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

==== 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>