Editing IPCC:AR6/WGI/Chapter-7 (section)

=== 7.4.4 Relationship Between Feedbacks and Temperature Patterns ===

<div id="h2-18-siblings" class="h2-siblings"></div>

The large-scale patterns of surface warming in observations since the 19th century ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] ) and climate model simulations ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.1|Section 4.3.1]] and Figure 7.12a) share several common features. In particular, surface warming in the Arctic is greater than for the global average and greater than in the Southern Hemisphere (SH) high latitudes; and surface warming is generally greater over land than over the ocean. Observations and climate model simulations also show some notable differences. ESMs generally simulate a weakening of the equatorial Pacific Ocean zonal (east–west) SST gradient on multi-decadal to centennial time scales, with greater warming in the east than the west, but this trend has not been seen in observations ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1|Section 9.2.1]] and Figure 2.11b).

[[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ) discusses patterns of surface warming for 21st-century climate projections under the Shared Socio-economic Pathways (SSP) scenarios. [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] [[IPCC:Wg1:Chapter:Chapter-9#9.2.1|Section 9.2.1]] ) assesses historical SST trends and the ability of coupled ESMs to replicate the observed changes. [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ) discusses the processes that cause the land to warm more than the ocean (land–ocean warming contrast). This section assesses process understanding of the large-scale patterns of surface temperature response from the perspective of a regional energy budget. It then assesses evidence from the paleoclimate proxy record for patterns of surface warming during past time periods associated with changes in atmospheric CO <sub>2</sub> concentrations. Finally, it assesses how radiative feedbacks depend on the spatial pattern of surface temperature, and thus how they can change in magnitude as that pattern evolves over time, with implications for the assessment of ECS based on historical warming (Sections 7.4.4.3 and 7.5.2.1).

<div id="7.4.4.1" class="h3-container"></div>

<span id="polar-amplification"></span>
==== 7.4.4.1 Polar Amplification ====

<div id="h3-35-siblings" class="h3-siblings"></div>

Polar amplification describes the phenomenon where surface temperature change at high latitudes exceeds the global average surface temperature change in response to radiative forcing of the climate system. Arctic amplification, often defined as the ratio of Arctic to global surface warming, is a ubiquitous emergent feature of climate model simulations ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] and Figure 7.12a; [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ) and is also seen in observations ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] ). However, both climate models and observations show relatively less warming of the SH high latitudes compared to the Northern Hemisphere (NH) high latitudes over the historical record ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] ), a characteristic that is projected to continue over the 21st century ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ). Since AR5 there is a much-improved understanding of the processes that drive polar amplification in the NH and delay its emergence in the Sh ( [[#7.4.4.1.1|Section 7.4.4.1.1]] ). Furthermore, the paleoclimate record provides evidence for polar amplification from multiple time periods associated with changes in CO <sub>2</sub> ( [[#Hollis--2019|Hollis et al., 2019]] ; [[#Cleator--2020|Cleator et al., 2020]] ; [[#McClymont--2020|McClymont et al., 2020]] ; [[#Tierney--2020b|Tierney et al., 2020b]] ), and allows an evaluation of polar amplification in model simulations of these periods ( [[#7.4.4.1.2|Section 7.4.4.1.2]] ). Research since AR5 identifies changes in the degree of polar amplification over time, particularly in the SH, as a key factor affecting how radiative feedbacks may evolve in the future ( [[#7.4.4.3|Section 7.4.4.3]] ).

<div id="_idContainer048" class="Basic-Text-Frame"></div>

[[File:06a8b9db625dc756a4847f3a68f254f1 IPCC_AR6_WGI_Figure_7_12.png]]

'''Figure 7.12''' '''|''' '''Contributions of effective radiative forcing, ocean heat uptake, atmospheric heat transport, and radiative feedbacks to regional surface temperature changes at year 100 of''' ''abrupt 4xCO2'' '''simulations of CMIP6 Earth system models (ESMs).''' '''Figure 7.12: (a)''' Pattern of near-surface air temperature change. '''(b–d)''' Contributions to net Arctic (&gt;60°N), tropical (30°S–30°N), and Antarctic (&lt;60°S) warming calculated by dividing regional-average energy inputs by the magnitude of the regional-average Planck response. The contributions from radiative forcing, changes in moist, dry-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 warming contributions associated with individual feedbacks, all summing to the total feedback contribution. Uncertainties (represented by black whiskers) show the interquartile range (25th and 75th percentiles) across models. The warming contributions (units of °C) for each process are diagnosed by calculating the energy flux (units of W m <sup>–2</sup> ) that each process 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 response (around 3.2 W m <sup>–2</sup> °C <sup>–1</sup> but varying with region). By construction, the individual warming contributions sum to the total warming in each region. Radiative kernel methods ( [[#7.4.1|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 albedo ( [[#Zelinka--2020|Zelinka et al. (2020)]] using the [[#Huang--2017|Huang et al. (2017)]] radiative kernel). The CMIP6 models included are those analysed by [[#Zelinka--2020|Zelinka et al. (2020)]] and the warming contribution analysis is based on that of [[#Goosse--2018|Goosse et al. (2018)]] . Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

<div id="7.4.4.1.1" class="h4-container"></div>

<span id="critical-processes-driving-polar-amplification"></span>
===== 7.4.4.1.1 Critical processes driving polar amplification =====

<div id="h4-13-siblings" class="h4-siblings"></div>

Several processes contribute to polar amplification under greenhouse gas forcing, including the loss of sea ice and snow (an amplifying surface-albedo feedback), the confinement of warming to near the surface in the polar atmosphere (an amplifying lapse-rate feedback), and increases in poleward atmospheric and oceanic heat transport ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Goosse--2018|Goosse et al., 2018]] ; [[#Dai--2019|Dai et al., 2019]] ; [[#Feldl--2020|Feldl et al., 2020]] ). Modelling and process studies since AR5 have led to an improved understanding of the combined effect of these different processes in driving polar amplification and how they differ between the hemispheres.

Idealized modelling studies suggest that polar amplification would occur even in the absence of any amplifying polar surface-albedo or lapse-rate feedbacks owing to changes in poleward atmospheric heat transport under global warming ( [[#Hall--2004|Hall, 2004]] ; [[#Alexeev--2005|Alexeev et al., 2005]] ; [[#Graversen--2009|Graversen and Wang, 2009]] ; [[#Alexeev--2013|Alexeev and Jackson, 2013]] ; [[#Graversen--2014|Graversen et al., 2014]] ; [[#Roe--2015|Roe et al., 2015]] ; [[#Merlis--2018|Merlis and Henry, 2018]] ; [[#Armour--2019|Armour et al., 2019]] ). Poleward heat transport changes reflect compensating changes in the transport of latent energy (moisture) and dry-static energy (sum of sensible and potential energy) by atmospheric circulations ( [[#Alexeev--2005|Alexeev et al., 2005]] ; [[#Held--2006|Held and Soden, 2006]] ; [[#Hwang--2010|Hwang and Frierson, 2010]] ; [[#Hwang--2011|Hwang et al., 2011]] ; [[#Kay--2012|Kay et al., 2012]] ; [[#Huang--2014|Huang and Zhang, 2014]] ; [[#Feldl--2017a|Feldl et al., 2017a]] ; [[#Donohoe--2020|Donohoe et al., 2020]] ). ESMs project that within the mid-latitudes, where eddies dominate the heat transport, an increase in poleward latent energy transport arises from an increase in the equator-to-pole gradient in atmospheric moisture with global warming, with moisture in the tropics increasing more than at the poles as described by the Clausius–Clapeyron relation ( [[IPCC:Wg1:Chapter:Chapter-8#8.2|Section 8.2]] ). This change is partially compensated by a decrease in dry-static energy transport arising from a weakening of the equator-to-pole temperature gradient as the polar regions warm more than the tropics.

