Using the Radiative Kernel Technique to Calculate Climate Feedbacks in NCAR’s Community Atmospheric Model

Abstract

Climate models differ in their responses to imposed forcings, such as increased greenhouse gas concentrations, due to different climate feedback strengths. Feedbacks in NCAR's Community Atmospheric Model (CAM) are separated into two components: the change in climate components in response to an imposed forcing and the "radiative kernel," the effect that climate changes have on the top-of-theatmosphere (TOA) radiative budget. This technique's usefulness depends on the linearity of the feedback processes. For the case of CO 2 doubling, the sum of the effects of water vapor, temperature, and surface albedo changes on the TOA clear-sky flux is similar to the clear-sky flux changes directly calculated by CAM. When monthly averages are used rather than values from every time step, the global-average TOA shortwave change is underestimated by a quarter, partially as a result of intramonth correlations of surface albedo with the radiative kernel. The TOA longwave flux changes do not depend on the averaging period. The longwave zonal averages are within 10% of the model-calculated values, while the global average differs by only 2%. Cloud radiative forcing (⌬CRF) is often used as a diagnostic of cloud feedback strength. The net effect of the water vapor, temperature, and surface albedo changes on ⌬CRF is Ϫ1.6 W m Ϫ2 , based on the kernel technique, while the total ⌬CRF from CAM is Ϫ1.3 W m Ϫ2 , indicating these components contribute significantly to ⌬CRF and make it more negative. Assuming linearity of the ⌬CRF contributions, these results indicate that the net cloud feedback in CAM is positive.

On the Longwave Climate Feedbacks

“…The magnitudes of these residuals (an ensemble mean of 1.23 W m 22 ) are apparently nonnegligible, compared to the overall OLR changes (0.70 W m 22 ). The magnitude of the residuals is so big that they cannot be simply attributed to the nonlinear effect, which should be very small as shown by many previous studies (e.g., Soden and Held 2006;Huang et al 2007;Shell et al 2008;Soden et al 2008;Huang et al 2010). Moreover, there is a substantial spread (a standard deviation of 0.47 W m 22 ) of the residuals across these models; also, a significant correlation (a correlation coefficient of 0.55) exists between these residuals and global mean surface temperature changes.…”

“…There are 18 models that submitted necessary data for analyzing the longwave feedbacks. The noncloud feedbacks are computed by multiplying the kernels of Shell et al (2008) with the linear trends in monthly mean temperature and water vapor as simulated by each GCM. The tropopause is set to linearly increase from 100 hPa at the equator to 300 hPa at the poles following the previous analyses (e.g., Soden and Held 2006;Soden et al 2008;Shell et al 2008).…”

“…The noncloud feedbacks are computed by multiplying the kernels of Shell et al (2008) with the linear trends in monthly mean temperature and water vapor as simulated by each GCM. The tropopause is set to linearly increase from 100 hPa at the equator to 300 hPa at the poles following the previous analyses (e.g., Soden and Held 2006;Soden et al 2008;Shell et al 2008). The cloud feedback is then obtained by using the cloud forcing adjustment (CFA) method (Soden et al 2008;Shell et al 2008), which combines with Eq.…”

“…The tropopause is set to linearly increase from 100 hPa at the equator to 300 hPa at the poles following the previous analyses (e.g., Soden and Held 2006;Soden et al 2008;Shell et al 2008). The cloud feedback is then obtained by using the cloud forcing adjustment (CFA) method (Soden et al 2008;Shell et al 2008), which combines with Eq. (3) the clear-sky OLR decomposition:…”

“…From a diagnostic point of view, the value of S can be thought to be determined by the overall effect of a variety of ''feedbacks'' (see appendix A). Here, a feedback refers to a physical quantity X other than forcing that influences R, and the strength of the feedback DR X can be assessed by the partial contribution of this factor to the change in R. The kernel method (Soden et al 2008;Shell et al 2008) is a widely used method for analyzing feedback strength. In this method, a noncloud feedback is approximated by multiplying a precalculated first-order sensitivity kernel ›R/›X and a climate response DX:…”