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

==== 7.5.2.2 Estimates of ECS and TCR Based on Climate Model Emulators ====

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Energy budget emulators are far less complex than comprehensive ESMs ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.3|Section 1.5.3]] and Cross-Chapter Box 7.1). For example, an emulator could represent the atmosphere, ocean, and land using a small number of connected boxes (e.g., [[#Goodwin--2016|Goodwin, 2016]] ), or it could represent the global mean climate using two connected ocean layers (e.g., Cross-Chapter Box 7.1 and Supplementary Material 7.SM.2). The numerical efficiency of emulators means that they can be empirically constrained by observations: a large number of possible parameter values (e.g., feedback parameter, aerosol radiative forcing, and ocean diffusivity) are randomly drawn from prior distributions; forward integrations of the model are performed with these parameters and weighted against observations of surface or ocean warming, producing posterior estimates of quantities of interest such as TCR, ECS and aerosol forcing ( [[#7.3|Section 7.3]] ). Owing to their reduced complexity, emulators lack full representations of the spatial patterns of sea surface temperature and radiative responses to changes in those patterns (discussed in ( [[#7.4.4.3|Section 7.4.4.3]] ) and many represent the net feedback parameter using a constant value. The ranges of ECS reported by studies using emulators are thus interpreted here as representative of the effective ECS over the historical record rather than of the true ECS.

Improved estimates of ocean heat uptake over the past two decades ( [[#7.2|Section 7.2]] ) have diminished the role of ocean diffusivity in driving uncertainty in ECS estimates, leaving the main trade-off between posterior ranges in ECS and aerosol radiative forcing ( [[#Forest--2002|Forest, 2002]] ; [[#Knutti--2002|Knutti et al., 2002]] ; [[#Frame--2005|Frame et al., 2005]] ). The AR5 ( [[#Bindoff--2013|Bindoff et al., 2013]] ) assessed a variety of estimates of ECS based on emulators and found that they were sensitive to the choice of prior parameter distributions and temperature datasets used, particularly for the upper end of the ECS range, though priors can be chosen to minimize the effect on results (e.g., [[#Lewis--2013|Lewis, 2013]] ). Emulators generally produced estimates of effective ECS between 1°C and 5°C and ranges of TCR between 0.9°C and 2.6°C. [[#Padilla--2011|Padilla et al. (2011)]] use a simple global-average emulator with two time scales ( [[#7.5.1.2|Section 7.5.1.2]] ; Supplementary Material 7.SM.2) to estimate a TCR of 1.6 [1.3 to 2.6] °C. Using the same model, [[#Schwartz--2012|Schwartz (2012)]] finds TCR in the range 0.9°C–1.9°C while [[#Schwartz--2018|Schwartz (2018)]] finds that an effective ECS of 1.7°C provides the best fit to the historical global surface temperature record while also finding a median aerosol forcing that is smaller than that assessed in ( [[#7.3|Section 7.3]] . Using an eight-box representation of the atmosphere–ocean–terrestrial system constrained by historical warming, [[#Goodwin--2016|Goodwin (2016)]] found an effective ECS of 2.4 [1.4 to 4.4] °C while [[#Goodwin--2018|Goodwin (2018)]] found effective ECS to be in the range 2°C–4.3°C when using a prior for ECS based on paleoclimate constraints.

Using an emulator comprised of Northern and Southern hemispheres and an upwelling-diffusive ocean ( [[#Aldrin--2012|Aldrin et al., 2012]] ), with surface temperature and ocean heat content datasets updated to 2014, [[#Skeie--2018|Skeie et al. (2018)]] estimate a TCR of 1.4 [0.9 to 2.0] °C and a median effective ECS of 1.9 [1.2 to 3.1] °C. Using a similar emulator comprised of land and ocean regions and an upwelling-diffusive ocean, with global surface temperature and ocean heat content datasets up to 2011, [[#Johansson--2015|Johansson et al. (2015)]] find an effective ECS of 2.5 [2.0 to 3.2] °C. The estimate is found to be sensitive to the choice of dataset endpoint and the representation of internal variability meant to capture the El Niño–Southern Oscillation and Pacific Decadal Variability. Differences between these two studies arise, in part, from their different global surface temperature and ocean heat content datasets, different radiative forcing uncertainty ranges, different priors for model parameters, and different representations of internal variability. This leads to different estimates of effective ECS, with the median estimate of [[#Skeie--2018|Skeie et al. (2018)]] lying below the 5–95% range of effective ECS from [[#Johansson--2015|Johansson et al. (2015)]] . Moreover, while the [[#Skeie--2018|Skeie et al. (2018)]] emulator has a constant value of the net feedback parameter, the [[#Johansson--2015|Johansson et al. (2015)]] emulator allows distinct radiative feedbacks for land and ocean, contributing to the different results.

The median estimates of TCR and effective ECS inferred from emulator studies generally lie within the 5–95% ranges of those inferred from historical global energy budget constraints (1.3 to 2.7 °C for TCR and 1.6 to 4.8 °C for effective ECS). Their estimates would be consistent with still-higher values of ECS when accounting for changes in radiative feedbacks as the spatial pattern of global warming evolves in the future ( [[#7.5.2.1|Section 7.5.2.1]] ). Cross-Chapter Box 7.1 and references therein show that four very different physically based emulators can be calibrated to match the assessed ranges of historical GSAT change, ERF, ECS and TCR from across the report. Therefore, the fact that the emulator effective ECS values estimated from previous studies tend to lie at the lower end of the range inferred from historical global energy budget constraints may reflect that the energy budget constraints in ( [[#7.5.2.1|Section 7.5.2.1]] use updated estimates of Earth’s energy imbalance, GSAT trends and ERF, rather than any methodological differences between the lines of evidence. The ‘emergent constraints’ on ECS based on observations of climate variability used in conjunction with comprehensive ESMs are assessed in ( [[#7.5.4.1|Section 7.5.4.1]] .

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