Energy balance models that approximate atmospheric heat transport in terms of a diffusive flux down the meridional gradient of near-surface moist static energy (sum of dry-static and latent energy) are able to reproduce the atmospheric heat transport changes seen within ESMs ( [[#Flannery--1984|Flannery, 1984]] ; [[#Hwang--2010|Hwang and Frierson, 2010]] ; [[#Hwang--2011|Hwang et al., 2011]] ; [[#Rose--2014|Rose et al., 2014]] ; [[#Roe--2015|Roe et al., 2015]] ; [[#Merlis--2018|Merlis and Henry, 2018]] ), including the partitioning of latent and dry-static energy transports ( [[#Siler--2018b|Siler et al., 2018b]] ; [[#Armour--2019|Armour et al., 2019]] ). These models suggest that polar amplification is driven by enhanced poleward latent heat transport and that the magnitude of polar amplification can be enhanced or diminished by the latitudinal structure of radiative feedbacks. Amplifying polar feedbacks enhance polar warming and in turn cause a decrease in the dry-static energy transport to high latitudes ( [[#Alexeev--2013|Alexeev and Jackson, 2013]] ; [[#Rose--2014|Rose et al., 2014]] ; [[#Roe--2015|Roe et al., 2015]] ; [[#Bonan--2018|Bonan et al., 2018]] ; [[#Merlis--2018|Merlis and Henry, 2018]] ; [[#Armour--2019|Armour et al., 2019]] ; [[#Russotto--2020|Russotto and Biasutti, 2020]] ). Poleward latent heat transport changes act to favour polar amplification and inhibit tropical amplification ( [[#Armour--2019|Armour et al., 2019]] ), resulting in a strongly polar-amplified warming response to polar forcing and a more latitudinally uniform warming response to tropical forcing within ESMs ( [[#Alexeev--2005|Alexeev et al., 2005]] ; [[#Rose--2014|Rose et al., 2014]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ). The important role for poleward latent energy transport in polar amplification is supported by studies of atmospheric reanalyses and ESMs showing that episodic increases in latent heat transport into the Arctic can enhance surface downwelling radiation and drive sea ice loss on sub-seasonal time scales ( [[#Woods--2016|Woods and Caballero, 2016]] ; [[#Gong--2017|Gong et al., 2017]] ; [[#Lee--2017|Lee et al., 2017]] ; B. [[#Luo--2017|]] [[#Luo--2017|Luo et al., 2017]] ), however this may be a smaller driver of sea ice variability than atmospheric temperature fluctuations ( [[#Olonscheck--2019|Olonscheck et al., 2019]] ).

Regional energy budget analyses are commonly used to diagnose the relative contributions of radiative feedbacks and energy fluxes to polar amplification as projected by ESMs under increased CO <sub>2</sub> concentrations (Figure 7.12; [[#Feldl--2013|Feldl and Roe, 2013]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Goosse--2018|Goosse et al., 2018]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ). These analyses suggest that a primary cause of amplified Arctic warming in ESMs is the latitudinal structure of radiative feedbacks, which warm the Arctic more than the tropics (Figure 7.12b), and enhanced latent energy transport into the Arctic. That net atmospheric heat transport into the Arctic does not change substantially within ESMs, on average, under CO <sub>2</sub> forcing (Figure 7.12b) reflects a compensating decrease in poleward dry-static energy transport as a response to polar amplified warming ( [[#Hwang--2011|Hwang et al., 2011]] ; [[#Armour--2019|Armour et al., 2019]] ; [[#Donohoe--2020|Donohoe et al., 2020]] ). The latitudinal structure of radiative feedbacks primarily reflects that of the surface-albedo and lapse-rate feedbacks, which preferentially warm the Arctic ( [[#Graversen--2014|Graversen et al., 2014]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Goosse--2018|Goosse et al., 2018]] ). Latitudinal structure in the lapse-rate feedback reflects weak radiative damping to space with surface warming in polar regions, where atmospheric warming is constrained to the lower troposphere owing to stably stratified conditions, and strong radiative damping in the tropics, where warming is enhanced in the upper troposphere owing to moist convective processes. This is only partially compensated by latitudinal structure in the water-vapour feedback ( [[#Taylor--2013|Taylor et al., 2013]] ), which favours tropical warming ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ). While cloud feedbacks have been found to play little role in Arctic amplification in CMIP5 models ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Goosse--2018|Goosse et al., 2018]] ; Figure 7.12b), less-negative cloud feedbacks at high latitude, as seen within some CMIP6 models ( [[#Zelinka--2020|Zelinka et al., 2020]] ), tend to favour stronger polar amplification ( [[#Dong--2020|Dong et al., 2020]] ). A weaker Planck response at high latitudes, owing to less efficient radiative damping where surface and atmospheric temperatures are lower, also contributes to polar amplification ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ). The effective radiative forcing of CO <sub>2</sub> is larger in the tropics than at high latitudes, suggesting that warming would be tropically amplified if not for radiative feedbacks and poleward latent heat transport changes (Figure 7.12b–d; [[#Stuecker--2018|Stuecker et al., 2018]] ).

While the contributions to regional warming can be diagnosed within ESM simulations (Figure 7.12), assessment of the underlying role of individual factors is limited by interactions inherent to the coupled climate system. For example, polar feedback processes are coupled and influenced by warming at lower latitudes ( [[#Screen--2012|Screen et al., 2012]] ; [[#Alexeev--2013|Alexeev and Jackson, 2013]] ; [[#Graversen--2014|Graversen et al., 2014]] ; [[#Graversen--2016|Graversen and Burtu, 2016]] ; [[#Rose--2016|Rose and Rencurrel, 2016]] ; [[#Feldl--2017a|Feldl et al., 2017a]] , 2020; [[#Yoshimori--2017|Yoshimori et al., 2017]] ; [[#Garuba--2018|Garuba et al., 2018]] ; [[#Po-Chedley--2018b|Po-Chedley et al., 2018b]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ; [[#Dai--2019|Dai et al., 2019]] ), while atmospheric heat transport changes are in turn influenced by the latitudinal structure of regional feedbacks, radiative forcing, and ocean heat uptake ( [[#Hwang--2011|Hwang et al., 2011]] ; [[#Zelinka--2012|Zelinka and Hartmann, 2012]] ; [[#Feldl--2013|Feldl and Roe, 2013]] ; [[#Huang--2014|Huang and Zhang, 2014]] ; [[#Merlis--2014|Merlis, 2014]] ; [[#Rose--2014|Rose et al., 2014]] ; [[#Roe--2015|Roe et al., 2015]] ; [[#Feldl--2017b|Feldl et al., 2017b]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ; [[#Armour--2019|Armour et al., 2019]] ). The use of different feedback definitions, such as a lapse-rate feedback partitioned into upper and lower tropospheric components ( [[#Feldl--2020|Feldl et al., 2020]] ) or including the influence of water vapour at constant relative humidity ( [[#Held--2012|Held and Shell, 2012]] ; [[#7.4.2|Section 7.4.2]] ), would also change the interpretation of which feedbacks contribute most to polar amplification.

The energy budget analyses (Figure 7.12) suggest that greater surface warming in the Arctic than the Antarctic under greenhouse gas forcing arises from two main processes. The first is large surface heat uptake in the Southern Ocean (Figure 7.12c) driven by the upwelling of deep waters that have not yet felt the effects of the radiative forcing; the heat taken up is predominantly transported away from Antarctica by northward-flowing surface waters ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1|Section 9.2.1]] ; [[#Marshall--2015|Marshall et al., 2015]] ; [[#Armour--2016|Armour et al., 2016]] ). Strong surface heat uptake also occurs in the subpolar North Atlantic Ocean under global warming ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1|Section 9.2.1]] ). However, this heat is partially transported northward into the Arctic, which leads to increased heat fluxes into the Arctic atmosphere (Figure 7.12b; [[#Rugenstein--2013|Rugenstein et al., 2013]] ; [[#Jungclaus--2014|Jungclaus et al., 2014]] ; [[#Koenigk--2014|Koenigk and Brodeau, 2014]] ; [[#Marshall--2015|Marshall et al., 2015]] ; [[#Nummelin--2017|Nummelin et al., 2017]] ; [[#Singh--2017|Singh et al., 2017]] ; [[#Oldenburg--2018|Oldenburg et al., 2018]] ). The second main process contributing to differences in Arctic and Antarctic warming is the asymmetry in radiative feedbacks between the poles ( [[#Yoshimori--2017|Yoshimori et al., 2017]] ; [[#Goosse--2018|Goosse et al., 2018]] ). This primarily reflects the weaker lapse-rate and surface-albedo feedbacks and more-negative cloud feedbacks in the SH high latitudes (Figure 7.12). However, note the SH cloud feedbacks are uncertain due to possible biases in the treatment of mixed phase clouds ( [[#Hyder--2018|Hyder et al., 2018]] ). Idealized modelling suggests that the asymmetry in the polar lapse-rate feedback arises from the height of the Antarctic Ice Sheet precluding the formation of deep atmospheric inversions that are necessary to produce the stronger positive lapse-rate feedbacks seen in the Arctic ( [[#Salzmann--2017|Salzmann, 2017]] ; [[#Hahn--2020|Hahn et al., 2020]] ). ESM projections of the equilibrium response to CO <sub>2</sub> forcing show polar amplification in both hemispheres, but generally with less warming in the Antarctic than the Arctic (C. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ; [[#Yoshimori--2017|Yoshimori et al., 2017]] ).

Because multiple processes contribute to polar amplification, it is a robust feature of the projected long-term response to greenhouse gas forcing in both hemispheres. At the same time, contributions from multiple processes make projections of the magnitude of polar warming inherently more uncertain than global mean warming ( [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Roe--2015|Roe et al., 2015]] ; [[#Bonan--2018|Bonan et al., 2018]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ). The magnitude of Arctic amplification ranges from a factor of two to four in ESM projections of 21st-century warming ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ). While uncertainty in both global and tropical warming under greenhouse gas forcing is dominated by cloud feedbacks ( [[#7.5.7|Section 7.5.7]] ; [[#Vial--2013|Vial et al., 2013]] ), uncertainty in polar warming arises from polar surface-albedo, lapse-rate, and cloud feedbacks, changes in atmospheric and oceanic poleward heat transport, and ocean heat uptake ( [[#Hwang--2011|Hwang et al., 2011]] ; [[#Mahlstein--2011|Mahlstein and Knutti, 2011]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Bonan--2018|Bonan et al., 2018]] ).

