  al., \r\n2018), causing a  global increase of 32,500 m3 s-1 due to human \r\nactivity. Changes in evaporation and transpiration affect the latent \r\nheat budget, but do not directly affect the top-of-atmosphere (TOA) \r\nradiative fluxes"


id: e34899813f1ef5cf2680f459127a9675
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 33252
content:
" The lifetime of water vapour is so short that the \r\neffect of changes in evaporation on the greenhouse contribution \r\nof water vapour are negligible (Sherwood et  al., 2018). However, \r\nevaporation can affect the ERF through adjustments, particularly \r\nthrough changes in low-cloud amounts. Land management affects \r\nthe emissions or removal of GHGs from the atmosphere (such as \r\nCO2, CH4, N2O). These emissions changes have the greatest effect on \r\nclimate (Ward et al., 2014), however they are already included in GHG \r\ninventories. Land-use change also affects the emissions of dust and \r\nbiogenic volatile organic compounds (BVOCs), which form aerosols \r\nand affect the atmospheric concentrations of ozone and methane \r\n(Section 6.2.2). The effects of land use on surface temperature and \r\nhydrology were recently assessed in SRCCL (Jia et al., 2019)."


id: e833fea9df82b7f1607979ef57004bb1
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 34040
content:
"Using the definition of ERF from Section  7.1, the adjustment in \r\nland-surface temperature is excluded from the definition of ERF, \r\nbut changes in vegetation and snow cover (resulting from land-use \r\nchange) are included (Boisier et al., 2013). Land-use change in the \r\nmid-latitudes induces a substantial amplifying adjustment in snow \r\ncover. Few climate model studies have attempted to quantify the \r\nERF of land-use change. T. Andrews et al. (2017) calculated a very \r\nlarge surface albedo ERF (-0.47 W m-2) from 1860 to 2005 in the \r\nHadGEM2-ES model, although they did not separate out the surface \r\nalbedo change from snow cover change. HadGEM2-ES is known \r\nto overestimate the amount of boreal trees and shrubs in the \r\nunperturbed state (Collins et al., 2011) so will tend to overestimate \r\nthe ERF associated with land-use change. The increases in dust in \r\nHadGEM2-ES contributed an extra -0.25 W m-2, whereas cloud cover \r\nchanges added a small positive adjustment (0"


id: 7ce55377bcfcce39bcda5b3504cce1a4
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 17249
content:
"in the 200-400 nanometre (nm) band than for the broad visible/\r\ninfrared band that dominates TSI (Krivova et al., 2006). The IRF can be \r\nderived simply by DTSI x (1 - albedo)/4 irrespective of wavelength, \r\nwhere the best estimate of the planetary albedo is usually taken to \r\nbe 0.29 and DTSI represents the change in total solar irradiance \r\n(Stephens et al., 2015). (The factor 4 arises because TSI is per unit \r\narea of Earth  cross section presented to the Sun and IRF is per \r\nunit area of Earth's surface). The adjustments are expected to be \r\nwavelength dependent. Gray et al. (2009) determined a stratospheric \r\ntemperature adjustment of -22% to spectrally resolved changes in \r\nthe solar radiance over one solar cycle. This negative adjustment is \r\ndue to stratospheric heating from increased absorption by ozone at \r\nthe short wavelengths, increasing the outgoing longwave radiation \r\nto space. A multi-model comparison (Smith et al"


id: 6716c5608e4abde59022db7011871c45
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 13811
content:
", 2018b) calculated \r\nadjustments of -4% due to stratospheric temperatures and -6% due \r\nto tropospheric processes (mostly clouds), for a change in TSI across \r\nthe spectrum (Figure 7.4). The smaller magnitude of the stratospheric \r\ntemperature adjustment is consistent with the broad spectral change \r\nrather than the shorter wavelengths characteristic of solar variation. \r\nA single-model study also found an adjustment that acts to reduce \r\nthe forcing (Modak et  al., 2016). While there has not yet been \r\na  calculation based on the appropriate spectral change, the -6% \r\ntropospheric adjustment from Smith et al. (2018b) is adopted along \r\nwith the Gray et  al. (2009) stratospheric temperature adjustment. \r\nThe ERF due to solar variability over the historical period is therefore \r\nrepresented by 0.72 x DTSI x (1 - albedo)/4 using the TSI timeseries \r\nfrom Chapter 2 (Section 2.2.1)."


id: 3030ccb833a7d3fb3190f17811bf3187
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 5008
content:
"Most radiative forcing estimates have a  regional emphasis. \r\nThe regional focus makes estimating a global mean radiative forcing \r\nfrom aggregating different studies challenging, and the relative \r\nimportance of each region is expected to change if the global pattern \r\nof emissions sources changes (Bauer et al., 2013). The lower bound \r\nof the assessed range of BC on snow and ice is extended to zero \r\nto encompass Lin et al. (2014), with the best estimate unchanged, \r\nresulting in 0.04 [0.00 to 0.09] W m-2. The efficacy of BC on snow \r\nforcing was estimated to be 2 to 4 times as large as for an equivalent \r\nCO2 forcing as the effects are concentrated at high latitudes in the \r\ncryosphere (Bond et al., 2013). However, it is unclear how much of \r\nthis effect is due to radiative adjustments leading to a higher ERF, \r\nand how much comes from a less negative feedback a due to the \r\nhigh-latitude nature of the forcing"


id: d6c9b2c5444978506d90bc6b9d3e8a3
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 31301
content:
" To estimate the overall ERF, \r\nthe IRF is doubled assuming that part of the increased efficacy is due \r\nto adjustments. This gives an overall assessed ERF of +0.08 [0.00 to \r\n0.18] W m-2, with low confidence."


id: c1c439f238610674158ba6d0dced6a2c
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 28070
content:
"In this assessment the TSI from the Paleoclimate Model \r\nIntercomparison Project Phase 4 (PMIP4) reconstruction is used \r\n(Section 2.2.1; Jungclaus et al., 2017). Proxies constructed from the \r\n14C and 10Be radiogenic records for the SATIRE-M model (Vieira et al., \r\n2011) and 14C record for the PMOD model (Shapiro et  al., 2011) \r\nfor the 1745 solar minimum provide ERFs for 1745-2008 of -0.01, \r\n-0.02 and 0.00 W m-2 respectively. An independent dataset from the \r\nNational Oceanic and Atmospheric Administration's Climate Data \r\nRecord (Coddington et  al., 2016; Lean, 2018) provides an ERF for \r\n1745-2008 of +0.03 W m-2. One substantially higher ERF estimate of \r\n+0.35 W m-2 derived from TSI reconstructions is provided by Egorova \r\net  al. (2018). However, the estimate from Egorova et  al. (2018) \r\nhinges on assumptions about long-term changes in the quiet Sun \r\nfor which there is no observed evidence"


id: b8fcd2bd42e1421ab4dc156e1aa8ecb4
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 26638
content:
" Lockwood and Ball (2020) \r\nanalysed the relationship between observed changes in cosmic ray \r\nfluxes and recent, more accurate, TSI data and derived ERF between \r\n-0.01 and +0.02 W m-2, and Yeo et al. (2020) modelling showed the "


id: bf4234f27e59f8b76672579ccf2a8dd9
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 27642
content:
"There is large episodic negative radiative forcing associated with \r\nsulphur dioxide (SO2) being ejected into the stratosphere from \r\nexplosive volcanic eruptions, accompanied by more frequent smaller \r\neruptions (Figure  2.2 and Cross-Chapter Box  4.1). From SO2 gas, \r\nreflective sulphate aerosol is formed in the stratosphere where it may \r\npersist for months to years, reducing the incoming solar radiation. \r\nThe volcanic SARF in AR5 (Myhre et  al., 2013b) was derived by \r\nscaling the stratospheric aerosol optical depth (SAOD) by a factor of \r\n-25 W m-2 per unit SAOD from Hansen et al. (2005b). Quantification \r\nof the adjustments to SAOD perturbations from climate model \r\nsimulations have determined a significant positive adjustment driven \r\nby a reduction in cloud amount (Figure 7.4; Marshall et al., 2020). \r\nAnalysis of CMIP5 models provides a mean ERF of -20 W m-2 per \r\nunit SAOD (Larson and Portmann, 2016)"


id: 8612aa66388e65d8cacc81e67769fdc0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 18632
content:
" Single-model studies with \r\nsuccessive generations of Hadley Centre climate models produce \r\nestimates between -17 and -19 W m-2 per unit SAOD (Gregory et al., \r\n2016; Marshall et al., 2020), with some evidence that ERF may be non\u0002linear with SAOD for large eruptions (Marshall et al., 2020). Analysis \r\nof the volcanically active periods of 1982-1985 and 1990-1994 using \r\nthe CESM1(WACCM) aerosol-climate model provided an SAOD\u0002to-ERF relationship of -21.5 (+- 1.1) W m-2 per unit SAOD (Schmidt \r\net al., 2018). Volcanic SO2 emissions may contribute a positive forcing \r\nthrough effects on upper tropospheric ice clouds, due to additional \r\nice nucleation on volcanic sulphate particles (Friberg et  al., 2015; \r\nSchmidt et  al., 2018), although one observational study found no \r\nsignificant effect (Meyer et  al., 2015). Due to low agreement, the \r\ncontribution of sulphate aerosol effects on ice clouds to volcanic ERF \r\nis not included in the overall assessment."


id: aec90f68e7bfbab960e525c1e5385db2
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 25032
content:
"Material 7.SM.2). The TCR has an ensemble median value of 1.81degC, \r\nin good agreement with Section  7.5.5. Two error bars are shown \r\nin Figure  7.7. The dashed error bar shows the contribution of ERF \r\nuncertainty (as assessed in the subsections of Section 7.3) employing \r\nthe best estimate of climate response with an ECS of 3.0degC. The solid \r\nbar is the total response uncertainty using the Section 7.5.5 assessment \r\nof ECS. The uncertainty in the historical temperature contributions \r\nofthe different forcing agents is mostly due to uncertainties in ERF, \r\nyet for the WMGHG the uncertainty is dominated by the climate \r\nresponse as its ERF is relatively well known (Figure 7.7). From the \r\nassessment of emulator responses in Cross-Chapter Box 7"


id: 2d1055bbe8f57b657a3514b3efe473cb
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 4529
content:
"1, there is \r\nhigh confidence that calibrated emulators such as the one employed \r\nhere can represent the historical GSAT change between 1850-1900 \r\nand 1995-2014 to within 5% for the best estimate and 10% for the \r\nvery likely range (Supplementary Material, Table 7.SM.4). This gives \r\nhigh confidence in the overall assessment of GSAT change for the \r\nresponse to ERFs over 1750-2019 derived from the emulator."


id: 83c8710b1df95ff60a320da1a98f8526
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 18266
content:
"These ERF timeseries are combined with a  two-layer emulator \r\n(Cross-Chapter Box 7.1 and Supplementary Material 7.SM.2) using \r\na  2237-member constrained Monte Carlo sample of both forcing \r\nuncertainty (by sampling ERF ranges) and climate response (by \r\nsampling ECS, TCR and ocean heat capacity ranges). The net model \r\nwarming over the historical period is matched to the assessment \r\nof historical GSAT warming from 1850-1900 to 1995-2014 of \r\n0.85 [0.67 to 0.98] degC (Cross-Chapter Box 2.3) and ocean heat content \r\nchange from 1971 to 2018 (Section  7.2.2.2). Therefore the model \r\ngives the breakdown of the GSAT trend associated with different \r\nforcing mechanisms that are consistent with the overall GSAT \r\nchange. The model assumes that there is no variation in feedback \r\nparameter across forcing mechanisms (Section 7.3.1) and variations \r\nin the effective feedback parameter over the historical record \r\n(Section 7.4.4). The distribution of ECS was informed by Section 7.5"


id: 8f957b635fc8c09607db7ba780e6de9b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 20138
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\nother gas cycles and can combine uncertainties along the cause-effect chain, from emissions to temperature response. AR5 (M. Collins \r\net al., 2013) used the MAGICC6 emulator (Meinshausen et al., 2011a) in a probabilistic setup (Meinshausen et al., 2009) to explore \r\nthe uncertainty in future projections. A simple impulse response emulator (Good et al., 2011) was also used to ensure a consistent \r\nset 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\nemulator for quantifying global temperature-change potentials (GTP). In AR5 WGIII (Clarke et al., 2014), MAGICC6 was also used \r\nfor the classification of scenarios, and in AR5 Synthesis Report (IPCC, 2014) this information was used to estimate carbon budgets. \r\nIn SR1"


id: d935f3ad659f2981bf7c3804f541a95
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 31684
content:
"5, two emulators were used to provide temperature projections of scenarios: the MAGICC6 model, which was used for the \r\nscenario classification, and the FaIR1.3 model (Millar et al., 2017; Smith et al., 2018a)."


id: 19b3503c17cc5ba8e2f5d62ac56ff4a2
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 1515
content:
"The SR1.5 found that the physically based emulators produced different projected non-CO2 forcing and identified the largely \r\nunexplained differences between the two emulators used as a  key knowledge gap (Forster et  al., 2018). This led to a  renewed \r\neffort to test the skill of various emulators. The Reduced Complexity Model Intercomparison Project (RCMIP; Nicholls et al., 2020) \r\nfound that the latest generation of the emulators can reproduce key characteristics of the observed changes in global surface air \r\ntemperature (GSAT) together with other key responses of ESMs (Cross-Chapter Box 7.1, Figure 1a). In particular, despite their reduced \r\nstructural complexity, some emulators are able to replicate the non-linear aspects of ESM GSAT response over a range of scenarios. \r\nGSAT emulation has been more thoroughly explored in the literature than other types of emulation"


id: a15bd26623bee1788c2166bba468d583
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 22948
content:
" Structural differences between \r\nemulation approaches lead to different outcomes and there are problems with emulating particular ESMs. In conclusion, there is \r\nmedium confidence that emulators calibrated to single ESM runs can reproduce ESM projections of the forced GSAT response to \r\nother similar emissions scenarios to within natural variability (Meinshausen et al., 2011b; Geoffroy et al., 2013a; Dorheim et al., 2020; \r\nNicholls et al., 2020; Tsutsui, 2020), although larger differences can remain for scenarios with very different forcing characteristics. \r\nFor variables other than GSAT there has not yet been a comprehensive effort to evaluate the performance of emulators."


id: 1001dc39cd5098942d8b56bcfc3f7d80
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 2
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\nGSAT change from effective radiative forcing (ERF) or concentration changes, where various versions of a two-layer energy budget \r\nemulator are used. The two-layer emulator is equivalent to a two-timescale impulse-response model (Supplementary Material 7.SM.2; \r\nGeoffroy et al., 2013b). Both a single configuration version and probabilistic forms are used. The emulator is an extension of the energy \r\nbudget equation (Box 7.1, Equation 7.1) and allows for heat exchange between the upper- and deep-ocean layers, mimicking the \r\nocean heat uptake that reduces the rate of surface warming under radiative forcing (Gregory, 2000; Held et al., 2010; Winton et al., \r\n2010; Armour, 2017; Mauritsen and Pincus, 2017; Rohrschneider et al., 2019)"


id: 890db2c31a1e1ba2a3bdcc3cc407a97d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 19103
content:
" Although the same energy budget emulator approach \r\nis used, different calibrations are employed in various sections, to serve different purposes and keep lines of evidence as independent \r\nas possible. Chapter 9 additionally employs projections of ocean heat content from the Chapter 7 two-layer emulator to estimate the \r\nthermostatic component of future sea level rise (Section 9.6.3 and Supplementary Material 7.SM.2)."


id: 82fac39c7d7a189496409c8d2dd71628
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 18136
content:
"Emissions-driven emulators (as opposed to ERF-driven or concentration-driven emulators) are also used in the Report. In Chapter 4 \r\n(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\nto represent the behaviour of ESMs. In Chapter  5 (Section  5.5) MAGICC7 is used to explore the non-CO2 GSAT contribution in \r\nemissions scenarios. In Chapter 6 and Chapter 7 (Section 7.6), two-layer model configurations are tuned to match the probabilistic \r\nGSAT responses of FaIRv1.6.2 and MAGICC7 emissions-driven emulators. For Chapter 6 the two median values from FaIRv1.6.2 and \r\nMAGICC7 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\nthe best-estimate ERF due to a doubling of CO2 of 3.93 W m-2 (Table 7.4). For Section 7"


id: afcd4fdaef220faa6ceaec2e79389d40
created_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 25178
content:
"6 a distribution of responses is used from the \r\ntwo emulators to estimate uncertainties in global temperature change potentials (GTP)."


id: 35bd4cfd88ea09a5427fe13d0ba5795e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 5866
content:
"Four emissions-driven emulators have been considered as tools for WGIII to explore the range of GSAT response to multiple scenarios \r\nbeyond those assessed in WGI. The four emulators are CICERO-SCM (Skeie et al., 2017, 2021), FaIRv1.6.2 (Millar et al., 2017; Smith \r\net al., 2018a), MAGICC7 (Meinshausen et al., 2009) and OSCARv3.1.1 (Gasser et al., 2017a, 2020). Each emulator's probabilistic \r\ndistribution has been calibrated to capture the relationship between emissions and GSAT change. The calibration is informed by \r\nthe WGI assessed ranges of ECS, TCR, historical GSAT change, ERF, carbon cycle metrics and future warming projections under the \r\n(concentration-driven) SSP scenarios. The emulators are then provided as a tool for WGIII to perform a GSAT-based classification of \r\nmitigation scenarios consistent with the physical understanding assessed in WGI. The calibration step reduced the emulator differences \r\nidentified in SR1.5"


id: c9d22945b3fdd83d5517a0ff61679dc1
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 29308
content:
" Note that evaluation of both central and range estimates of each emulator's probabilistic projections is important \r\nto assess the fitness-for-purpose for the classification of scenarios in WGIII, based on information beyond the central estimate of \r\nGSAT warming."


id: b9342b585167358df89a4bb41d12e1cf
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 26673
content:
"MAGICC7 and FaIRv1.6.2 emissions-based emulators are able to represent the WGI assessment to within small differences (defined \r\nhere as within typical rounding precisions of +-5% for central estimates and +-10% for ranges) across more than 80% of metric ranges \r\n(Cross-Chapter Box 7.1, Table 2). Both calibrated emulators are consistent with assessed ranges of ECS, historical GSAT, historical \r\nocean heat uptake, total greenhouse gas ERF, methane ERF and the majority of the assessed SSP warming ranges. FaIRv1.6.2 also \r\nmatches the assessed central value of TCRE and airborne fraction. Whereas, MAGICC7 matches the assessed TCR ranges as well as \r\nproviding a closer fit to the SSP warming ranges for the lower-emissions scenarios"


id: 11bac5ba59144a1772e0eaaf695f3e24
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 284
content:
" In the evaluation framework considered here, \r\nCICERO-SCM represents historical warming to within 2% of the assessed ranges and also represents future temperature ranges \r\nacross the majority of the assessment, although it lacks the representation of the carbon cycle. In this framework, OSCARv3.1.1 is less \r\nable to represent the assessed projected GSAT ranges although it matches the range of airborne fraction estimates closely and the \r\nassessed historical GSAT likely range to within 0.5%. Despite these identified limitations, both CICERO-SCM and OSCARv3.1.1 provide \r\nadditional information for evaluating the sensitivity of scenario classification to model choice."


id: b3474a08bc5f179d02f896c181bbc0aa
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 25719
content:
"Cross-Chapter Box 7.1, Figure 1 | A comparison between the global surface air temperature (GSAT) response of various calibrated simple \r\nclimate 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\nmulti-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\nscenario 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\n(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\nas 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)"


id: b35abbd04ebdc0466cb445f5cc24635c
created_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 25729
content:
" Further \r\ndetails on data sources and processing are available in the chapter data table (Table 7.SM.14)."


id: 54b08a54a61ee117795e37665cb93b14
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 10780
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\ntransient 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\n(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\nanalysed 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\nterm (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\nof this table with the absolute values rather than percentage differences"


id: 38fd58477fd8a08341de5e3beeab5f8f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 6336
content:
" The columns labelled 'upper' and 'lower' indicate 5-95% ranges, except for the variables \r\ndemarcated 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\ncombination 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\nnarrow 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\ndifferences (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\nblue 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\nthat values are rounded after the colours are applied."


id: ba24bb080bf5b98e453887c3e89b2f5f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 26828
content:
"The magnitude of global surface temperature change primarily \r\ndepends on the strength of the radiative forcings and feedbacks, \r\nthe latter defined as the changes of the net energy budget at the \r\ntop-of-atmosphere (TOA) in response to a change in the GSAT (Box 7.1, \r\nEquation 7.1). Feedbacks in the Earth system are numerous, and it can \r\nbe helpful to categorize them into three groups: (i) physical feedbacks; \r\n(ii) biogeophysical and biogeochemical feedbacks; and (iii) long-term \r\nfeedbacks associated with ice sheets. The physical feedbacks (e.g., those \r\nassociated with changes in lapse rate, water vapour, surface albedo, or \r\nclouds; Sections 7.4.2.1-7.4.2.4) and biogeophysical/biogeochemical \r\nfeedbacks (e.g., those associated with changes in methane, aerosols, \r\nozone, or vegetation; Section 7.4.2.5) act both on time scales that are \r\nused to estimate the equilibrium climate sensitivity (ECS) in models \r\n(typically 150 years, see Box 7"


id: e574a9e26318dede0aba27b378617b85
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 33606
content:
"1) and on longer time scales required \r\nto reach equilibrium. Long-term feedbacks associated with ice sheets \r\n(Section  7.4.2.6) are relevant primarily after several centuries or \r\nmore. The feedbacks associated with biogeophysical/biogeochemical \r\nprocesses and ice sheets, often collectively referred to as Earth system \r\nfeedbacks, had not been included in conventional estimates of the \r\nclimate feedback (e.g., Hansen et al., 1984), but the former can now \r\nbe quantified and included in the assessment of the total (net) climate \r\nfeedback. Feedback analysis represents a formal framework for the \r\nquantification of the coupled interactions occurring within a complex \r\nEarth system in which everything influences everything else (e.g., Roe, \r\n2009). As  used here (as presented in Section  7.4.1), the primary \r\nobjective of feedback analysis is to identify and understand the key \r\nprocesses that determine the magnitude of the surface temperature \r\nresponse to an external forcing"


id: ccc7b60681cf838d254b95c777a3ff87
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 29759
content:
" For each feedback, the basic underlying \r\nmechanisms and their assessments are presented in Section 7.4.2."


id: d88717363510a78f00b5419c55ad6f06
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 31586
content:
"In global climate models, the feedback parameters ax in global \r\nwarming conditions are often estimated as the mean differences in \r\nthe radiative fluxes between atmosphere-only simulations in which \r\nthe change in SST is prescribed (Cess et al., 1990), or as the regression \r\nslope of change in radiation flux against change in GSAT using \r\natmosphere-ocean coupled simulations with abrupt CO2 changes \r\n(abrupt4xCO2) for 150 years (Box 7.1; Gregory et al., 2004; Andrews \r\net  al., 2012; Caldwell et  al., 2016). Neither method is perfect, but \r\nboth are useful and yield consistent results (Ringer et al., 2014). In \r\nthe regression method, the radiative effects of land warming are \r\nexcluded from the ERF due to doubling of CO2 (Section 7.3.2), which \r\nmay overestimate feedback values by about 15%. At the same time, \r\nthe feedback calculated using the regression over years 1-150 ignores \r\nits state-dependence on multi-centennial time scales (Section 7.4"


id: 6baa158a24c8230bcf0f47ac68dbbd92
created_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 14565
content:
"3), \r\nprobably giving an underestimate of a  by about 10% (Rugenstein \r\net al., 2019). These effects are both small and approximately cancel \r\neach other in the ensemble mean, justifying the use of regression over \r\n150 years as an approximation to feedbacks in ESMs."


id: 114814fd799318f1a24152eae6800b52
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 198
content:
"The Planck response represents the additional thermal or longwave (LW) \r\nemission to space arising from vertically uniform warming of the surface \r\nand the atmosphere. The Planck response aP, often called the Planck \r\nfeedback, plays a fundamental stabilizing role in Earth's climate and has \r\na value that is strongly negative: a warmer planet radiates more energy \r\nto space. A  crude estimate of aP can be made using the normalized \r\ngreenhouse effect g, defined as the ratio between the greenhouse effect \r\nG and the upwelling LW flux at the surface (Raval and Ramanathan, \r\n1989). Current estimates (Section 7.2, Figure 7.2) give G = 159 W m-2 and \r\ng 0.4. Assuming gis constant, one obtains for a surface temperature \r\nTs = 288 K, aP = (g - 1) 4 s T 3\r\ns  -3.3 W m-2 degC-1, where s is the Stefan-\r\nBoltzmann constant"


id: 48eb422f391fcf06c81f69e6bb7d0081
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 8860
content:
" This parameter aP is estimated more accurately \r\nusing kernels obtained from meteorological reanalysis or climate \r\nsimulations (Soden and Held, 2006; Dessler, 2013; Vial et  al., 2013; \r\nCaldwell et al., 2016; Colman and Hanson, 2017; Zelinka et al., 2020). \r\nDiscrepancies among estimates primarily arise because differences \r\nin cloud distributions make the radiative kernels differ (Kramer et al., \r\n2019). Using six different kernels, Zelinka et  al. (2020) obtained \r\na  spread of +-0.1 W m-2 degC-1 (one standard deviation). Discrepancies \r\namong estimates secondarily arise from differences in the pattern of \r\nequilibrium surface temperature changes among ESMs. For the CMIP5 \r\nand CMIP6 models this introduces a  spread of +-0.04  W  m-2  degC-1\r\n(one  standard deviation). The multi-kernel and multi-model mean of \r\naP is equal to -3.20 W m-2 degC-1 for the CMIP5 and -3.22 W m-2 degC-1\r\nfor the CMIP6 models (Supplementary Material, Table 7.SM.5)"


id: bd4f91ab13f57ff9877d0bad4c065489
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 27347
content:
" Overall, \r\nthere is high confidence in the estimate of the Planck response, which is \r\nassessed to be aP = -3.22 W m-2 degC-1 with a very likely range of -3.4 to \r\n-3.0 W m-2 degC-1 and a likely range of -3.3 to -3.1 W m-2 degC-1."


