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

=== 7.5.7 Processes Underlying Uncertainty in the Global Temperature Response to Forcing ===

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While the magnitude of global warming by the end of the 21st century is dominated by future GHG emissions, the uncertainty in warming for a given ERF change is dominated by the uncertainty in ECS and TCr ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ). The proportion of variation explained by ECS and TCR varies with scenario and the time period considered, but within CMIP5 models around 60–90% of the globally averaged projected surface warming range in 2100 can be explained by the model range of these metrics ( [[#Grose--2018|Grose et al., 2018]] ). Uncertainty in the long-term global surface temperature change can further be understood in terms of the processes affecting the global TOA energy budget, namely the ERF, the radiative feedbacks which govern the efficiency of radiative energy loss to space with surface warming, and the increase in the global energy inventory (dominated by ocean heat uptake) which reduces the transient surface warming. A variety of studies evaluate the effect of each of these processes on surface changes within coupled ESM simulations by diagnosing so-called ‘warming contributions’ ( [[#Dufresne--2008|Dufresne and Bony, 2008]] ; [[#Crook--2011|Crook et al., 2011]] ; [[#Feldl--2013|Feldl and Roe, 2013]] ; [[#Vial--2013|Vial et al., 2013]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Goosse--2018|Goosse et al., 2018]] ). By construction, the individual warming contributions sum to the total global surface warming (Figure 7.20b). For long-term warming in response to CO <sub>2</sub> forcing in CMIP5 models, the energy added to the climate system by radiative feedbacks is larger than the ERF of CO <sub>2</sub> (Figure 7.20a), implying that feedbacks more than double the magnitude of global warming (Figure 7.20b). Radiative kernel methods (see ( [[#7.4.1|Section 7.4.1]] ) can be used to decompose the net energy input from radiative feedbacks into its components. The water-vapour, cloud and surface-albedo feedbacks enhance global warming, while the lapse-rate feedback reduces global warming. Ocean heat uptake reduces the rate of global surface warming by sequestering heat at depth away from the ocean surface. [[#7.4.4.1|Section 7.4.4.1]] shows the warming contributions from these factors at the regional scale.

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'''Figure 7.20''' '''|''' '''Contributions of effective radiative forcing, ocean heat uptake and radiative feedbacks to global atmospheric energy input and ne''' '''ar-su''' '''rface air temperature change at year 100 of''' ''abrupt 4xCO2'' '''simulations of CMIP6 models. (a)''' The energy flux to the global atmosphere associated with the effective CO <sub>2</sub> forcing, global ocean heat uptake, Planck response, and radiative feedbacks, which together sum to zero. The inset shows energy input from individual feedbacks, summing to the total feedback energy input. '''(b)''' Contributions to net global warming are calculated by dividing the energy inputs by the ''magnitude'' of the global Planck response (3.2 W m <sup>–2</sup> °C <sup>–1</sup> ), with the contributions from radiative forcing, ocean heat uptake, and radiative feedbacks (orange bars) summing to the value of net warming (grey bar). The inset shows warming contributions associated with individual feedbacks, summing to the total feedback contribution. Uncertainties show the interquartile range (25th and 75th percentiles) across models. Radiative kernel methods (see ( [[#7.4.1|Section 7.4.1]] ) were 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).

Differences in projected transient global warming across ESMs are dominated by differences in their radiative feedbacks, while differences in ocean heat uptake and radiative forcing play secondary roles (Figure 7.20b; [[#Vial--2013|Vial et al., 2013]] ). The uncertainty in projected global surface temperature change associated with inter-model differences in cloud feedbacks is the largest source of uncertainty in CMIP5 and CMIP6 models (Figure 7.20b), just as they were for CMIP3 models ( [[#Dufresne--2008|Dufresne and Bony, 2008]] ). Extending this energy budget analysis to equilibrium surface warming suggests that about 70% of the inter-model differences in ECS arises from uncertainty in cloud feedbacks, with the largest contribution to that spread coming from shortwave low-cloud feedbacks ( [[#Vial--2013|Vial et al., 2013]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ).

Interactions between different feedbacks within the coupled climate system pose a challenge to our ability to understand global warming and its uncertainty based on energy budget diagnostics ( [[#7.4.2|Section 7.4.2]] ). For example, water-vapour and lapse-rate feedbacks are correlated ( [[#Held--2006|Held and Soden, 2006]] ) owing to their joint dependence on the spatial pattern of warming ( [[#Po-Chedley--2018b|Po-Chedley et al., 2018b]] ). Moreover, feedbacks are not independent of ocean heat uptake because the uptake and transport of heat by the ocean influences the SST pattern on which global feedbacks depend ( [[#7.4.4.3|Section 7.4.4.3]] ). However, alternative decompositions of warming contributions that better account for correlations between feedbacks produce similar results ( [[#Caldwell--2016|Caldwell et al., 2016]] ). The key role of radiative feedbacks in governing the magnitude of global warming is also supported by the high correlation between radiative feedbacks (or ECS) and transient 21st-century warming within ESMs ( [[#Grose--2018|Grose et al., 2018]] ).