The magnitude of polar amplification also depends on the type of radiative forcing applied ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.1|Section 4.5.1.1]] ; [[#Stjern--2019|Stjern et al., 2019]] ), with ( [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Section 6.4.3) discussing changes in sulphate aerosol emissions and the deposition of black carbon aerosols on ice and snow as potential drivers of amplified Arctic warming. The timing of the emergence of SH polar amplification remains uncertain due to insufficient knowledge of the time scales associated with Southern Ocean warming and the response to surface wind and freshwater forcing ( [[#Bintanja--2013|Bintanja et al., 2013]] ; [[#Kostov--2017|Kostov et al., 2017]] , 2018; [[#Pauling--2017|Pauling et al., 2017]] ; [[#Purich--2018|Purich et al., 2018]] ). ESM simulations indicate that freshwater input from melting ice shelves could reduce Southern Ocean warming by up to several tenths of a °C over the 21st century by increasing stratification of the surface ocean around Antarctica ( ''low confidence'' due to ''medium agreement'' but ''limited evidence'' ) (Sections 7.4.2.6 and 9.2.1, and Box 9.3; [[#Bronselaer--2018|Bronselaer et al., 2018]] ; [[#Golledge--2019|Golledge et al., 2019]] ; [[#Lago--2019|Lago and England, 2019]] ). However, even a large reduction in the Atlantic Meridional Overturning Circulation (AMOC) and associated northward heat transport due, for instance, to greatly increased freshwater runoff from Greenland would be insufficient to eliminate Arctic amplification ( ''medium confidence'' based on ''medium agreement'' and ''medium evidence'' ) ( [[#Liu--2017|Liu et al., 2017]] ; Y. [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ; [[#Wen--2018|Wen et al., 2018]] ).

Arctic amplification has a distinct seasonality with a peak in early winter (November to January) owing to sea ice loss and associated increases in heat fluxes from the ocean to the atmosphere resulting in strong near-surface warming ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Dai--2019|Dai et al., 2019]] ). Surface warming may be further amplified by positive cloud and lapse-rate feedbacks in autumn and winter ( [[#Burt--2016|Burt et al., 2016]] ; [[#Morrison--2019|Morrison et al., 2019]] ; [[#Hahn--2020|Hahn et al., 2020]] ). Arctic amplification is weak in summer owing to surface temperatures remaining stable as excess energy goes into thinning the summertime sea ice cover, which remains at the melting point, or into the ocean mixed layer. Arctic amplification can also be interpreted through changes in the surface energy budget ( [[#Burt--2016|Burt et al., 2016]] ; [[#Woods--2016|Woods and Caballero, 2016]] ; [[#Boeke--2018|Boeke and Taylor, 2018]] ; [[#Kim--2019|Kim et al., 2019]] ), however such analyses are complicated by the finding that a large portion of the changes in downward longwave radiation can be attributed to the lower troposphere warming along with the surface itself ( [[#Vargas%20Zeppetello--2019|Vargas Zeppetello et al., 2019]] ).

<div id="7.4.4.1.2" class="h4-container"></div>

<span id="polar-amplification-from-proxies-and-models-during-past-climates-associated-with-co-2-change"></span>
===== 7.4.4.1.2 Polar amplification from proxies and models during past climates associated with CO 2 change =====

<div id="h4-14-siblings" class="h4-siblings"></div>

Paleoclimate proxy data provide observational evidence of large-scale patterns of surface warming in response to past forcings, and allow an evaluation of the modelled response to these forcings (Sections 3.3.1.1 and 3.8.2.1). In particular, paleoclimate data provide evidence for long-term changes in polar amplification during time periods in which the primary forcing was a change in atmospheric CO <sub>2</sub> , although data sparsity means that for some time periods this evidence may be limited to a single hemisphere or ocean basin, or the evidence may come primarily from the mid-latitudes as opposed to the polar regions. In this context, there has been a modelling and data focus on the Last Glacial Maximum (LGM) in the context of PMIP4 ( [[#Cleator--2020|Cleator et al., 2020]] ; [[#Tierney--2020b|Tierney et al., 2020b]] ; [[#Kageyama--2021|Kageyama et al., 2021]] ), the mid-Pliocene Warm Period (MPWP) in the context of PlioMIP2 (Cross-Chapter Box 2.4; [[#Salzmann--2013|Salzmann et al., 2013]] ; [[#Haywood--2020|Haywood et al., 2020]] ; [[#McClymont--2020|McClymont et al., 2020]] ), the Early Eocene Climatic Optimum (EECO) in the context of DeepMIP ( [[#Hollis--2019|Hollis et al., 2019]] ; [[#Lunt--2021|Lunt et al., 2021]] ), and there is growing interest in the Miocene ( [[#Goldner--2014b|Goldner et al., 2014b]] ; [[#Steinthorsdottir--2021|Steinthorsdottir et al., 2021]] ; for definitions of time periods see Cross-Chapter Box 2.1). For all these time periods, in addition to the CO <sub>2</sub> forcing there are long-term feedbacks associated with ice sheets ( [[#7.4.2.6|Section 7.4.2.6]] ), and in particular for the Early Eocene there is a forcing associated with paleogeographic change ( [[#Farnsworth--2019|Farnsworth et al., 2019]] ). However, because these non-CO <sub>2</sub> effects can all be included as boundary conditions in model simulations, these time periods allow an assessment of the patterns of modelled response to known forcing (although uncertainty in the forcing increases further back in time). Because these changes to boundary conditions can be complex to implement in models, and because long simulations (typically longer than 500 years) are required to approach equilibrium, these simulations have been carried out mostly by pre-CMIP6 models, with relatively few (or none for the Early Eocene) fully coupled CMIP6 models in the ensembles.

At the time of AR5, polar amplification was evident in proxy reconstructions of paleoclimate sea surface temperature (SST) and surface air temperature (SAT) from the LGM, MPWP and the Early Eocene, but uncertainties associated with proxy calibrations ( [[#Waelbroeck--2009|Waelbroeck et al., 2009]] ; [[#Dowsett--2012|Dowsett et al., 2012]] ; [[#Lunt--2012a|Lunt et al., 2012a]] ) and the role of orbital forcing (for the MPWP; [[#Lisiecki--2005|Lisiecki and Raymo, 2005]] ) meant that the degree of polar amplification during these time periods was not accurately known. Furthermore, although some models (CCSM3; [[#Winguth--2010|Winguth et al., 2010]] ; [[#Huber--2011|Huber and Caballero, 2011]] ) at that time were able to reproduce the strong polar amplification implied by temperature proxies of the Early Eocene, this was achieved at higher CO <sub>2</sub> concentrations (&gt;2000 ppm) than those indicated by CO <sub>2</sub> proxies (&lt;1500 ppm; [[#Beerling--2011|Beerling and Royer, 2011]] ).

Since AR5 there has been progress in improving the accuracy of proxy temperature reconstructions of the LGM ( [[#Cleator--2020|Cleator et al., 2020]] ; [[#Tierney--2020b|Tierney et al., 2020b]] ), the MPWP ( [[#McClymont--2020|McClymont et al., 2020]] ), and the Early Eocene ( [[#Hollis--2019|Hollis et al., 2019]] ) time periods. In addition, reconstructions of the MPWP have been focused on a short time slice with an orbit similar to modern-day (isotopic stage KM5C; [[#Haywood--2013|Haywood et al., 2013]] , 2016b). Furthermore, there are more robust constraints on CO <sub>2</sub> concentrations from the MPWP ( [[#Martínez-Botí--2015|Martínez-Botí et al., 2015]] ; [[#de%20la%20Vega--2020|de la Vega et al., 2020]] ) and the Early Eocene ( [[#Anagnostou--2016|Anagnostou et al., 2016]] , 2020). As such, polar amplification during the LGM, MPWP, and Early Eocene time periods can now be better quantified than at the time of AR5, and the ability of climate models to reproduce this pattern can be better assessed; model-data comparisons for SAT and SST for these three time periods are shown in Figure 7.13.