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vector_id: 19644
content:
"Feedback parameters from lines of evidence other than global \r\nmodels are estimated in various ways. For example, observational \r\ndata combined with GCM simulations could produce an emergent \r\nconstraint on a  particular feedback (Hall and Qu, 2006; Klein and \r\nHall, 2015), or the observed interannual fluctuations in the global \r\nmean TOA radiation and the surface air temperature, to which the \r\nlinear regression analysis is applied, could generate a direct estimate \r\nof the climate feedback, assuming that the feedback associated with \r\ninternal climate variability at short time scales can be a surrogate \r\nof the feedback to CO2-induced warming (Dessler, 2013; Loeb et al., \r\n2016). The assumption is not trivial, but can be justified given that the \r\nclimate feedbacks are fast enough to occur at the interannual time \r\nscale"


id: 2be11f665dd94ec5a2c07466e82cf9c6
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 4330
content:
" Indeed, a  broad agreement has been obtained in estimates \r\nof individual physical climate feedbacks based on interannual \r\nvariability and longer climate change time scales in GCMs (Zhou \r\net al., 2015; Colman and Hanson, 2017). This means that the climate \r\nfeedbacks estimated from the observed interannual fluctuations are \r\nrepresentative of the longer-term feedbacks (decades to centuries). \r\nCare must be taken for these observational estimates because they \r\ncan be sensitive to details of the calculation such as data sets and \r\nperiods used (Dessler, 2013; Proistosescu et al., 2018). In particular, \r\nthere would be a dependence of physical feedbacks on the surface \r\nwarming pattern at the interannual time scale due, for example, to \r\nEl Nino-Southern Oscillation. However, this effect both amplifies and \r\nsuppresses the feedback when data include the positive and negative \r\nphases of the interannual fluctuation, and therefore the net bias will \r\nbe small."


id: d2333332c81a0ff19beb2e6aa34100db
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 30600
content:
"The second decomposition was proposed by Held and Shell (2012) to \r\nseparate the response that would occur under the assumption that \r\nrelative humidity remains constant from that due to the change in \r\nrelative humidity. The feedback is decomposed into three: (i) change \r\nin  water vapour due to an identical temperature increase at the \r\nsurface and throughout the troposphere assuming constant relative \r\nhumidity, which will be called the Clausius-Clapeyron (CC) feedback \r\nhere; (ii) change in LR assuming constant relative humidity (LR*); \r\n(iii) change in relative humidity (RH). Since AR5 it has been clarified \r\nthat by construction, the sum of the temperature lapse rate and specific \r\nhumidity (LR + WV) feedbacks is equal to the sum of the Clausius-\r\nClapeyron feedback, the lapse rate feedback assuming constant \r\nrelative humidity, and the feedback from changes in relative humidity \r\n(that is, CC + LR* + RH)"


id: d5e504db557da9e9091dbdc8fe1d3fdb
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 31168
content:
" Therefore, each of these two sums may \r\nsimply be referred to as the 'water-vapour plus lapse-rate' feedback."


id: ddfba76d2f6358538349273bf6dadd07
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 32425
content:
"The specific humidity (WV) feedback, also known as the water-vapour \r\nfeedback, quantifies the change in radiative flux at the TOA due to \r\nchanges in atmospheric water vapour concentration associated with \r\na change in global mean surface air temperature. According to theory, \r\nobservations and models, the water vapour increase approximately \r\nfollows the Clausius-Clapeyron relationship at the global scale \r\nwith regional differences dominated by dynamical processes \r\n(Section 8.2.1; Sherwood et al., 2010a; Chung et al., 2014; Romps, \r\n2014; R. Liu et al., 2018; Schroder et al., 2019). Greater atmospheric \r\nwater vapour content, particularly in the upper troposphere, results in \r\nenhanced absorption of LW and SW radiation and reduced outgoing \r\nradiation. This is a  positive feedback. Atmospheric moistening \r\nhas been detected in satellite records (Section  2.3.1.3.3), it is \r\nsimulated by climate models (Section  3.3.2"


id: d868b4b82d795f82bf903c681973308a
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 31575
content:
"2), and the estimates \r\nagree within model and observational uncertainty (Soden et  al., \r\n2005; Dessler, 2013; Gordon et  al., 2013; Chung et  al., 2014). \r\nThe estimate of this feedback inferred from satellite observations is \r\naWV = 1.85 +- 0.32 W m-2 degC-1 (R. Liu et al., 2018). This is consistent \r\nwith the value aWV= 1.77 +- 0.20 W m-2 degC-1 (one standard deviation) \r\nobtained with CMIP5 and CMIP6 models (Zelinka et al., 2020)."


id: 5923c69b65d24461ef165ed93920280b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 11520
content:
"The CC feedback has a large positive value due to well understood \r\nthermodynamic and radiative processes: aCC= 1.36 +- 0.04 W m-2 degC-1\r\n(one standard deviation; Held and Shell, 2012; Zelinka et al., 2020). \r\nThe lapse-rate feedback assuming a constant relative humidity (LR*) in \r\nCMIP6 models has small absolute values (aLR* = -0.10 +- 0.07 W m-2 degC-1\r\n(one standard deviation)), as expected from theoretical arguments \r\n(Ingram, 2010, 2013). It includes the pattern effect of surface warming \r\nthat modulates the lapse rate and associated specific humidity \r\nchanges (Po-Chedley et al., 2018b). The relative humidity feedback is \r\nclose to zero (aRH = 0.00 +- 0.06 W m-2 degC-1 (one standard deviation)) \r\nand the spread among models is confined to the tropics (Sherwood \r\net  al.,  2010b; Vial et  al., 2013; Takahashi et  al., 2016; Po-Chedley \r\net al., 2018b). The change in upper tropospheric RH is closely related \r\nto model representation of current climate (Sherwood et al"


id: 1603f89f28d3de46ae77ebe403f52a63
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 988
content:
", 2010b; \r\nPo-Chedley et  al., 2019), and a  reduction in model RH biases is \r\nexpected to reduce the uncertainty of the RH feedback. At interannual \r\ntime scales, it has been shown that the change in RH in the tropics is \r\nrelated to the change of the spatial organization of deep convection \r\n(Holloway et al., 2017; Bony et al., 2020)."


id: 2da60df34f93e72d2a541852a0de5bcb
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 4621
content:
"The lapse-rate (LR) feedback quantifies the change in radiative flux \r\nat the TOA due to a nonuniform change in the vertical temperature \r\nprofile. In the tropics, the vertical temperature profile is mainly driven \r\nby moist convection and is close to a moist adiabat. The warming is \r\nlarger in the upper troposphere than in the lower troposphere (Manabe \r\nand Wetherald, 1975; Santer et al., 2005; Bony et al., 2006), leading \r\nto a  larger radiative emission to space and therefore a  negative \r\nfeedback. This larger warming in the upper troposphere than at \r\nthe surface has been observed over the last 20 years thanks to the \r\navailability of sufficiently accurate observations (Section 2.3.1.2.2). \r\nIn the extratropics, the vertical temperature profile is mainly driven \r\nby a balance between radiation, meridional heat transport and ocean \r\nheat uptake (Rose et al., 2014). Strong winter temperature inversions \r\nlead to warming that is larger in the lower troposphere (Payne et al"


id: a74674c38e1f98d1e6f73425b909910d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 23832
content:
", \r\n2015; Feldl et al., 2017a) and a positive LR feedback in polar regions \r\n(Section 7.4.4.1; Manabe and Wetherald, 1975; Bintanja et al., 2012; \r\nPithan and Mauritsen, 2014). However, the tropical contribution \r\ndominates, leading to a negative global mean LR feedback (Soden \r\nand Held, 2006; Dessler, 2013; Vial et al., 2013; Caldwell et al., 2016). "


id: 7991afd4def9c4767b3f43371a7b57bd
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 16699
content:
"CMIP5 and CMIP6 models show moderate spread in global aA, \r\ndetermined from century time scale changes (Qu and Hall, 2014; \r\nSchneider et  al., 2018; Thackeray and Hall, 2019; Zelinka et  al., \r\n2020), owing to variations in modelled sea ice loss and snow cover \r\nresponse in boreal forest regions. The multi-model mean global-scale \r\naA (from all contributions) over the 21st century in CMIP5 models \r\nunder the RCP8.5 scenario was derived by Schneider et  al. (2018) \r\nto be 0.40 +- 0.10 W m-2 degC-1 (one standard deviation). Moreover, \r\nthey found that modelled aA does not decline over the 21st century, \r\ndespite large losses of snow and sea ice, though a weakened feedback \r\nis apparent after 2100. Using the idealized abrupt4xCO2, as for the \r\nother feedbacks, the estimate of the global-scale albedo feedback in \r\nthe CMIP5 models is 0.35 +- 0.08 W m-2 degC-1 (one standard deviation; \r\nVial et  al., 2013; Caldwell et  al., 2016). The CMIP6 multi-model \r\nmean varies from 0.3 to 0"


id: c206108cfb2878b0e3afbf7718d91b96
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 28108
content:
"5 W m-2 degC-1 depending on the kernel \r\nused (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\nfrom the CMIP5 abrupt4xCO2 ensemble, employing model-specific \r\nestimates of atmospheric attenuation and thereby avoiding bias \r\nassociated with use of a single radiative kernel."


id: 4068d7b3cefe735742acf0c64ece2621
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 7519
content:
"Surface albedo is determined primarily by reflectance at Earth's surface, \r\nbut also by the spectral and angular distribution of incident solar \r\nradiation. Changes in surface albedo result in changes in planetary \r\nalbedo that are roughly reduced by two-thirds, owing to atmospheric \r\nabsorption and scattering, with variability and uncertainty arising \r\nprimarily from clouds (Bender, 2011; Donohoe and Battisti, 2011; Block \r\nand Mauritsen, 2013). Temperature change induces surface-albedo \r\nchange through several direct and indirect means. In the present climate \r\nand at multi-decadal time scales, the largest contributions by far are \r\nchanges in the extent of sea ice and seasonal snow cover, as these \r\nmedia are highly reflective and are located in regions that are close \r\nto the melting temperature (Sections 2.3.2.1 and 2.3.2.2). Reduced \r\nsnow cover on sea ice may contribute as much to albedo feedback as \r\nreduced extent of sea ice (Zhang et al., 2019)"


id: 21e2ba320035ee1acc66da1c09c1bdc
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 2772
content:
" Changes in the snow \r\nmetamorphic rate, which generally reduces snow albedo with warmer \r\ntemperature, and warming-induced consolidation of light-absorbing \r\nimpurities near the surface, also contribute secondarily to the albedo \r\nfeedback (Flanner and Zender, 2006; Qu and Hall, 2007; Doherty et al., \r\n2013; Tuzet et al., 2017). Other contributors to albedo change include \r\nvegetation state (assessed separately in Section 7.4.2.5), soil wetness \r\nand ocean roughness."


id: 42a5ba0a1c31be53629050bf1371f1b0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 7878
content:
"The surface-albedo feedback estimates using centennial changes \r\nhave been shown to be highly correlated to those using seasonal \r\nregional changes for NH land snow (Qu and Hall, 2014) and Arctic \r\nsea ice (Thackeray and Hall, 2019). For the NH land snow, because \r\nthe physics underpinning this relationship are credible, this opens the \r\npossibility to use it as an emergent constraint (Qu and Hall, 2014). \r\nConsidering only the eight models whose seasonal cycle of albedo \r\nfeedback falls within the observational range does not change \r\nthe multi-model mean contribution to global aA (0.08 W m-2 degC-1) \r\nbut decreases the inter-model spread by a  factor of two (from \r\n+-0.03 to  +-0.015 W m-2 degC-1; Qu and Hall, 2014). For Arctic sea \r\nice, Thackeray and Hall (2019) show that the seasonal cycle also \r\nprovides an emergent constraint, at least until mid-century when the \r\nrelationship degrades. They find that the CMIP5 multi-model mean \r\nof the Arctic sea ice contribution to aA is 0"


id: d5c97daad99162d0b42721a68c1e5e14
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 31153
content:
"13 W m-2 degC-1 and that \r\nthe inter-model spread is reduced by a factor of two (from +-0.04 to \r\n+-0.02 W m-2  degC-1) when the emergent constraint is used. This model \r\nestimate is smaller than observational estimates (Pistone et al., 2014; \r\nCao et al., 2015) except those of Donohoe et al. (2020). This can be \r\ntraced to CMIP5 models generally underestimating the rate of Arctic \r\nsea ice loss during recent decades (Section 9.3.1; Stroeve et al., 2012; "


id: 83ca6a01e0359b43fdcb6bbbcfe38b85
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 18267
content:
"Clouds have various types, from optically thick convective clouds \r\nto thin stratus and cirrus clouds, depending upon thermodynamic \r\nconditions and large-scale circulation (Figure 7.9). Over the equatorial \r\nwarm pool and inter-tropical convergence zone (ITCZ) regions, high \r\nSSTs stimulate the development of deep convective cloud systems, \r\nwhich are accompanied by anvil and cirrus clouds near the tropopause \r\nwhere the convective air outflows. The large-scale circulation \r\nassociated with these convective clouds leads to subsidence over \r\nthe subtropical cool ocean, where deep convection is suppressed by \r\na  lower tropospheric inversion layer maintained by the subsidence \r\nand promoting the formation of shallow cumulus and stratocumulus \r\nclouds. In the extratropics, mid-latitude storm tracks control cloud \r\nformation, which occurs primarily in the frontal bands of extratropical \r\ncyclones"


id: 5721e9406242c56765adff7163f6d326
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 11190
content:
" Since liquid droplets do not freeze spontaneously at \r\ntemperatures warmer than approximately -40degC and ice nucleating \r\nparticles that can aid freezing at warmer temperatures are scarce \r\n(see Section 7.3.3), extratropical clouds often consist both of super\u0002cooled liquid and ice crystals, resulting in mixed-phase clouds."


id: 8cf985e20ee8442f140bb81be412b7fd
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 19701
content:
"increase even though the surface warms, resulting in an enhancement \r\nof the high-cloud greenhouse effect (a positive feedback; Yoshimori \r\net al. (2020)). The upward shift of high-clouds with surface warming \r\nis detected in observed interannual variability and trends in satellite \r\nrecords for recent decades (Chepfer et al., 2014; Norris et al., 2016; \r\nSaint-Lu et  al., 2020). The observational detection is not always \r\nsuccessful (Davies et al., 2017), but the cloud altitude shifts similarly in \r\nmany CRM experiments (Khairoutdinov and Emanuel, 2013; Tsushima \r\net al., 2014; Narenpitak et al., 2017). The high-cloud altitude feedback \r\nwas estimated to be 0.5 W m-2 degC-1 based on GCMs in AR5, but is \r\nrevised, using a recent re-evaluation that excludes aliasing effects by \r\nreduced low-cloud amounts, downward to 0.22 +- 0.12 W m-2 degC-1\r\n(one standard deviation; Zhou et  al., 2014; Zelinka et  al., 2020)"


id: 9e5631535b64bfb713e35c49e1cf3572
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 22437
content:
" \r\nIn conclusion, there is high confidence in the positive high-cloud \r\naltitude feedback simulated in ESMs as it is supported by theoretical, \r\nobservational, and process modelling studies."


id: 1b3673795582447364b3ad2ab644968f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 1747
content:
"In a  first attempt to systematically evaluate equilibrium climate \r\nsensitivity (ECS) based on fully coupled general circulation models \r\n(GCMs) in AR4, diverging cloud feedbacks were recognized as \r\na dominant source of uncertainty. An advance in understanding the \r\ncloud feedback was to assess feedbacks separately for different cloud \r\nregimes (Gettelman and Sherwood, 2016). A  thorough assessment \r\nof cloud feedbacks in different cloud regimes was carried out in AR5 \r\n(Boucher et al., 2013), which assigned high or medium confidence for \r\nsome cloud feedbacks but low or no confidence for others (Table 7.9). \r\nMany studies that estimate the net cloud feedback using CMIP5 \r\nsimulations (Vial et  al., 2013; Caldwell et  al., 2016; Zelinka et  al"


id: 2e0f5746fb52070e5b7aee4504ac4e3f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 4684
content:
", \r\n2016; Colman and Hanson, 2017) show different values depending on \r\nthe methodology and the set of models used, but often report a large \r\ninter-model spread of the feedback, with the 90% confidence interval \r\nspanning both weak negative and strong positive net feedbacks. \r\nPart of this diversity arises from the dependence of the model cloud \r\nfeedbacks on the parametrization of clouds and their coupling to \r\nother sub-grid-scale processes (Zhao et al., 2015)."


id: ce4d487a13092271db3ad50a483fe5b7
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 29986
content:
"Since AR5, community efforts have been undertaken to understand \r\nand quantify the cloud feedbacks in various cloud regimes coupled \r\nwith large-scale atmospheric circulation (Bony et al., 2015). For some \r\ncloud regimes, alternative tools to ESMs, such as observations, theory, \r\nhigh-resolution cloud resolving models (CRMs), and large eddy \r\nsimulations (LES), help quantify the feedbacks. Consequently, the net \r\ncloud feedback derived from ESMs has been revised by assessing \r\nthe regional cloud feedbacks separately and summing them with \r\nweighting by the ratio of fractional coverage of those clouds over the \r\nglobe to give the global feedback, following an approach adopted \r\nin Sherwood et al. (2020). This 'bottom-up' assessment is explained \r\nbelow with a  summary of updated confidence of individual cloud \r\nfeedback components (Table 7.9). Dependence of cloud feedbacks on \r\nevolving patterns of surface warming will be discussed in Section 7.4"


id: 712fe8055d0227ca4650c252aadff380
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 15444
content:
"(one  standard deviation; Zhou et  al., 2014). The negative LW \r\nfeedback due to reduced amount of thick high-clouds is partly \r\ncompensated by the positive SW feedback (due to less reflection of \r\nsolar radiation), so that the tropical high-cloud amount feedback \r\nis assessed to be equal to or smaller than their sum. Consistently, \r\nthe net high-cloud feedback in the tropical convective regime, \r\nincluding a  part of the altitude feedback, is estimated to have \r\nthe global contribution of -0.13 +- 0.06 W m-2 degC-1 (one standard \r\ndeviation; Williams and Pierrehumbert, 2017). The negative cloud \r\nLW feedback is considerably biased in CMIP5 GCMs (Mauritsen and \r\nStevens, 2015; Su et al., 2017; Li et al., 2019) and highly uncertain, \r\nprimarily due to differences in the convective parametrization (Webb \r\net al., 2015)"


id: c290b4577751d44853e188727960e58b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 28205
content:
" Furthermore, high-resolution CRM simulations cannot \r\nalone be used to constrain uncertainty because the results depend \r\non parametrized cloud microphysics and turbulence (Bretherton \r\net  al., 2014; Ohno et  al., 2019). Therefore, the tropical high-cloud \r\namount feedback is assessed as negative but with low confidence \r\ngiven the lack of modelling evidence. Taking observational estimates \r\naltogether and methodological uncertainty into account, the global \r\ncontribution of the high-cloud amount feedback is assessed to be \r\n-0.15 +- 0.2 W m-2 degC-1 (one standard deviation)."


id: b9d8cd0a82bfaca441409b6b03e51011
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 26778
content:
"Intensification of the global hydrological cycle is a  robust feature \r\nof global warming, but at the same time, many land areas in \r\nthe subtropics will experience drying at the surface and in the \r\natmosphere (Section  8.2.2). This occurs due to limited water \r\navailability in these regions, where the cloudiness is consequently \r\nexpected to decrease. Reduction in clouds over land is consistently \r\nidentified in the CMIP5 models and also in a  GCM with explicit \r\nconvection (Bretherton et al., 2014; Kamae et al., 2016a). Because \r\nlow-clouds make up the majority of subtropical land clouds, this \r\nreduced amount of low-clouds reflects less solar radiation and leads \r\nto a positive feedback similar to the marine low-clouds. The mean \r\nestimate of the global land cloud  feedback in CMIP5 models is \r\nsmaller than the marine low-cloud feedback, 0.08 +- 0.08 W m-2 degC-1 \r\n(Zelinka et al., 2016). These values are nearly unchanged in CMIP6 \r\n(Zelinka et al., 2020)"


id: 908250bfa0e65c11f8a26b515c281a4e
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 20271
content:
" However, ESMs still have considerable biases \r\nin the climatological temperature and cloud fraction over land, \r\nand the magnitude of this feedback has not yet been supported by \r\nobservational evidence. Therefore, the feedback due to decreasing \r\nland clouds is assessed to be 0.08 +- 0.08 W m-2 degC-1 (one standard \r\ndeviation) with low confidence."


id: 1002f1778b09f84b8125c4fba7c4783
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 4
content:
"In order to disentangle the large-scale processes that cause the cloud \r\namount either to increase or decrease in response to the surface \r\nwarming, the cloud feedback has been expressed in terms of several \r\n'cloud controlling factors' (Qu et al., 2014, 2015; Zhai et al., 2015; \r\nBrient and Schneider, 2016; Myers and Norris, 2016; McCoy et al., \r\n2017a). The advantage of this approach over conventional calculation \r\nof cloud feedbacks is that the temperature-mediated cloud response \r\ncan be estimated without using information of the simulated cloud \r\nresponses that are less well-constrained than the changes in the \r\nenvironmental conditions. Two dominant factors are identified for \r\nthe subtropical low-clouds: a thermodynamic effect due to rising SST \r\nthat acts to reduce low-cloud by enhancing cloud-top entrainment \r\nof dry air, and a  stability effect accompanied by an enhanced \r\ninversion strength that acts to increase low-cloud (Qu et al., 2014, \r\n2015; Kawai et  al., 2017)"


id: 1a6b35c8afee51121f774648a0529552
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 1636
content:
" These controlling factors compensate \r\nwith a  varying degree in different ESMs, but can be constrained \r\nby referring to the observed seasonal or interannual relationship \r\nbetween the low-cloud amount and the controlling factors in the \r\nenvironment as a  surrogate. The analysis leads to a  positive local \r\nfeedback that has the global contribution of 0.14 to 0.36 W m-2 degC-1\r\n(Klein et  al., 2017), to which the feedback in the stratocumulus \r\nregime dominates over the feedback in the trade cumulus regime "


id: 8ffbdd093dafa2d0c40294c74b97a60f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 20180
content:
"Poleward shifts in the mid-latitude jets are evident since the 1980s \r\n(Section  2.3.1.4.3) and are a  feature of the large-scale circulation \r\nchange in future projections (Section 4.5.1.6). Because mid-latitude \r\nclouds over the North Pacific, North Atlantic and Southern Ocean \r\nare induced mainly by extratropical cyclones in the storm tracks \r\nalong the jets, it has been suggested that the jet shifts should be \r\naccompanied by poleward shifts in the mid-latitude clouds, which \r\nwould result in a positive feedback through the reduced reflection \r\nof insolation (Boucher et al., 2013). However, studies since AR5 have \r\nrevealed that this proposed mechanism does not apply in practice \r\n(Ceppi and Hartmann, 2015). While a poleward shift of mid-latitude \r\ncloud maxima in the free troposphere has been identified in satellite \r\nand ground-based observations (Bender et al"


id: 11aa89c53b18d44dd917f10781a2158d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 270
content:
", 2012; Eastman and \r\nWarren, 2013), associated changes in net CRE are small because \r\nthe responses in high and low-clouds to the jet shift act to cancel \r\neach other (Grise and Medeiros, 2016; Tselioudis et al., 2016; Zelinka \r\net  al., 2018). This cancellation is not well captured in ESMs (Lipat \r\net al., 2017), but the above findings show that the mid-latitude cloud \r\nfeedback is not dynamically driven by the poleward jet shifts, which \r\nare rather suggested to occur partly in response to changes in high \r\nclouds (Y. Li et al., 2018)."


id: 572d7fa02bc541933e45a3d220c8e524
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 11203
content:
"Thermodynamics play an important role in controlling extratropical \r\ncloud amount equatorward of about 50deg latitude. Recent studies \r\nshowed, using observed cloud controlling factors, that the mid-latitude \r\nlow-cloud fractions decrease with rising SST, which also acts to \r\nweaken stability of the atmosphere unlike in the subtropics (McCoy \r\net al., 2017a). ESMs consistently show a decrease of cloud amounts \r\nand a resultant positive SW feedback in the 30deg-40deg latitude bands, \r\nwhich can be constrained using observations of seasonal migration \r\nof cloud amount (Zhai et  al., 2015). Based on the qualitative \r\nagreement between observations and ESMs, the mid-latitude cloud \r\namount feedback is assessed as positive with medium confidence. \r\nFollowing these emergent constraint studies using observations \r\nand CMIP5/6 models, the global contribution of net cloud amount \r\nfeedback over 30deg-60deg ocean areas, covering 27% of the globe, \r\nis assessed at 0.09  +- 0"


id: 41a9c0735ff78c60638f93f91dfc1c1
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 7731
content:
"1 W m-2 degC-1 (one standard deviation), in \r\nwhich the uncertainty reflects potential errors in models' low-cloud \r\nresponse to changes in thermodynamic conditions."


id: 294a14b4d35d5e02ab0b0fae6da8cc6d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 3933
content:
"Arctic cloud feedback \r\nClouds in polar regions, especially over the Arctic, form at low altitude \r\nabove or within a stable to neutral boundary layer and are known to \r\nco-vary with sea ice variability beneath. Because the clouds reflect \r\nsunlight during summer but trap LW radiation throughout the year, \r\nseasonality plays an important role in cloud effects on Arctic climate \r\n(Kay et al., 2016b). AR5 assessed that Arctic low-cloud amount will \r\nincrease in boreal autumn and winter in response to declining sea \r\nice in a  warming climate, due primarily to an enhanced upward \r\nmoisture flux over open water. The cloudier conditions during these \r\nseasons result in more downwelling LW radiation, acting as a positive \r\nfeedback on surface warming (Kay and Gettelman, 2009). Over \r\nrecent years, further evidence of the cloud contribution to the Arctic \r\namplification has been obtained (Section 7.4.4.1; Goosse et al., 2018)"


id: 8f3ae893b05331d6d7b6de972b155fc9
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 20078
content:
" \r\nSpace-borne lidar (light detection and ranging) observations show \r\nthat the cloud response to summer sea ice loss is small and cannot \r\novercome the cloud effect in autumn (Taylor et al., 2015; Morrison \r\net al., 2019). The seasonality of the cloud response to sea ice variability \r\nis reproduced in GCM simulations (Laine et al., 2016; Yoshimori et al., \r\n2017). The agreement between observations and models indicates \r\nthat the Arctic cloud feedback is positive at the surface. This leads \r\nto an Arctic cloud feedback at TOA that is likely positive, but very \r\nsmall in magnitude, as found in some climate models (Pithan and \r\nMauritsen, 2014; Morrison et al., 2019). The observational estimates \r\nare sensitive to the analysis period and the choice of reanalysis data, \r\nand a recent estimate of the TOA cloud feedback over 60degN-90degN \r\nusing atmospheric reanalysis data and CERES satellite observations \r\nsuggests a regional value ranging from -0.3 to +0"


id: 80ed52da122b3c10b284cfc46298d7af
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 17804
content:
"5 W m-2 degC-1, which \r\ncorresponds to a global contribution of -0.02 to +0.03 W m-2 degC-1\r\n(R. Zhang et al., 2018). Based on the overall agreement between ESMs \r\nand observations, the Arctic cloud feedback is assessed to be small \r\npositive and has the value of 0.01 +- 0.05 W m-2 degC-1 (one standard \r\ndeviation). The assessed range indicates that a negative feedback is \r\nalmost as probable as a positive feedback, and the assessment that \r\nthe Arctic cloud feedback is positive is therefore given low confidence."


id: fc65216026dabb5c6e3e827ca1da51ce
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 37215
content:
"Due to insufficient amounts of super-cooled liquid water in the \r\nsimulated atmospheric mean state, many CMIP5 models overestimated \r\nthe conversion from ice to liquid clouds with climate warming and \r\nthe resultant negative phase-change feedback (Kay et  al., 2016a; \r\nTan et al., 2016; Lohmann and Neubauer, 2018). This feedback can \r\nbe constrained using satellite-derived LWP observations over the \r\npast 20 years that enable estimates of both long-term trends and \r\nthe interannual relationship with SST variability (Gordon and Klein, \r\n2014; 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\n40degS-70degS depending on the methodology (Ceppi et al., 2016; Terai \r\net al., 2016). In some CMIP6 models, representation of super-cooled \r\nliquid water content has been improved, leading to weaker \r\nnegative optical depth feedback over the Southern Ocean closer \r\nto observational estimates (Bodas-Salcedo et  al"


id: 9aebd430e6089c123088afe47e49f97
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 21907
content:
", 2019; Gettelman \r\net al., 2019). This improvement at the same time results in a positive \r\noptical depth feedback over other extratropical ocean where LWP \r\ndecreased in response to reduced stability in those CMIP6 models \r\n(Zelinka et al., 2020). Given the accumulated observational estimates \r\nand an improved agreement between ESMs and observations, the \r\nextratropical optical depth feedback is assessed to be small negative \r\nwith medium confidence. Quantitatively, the global contribution of \r\nthis feedback is assessed to have a value of -0.03 +- 0.05 W m-2 degC-1 "


id: 6f0fdec85d7bbc74e9df13c34bf3b50b
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 15099
content:
"climate feedbacks associated with these variables has been recognized \r\nsince early studies of climate change. However, in addition to these \r\nphysical climate feedbacks, the Earth system includes feedbacks for \r\nwhich the effect of global mean surface temperature change on the \r\nTOA energy budget is mediated through other mechanisms, such \r\nas the chemical composition of the atmosphere, or by vegetation \r\nchanges. Among these additional feedbacks, the most important is \r\nthe CO2 feedback that describes how a change of the global surface \r\ntemperature affects the atmospheric CO2 concentration. In  ESM \r\nsimulations in which CO2 emissions are prescribed, changes in surface \r\ncarbon fluxes affect the CO2 concentration in the atmosphere, the \r\nTOA radiative energy budget, and eventually the global mean surface \r\ntemperature"


id: e82ac26b51c122e2762135cf9f638817
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 34033
content:
" In ESM simulations in which the CO2 concentration \r\nis prescribed, changes in the carbon cycle allow compatible CO2\r\nemissions to be calculated, that is, the CO2 emissions that are \r\ncompatible with both the prescribed CO2 concentration and the \r\nrepresentation of the carbon cycle in the ESM. The CO2 feedback \r\nis assessed in Chapter 5 (Section 5.4). The framework presented in \r\nthis chapter assumes that the CO2 concentration is prescribed, and \r\nour assessment of the net feedback parameter, a, does not include \r\ncarbon cycle feedbacks on the atmospheric CO2 concentration \r\n(Section  7.1 and Box  7.1).  However, our assessment of a does \r\ninclude non-CO2 biogeochemical feedbacks (including effects due to \r\nchanges in atmospheric methane concentration; Section  7.4.2.5.1) \r\nand biogeophysical feedbacks (Section  7.4.2.5.2). A  synthesis of \r\nthe combination of biogeophysical and non-CO2 biogeochemical \r\nfeedbacks is given in Section 7.4.2.5.3."


id: 1d5c78d89877a75fa1d64a79d7c1e9a2
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 2064
content:
"of -0.10 to +0.94 W m-2 degC-1 (Table 7.10). This approach potentially \r\nmisses feedbacks from cloud regimes that are not assessed, but almost \r\nall the major cloud regimes were taken into consideration (Gettelman \r\nand Sherwood, 2016) and therefore additional uncertainty will be \r\nsmall. This argument is also supported by an agreement between \r\nthe net cloud feedback assessed here and the net cloud feedback \r\ndirectly estimated using observations. The observational estimate, \r\nwhich is sensitive to the period considered and is based on two \r\natmospheric reanalyses (ERA-Interim and MERRA) and TOA radiation \r\nbudgets derived from the CERES satellite observations for the years \r\n2000-2010, is 0.54 +- 0.7 W m-2 degC-1 (one standard deviation; Dessler, \r\n2013). The observational estimate overlaps with the assessed range \r\nof the net cloud feedback"


id: a64bf22dbcaf06965ed147805b2d9aec
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 23697
content:
" The assessed very likely range is reduced \r\nby about 50% compared to AR5, but is still wide compared to those \r\nof other climate feedbacks (Table 7.10). The largest contribution to \r\nthis uncertainty range is the estimate of tropical high-cloud amount \r\nfeedback which is not yet well quantified using models."


id: bedac73ff6f43ee0a4969bae09d944f3
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 27581
content:
"scales these emissions would eventually substantially decline as the \r\npermafrost carbon pools were depleted (Schneider von Deimling \r\net  al., 2012, 2015). This leaves the wetland CH4, land N2O, and \r\nocean N2O feedbacks, the assessed mean values of which sum to \r\na  positive feedback parameter of +0.04 [0.02 to 0.06] W m-2 degC-1\r\n(Section  5.4.7). Other non-CO2 biogeochemical feedbacks that are \r\nrelevant to the net feedback parameter are assessed in Chapter 6 \r\n(Section 6.4.5 and Table 6.8). These feedbacks are associated with \r\nsea salt, dimethyl sulphide, dust, ozone, biogenic volatile organic \r\ncompounds, lightning, and CH4 lifetime, and sum to a  negative \r\nfeedback parameter of -0.20 [-0.41 to +0.01] W m-2 degC-1. The \r\noverall feedback parameter for non-CO2 biogeochemical feedbacks \r\nis obtained by summing the Chapter 5 and Chapter 6 assessments, \r\nwhich gives -0.16 [-0.37 to +0.05] W m-2 degC-1"


id: 9ecb5fead35034949fd6a4dec8cd47b3
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 22525
content:
" However, there is \r\nlow confidence in the estimates of both the individual non-CO2\r\nbiogeochemical feedbacks as well as their total effect, as evident \r\nfrom the large range in the magnitudes of a from different studies, \r\nwhich can be attributed to diversity in how models account for these \r\nfeedbacks and limited process-level understanding."


