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

==== 7.5.2.3 Estimates of ECS Based on Variability in Earth’s Top-of-atmosphere Radiation Budget ====

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While continuous satellite measurements of top-of-atmosphere (TOA) radiative fluxes (Figure 7.3) do not have sufficient accuracy to determine the absolute magnitude of Earth’s energy imbalance ( [[#7.2.1|Section 7.2.1]] ), they provide accurate estimates of its variations and trends since the year 2002 that agree well with estimates based on observed changes in global ocean heat content ( [[#Loeb--2012|Loeb et al., 2012]] ; [[#Johnson--2016|Johnson et al., 2016]] ; [[#Palmer--2017|Palmer, 2017]] ). When combined with global surface temperature observations and simple models of global energy balance, satellite measurements of TOA radiation afford estimates of the net feedback parameter associated with recent climate variability ( [[#Tsushima--2013|Tsushima and Manabe, 2013]] ; [[#Donohoe--2014|Donohoe et al., 2014]] ; [[#Dessler--2018|Dessler and Forster, 2018]] ). These feedback estimates, derived from the regression of TOA radiation on surface temperature variability, imply values of ECS that are broadly consistent with those from other lines of evidence ( [[#Forster--2016|Forster, 2016]] ; [[#Knutti--2017|Knutti et al., 2017]] ). A history of regression-based feedbacks and their uncertainties is summarized in [[#Bindoff--2013|Bindoff et al. (2013)]] , [[#Forster--2016|Forster (2016)]] , and [[#Knutti--2017|Knutti et al. (2017)]] .

Research since AR5 has noted that regression-based feedback estimates depend on whether annual- or monthly-mean data are used and on the choice of lag employed in the regression, complicating their interpretation ( [[#Forster--2016|Forster, 2016]] ). The observed lead–lag relationship between global TOA radiation and global surface temperature, and its dependence on sampling period, is well replicated within unforced simulations of ESMs ( [[#Dessler--2011|Dessler, 2011]] ; [[#Proistosescu--2018|Proistosescu et al., 2018]] ). These features arise because the regression between global TOA radiation and global surface temperature reflects a blend of different radiative feedback processes associated with several distinct modes of variability acting on different time scales (Annex IV), such as monthly atmospheric variability and interannual El Niño–Southern Oscillation (ENSO) variability ( [[#Lutsko--2018|Lutsko and Takahashi, 2018]] ; [[#Proistosescu--2018|Proistosescu et al., 2018]] ). Regression-based feedbacks thus provide estimates of the radiative feedbacks that are associated with internal climate variability (e.g., [[#Brown--2014|Brown et al., 2014]] ), and do not provide a direct estimate of ECS ( ''high confidence'' ). Moreover, variations in global surface temperature that do not directly affect TOA radiation may lead to a positive bias in regression-based feedback, although this bias appears to be small, particularly when annual-mean data are used ( [[#Murphy--2010|Murphy and Forster, 2010]] ; [[#Spencer--2010|Spencer and Braswell, 2010]] , 2011; [[#Proistosescu--2018|Proistosescu et al., 2018]] ). When tested within ESMs, regression-based feedbacks have been found to be only weakly correlated with values of ECS ( [[#Chung--2010|Chung et al., 2010]] ), although cloudy-sky TOA radiation fluxes have been found to be moderately correlated with ECS at ENSO time scales within CMIP5 models ( [[#Lutsko--2018|Lutsko and Takahashi, 2018]] ).

Finding such correlations within models requires simulations that span multiple centuries, suggesting that the satellite record may not be of sufficient length to produce robust feedback estimates. However, correlations between regression-based feedbacks and long-term feedbacks have been found to be higher when focused on specific processes or regions, such as for the cloud- or water-vapour feedbacks ( [[#7.4.2|Section 7.4.2]] ; [[#Dessler--2013|Dessler, 2013]] ; [[#Zhou--2015|Zhou et al., 2015]] ). Assessing the global radiative feedback in terms of the more stable relationship between tropospheric temperature and TOA radiation offers another potential avenue for constraining ECS. The ‘emergent constraints’ on ECS based on variability in the TOA energy budget are assessed in ( [[#7.5.4.1|Section 7.5.4.1]] .

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