Since AR5, there has been progress in the simulation of polar amplification by paleoclimate models of the Early Eocene. Initial work indicated that changes to model parameters associated with aerosols and/or clouds could increase simulated polar amplification and improve agreement between models and paleoclimate data ( [[#Kiehl--2013|Kiehl and Shields, 2013]] ; [[#Sagoo--2013|Sagoo et al., 2013]] ), but such parameter changes were not physically based. In support of these initial findings, a more recent (CMIP5) climate model, that includes a process-based representation of cloud microphysics, exhibits polar amplification in better agreement with proxies when compared to the models assessed in AR5 ( [[#Zhu--2019a|Zhu et al., 2019a]] ). Since then, some other CMIP3 and CMIP5 models in the DeepMIP multi-model ensemble ( [[#Lunt--2021|Lunt et al., 2021]] ) have obtained polar amplification for the EECO that is consistent with proxy indications of both polar amplification and CO <sub>2</sub> . Although there is a lack of tropical proxy SAT estimates, both proxies and DeepMIP models show greater terrestrial warming in the high latitudes than the mid-latitudes in both hemispheres (Figure 7.13a,d). SST proxies also exhibit polar amplification in both hemispheres, but the magnitude of this polar amplification is too low in the models, in particular in the south-west Pacific (Figure 7.13g,j).

For the MPWP, model simulations are now in better agreement with proxies than at the time of AR5 ( [[#Haywood--2020|Haywood et al., 2020]] ; [[#McClymont--2020|McClymont et al., 2020]] ). In particular, in the tropics new proxy reconstructions of SSTs are warmer and in better agreement with the models, due in part to the narrower time window in the proxy reconstructions. There is also better agreement at higher latitudes (primarily in the North Atlantic), due in part to the absence of some very warm proxy SSTs due to the narrower time window ( [[#McClymont--2020|McClymont et al., 2020]] ), and in part to a modified representation of Arctic gateways in the most recent Pliocene model simulations ( [[#Otto-Bliesner--2017|Otto-Bliesner et al., 2017]] ), which have resulted in warmer modelled SSTs in the North Atlantic ( [[#Haywood--2020|Haywood et al., 2020]] ). Furthermore, as for the Eocene, improvements in the representation of aerosol–cloud interactions have also led to improved model-data consistency at high latitudes ( [[#Feng--2019|Feng et al., 2019]] ). Although all PlioMIP2 models exhibit polar amplification of SAT, due to the relatively narrow time window there are insufficient terrestrial proxies to assess this (Figure 7.13b,e). However, polar SST amplification in the PlioMIP2 ensemble mean is in reasonably good agreement with that from SST proxies in the Northern Hemisphere (Figure 7.13h,k).

The Last Glacial Maximum (LGM) also gives an opportunity to evaluate model simulation of polar amplification under CO <sub>2</sub> forcing, albeit under colder conditions than today ( [[#Kageyama--2021|Kageyama et al., 2021]] ). Terrestrial SAT and marine SST proxies exhibit clear polar amplification in the Northern Hemisphere, and the PMIP4 models capture this well (Figure 7.13c,f,i,l), particularly for SAT. There is less proxy data in the mid- to high latitudes of the Southern Hemisphere, but here the models exhibit polar amplification of both SST and SAT. LGM regional model-data agreement is also assessed in ( [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] [[IPCC:Wg1:Chapter:Chapter-3#3.8.2|Section 3.8.2]] ).

Overall, the proxy reconstructions give ''high confidence'' that there was polar amplification in the LGM, MPWP and EECO, and this is further supported by model simulations of these time periods (Figure 7.13; [[#Zhu--2019a|Zhu et al., 2019a]] ; [[#Haywood--2020|Haywood et al., 2020]] ; [[#Kageyama--2021|Kageyama et al., 2021]] ; [[#Lunt--2021|Lunt et al., 2021]] ). For both the MPWP and EECO, models are more consistent with the temperature and CO <sub>2</sub> proxies than at the time of AR5 ( ''high confidence'' ). For the LGM Northern Hemisphere, which is the region with the most data and the time period with the least uncertainty in model boundary conditions, polar amplification in the PMIP4 ensemble mean is in good agreement with the proxies, especially for SAT ( ''medium confidence'' ). Overall, the confidence in the ability of models to accurately simulate polar amplification is higher than at the time of AR5, but a more complete model evaluation could be carried out if there were more CMIP6 paleoclimate simulations included in the assessment.

<div id="7.4.4.1.3" class="h4-container"></div>

<span id="overall-assessment-of-polar-amplification"></span>
===== 7.4.4.1.3 Overall assessment of polar amplification =====

<div id="h4-15-siblings" class="h4-siblings"></div>

Based on mature process understanding of the roles of poleward latent heat transport and radiative feedbacks in polar warming, a high degree of agreement across a hierarchy of climate models, observational evidence, paleoclimate proxy records of past climates associated with CO <sub>2</sub> change, and ESM simulations of those past climates, there is ''high confidence'' that polar amplification is a robust feature of the long-term response to greenhouse gas forcing in both hemispheres. Stronger warming in the Arctic than the global average has already been observed ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] ) and its causes are well understood. It is ''very likely'' that the warming in the Arctic will be more pronounced than the global average over the 21st century ( ''high confidence'' ) [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.1|Section 4.5.1.1]] ). This is supported by models’ improved ability to simulate polar amplification during past time periods, compared with at the time of AR5 ( ''high confidence'' ); although this is based on an assessment of mostly non-CMIP6 models.

Southern Ocean SSTs have been slow to warm over the instrumental period, with cooling since about 1980 owing to a combination of upper-ocean freshening from ice-shelf melt, intensification of surface westerly winds from ozone depletion, and variability in ocean convection ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1|Section 9.2.1]] ). This stands in contrast to the equilibrium warming pattern either inferred from the proxy record or simulated by ESMs under CO <sub>2</sub> forcing. There is ''high confidence'' that the SH high latitudes will warm more than the tropics on centennial time scales as the climate equilibrates with radiative forcing and Southern Ocean heat uptake is reduced. However, there is only ''low confidence'' that this feature will emerge this century.

<div id="7.4.4.2" class="h3-container"></div>

<span id="tropical-pacific-sea-surface-temperature-gradients"></span>

==== 7.4.4.2 Tropical Pacific Sea Surface Temperature Gradients ====

<div id="h3-36-siblings" class="h3-siblings"></div>

Research published since AR5 identifies changes in the tropical Pacific Ocean zonal SST gradient over time as a key factor affecting how radiative feedbacks may evolve in the future ( [[#7.4.4.3|Section 7.4.4.3]] ). There is now a much-improved understanding of the processes that govern the tropical Pacific SST gradient ( [[#7.4.4.2.1|Section 7.4.4.2.1]] ) and the paleoclimate record provides evidence for its equilibrium changes from time periods associated with changes in CO <sub>2</sub> [[#7.4.4.2.2|Section 7.4.4.2.2]] ).

<div id="7.4.4.2.1" class="h4-container"></div>

<span id="critical-processes-determining-changes-in-tropical-pacific-sea-surface-temperature-gradients"></span>
===== 7.4.4.2.1 Critical processes determining changes in tropical Pacific sea surface temperature gradients =====

<div id="h4-16-siblings" class="h4-siblings"></div>

A weakening of the equatorial Pacific Ocean east–west SST gradient, with greater warming in the east than the west, is a common feature of the climate response to greenhouse gas forcing as projected by ESMs on centennial and longer time scales (e.g., Figure 7.14b; see ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ). There are thought to be several factors contributing to this pattern. In the absence of any changes in atmospheric or oceanic circulations, the east–west surface temperature difference is theorized to decrease owing to weaker evaporative damping, and thus greater warming in response to forcing, where climatological temperatures are lower in the eastern Pacific cold tongue ( [[#Xie--2010|Xie et al., 2010]] ; [[#Luo--2015|Luo et al., 2015]] ). Within atmospheric ESMs coupled to a mixed-layer ocean, this gradient in damping has been linked to the rate of change with warming of the saturation specific humidity, which is set by the Clausius–Clapeyron relation ( [[#Merlis--2011|Merlis and Schneider, 2011]] ). Gradients in low-cloud feedbacks may also favour eastern equatorial Pacific warming ( [[#DiNezio--2009|DiNezio et al., 2009]] ).