id: 42d38a1f2c6349a7de2b1947af3e79f0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 7903
content:
"Since AR5, several studies have confirmed that a shift from tundra to \r\nboreal forests and the associated albedo change leads to increased \r\nwarming in Northern Hemisphere high latitudes (high confidence) \r\n(Willeit et al., 2014; W. Zhang et al., 2018; Armstrong et al., 2019). \r\nHowever, regional modelling indicates that vegetation feedbacks may \r\nact to cool climate in the Mediterranean (Alo and Anagnostou, 2017), \r\nand in the tropics and subtropics the regional response is in general \r\nnot consistent across models. On a global scale, several modelling \r\nstudies have either carried out a feedback analysis (Stocker et al., 2013; \r\nWilleit et al., 2014) or presented simulations that allow a feedback \r\nparameter to be estimated (O'ishi et al., 2009; Armstrong et al., 2019), \r\nin such a way that the physiological response can be accounted for \r\nas a  forcing rather than a  feedback"


id: 488ea65929663ba7ca7d2ff084cb38b3
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 8790
content:
" The central estimates of the \r\nbiogeophysical feedback parameter from these studies range from \r\nclose to zero (Willeit et al., 2014) to +0.13 W m-2 degC-1 (Stocker et al., \r\n2013). An additional line of evidence comes from the mid-Pliocene \r\nwarm period (MPWP, Chapter 2, Cross-Chapter Box 2.1), for which \r\npaleoclimate proxies provide evidence of vegetation distribution \r\nand CO2 concentrations. Model simulations that include various \r\ncombinations of modern versus MPWP vegetation and CO2 allow an \r\nassociated feedback parameter to be estimated, as long as account \r\nis also taken of the orographic forcing (Lunt et al., 2010, 2012b). This \r\napproach has the advantage over pure modelling studies in that the \r\nreconstructed vegetation is based on (paleoclimate) observations, \r\nand is in equilibrium with the CO2 forcing"


id: 450922dedbbcd909a6138aa690651d87
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 8286
content:
" However, there are \r\nuncertainties in the vegetation reconstruction in regions with little \r\nor no proxy data, and it is uncertain how much of the vegetation \r\nchange is associated with the physiological response to CO2. This \r\npaleoclimate approach gives an estimate for the biogeophysical \r\nfeedback parameter of +0.3 W m-2 degC-1."


id: f02de85073b05799fe4f6a4de4d7234d
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 35292
content:
"Biogeophysical feedbacks are associated with changes in the spatial \r\ndistribution and/or biophysical properties of vegetation, induced \r\nby surface temperature change and attendant hydrological cycle \r\nchange. These vegetation changes can alter radiative fluxes directly \r\nvia albedo changes, or via surface momentum or moisture flux \r\nchanges and hence changes in cloud properties. However, the direct \r\nphysiological response of vegetation to changes in CO2, including \r\nchanges in stomatal conductance, is considered part of the CO2\r\neffective radiative forcing rather than a feedback (Section 7.3.2.1). \r\nThe time scale on which vegetation responds to climate change is \r\nrelatively uncertain but can be from decades to hundreds of years \r\n(Willeit et al., 2014), and could occur abruptly or as a tipping point \r\n(Sections 5.4.9.1.1, 8.6.2.1 and 8.6.2"


id: 30df0c1f6a4d676a9b076ce74c4c952a
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 5111
content:
"2); equilibrium only occurs \r\nwhen the soil system and associated nutrient and carbon pools \r\nequilibrate, which can take millennia (Brantley, 2008; Sitch et  al., \r\n2008). The overall effects of climate-induced vegetation changes may \r\nbe comparable in magnitude to those from anthropogenic land-use \r\nand land-cover change (Davies-Barnard et al., 2015). Climate models \r\nthat include a  dynamical representation of vegetation (e.g.,  Reick \r\net al., 2013; Harper et al., 2018) are used to explore the importance \r\nof biogeophysical feedbacks (Notaro et  al., 2007; Brovkin et  al., \r\n2009; O'ishi et al., 2009; Port et al., 2012; Willeit et al., 2014; Alo and \r\nAnagnostou, 2017; W. Zhang et al., 2018; Armstrong et al., 2019)"


id: 7fe7c4b6da8738c212eda810376ad9e0
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 17669
content:
" \r\nIn AR5, it was discussed that such model experiments predicted \r\nthat expansion of vegetation in the high latitudes of the Northern \r\nHemisphere would enhance warming due to the associated surface\u0002albedo change, and that reduction of tropical forests in response \r\nto climate change would lead to regional surface warming, due to \r\nreduced evapotranspiration (M. Collins et al., 2013), but there was no \r\nassessment of the associated feedback parameter. The SRCCL stated \r\nthat regional climate change can be dampened or enhanced by \r\nchanges in local land cover, but that this depends on the location and \r\nthe season; however, in general the focus was on anthropogenic land\u0002cover change, and no assessment of the biogeophysical feedback \r\nparameter was carried out. There are also indications of a  marine "


id: 26396c5ded79e9a80c4be1168fc6db29
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 3430
content:
"Some supporting evidence for this overall assessment can be \r\nobtained from the CMIP6 ensemble, which provides some pairs of \r\ninstantaneous 4xCO2 simulations carried out using related models, \r\nwith and without biogeophysical and non-CO2 biogeochemical \r\nfeedbacks. This is not a  direct comparison because these pairs of \r\nsimulations may differ by more than just their inclusion of these \r\nadditional feedbacks; furthermore, not all biogeophysical and \r\nnon-CO2 biogeochemical feedbacks are fully represented. However, \r\na comparison of the pairs of simulations does provide a first-order \r\nestimate of the magnitude of these additional feedbacks. Seferian \r\net  al. (2019) find a  slightly more negative feedback parameter in \r\nCNRM-ESM2-1 (with additional feedbacks) then in CNRM-CM6-1 \r\n(a decrease of 0.02 W m-2 degC-1, using the linear regression method \r\nfrom years 10-150). Andrews et al"


id: 35ce7efd94549fcc3357bfb49fd84866
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 5878
content:
" (2019) also find a slightly more \r\nnegative feedback parameter when these additional feedbacks are \r\nincluded (a decrease of 0.04 W m-2 degC-1 in UKESM1 compared with \r\nHadGEM3-GC3.1). Both of these studies suggest a small but slightly \r\nnegative feedback parameter for the combination of biogeophysical \r\nand non-CO2 biogeochemical feedbacks, but with relatively large \r\nuncertainty given (i) interannual variability and (ii) that feedbacks \r\nassociated with natural terrestrial emissions of CH4 and N2O were not \r\nrepresented in either pair."


id: f6409e1681401e6c691be8f098b429dc
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 36251
content:
"The magnitude of the radiative feedback associated with changes to \r\nice sheets can be quantified by comparing the global mean long-term \r\nequilibrium temperature response to increased CO2 concentrations in \r\nsimulations that include interactive ice sheets with that of simulations \r\nthat do not include the associated ice sheet-climate interactions \r\n(Swingedouw et al., 2008; Vizcaino et al., 2010; Goelzer et al., 2011; \r\nBronselaer et  al., 2018; Golledge et  al., 2019). These simulations \r\nindicate that on multi-centennial time scales, ice-sheet mass loss leads \r\nto freshwater fluxes that can modify ocean circulation (Swingedouw \r\net  al., 2008; Goelzer et  al., 2011; Bronselaer et  al., 2018; Golledge \r\net al., 2019). This leads to reduced surface warming (by about 0.2degC \r\nin the global mean after 1000 years; Section 7.4.4.1.1; Goelzer et al., \r\n2011), although other work suggests no net global temperature \r\neffect of ice-sheet mass loss (Vizcaino et  al., 2010)"


id: c1bd557d24cf5237bbaa7a88542de820
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 28067
content:
" However, \r\nmodel simulations in which the Antarctic Ice Sheet is removed \r\ncompletely in a  paleoclimate context indicate a  positive global \r\nmean feedback on multi-millennial time scales due primarily to the \r\nsurface-albedo change (Goldner et al., 2014a; Kennedy-Asser et al., \r\n2019); in Chapter 9 (Section 9.6.3) it is assessed that such ice-free \r\nconditions could eventually occur given 7degC-13degC of warming. This \r\nnet positive feedback from ice-sheet mass loss on long time scales \r\nis also supported by model simulations of the mid-Pliocene Warm \r\nPeriod (MPWP; Cross-chapter Box  2.1) in which the volume and \r\narea of the Greenland and West Antarctic ice sheets are reduced in \r\nmodel simulations in agreement with geological data (Chandan and \r\nPeltier, 2018), leading to surface warming"


id: 4694c4407c1615f2bf7e501b69b12059
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 8486
content:
" As such, overall, on multi\u0002centennial time scales the feedback parameter associated with ice \r\nsheets is likely negative (medium confidence), but on multi-millennial \r\ntime scales by the time the ice sheets reach equilibrium, the feedback \r\nparameter is very likely positive (high confidence) (Table  7.10). \r\nHowever, a relative lack of models carrying out simulations with and \r\nwithout interactive ice sheets over centennial to millennial time scales \r\nmeans that there is currently not enough evidence to quantify the \r\nmagnitude of these feedbacks, or the time scales on which they act."


id: f077395d1be70bb4402719dafc020a45
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
index: climate_gpt_only_giec
vector_id: 35332
content:
"Earth's ice sheets (Greenland and Antarctica) are sensitive to climate \r\nchange (Section  9.4; Pattyn et  al., 2018). Their time evolution is \r\ndetermined by both their surface mass balance and ice dynamic \r\nprocesses, with the latter being particularly important for the West \r\nAntarctic Ice Sheet. Surface mass balance depends on the net energy \r\nand hydrological fluxes at their surface, and there are mechanisms of \r\nice-sheet instability that depend on ocean temperatures and basal \r\nmelt rates (Section 9.4.1.1). The presence of ice sheets affects Earth's \r\nradiative budget, hydrology, and atmospheric circulation due to their \r\ncharacteristic high albedo, low roughness length, and high altitude, \r\nand they influence ocean circulation through freshwater input from \r\ncalving and melt (e.g.,  Fyke et  al., 2018). Ice-sheet changes also \r\nmodify surface albedo through the attendant change in sea level \r\nand therefore land area (Abe-Ouchi et al., 2015)"


id: 48a4c7239c7c63d83691f2f360687a2f
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
content_type: text
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vector_id: 8804
content:
" The time scale for \r\nice sheets to reach equilibrium is of the order of thousands of years \r\n(Clark et al., 2016). Due to the long time scales involved, it is a major \r\nchallenge to run coupled climate-ice sheet models to equilibrium, "


id: 49653a2fb7b365fcc1089455afcd0b98
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 8934
content:
"distribution. It is virtually certain that the net climate feedback is \r\nnegative, primarily due to the Planck temperature response, indicating \r\nthat climate acts to stabilize in response to radiative forcing imposed \r\nto the system. Supported by the level of confidence associated with \r\nthe individual feedbacks, it is also virtually certain that the sum of \r\nthe non-Planck feedbacks is positive. Based on Table  7.10 these \r\nclimate feedbacks amplify the Planck temperature response by about \r\n2.8 [1.9 to 5.9] times. Cloud feedback remains the largest contributor \r\nto uncertainty of the net feedback, but the uncertainty is reduced \r\ncompared to AR5. A  secondary contribution to the net feedback \r\nuncertainty is the biogeophysical and non-CO2 biogeochemical \r\nfeedbacks, which together are assessed to have a central value near \r\nzero and thus do not affect the central estimate of ECS. The net climate \r\nfeedback is assessed to be -1.16 W m-2 degC-1, likely from -1.54  to \r\n-0"


id: e35ffc3700052b7f6aea8381a29c5195
created_at: 2023-03-16 14:02:02
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vector_id: 33256
content:
"Feedback parameters in climate models are calculated assuming \r\nthat they are independent of each other, except for a  well-known \r\nco-dependency between the water vapour (WV) and lapse rate (LR) \r\nfeedbacks. When the inter-model spread of the net climate feedback is \r\ncomputed by adding in quadrature the inter-model spread of individual \r\nfeedbacks, it is 17% wider than the spread of the net climate feedback \r\ndirectly derived from the ensemble. This indicates that the feedbacks in \r\nclimate models are partly co-dependent. Two possible co-dependencies \r\nhave been suggested (Huybers, 2010; Caldwell et  al., 2016). One is \r\na negative covariance between the LR and longwave cloud feedbacks, \r\nwhich may be accompanied by a  deepening of the troposphere \r\n(O'Gorman and Singh, 2013; Yoshimori et al., 2020) leading both to \r\ngreater rising of high-clouds and a larger upper-tropospheric warming"


id: 6f88ee6e9f6356d8f87d29cd16ba413e
created_at: 2023-03-16 14:02:02
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content:
" \r\nThe other is a  negative covariance between albedo and shortwave \r\ncloud feedbacks, which may originate from the Arctic regions: \r\na reduction in sea ice enhances the shortwave cloud radiative effect \r\nbecause the ocean surface is darker than sea ice (Gilgen et al., 2018). \r\nThis covariance is reinforced as the decrease of sea ice leads to an \r\nincrease in low-level clouds (Mauritsen et  al., 2013). However, the \r\nmechanism causing these co-dependences between feedbacks is not \r\nwell understood yet and a quantitative assessment based on multiple \r\nlines of evidence is difficult. Therefore, this synthesis assessment does \r\nnot consider any co-dependency across individual feedbacks."


id: bcc4ed9f994761e933cda75efb6bb8a2
created_at: 2023-03-16 14:02:02
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vector_id: 27269
content:
"A remarkable improvement of cloud representation in some CMIP6 \r\nmodels is the reduced error of the too-weak negative shortwave CRE \r\nover the Southern Ocean (Bodas-Salcedo et al., 2019; Gettelman et al., \r\n2019) due to a more realistic simulation of supercooled liquid droplets \r\nand associated cloud optical depths that were biased low commonly \r\nin CMIP5 models (McCoy et  al., 2014a, b). Because the negative \r\ncloud optical depth feedback occurs due to 'brightening' of clouds via \r\nphase change from ice to liquid cloud particles in response to surface \r\nwarming (Cesana and Storelvmo, 2017), the extratropical cloud \r\nshortwave feedback tends to be less negative or even slightly positive \r\nin models with reduced errors (Bjordal et al., 2020; Zelinka et al., 2020). \r\nThe assessment of cloud feedbacks in Section  7.4.2.4 incorporates \r\nestimates from these improved ESMs. Yet, there still remain other \r\nshared model errors, such as in the subtropical low-clouds (Calisto \r\net al"


id: fd7e5683bcb2df6f2b6659107368c38c
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", 2014) and tropical anvil clouds (Mauritsen and Stevens, 2015), \r\nhampering an assessment of feedbacks associated with these cloud \r\nregimes based only on ESMs (Section 7.4.2.4)."


id: 307216f6c59c20f53231928874b746ab
created_at: 2023-03-16 14:02:02
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"There are several modelling studies since AR5 in which ESMs \r\nof varying complexity have been used to explore temperature \r\ndependence of feedbacks, either under modern (Hansen et al., 2013; \r\nJonko et  al., 2013; Meraner et  al., 2013; Good et  al., 2015; Duan \r\net al., 2019; Mauritsen et al., 2019; Rohrschneider et al., 2019; Stolpe \r\net al., 2019; Bloch-Johnson et al., 2020; Rugenstein et al., 2020) or \r\npaleo (Caballero and Huber, 2013; Zhu et al., 2019a) climate conditions, \r\ntypically by carrying out multiple simulations across successive CO2\r\ndoublings. A  non-linear temperature response to these successive \r\ndoublings may be partly due to forcing that increases more (or less) \r\nthan expected from a purely logarithmic dependence (Section 7.3.2; \r\nEtminan et  al., 2016), and partly due to state-dependence in \r\nfeedbacks; however, not all modelling studies have partitioned the \r\nnon-linearities in temperature response between these two effects"


id: 53100d629bc2c8d93f9a46807e631689
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" \r\nNonetheless, there is general agreement among ESMs that the net \r\nfeedback parameter, a, increases (i.e., becomes less negative) as \r\ntemperature increases from pre-industrial levels (i.e.,  sensitivity to \r\nforcing increases as temperature increases; e.g., Meraner et al., 2013; \r\nsee Figure 7.11). The associated increase in sensitivity to forcing is, \r\nin most models, due to the water vapour (Section 7.4.2.2) and cloud \r\n(Section  7.4.2.4) feedback parameters increasing with warming \r\n(Caballero and Huber, 2013; Meraner et al., 2013; Zhu et al., 2019a; \r\nRugenstein et al., 2020; Sherwood et al., 2020). These changes are \r\noffset partially by the surface-albedo feedback parameter decreasing \r\n(Jonko et  al., 2013; Meraner et  al., 2013; Rugenstein et  al., 2020), \r\nas a consequence of a reduced amount of snow and sea ice cover \r\nin a much warmer climate. At the same time, there is little change \r\nin the Planck response (Section 7.4.2"


id: f5557d3743b9ae8cbf0efc1d834c90d1
created_at: 2023-03-16 14:02:02
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content:
"1), which has been shown in one \r\nmodel to be due to competing effects from increasing Planck emission \r\nat warmer temperatures and decreasing planetary emissivity due to \r\nincreased CO2 and water vapour (Mauritsen et al., 2019). Analysis of \r\nthe spatial patterns of the non-linearities in temperature response \r\n(Good et al., 2015) suggests that these patterns are linked to a reduced \r\nweakening of the AMOC, and changes to evapotranspiration. The \r\ntemperature dependence of a is also found in model simulations of \r\nhigh-CO2 paleoclimates (Caballero and Huber, 2013; Zhu et al., 2019a). \r\nThe temperature dependence is not only evident at very high CO2\r\nconcentrations in excess of 4xCO2, but also apparent in the difference \r\nin temperature response to a  2xCO2 forcing compared with to \r\na 4xCO2 forcing (Mauritsen et al., 2019; Rugenstein et al., 2020), and \r\nas such is relevant for interpreting century-scale climate projections"


id: fe6982d45cb73e2563499bf7136a9899
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"\r\nDespite the general agreement that a increases as temperature \r\nincreases from pre-industrial levels (Figure  7.11), other modelling \r\nstudies have found the opposite (Duan et  al., 2019; Stolpe et  al., \r\n2019). Modelling studies exploring state-dependence in climates \r\ncolder than today, including in cold paleoclimates such as the "


id: e85553a59e5a1be6d2963a195f8243bf
created_at: 2023-03-16 14:02:02
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vector_id: 34060
content:
"(Kohler et  al., 2015; Stap et  al., 2019), which is not well known, \r\ndue to a  relative lack of proxy indicators of ice-sheet extent and \r\ndistribution prior to the LGM (Cross-Chapter Box 2.1). Furthermore, \r\nmany of these glacial-interglacial studies estimate a  very strong \r\ntemperature-dependence of a (Figure 7.11) that is hard to reconcile \r\nwith the other lines of evidence, including proxy estimates from \r\nwarmer paleoclimates. However, if the analysis excludes time periods \r\nwhen the temperature and CO2 data are not well correlated, which \r\noccurs in general at times when sea level is falling and obliquity \r\nis decreasing, the state-dependence reduces (Kohler et  al., 2018)"


id: bde218d2fd5a7a4f4a981601e4efa8c
created_at: 2023-03-16 14:02:02
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" \r\nDespite these uncertainties, due to the agreement in the sign of the \r\ntemperature-dependence from all these studies, there is medium \r\nconfidence from the paleoclimate proxy record that the net feedback \r\nparameter, a, was less negative in the warm periods than in the cold \r\nperiods of the glacial-interglacial cycles."


id: 9ffced510fcf8b7e4159bfa7553272a4
created_at: 2023-03-16 14:02:02
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vector_id: 22711
content:
"Figure 7.11 | Feedback parameter, a (W m-2 degC-1), as a function of global \r\nmean surface air temperature anomaly relative to pre-industrial, for ESM \r\nsimulations (red circles and lines) (Caballero and Huber, 2013; Jonko et al., 2013; \r\nMeraner et al., 2013; Good et al., 2015; Duan et al., 2019; Mauritsen et al., 2019; \r\nStolpe et al., 2019; Zhu et al., 2019a), and derived from paleoclimate proxies \r\n(grey squares and lines) (von der Heydt et al., 2014; Anagnostou et al., 2016, \r\n2020; Friedrich et al., 2016; Royer, 2016; Shaffer et al., 2016; Kohler et al., 2017; \r\nSnyder, 2019; Stap et al., 2019). For the ESM simulations, the value on the x-axis \r\nrefers to the average of the temperature before and after the system has equilibrated \r\nto a forcing (in most cases a CO2 doubling), and is expressed as an anomaly relative \r\nto an associated pre-industrial global mean temperature from that model. The light \r\nblue shaded square extends across the assessed range of a (Table 7"


id: cf1aa7683653773827d7d0d3bf882674
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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index: climate_gpt_only_giec
vector_id: 30121
content:
"10) on the y-axis, \r\nand on the x-axis extends across the approximate temperature range over which the \r\nassessment of a is based (taken as from zero to the assessed central value of ECS; \r\nsee Table 7.13). Further details on data sources and processing are available in the \r\nchapter data table (Table 7.SM.14)."


id: 1a56e2b28460de098269594f8f1c3bd7
created_at: 2023-03-16 14:02:02
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vector_id: 1613
content:
"Overall, independent lines of evidence from models (Section 7.4.3.1) \r\nand from the paleoclimate proxy record (Section  7.4.3.2) lead to \r\nhigh confidence that the net feedback parameter, a, increases \r\n(i.e., becomes less negative) as temperature increases; that is, \r\nthat sensitivity to forcing increases as temperature increases \r\n(Figure  7.11). This temperature-dependence should be considered \r\nwhen estimating ECS from ESM simulations in which CO2 is \r\nquadrupled (Section 7.5.5) or from paleoclimate observations from \r\npast time periods colder or warmer than today (Section  7.5.4). \r\nAlthough individual lines of evidence give only medium confidence, \r\nthe overall high confidence comes from the multiple models that \r\nshow the same sign of the temperature-dependence of a, the \r\ngeneral agreement in evidence from the paleo proxy and modelling \r\nlines of evidence, and the agreement between proxy evidence from \r\nboth cold and warm past climates"


id: 5ce4ed173c8e1c7069071fae785de97b
created_at: 2023-03-16 14:02:02
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vector_id: 12132
content:
" However, due to the large range \r\nin estimates of the magnitude of the temperature-dependence of \r\na across studies (Figure  7.11), a  quantitative assessment cannot \r\ncurrently be given, which provides a  challenge for including this \r\ntemperature-dependence in emulator-based future projections \r\n(Cross-Chapter Box 7.1). Greater confidence in the modelling lines of \r\nevidence could be obtained from simulations carried out for several \r\nhundreds of years (Rugenstein et al., 2020), substantially longer than \r\nin many studies, and from more models carrying out simulations at \r\nmultiple CO2 concentrations. Greater confidence in the paleoclimate "


id: 255685ff9238ac7077a64f219ffde4db
created_at: 2023-03-16 14:02:02
updated_at: 2023-03-16 14:02:02
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vector_id: 3304
content:
"Polar amplification describes the phenomenon where surface \r\ntemperature change at high latitudes exceeds the global average \r\nsurface temperature change in response to radiative forcing of the \r\nclimate system. Arctic amplification, often defined as the ratio of \r\nArctic to global surface warming, is a ubiquitous emergent feature \r\nof climate model simulations (Section  4.5.1 and Figure  7.12a; \r\nHolland and Bitz, 2003; Pithan and Mauritsen, 2014) and is also \r\nseen in observations (Section 2.3.1). However, both climate models \r\nand observations show relatively less warming of the SH high \r\nlatitudes compared to the Northern Hemisphere (NH) high latitudes \r\nover the historical record (Section  2.3.1), a  characteristic that is \r\nprojected to continue over the 21st  century (Section  4.5.1). Since \r\nAR5 there is a  much-improved understanding of the processes \r\nthat drive polar amplification in the NH and delay its emergence \r\nin the SH (Section 7.4.4.1.1)"


id: e65f0315c8246348e22127d73a49d840
created_at: 2023-03-16 14:02:02
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vector_id: 33733
content:
" Furthermore, the paleoclimate record \r\nprovides evidence for polar amplification from multiple time periods \r\nassociated with changes in CO2 (Hollis et  al., 2019; Cleator et  al., \r\n2020; McClymont et  al., 2020; Tierney et  al., 2020b), and allows \r\nan evaluation of polar amplification in model simulations of these \r\nperiods (Section 7.4.4.1.2). Research since AR5 identifies changes in \r\nthe degree of polar amplification over time, particularly in the SH, \r\nas a key factor affecting how radiative feedbacks may evolve in the \r\nfuture (Section 7.4.4.3)."


id: d7df699385afb55064d0070e7aa64272
created_at: 2023-03-16 14:02:02
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vector_id: 31491
content:
"Energy balance models that approximate atmospheric heat transport \r\nin terms of a  diffusive flux down the meridional gradient of \r\nnear-surface moist static energy (sum of dry-static and latent energy) \r\nare able to reproduce the atmospheric heat transport changes seen \r\nwithin ESMs (Flannery, 1984; Hwang and Frierson, 2010; Hwang \r\net al., 2011; Rose et al., 2014; Roe et al., 2015; Merlis and Henry, \r\n2018), including the partitioning of latent and dry-static energy \r\ntransports (Siler et  al., 2018b; Armour et  al., 2019). These models \r\nsuggest that polar amplification is driven by enhanced poleward \r\nlatent heat transport and that the magnitude of polar amplification \r\ncan be enhanced or diminished by the latitudinal structure of radiative \r\nfeedbacks. Amplifying polar feedbacks enhance polar warming and \r\nin turn cause a decrease in the dry-static energy transport to high \r\nlatitudes (Alexeev and Jackson, 2013; Rose et al., 2014; Roe et al., \r\n2015; Bonan et  al"


id: dc9bf329ae6ee0e4fe50aad6ed42083d
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", 2018; Merlis and Henry, 2018; Armour et  al., \r\n2019; Russotto and Biasutti, 2020). Poleward latent heat transport \r\nchanges act to favour polar amplification and inhibit tropical \r\namplification (Armour et  al., 2019), resulting in a  strongly polar\u0002amplified warming response to polar forcing and a more latitudinally \r\nuniform warming response to tropical forcing within ESMs (Alexeev \r\net al., 2005; Rose et al., 2014; Stuecker et al., 2018). The important \r\nrole for poleward latent energy transport in polar amplification is \r\nsupported by studies of atmospheric reanalyses and ESMs showing \r\nthat episodic increases in latent heat transport into the Arctic can \r\nenhance surface downwelling radiation and drive sea ice loss on \r\nsub-seasonal time scales (Woods and Caballero, 2016; Gong et al., \r\n2017; Lee et  al., 2017; B. Luo et  al., 2017), however this may be \r\na smaller driver of sea ice variability than atmospheric temperature \r\nfluctuations (Olonscheck et al., 2019)."


id: 9ce905840a67fb92902fadfcd3240bd8
created_at: 2023-03-16 14:02:02
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vector_id: 22223
content:
"Figure 7.12 (continued): (a) Pattern of near-surface air temperature change. (b-d) Contributions to net Arctic (>60degN), tropical (30degS-30degN), and Antarctic (<60degS) warming \r\ncalculated by dividing regional-average energy inputs by the magnitude of the regional-average Planck response. The contributions from radiative forcing, changes in moist, \r\ndry-static, and total atmospheric energy transport, ocean heat uptake, and radiative feedbacks (orange bars) all sum to the value of net warming (grey bar). Inset shows regional \r\nwarming contributions associated with individual feedbacks, all summing to the total feedback contribution. Uncertainties (represented by black whiskers) show the interquartile \r\nrange (25th and 75th percentiles) across models"


id: 43bd78bc0b81bca275338c5d23721d3b
created_at: 2023-03-16 14:02:02
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vector_id: 8067
content:
" The warming contributions (units of degC) for each process are diagnosed by calculating the energy flux (units of W m-2) that each \r\nprocess contributes to the atmosphere over a given region, either at the top-of-atmosphere or surface, then dividing that energy flux by the magnitude of the regional Planck \r\nresponse (around 3.2 W m-2 degC-1 but varying with region). By construction, the individual warming contributions sum to the total warming in each region. Radiative kernel methods \r\n(Section 7.4.1) are used to decompose the net energy input from radiative feedbacks into contributions from changes in atmospheric water vapour, lapse rate, clouds, and surface \r\nalbedo (Zelinka et al. (2020) using the Huang et al. (2017) radiative kernel). The CMIP6 models included are those analysed by Zelinka et al. (2020) and the warming contribution \r\nanalysis is based on that of Goosse et al. (2018). Further details on data sources and processing are available in the chapter data table (Table 7.SM"


id: 6280636231a6064ea198c6db92df4fa7
created_at: 2023-03-16 14:02:02
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content:
"waters that have not yet felt the effects of the radiative forcing; the \r\nheat taken up is predominantly transported away from Antarctica \r\nby northward-flowing surface waters (Section  9.2.1; Marshall \r\net al., 2015; Armour et al., 2016). Strong surface heat uptake also \r\noccurs in the subpolar North Atlantic Ocean under global warming \r\n(Section 9.2.1). However, this heat is partially transported northward \r\ninto the Arctic, which leads to increased heat fluxes into the Arctic \r\natmosphere (Figure 7.12b; Rugenstein et al., 2013; Jungclaus et al., \r\n2014; Koenigk and Brodeau, 2014; Marshall et al., 2015; Nummelin \r\net al., 2017; Singh et al., 2017; Oldenburg et al., 2018). The second \r\nmain process contributing to differences in Arctic and Antarctic \r\nwarming is the asymmetry in radiative feedbacks between the poles \r\n(Yoshimori et al., 2017; Goosse et al., 2018)"


id: 36111f29a1d75988a2a1abb9926d4583
created_at: 2023-03-16 14:02:02
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vector_id: 5912
content:
" This primarily reflects the \r\nweaker lapse-rate and surface-albedo feedbacks and more-negative \r\ncloud feedbacks in the SH high latitudes (Figure  7.12). However, \r\nnote the SH cloud feedbacks are uncertain due to possible biases in \r\nthe treatment of mixed phase clouds (Hyder et al., 2018). Idealized \r\nmodelling suggests that the asymmetry in the polar lapse-rate \r\nfeedback arises from the height of the Antarctic Ice Sheet precluding \r\nthe formation of deep atmospheric inversions that are necessary \r\nto produce the stronger positive lapse-rate feedbacks seen in the \r\nArctic (Salzmann, 2017; Hahn et al., 2020). ESM projections of the \r\nequilibrium response to CO2 forcing show polar amplification in both \r\nhemispheres, but generally with less warming in the Antarctic than \r\nthe Arctic (C. Li et al., 2013; Yoshimori et al., 2017)."