Another approach to evaluating the roles of forcing, feedbacks and ocean heat uptake in projected warming employs idealized energy balance models that emulate the response of ESMs, and which preserve the interactions between system components. One such emulator, used in ( [[#7.5.1.2|Section 7.5.1.2]] , resolves the heat capacity of both the surface components of the climate system and the deep ocean ( [[#Held--2010|Held et al., 2010]] ; [[#Geoffroy--2013a|Geoffroy et al., 2013a]] , b; [[#Kostov--2014|Kostov et al., 2014]] ; [[#Armour--2017|Armour, 2017]] ). Using this emulator, [[#Geoffroy--2012|Geoffroy et al. (2012)]] find that: under an idealized 1% per year increase in atmospheric CO <sub>2</sub> , radiative feedbacks constitute the greatest source of uncertainty (about 60% of variance) in transient warming beyond several decades; ERF uncertainty plays a secondary but important role in warming uncertainty (about 20% of variance) that diminishes beyond several decades; and ocean heat uptake processes play a minor role in warming uncertainty (less than 10% of variance) at all time scales.

More computationally intensive approaches evaluate how the climate response depends on perturbations to key parameter or structural choices within ESMs. Large ‘perturbed parameter ensembles’, wherein a range of parameter settings associated with cloud physics are explored within atmospheric ESMs, produce a wide range of ECS due to changes in cloud feedbacks, but often produce unrealistic climate states ( [[#Joshi--2010|Joshi et al., 2010]] ). [[#Rowlands--2012|Rowlands et al. (2012)]] generated an ESM perturbed-physics ensemble of several thousand members by perturbing model parameters associated with radiative forcing, cloud feedbacks and ocean vertical diffusivity (an important parameter for ocean heat uptake). After constraining the ensemble to have a reasonable climatology and to match the observed historical surface warming, they found a wide range of projected warming by the year 2050 under the SRES A1B scenario (1.4°C–3°C relative to the 1961–1990 average) that is dominated by differences in cloud feedbacks. The finding that cloud feedbacks are the largest source of spread in the net radiative feedback has since been confirmed in perturbed parameter ensemble studies using several different ESMs ( [[#Gettelman--2012|Gettelman et al., 2012]] ; [[#Tomassini--2015|Tomassini et al., 2015]] ; [[#Kamae--2016b|Kamae et al., 2016b]] ; [[#Rostron--2020|Rostron et al., 2020]] ; [[#Tsushima--2020|Tsushima et al., 2020]] ). By swapping out different versions of the atmospheric or oceanic components in a coupled ESM, [[#Winton--2013|Winton et al. (2013)]] found that TCR and ECS depend on which atmospheric component was used (using two versions with different atmospheric physics), but that only TCR is sensitive to which oceanic component of the model was used (using two versions with different vertical coordinate systems, among other differences); TCR and ECS changed by 0.4°C and 1.4°C, respectively, when the atmospheric model component was changed, while TCR and ECS changed by 0.3°C and less than 0.05°C, respectively, when the oceanic model component was changed. By perturbing ocean vertical diffusivities over a wide range, [[#Watanabe--2020|Watanabe et al. (2020)]] found that TCR changed by 0.16°C within the model MIROC5.2 while [[#Krasting--2018|Krasting et al. (2018)]] found that ECS changed by about 0.6°C within the model GFDL-ESM2G, with this difference linked to different radiative feedbacks associated with different spatial patterns of sea surface warming ( [[#7.4.4.3|Section 7.4.4.3]] ). By comparing simulations of CMIP6 models with and without the effects of CO <sub>2</sub> on vegetation, [[#Zarakas--2020|Zarakas et al. (2020)]] find a physiological contribution to TCR of 0.12°C (range 0.02°C–0.29°C across models) owing to physiological adjustments to the CO <sub>2</sub> eRf ( [[#7.3.2.1|Section 7.3.2.1]] ).

There is ''robust evidence'' and ''high agreement'' across a diverse range of modelling approaches and thus ''high confidence'' that radiative feedbacks are the largest source of uncertainty in projected global warming out to 2100 under increasing or stable emissions scenarios, and that cloud feedbacks in particular are the dominant source of that uncertainty. Uncertainty in radiative forcing plays an important but generally secondary role. Uncertainty in global ocean heat uptake plays a lesser role in global warming uncertainty, but ocean circulation could play an important role through its effect on sea surface warming patterns which in turn project onto radiative feedbacks through the pattern effect ( [[#7.4.4.3|Section 7.4.4.3]] ).

The spread in historical surface warming across CMIP5 ESMs shows a weak correlation with inter-model differences in radiative feedback or ocean heat uptake processes but a high correlation with inter-model differences in radiative forcing owing to large variations in aerosol forcing across models ( [[#Forster--2013|Forster et al., 2013]] ). Likewise, the spread in projected 21st-century warming across ESMs depends strongly on which emissions scenario is employed ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.1|Section 4.3.1]] ; [[#Hawkins--2012|Hawkins and Sutton, 2012]] ). Strong emissions reductions would remove aerosol forcing (Section 6.7.2) and this could dominate the uncertainty in near-term warming projections ( [[#Armour--2011|Armour and Roe, 2011]] ; [[#Mauritsen--2017|Mauritsen and Pincus, 2017]] ; [[#Schwartz--2018|Schwartz, 2018]] ; [[#Smith--2019|Smith et al., 2019]] ). On post-2100 time scales carbon cycle uncertainty such as that related to permafrost thawing could become increasingly important, especially under high-emissions scenarios (Figure 5.30).

In summary, there is ''high confidence'' that cloud feedbacks are the dominant source of uncertainty for late 21st-century projections of transient global warming under increasing or stable emissions scenarios, whereas uncertainty is dominated by aerosol ERF in strong mitigation scenarios. Global ocean heat uptake is a smaller source of uncertainty in long-term surface warming ( ''high confidence'' ).

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