<div id="_idContainer054" class="Basic-Text-Frame"></div>

[[File:60bc04f65baf68bf30b88df95e74c1aa IPCC_AR6_WGI_Figure_7_14.png]]

'''Figure''' '''7.14 |''' '''Illustration of tropospheric temperature and low-cloud response to observed and projected Pacific Ocean sea surface temperature trends. (a)''' Atmospheric response to linear sea surface temperature trend observed over 1870–2019 (HadISST1 dataset; [[#Rayner--2003|Rayner et al., 2003]] ). '''(b)''' Atmospheric response to linear sea-surface temperature trend over 150 years following ''abrupt 4xCO2'' forcing as projected by CMIP6 ESMs ( [[#Dong--2020|Dong et al., 2020]] ). Relatively large historical warming in the western tropical Pacific has been communicated aloft (a shift from grey to red atmospheric temperature profile), remotely warming the tropical free troposphere and increasing the strength of the inversion in regions of the tropics where warming has been slower, such as the eastern equatorial Pacific. In turn, an increased inversion strength has increased the low-cloud cover ( [[#Zhou--2016|Zhou et al., 2016]] ) causing an anomalously negative cloud and lapse-rate feedbacks over the historical record ( [[#Andrews--2018|Andrews et al., 2018]] ; [[#Marvel--2018|Marvel et al., 2018]] ). Relatively large projected warming in the eastern tropical Pacific is trapped near the surface (shift from grey to red atmospheric temperature profile), decreasing the strength of the inversion locally. In turn, a decreased inversion strength combined with surface warming is projected to decrease the low-cloud cover, causing the cloud and lapse-rate feedbacks to become less negative in the future. Figure adapted from [[#Mauritsen--2016|Mauritsen (2016)]] . Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

In the coupled climate system, changes in atmospheric and oceanic circulations will influence the east-west temperature gradient as well. It is expected that as global temperature increases and as the east–west temperature gradient weakens, east–west sea level pressure gradients and easterly trade winds (characterizing the Walker circulation) will weaken as well (Sections 4.5.3, 8.2.2.2 and 8.4.2.3, and Figure 7.14b; [[#Vecchi--2006|Vecchi et al., 2006]] , 2008). This would, in turn, weaken the east–west temperature gradient through a reduction of equatorial upwelling of cold water in the east Pacific and a reduction in the transport of warmer water to the western equatorial Pacific and Indian Ocean ( [[#England--2014|England et al., 2014]] ; [[#Dong--2017|Dong and McPhaden, 2017]] ; [[#Li--2017|Li et al., 2017]] ; [[#Maher--2018|Maher et al., 2018]] ).

Research published since AR5 ( [[#Burls--2014b|Burls and Fedorov, 2014b]] ; [[#Fedorov--2015|Fedorov et al., 2015]] ; [[#Erfani--2019|Erfani and Burls, 2019]] ) has built on an earlier theory ( [[#Liu--1997|Liu and Huang, 1997]] ; [[#Barreiro--2008|Barreiro and Philander, 2008]] ) linking the east–west temperature gradient to the north–south temperature gradient. In particular, model simulations suggest that a reduction in the equator-to-pole temperature gradient (polar amplification) increases the temperature of water subducted in the extra-tropics, which in turn is upwelled in the eastern Pacific. Thus, polar amplified warming, with greater warming in the mid-latitudes and subtropics than in the deep tropics, is expected to contribute to the weakening of the east–west equatorial Pacific SST gradient on decadal to centennial time scales.

The transient adjustment of the equatorial Pacific SST gradient is influenced by upwelling waters which delay surface warming in the east since they have not been at the surface for years-to-decades to experience the greenhouse gas forcing. This ‘thermostat mechanism’ ( [[#Clement--1996|Clement et al., 1996]] ; [[#Cane--1997|Cane et al., 1997]] ) is not thought to persist to equilibrium since it does not account for the eventual increase in temperatures of upwelled waters ( [[#Liu--2005|Liu et al., 2005]] ; [[#Xie--2010|Xie et al., 2010]] ; Y. [[#Luo--2017|]] [[#Luo--2017|Luo et al., 2017]] ) which will occur as the subducting waters in mid-latitudes warm by more than the tropics on average as polar amplification emerges. An individual CMIP5 ESM (GFDL’s ESM2M) has been found to exhibit a La Niña-like pattern of Pacific temperature change through the 21st century, similar to the SST trends seen over the historical record ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1|Section 9.2.1]] and Figure 7.14a), owing to a weakening asymmetry between El Niño and La Niña events ( [[#Kohyama--2017|Kohyama et al., 2017]] ), but this pattern of warming may not persist to equilibrium ( [[#Paynter--2018|Paynter et al., 2018]] ).

Since 1870, observed SSTs in the tropical western Pacific Ocean have increased while those in the tropical eastern Pacific Ocean have changed less (Figure 7.14a and ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1|Section 9.2.1]] ). Much of the resultant strengthening of the equatorial Pacific temperature gradient has occurred since about 1980 due to strong warming in the west and cooling in the east (Figure 2.11b) concurrent with an intensification of the surface equatorial easterly trade winds and Walker circulation (Sections 3.3.3.1, 3.7.6, 8.3.2.3 and 9.2, and Figures 3.16f and 3.39f; [[#England--2014|England et al., 2014]] ). This temperature pattern is also reflected in regional ocean heat content trends and sea level changes observed from satellite altimetry since 1993 ( [[#Bilbao--2015|Bilbao et al., 2015]] ; [[#Richter--2020|Richter et al., 2020]] ). The observed changes may have been influenced by one or a combination of temporary factors including sulphate aerosol forcing ( [[#Smith--2016|Smith et al., 2016]] ; [[#Takahashi--2016|Takahashi and Watanabe, 2016]] ; [[#Hua--2018|Hua et al., 2018]] ), internal variability within the Indo-Pacific Ocean ( [[#Luo--2012|Luo et al., 2012]] ; [[#Chung--2019|Chung et al., 2019]] ), teleconnections from multi-decadal tropical Atlantic SST trends ( [[#Kucharski--2011|Kucharski et al., 2011]] , 2014, 2015; [[#McGregor--2014|McGregor et al., 2014]] ; [[#Chafik--2016|Chafik et al., 2016]] ; X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Kajtar--2017|Kajtar et al., 2017]] ; [[#Sun--2017|Sun et al., 2017]] ), teleconnections from multi-decadal Southern Ocean SST trends ( [[#Hwang--2017|Hwang et al., 2017]] ), and coupled ocean–atmosphere dynamics which slow warming in the equatorial eastern Pacific ( [[#Clement--1996|Clement et al., 1996]] ; [[#Cane--1997|Cane et al., 1997]] ; [[#Seager--2019|Seager et al., 2019]] ). CMIP3 and CMIP5 ESMs have difficulties replicating the observed trends in the Walker circulation and Pacific Ocean SSTs over the historical record ( [[#Sohn--2013|Sohn et al., 2013]] ; [[#Zhou--2016|Zhou et al., 2016]] ; [[#Coats--2017|Coats and Karnauskas, 2017]] ), possibly due to model deficiencies including insufficient multi-decadal Pacific Ocean SST variability ( [[#Laepple--2014|Laepple and Huybers, 2014]] ; [[#Bilbao--2015|Bilbao et al., 2015]] ; [[#Chung--2019|Chung et al., 2019]] ), mean state biases affecting the forced response or the connection between Atlantic and Pacific basins ( [[#Kucharski--2014|Kucharski et al., 2014]] ; [[#Kajtar--2018|Kajtar et al., 2018]] ; [[#Luo--2018|Luo et al., 2018]] ; [[#McGregor--2018|McGregor et al., 2018]] ; [[#Seager--2019|Seager et al., 2019]] ), and/or a misrepresentation of radiative forcing (Sections 9.2.1 and 3.7.6). However, the observed trends in the Pacific Ocean SSTs are still within the range of internal variability as simulated by large initial condition ensembles of CMIP5 and CMIP6 models ( [[#Olonscheck--2020|Olonscheck et al., 2020]] ; Watanabe et al., 2021). Because the causes of observed equatorial Pacific temperature gradient and Walker circulation trends are not well understood ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.1|Section 3.3.3.1]] ), there is ''low confidence'' in their attribution to anthropogenic influences ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.3|Section 8.3.2.3]] ), while there is ''medium confidence'' that the observed changes have resulted from internal variability (Sections 3.7.6 and 8.2.2.2).

<div id="7.4.4.2.2" class="h4-container"></div>

<span id="tropical-pacific-temperature-gradients-in-past-high-co-2-climates"></span>
===== 7.4.4.2.2 Tropical Pacific temperature gradients in past high-CO 2 climates =====

<div id="h4-17-siblings" class="h4-siblings"></div>

The AR5 stated that paleoclimate proxies indicate a reduction in the longitudinal SST gradient across the equatorial Pacific during the Mid-Pliocene Warm Period (MPWP; [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ; see Cross-Chapter Box 2.1 and Cross-Chapter Box 2.4 in this Report). This assessment was based on SST reconstructions between two sites situated very close to the equator in the heart of the western Pacific warm pool and eastern Pacific cold tongue, respectively. Multiple SST reconstructions based on independent paleoclimate proxies generally agreed that during the Pliocene the SST gradient between these two sites was reduced compared with the modern long-term mean ( [[#Wara--2005|Wara et al., 2005]] ; [[#Dekens--2008|Dekens et al., 2008]] ; [[#Fedorov--2013|Fedorov et al., 2013]] ).