id: b1a92b83aafe8d99bcd1d5bdbfcdec4d
created_at: 2023-03-16 14:02:03
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vector_id: 25448
content:
"the surface-albedo and lapse-rate feedbacks,  which preferentially \r\nwarm the Arctic (Graversen et al., 2014; Pithan and Mauritsen, 2014; \r\nGoosse et al., 2018). Latitudinal structure in the lapse-rate feedback \r\nreflects weak radiative damping to space with surface warming in \r\npolar regions, where atmospheric warming is constrained to the \r\nlower troposphere owing to stably stratified conditions, and strong \r\nradiative damping in the tropics, where warming is enhanced in the \r\nupper troposphere owing to moist convective processes. This is only \r\npartially compensated by latitudinal structure in the water-vapour \r\nfeedback (Taylor et al., 2013), which favours tropical warming (Pithan \r\nand Mauritsen, 2014). While cloud feedbacks have been found to \r\nplay little role in Arctic amplification in CMIP5 models (Pithan and \r\nMauritsen, 2014; Goosse et  al., 2018; Figure  7.12b), less-negative \r\ncloud feedbacks at high latitude, as seen within some CMIP6 models \r\n(Zelinka et  al"


id: 7a29c6a8b25b82a0ca487dbfcf34ca37
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vector_id: 16793
content:
", 2020), tend to favour stronger polar amplification \r\n(Dong et al., 2020). A weaker Planck response at high latitudes, owing \r\nto less efficient radiative damping where surface and atmospheric \r\ntemperatures are lower, also contributes to polar amplification (Pithan \r\nand Mauritsen, 2014). The effective radiative forcing of CO2 is larger in \r\nthe tropics than at high latitudes, suggesting that warming would be \r\ntropically amplified if not for radiative feedbacks and poleward latent \r\nheat transport changes (Figure 7.12b-d; Stuecker et al., 2018)."


id: 99c224b6078ea372865ac96c55cd8f59
created_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 21710
content:
"While the contributions to regional warming can be diagnosed within \r\nESM simulations (Figure 7.12), assessment of the underlying role of \r\nindividual factors is limited by interactions inherent to the coupled \r\nclimate system. For example, polar feedback processes are coupled and \r\ninfluenced by warming at lower latitudes (Screen et al., 2012; Alexeev \r\nand Jackson, 2013; Graversen et  al., 2014; Graversen and Burtu, \r\n2016; Rose and Rencurrel, 2016; Feldl et al., 2017a, 2020; Yoshimori \r\net al., 2017; Garuba et al., 2018; Po-Chedley et al., 2018b; Stuecker \r\net al., 2018; Dai et al., 2019), while atmospheric heat transport changes \r\nare in turn influenced by the latitudinal structure of regional feedbacks, \r\nradiative forcing, and ocean heat uptake (Hwang et al., 2011; Zelinka \r\nand Hartmann, 2012; Feldl and Roe, 2013; Huang and Zhang, 2014; \r\nMerlis, 2014; Rose et al., 2014; Roe et al., 2015; Feldl et al., 2017b; \r\nStuecker et al., 2018; Armour et al., 2019)"


id: fe491684cb39d11d6dbefc44ec753291
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 37536
content:
" The use of different feedback \r\ndefinitions, such as a lapse-rate feedback partitioned into upper and \r\nlower tropospheric components (Feldl et  al., 2020) or including the \r\ninfluence of water vapour at constant relative humidity (Held and Shell, \r\n2012; Section 7.4.2), would also change the interpretation of which \r\nfeedbacks contribute most to polar amplification."


id: 7d690151a24a7399d913c140050ab44a
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 17303
content:
"Because multiple processes contribute to polar amplification, it is \r\na robust feature of the projected long-term response to greenhouse \r\ngas forcing in both hemispheres. At the same time, contributions \r\nfrom multiple processes make projections of the magnitude of polar \r\nwarming inherently more uncertain than global mean warming \r\n(Holland and Bitz, 2003; Roe et  al., 2015; Bonan et  al., 2018; \r\nStuecker et al., 2018). The magnitude of Arctic amplification ranges \r\nfrom a  factor of two to four in ESM projections of 21st-century \r\nwarming (Section  4.5.1). While uncertainty in both global and \r\ntropical warming  under greenhouse gas forcing is dominated by \r\ncloud feedbacks (Section  7.5.7; Vial et  al., 2013), uncertainty in \r\npolar warming arises from polar surface-albedo, lapse-rate, and \r\ncloud feedbacks, changes in atmospheric and oceanic poleward heat \r\ntransport, and ocean heat uptake (Hwang et al., 2011; Mahlstein and \r\nKnutti, 2011; Pithan and Mauritsen, 2014; Bonan et al"


id: 9c6fe7ac1c95deda4ad2b0ab4ee7e974
created_at: 2023-03-16 14:02:03
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vector_id: 22169
content:
"the emergence of SH polar amplification remains uncertain due to \r\ninsufficient knowledge of the time scales associated with Southern \r\nOcean warming and the response to surface wind and freshwater \r\nforcing (Bintanja et al., 2013; Kostov et al., 2017, 2018; Pauling et al., \r\n2017; Purich et  al., 2018). ESM simulations indicate that freshwater \r\ninput from melting ice shelves could reduce Southern Ocean warming \r\nby up to several tenths of a  degC over the 21st  century by increasing \r\nstratification of the surface ocean around Antarctica (low confidence\r\ndue to medium agreement but limited evidence) (Sections 7.4.2.6 \r\nand 9.2.1, and Box 9.3; Bronselaer et al., 2018; Golledge et al., 2019; \r\nLago and England, 2019)"


id: a730289579a7f623fab7f8a6f42ce8d8
created_at: 2023-03-16 14:02:03
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vector_id: 23813
content:
" However, even a large reduction in the Atlantic \r\nMeridional Overturning Circulation (AMOC) and associated northward \r\nheat transport due, for instance, to greatly increased freshwater runoff \r\nfrom Greenland would be insufficient to eliminate Arctic amplification \r\n(medium confidence based on medium agreement and medium \r\nevidence) (Liu et al., 2017; Y. Liu et al., 2018; Wen et al., 2018)."


id: afba80c06c4cb31b9fe2b23c8227bb39
created_at: 2023-03-16 14:02:03
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vector_id: 25160
content:
"Arctic amplification has a distinct seasonality with a peak in early \r\nwinter (November to January) owing to sea ice loss and associated \r\nincreases in heat fluxes from the ocean to the atmosphere resulting \r\nin strong near-surface warming (Pithan and Mauritsen, 2014; Dai \r\net al., 2019). Surface warming may be further amplified by positive \r\ncloud and lapse-rate feedbacks in autumn and winter (Burt et  al., \r\n2016; Morrison et al., 2019; Hahn et al., 2020). Arctic amplification \r\nis weak in summer owing to surface temperatures remaining stable \r\nas excess energy goes into thinning the summertime sea ice cover, \r\nwhich remains at the melting point, or into the ocean mixed layer. \r\nArctic amplification can also be interpreted through changes in \r\nthe surface energy budget (Burt et al., 2016; Woods and Caballero, \r\n2016; Boeke and Taylor, 2018; Kim et  al"


id: 66d323861e921879d28dac1153585162
created_at: 2023-03-16 14:02:03
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vector_id: 13772
content:
", 2019), however such \r\nanalyses are complicated by the finding that a  large portion of \r\nthe changes in downward longwave radiation can be attributed to \r\nthe lower troposphere warming along with the surface itself (Vargas \r\nZeppetello et al., 2019)."


id: c76cbf348dba42018d4d3e3598430469
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 28961
content:
"Paleoclimate proxy data provide observational evidence of large-scale \r\npatterns of surface warming in response to past forcings, and allow an \r\nevaluation of the modelled response to these forcings (Sections 3.3.1.1 \r\nand 3.8.2.1). In particular, paleoclimate data provide evidence for \r\nlong-term changes in polar amplification during time periods in which \r\nthe primary forcing was a change in atmospheric CO2, although data \r\nsparsity means that for some time periods this evidence may be limited \r\nto a  single hemisphere or ocean basin, or the evidence may come \r\nprimarily from the mid-latitudes as opposed to the polar regions. In \r\nthis context, there has been a modelling and data focus on the Last \r\nGlacial Maximum (LGM) in the context of PMIP4 (Cleator et al., 2020; \r\nTierney et al., 2020b; Kageyama et al., 2021), the mid-Pliocene Warm \r\nPeriod (MPWP) in the context of PlioMIP2 (Cross-Chapter Box  2.4; \r\nSalzmann et  al., 2013; Haywood et  al., 2020; McClymont et  al"


id: 7b1dfd1d54309194fbe503b5e64b1ac
created_at: 2023-03-16 14:02:03
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vector_id: 16964
content:
", \r\n2020), the Early Eocene Climatic Optimum (EECO) in the context of \r\nDeepMIP (Hollis et al., 2019; Lunt et al., 2021), and there is growing \r\ninterest in the Miocene (Goldner et al., 2014b; Steinthorsdottir et al., \r\n2021; for definitions of time periods see Cross-Chapter Box  2.1). \r\nFor all these time periods, in addition to the CO2 forcing there are "


id: a243b8f5f75fc02f3b9942b130424c18
created_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 23097
content:
"Since AR5, there has been progress in the simulation of polar \r\namplification by paleoclimate models of the Early Eocene. Initial \r\nwork indicated that changes to model parameters associated with \r\naerosols and/or clouds could increase simulated polar amplification \r\nand improve agreement between models and paleoclimate data \r\n(Kiehl and Shields, 2013; Sagoo et  al., 2013), but such parameter \r\nchanges were not physically based. In support of these initial findings, \r\na more recent (CMIP5) climate model, that includes a process-based \r\nrepresentation of cloud microphysics, exhibits polar amplification \r\nin better agreement with proxies when compared to the models \r\nassessed in AR5 (Zhu et al., 2019a). Since then, some other CMIP3 \r\nand CMIP5 models in the DeepMIP multi-model ensemble (Lunt \r\net  al., 2021) have obtained polar amplification for the EECO that \r\nis consistent with proxy indications of both polar amplification and \r\nCO2"


id: 1d9594486393f24d58fe1b9dce98c6c7
created_at: 2023-03-16 14:02:03
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vector_id: 2095
content:
"Figure 7.13 (continued): Temperature anomalies compared with pre-industrial (equivalent to CMIP6 simulation 'piControl') are shown for the high-CO2 EECO and MPWP \r\ntime periods, and for the low-CO2 LGM (expressed as pre-industrial minus LGM). (a), (b) and (c) Modelled near-surface air temperature anomalies for ensemble-mean \r\nsimulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). Also shown are proxy \r\nnear-surface air temperature anomalies (coloured circles). (d), (e) and (f) Proxy near-surface air temperature anomalies (grey circles), including published uncertainties (grey \r\nvertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (a-c), light-red lines show the modelled temperature anomaly \r\nfor the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5)"


id: 46ce694c092215378c73305c9fc4b9e
created_at: 2023-03-16 14:02:03
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vector_id: 8525
content:
" Black dashed lines show the average of the proxy values in each latitude band: \r\n90degS-30degS, 30degS-30degN, and 30degN-90degN. Red dashed lines show the same banded average in the model ensemble mean, calculated from the same locations as the proxies. \r\nBlack and red dashed lines are only shown if there are five or more proxy points in that band. Mean differences between the 90degS/N to 30degS/N and 30degS to 30degN bands are \r\nquantified for the models and proxies in each plot. Panels (g), (h) and (i) are like panels (d-f) but for sea surface temperature (SST) instead of near-surface air temperature. \r\nPanels (j), (k) and (l) are like panels (a-c) but for SST instead of near-surface air temperature. For the EECO maps - (a) and (j) - the anomalies are relative to the zonal mean of \r\nthe pre-industrial, due to the different continental configuration. Proxy datasets are: (a) and (d) Hollis et al. (2019); (b) and (e) Salzmann et al. (2013); Vieira et al"


id: e44042cc72b81c02ecf3c2082128cb71
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 33390
content:
" (2018), (c) and \r\n(f) Cleator et al. (2020) at the sites defined in Bartlein et al. (2011); (g) and (j) Hollis et al. (2019); (h) and (k) McClymont et al. (2020); (i) and (l) Tierney et al. (2020b). Where \r\nthere are multiple proxy estimations at a single site, a mean is taken. Model ensembles are (a), (d), (g) and (j) DeepMIP (only model simulations carried out with a mantle-frame \r\npaleogeography, and carried out under CO2 concentrations within the range assessed in Table 2.2, are shown); (b), (e), (h) and (k) PlioMIP; and (c), (f), (i) and (l) PMIP4. Further \r\ndetails on data sources and processing are available in the chapter data table (Table 7.SM.14)."


id: 188407c1b0b8b0a9ca84eaa6d96f3ae1
created_at: 2023-03-16 14:02:03
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vector_id: 1349
content:
"For the MPWP, model simulations are now in better agreement with \r\nproxies than at the time of AR5 (Haywood et al., 2020; McClymont \r\net  al., 2020). In particular, in the tropics new proxy reconstructions \r\nof SSTs are warmer and in better agreement with the models, due in \r\npart to the narrower time window in the proxy reconstructions. There \r\nis also better agreement at higher latitudes (primarily in the North \r\nAtlantic), due in part to the absence of some very warm proxy SSTs \r\ndue to the narrower time window (McClymont et al., 2020), and in \r\npart to a modified representation of Arctic gateways in the most recent \r\nPliocene model simulations (Otto-Bliesner et  al., 2017), which have \r\nresulted in warmer modelled SSTs in the North Atlantic (Haywood \r\net  al., 2020). Furthermore, as for the Eocene, improvements in the \r\nrepresentation of aerosol-cloud interactions have also led to improved \r\nmodel-data consistency at high latitudes (Feng et al., 2019)"


id: 244a124fe2240637d329853a965fc557
created_at: 2023-03-16 14:02:03
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vector_id: 3160
content:
" Although \r\nall PlioMIP2 models exhibit polar amplification of SAT, due to the \r\nrelatively narrow time window there are insufficient terrestrial proxies \r\nto assess this (Figure 7.13b,e). However, polar SST amplification in the \r\nPlioMIP2 ensemble mean is in reasonably good agreement with that \r\nfrom SST proxies in the Northern Hemisphere (Figure 7.13h,k)."


id: d7579b5c4a1e975de49395335928d6ec
created_at: 2023-03-16 14:02:03
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vector_id: 31397
content:
"Based on mature process understanding of the roles of poleward \r\nlatent heat transport and radiative feedbacks in polar warming, \r\na high degree of agreement across a hierarchy of climate models, \r\nobservational evidence, paleoclimate proxy records of past climates \r\nassociated with CO2 change, and ESM simulations of those past \r\nclimates, there is high confidence that polar amplification is a robust \r\nfeature of the long-term response to greenhouse gas forcing in both \r\nhemispheres. Stronger warming in the Arctic than the global average \r\nhas already been observed (Section  2.3.1) and its causes are well \r\nunderstood. It is very likely that the warming in the Arctic will be \r\nmore pronounced than the global average over the 21st century (high \r\nconfidence) (Section 4.5.1.1)"


id: 405f2bbce056472af83ee76f8af2049b
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content:
" This is supported by models' improved \r\nability to simulate polar amplification during past time periods, \r\ncompared with at the time of AR5 (high confidence); although this is \r\nbased on an assessment of mostly non-CMIP6 models."


id: a922b89cdd3dade5c977919d637f7c0a
created_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 24143
content:
"A weakening of the equatorial Pacific Ocean east-west SST gradient, \r\nwith greater warming in the east than the west, is a common feature \r\nof the climate response to greenhouse gas forcing as projected by \r\nESMs on centennial and longer time scales (e.g., Figure 7.14b; see \r\nSection 4.5.1). There are thought to be several factors contributing to \r\nthis pattern. In the absence of any changes in atmospheric or oceanic \r\ncirculations, the east-west surface temperature difference is theorized \r\nto decrease owing to weaker evaporative damping, and thus greater \r\nwarming in response to forcing, where climatological temperatures \r\nare lower in the eastern Pacific cold tongue (Xie et  al., 2010; Luo \r\net  al., 2015). Within atmospheric ESMs coupled to a  mixed-layer \r\nocean, this gradient in damping has been linked to the rate of change \r\nwith warming of the saturation specific humidity, which is set by the \r\nClausius-Clapeyron relation (Merlis and Schneider, 2011)"


id: 5f31f7abea6cb6169604712b31deb27
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vector_id: 12541
content:
" Gradients \r\nin low-cloud feedbacks may also favour eastern equatorial Pacific \r\nwarming (DiNezio et al., 2009)."


id: d9797a121620c257a4cb967f3dc6f68d
created_at: 2023-03-16 14:02:03
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vector_id: 31731
content:
"Since 1870, observed SSTs in the tropical western Pacific Ocean have \r\nincreased while those in the tropical eastern Pacific Ocean \r\nhave  changed less (Figure  7.14a and Section  9.2.1). Much of the \r\nresultant strengthening of the equatorial Pacific temperature gradient \r\nhas occurred since about 1980 due to strong warming in the west and \r\ncooling in the east (Figure 2.11b) concurrent with an intensification \r\nof the surface equatorial easterly trade winds and Walker circulation \r\n(Sections 3.3.3.1, 3.7.6, 8.3.2.3 and 9.2, and Figures 3.16f and 3.39f; \r\nEngland et  al., 2014). This temperature pattern is also reflected in \r\nregional ocean heat content trends and sea level changes observed \r\nfrom satellite altimetry since 1993 (Bilbao et al., 2015; Richter et al., \r\n2020). The observed changes may have been influenced by one or \r\na combination of temporary factors including sulphate aerosol forcing \r\n(Smith et al., 2016; Takahashi and Watanabe, 2016; Hua et al"


id: b42e2232970fedfabe2cfda5ef0efa34
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vector_id: 25863
content:
", 2018), \r\ninternal variability within the Indo-Pacific Ocean (Luo et al., 2012; Chung \r\net al., 2019), teleconnections from multi-decadal tropical Atlantic SST \r\ntrends (Kucharski et  al., 2011, 2014, 2015; McGregor et  al.,  2014; \r\nChafik et al., 2016; X. Li et al., 2016; Kajtar et al., 2017; Sun et al., \r\n2017), teleconnections from multi-decadal Southern Ocean SST trends \r\n(Hwang et al., 2017), and coupled ocean-atmosphere dynamics which \r\nslow warming in the equatorial eastern Pacific (Clement et al., 1996; \r\nCane et al., 1997; Seager et al., 2019). CMIP3 and CMIP5 ESMs have \r\ndifficulties replicating the observed trends in the Walker circulation \r\nand Pacific Ocean SSTs over the historical record (Sohn et al., 2013; \r\nZhou et al., 2016; Coats and Karnauskas, 2017), possibly due to model \r\ndeficiencies including insufficient multi-decadal Pacific Ocean SST \r\nvariability (Laepple and Huybers, 2014; Bilbao et  al., 2015; Chung \r\net al"


id: 3c7cc069a249ad461b80935653ccb6a4
created_at: 2023-03-16 14:02:03
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content:
", 2019), mean state biases affecting the forced response or the \r\nconnection between Atlantic and Pacific basins (Kucharski et al., 2014; \r\nKajtar et al., 2018; Luo et al., 2018; McGregor et al., 2018; Seager et al., \r\n2019), and/or a misrepresentation of radiative forcing (Sections 9.2.1 \r\nand 3.7.6). However, the observed trends in the Pacific Ocean SSTs \r\nare still within the range of internal variability as simulated by large \r\ninitial condition ensembles of CMIP5 and CMIP6 models (Olonscheck \r\net al., 2020; Watanabe et al., 2021). Because the causes of observed \r\nequatorial Pacific temperature gradient and Walker circulation trends \r\nare not well understood (Section 3.3.3.1), there is low confidence in \r\ntheir attribution to anthropogenic influences (Section 8.3.2.3), while \r\nthere is medium confidence that the observed changes have resulted \r\nfrom internal variability (Sections 3.7.6 and 8.2.2.2)."


id: baa4b786da8320b2279c063e75902406
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vector_id: 26903
content:
"Since AR5, the generation of new SST records has led to a variety \r\nof revised gradient estimates, specifically the generation of a new \r\nrecord for the warm pool (Zhang et al., 2014), the inclusion of SST \r\nreconstructions from sites in the South China Sea as warm pool \r\nestimates (O'Brien et al., 2014; Zhang et al., 2014), and the inclusion \r\nof several new sites from the eastern Pacific as cold tongue estimates \r\n(Zhang et al., 2014; Fedorov et al., 2015). Published estimates of the \r\nreduction in the longitudinal SST difference for the Late Pliocene, \r\nrelative to either Late Quaternary (0-0.5 million years ago) or \r\npre-industrial values, include 1degC to 1.5degC (Zhang et al., 2014), 0.1degC \r\nto 1.9degC (Tierney et al., 2019), and about 3degC (Ravelo et al., 2014; \r\nFedorov et al., 2015; Wycech et al., 2020). All of these studies report \r\na further weakening of the longitudinal gradient based on records \r\nextending into the Early Pliocene"


id: 34edc06a8fc836e55cca17a98e31282c
created_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 5737
content:
" While these revised estimates differ \r\nin magnitude due to differences in the sites and SST proxies used, \r\nthey all agree that the longitudinal gradient was weaker, and this \r\nis supported by the probabilistic approach of Tierney et al. (2019). \r\nHowever, given that there are currently relatively few western \r\nequatorial Pacific records from independent site locations, and due \r\nto uncertainties associated with the proxy calibrations (Haywood \r\net  al., 2016a), there is only medium confidence that the average \r\nlongitudinal gradient in the tropical Pacific was weaker during the \r\nPliocene than during the Late Quaternary."


id: 4cfd6c8c32d2da375c786c2bdb2ee0b2
created_at: 2023-03-16 14:02:03
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vector_id: 9534
content:
"To avoid the influence of local biases, changes in the longitudinal \r\ntemperature difference within Pliocene model simulations are typically \r\nevaluated using domain-averaged SSTs within chosen east and west \r\nPacific regions and as such there is sensitivity to methodology. Unlike \r\nthe reconstructed estimates, longitudinal gradient changes simulated \r\nby the Pliocene Model Intercomparison Project Phase 1 (PlioMIP1) \r\nmodels do not agree on the change in sign and are reported as \r\nspanning approximately -0.5degC to +0.5degC by Brierley et  al. (2015) \r\nand approximately -1degC to +1degC by Tierney et  al. (2019). Initial \r\nPlioMIP  Phase 2 (PlioMIP2) analysis suggests responses similar to \r\nPlioMIP1 (Feng et al., 2019; Haywood et al., 2020). Models that include \r\nhypothetical modifications to cloud albedo or ocean mixing are \r\nrequired to simulate the substantially weaker longitudinal differences \r\nseen in reconstructions of the Early Pliocene (Fedorov et  al"


id: dd0f4bff3557be46c6a2811857072654
created_at: 2023-03-16 14:02:03
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vector_id: 32279
content:
"The expected time-evolution of the spatial pattern of surface warming \r\nin the future has important implications for values of ECS inferred \r\nfrom the historical record of observed warming. In particular, changes \r\nin the global top-of-atmosphere (TOA) radiative energy budget can be \r\ninduced by changes in the regional variations of surface temperature, \r\neven without a change in the global mean temperature (Zhou et al., \r\n2016; Ceppi and Gregory, 2019). Consequently, the global radiative \r\nfeedback, characterizing the net TOA radiative response to global \r\nsurface warming, depends on the spatial pattern of that warming. \r\nTherefore, if the equilibrium warming pattern under CO2 forcing \r\n(similar to CMIP6 projections in Figure 7.12a) is distinct from that \r\nobserved over the historical record or indicated by paleoclimate \r\nproxies (Sections 7.4.4.1 and 7.4.4.2), then ECS will be different \r\nfrom the effective ECS (Box 7.1) that is inferred from those periods"


id: 9b91b2e9aee10d96e4cfc90816caa902
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index: climate_gpt_only_giec
vector_id: 22034
content:
" \r\nAccounting for the dependence of radiative feedbacks on the spatial \r\npattern of warming has helped to reconcile values of ECS inferred \r\nfrom the historical record with values of ECS based on other lines of \r\nevidence and simulated by climate models (Section 7.5.2.1; Armour, \r\n2017; Proistosescu and Huybers, 2017; Andrews et al., 2018) but has \r\nnot yet been examined in the paleoclimate context."


id: 80b950e13ea776a411f6539e7324c05d
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vector_id: 17773
content:
"This temperature 'pattern effect' (Stevens et  al., 2016) can result \r\nfrom both internal variability and radiative forcing of the climate \r\nsystem. Importantly, it is distinct from potential radiative feedback \r\ndependencies on the global surface temperature, which are assessed \r\nin Section 7.4.3. While changes in global radiative feedbacks under \r\ntransient warming have been documented in multiple generations \r\nof climate models (Williams et al., 2008; Andrews et al., 2015; Ceppi \r\nand Gregory, 2017; Dong et  al., 2020), research published since \r\nAR5 has developed a much-improved understanding of the role of \r\nevolving SST patterns in driving feedback changes (Armour et  al., \r\n2013; Andrews et  al., 2015, 2018; Gregory and Andrews, 2016; \r\nZhou et al., 2016, 2017b; Ceppi and Gregory, 2017; Haugstad et al., \r\n2017; Proistosescu and Huybers, 2017; Andrews and Webb, 2018; \r\nMarvel et al., 2018; Silvers et al., 2018; Dong et al., 2019, 2020)"


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content:
" This \r\nsection assesses process understanding of the pattern effect, which \r\nis dominated by the evolution of SSTs. Section 7.5.2.1 describes how \r\npotential feedback changes associated with the pattern effect are \r\nimportant to interpreting ECS estimates based on historical warming."


id: 2da24dc7bd2416b6eb847e4c7c1518df
created_at: 2023-03-16 14:02:03
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content:
"The radiation changes most sensitive to warming patterns are those \r\nassociated with low-cloud cover (affecting global albedo) and the \r\ntropospheric temperature profile (affecting thermal emission to \r\nspace) (Ceppi and Gregory, 2017; Zhou et al., 2017b; Andrews et al., \r\n2018; Dong et  al., 2019). The mechanisms and radiative effects \r\nof these changes are illustrated in Figure  7.14a,b. SSTs in regions \r\nof deep convective ascent (e.g., in the western Pacific warm pool) \r\ngovern the temperature of the tropical free troposphere and, in turn, \r\naffect low-clouds through the strength of the inversion that caps the \r\nboundary layer (i.e., the lower-tropospheric stability) in subsidence \r\nregions (Wood and Bretherton, 2006; Klein et  al., 2017). Surface \r\nwarming within ascent regions thus warms the free troposphere \r\nand increases low-cloud cover, causing an increase in emission \r\nof thermal radiation to space and a  reduction in absorbed solar \r\nradiation"


id: fdbdc39603ccbfb9d9488bb4f90b59a8
created_at: 2023-03-16 14:02:03
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content:
" In contrast, surface warming in regions of overall descent \r\npreferentially warms the boundary layer and enhances convective \r\nmixing with the dry free troposphere, decreasing low-cloud cover \r\n(Bretherton et al., 2013; Qu et al., 2014; Zhou et al., 2015). This leads \r\nto an increase in absorption of solar radiation but little change in \r\nthermal emission to space. Consequently, warming in tropical ascent \r\nregions results in negative lapse-rate and cloud feedbacks while \r\nwarming in tropical descent regions results in positive lapse-rate \r\nand cloud feedbacks (Figure  7.14; Rose and Rayborn, 2016; Zhou \r\net al., 2017b; Andrews and Webb, 2018; Dong et al., 2019). Surface \r\nwarming in mid-to-high latitudes causes a weak radiative response \r\nowing to compensating changes in thermal emission (Planck and \r\nlapse-rate feedbacks) and absorbed solar radiation (shortwave cloud \r\nand surface-albedo feedbacks; Rose and Rayborn, 2016; Dong et al"


id: 972add5fce7331880f262ca1054751a1
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 21298
content:
", \r\n2019), however this compensation may weaken due to less-negative \r\nshortwave cloud feedbacks at high warming (Frey and Kay, 2018; \r\nBjordal et al., 2020; Dong et al., 2020)."


id: ca4692f7a2c517fd66ef34415ad30818
created_at: 2023-03-16 14:02:03
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vector_id: 29387
content:
"Figure  7.14 | Illustration of tropospheric temperature and low-cloud \r\nresponse to observed and projected Pacific Ocean sea surface temperature \r\ntrends. (a) Atmospheric response to linear sea surface temperature trend observed \r\nover 1870-2019 (HadISST1 dataset; Rayner et al., 2003). (b) Atmospheric response \r\nto linear sea-surface temperature trend over 150 years following abrupt4xCO2 \r\nforcing as projected by CMIP6 ESMs (Dong et al., 2020). Relatively large historical \r\nwarming in the western tropical Pacific has been communicated aloft (a shift from \r\ngrey to red atmospheric temperature profile), remotely warming the tropical free \r\ntroposphere and increasing the strength of the inversion in regions of the tropics \r\nwhere warming has been slower, such as the eastern equatorial Pacific. In turn, an \r\nincreased inversion strength has increased the low-cloud cover (Zhou et al"


id: 3dc1ef612b95a16f202b71a342b58b4c
created_at: 2023-03-16 14:02:03
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vector_id: 7070
content:
", 2016) \r\ncausing an anomalously negative cloud and lapse-rate feedbacks over the historical \r\nrecord (Andrews et al., 2018; Marvel et al., 2018). Relatively large projected warming \r\nin the eastern tropical Pacific is trapped near the surface (shift from grey to red \r\natmospheric temperature profile), decreasing the strength of the inversion locally. \r\nIn turn, a decreased inversion strength combined with surface warming is projected to \r\ndecrease the low-cloud cover, causing the cloud and lapse-rate feedbacks to become \r\nless negative in the future. Figure adapted from Mauritsen (2016). Further details on \r\ndata sources and processing are available in the chapter data table (Table 7.SM.14)."


