Assessment of radiative feedback in climate models using satellite observations of annual flux variation

Abstract

In the climate system, two types of radiative feedback are in operation. The feedback of the first kind involves the radiative damping of the vertically uniform temperature perturbation of the troposphere and Earth's surface that approximately follows the StefanBoltzmann law of blackbody radiation. The second kind involves the change in the vertical lapse rate of temperature, water vapor, and clouds in the troposphere and albedo of the Earth's surface. Using satellite observations of the annual variation of the outgoing flux of longwave radiation and that of reflected solar radiation at the top of the atmosphere, this study estimates the so-called "gain factor," which characterizes the strength of radiative feedback of the second kind that operates on the annually varying, global-scale perturbation of temperature at the Earth's surface. The gain factor is computed not only for all sky but also for clear sky. The gain factor of socalled "cloud radiative forcing" is then computed as the difference between the two. The gain factors thus obtained are compared with those obtained from 35 models that were used for the fourth and fifth Intergovernmental Panel on Climate Change assessment. Here, we show that the gain factors obtained from satellite observations of cloud radiative forcing are effective for identifying systematic biases of the feedback processes that control the sensitivity of simulated climate, providing useful information for validating and improving a climate model.O ne of the most challenging tasks of climate science is to determine climate sensitivity. It is often defined as the equilibrium response of the global mean surface temperature to the doubling of atmospheric CO 2 . Unfortunately, currently available models have sensitivities that vary across a wide range. According to the report of the fourth Intergovernmental Panel on Climate Change (IPCC) assessment (1), about two-thirds of the current climate models have sensitivities that range between 2°C and 4.5°C. Although this range is itself large, the sensitivities of one-third of the models lie outside of this range. The need to reduce this sizable uncertainty is one of the important reasons it is urgent to understand and reliably quantify the mechanisms that determine climate sensitivity.Climate sensitivity is inversely proportional to the strength of the radiative feedback that operates on the global-scale perturbation of surface temperature. Here, we describe our attempt to estimate the strength of the radiative feedback that operates on the global-scale perturbation of surface temperature in many current climate models and compare the results with those obtained from satellite observations. The geographical pattern of the perturbation chosen for the present analysis, however, is the annual variation, rather than global warming.The annual variation of the global mean surface temperature is attributable mainly to the difference in the amplitude of the seasonal variation of the hemispheric mean temperature that is out of phase between the two he...

An Estimate of Equilibrium Climate Sensitivity From Interannual Variability

“…There are several reasons why ECS estimated from the revised energy balance framework (equation ) should be considered more reliable than that estimated from the traditional framework (equation ) used in previous papers (e.g., Chung et al, ; Dessler, ; Donohoe et al, ; Forster, ; Forster & Gregory, ; Tsushima et al, ; Tsushima & Manabe, ). Figure shows the main advantage—that Θ iv,obs is better constrained than λ iv,obs This is what leads to the narrower distributions of ECS in Figure than in Figure .…”

“…Thus, there is great value in finding alternate ways to approach the problem. Relatively few papers have attempted to use short-term interannual variability to estimate ECS (e.g., Chung et al, 2010;Dessler, 2013;Donohoe et al, 2014;Forster, 2016;Forster & Gregory, 2006;Tsushima et al, 2005;Tsushima & Manabe, 2013). Papers that do typically yield estimates of ECS are consistent with the IPCC's canonical ECS range of 1.5-4.5°C, but their uncertainty is so large as to provide no meaningful constraint of the range.…”

Radiative Feedbacks From Stochastic Variability in Surface Temperature and Radiative Imbalance

“…Consequently, significant effort has gone into estimating radiative feedbacks by regressing unforced fluctuations in global-mean TOA radiative imbalance against fluctuations in global-mean near-surface air temperature. of climate sensitivity to greenhouse gases (e.g., Chung et al, 2010;Dessler, 2010;Donohoe et al, 2014;Forster, 2016;Forster & Gregory, 2006;Trenberth et al, 2015;Tsushima & Manabe, 2013;Zhou et al, 2014) or used as an emergent constraint on long-term climate response (e.g., Zhou et al, 2015). Greenhouse gas and aerosol forcing is removed, and data are processed as in Donohoe et al (2014).…”

Beyond equilibrium climate sensitivity

“…However, short term climate variations have long been known to provide a poor and unreliable constraint on the long term response 132,169 . Short term variations in the energy budget can be informative to compare feedbacks in models and observation, and relate them to long-term feedbacks in the form of an emergent constraint 24,225,[265][266][267][268] , but feedbacks depend on the timescale and are different for variability 231,269,270 . The results in such studies also hinge on assumptions on the response timescales, and are affected by limitations of simple models fitted.…”