Since AR5, the generation of new SST records has led to a variety of revised gradient estimates, specifically the generation of a new record for the warm pool ( [[#Zhang--2014|Zhang et al., 2014]] ), the inclusion of SST reconstructions from sites in the South China Sea as warm pool estimates ( [[#O’Brien--2014|O’Brien et al., 2014]] ; [[#Zhang--2014|Zhang et al., 2014]] ), and the inclusion of several new sites from the eastern Pacific as cold tongue estimates ( [[#Zhang--2014|Zhang et al., 2014]] ; [[#Fedorov--2015|Fedorov et al., 2015]] ). Published estimates of the reduction in the longitudinal SST difference for the Late Pliocene, relative to either Late Quaternary (0–0.5 million years ago) or pre-industrial values, include 1°C to 1.5°C ( [[#Zhang--2014|Zhang et al., 2014]] ), 0.1°C to 1.9°C ( [[#Tierney--2019|Tierney et al., 2019]] ), and about 3°C ( [[#Ravelo--2014|Ravelo et al., 2014]] ; [[#Fedorov--2015|Fedorov et al., 2015]] ; [[#Wycech--2020|Wycech et al., 2020]] ). All of these studies report a further weakening of the longitudinal gradient based on records extending into the Early Pliocene. While these revised estimates differ in magnitude due to differences in the sites and SST proxies used, they all agree that the longitudinal gradient was weaker, and this is supported by the probabilistic approach of [[#Tierney--2019|Tierney et al. (2019)]] . However, given that there are currently relatively few western equatorial Pacific records from independent site locations, and due to uncertainties associated with the proxy calibrations ( [[#Haywood--2016a|Haywood et al., 2016a]] ), there is only ''medium confidence'' that the average longitudinal gradient in the tropical Pacific was weaker during the Pliocene than during the Late Quaternary.

To avoid the influence of local biases, changes in the longitudinal temperature difference within Pliocene model simulations are typically evaluated using domain-averaged SSTs within chosen east and west Pacific regions and as such there is sensitivity to methodology. Unlike the reconstructed estimates, longitudinal gradient changes simulated by the Pliocene Model Intercomparison Project Phase 1 (PlioMIP1) models do not agree on the change in sign and are reported as spanning approximately –0.5°C to +0.5°C by [[#Brierley--2015|Brierley et al. (2015)]] and approximately –1°C to +1°C by [[#Tierney--2019|Tierney et al. (2019)]] . Initial PlioMIP Phase 2 (PlioMIP2) analysis suggests responses similar to PlioMIP1 ( [[#Feng--2019|Feng et al., 2019]] ; [[#Haywood--2020|Haywood et al., 2020]] ). Models that include hypothetical modifications to cloud albedo or ocean mixing are required to simulate the substantially weaker longitudinal differences seen in reconstructions of the Early Pliocene ( [[#Fedorov--2013|Fedorov et al., 2013]] ; [[#Burls--2014a|Burls and Fedorov, 2014a]] ).

While more western Pacific warm pool temperature reconstructions are needed to refine estimates of the longitudinal gradient, several Pliocene SST reconstructions from the east Pacific indicate enhanced warming in the centre of the eastern equatorial cold tongue upwelling region ( [[#Liu--2019|Liu et al., 2019]] ). This enhanced warming in the east Pacific cold tongue appears to be dynamically consistent with reconstruction of enhanced subsurface warming ( [[#Ford--2015|Ford et al., 2015]] ) and enhanced warming in coastal upwelling regions, suggesting that the tropical thermocline was deeper and/or less stratified during the Pliocene. The Pliocene data therefore suggest that the observed cooling trend over the last 60 years in parts of the eastern equatorial Pacific ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1.1|Section 9.2.1.1]] and Figure 9.3; [[#Seager--2019|Seager et al., 2019]] ), whether forced or due to internal variability, involves transient processes that are probably distinct from the longer-time scale process ( [[#Burls--2014a|Burls and Fedorov, 2014a]] , b; [[#Luo--2015|Luo et al., 2015]] ; [[#Heede--2020|Heede et al., 2020]] ) that maintained warmer eastern Pacific SST during the Pliocene.

<div id="7.4.4.2.3" class="h4-container"></div>

<span id="overall-assessment-of-tropical-pacific-sea-surface-temperature-gradients-under-co-2-forcing"></span>
===== 7.4.4.2.3 Overall assessment of tropical Pacific sea surface temperature gradients under CO 2 forcing =====

<div id="h4-18-siblings" class="h4-siblings"></div>

The paleoclimate proxy record and ESM simulations of the MPWP, process understanding, and ESM projections of climate response to CO <sub>2</sub> forcing provide ''medium evidence'' and a ''medium agreement'' and thus ''medium confidence'' that equilibrium warming in response to elevated CO <sub>2</sub> will be characterized by a weakening of the east–west tropical Pacific SST gradient.

Overall the observed pattern of warming over the instrumental period, with a warming minimum in the eastern tropical Pacific Ocean (Figure 7.14a), stands in contrast to the equilibrium warming pattern either inferred from the MPWP proxy record or simulated by ESMs under CO <sub>2</sub> forcing. There is ''medium confidence'' that the observed strengthening of the east–west SST gradient is temporary and will transition to a weakening of the SST gradient on centennial time scales. However, there is only ''low confidence'' that this transition will emerge this century owing to a low degree of agreement across studies about the factors driving the observed strengthening of the east–west SST gradient and how those factors will evolve in the future. These trends in tropical Pacific SST gradients reflect changes in the climatology, rather than changes in ENSO amplitude or variability, which are assessed in ( [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] [[IPCC:Wg1:Chapter:Chapter-4#4.3.3|Section 4.3.3]] ).

<div id="7.4.4.3" class="h3-container"></div>

<span id="dependence-of-feedbacks-on-temperature-patterns"></span>
==== 7.4.4.3 Dependence of Feedbacks on Temperature Patterns ====

<div id="h3-37-siblings" class="h3-siblings"></div>

The expected time-evolution of the spatial pattern of surface warming in the future has important implications for values of ECS inferred from the historical record of observed warming. In particular, changes in the global top-of-atmosphere (TOA) radiative energy budget can be induced by changes in the regional variations of surface temperature, even without a change in the global mean temperature ( [[#Zhou--2016|Zhou et al., 2016]] ; [[#Ceppi--2019|Ceppi and Gregory, 2019]] ). Consequently, the global radiative feedback, characterizing the net TOA radiative response to global surface warming, depends on the ''spatial pattern'' of that warming. Therefore, if the equilibrium warming pattern under CO <sub>2</sub> forcing (similar to CMIP6 projections in Figure 7.12a) is distinct from that observed over the historical record or indicated by paleoclimate proxies (Sections 7.4.4.1 and 7.4.4.2), then ECS will be different from the effective ECS (Box 7.1) that is inferred from those periods. Accounting for the dependence of radiative feedbacks on the spatial pattern of warming has helped to reconcile values of ECS inferred from the historical record with values of ECS based on other lines of evidence and simulated by climate models ( [[#7.5.2.1|Section 7.5.2.1]] ; [[#Armour--2017|Armour, 2017]] ; [[#Proistosescu--2017|Proistosescu and Huybers, 2017]] ; [[#Andrews--2018|Andrews et al., 2018]] ) but has not yet been examined in the paleoclimate context.

This temperature ‘pattern effect’ ( [[#Stevens--2016|Stevens et al., 2016]] ) can result from both internal variability and radiative forcing of the climate system. Importantly, it is distinct from potential radiative feedback dependencies on the global surface temperature, which are assessed in ( [[#7.4.3|Section 7.4.3]] . While changes in global radiative feedbacks under transient warming have been documented in multiple generations of climate models ( [[#Williams--2008|Williams et al., 2008]] ; [[#Andrews--2015|Andrews et al., 2015]] ; [[#Ceppi--2017|Ceppi and Gregory, 2017]] ; [[#Dong--2020|Dong et al., 2020]] ), research published since AR5 has developed a much-improved understanding of the role of evolving SST patterns in driving feedback changes ( [[#Armour--2013|Armour et al., 2013]] ; [[#Andrews--2015|Andrews et al., 2015]] , 2018; [[#Gregory--2016|Gregory and Andrews, 2016]] ; [[#Zhou--2016|Zhou et al., 2016]] , 2017b; [[#Ceppi--2017|Ceppi and Gregory, 2017]] ; [[#Haugstad--2017|Haugstad et al., 2017]] ; [[#Proistosescu--2017|Proistosescu and Huybers, 2017]] ; [[#Andrews--2018|Andrews and Webb, 2018]] ; [[#Marvel--2018|Marvel et al., 2018]] ; [[#Silvers--2018|Silvers et al., 2018]] ; [[#Dong--2019|Dong et al., 2019]] , 2020). This section assesses process understanding of the pattern effect, which is dominated by the evolution of SSTs. [[#7.5.2.1|Section 7.5.2.1]] describes how potential feedback changes associated with the pattern effect are important to interpreting ECS estimates based on historical warming.