id: 9a44b87b33c5c8dbe19997769ab6399b
created_at: 2023-03-16 14:02:03
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vector_id: 21796
content:
"that found when prescribing observed warming patterns within \r\natmosphere-only ESMs (Figure  7.15; Andrews et  al., 2018). This \r\narises from the fact that the forced spatial pattern of warming within \r\ntransient simulations of most coupled ESMs are distinct from observed \r\nwarming patterns over the historical record in key regions such as \r\nthe equatorial Pacific Ocean and Southern Ocean (Sections 7.4.4.1 \r\nand 7.4.4.2), while being more similar to the equilibrium pattern \r\nsimulated under abrupt4xCO2. However, historical simulations \r\nwith HadGEM3-GC3.1-LL (Andrews et  al., 2019) and GFDL CM4.0 \r\n(Winton et  al., 2020) show substantial spread in the value of \r\na'  across ensemble members, indicating a  potentially important \r\nrole for internal variability in setting the magnitude of the pattern \r\neffect over the historical period. Using the  100-member historical \r\nsimulation ensemble of MPI-ESM1.1, Dessler et al. (2018) find that \r\ninternal climate variability alone results in a 0"


id: 8249ca9ad6463865978cb9df77f1189
created_at: 2023-03-16 14:02:03
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vector_id: 18025
content:
"5 W m-2 degC-1 spread in \r\nthe historical effective feedback parameter, and thus also in the value \r\nof a'. Estimates of a' using prescribed historical warming patterns \r\nprovide a more realistic representation of the historical pattern effect \r\nbecause they account for the net effect of the transient response \r\nto historical forcing and internal variability in the observed record \r\n(Andrews et al., 2018)."


id: 103154724b218e61273425a8a2c3040f
created_at: 2023-03-16 14:02:03
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vector_id: 37
content:
"Andrews et al. (2018) analysed available CMIP5/6 ESM simulations \r\n(six in total) comparing effective feedback parameters diagnosed \r\nwithin atmosphere-only ESMs using prescribed historical SST and sea \r\nice concentration patterns with the equilibrium feedback parameters \r\nas estimated within coupled ESMs (using identical atmospheres) \r\ndriven by abrupt 4xCO2 forcing. The atmosphere-only ESMs show \r\npronounced multi-decadal variations in their effective feedback \r\nparameters over the last  century, with a  trend towards strongly \r\nnegative values since about 1980 owing primarily to negative \r\nshortwave cloud feedbacks driven by warming in the western \r\nequatorial Pacific Ocean and cooling in the eastern equatorial \r\nPacific Ocean (Zhou et al., 2016; Andrews et al., 2018; Marvel et al., \r\n2018; Dong et  al., 2019). Yet, all six models show a  less-negative \r\nnet feedback parameter under abrupt4xCO2 than for the historical \r\nperiod (based on regression since 1870 following Andrews et  al"


id: ea30b4432fde68246e91f4d5cadedc0
created_at: 2023-03-16 14:02:03
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vector_id: 34348
content:
", \r\n2018). The average change in net feedback parameter between \r\nthe historical period and the equilibrium response to CO2 forcing, \r\ndenoted here as a', for these simulations is a' = +0.6 W m-2 degC-1\r\n(+0.3 to +1.0 W m-2 degC-1 range across models; Figure 7.15b). These \r\nfeedback parameter changes imply that the value of ECS may be \r\nsubstantially larger than that inferred from the historical record \r\n(Section  7.5.2.1). These findings can be understood from the fact \r\nthat, due to a  combination of internal variability and transient \r\nresponse to forcing (Section 7.4.4.2), historical sea surface warming \r\nhas been relatively large in regions of tropical ascent (Figure 7.14a), \r\nleading to an anomalously large net negative radiative feedback; \r\nhowever, future warming is expected to be largest in tropical \r\ndescent regions, such as the eastern equatorial Pacific, and at high \r\nlatitudes (Sections 7.4.4.1 and 7.4.4.2 and Figure 7"


id: a095563ebd0b2dd927ece8a2a333ccdc
created_at: 2023-03-16 14:02:03
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vector_id: 22801
content:
"The magnitude of a', as quantified by ESMs, depends on the accuracy \r\nof both the projected patterns of SST and sea ice concentration \r\nchanges in response to CO2 forcing and the radiative response to \r\nthose patterns (Andrews et al., 2018). Model biases that affect the \r\nlong-term warming pattern (e.g.,  SST and relative humidity biases \r\nin the equatorial Pacific cold tongue as suggested by Seager et al., \r\n2019) will affect the value of a'. The value of a' also depends on the \r\naccuracy of the historical SST and sea ice concentration conditions \r\nprescribed within atmosphere-only versions of ESMs to quantify \r\nthe historical radiative feedback (Figure 7.15b). Historical SSTs are \r\nparticularly uncertain for the early portion of the historical record \r\n(Section 2.3.1), and there are few constraints on sea ice concentration \r\nprior to the satellite era. Using alternative SST datasets, Andrews \r\net al"


id: 11dff8d93978c19d4d83bc9c2d4bf382
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 309
content:
" (2018) found little change in the value of a' within two models \r\n(HadGEM3 and HadAM3), while Lewis and Mauritsen (2021) found \r\na smaller value of a' within two other models (ECHAM6.3 and CAM5). \r\nThe sensitivity of results to the choice of dataset represents a major \r\nsource of uncertainty in the quantification of the historical pattern \r\neffect using atmosphere-only ESMs that has yet to be systematically \r\nexplored, but the preliminary findings of Lewis and Mauritsen (2021) \r\nand Fueglistaler and Silvers (2021) suggest that a' could be smaller \r\nthan the values reported in Andrews et al. (2018)."


id: eb643dc68904f03d9c17e0c4644a32e6
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 34530
content:
"A similar behaviour is seen within transient simulations of coupled \r\nESMs, which project SST warming patterns that are initially \r\ncharacterized by relatively large warming rates in the western \r\nequatorial Pacific Ocean on decadal time scales and relatively large \r\nwarming in the eastern equatorial Pacific and Southern Ocean \r\non centennial time scales (Andrews et  al., 2015; Proistosescu \r\nand Huybers, 2017; Dong et  al., 2020). Recent studies based on \r\nsimulations of 1%  yr -1 CO2 increase (1pctCO2) or abrupt4xCO2 as \r\nanalogues for historical warming suggest characteristic values of \r\na' = +0.05 W m-2 degC-1 (-0.2 to +0.3 W m-2 degC-1 range across models) \r\nbased on CMIP5 and CMIP6 ESMs (Armour 2017, Lewis and Curry \r\n2018, Dong et al. 2020). Using historical simulations of one CMIP6 ESM \r\n(HadGEM3-GC3.1-LL), Andrews et al. (2019) find an average feedback \r\nparameter change of a' = +0.2 W m-2 degC-1 (-0.2 to +0.6 W m-2 degC-1 \r\nrange across four ensemble members)"


id: 45a1f2f8c6756a69744895c8bf144f92
created_at: 2023-03-16 14:02:03
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vector_id: 8360
content:
" Using historical simulations \r\nfrom another CMIP6 ESM (GFDL CM4.0), Winton et al. (2020) find \r\nan average feedback parameter change of a' = +1.5 W m-2 degC-1 \r\n(+1.2  to +1.7 W m-2 degC-1 range across three ensemble members). \r\nThis value is larger than the a' = +0.7 W m-2 degC-1 within GFDL CM4.0 \r\nfor historical CO2 forcing only, suggesting that the value of a' may \r\ndepend on historical non-CO2 forcings such as those associated with \r\ntropospheric and stratospheric aerosols (Marvel et al., 2016; Gregory \r\net al., 2020; Winton et al., 2020)."


id: 91787ef10f594b9bacb02a4a68650ae3
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 20422
content:
"equilibrium - an improbable scenario given that Earth is in a relatively \r\nearly phase of transient warming and that reaching equilibrium would \r\ntake multiple millennia (C. Li et  al., 2013). Moreover, paleoclimate \r\nproxies, ESM simulations, and process understanding indicate that \r\nstrong warming in the eastern equatorial Pacific Ocean (with medium \r\nconfidence) and Southern Ocean (with high confidence) will emerge \r\non centennial time scales as the response to CO2 forcing dominates \r\ntemperature changes in these regions (Sections 7.4.4.1, 7.4.4.2 \r\nand 9.2.1). However, there is low confidence that these features, which \r\nhave been largely absent over the historical record, will emerge this \r\ncentury (Sections 7.4.4.1, 7.4.4.2 and Section 9.2.1)"


id: cf6015980ed8792110977bf4f2bb403
created_at: 2023-03-16 14:02:03
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vector_id: 30159
content:
" This leads to high \r\nconfidence that radiative feedbacks will become less negative as the \r\nCO2-forced pattern of surface warming emerges (a' > 0 W m-2 degC-1), \r\nbut low confidence that these feedback changes will be realized this \r\ncentury. There is also substantial uncertainty in the magnitude of the \r\nnet radiative feedback change between the present warming pattern \r\nand the projected equilibrium warming pattern in response to CO2\r\nforcing owing to the fact that its quantification currently relies solely \r\non ESM results and is subject to uncertainties in historical SST patterns. \r\nThus, based on the pattern of warming since 1870, a' is estimated to \r\nbe in the range 0.0 to 1.0 W m-2 degC-1 but with a low confidence in \r\nthe upper end of this range. A value of a' = +0.5 +- 0.5 W m-2 degC -1\r\nis used to represent this range in Box 7.2 and Section 7.5"


id: 668d6176fcfcd09f0fa0c3f37be3e0a7
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content:
"2, which \r\nrespectively assess the implications of changing radiative feedbacks \r\nfor Earth's energy imbalance and estimates of ECS based on the \r\ninstrumental record. The value of a' is larger if quantified based on \r\nthe observed pattern of warming since 1980 (Figure 2.11b) which is "


id: a131c36e40b59cd88d2c9bd6fce7abf5
created_at: 2023-03-16 14:02:03
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vector_id: 22921
content:
"Before AR6, the assessment of ECS relied on either CO2-doubling \r\nexperiments using global atmospheric models coupled with \r\nmixed-layer ocean or standardized CO2-quadrupling (abrupt4xCO2) \r\nexperiments using fully coupled ocean-atmosphere models or \r\nEarth system models (ESMs). The TCR has similarly been diagnosed \r\nfrom ESMs in which the CO2 concentration is increased at 1% yr -1\r\n(1pctCO2, an approximately linear increase in ERF over time) and is \r\nin practice estimated as the average over a 20-year period centred \r\nat the time of atmospheric CO2 doubling, that is, year 70. In AR6, \r\nthe assessments of ECS and TCR are made based on multiple lines \r\nof evidence, with ESMs representing only one of several sources \r\nof information. The constraints on these climate metrics are based \r\non radiative forcing and climate feedbacks assessed from process \r\nunderstanding (Section  7.5.1), climate change and variability seen \r\nwithin the instrumental record (Section 7.5"


id: a940e180c11259d4d341ea8645a08a8a
created_at: 2023-03-16 14:02:03
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vector_id: 24157
content:
"2), paleoclimate evidence \r\n(Section 7.5.3), emergent constraints (Section 7.5.4), and a synthesis \r\nof all lines of evidence (Section 7.5.5). In AR5, these lines of evidence \r\nwere not explicitly combined in the assessment of climate sensitivity, \r\nbut as demonstrated by Sherwood et  al. (2020) their combination \r\nnarrows the uncertainty ranges of ECS compared to that assessed \r\nin AR5. ECS values found in CMIP6 models, some of which exhibit \r\nvalues higher than 5degC (Meehl et al., 2020; Zelinka et al., 2020), are \r\ndiscussed in relation to the AR6 assessment in section 7.5.6."


id: 7b238c7fc5d5fb552da2630ac90a1200
created_at: 2023-03-16 14:02:03
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vector_id: 16966
content:
"The wide range of the process-based ECS estimate is not due \r\nsolely to uncertainty in the estimates of DF2xCO2 and a, but is partly \r\nexplained by the assumption that DF2xCO2 and a are independent in \r\nthis approach. In CMIP5 and CMIP6 ensembles, DF2xCO2 and a are \r\nnegatively correlated when they are calculated using linear regression \r\nin abrupt4xCO2 simulations (r 2 = 0.34; Andrews et al., 2012; Webb \r\net al., 2013; Zelinka et al., 2020). The negative correlation leads to \r\ncompensation between the inter-model spreads of these quantities, \r\nthereby reducing the ECS range estimated directly from the models. \r\nIf the process-based ECS distribution is reconstructed from probability \r\ndistributions of DF2xCO2 and a assuming that they are correlated as in \r\nCMIP model ensembles, the range of ECS will be narrower by 14% \r\n(pink curved bar in Figure 7.16). If, however, the covariance between \r\nDF2xCO2 and a is not adopted, there is no change in the mean, but the \r\nwide range still applies"


id: b4c67889097553596e502230a1ba0651
created_at: 2023-03-16 14:02:03
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vector_id: 25976
content:
"A significant correlation between DF2xCO2 and a also occurs when \r\nthe two parameters are estimated separately from atmospheric ESM \r\nfixed-SST experiments (Section  7.3.1) or fixed CO2 concentration \r\nexperiments (Section  7.4.1; Ringer et  al., 2014; Chung and Soden, \r\n2018). Hence the relationship is not expected to be an artefact of \r\ncalculating the parameters using linear regression in abrupt4xCO2\r\nsimulations. A  possible physical cause of the correlation may be \r\na  compensation between the cloud adjustment and the cloud \r\nfeedback over the tropical ocean (Ringer et  al., 2014; Chung and \r\nSoden, 2018). It has been shown that the change in the hydrological \r\ncycle is a controlling factor for the low-cloud adjustment (Dinh and \r\nFueglistaler, 2019) and for the low-cloud feedback (Watanabe et al., \r\n2018), and therefore the responses of these clouds to the direct CO2\r\nradiative forcing and to the surface warming may not be independent"


id: 1fc4fefc6dbde5f5b15fd88c54b03ac
created_at: 2023-03-16 14:02:03
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vector_id: 2429
content:
" \r\nHowever, robust physical mechanisms are not yet established, \r\nand furthermore, the process-based assessment of the tropical \r\nlow-cloud feedback is only indirectly based on ESMs given that \r\nphysical processes which control the low-clouds are not sufficiently \r\nwell-simulated in models (Section  7.4.2.4). For these reasons, the \r\nco-dependency between DF2xCO2 and a is assessed to have low \r\nconfidence and, therefore, the more conservative assumption that \r\nthey are independent for the process-based assessment of ECS \r\nis retained."


id: 118bb8f566b80a669dc5ee94fa9371e4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 251
content:
"The analytical solution of the energy balance emulator reveals \r\nthat the global surface temperature change to abrupt increase of \r\nthe atmospheric CO2 concentration is expressed by a  combination \r\nof a  fast adjustment of the surface components of the climate \r\nsystem and a  slow response of the deep ocean, with time scales \r\nof several years and several centuries, respectively (grey curve in \r\nFigure 7.17b). The equilibrium response of upper ocean temperature, \r\napproximating SST and the surface air temperature response, \r\ndepends, by definition, only on the radiative forcing and the net \r\nfeedback parameter. Uncertainty in a dominates (80-90%) the \r\ncorresponding uncertainty range for ECS in CMIP5 models (Vial et al., \r\n2013), and also an increase of ECS in CMIP6 models (Section 7.5.5) \r\nis attributed by about 60-80% to a  change in a (Zelinka et  al., \r\n2020)"


id: 9163a55a631b18e8fc3132278758f25d
created_at: 2023-03-16 14:02:03
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vector_id: 20404
content:
" For the range of TCR, the contribution from uncertainty in a is \r\nreduced to 50-60% while uncertainty in DF2xCO2 becomes relatively \r\nmore important (Geoffroy et al., 2012; Lutsko and Popp, 2019). TCR \r\nreflects the fast response occurring approximately during the first \r\n20 years in the abrupt4xCO2 simulation (Held et al., 2010), but the \r\nfast response is not independent of the slow response because there \r\nis a non-linear co-dependence between them (Andrews et al., 2015). \r\nThe non-linear  relationship between ECS and TCR indicates that \r\nthe probability of high TCR is not very sensitive to changes in the \r\nprobability of high ECS (Meehl et al., 2020)."


id: 4537603be186230d982b7965bb74619c
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 8318
content:
"Considering an idealized time evolution of ERF (1% increase per year \r\nuntil CO2 doubling and held fixed afterwards, see Figure  7.17a), \r\nthe TCR defined by the surface temperature response at year 70 is \r\nderived by substituting the process-based ECS into the analytical \r\nsolution of the emulator (Figure  7.17b, see also Supplementary \r\nMaterial 7.SM.2.1). When additional parameters in the emulator \r\nare prescribed by using CMIP6 multi-model mean values of those \r\nestimates (Smith et al., 2020b), this calculation translates the range of \r\nECS in Section 7.5.2.1 to the range of TCR. The transient temperature \r\nresponse, in reality, varies with different estimates of the ocean \r\nheat uptake efficiency (g) and efficacy (e). When the emulator was \r\ncalibrated to the transient responses in CMIP5 models, it shows that \r\nuncertainty in heat capacities is negligible and differences in g and \r\ne explain 10-20% of the inter-model spread of TCR among GCMs \r\n(Geoffroy et al., 2012)"


id: 574cd10ad0d90365ef664d3ce84bd95e
created_at: 2023-03-16 14:02:03
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vector_id: 11223
content:
" Specifically, their product, k = ge, appearing \r\nin a simplified form of the solution, that is, TCR  -DF2xCO2/(a - k), \r\ngives a  single parameter quantifying the damping effects of heat \r\nuptake (Jimenez-de-la-Cuesta and Mauritsen, 2019). This parameter \r\nis positive and acts to slow down the temperature response in \r\na similar manner to the 'pattern effect' (Sections 7.4.4.3 and 7.5.2.1). \r\nThe ocean heat uptake in nature is controlled by multiple processes \r\nassociated with advection and mixing (Exarchou et al., 2014; Kostov \r\net al., 2014; Kuhlbrodt et al., 2015) but is simplified to be represented \r\nby a  single term of heat exchange between the upper and deep \r\nocean in the emulator. Therefore, it is challenging to constrain \r\ng and e from process-based understanding (Section 7.5.2). Because \r\nthe estimated values are only weakly correlated across models, \r\nthe mean value and one standard deviation of k are calculated as "


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vector_id: 83
content:
"The GSAT change from 1850-1900 to 2006-2019 is estimated to be \r\n1.03 [0.86 to 1.18] degC (Cross-chapter Box 2.3). Together with estimates \r\nof Earth's energy imbalance (Section 7.2.2) and the global ERF that \r\nhas driven the observed warming (Section  7.3), the instrumental \r\ntemperature record enables global energy budget estimates of ECS \r\nand TCR. While energy budget estimates use instrumental data, they \r\nare not based purely on observations. A conceptual model typically \r\nbased on the global mean forcing and response energy budget \r\nframework (Box 7.1) is needed to relate ECS and TCR to the estimates \r\nof global warming, ERF and Earth's energy imbalance (Forster, 2016; \r\nKnutti et al., 2017). Moreover, ESM simulations partly inform estimates \r\nof the historical ERF (Section 7.3) as well as Earth's energy imbalance \r\nin the 1850-1900 climate (the period against which changes are \r\nmeasured; Forster, 2016; Lewis and Curry, 2018)"


id: fdb836092eb8ccf77a46abf3b7fd0e10
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 37437
content:
" ESMs are also used \r\nto estimate uncertainty due the internal climate variability that may \r\nhave contributed to observed changes in temperature and energy \r\nimbalance (e.g., Palmer and McNeall, 2014; Sherwood et al., 2020). \r\nResearch since AR5 has shown that global energy budget estimates \r\nof ECS may be biased low when they do not take into account how \r\nradiative feedbacks depend on the spatial pattern of surface warming \r\n(Section 7.4.4.3) or when they do not incorporate improvements in \r\nthe estimation of global surface temperature trends which take better \r\naccount of data-sparse regions and are more consistent in their \r\ntreatment of surface temperature data (Section 2.3.1). Together with \r\nupdated estimates of global ERF and Earth's energy imbalance, these \r\nadvances since AR5 have helped to reconcile energy budget estimates \r\nof ECS with estimates of ECS from other lines of evidence."


id: ed8c88470f2bb25d3cafa0c7e47127f7
created_at: 2023-03-16 14:02:03
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vector_id: 34837
content:
"The traditional global mean forcing and response energy budget \r\nframework (Section 7.4.1 and Box 7.1; Gregory et al., 2002) relates \r\nthe difference between the ERF (DF) and the radiative response to \r\nobserved global warming (aDT) to the Earth's energy imbalance (DN): \r\nDN = aDT + DF. Given the relationship ECS = -DF2xCO2/a, where DF2xCO2\r\nis the ERF from CO2 doubling, ECS can be estimated from historical \r\nestimates of DT, DF, DN and DF2xCO2: ECS = DF2xCO2DT/(DF - DN). \r\nSince TCR is defined as the temperature change at the time of \r\nCO2 doubling under an idealized 1% yr-1 CO2 increase, it can be \r\ninferred from the historical record as: TCR  =  DF2xCO2 DT/DF, under \r\nthe assumption that radiative forcing increases quickly compared to \r\nthe adjustment time scales of the deep ocean, but slowly enough \r\nand over a sufficiently long time that the upper ocean is adjusted, \r\nso that DT and DN increases approximately in proportion to DF"


id: f98e08da8a2c95e1902d5bb4a82c7eba
created_at: 2023-03-16 14:02:03
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vector_id: 36784
content:
" \r\nBecause DN is positive, TCR is always smaller than ECS, reflecting \r\nweaker transient warming than equilibrium warming. TCR is better \r\nconstrained than ECS owing to the fact that the denominator of TCR, \r\nwithout the quantity DN, is more certain and further from zero than \r\nis the denominator of ECS. The upper bounds of both TCR and ECS \r\nestimated from historical warming are inherently less certain than \r\ntheir lower bounds because DF is uncertain and in the denominator."


id: 913b3b2250109d26665c0635a4897091
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 20387
content:
"The traditional energy budget framework lacks a representation of \r\nhow radiative feedbacks depend on the spatial pattern of warming. \r\nThus, studies employing this framework (Otto et  al., 2013; Lewis \r\nand Curry, 2015, 2018; Forster, 2016) implicitly assume that the net \r\nradiative feedback has a constant magnitude, producing an estimate \r\nof the effective ECS (defined as the value of ECS that would occur \r\nif a does not change from its current value) rather than of the true \r\nECS. As summarized in Section  7.4.4.3, there are now multiple \r\nlines of evidence providing high confidence that the net radiative \r\nfeedback will become less negative as the warming pattern evolves \r\nin the future (the pattern effect). This arises because historical \r\nwarming has been relatively larger in key negative feedback regions \r\n(e.g.,  western tropical Pacific Ocean) and relatively smaller in key \r\npositive feedback regions (e.g"


id: d24dd7f5807d068be94c96e405fcd805
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 30614
content:
",  eastern tropical Pacific Ocean and \r\nSouthern Ocean) than is projected in the near-equilibrium response \r\nto CO2 forcing (Section 7.4.4.3; Held et al., 2010; Proistosescu and \r\nHuybers, 2017; Dong et  al., 2019), implying that the true ECS will \r\nbe larger than the effective ECS inferred from historical warming. \r\nThis section first assesses energy budget constraints on TCR and the \r\neffective ECS based on updated estimates of historical warming, ERF, \r\nand Earth's energy imbalance. It then assesses what these energy \r\nbudget constraints imply for values of ECS once the pattern effect is \r\naccounted for."


id: 4dd5d9a12b544ce16ea500da8f25acc3
created_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 9672
content:
"balance modelling (Armour, 2017), inferred ocean warming given \r\nobserved SSTs using ocean models (Gebbie and Huybers, 2019; \r\nZanna et  al., 2019), and ocean warming reconstructed from noble \r\ngas thermometry (Baggenstos et  al., 2019) suggest a  1850-1900 \r\nEarth energy imbalance of 0.2 +- 0.2 W m-2. Combined with estimates \r\nof internal variability in Earth's energy imbalance, calculated using \r\nperiods of equivalent lengths of years as used in unforced ESM \r\nsimulations (Palmer and McNeall, 2014; Sherwood et al., 2020), the \r\nanomalous energy imbalance between 1850-1900 and 2006-2019 \r\nis estimated to be DN = 0.59 +- 0.35 W m-2. GSAT change between \r\n1850-1900 and 2006-2019 is estimated to be DT = 1.03degC +- 0.20 degC \r\n(Cross-Chapter Box  2.3 and Box  7.2) after accounting for internal \r\ntemperature variability derived from unforced ESM simulations \r\n(Sherwood et al., 2020). The ERF change between 1850-1900 and \r\n2006-2019 is estimated to be DF  =  2.20 [1.53 to 2"


id: a36c953483562a341230dd97354a2a37
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 23274
content:
"91] W  m-2\r\n(Section 7.3.5) and the ERF for a doubling of CO2 is estimated to be \r\nDF2xCO2 = 3.93 +- 0.47 W m-2 (Section 7.3.2). Employing these values \r\nwithin the traditional global energy balance framework described \r\nabove (following the methods of Otto et al. (2013) and accounting \r\nfor correlated uncertainties between DF and DF2xCO2) produces a TCR \r\nof 1.9 [1.3 to 2.7] degC and an effective ECS of 2.5 [1.6 to 4.8] degC. \r\nThese TCR and effective ECS values are higher than those in the \r\nrecent literature (Otto et al., 2013; Lewis and Curry, 2015, 2018) but \r\nare comparable to those of Sherwood et al. (2020) who also used \r\nupdated estimates of observed warming, Earth's energy imbalance, \r\nand ERF."


id: a6ffaf8b7d89551f77b8114eaa9b3fa5
created_at: 2023-03-16 14:02:03
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vector_id: 23786
content:
"Energy budget estimates of TCR and ECS have evolved in the \r\nliterature over recent decades. Prior to AR4, the global energy \r\nbudget provided relatively weak constraints, primarily due to large \r\nuncertainty in the tropospheric aerosol forcing, giving ranges of the \r\neffective ECS that typically included values above 10degC (Forster, 2016; \r\nKnutti et  al., 2017). Revised estimates of aerosol forcing together \r\nwith a larger greenhouse gas forcing by the time of AR5 led to an \r\nestimate of DF that was more positive and with reduced uncertainty \r\nrelative to AR4. Using energy budget estimates and radiative forcing \r\nestimates updated to 2009, Otto et  al. (2013) estimated that TCR \r\nwas 1.3 [0.9 to 2.0] degC, and that the effective ECS was 2.0 [1.2 to \r\n3.9] degC. This AR5-based energy budget estimate of ECS was lower \r\nthan estimates based on other lines of evidence, leading AR5 to \r\nexpand the assessed likely range of ECS to include lower values \r\nrelative to AR4"


id: 965bf8fa88b859f07c888cafa8302f21
created_at: 2023-03-16 14:02:03
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vector_id: 21175
content:
" Studies since AR5 using similar global energy budget \r\nmethods have produced similar or slightly narrower ranges for TCR \r\nand effective ECS (Forster, 2016; Knutti et al., 2017)."


id: 557d1f21ce42497a7b29acb957615147
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 10928
content:
"The trend estimation method applied to global surface temperature \r\naffects derived values of ECS and TCR from the historical record. \r\nIn this Report, the effective ECS is inferred from estimates that \r\nuse global coverage of GSAT to estimate the surface temperature \r\ntrends. The GSAT trend is assessed to have the same best estimate \r\nas the observed global mean surface temperature (GMST), \r\nalthough the  GSAT trend is assessed to have larger uncertainty \r\n(see Cross-Chapter Box 2.3). Many previous studies have relied on \r\nHadCRUT4 GMST estimates that used the blended observations \r\nand did not interpolate over regions of incomplete observational \r\ncoverage such as the Arctic. As a result, the ECS and TCR derived \r\nfrom these studies has smaller ECS and TCR values than those \r\nderived from model-inferred estimates (M. Richardson et al., 2016, \r\n2018). The energy budget studies assessing ECS in AR5 employed \r\nHadCRUT4 or similar measures of GMST trends"


id: 573906db8b2c15304b45aa2ff204ddc4
created_at: 2023-03-16 14:02:03
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vector_id: 11211
content:
" As other lines of \r\nevidence in that report used GSAT trends, this could partly explain \r\nwhy AR5-based energy budget estimates of ECS were lower than \r\nthose estimated from other lines of evidence, adding to the overall \r\ndisparity in M. Collins et al. (2013). In this report, GSAT is chosen \r\nas the standard measure of global surface temperature to aid \r\ncomparison with previous model- and process-based estimates of \r\nECS, TCR and climate feedbacks (see Cross-Chapter Box 2.3)."


id: 3324f5c29e29e78db7d722f119492d71
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 5466
content:
"(HadGEM3-GC3.1-LL) the effective ECS derived from historical energy \r\nbudget constraints is 4.1degC (average of four ensemble members) while \r\nECS is estimated to be 5.5degC (Andrews et al., 2019). These modelling \r\nresults suggest that the effective ECS under historical forcing could be \r\nlower than the true ECS owing to differences in radiative feedbacks \r\ninduced by the distinct patterns of historical and equilibrium warming \r\n(Section 7.4.4.3). Using GFDL-CM4, Winton et al. (2020) also find that \r\nthe value of TCR estimated from energy budget constraints within \r\na  historical simulation (1.3degC) is substantially lower than the true \r\nvalue of TCR (2.1degC) diagnosed within a 1pctCO2 simulation owing to \r\na combination of the pattern effect and differences in the efficiency \r\nof ocean heat uptake between historical and 1pctCO2 forcing. This \r\nsection next considers how the true ECS can be estimated from \r\nthe historical energy budget by accounting for the pattern effect"


id: 125c4fbbd666159135bc7fb31da5cd9e
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 392
content:
" \r\nHowever, owing to limited evidence this section does not attempt to \r\naccount for these effects in estimates of TCR."


