The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models

Abstract

Experiments with CO2 instantaneously quadrupled and then held constant are used to show that the relationship between the global-mean net heat input to the climate system and the global-mean surface air temperature change is nonlinear in phase 5 of the Coupled Model Intercomparison Project (CMIP5) atmosphere–ocean general circulation models (AOGCMs). The nonlinearity is shown to arise from a change in strength of climate feedbacks driven by an evolving pattern of surface warming. In 23 out of the 27 AOGCMs examined, the climate feedback parameter becomes significantly (95% confidence) less negative (i.e., the effective climate sensitivity increases) as time passes. Cloud feedback parameters show the largest changes. In the AOGCM mean, approximately 60% of the change in feedback parameter comes from the tropics (30°N–30°S). An important region involved is the tropical Pacific, where the surface warming intensifies in the east after a few decades. The dependence of climate feedbacks on an evolving pattern of surface warming is confirmed using the HadGEM2 and HadCM3 atmosphere GCMs (AGCMs). With monthly evolving sea surface temperatures and sea ice prescribed from its AOGCM counterpart, each AGCM reproduces the time-varying feedbacks, but when a fixed pattern of warming is prescribed the radiative response is linear with global temperature change or nearly so. It is also demonstrated that the regression and fixed-SST methods for evaluating effective radiative forcing are in principle different, because rapid SST adjustment when CO2 is changed can produce a pattern of surface temperature change with zero global mean but nonzero change in net radiation at the top of the atmosphere (~−0.5 W m−2 in HadCM3).

The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity

“…It has been previously noted in analyses of the historical record that l exhibits significant 234 interdecadal variability (Andrews et al, 2015;Gregory and Andrews, 2016;Zhou et al, 2016). 235 We can reproduce this in a 2000-year control run (a run with fixed pre-industrial boundary 236 conditions) of the MPI-ESM1.1 model.…”

Observations of Local Positive Low Cloud Feedback Patterns and Their Role in Internal Variability and Climate Sensitivity

“…Analyses of CO2 quadrupling experiments have demonstrated that implied climate sensitivity generally increases with time. It happens when forced SST warming eventually overwhelms SST cooling due to internal variability or ocean heat uptake in the upwelling regions, which gets further amplified by positive cloud feedback (Andrews et al, 2015). We refer to this warming as latent warming here.…”

“…Both internal variability and changing ( F ( t )) can make $\frac{\mathrm{normal\Delta }T}{\mathrm{normal\Delta }t}$ and warming patterns change with time (Figures 1b and 1c). Model analyses suggest that time‐varying patterns of sea surface temperature (SST) can actively change α ( t ), and therefore $\frac{\mathrm{normal\Delta }T}{\mathrm{normal\Delta }t}$, by convolving with individual feedback processes (Andrews et al, 2015; Armour et al, 2013; Held et al, 2010). The low cloud feedback plays a critical role in this process (Andrews et al, 2015, 2012; Gregory & Andrews, 2016; Rose et al, 2014; Zhou et al, 2016).…”

“…Model analyses suggest that time‐varying patterns of sea surface temperature (SST) can actively change α ( t ), and therefore $\frac{\mathrm{normal\Delta }T}{\mathrm{normal\Delta }t}$, by convolving with individual feedback processes (Andrews et al, 2015; Armour et al, 2013; Held et al, 2010). The low cloud feedback plays a critical role in this process (Andrews et al, 2015, 2012; Gregory & Andrews, 2016; Rose et al, 2014; Zhou et al, 2016). It is perhaps best illustrated in an idealized model experiment where simply changing the SST pattern, without altering the global mean T , produces a nonzero N ( t ) due to cloud anomalies (Andrews et al, 2015; Zhou et al, 2016).…”

“…The low cloud feedback plays a critical role in this process (Andrews et al, 2015, 2012; Gregory & Andrews, 2016; Rose et al, 2014; Zhou et al, 2016). It is perhaps best illustrated in an idealized model experiment where simply changing the SST pattern, without altering the global mean T , produces a nonzero N ( t ) due to cloud anomalies (Andrews et al, 2015; Zhou et al, 2016). Recent modeling studies also show that observed time‐varying patterns of SST in recent decades have significantly changed α ( t ) (Gregory & Andrews, 2016; Marvel et al, 2018; Zhou et al, 2016).…”