<div id="_idContainer051" class="Basic-Text-Frame _idGenObjectStyleOverride-1"></div>

[[File:84e2e550237af9033963917321af870b IPCC_AR6_WGI_Figure_7_13.png]]

'''Figure 7.13''' '''|''' '''Polar amplification in paleo proxies and models of the Early Eocene Climatic Optimum (EECO), the Mid-Pliocene Warm Period (MPWP) and the Last Glacial Maximum (LGM).''' '''Figure 7.13:''' Temperature anomalies compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’) are shown for the high-CO <sub>2</sub> EECO and MPWP time periods, and for the low-CO <sub>2</sub> LGM (expressed as pre-industrial minus LGM). '''(a), (b) and (c)''' Modelled near-surface air temperature anomalies for ensemble-mean simulations of the (a) EECO ( [[#Lunt--2021|Lunt et al., 2021]] ); (b) MPWP ( [[#Haywood--2020|Haywood et al., 2020]] ; [[#Zhang--2021|Zhang et al., 2021]] ); and (c) LGM ( [[#Kageyama--2021|Kageyama et al., 2021]] ; [[#Zhu--2021|Zhu et al., 2021]] ). Also shown are proxy near-surface air temperature anomalies (coloured circles). '''(d), (e) and (f)''' Proxy near-surface air temperature anomalies (grey circles), including published uncertainties (grey vertical 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 for the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5). Black dashed lines show the average of the proxy values in each latitude band: 90°S–30°S, 30°S–30°N, and 30°N–90°N. Red dashed lines show the same banded average in the model ensemble mean, calculated from the same locations as the proxies. Black and red dashed lines are only shown if there are five or more proxy points in that band. Mean differences between the 90°S/N to 30°S/N and 30°S to 30°N bands are quantified 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. Panels '''(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 the pre-industrial, due to the different continental configuration. Proxy datasets are: (a) and (d) [[#Hollis--2019|Hollis et al. (2019)]] ; (b) and (e) [[#Salzmann--2013|Salzmann et al. (2013)]] ; [[#Vieira--2018|Vieira et al. (2018)]] , (c) and (f) [[#Cleator--2020|Cleator et al. (2020)]] at the sites defined in [[#Bartlein--2011|Bartlein et al. (2011)]] ; (g) and (j) [[#Hollis--2019|Hollis et al. (2019)]] ; (h) and (k) [[#McClymont--2020|McClymont et al. (2020)]] ; (i) and (l) [[#Tierney--2020b|Tierney et al. (2020b)]] . Where there 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 paleogeography, and carried out under CO <sub>2</sub> 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 details on data sources and processing are available in the chapter data table (Table 7.SM.14).

The radiation changes most sensitive to warming patterns are those associated with low-cloud cover (affecting global albedo) and the tropospheric temperature profile (affecting thermal emission to space) ( [[#Ceppi--2017|Ceppi and Gregory, 2017]] ; [[#Zhou--2017b|Zhou et al., 2017b]] ; [[#Andrews--2018|Andrews et al., 2018]] ; [[#Dong--2019|Dong et al., 2019]] ). The mechanisms and radiative effects of these changes are illustrated in Figure 7.14a,b. SSTs in regions of deep convective ascent (e.g., in the western Pacific warm pool) govern the temperature of the tropical free troposphere and, in turn, affect low-clouds through the strength of the inversion that caps the boundary layer (i.e., the lower-tropospheric stability) in subsidence regions ( [[#Wood--2006|Wood and Bretherton, 2006]] ; [[#Klein--2017|Klein et al., 2017]] ). Surface warming within ascent regions thus warms the free troposphere and increases low-cloud cover, causing an increase in emission of thermal radiation to space and a reduction in absorbed solar radiation. In contrast, surface warming in regions of overall descent preferentially warms the boundary layer and enhances convective mixing with the dry free troposphere, decreasing low-cloud cover ( [[#Bretherton--2013|Bretherton et al., 2013]] ; [[#Qu--2014|Qu et al., 2014]] ; [[#Zhou--2015|Zhou et al., 2015]] ). This leads to an increase in absorption of solar radiation but little change in thermal emission to space. Consequently, warming in tropical ascent regions results in negative lapse-rate and cloud feedbacks while warming in tropical descent regions results in positive lapse-rate and cloud feedbacks (Figure 7.14; [[#Rose--2016|Rose and Rayborn, 2016]] ; [[#Zhou--2017b|Zhou et al., 2017b]] ; [[#Andrews--2018|Andrews and Webb, 2018]] ; [[#Dong--2019|Dong et al., 2019]] ). Surface warming in mid-to-high latitudes causes a weak radiative response owing to compensating changes in thermal emission (Planck and lapse-rate feedbacks) and absorbed solar radiation (shortwave cloud and surface-albedo feedbacks; [[#Rose--2016|Rose and Rayborn, 2016]] ; [[#Dong--2019|Dong et al., 2019]] ), however this compensation may weaken due to less-negative shortwave cloud feedbacks at high warming ( [[#Frey--2018|Frey and Kay, 2018]] ; [[#Bjordal--2020|Bjordal et al., 2020]] ; [[#Dong--2020|Dong et al., 2020]] ).

The spatial pattern of SST changes since 1870 shows relatively little warming in key regions of less-negative radiative feedbacks, including the eastern tropical Pacific Ocean and Southern Ocean (Sections 7.4.4.1 and 7.4.4.2, and Figures 2.11b and 7.14a). Cooling in these regions since 1980 has occurred along with an increase in the strength of the capping inversion in tropical descent regions, resulting in an observed increase in low-cloud cover over the tropical eastern Pacific (Figure 7.14a; [[#Zhou--2016|Zhou et al., 2016]] ; [[#Ceppi--2017|Ceppi and Gregory, 2017]] ; [[#Fueglistaler--2021|Fueglistaler and Silvers, 2021]] ). Thus, tropical low-cloud cover increased over recent decades even as global surface temperature increased, resulting in a negative low-cloud feedback which is at odds with the positive low-cloud feedback expected for the pattern of equilibrium warming under CO <sub>2</sub> forcing ( [[#7.4.2.4|Section 7.4.2.4]] and Figure 7.14b).

[[#Andrews--2018|Andrews et al. (2018)]] analysed available CMIP5/6 ESM simulations (six in total) comparing effective feedback parameters diagnosed within atmosphere-only ESMs using prescribed historical SST and sea ice concentration patterns with the equilibrium feedback parameters as estimated within coupled ESMs (using identical atmospheres) driven by abrupt 4×CO <sub>2</sub> forcing. The atmosphere-only ESMs show pronounced multi-decadal variations in their effective feedback parameters over the last century, with a trend towards strongly negative values since about 1980 owing primarily to negative shortwave cloud feedbacks driven by warming in the western equatorial Pacific Ocean and cooling in the eastern equatorial Pacific Ocean ( [[#Zhou--2016|Zhou et al., 2016]] ; [[#Andrews--2018|Andrews et al., 2018]] ; [[#Marvel--2018|Marvel et al., 2018]] ; [[#Dong--2019|Dong et al., 2019]] ). Yet, all six models show a less-negative net feedback parameter under ''abrupt 4xCO2'' than for the historical period (based on regression since 1870 following [[#Andrews--2018|Andrews et al., 2018]] ). The average change in net feedback parameter between the historical period and the equilibrium response to CO <sub>2</sub> forcing, denoted here as α ''’'' , for these simulations is α ''’'' = +0.6 W m <sup>–2</sup> °C <sup>–1</sup> (+0.3 to +1.0 W m <sup>–2</sup> °C <sup>–1</sup> range across models; Figure 7.15b). These feedback parameter changes imply that the value of ECS may be substantially larger than that inferred from the historical record ( [[#7.5.2.1|Section 7.5.2.1]] ). These findings can be understood from the fact that, due to a combination of internal variability and transient response to forcing ( [[#7.4.4.2|Section 7.4.4.2]] ), historical sea surface warming has been relatively large in regions of tropical ascent (Figure 7.14a), leading to an anomalously large net negative radiative feedback; however, future warming is expected to be largest in tropical descent regions, such as the eastern equatorial Pacific, and at high latitudes (Sections 7.4.4.1 and 7.4.4.2 and Figure 7.14b), leading to a less-negative net radiative feedback and higher ECS.