id: 90e6dd671f6c4fd9e30bbfc1682b8adc
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 20345
content:
"Overall, there is high confidence that the true ECS is higher than the \r\neffective ECS as inferred from the historical global energy budget, \r\nbut there is substantial uncertainty in how much higher because of \r\nlimited evidence regarding how radiative feedbacks may change in \r\nthe future. While several lines of evidence indicate that a' > 0, the \r\nquantitative accuracy of feedback changes is not known at this time \r\n(Section 7.4.4.3). Global energy budget constraints thus provide high \r\nconfidence in the lower bound of ECS which is not sensitive to the \r\nvalue of a': ECS is extremely unlikely to be less than 1.6degC. Estimates \r\nof a' that are informed by idealized CO2 forcing simulations of \r\ncoupled ESMs (Armour, 2017; Lewis and Curry, 2018; Andrews et al., \r\n2019; Dong et al., 2020; Winton et al., 2020) indicate a median value \r\nof ECS of around 2.7degC while estimates of a' that are informed by \r\nobserved historical sea surface temperature patterns (Andrews et al"


id: 4202f839d78d29b7a5944c6a4b46de1d
created_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 7791
content:
", \r\n2018) indicate a  median value of ECS of around 3.5degC. Owing to \r\nlarge uncertainties in future feedback changes, the historical energy \r\nbudget currently provides little information about the upper end of \r\nthe ECS range."


id: 6ca6096b556086f4f5f03d6db8b6faab
created_at: 2023-03-16 14:02:03
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vector_id: 14713
content:
"Research since AR5 has introduced extensions to the traditional \r\nenergy budget framework that account for the feedback dependence \r\non temperature patterns by allowing for multiple radiative \r\nfeedbacks operating on different time scales (Armour et al., 2013; \r\nGeoffroy et  al., 2013a; Armour, 2017; Proistosescu and Huybers, \r\n2017; Goodwin, 2018; Rohrschneider et  al., 2019), by allowing \r\nfeedbacks to vary with the spatial pattern or magnitude of ocean \r\nheat uptake (Winton et al., 2010; Rose et al., 2014; Rugenstein et al., \r\n2016a), or by allowing feedbacks to vary with the type of radiative \r\nforcing agent (Kummer and Dessler, 2014; Shindell, 2014; Marvel \r\net al., 2016; Winton et al., 2020). A direct way to account for the \r\npattern effect is to use the relationship ECS = -DF2xCO2/(a  + a'), \r\nwhere a = (DN  - DF)/DT is the effective feedback parameter \r\n(Box 7"


id: e6271d13f8c4ff351998902fa9f98d1c
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content:
"1) estimated from historical global energy budget changes \r\nand a' represents the change in the feedback parameter between \r\nthe historical period and the equilibrium response to CO2 forcing, \r\nwhich can be estimated using ESMs (Section 7.4.4.3; Armour, 2017; \r\nAndrews et  al., 2018, 2019; Lewis and Curry, 2018; Dong et  al., \r\n2020; Winton et al., 2020)."


id: 662d40a79bdcf9a336da41d10dcb5790
created_at: 2023-03-16 14:02:03
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content:
"Energy budget emulators are far less complex than comprehensive \r\nESMs (Section  1.5.3 and Cross-Chapter Box  7.1). For example, an \r\nemulator could represent the atmosphere, ocean, and land using \r\na small number of connected boxes (e.g., Goodwin, 2016), or it could \r\nrepresent the global mean climate using two connected ocean layers \r\n(e.g., Cross-Chapter Box 7.1 and Supplementary Material 7.SM.2). The \r\nnumerical efficiency of emulators means that they can be empirically \r\nconstrained by observations: a  large number of possible parameter \r\nvalues (e.g., feedback parameter, aerosol radiative forcing, and ocean \r\ndiffusivity) are randomly drawn from prior distributions; forward \r\nintegrations of the model are performed with these parameters and \r\nweighted against observations of surface or ocean warming, producing \r\nposterior estimates of quantities of interest such as TCR, ECS and \r\naerosol forcing (Section  7.3)"


id: 806f945889c3955beb12296a7d462f70
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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content:
" Owing to their reduced complexity, \r\nemulators lack full representations of the spatial patterns of sea surface \r\ntemperature and radiative responses to changes in those patterns \r\n(discussed in Section  7.4.4.3) and many represent the net feedback \r\nparameter using a  constant value. The ranges of ECS reported by \r\nstudies using emulators are thus interpreted here as representative of \r\nthe effective ECS over the historical record rather than of the true ECS."


id: d311d6764a55fcdfa8a60b15919863fc
created_at: 2023-03-16 14:02:03
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vector_id: 30730
content:
"The net radiative feedback change between the historical warming \r\npattern and the projected equilibrium warming pattern in response to \r\nCO2 forcing (a') is estimated to be in the range 0.0 to 1.0 W m-2 degC-1 \r\n(Figure  7.15). Using the value a' = +0.5 +- 0.5 W  m-2 degC  -1 to \r\nrepresent this range illustrates the effect of changing radiative \r\nfeedbacks on estimates of ECS. While the effective ECS inferred from \r\nhistorical warming is 2.5 [1.6 to 4.8] degC, ECS = -DF2xCO2/(a + a') \r\nis 3.5 [1.7  to 13.8] degC. For comparison, values of a' derived from \r\nthe response to historical and idealized CO2 forcing within coupled \r\nclimate models (Armour, 2017; Lewis and Curry, 2018; Andrews et al., \r\n2019; Dong et al., 2020; Winton et al., 2020) can be approximated \r\nas a' = +0.1 +- 0.3 W m-2 degC-1 (Section 7.4.4.3), corresponding to \r\na value of ECS of 2.7 [1.7 to 5.9] degC"


id: 32c4737bc35dfecd6242f9d1a1f2fd88
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 5407
content:
" In both cases, the low end of \r\nthe ECS range is similar to that of the effective ECS inferred using the \r\ntraditional energy balance model framework that assumes a' = 0, \r\nreflecting a weak dependence on the value of a' when ECS is small \r\n(Armour, 2017; Andrews et al., 2018); the low end of the ECS range \r\nis robust even in the hypothetical case that a' is slightly negative. \r\nHowever, the high end of the ECS range is substantially larger than \r\nthat of the effective ECS and strongly dependent on the value of a'."


id: 38d44a74b7f5c33ab7ba043693a58870
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 6312
content:
"Improved estimates of ocean heat uptake over the past two decades \r\n(Section 7.2) have diminished the role of ocean diffusivity in driving \r\nuncertainty in ECS estimates, leaving the main trade-off between \r\nposterior ranges in ECS and aerosol radiative forcing (Forest, 2002; \r\nKnutti et al., 2002; Frame et al., 2005). The AR5 (Bindoff et al., 2013) \r\nassessed a variety of estimates of ECS based on emulators and found \r\nthat they were sensitive to the choice of prior parameter distributions \r\nand temperature datasets used, particularly for the upper end of the \r\nECS range, though priors can be chosen to minimize the effect on \r\nresults (e.g., Lewis, 2013). Emulators generally produced estimates \r\nof effective ECS between 1degC and 5degC and ranges of TCR between \r\n0.9degC and 2.6degC. Padilla et al. (2011) use a simple global-average \r\nemulator with two time scales (Section  7.5.1.2; Supplementary \r\nMaterial 7.SM.2) to estimate a TCR of 1.6 [1.3 to 2.6] degC"


id: 69dbeb7047b2c24f756e3c7b5cec330a
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 14253
content:
" Using the \r\nsame model, Schwartz (2012) finds TCR in the range 0.9degC-1.9degC \r\nwhile Schwartz (2018) finds that an effective ECS of 1.7degC provides \r\nthe best fit to the historical global surface temperature record \r\nwhile also finding a  median aerosol forcing that is smaller than \r\nthat assessed in Section  7.3. Using an eight-box representation \r\nof the atmosphere-ocean-terrestrial system constrained by \r\nhistorical warming, Goodwin (2016) found an effective ECS of \r\n2.4  [1.4 to 4.4] degC while Goodwin (2018) found effective ECS \r\nto be in the range 2degC-4.3degC when using a prior for ECS based on \r\npaleoclimate constraints."


id: a2121d8ab583c0264910e73e1d0a6241
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 23063
content:
"While continuous satellite measurements of top-of-atmosphere \r\n(TOA) radiative fluxes (Figure  7.3) do not have sufficient accuracy \r\nto determine the absolute magnitude of Earth's energy imbalance \r\n(Section 7.2.1), they provide accurate estimates of its variations and \r\ntrends since the year 2002 that agree well with estimates based on \r\nobserved changes in global ocean heat content (Loeb et al., 2012; \r\nJohnson et  al., 2016; Palmer, 2017). When combined with global \r\nsurface temperature observations and simple models of global energy \r\nbalance, satellite measurements of TOA radiation afford estimates \r\nof the net feedback parameter associated with recent climate \r\nvariability (Tsushima and Manabe, 2013; Donohoe et  al., 2014; \r\nDessler and Forster, 2018). These feedback estimates, derived from \r\nthe regression of TOA radiation on surface temperature variability, \r\nimply values of ECS that are broadly consistent with those from \r\nother lines of evidence (Forster, 2016; Knutti et al"


id: f2e4fb20924c8a8128cbd18a3dec1b75
created_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 35740
content:
", 2017). A history of \r\nregression-based feedbacks and their uncertainties is summarized in \r\nBindoff et al. (2013), Forster (2016), and Knutti et al. (2017)."


id: 91524c725a2f0400267cfb2d23bb664e
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 20392
content:
"Using an emulator comprised of Northern and Southern hemispheres \r\nand an upwelling-diffusive ocean (Aldrin et al., 2012), with surface \r\ntemperature and ocean heat content datasets updated to 2014, \r\nSkeie et al. (2018) estimate a TCR of 1.4 [0.9 to 2.0] degC and a median \r\neffective ECS of 1.9 [1.2 to 3.1] degC. Using a  similar emulator \r\ncomprised of land and ocean regions and an upwelling-diffusive \r\nocean, with global surface temperature and ocean heat content \r\ndatasets up to 2011, Johansson et al. (2015) find an effective ECS of \r\n2.5 [2.0 to 3.2] degC. The estimate is found to be sensitive to the choice \r\nof dataset endpoint and the representation of internal variability \r\nmeant to capture the El  Nino-Southern Oscillation and Pacific \r\nDecadal Variability"


id: 9d070e2ea251b790e81ced3088e2a6a5
created_at: 2023-03-16 14:02:03
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vector_id: 22248
content:
" Differences between these two studies arise, \r\nin part, from their different global surface temperature and ocean \r\nheat content datasets, different radiative forcing uncertainty ranges, \r\ndifferent priors for model parameters, and different representations \r\nof internal variability. This leads to different estimates of effective \r\nECS, with the median estimate of Skeie et  al. (2018) lying below \r\nthe 5-95% range of effective ECS from Johansson et  al. (2015). \r\nMoreover, while the Skeie et al. (2018) emulator has a constant value \r\nof the net feedback parameter, the Johansson et al. (2015) emulator \r\nallows distinct radiative feedbacks for land and ocean, contributing \r\nto the different results."


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created_at: 2023-03-16 14:02:03
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"Research since AR5 has noted that regression-based feedback \r\nestimates depend on whether annual- or monthly-mean data are used \r\nand on the choice of lag employed in the regression, complicating \r\ntheir interpretation (Forster, 2016). The observed lead-lag relationship \r\nbetween global TOA radiation and global surface temperature, and \r\nits dependence on sampling period, is well replicated within unforced \r\nsimulations of ESMs (Dessler, 2011; Proistosescu et al., 2018). These \r\nfeatures arise because the regression between global TOA radiation \r\nand global surface temperature reflects a blend of different radiative \r\nfeedback processes associated with several distinct modes of \r\nvariability acting on different time scales (Annex IV), such as monthly \r\natmospheric variability and interannual El Nino-Southern Oscillation \r\n(ENSO) variability (Lutsko and Takahashi, 2018; Proistosescu et al., \r\n2018)"


id: ce2c686251e2fbb0d25983770b866546
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content:
" Regression-based feedbacks thus provide estimates of \r\nthe radiative feedbacks that are associated with internal climate \r\nvariability (e.g.,  Brown et  al., 2014), and do not provide a  direct \r\nestimate of ECS (high confidence). Moreover, variations in global \r\nsurface temperature that do not directly affect TOA radiation may \r\nlead to a positive bias in regression-based feedback, although this \r\nbias appears to be small, particularly when annual-mean data are \r\nused (Murphy and Forster, 2010; Spencer and Braswell, 2010, 2011; \r\nProistosescu et al., 2018). When tested within ESMs, regression-based \r\nfeedbacks have been found to be only weakly correlated with values \r\nof ECS (Chung et al., 2010), although cloudy-sky TOA radiation fluxes \r\nhave been found to be moderately correlated with ECS at ENSO time \r\nscales within CMIP5 models (Lutsko and Takahashi, 2018)."


id: 261359990ad5606ff137a8e7ebe825ac
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content:
"Estimates of ECS based on paleoclimate data are complementary \r\nto, and largely independent from, estimates based on process-based \r\nstudies (Section  7.5.1) and the instrumental record (Section  7.5.2). \r\nThe  strengths of using paleoclimate data to estimate ECS include: \r\n(i) the estimates are based on observations of a real-world Earth system \r\nresponse to a forcing, in contrast to using estimates from process\u0002based modelling studies or directly from models; (ii) the forcings \r\nare often relatively large (similar in magnitude to a CO2 doubling or \r\nmore), in contrast to data from the instrumental record; (iii) the forcing \r\noften changes relatively slowly so the system is close to equilibrium; \r\nas such, all individual feedback parameters, ax, are included, and \r\ncomplications associated with accounting for ocean heat uptake are \r\nreduced or eliminated, in contrast to the instrumental record"


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" However, \r\nthere can be relatively large uncertainties on estimates of both the \r\npaleo forcing and paleo global surface temperature response, and \r\ncare must be taken to account for long-term feedbacks associated \r\nwith ice sheets (Section 7.4.2.6), which often play an important role \r\nin the paleoclimate response to forcing, but which are not included in \r\nthe definition of ECS. Furthermore, the state-dependence of feedbacks \r\n(Section 7.4.3) means that climate sensitivity during Earth's past may \r\nnot be the same as it is today, which should be accounted for when \r\ninterpreting paleoclimate estimates of ECS."


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"A number of studies consider the observed climate response to volcanic \r\neruptions over the 20th  century (Section  3.3.1 and Cross-Chapter \r\nBox 4.1; Knutti et al., 2017). However, the direct constraint on ECS \r\nis weak, particularly at the high end, because the temperature \r\nresponse to short-term forcing depends only weakly on radiative \r\nfeedbacks and because it can take decades of a sustained forcing \r\nbefore the magnitude of temperature changes reflects differences in \r\nECS across models (Geoffroy et al., 2013b; Merlis et al., 2014). It is \r\nalso a challenge to separate the response to volcanic eruptions from \r\ninternal climate variability in the years that follow them (Wigley et al., \r\n2005). Based on ESM simulations, radiative feedbacks governing \r\nthe global surface temperature response to volcanic eruptions can \r\nbe substantially different than those governing long-term global \r\nwarming (Merlis et al., 2014; Marvel et al., 2016; Ceppi and Gregory, \r\n2019)"


id: a8cd9233cfe19fa4078805a5f85a40ae
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content:
" Estimates based on the response to volcanic eruptions agree \r\nwith other lines of evidence (Knutti et  al., 2017), but they do not \r\nconstitute a direct estimate of ECS (high confidence). The 'emergent \r\nconstraints' on ECS based on climate variability, including volcanic \r\neruptions, are summarized in Section 7.5.4.1."


id: ff36013abbb89fc8393534bb5a830de5
created_at: 2023-03-16 14:02:03
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content:
"Evidence from the instrumental temperature record, including \r\nestimates using global energy budget changes (Section  7.5.2.1), \r\nclimate emulators (Section 7.5.2.2), variability in the TOA radiation \r\nbudget (Section  7.5.2.3), and the climate response to volcanic \r\neruptions (Section 7.5.2.4) produce median ECS estimates that range \r\nbetween 2.5degC and 3.5degC, but a best estimate value cannot be given \r\nowing to a strong dependence on assumptions about how radiative \r\nfeedbacks will change in the future. However, there is robust \r\nevidence and high agreement across the lines of evidence that ECS is \r\nextremely likely greater than 1.6degC (high confidence). There is robust \r\nevidence and medium agreement across the lines of evidence that \r\nECS is very likely greater than 1.8degC and likely greater than 2.2degC \r\n(high confidence). These ranges of ECS correspond to estimates \r\nbased on historical global energy budget constraints (Section 7.5.2"


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"1) \r\nunder the assumption of no feedback dependence on evolving SST \r\npatterns (i.e., a' = 0) and thus represent an underestimate of the \r\ntrue ECS ranges that can be inferred from this line of evidence (high \r\nconfidence). Historical global energy budget changes do not provide "


id: 21f20747e00af8e3530d7a34063f57c8
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"(von  der  Heydt  et  al., 2014; Kohler et  al., 2015, 2017, 2018; \r\nFriedrich et al., 2016; Royer, 2016; Snyder, 2019; Stap et al., 2019; \r\nFriedrich and Timmermann, 2020; see Table  7.11). Compared to \r\nthe LGM, uncertainties in the derived ECS from these periods are \r\nin general greater, due to greater uncertainty in global surface \r\ntemperature (due to fewer individual sites with proxy temperature \r\nrecords), ice-sheet forcing (due to a  lack of detailed ice-sheet \r\nreconstructions), and CO2 forcing (for those studies that include \r\nthe pre-ice-core period, where CO2 reconstructions are substantially \r\nmore uncertain). Furthermore, accounting for varying orbital forcing \r\nin the traditional global mean forcing and response energy budget \r\nframework (Box 7.1) is challenging (Schmidt et al"


id: b83691b294bb353ecba0509e461bb379
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", 2017b), due to \r\nseasonal and latitudinal components of the forcing that, despite \r\na close-to-zero orbital forcing in the global annual mean, can directly \r\nresult in responses in annual mean global surface temperature \r\n(Liu et al., 2014), ice volume (Abe-Ouchi et al., 2013), and feedback \r\nprocesses such as those associated with methane (Singarayer et al., \r\n2011). In addition, for time periods in which the forcing relative to \r\nthe modern era is small (interglacials), the inferred ECS has relatively \r\nlarge uncertainties because the forcing and temperature response \r\n(DF and DT in Equation 7.1, Box 7.1) are both close to zero."


id: 331980c1c47af32a9c0eaa0b2df955a8
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"The LGM (Cross-Chapter Box 2.1) has been used to provide estimates \r\nof ECS (see Table 7.11 for estimates since AR5; Sherwood et al., 2020; \r\nTierney et al., 2020b). The major forcings and feedback processes that \r\nled to the cold climate at that time (e.g., CO2, non-CO2 greenhouse \r\ngases, and ice sheets) are relatively well-known (Section  5.1), \r\norbital forcing relative to pre-industrial was negligible, and there \r\nare relatively high spatial resolution and well-dated paleoclimate \r\ntemperature data available for this time period (Section  2.3.1). \r\nUncertainties in deriving global surface temperature from the LGM \r\nproxy data arise partly from uncertainties in the calibration from the \r\npaleoclimate data to local annual mean surface temperature, and \r\npartly from uncertainties in the conversion of the local temperatures to \r\nan annual mean global surface temperature"


id: 156d3bd5a3bbdce9850c3f6f578165d3
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content:
" Overall, the global mean \r\nLGM cooling relative to pre-industrial is assessed to be very likely\r\nfrom 5 to 7 degC (Section 2.3.1). The LGM climate is often assumed to \r\nbe in full equilibrium with the forcing, such that DN in Equation 7.1, \r\nBox 7.1, is zero. A calculation of sensitivity using solely CO2 forcing, \r\nand assuming that the LGM ice sheets were in equilibrium with that \r\nforcing, would give an Earth System Sensitivity (ESS) rather than an \r\nECS (see Box 7.1). In order to calculate an ECS, which is defined here \r\nto include all feedback processes except ice sheets, the approach \r\nof Rohling et  al. (2012) can be used. This approach introduces an \r\nadditional forcing term in Equation 7.1, Box 7.1, that quantifies the \r\nresulting forcing associated with the ice-sheet feedback (primarily \r\nan estimate of the radiative forcing associated with the change in \r\nsurface albedo)"


id: 774efd16d354abd48185b8beacd28be1
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content:
" However, differences between studies as to which \r\nprocesses are considered as forcings (for example, some studies \r\nalso include vegetation and/or aerosols, such as dust, as forcings), \r\nmeans that published estimates are not always directly comparable. \r\nAdditional uncertainty arises from the magnitude of the ice-sheet \r\nforcing itself (Stap et  al., 2019; Zhu and Poulsen, 2021), which is \r\noften estimated using ESMs. Furthermore, the ECS at the LGM may \r\ndiffer from that of today due to state-dependence (Section  7.4.3). \r\nHere, only studies that report values of ECS that have accounted for \r\nthe long-term feedbacks associated with ice sheets, and therefore \r\nmost closely estimate ECS as defined in this chapter, are assessed \r\nhere (Table 7.11)."


id: 1746df352b833f1439473ffd761c930c
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"In the pre-Quaternary (prior to about 2.5 million years ago), \r\nthe forcings and response are generally of the same sign and \r\nsimilar magnitude as future projections of climate change (Burke \r\net  al., 2018; Tierney et  al., 2020a). Similar uncertainties as for \r\nthe LGM apply, but in this case a major uncertainty relates to the \r\nforcing, because prior to the ice-core record there are only indirect \r\nestimates of CO2 concentration. However, advances in pre-ice-core \r\nCO2 reconstruction (e.g.,  Foster and Rae, 2016; Super et  al., 2018; \r\nWitkowski et al., 2018) mean that the estimates of pre-Quaternary \r\nCO2 have less uncertainty than at the time of AR5, and these time \r\nperiods can now contribute to an assessment of climate sensitivity \r\n(Table 7.11). The mid-Pliocene Warm Period (MPWP; Cross-Chapter \r\nBox 2.1 and Cross-Chapter Box 2.4) has been targeted for constraints \r\non ECS (Martinez-Boti et al., 2015; Sherwood et al"


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", 2020), due to \r\nthe fact that CO2 concentrations were relatively high at this time \r\n(350-425  ppm) and because the MPWP is sufficiently recent that \r\ntopography and continental configuration are similar to modern-day. \r\nAs such, a comparison of the MPWP with the pre-industrial climate \r\nprovides probably the closest natural geological analogue for the \r\nmodern day that is useful for assessing constraints on ECS, despite \r\nthe effects of different geographies not being negligible (global \r\nsurface temperature patterns; ocean circulation). Furthermore, \r\nthe global surface temperature of the MPWP was such that non\u0002linearities in feedbacks (Section 7.4.3) were relatively modest. Within \r\nthe MPWP, the KM5c interglacial has been identified as a particularly \r\nuseful time period for assessing ECS (Haywood et al., 2013, 2016b) \r\nbecause Earth's orbit during that time was very similar to that of the \r\nmodern day."


id: 932e1cdca5a90079ca7ad4b59c0f9f95
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"lines of paleoclimate evidence regarding ECS, from differing past \r\ntime periods: LGM (Sherwood et  al., 2020; Tierney et  al., 2020b); \r\nglacial-interglacial (Royer, 2016; Kohler et  al., 2017; Snyder, \r\n2019; Friedrich and Timmermann, 2020); Pliocene (Martinez-Boti \r\net  al., 2015; Sherwood et  al., 2020); and the Eocene (Anagnostou \r\net  al.,  2016, 2020; Shaffer et  al., 2016; Inglis et  al., 2020), with \r\ndiffering proxies for estimating forcing (e.g.,  CO2 from ice cores \r\nor boron isotopes) and response (e.g.,  global surface temperature \r\nfrom d18O, Mg/Ca or Antarctic dD). Furthermore, although different \r\nstudies have uncertainty estimates that account for differing sources \r\nof uncertainty, some studies (Snyder, 2019; Inglis et  al., 2020; \r\nSherwood et al., 2020; Tierney et al., 2020b) do consider many of the \r\nuncertainties discussed in Sections 7.5.3.1-7.5.3.3"


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" All the studies \r\nbased on glacial-interglacial cycles account for some aspects of the \r\nstate-dependence of climate sensitivity (Section 7.4.3) by considering \r\nonly the warm phases of the Pleistocene, although what constitutes \r\na warm phase is defined differently across the studies."


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"the sum of all feedbacks, and furthermore unlike constraints on the \r\ninstrumental record they are based on climate states that are at, or \r\nclose to, equilibrium. So far, these emergent constraints have been \r\nlimited to the Last Glacial Maximum (LGM; Cross-Chapter Box 2.1) \r\ncooling (Hargreaves et al., 2012; Schmidt et al., 2014; Renoult et al., \r\n2020) and warming in the mid-Pliocene Warm Period (MPWP; \r\nCross-Chapter Box 2.1 and Cross-Chapter Box 2.4; Hargreaves and \r\nAnnan, 2016; Renoult et al., 2020) due to the availability of sufficiently \r\nlarge multi-model ensembles for these two cases. The paleoclimate \r\nemergent constraints are limited by structural uncertainties in the \r\nproxy-based global surface temperature and forcing reconstructions \r\n(Section  7.5.3), possible differences in equilibrium sea surface \r\ntemperature patterns between models and the real world, and \r\na small number of model simulations participating, which has led to \r\ndivergent results"


id: cfa4fdc483382018d78cace25380e829
created_at: 2023-03-16 14:02:03
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" For example, Hopcroft and Valdes (2015) repeated \r\nthe study based on the LGM by Hargreaves et  al. (2012) using \r\nanother model ensemble and found that the emergent constraint \r\nwas not robust, whereas studies using multiple available ensembles \r\nretain useful constraints (Schmidt et al., 2014; Renoult et al., 2020). \r\nAlso, the results are somewhat dependent on the applied statistical \r\nmethods (Hargreaves and Annan, 2016). However, Renoult et  al. \r\n(2020) explored this and found 95th percentiles of ECS below 6degC for \r\nLGM and Pliocene individually, regardless of statistical approach, and \r\nby combining the two estimates the 95th percentile dropped to 4.0degC. \r\nThe consistency between the cold LGM and warm MPWP emergent \r\nconstraint estimates increases confidence in these estimates, and \r\nfurther suggests that the dependence of feedback on climate mean \r\nstate (Section 7.4.3) as represented in PMIP models used in these \r\nstudies is reasonable."


