Variation in climate sensitivity and feedback parameters during the historical period

Abstract

We investigate the climate feedback parameter α (W m−2 K−1) during the historical period (since 1871) in experiments using the HadGEM2 and HadCM3 atmosphere general circulation models (AGCMs) with constant preindustrial atmospheric composition and time‐dependent observational sea surface temperature (SST) and sea ice boundary conditions. In both AGCMs, for the historical period as a whole, the effective climate sensitivity is ∼2 K (α≃1.7 W m−2 K−1), and α shows substantial decadal variation caused by the patterns of SST change. Both models agree with the AGCMs of the latest Coupled Model Intercomparison Project in showing a considerably smaller effective climate sensitivity of ∼1.5 K (α = 2.3 ± 0.7 W m−2 K−1), given the time‐dependent changes in sea surface conditions observed during 1979–2008, than the corresponding coupled atmosphere‐ocean general circulation models (AOGCMs) give under constant quadrupled CO2 concentration. These findings help to relieve the apparent contradiction between the larger values of effective climate sensitivity diagnosed from AOGCMs and the smaller values inferred from historical climate change.

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.…”

Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

“…When more warming later appears in the subsidence regions, tropical low-cloud feedbacks will become more positive. This behavior is most apparent in the simulations with abrupt quadrupling of CO 2 (Andrews et al 2015;Rugenstein et al 2016), but it also occurs in decadal feedbacks inferred for the last century (Gregory and Andrews 2016;Zhou et al 2016). This does not negate the framework of Eqs.…”

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

“… α ( t ), the time‐dependent climate feedback parameter, measures the global radiative response per degree of surface air temperature T change. Its reciprocal $\lambda \left(t\right)=\frac{1}{\alpha \left(t\right)}$, called the climate sensitivity parameter, measures the warming per unit increase in radiative forcing (Gregory & Andrews, 2016). Assuming α ( t ) to be constant, equilibrium climate sensitivity can be estimated by quantifying changes in temperature (Δ T ), ocean heat uptake (excellent proxy for Δ N ), and radiative forcing (Δ F ) over a period according to ${\lambda }_{\mathrm{eq}}=\frac{\mathrm{normal\Delta }T}{\left(\mathrm{normal\Delta }F-\mathrm{normal\Delta }N\right)}$.…”

“…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).…”