<div id="_idContainer056" class="Basic-Text-Frame"></div>

[[File:b7b4983aa5e6275bb417a83cb5cbc191 IPCC_AR6_WGI_Figure_7_15.png]]

'''Figure 7.15''' '''|''' '''Relationship between''' ''historical'' '''and''' ''abrupt 4xCO2'' '''net radiative feedbacks in ESMs. (a)''' Radiative feedbacks in CMIP6 ESMs estimated under historical forcing (values for GFDL CM4.0 and HadGEM3-CG3.1-LL from [[#Winton--2020|Winton et al. (2020)]] and [[#Andrews--2019|Andrews et al. (2019)]] , respectively); horizontal lines show the range across ensemble members. The other points show effective feedback values for 29 ESMs estimated using regression over the first 50 years of ''abrupt 4xCO2'' simulations as an analogue for historical warming ( [[#Dong--2020|Dong et al., 2020]] ). '''(b)''' Historical radiative feedbacks estimated from atmosphere-only ESMs with prescribed observed sea-surface temperature and sea-ice concentration changes ( [[#Andrews--2018|Andrews et al., 2018]] ) based on a linear regression of global top-of-atmosphere (TOA) radiation against global near-surface air temperature over the period 1870–2010 (pattern of warming similar to Figure 7.14a) and compared with equilibrium feedbacks in ''abrupt 4xCO2'' simulations of coupled versions of the same ESMs (pattern of warming similar to Figure 7.14b). In all cases, the equilibrium feedback magnitudes are estimated as CO <sub>2</sub> ERF divided by ECS where ECS is derived from regression over years 1–150 of ''abrupt 4xCO2'' simulations (Box 7.1); similar results are found if the equilibrium feedback is estimated directly from the slope of the linear regression. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

A similar behaviour is seen within transient simulations of coupled ESMs, which project SST warming patterns that are initially characterized by relatively large warming rates in the western equatorial Pacific Ocean on decadal time scales and relatively large warming in the eastern equatorial Pacific and Southern Ocean on centennial time scales ( [[#Andrews--2015|Andrews et al., 2015]] ; [[#Proistosescu--2017|Proistosescu and Huybers, 2017]] ; [[#Dong--2020|Dong et al., 2020]] ). Recent studies based on simulations of 1% yr <sup>–1</sup> CO <sub>2</sub> increase ( ''1pctCO'' 2 ) or ''abrupt 4xCO2'' as analogues for historical warming suggest characteristic values of α ''’'' = +0.05 W m <sup>–2</sup> °C <sup>–1</sup> (–0.2 to +0.3 W m <sup>–2</sup> °C <sup>–1</sup> range across models) based on CMIP5 and CMIP6 ESMs (Armour 2017, Lewis and Curry 2018, Dong et al. 2020). Using historical simulations of one CMIP6 ESM (HadGEM3-GC3.1-LL), [[#Andrews--2019|Andrews et al. (2019)]] find an average feedback parameter change of α ''’'' = +0.2 W m <sup>–2</sup> °C <sup>–1</sup> (–0.2 to +0.6 W m <sup>–2</sup> °C <sup>–1</sup> range across four ensemble members). Using historical simulations from another CMIP6 ESM (GFDL CM4.0), [[#Winton--2020|Winton et al. (2020)]] find an average feedback parameter change of α ''’'' = +1.5 W m <sup>–2</sup> °C <sup>–1</sup> (+1.2 to +1.7 W m <sup>–2</sup> °C <sup>–1</sup> range across three ensemble members). This value is larger than The α ''’'' = +0.7 W m <sup>–2</sup> °C <sup>–1</sup> within GFDL CM4.0 for historical CO <sub>2</sub> forcing only, suggesting that the value of α ''’'' may depend on historical non-CO <sub>2</sub> forcings such as those associated with tropospheric and stratospheric aerosols ( [[#Marvel--2016|Marvel et al., 2016]] ; [[#Gregory--2020|Gregory et al., 2020]] ; [[#Winton--2020|Winton et al., 2020]] ).

The magnitude of the net feedback parameter change α ''’'' found within coupled CMIP5 and CMIP6 ESMs is generally smaller than that found when prescribing observed warming patterns within atmosphere-only ESMs (Figure 7.15; [[#Andrews--2018|Andrews et al., 2018]] ). This arises from the fact that the forced spatial pattern of warming within transient simulations of most coupled ESMs are distinct from observed warming patterns over the historical record in key regions such as the equatorial Pacific Ocean and Southern Ocean (Sections 7.4.4.1 and 7.4.4.2), while being more similar to the equilibrium pattern simulated under ''abrupt 4xCO2'' . However, historical simulations with HadGEM3-GC3.1-LL ( [[#Andrews--2019|Andrews et al., 2019]] ) and GFDL CM4.0 ( [[#Winton--2020|Winton et al., 2020]] ) show substantial spread in the value of α ''’'' across ensemble members, indicating a potentially important role for internal variability in setting the magnitude of the pattern effect over the historical period. Using the 100-member historical simulation ensemble of MPI-ESM1.1, [[#Dessler--2018|Dessler et al. (2018)]] find that internal climate variability alone results in a 0.5 W m <sup>–2</sup> °C <sup>–1</sup> spread in the historical effective feedback parameter, and thus also in the value of α ''’'' . Estimates of α ''’'' using prescribed historical warming patterns provide a more realistic representation of the historical pattern effect because they account for the net effect of the transient response to historical forcing and internal variability in the observed record ( [[#Andrews--2018|Andrews et al., 2018]] ).

The magnitude of α ''’'' , as quantified by ESMs, depends on the accuracy of both the projected patterns of SST and sea ice concentration changes in response to CO <sub>2</sub> forcing and the radiative response to those patterns ( [[#Andrews--2018|Andrews et al., 2018]] ). Model biases that affect the long-term warming pattern (e.g., SST and relative humidity biases in the equatorial Pacific cold tongue as suggested by [[#Seager--2019|Seager et al., 2019]] ) will affect the value of α ''’'' . The value of α ''’'' also depends on the accuracy of the historical SST and sea ice concentration conditions prescribed within atmosphere-only versions of ESMs to quantify the historical radiative feedback (Figure 7.15b). Historical SSTs are particularly uncertain for the early portion of the historical record ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] ), and there are few constraints on sea ice concentration prior to the satellite era. Using alternative SST datasets, [[#Andrews--2018|Andrews et al. (2018)]] found little change in the value of α ''’'' within two models (HadGEM3 and HadAM3), while [[#Lewis--2021|Lewis and Mauritsen (2021)]] found a smaller value of α ''’'' within two other models (ECHAM6.3 and CAM5). The sensitivity of results to the choice of dataset represents a major source of uncertainty in the quantification of the historical pattern effect using atmosphere-only ESMs that has yet to be systematically explored, but the preliminary findings of [[#Lewis--2021|Lewis and Mauritsen (2021)]] and [[#Fueglistaler--2021|Fueglistaler and Silvers (2021)]] suggest that α ''’'' could be smaller than the values reported in [[#Andrews--2018|Andrews et al. (2018)]] .

While there are not yet direct observational constraints on the magnitude of the pattern effect, satellite measurements of variations in TOA radiative fluxes show strong co-variation with changing patterns of SSTs, with a strong dependence on SST changes in regions of deep convective ascent (e.g., in the western Pacific warm pool; [[#Loeb--2018a|Loeb et al., 2018a]] ; [[#Fueglistaler--2019|Fueglistaler, 2019]] ). Cloud and TOA radiation responses to observed warming patterns in atmospheric models have been found to compare favourably with those observed by satellite ( [[#7.2.2.1|Section 7.2.2.1]] and Figure 7.3; [[#Zhou--2016|Zhou et al., 2016]] ; [[#Loeb--2020|Loeb et al., 2020]] ). This observational and modelling evidence indicates the potential for a strong pattern effect in nature that will only be negligible if the observed pattern of warming since pre-industrial levels persists to equilibrium – an improbable scenario given that Earth is in a relatively early phase of transient warming and that reaching equilibrium would take multiple millennia (C. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ). Moreover, paleoclimate proxies, ESM simulations, and process understanding indicate that strong warming in the eastern equatorial Pacific Ocean (with ''medium confidence'' ) and Southern Ocean (with ''high confidence'' ) will emerge on centennial time scales as the response to CO <sub>2</sub> forcing dominates temperature changes in these regions (Sections 7.4.4.1, 7.4.4.2 and 9.2.1). However, there is ''low confidence'' that these features, which have been largely absent over the historical record, will emerge this century (Sections 7.4.4.1, 7.4.4.2 and ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1|Section 9.2.1]] ). This leads to ''high confidence'' that radiative feedbacks will become less negative as the CO <sub>2</sub> -forced pattern of surface warming emerges ( α ''’'' &gt; 0 W m <sup>–2</sup> °C <sup>–1</sup> ), but ''low confidence'' that these feedback changes will be realized this century. There is also substantial uncertainty in the magnitude of the net radiative feedback change between the present warming pattern and the projected equilibrium warming pattern in response to CO <sub>2</sub> forcing owing to the fact that its quantification currently relies solely on ESM results and is subject to uncertainties in historical SST patterns. Thus, based on the pattern of warming since 1870, α ''’'' is estimated to be in the range 0.0 to 1.0 W m <sup>–2</sup> °C <sup>–1</sup> but with a ''low confidence'' in the upper end of this range. A value of α ''’'' = +0.5 ± 0.5 W m <sup>–2</sup> °C <sup>–1</sup> is used to represent this range in Box 7.2 and ( [[#7.5.2|Section 7.5.2]] , which respectively assess the implications of changing radiative feedbacks for Earth’s energy imbalance and estimates of ECS based on the instrumental record. The value of α ''’'' is larger if quantified based on the observed pattern of warming since 1980 (Figure 2.11b) which is more distinct from the equilibrium warming pattern expected under CO <sub>2</sub> forcing ( ''high confidence'' ) (similar to CMIP6 projections shown in Figure 7.12a; [[#Andrews--2018|Andrews et al., 2018]] ).

<div id="7.5" class="h1-container"></div>

<span id="estimates-of-ecs-and-tcr"></span>