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"A number of considerations must be taken into account when \r\nassessing the diverse literature on ECS and TCR emergent constraints. \r\nFor instance, it is important to have physical and theoretical bases \r\nfor the connection between the observable and modelled ECS or \r\nTCR since in model ensembles thousands of relationships that pass \r\nstatistical significance can be found simply by chance (Caldwell \r\net al., 2014). It is also important that the underlying model ensemble \r\ndoes not exhibit a shared bias that influences the simulation of the \r\nobservable quantity on which the emergent constraint is based. \r\nAlso, correctly accounting for uncertainties in both the observable \r\n(including measurement uncertainty and natural variability) and \r\nthe emergent constraint statistical relationship can be challenging, \r\nin particular in cases where the latter is not expected to be linear \r\n(Annan et  al., 2020)"


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" A  number of proposed emergent constraints \r\nleverage variations in modelled ECS arising from tropical low-clouds, \r\nwhich was the dominant source of inter-model spread in the CMIP5 \r\nensemble used in most emergent constraint studies. Since ECS is \r\ndependent on the sum of individual feedbacks (Section 7.5.1) these \r\nstudies implicitly assume that all other feedback processes in models \r\nare unbiased and should therefore rather be thought of as constraints \r\non tropical low-cloud feedback (Klein and Hall, 2015; Qu et al., 2018; \r\nSchlund et al., 2020). The following sections go through a range of \r\nemergent constraints and assess their strengths and limitations."


id: db4383cdbe6098655a52bfe6c41dbd09
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"Various emergent constraint approaches using global warming over \r\nthe instrumental record have been proposed. These benefit from more \r\naccurate data compared with paleoclimates, but suffer from the fact \r\nthat the climate is not in equilibrium, thereby assuming that ESMs \r\non average accurately depict the ratio of short-term to long-term \r\nglobal warming. Global warming in climate models over 1850 to the \r\npresent day exhibits no correlation with ECS, which is partly due to \r\na  substantial number of models exhibiting compensation between \r\na high climate sensitivity with strong historical aerosol cooling (Kiehl, \r\n2007; Forster et al., 2013; Nijsse et al., 2020). However, the aerosol \r\ncooling increased up until the 1970s, when air quality regulations \r\nreduced the emissions from Europe and North America whereas \r\nother regions saw increases resulting in a subsequently reduced pace \r\nof global mean aerosol ERF increase (Section 2.2.8 and Figure 2.10)"


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" \r\nEnergy balance considerations over the 1970-2010 period gave \r\na  best estimate ECS of 2.0degC (Bengtsson and Schwartz, 2013), \r\nhowever this estimate did not account for pattern effects. To address \r\nthis limitation an emergent constraint on 1970-2005 global warming \r\nwas demonstrated to yield a  best estimate ECS of 2.83 [1.72 to \r\n4.12] degC (Jimenez-de-la-Cuesta and Mauritsen, 2019). The study \r\nwas followed up using CMIP6 models yielding a best estimate ECS \r\nof 2.6 [1.5 to 4.0] degC based on 1975-2019 global warming (Nijsse \r\net  al., 2020), thereby confirming the emergent constraint. Internal \r\nvariability and forced or unforced pattern effects may influence the \r\nresults (Jimenez-de-la-Cuesta and Mauritsen, 2019; Nijsse et  al., \r\n2020). For instance the Atlantic Multi-decadal Oscillation changed \r\nfrom negative to positive anomaly, while the Indo-Pacific Oscillation "


id: a893caa3171d5fdb52a3d45e32769485
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"A substantial number of emergent constraint studies focus on \r\nobservables that are related to tropical low-cloud feedback processes \r\n(Volodin, 2008; Sherwood et al., 2014; Zhai et al., 2015; Brient and \r\nSchneider, 2016; Brient et al., 2016). These studies yield median ECS \r\nestimates of 3.5degC-4degC and in many cases indicate low likelihoods \r\nof values below 3degC. The approach has attracted attention since \r\nmost of the spread in climate sensitivity seen in CMIP5, and earlier \r\nclimate model ensembles, arises from uncertainty in low-cloud \r\nfeedbacks (Bony and Dufresne, 2005; Wyant et  al., 2006; Randall \r\net al., 2007; Vial et al., 2013). Nevertheless, this approach assumes \r\nthat all other feedback processes are unbiased (Klein and Hall, 2015; \r\nQu et al., 2018; Schlund et al., 2020), for instance the possibly missing \r\nnegative anvil area feedback or the possibly exaggerated mixed\u0002phase cloud feedback (Section 7.4.2.4)"


id: 49677a0f8b19242872f4df00e41bdf38
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content:
" Thus, the subset of emergent \r\nconstraints that focus on low-level tropical clouds are not necessarily \r\ninconsistent with other emergent constraints of ECS. Related \r\nemergent constraints that focus on aspects of the tropical circulation \r\nand ECS have led to conflicting results (Su et al., 2014; Tian, 2015; \r\nLipat et  al., 2017; Webb and Lock, 2020), possibly because these \r\nprocesses are not the dominant factors in causing the inter-model \r\nspread (Caldwell et al., 2018)."


id: 67d16cafe6322647a27a74a5f8576855
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vector_id: 13919
content:
"Lagged correlations present in short-term variations in the global surface \r\ntemperature can be linked to ECS through the fluctuation-dissipation \r\ntheorem, which is derived from a single heat-reservoir model (Einstein, \r\n1905; Hasselmann, 1976; Schwartz, 2007; Cox et al., 2018a). From this \r\nit follows that the memory carried by the heat capacity of the ocean \r\nresults in low-frequency global temperature variability (red noise) \r\narising from high-frequency (white noise) fluctuations in the radiation \r\nbalance, for example, caused by weather. Initial attempts to apply the \r\ntheorem to observations yielded a fairly low median ECS estimate of \r\n1.1degC (Schwartz, 2007), a result that was disputed (Foster et al., 2008; \r\nKnutti et al., 2008). Recently it was proposed by Cox et al. (2018a) to use \r\nvariations in the historical experiments of the CMIP5 climate models as \r\nan emergent constraint giving a median ECS estimate of 2.8 [1.6 to \r\n4.0] degC"


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" A  particular challenge associated with these approaches is \r\nto separate short-term from long-term variability, and slightly arbitrary \r\nchoices regarding the methodology of separating these in the global \r\nsurface temperature from long-term signals in the historical record, \r\nomission of the more strongly forced period after 1962, as well as input \r\ndata choices, can lead to median ECS estimates ranging from 2.5degC to \r\n3.5degC (Brown et al., 2018; Po-Chedley et al., 2018a; Rypdal et al., 2018). \r\nCalibrating the emergent constraint using CMIP5 modelled internal \r\nvariability as measured in historical control simulations (Po-Chedley \r\net  al., 2018a) will inevitably lead to an overestimated ECS due to \r\nexternally forced short-term variability present in the historical record \r\n(Cox et  al., 2018b). Contrary to constraints based on paleoclimates \r\nor global warming since the 1970s, when based on CMIP6 models \r\na  higher, yet still well-bounded ECS estimate of 3.7 [2.6 to 4"


id: 4fde107950f799a36cff6e8aa014f069
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content:
"8] degC \r\nis obtained (Schlund et al., 2020). A more problematic issue is raised \r\nby Annan et  al. (2020) who showed that the upper bound on ECS \r\nestimated this way is less certain when considering deep-ocean heat \r\nuptake. In conclusion, even if not inconsistent, these limitations prevent \r\nus from directly using this type of constraint in the assessment."


id: 29393a3fa53b4ed1b0ae795c45427f22
created_at: 2023-03-16 14:02:03
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content:
"(Gillett et al., 2013), and updated by Schurer et al. (2018). A related \r\ndata-assimilation-based approach that accounted also for uncertainty \r\nin response patterns gave 1.33degC-2.36degC (Ribes et al., 2021), but is \r\ndependent on the choice of prior ensemble distribution (CMIP5 or \r\nCMIP6). Another study used the response to the Pinatubo volcanic \r\neruption to obtain a  range of 0.8degC-2.3degC (Bender et  al., 2010). \r\nA tighter range, notably at the lower end, was found in an emergent \r\nconstraint focusing on the post-1970s warming exploiting the lower \r\nspread in aerosol forcing change over this period (Jimenez-de-la\u0002Cuesta and Mauritsen, 2019). Their estimate was 1.67  [1.17 to \r\n2.16] degC. Two studies tested this idea: Tokarska et al. (2020) estimates \r\nTCR was 1.60 [0.90 to 2.27] degC based on CMIP6 models, whereas \r\nNijsse et al. (2020) found 1.68 [1.0 to 2.3] degC"


id: 6f1c8a8c1cfb4fd3740c0bf26ea79839
created_at: 2023-03-16 14:02:03
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content:
" In both cases there was \r\na small sensitivity to choice of ensemble, with CMIP6 models yielding \r\nslightly lower values and ranges. Combining these studies gives a best \r\nestimate of 1.7degC and a very likely range of TCR of 1.1 to 2.3 degC with \r\nhigh confidence."


id: ea322a20b995e52a7b1da47b44de308a
created_at: 2023-03-16 14:02:03
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"In the first group of emergent constraints there is broad agreement \r\non the best estimate of ECS ranging from 2.4degC-3.3degC. At the lower \r\nend, nearly all studies find lower bounds (5th percentiles) around \r\n1.5degC, whereas several studies indicate 95th percentiles as low \r\nas 4degC. Considering both classes of studies, none of them yield \r\nupper very likely bounds above 5degC. Since several of the emergent \r\nconstraints can be considered nearly independent one could assume \r\nthat emergent constraints provide very strong evidence on ECS by \r\ncombining them. Nevertheless, this is not done here because there \r\nare sufficient cross-dependencies, as for instance models are re-used \r\nin many of the derived emergent constraints, and furthermore the \r\nmethodology has not yet reached a sufficient level of maturity since \r\nsystematic biases may not have been accounted for. Uncertainty is \r\ntherefore conservatively added to reflect these potential issues"


id: f1ac782508fc5660c7daf95c48090a9d
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" This \r\nleads to the assessment that ECS inferred from emergent constraints \r\nis very likely 1.5 to 5 degC with medium confidence."


id: 724cb4b737018370d866202b9388805d
created_at: 2023-03-16 14:02:03
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content:
"considered. As in earlier reports, a 0.5degC precision is used. Starting \r\nwith the very likely lower bound, there is broad support for a value of \r\n2.0degC, including process understanding and the instrumental record \r\n(Table 7.13). For the very likely upper bound, emergent constraints \r\ngive a  value of 5.0degC whereas the three other lines of evidence \r\nare individually less tightly constrained. Nevertheless, emergent \r\nconstraints are a relatively recent field of research, in part taken into \r\naccount by adding uncertainty to the upper bound (Section 7.5.4.3), \r\nand the underlying studies use, to a varying extent, information that \r\nis also used in the other three lines of evidence, causing statistical \r\ndependencies. However, omitting emergent constraints and \r\nstatistically combining the remaining lines of evidence likewise yields \r\n95th percentiles close to 5.0degC (Sherwood et al., 2020)"


id: 84cad38eb7de3c9069fa7a20994a5faa
created_at: 2023-03-16 14:02:03
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content:
" Information \r\nfor the likely range is partly missing or one-sided, however it must \r\nnecessarily reside inside the very likely range and is therefore \r\nsupported by evidence pertaining to both the likely and very likely\r\nranges. Hence, the upper likely bound is assessed to be about halfway \r\nbetween the best estimate and the upper very likely bound while \r\nthe lower likely bound is assessed to be about halfway between the \r\nbest estimate and the lower very likely bound. In summary, based on \r\nmultiple lines of evidence the best estimate of ECS is 3degC, it is likely\r\nwithin the range 2.5 to 4 degC and very likely within the range 2 to 5 degC. \r\nIt is virtually certain that ECS is larger than 1.5degC. Whereas there is \r\nhigh confidence based on mounting evidence that supports the best \r\nestimate, likely range and very likely lower end, a higher ECS than \r\n5degC cannot be ruled out, hence there is medium confidence in the \r\nupper end of the very likely range"


id: bf7ab457dc9a296f6738dc4e923adf46
created_at: 2023-03-16 14:02:03
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content:
" Note that the best estimate of ECS \r\nmade here corresponds to a feedback parameter of -1.3 W m-2 degC-1\r\nwhich is slightly more negative than the feedback parameter from \r\nprocess-based evidence alone that is assessed in Section 7.4.2.7."


id: 24ee0a142f095db84e5f0e31b1a7e354
created_at: 2023-03-16 14:02:03
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content:
"The AR5 assessed ECS to have a  likely range from 1.5 to 4.5 degC \r\n(M. Collins et al., 2013) based on the majority of studies and evidence \r\navailable at the time. The broader evidence base presented in this \r\nReport and the general agreement among different lines of evidence \r\nmeans that they can be combined to yield a narrower range of ECS \r\nvalues. This can be done formally using Bayesian statistics, though \r\nsuch a  process is complex and involves formulating likelihoods \r\nand priors (Annan and Hargreaves, 2006; Stevens et  al., 2016; \r\nSherwood et al., 2020). However, it can be understood that if two \r\nlines of independent evidence each give a  low probability of an \r\noutcome being true, for example, that ECS is less than 2.0degC, then \r\nthe combined probability that ECS is less than 2.0degC is lower than \r\nthat of either line of evidence"


id: 9218976bda1021fc937a31ddbd60e688
created_at: 2023-03-16 14:02:03
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" On the contrary, if one line of evidence \r\nis unable to rule out an outcome, but another is able to assign a low \r\nprobability, then there is a low probability that the outcome is true \r\n(Stevens et al., 2016). This general principle applies even when there \r\nis some dependency between the lines of evidence (Sherwood et al., \r\n2020), for instance between historical energy budget constraints \r\n(Section  7.5.2.1) and those emergent constraints that use the \r\nhistorically observed global warming (Section 7.5.4.1). Even in this \r\ncase the combined constraint will be closer to the narrowest range \r\nassociated with the individual lines of evidence."


id: ac7cf44575fcae46d47881fb02ddaa71
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 24678
content:
"arguing that the atmosphere was already saturated in infrared \r\nabsorption such that adding more CO2 would not lead to warming. \r\nThe assertion of Angstrom was understood half a century later to be \r\nincorrect. History has seen a multitude of studies (e.g., Svensmark, \r\n1998; Lindzen et al., 2001; Schwartz, 2007) mostly implying lower \r\nECS than the range assessed as very likely here. However, there are \r\nalso examples of the opposite, such as very large ECS estimates based \r\non the Pleistocene records (Snyder, 2016), which have been shown to \r\nbe overestimated due to a lack of accounting for orbital forcing and \r\nlong-term ice-sheet feedbacks (Schmidt et al., 2017b), or suggestions \r\nthat global climate instabilities may occur in the future (Steffen et al., \r\n2018; Schneider et  al., 2019). There is, however, no evidence for \r\nunforced instabilities of such magnitude occurring in the paleo-record \r\ntemperatures of the past 65 million years (Westerhold et al"


id: 4277af4a0f634dc258ef32c7a3795fc3
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", 2020), \r\npossibly short of the Paleocene-Eocene Thermal Maximum (PETM) \r\nexcursion (Section 5.3.1.1) that occurred at more than 10degC above \r\npresent-day levels (Anagnostou et  al., 2020). Looking back, the \r\nresulting debates have led to a deeper understanding, strengthened \r\nthe consensus, and have been scientifically valuable."


id: c13b8e0f0a0f32a47bda2d39022c4251
created_at: 2023-03-16 14:02:03
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vector_id: 27983
content:
"less than ECS (see the introduction to Section  7.5). Furthermore, \r\nestimates of TCR from the historical record are not as strongly \r\ninfluenced by externally forced surface temperature pattern effects \r\nas estimates of ECS are since both historical transient warming and \r\nTCR are affected by this phenomenon (Section  7.4.4). A  slightly \r\nhigher weight is given to instrumental record warming and emergent \r\nconstraints since these are based on observed transient warming, \r\nwhereas the process-understanding estimate relies on pattern \r\neffects and ocean heat uptake efficiency from ESMs to represent \r\nthe transient dampening effects of the ocean. If these effects are \r\nunderestimated by ESMs then the resulting TCR would be lower. \r\nGiven the interdependencies of the other two lines of evidence, \r\na  conservative approach to combining them as reflected in the \r\nassessment is adopted. Since uncertainty is substantially lower than \r\nin AR5 a 0.1degC precision is therefore used here"


id: 43f30d426289819407e6745930359515
created_at: 2023-03-16 14:02:03
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vector_id: 8106
content:
" Otherwise the same \r\nmethodology for combining the lines of evidence as applied to ECS \r\nis used for TCR. Based on process understanding, warming over the \r\ninstrumental record and emergent constraints, the best estimate TCR \r\nis 1.8degC, it is likely 1.4 to 2.2 degC and very likely 1.2 to 2.4 degC. The \r\nassessed ranges are all assigned high confidence due to the high \r\nlevel of agreement among the lines of evidence."


id: 2234d817970f0d70b6f19357d4817203
created_at: 2023-03-16 14:02:03
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vector_id: 2829
content:
"In the climate sciences, there are often good reasons to consider \r\nrepresenting deep uncertainty, or what are sometimes referred to as \r\n'unknown unknowns'. This is natural in a field that considers a system \r\nthat is both complex and at the same time challenging to observe. \r\nFor instance, since emergent constraints represent a relatively new \r\nline of evidence, important feedback mechanisms may be biased \r\nin process-level understanding; pattern effects and aerosol cooling \r\nmay be large; and paleo evidence inherently builds on indirect and \r\nincomplete evidence of past climate states, there certainly can be \r\nvalid reasons to add uncertainty to the ranges assessed on individual \r\nlines of evidence. This has indeed been addressed throughout \r\nSections  7.5.1-7.5.4"


id: 82f2ff616de765971126a6a6a3a724b0
created_at: 2023-03-16 14:02:03
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content:
" Since it is neither probable that all lines of \r\nevidence assessed here are collectively biased nor is the assessment \r\nsensitive to single lines of evidence, deep uncertainty is not considered \r\nas necessary to frame the combined assessment of ECS."


id: 21f8b6c8013a29603f248d4b9d5f16b3
created_at: 2023-03-16 14:02:03
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content:
"Coupled climate models, such as those participating in CMIP, have \r\nlong played a central role in assessments of ECS and TCR. In reports \r\nup to and including the IPCC Third Assessment Report (TAR), climate \r\nsensitivities derived directly from ESMs were the primary line of \r\nevidence. However, since AR4, historical warming and paleoclimate \r\ninformation provided useful additional evidence and it was noted \r\nthat assessments based on models alone were problematic (Knutti, \r\n2010). As new lines of evidence have evolved, in AR6 various \r\nnumerical models are used where they are considered accurate, or \r\nin some cases the only available source of information, and thereby \r\nsupport all four lines of evidence (Sections 7.5.1-7.5.4). However, \r\nAR6 differs from previous IPCC reports in excluding direct estimates \r\nof ECS and TCR from ESMs in the assessed ranges (Section 7.5.5), \r\nfollowing several recent studies (Annan and Hargreaves, 2006; \r\nStevens et  al., 2016; Sherwood et  al., 2020)"


id: 7ba1a0207931358ca6ca8b2aa62d3ba2
created_at: 2023-03-16 14:02:03
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content:
" The purpose of this \r\nsection is to explain why this approach has been taken and to \r\nprovide a perspective on the interpretation of the climate sensitivities \r\nexhibited in CMIP6 models."


id: ca533261c88132c3c23c33fd83c8d7d3
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"cases entire sub-model systems are shared and slightly modified. \r\nTherefore, models cannot be considered independent developments, \r\nbut rather families of models with interdependencies (Knutti et al., \r\n2013). It is therefore difficult to interpret the collection of models \r\n(Knutti, 2010), and it cannot be ruled out that there are common \r\nlimitations and therefore systematic biases to model ensembles \r\nthat are reflected in the distribution of ECS as derived from them. \r\nAlthough ESMs are typically well-documented, in ways that \r\nincreasingly include information on critical decisions regarding tuning \r\n(Mauritsen et al., 2012; Hourdin et al., 2017; Schmidt et al., 2017a; \r\nMauritsen  and Roeckner, 2020), the full history of development \r\ndecisions could involve both process-understanding and sometimes \r\nalso other information such as historical warming"


id: adf2c1bd4e4a1edac98064127f28e8eb
created_at: 2023-03-16 14:02:03
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content:
" As outlying or \r\npoorly performing models emerge from the development process, \r\nthey can become re-tuned, reconfigured or discarded and so might \r\nnot see publication (Hourdin et al., 2017; Mauritsen and Roeckner, \r\n2020). In the process of addressing such issues, modelling groups \r\nmay, whether intentionally or not, modify the modelled ECS."


id: 4e28b769999f20f75ec4887eca07b005
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 9720
content:
"The ECS of a model is the net result of the model's effective radiative \r\nforcing from a doubling of CO2 and the sum of the individual feedbacks \r\nand their interactions. It is well known that most of the model spread \r\nin ECS arises from cloud feedbacks, and particularly the response of \r\nlow-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since \r\nthese clouds are small-scale and shallow, their representation in climate \r\nmodels is mostly determined by sub-grid-scale parametrizations. \r\nIt is sometimes assumed that parametrization improvements will \r\neventually lead to convergence in model response and therefore \r\na decrease in the model spread of ECS. However, despite decades of \r\nmodel development, increases in model resolution and advances in \r\nparametrization schemes, there has been no systematic convergence \r\nin model estimates of ECS"


id: 64d4f7f28f6fadb05edd97ca1e347c23
created_at: 2023-03-16 14:02:03
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content:
" In fact, the overall inter-model spread \r\nin ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are \r\ngiven for CMIP6 models in Supplementary Material 7.SM.4 based \r\non Schlund et al. (2020) for ECS and Meehl et al. (2020) for TCR (see \r\nalso Figure  7.18 and FAQ 7.3). The upward shift does not apply to \r\nall models traceable to specific modelling centres, but a substantial \r\nsubset of models have seen an increase in ECS between the two model \r\ngenerations. The increased ECS values, as discussed in Section 7.4.2.8, \r\nare partly due to shortwave cloud feedbacks (Flynn and Mauritsen, \r\n2020) and it appears that in some models extra-tropical clouds with \r\nmixed ice and liquid phases are central to the behaviour (Zelinka et al., \r\n2020), probably borne out of a recent focus on biases in these types of \r\nclouds (McCoy et al., 2016; Tan et al., 2016)"


id: 12ba2fffbf7daf7d013b582fe87d2e14
created_at: 2023-03-16 14:02:03
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" These biases have recently \r\nbeen reduced in many ESMs, guided by process understanding from \r\nlaboratory experiments, field measurements and satellite observations \r\n(Lohmann and Neubauer, 2018; Bodas-Salcedo et al., 2019; Gettelman \r\net  al., 2019). However, this and other known model biases are \r\nalready factored into the process-level assessment of cloud feedback \r\n(Section 7.4.2.4), and furthermore the emergent constraints used here \r\nfocus on global surface temperature change and are therefore less \r\nsusceptible to shared model biases in individual feedback parameters \r\nthan emergent constraints that focus on specific physical processes \r\n(Section 7.5.4). The high values of ECS and TCR in some CMIP6 models \r\nlead to higher levels of surface warming than CMIP5 simulations and \r\nalso the AR6 projections based on the assessed ranges of ECS, TCR \r\nand ERF (Box 4.1 and FAQ 7.3; Forster et al., 2020)"


id: 17c53bdfed3f87a55ade8a62f736a7cf
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content:
"\r\nIt is generally difficult to determine which information enters the \r\nformulation and development of parametrizations used in ESMs. \r\nClimate models frequently share code components, and in some "


id: 4ee7e1acce5fac05f92e549799f36fb2
created_at: 2023-03-16 14:02:03
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content:
"It is problematic and not obviously constructive to provide weights \r\nfor, or rule out, individual CMIP6 model ensemble members based \r\nsolely on their ECS and TCR values. Rather these models must be \r\ntested in a like-with-like way against observational evidence. Based \r\non the currently published CMIP6 models we provide such an analysis, \r\nmarking models with ECS above and below the assessed very likely\r\nrange (Figure 7.19). In the long-term historical warming (Figure 7.19a) \r\nboth low- and high-ECS models are able to match the observed \r\nwarming, presumably in part as a  result of compensating aerosol \r\ncooling (Kiehl, 2007; Forster et al., 2013; Wang et al., 2021). In several \r\ncases of high ECS models that apply strong aerosol cooling it is found \r\nto result in surface warming and ocean heat uptake evolutions that \r\nare inconsistent with observations (Golaz et al., 2019; Andrews et al., \r\n2020; Winton et  al., 2020)"


id: d9fc3c655cecdb0851ccc5cf89605450
created_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 31821
content:
" Modelled warming since the 1970s is \r\nless influenced by compensation between climate sensitivity and \r\naerosol cooling (Jimenez-de-la-Cuesta and Mauritsen, 2019; Nijsse \r\net  al., 2020) resulting in the high-ECS models in general warming \r\nmore than observed, whereas low-sensitivity models mostly perform \r\nbetter (Figure 7.19b); a result that may also have been influenced by \r\ntemporary pattern effects (Sections 7.4.4 and 7.5.4). Paleoclimates \r\nare not influenced by such transient pattern effects, but are limited by \r\nstructural uncertainties in the proxy-based temperature and forcing \r\nreconstructions as well as possible differences in equilibrium sea \r\nsurface temperature patterns between models and the real world \r\n(Section 7.5.4). Across the LGM, MPWP and EECO (Figure 7.19c-e), \r\nthe few high-ECS models that simulated these cases were outside the \r\nobserved very likely ranges (see also Feng et al., 2020; Renoult et al., \r\n2020; Zhu et al., 2020)"


id: c120d98032eab81fd49b5b93f5462558
created_at: 2023-03-16 14:02:03
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vector_id: 27965
content:
"Figure 7.20 | Contributions of effective radiative forcing, ocean heat uptake and radiative feedbacks to global atmospheric energy input and near-surface \r\nair temperature change at year 100 of abrupt4xCO2 simulations of CMIP6 models. (a) The energy flux to the global atmosphere associated with the effective CO2\r\nforcing, global ocean heat uptake, Planck response, and radiative feedbacks, which together sum to zero. The inset shows energy input from individual feedbacks, summing \r\nto the total feedback energy input. (b) Contributions to net global warming are calculated by dividing the energy inputs by the magnitude of the global Planck response \r\n(3.2 W m-2 degC-1), with the contributions from radiative forcing, ocean heat uptake, and radiative feedbacks (orange bars) summing to the value of net warming (grey bar). The \r\ninset shows warming contributions associated with individual feedbacks, summing to the total feedback contribution"


id: 9186ed60ac9e2c0fb699f16c3be4f035
created_at: 2023-03-16 14:02:03
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content:
" Uncertainties show the interquartile range (25th and \r\n75th percentiles) across models. Radiative kernel methods (see Section 7.4.1) were used to decompose the net energy input from radiative feedbacks into contributions from \r\nchanges in atmospheric water vapour, lapse rate, clouds, and surface albedo (Zelinka et al. (2020) using the Huang et al. (2017) radiative kernel). The CMIP6 models included \r\nare those analysed by Zelinka et al. (2020) and the warming contribution analysis is based on that of Goosse et al. (2018). Further details on data sources and processing are \r\navailable in the chapter data table (Table 7.SM.14)."


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vector_id: 36948
content:
"evaluate the effect of each of these processes on surface changes \r\nwithin coupled ESM simulations by diagnosing so-called 'warming \r\ncontributions' (Dufresne and Bony, 2008; Crook et  al., 2011; Feldl \r\nand Roe, 2013; Vial et al., 2013; Pithan and Mauritsen, 2014; Goosse \r\net  al., 2018). By construction, the individual warming contributions \r\nsum to the total global surface warming (Figure 7.20b). For long-term \r\nwarming in response to CO2 forcing in CMIP5 models, the energy \r\nadded to the climate system by radiative feedbacks is larger than the \r\nERF of CO2 (Figure 7.20a), implying that feedbacks more than double \r\nthe magnitude of global warming (Figure  7.20b). Radiative kernel \r\nmethods (see Section 7.4.1) can be used to decompose the net energy \r\ninput from radiative feedbacks into its components. The water-vapour, \r\ncloud and surface-albedo feedbacks enhance global warming, while \r\nthe lapse-rate feedback reduces global warming"


id: 72fb86b6a617d775a2210061f4d3e8fb
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 15702
content:
"Interactions between different feedbacks within the coupled \r\nclimate system pose a challenge to our ability to understand global \r\nwarming and its uncertainty based on energy budget diagnostics \r\n(Section 7.4.2). For example, water-vapour and lapse-rate feedbacks \r\nare correlated (Held and Soden, 2006) owing to their joint dependence \r\non the spatial pattern of warming (Po-Chedley et  al., 2018b). \r\nMoreover, feedbacks are not independent of ocean heat uptake \r\nbecause the uptake and transport of heat by the ocean influences \r\nthe SST pattern on which global feedbacks depend (Section 7.4.4.3). \r\nHowever, alternative decompositions of warming contributions that \r\nbetter account for correlations between feedbacks produce similar \r\nresults (Caldwell et al., 2016). The key role of radiative feedbacks in \r\ngoverning the magnitude of global warming is also supported by the \r\nhigh correlation between radiative feedbacks (or ECS) and transient \r\n21st-century warming within ESMs (Grose et al"


id: fdae2c7e458cb3489bc4c300b4808587
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 37428
content:
"Another approach to evaluating the roles of forcing, feedbacks and \r\nocean heat uptake in projected warming employs idealized energy \r\nbalance models that emulate the response of ESMs, and which \r\npreserve the interactions between system components. One such \r\nemulator, used in Section  7.5.1.2, resolves the heat capacity of \r\nboth the surface components of the climate system and the deep \r\nocean (Held et  al., 2010; Geoffroy et  al., 2013a, b; Kostov et  al., \r\n2014; Armour, 2017). Using this emulator, Geoffroy et al"


id: fc0c5c9a3fd9ebaab10bcdc43ffab3f2
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 37150
content:
" (2012) find \r\nthat: under an idealized 1% per year increase in atmospheric CO2, \r\nradiative feedbacks constitute the greatest source of uncertainty \r\n(about 60% of variance) in transient warming beyond several \r\ndecades; ERF uncertainty plays a  secondary but important role in \r\nwarming uncertainty (about 20% of variance) that diminishes beyond \r\nseveral decades; and ocean heat uptake processes play a minor role \r\nin warming uncertainty (less than 10% of variance) at all time scales."


id: 97e0a41c56aa200c0908efecde6f4c0a
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 21412
content:
"More computationally intensive approaches evaluate how the \r\nclimate response depends on perturbations to key parameter  or \r\nstructural choices within ESMs. Large 'perturbed parameter \r\nensembles', wherein a  range of parameter settings associated with \r\ncloud physics are explored within atmospheric ESMs, produce a wide \r\nrange of ECS due to changes in cloud feedbacks, but often produce \r\nunrealistic climate states (Joshi et al., 2010). Rowlands et al. (2012) \r\ngenerated an ESM perturbed-physics ensemble of several thousand \r\nmembers by perturbing model parameters associated with radiative \r\nforcing, cloud feedbacks and ocean vertical diffusivity (an important \r\nparameter for ocean heat uptake). After constraining the ensemble to \r\nhave a reasonable climatology and to match the observed historical \r\nsurface warming, they found a wide range of projected warming by \r\nthe year 2050 under the SRES A1B scenario (1"


id: df1c43521f6ad846d9986c390a069858
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 32597
content:
"4degC-3degC relative to \r\nthe 1961-1990 average) that is dominated by differences in cloud \r\nfeedbacks. The finding that cloud feedbacks are the largest source \r\nof spread in the net radiative feedback has since been confirmed in \r\nperturbed parameter ensemble studies using several different ESMs \r\n(Gettelman et al., 2012; Tomassini et al., 2015; Kamae et al., 2016b; \r\nRostron et al., 2020; Tsushima et al., 2020). By swapping out different \r\nversions of the atmospheric or oceanic components in a  coupled \r\nESM, Winton et al. (2013) found that TCR and ECS depend on which \r\natmospheric component was used (using two versions with different \r\natmospheric physics), but that only TCR is sensitive to which oceanic \r\ncomponent of the model was used (using two versions with different "


id: df66f4d889d3db30685a7c681119c7e5
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 32645
content:
"The physical emissions metrics described above are functions of time \r\nsince typically the physical effects reach a peak and then decrease in \r\nthe period after a pulse emission as the concentrations of the emitted \r\ncompound decay. The value of the metrics can therefore be strongly \r\ndependent on the time horizon of interest. All relative metrics (GWP, \r\nGTP etc.) are also affected by the time dependence of the CO2 metrics \r\nin the denominator. Instantaneous or endpoint metrics quantify the \r\nchange (e.g., in radiative forcing, global surface temperature, global \r\nmean sea level) at a particular time after the emission. These can be \r\nappropriate when the goal is to not exceed a  fixed target such as \r\na temperature or global mean sea level rise at a specific time. Emissions \r\nmetrics can also be integrated from the time of emission. The most \r\ncommon of these is the absolute global warming potential (AGWP), \r\nwhich is the integral of the AGFP"


id: d6429eae225f9abe2a6ad945016bb05b
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 31222
content:
" The physical effect is then in units \r\nof forcing-years, degree-years or metre-years for forcing, temperature, \r\nor sea level rise, respectively. These can be appropriate for trying to \r\nreduce the overall damage potential when the effect depends on \r\nhow long the change occurs for, not just how large the change is. \r\nThe integrated metrics still depend on the time horizon, though for \r\nthe shorter-lived compounds this dependence is somewhat smoothed \r\nby the integration. The integrated version of a metric is often denoted \r\nas iAGxx, although the integral of the forcing-based metric (iAGFP) \r\nis known as the AGWP. Both the endpoint and integrated absolute \r\nmetrics for non-CO2 species can be divided by the equivalent for CO2\r\nto give relative emissions metrics (e.g., GWP (=iGFP), GTP, iGTP)."


id: 979f52cfd7ce7ccc2184d0583e20f72c
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 21381
content:
"Any agent that warms the surface perturbs the terrestrial and oceanic \r\ncarbon fluxes (Sections 5.4.3 and 5.4.4), typically causing a net flux \r\nof CO2 into the atmosphere and hence further warming. This aspect is \r\nalready included in the carbon cycle models that are used to generate \r\nthe radiative effects of a  pulse of CO2 (Joos et  al., 2013), but was \r\nneglected for non-CO2 compounds in the conventional metrics so \r\nthis introduces an inconsistency and bias in the metric values (Gillett \r\nand Matthews, 2010; MacDougall et al., 2015; Tokarska et al., 2018). \r\nA  simplistic account of the carbon cycle response was tentatively \r\nincluded in AR5 based on a single study (W.J. Collins et al., 2013). \r\nSince AR5 this understanding has been revised (Gasser et al., 2017b; \r\nSterner and Johansson, 2017) using simple parametrized carbon cycle \r\nmodels to derive the change in CO2 surface flux for a unit temperature \r\npulse as an impulse response function to temperature. In W.J"


id: 7aa1d9933b33ac2c83c22bfd9b89d5d4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 16870
content:
" Collins \r\net al. (2013) this response function was assumed to be simply a delta \r\nfunction, whereas the newer studies include a  more complete \r\nfunctional form accounting for subsequent re-uptake of CO2 after the \r\nremoval of the temperature increase. Accounting for re-uptake has \r\nthe effect of reducing the carbon-cycle responses associated with \r\nthe metrics compared to AR5, particularly at large time horizons. The \r\nincrease in any metric due to the carbon cycle response can be derived \r\nfrom the convolution of the global surface temperature response with \r\nthe CO2 flux response to temperature and the equivalent metric for \r\nCO2 (Equation 7.SM.5.5 in the Supplementary Material). Including \r\nthis response also increases the duration of the effect of short-lived \r\nGHGs on climate (Fu et al., 2020)"


id: 3268734620cea3a75b71190addcb4c48
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 5340
content:
" An alternative way of accounting for \r\nthe carbon cycle temperature response would be to incorporate it into \r\nthe temperature response function (the response functions used here \r\nand given in Supplementary Material 7.SM.5.2 do not explicitly do \r\nthis). If this were done, the correction could be excluded from both the \r\nCO2 and non-CO2 forcing responses as, in Hodnebrog et al. (2020a).\r\nIncluding the carbon cycle response for non-CO2 treats CO2 and \r\nnon-CO2 compounds consistently and therefore we assess that its \r\ninclusion more accurately represents the climate effects of non-CO2"


id: 12b8fce760c77c23dc0503eaa36a8f27
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 447
content:
"species. There is high confidence in the methodology of using carbon \r\ncycle models for calculating the carbon cycle response. The magnitude \r\nof the carbon cycle response contributions to the emissions metrics \r\nvaries by a factor of two between Sterner and Johansson (2017) and \r\nGasser et al. (2017b). The central values are taken from Gasser et al. \r\n(2017b) as the OSCAR 2.2 model used is based on parameters derived \r\nfrom CMIP5 models, and the climate-carbon feedback magnitude is \r\ntherefore similar to the CMIP5 multi-model mean (Arora et al., 2013; \r\nLade et al., 2018). As values have only been calculated in two simple \r\nparametrized carbon cycle models the uncertainty is assessed to be \r\n+-100%. Due to there being few studies and a factor of two difference \r\nbetween them, there is low confidence that the magnitude of the \r\ncarbon cycle response is within the higher end of this uncertainty \r\nrange, but high confidence that the sign is positive"


id: 9f8eb52f6b9a4588b1be225becf9fc4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 22644
content:
" Carbon cycle \r\nresponses are included in all the metrics presented in Table 7.15 and \r\nSupplementary Table 7.SM.7. The carbon cycle contribution is lower \r\nthan in AR5, but there is high confidence in the need for its inclusion \r\nand the method by which it is quantified."


id: 18530c83a1f54d2667e76e5b85721648
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 1320
content:
"Oxidation of methane leads ultimately to the net production of \r\natmospheric CO2 (Boucher et al., 2009). This yield is less than 100% \r\n(on a molar basis) due to uptake by soils and some of the reaction \r\nproducts (mainly formaldehyde) being directly removed from the \r\natmosphere before being completely oxidized. Estimates of the yield \r\nare 61% (Boucher et al., 2009) and 88% (Shindell et al., 2017), so \r\nthe assessed range is 50-100% with a central value of 75% (low \r\nconfidence). For methane and hydrocarbons from fossil sources, \r\nthis will lead to additional fossil CO2 in the atmosphere whereas \r\nfor biogenic sources of methane or hydrocarbons, this replaces CO2\r\nthat has been recently removed from the atmosphere. Since the ratio \r\nof molar masses is 2.75, 1 kg of methane generates 2.1 +- 0.7 kgCO2\r\nfor a 75% yield. For biogenic methane the soil uptake and removal of \r\npartially oxidized products is equivalent to a sink of atmospheric CO2 \r\nof 0.7 +- 0.7 kg per kg methane"


id: be54ed4fe60c389c4da2905c548527e4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 27494
content:
" The contributions of this oxidation \r\neffect to the methane metric values allow for the time delay in the \r\noxidation of methane. Methane from fossil fuel sources has therefore \r\nslightly higher emissions metric values than those from biogenic \r\nsources (high confidence). The CO2 can already be included in carbon \r\nemissions totals (Munoz and Schmidt, 2016) so care needs to be \r\ntaken when applying the fossil correction to avoid double counting."


id: 75ea9a977c77b69b2939360e29047439
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 16144
content:
"Indirect contributions from chemical production or destruction of \r\nother GHGs are quantified in Chapter 6 (Section 6.4). For methane \r\n(CH4), AR5 (Myhre et al., 2013b) assessed that the contributions from \r\neffects on ozone and stratospheric water vapour add 50% +- 30% \r\nand 15% +- 11% to the emissions-based ERF, which were equivalent \r\nto 1.8 +- 0.7 x10-4 and 0.5 +- 0.4 x10-4 W m-2 ppb (CH4)\r\n-1. In AR6 \r\nthe radiative efficiency formulation is preferred as it is independent \r\nof the assumed radiative efficiency for methane. The assessed \r\ncontributions to the radiative efficiency for methane due to ozone \r\nare 1.4 +- 0.7 x10-4 W m-2 ppb (CH4)\r\n-1, based on 0.14 W  m-2\r\nforcing from a  1023 ppb (1850-2014) methane change (Thornhill \r\net  al., 2021b). The contribution from stratospheric water vapour is \r\n0.4 +- 0.4 x10-4 W m-2 ppb (CH4)\r\n-1, based on 0.05 W m-2 forcing from \r\na 1137 ppb (1750-2019) methane change (Section 7.3.2.6)"


id: 74290cfd8086bed2503e5fba2d17989
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 15869
content:
" Nitrous \r\noxide (N2O) depletes upper stratospheric ozone (a positive forcing) \r\nand reduces the methane lifetime. In AR5 the methane lifetime effect \r\nwas assessed to reduce methane concentrations by 0.36 ppb per ppb \r\nincrease in N2O, with no assessment of the effective radiative forcing \r\nfrom ozone. This is now increased to -1.7 ppb methane per ppb N2O \r\n(based on a methane lifetime decrease of 4% +- 4% for a 55 ppb \r\nincrease in N2O (Thornhill et al., 2021b) and a radiative efficiency of \r\n5.5 +- 0.4 x10-4 W m-2 ppb (N2O)-1 through ozone (Thornhill et al., \r\n2021b)). In summary, GWPs and GTPs for methane and nitrous oxide \r\nare slightly lower than in AR5 (medium confidence) due to revisions \r\nin their lifetimes and updates to their indirect chemical effects."


id: b49b09efa9b62330892865dc61d275ce
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 25949
content:
"Since AR5 there have been developments in how to account for \r\nthe different behaviours of short-lived and long-lived compounds. \r\nPulse-based emissions metrics for short-lived GHGs with lifetimes \r\nless than 20 years are very sensitive to the choice of time horizon \r\n(e.g.,  Pierrehumbert, 2014). Global surface temperature changes \r\nfollowing a  pulse of CO2 emission are roughly constant in time \r\n(the principle behind TCRE; Section 5.5.1 and Figure 7.21b) whereas \r\nthe temperature change following a  pulse of short-lived GHG \r\nemission declines with time. In contrast to a  one-off pulse, a  step \r\nchange in short-lived GHG emissions that is maintained indefinitely \r\ncauses a  concentration increase that eventually equilibrates to \r\na steady state in a way that is more comparable to a pulse of CO2. \r\nSimilarly the resulting change in global surface temperature from \r\na step change in short-lived GHGs (Figure 7"


id: 190c155ffa8302ba209ab3f4724a2068
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 1433
content:
"21a) after a few decades \r\nincreases only slowly (due to accumulation of heat in the deep ocean) \r\nand hence its effects are more similar to a pulse of CO2 (Smith et al., \r\n2012; Lauder et  al., 2013; Allen et  al., 2016, 2018b). The different \r\ntime dependence of short-lived and long-lived compounds can be "


id: fb644956d82d57830dcbf710c47fa41b
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 37056
content:
"The similarity in behaviour of step changes in short-lived GHG \r\nemissions and pulses of CO2 emissions has recently been used to \r\nformulate new emissions metric concepts (Collins et al., 2020). For \r\nshort-lived GHGs, these new concepts use a step change in the rate \r\nof emissions, in contrast to an instantaneous pulse in a given year \r\nthat is typically used (e.g.,  Myhre et  al., 2013b). Metrics for step \r\nemissions changes are denoted here by a superscript 'S' (e.g., AGTP S\r\nX\r\nis the absolute global surface temperature-change potential from \r\na  unit step change in emissions of species \"X\"). These can be \r\nderived by integrating the more standard pulse emission changes \r\nup to the time horizon. The response to a step emissions change is \r\ntherefore equivalent to the integrated response to a pulse emission \r\n(AGTP S\r\nX  =  iAGTPX); and the radiative forcing response to a  step \r\nemissions change AGFP S\r\nX is equivalent to the integrated forcing \r\nresponse iAGFPX which is the AGWP"


id: cfff4f13fcbebddb920b585418c62fa3
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 30252
content:
"actual temperature response computed with the two-layer emulator. \r\nNote that GWP and GTP metrics were not designed for use under \r\na cumulative carbon dioxide equivalent emissions framework (Shine \r\net al., 1990, 2005), even if they sometimes are (e.g., Cui et al., 2017; \r\nHoward et al., 2018) and analysing them in this way can give useful \r\ninsights into their physical properties. Using these standard metrics \r\nunder such frameworks, the cumulative CO2 equivalent emissions \r\nassociated with methane emissions would continue to rise if methane \r\nemissions were substantially reduced but remained above zero. \r\nIn reality, a decline in methane emissions to a smaller but still positive \r\nvalue could cause a declining warming. GSAT changes estimated with \r\ncumulative CO2 equivalent emissions computed with GWP-20 matches \r\nthe warming trend for a few decades but quickly overestimates the \r\nresponse"


id: 1719316c5ff430690f34ca4c98194a61
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 1153
content:
" Cumulative emissions using GWP-100 perform well when \r\nemissions are increasing but not when they are stable or decreasing. \r\nCumulative emissions using GTP-100 consistently underestimate \r\nthe warming. Cumulative emissions using either CGTP or GWP* \r\napproaches can more closely match the GSAT evolution (Allen et al., \r\n2018b; Cain et al., 2019; Collins et al., 2020; Lynch et al., 2020)."


id: d2278c71d02142921cc04e6032a26dbc
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 30589
content:
"When individual gases are treated separately in climate model emulators (Cross-Chapter Box  7.1), or weighted and aggregated \r\nusing an emissions metric approach (such as CGTP or GWP*) which translate the distinct behaviour from cumulative emissions of \r\nshort-lived gases, ambiguity in the future warming trajectory of a given emissions scenario can be substantially reduced (Cain et al., \r\n2019; Denison et al., 2019; Collins et al., 2020; Lynch et al., 2021). The degree of ambiguity varies with the emissions scenario. For \r\nmitigation pathways that limit warming to 2degC with an even chance, the ambiguity arising from using GWP-100 as sole constraint \r\non emissions of a mix of greenhouse gases (without considering their economic implications or feasibility) could be as much as \r\n0.17degC, which represents about one-fifth of the remaining global warming in those pathways (Denison et al., 2019)"


id: edd07a10f4b350fbf3cc49bbce143914
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 34877
content:
" If the evolution \r\nof the individual GHGs is not known, this can make it difficult to evaluate how a given global multi-gas emissions pathway specified \r\nonly in CO2 equivalent emissions would achieve (or not) global surface temperature goals. This is potentially an issue as Nationally \r\nDetermined Contributions frequently make commitments in terms of GWP-100-based CO2 equivalent emissions at 2030 without \r\nspecifying individual gases (Denison et al., 2019). Clear and transparent representation of the global warming implications of future \r\nemissions pathways including Nationally Determined Contributions could be achieved either by their detailing pathways for multiple \r\ngases or by detailing a pathway of cumulative carbon dioxide equivalent emissions approach aggregated across GHGs evaluated \r\nby either GWP* or CGTP metric approaches (Cain et  al., 2019; Collins et  al., 2020; Lynch et  al., 2021)"


id: 144a91281f8f889d10e75a15a254acb1
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 704
content:
" It should be noted that \r\nalthough the Paris Agreement Rulebook asks countries to report emissions of individual GHGs separately for the global stocktake \r\n(Decision 18/CMA.1, annex, paragraph 38), which can allow the current effects of their emissions on global surface temperature to \r\nbe accurately estimated, estimates of future warming are potentially ambiguous where emissions are aggregated using GWP-100 or \r\nother pulse metrics."


id: cb46252a72265f75fb9cc1feff3c288a
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 29536
content:
"is an active area of research (Schleussner et  al., 2016, 2019; \r\nFuglestvedt et al., 2018; Collins et al., 2020). Several possible scientific \r\ninterpretations of the Article 2 and 4 goals can be devised, and these, \r\nalong with emissions metric choice, have implications both for when \r\na balance in GHG emissions, net zero CO2 emissions or net zero GHG \r\nemissions are achieved, and for their meaning in terms of temperature \r\noutcome (Fuglestvedt et al., 2018; Rogelj et al., 2018; Wigley, 2018). \r\nIn AR6 net zero GHG emissions is defined as the condition in which \r\nmetric-weighted anthropogenic GHG emissions are balanced by \r\nmetric-weighted anthropogenic GHG removals over a specified period \r\n(see Box  1.4 and Appendix VII: Glossary). The quantification of net \r\nzero GHG emissions depends on the GHG emissions metric chosen \r\nto compare emissions and removals of different gases, as well as the \r\ntime horizon chosen for that metric"


id: 3fe1666f7dba5a46562d1bc1f0eeb741
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 7434
content:
" As the choice of emissions metric \r\naffects the quantification of net zero GHG emissions, it therefore \r\naffects the resulting temperature outcome after net zero emissions \r\nare achieved (Lauder et  al., 2013; Rogelj et  al., 2015; Fuglestvedt \r\net al., 2018; Schleussner et al., 2019). Schleussner et al. (2019) note \r\nthat declining temperatures may be a desirable outcome of net zero. \r\nRogelj and Schleussner (2019) also point out that the use of physical \r\nmetrics raises questions of equity and fairness between developed and \r\ndeveloping countries."


id: 89f0420147bb994c5504b0ff4849b1f4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 19230
content:
"One prominent use of emissions metrics is for comparison of efforts \r\nmeasured against climate change goals or targets. One of the most \r\ncommonly discussed goals is in Article 2 of the Paris Agreement \r\nwhich aims to limit the risks and impacts of climate change by setting \r\ntemperature goals. In addition, the Paris Agreement has important \r\nprovisions which relate to how the goals are to be achieved, including \r\nmaking emissions reductions in a  manner that does not threaten \r\nfood production (Article 2), an early emissions peaking target, and \r\nthe aim to 'achieve a balance between anthropogenic emissions by \r\nsources and removals by sinks of greenhouse gases in the second half \r\nof this century' (Article 4). Article 4 also contains important context \r\nregarding international equity, sustainable development, and poverty \r\nreduction"


id: 8a5f2adc4e641c6bd5c1d3422099fbe4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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content:
" Furthermore, the United Nations Framework Convention \r\non Climate Change (UNFCCC) sets out as its ultimate objective, the \r\n'stabilization of greenhouse gas concentrations in the atmosphere \r\nat a level that would prevent dangerous anthropogenic interference \r\nwith the climate system.'"


id: d16c9c40f9cb5868cf67a9968030ac6b
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 30488
content:
"Based on SR1.5 (Allen et al., 2018a), there is high confidence that \r\nachieving net zero CO\r\n2 emissions and declining non-CO2 radiative \r\nforcing would halt human-induced warming. Based on (Bowerman \r\net  al., 2013; Pierrehumbert, 2014; Fuglestvedt et  al., 2018; Tanaka \r\nand O'Neill, 2018; Schleussner et  al., 2019) there is also high \r\nconfidence that reaching net zero GHG emissions as quantified by \r\nGWP-100 typically leads to reductions from peak global surface \r\ntemperature after net zero GHGs emissions are achieved, depending \r\non the relative sequencing of mitigation of short-lived and long-lived \r\nspecies. If both short- and long-lived species are mitigated together, \r\nthen temperatures peak and decline. If mitigation of short-lived \r\nspecies occurs much earlier than that of long-lived species, then \r\ntemperatures stabilize very near peak values, rather than decline"


id: 580e7a0fbae594c8250e32ef82907fa1
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 11357
content:
" \r\nTemperature targets can be met even with positive net GHG emissions \r\nbased on GWP-100 (Tanaka and O'Neill, 2018). As demonstrated \r\nby Allen et al. (2018b), Cain et al. (2019), Schleussner et al. (2019) \r\nand Collins et  al. (2020) reaching net zero GHG emissions when \r\nquantified using the new emissions metric approaches such as \r\nCGTP or GWP* would lead to an approximately similar temperature \r\nevolution as achieving net zero CO\r\n2. Hence, net zero CO2 and net zero \r\nGHG, quantified using these new approaches, would both lead to \r\napproximately stable contributions to temperature change after net \r\nzero emissions are achieved (high confidence)."


id: bb9dd56466f35edc65bb5f328226b055
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 27076
content:
"Comparisons with emissions or global surface temperature \r\nstabilization goals are not the only role for emissions metrics. \r\nOther important roles include those in pricing approaches where \r\npolicymakers choose to compare short-lived and long-lived climate \r\nforcers (e.g.,  Manne and Richels, 2001), and in life cycle analyses \r\n(e.g., Hellweg and Mila i Canals, 2014). Several papers have reviewed \r\nthe issue of metric choice for life cycle analyses, noting that analysts \r\nshould be aware of the challenges and value judgements inherent in \r\nattempting to aggregate the effects of forcing agents with different \r\ntime scales onto a common scale (e.g., Mallapragada and Mignone, \r\n2017) and recommend aligning metric choice with policy goals as \r\nwell as testing sensitivities of results to metric choice (Cherubini et al., \r\n2016)"


id: bd2496e85672ed891506860ff1d3b2cd
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 27325
content:
" Furthermore, life cycle analyses approaches which are sensitive \r\nto choice of emissions metric benefit from careful communication of \r\nthe reasons for the sensitivity (Levasseur et al., 2016)."


id: 9e5c9357e09b754f8d58ded982f7c2a3
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
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vector_id: 22440
content:
"We measure the influence of various human and natural factors on the energy flows at the top of our atmosphere \r\nin terms of radiative forcings, where a positive radiative forcing has a warming effect and a negative radiative \r\nforcing has a cooling effect. In response to these forcings, the Earth system will either warm or cool, so as to \r\nrestore balance through changes in the amount of outgoing thermal radiation (the warmer the Earth, the more \r\nradiation it emits). Changes in Earth's temperature in turn lead to additional changes in the climate system \r\n(known as climate feedbacks) that either amplify or dampen the original effect. For example, Arctic sea ice has \r\nbeen melting as the Earth warms, reducing the amount of reflected sunlight and adding to the initial warming \r\n(an amplifying feedback). The most uncertain of those climate feedbacks are clouds, as they respond to warming in \r\ncomplex ways that affect both the emission of thermal radiation and the reflection of sunlight"


id: 8ce088eebb621d94339ddcf22e7c53f4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 19684
content:
" However, we are \r\nnow more confident that cloud changes, taken together, will amplify climate warming (see FAQ 7.2)."


id: f8af2441f0fc7ca0686ea49e8faf72eb
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 36640
content:
"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which \r\nform when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). \r\nClouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence \r\nEarth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and \r\nvaried. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by \r\npreventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap \r\n(i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming \r\neffect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, \r\nresulting in an overall net cooling effect on the present climate"


id: b8f797b6e93687a6cf3caf2fe78d7d35
created_at: 2023-03-16 14:02:03
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vector_id: 26634
content:
" Human activities since the pre-industrial era \r\nhave altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol \r\nparticles in the atmosphere and by warming the Earth's surface, primarily as a result of increases in greenhouse \r\ngas emissions."


id: 2e9f2291987c04dae3c5fbe4671fdaf8
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 4779
content:
"Since the last IPCC Report in 2013 (the Fifth Assessment Report, or AR5), understanding of cloud processes has \r\nadvanced with better observations, new analysis approaches and explicit high-resolution numerical simulation of \r\nclouds. Also, current global climate models simulate cloud behaviour better than previous models, due both to \r\nadvances in computational capabilities and process understanding. Altogether, this has helped to build a more \r\ncomplete picture of how clouds will change as the climate warms (FAQ 7.2, Figure 1). For example, the amount \r\nof low-clouds will reduce over the subtropical ocean, leading to less reflection of incoming solar energy, and \r\nthe altitude of high-clouds will rise, making them more prone to trapping outgoing energy; both processes \r\nhave a warming effect. In contrast, clouds in high latitudes will be increasingly made of water droplets rather \r\nthan ice crystals"


id: 5c3badc69e9a61937b36bd41b9ce32d7
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 12026
content:
" This shift from fewer, larger ice crystals to smaller but more numerous water droplets will \r\nresult in more of the incoming solar energy being reflected back to space and produce a cooling effect. Better \r\nunderstanding of how clouds respond to warming has led to more confidence than before that future changes \r\nin clouds will, overall, cause additional warming (i.e., by weakening the current cooling effect of clouds). This is \r\ncalled a positive net cloud feedback."


id: 4ef21e02f6f9ef7c5c1338b55b77a26b
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 9862
content:
"Gabriel Abramowitz (Australia), Gudfinna Adalgeirsdottir (Iceland), Andrea Alessandri \r\n(Italy), Robert  J.  Allen (United States of America), Kevin Anchukaitis (United States \r\nof America), Richard A. Betts (United Kingdom), Celine J. W. Bonfils (United States of America/\r\nFrance), Michael Bosilovich (United  States of America), Olivier  Boucher (France), \r\nJosephine Brown (Australia), Michael  P.  Byrne (United Kingdom/Ireland), Robin Chadwick \r\n(United  Kingdom), Sarah  Connors (France/United  Kingdom), Benjamin Cook (United  States \r\nof America), Erika Coppola (Italy), Alejandro  Di  Luca (Australia, Canada/Argentina), \r\nAida Diongue Niang (Senegal), Petra Doll (Germany), Ellen Douglas (United States of America), \r\nPaul  J.  Durack (United States of America/Australia), Hayley J.  Fowler (United  Kingdom), \r\nAlexander Gershunov (United States of America), Nicholas R"


id: 796d2261f361ffc2bf9071d0e0f18f06
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 16678
content:
"  Golledge (New  Zealand/\r\nUnited Kingdom), James Kossin (United States of America), Won-Tae Kwon (Republic of Korea), \r\nFlavio Lehner (United States of America/Switzerland), Eric Maloney (United States of America), \r\nVimal Mishra (India), Angeline Pendergrass (United States of America), Stefan Pfahl (Germany), \r\nCatherine Prigent (France), Catherine Rio (France), Alex C.  Ruane (United  States of America), \r\nBenjamin Sanderson (United Kingdom), Sonia I. Seneviratne (Switzerland), Shoichi Shige (Japan), \r\nVijay Singh (United States of America), Abigail Swann (United States of America), Richard G. Taylor \r\n(United  Kingdom/Canada, United  Kingdom), Laurent  Terray (France), Natalia  Tilinina \r\n(The Russian Federation), Bart van den Hurk (The Netherlands), Sergio M. Vicente-Serrano (Spain), \r\nMichael  Wehner (United  States of  America), Laura J.  Wilcox (United  Kingdom), Cunde  Xiao \r\n(China), Prodromos Zanis (Greece), Xuebin Zhang (Canada)"


id: 18c1ba26d960cafe4342ffb718333931
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 1395
content:
"Modifications of Earth's energy budget by anthropogenic \r\nradiative forcings drive substantial and widespread changes \r\nin the global water cycle. There is high confidence that global \r\nmean precipitation and evaporation increase with global warming, \r\nbut the estimated rate is model-dependent (very likely range of \r\n1-3% per 1degC). The global increase in precipitation is determined by \r\na robust response to global mean surface air temperature (very likely\r\n2-3% per 1degC) that is partly offset by fast atmospheric adjustments \r\nto atmospheric heating by greenhouse gases and aerosols. The overall \r\neffect of anthropogenic aerosols is to reduce global precipitation and \r\nalter large-scale atmospheric circulation patterns through their well\u0002understood surface radiative cooling effect (high confidence). Land\u0002use and land-cover changes also drive regional water cycle changes \r\nthrough their influence on surface water and energy budgets (high \r\nconfidence). {8.2.1, 8.2.3.4, 8.2.2.2, Box 8"


id: fe5e005c716611857a64388d4e422839
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 37555
content:
"Anthropogenic aerosols have driven detectable large-scale \r\nwater cycle changes since at least the mid-20th century (high \r\nconfidence). Shifts in the tropical rain belt are associated with \r\nthe inter-hemispheric temperature response to the time-evolving \r\nradiative influence of anthropogenic aerosols and the ongoing \r\nwarming influence of greenhouse gases (high confidence). Cooling in \r\nthe Northern Hemisphere by sulphate aerosols explained a southward \r\nshift in the tropical rain belt and contributed to the Sahel drought \r\nfrom the 1970s to the 1980s (high confidence), subsequent recovery \r\nfrom which has been linked with greenhouse gas warming (medium \r\nconfidence). Observed changes in regional monsoon precipitation, \r\nespecially over South Asia, East Asia and West Africa, have been \r\nlimited over much of the 20th century due to increases driven by \r\nwarming from greenhouse gases being counteracted by decreases \r\ndue to cooling from anthropogenic aerosols (high confidence). \r\n{8.3"


id: b4f1cca0932694005bf10d93e0e8f5fd
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 26006
content:
"Without large-scale reduction in greenhouse gas emissions, \r\nglobal warming is projected to cause substantial changes \r\nin the water cycle at both global and regional scales (high \r\nconfidence). Global annual precipitation over land is projected to \r\nincrease on average by 2.4 [-0.2 to +4.7] %  (likely  range) in the \r\nSSP1-1.9 low-emissions scenario and by 8.3 [0.9 to 12.9] % in the \r\nSSP5-8.5 very high-emissions scenario by 2081-2100, relative to \r\n1995-2014. It is virtually certain that evaporation will increase over \r\nthe oceans and very likely that evapotranspiration will increase over \r\nland with regional exceptions in drying areas. There is low confidence\r\nin the sign and magnitude of projected changes in global land runoff \r\nin all Shared Socio-economic Pathway scenarios. Projected increases \r\nin precipitation amount and intensity will be associated with \r\nincreased runoff in the northern high latitudes (high confidence)"


id: 56cc549d5b4957d7188401462734cbd4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 11130
content:
" \r\nThere is high confidence that mountain glaciers will diminish in \r\nall regions and that seasonal snow cover duration will generally \r\ndecrease. Runoff from small glaciers will typically decrease through \r\nloss of ice mass, while runoff from large glaciers is likely to increase \r\nwith increasing global warming until glacier mass becomes depleted \r\n(high confidence). {4.5.1, 8.4.1}"


id: 931dd23cbf8e35b32d1e8016314affc6
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 20662
content:
"Abrupt human-caused changes to the water cycle cannot be \r\nexcluded. There is evidence of abrupt change in some high-emissions \r\nscenarios, but there is no overall consistency regarding the magnitude \r\nand timing of such changes. Positive land surface feedbacks, including \r\nvegetation and dust, can contribute to abrupt changes in aridity, \r\nbut there is only low confidence that such changes will occur during \r\nthe 21st century. Continued Amazon deforestation, combined with \r\na  warming climate, raises the probability that this ecosystem will \r\ncross a  tipping point into a  dry state during the 21st century (low \r\nconfidence). The paleoclimate records show that a  collapse in the \r\nAtlantic Meridional Overturning Circulation (AMOC) causes abrupt \r\nshifts in the water cycle (high confidence), such as a southward shift in \r\nthe tropical rain belt, weakening of the African and Asian monsoons, \r\nstrengthening of Southern Hemisphere monsoons, and drying in \r\nEurope"


id: 7cd9808c82463e76ac3f212fb19f7289
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 17240
content:
" There is medium confidence that AMOC will not collapse \r\nbefore 2100, but should it collapse, it is very likely that there would be \r\nabrupt changes in the water cycle. {8.6.1, 8.6.2}"


id: 1f749effddf8354d0636fdc27d22064b
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 2377
content:
"Water is vital to all life on Earth. Seventy-one percent of the Earth \r\nis covered by water, with saline ocean water accounting for around \r\n97% of total water availability (Figure  8.1). Terrestrial freshwater \r\nrepresents less than 2% of all water on Earth, and the remainder \r\n(around 1-2%) is primarily made up of saline groundwater and saline \r\nlakes (Durack, 2015; Abbott et al., 2019). Ice sheets, glaciers and \r\nsnow pack account for approximately 96% of all freshwater, with less \r\nthan 4% of freshwater considered easily accessible and available for \r\nessential ecosystem functioning and human society's water resource \r\nneeds (Durack, 2015; Abbott et al., 2019). This very small fraction of \r\nfreshwater represents a total volume of about 835,000 km3, mostly \r\ncontained in groundwater (630,000 km3), the remaining 205,000 km3 \r\nbeing stored in lakes, rivers, wetlands and soils (Abbott et al., 2019)"


