The extent to which the climate will change due to an external forcing depends largely on radiative feedbacks, which act to amplify or damp the surface temperature response. There are a variety of issues that complicate the analysis of radiative feedbacks in global climate models, resulting in some confusion regarding their strengths and distributions. In this paper, the authors present a method for quantifying climate feedbacks based on “radiative kernels” that describe the differential response of the top-of-atmosphere radiative fluxes to incremental changes in the feedback variables. The use of radiative kernels enables one to decompose the feedback into one factor that depends on the radiative transfer algorithm and the unperturbed climate state and a second factor that arises from the climate response of the feedback variables. Such decomposition facilitates an understanding of the spatial characteristics of the feedbacks and the causes of intermodel differences. This technique provides a simple and accurate way to compare feedbacks across different models using a consistent methodology. Cloud feedbacks cannot be evaluated directly from a cloud radiative kernel because of strong nonlinearities, but they can be estimated from the change in cloud forcing and the difference between the full-sky and clear-sky kernels. The authors construct maps to illustrate the regional structure of the feedbacks and compare results obtained using three different model kernels to demonstrate the robustness of the methodology. The results confirm that models typically generate globally averaged cloud feedbacks that are substantially positive or near neutral, unlike the change in cloud forcing itself, which is as often negative as positive.

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.

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Abstract.Equilibrium climate sensitivity of the Community Climate System Model Version 4 (CCSM4) is 3.20• C for 1• horizontal resolution in each component. This is about a half degree Celsius higher than in the previous version (CCSM3). The transient climate sensitivity of CCSM4 at 1• resolution is 1.72• C, which is about 0.2 • C higher than in CCSM3. These higher climate sensitivities in CCSM4 cannot be explained by the change to a preindustrial baseline climate. We use the radiative kernel technique to show that from CCSM3 to CCSM4, the global mean lapse-rate feedback declines in magnitude, and the shortwave cloud feedback increases. These two warming effects are partially canceled by cooling due to slight decreases in the global mean water-vapor feedback and longwave cloud feedback from CCSM3 to CCSM4. A new formulation of the mixed-layer, slab ocean model in CCSM4 attempts to reproduce the SST and sea ice climatology from an integration with a full-depth ocean, and it is integrated with a dynamic sea ice model. These new features allow an isolation of the influence of ocean dynamical changes on the climate response when comparing integrations with the slab ocean and full-depth ocean. The transient climate response of the full-depth ocean version is 0.54 of the equilibrium climate sensitivity when estimated with the new slab ocean model version for both CCSM3 and CCSM4. We argue the ratio is the same in both versions because they have about the same zonal mean pattern of change in ocean surface heat flux, which broadly resembles the zonal mean pattern of net feedback strength.

The major evolution of the National Center for Atmospheric Research Community Atmosphere Model (CAM) is used to diagnose climate feedbacks, understand how climate feedbacks change with different physical parameterizations, and identify the processes and regions that determine climate sensitivity. In the evolution of CAM from version 4 to version 5, the water vapor, temperature, surface albedo, and lapse rate feedbacks are remarkably stable across changes to the physical parameterization suite. However, the climate sensitivity increases from 3.2 K in CAM4 to 4.0 K in CAM5. The difference is mostly due to (i) more positive cloud feedbacks and (ii) higher CO 2 radiative forcing in CAM5. The intermodel differences in cloud feedbacks are largest in the tropical trade cumulus regime and in the midlatitude storm tracks. The subtropical stratocumulus regions do not contribute strongly to climate feedbacks owing to their small area coverage. A ''modified Cess'' configuration for atmosphere-only model experiments is shown to reproduce slab ocean model results. Several parameterizations contribute to changes in tropical cloud feedbacks between CAM4 and CAM5, but the new shallow convection scheme causes the largest midlatitude feedback differences and the largest change in climate sensitivity. Simulations with greater cloud forcing in the mean state have lower climate sensitivity. This work provides a methodology for further analysis of climate sensitivity across models and a framework for targeted comparisons with observations that can help constrain climate sensitivity to radiative forcing.

An approach to climate change feedback analysis is described in which tropospheric relative humidity replaces specific humidity as the state variable that, along with the temperature structure, surface albedos, and clouds, controls the magnitude of the response of global mean surface temperature to a radiative forcing. Despite being simply a regrouping of terms in the feedback analysis, this alternative perspective has the benefit of removing most of the pervasive cancellation between water and lapse-rate feedbacks seen in models. As a consequence, the individual feedbacks have less scatter than in the traditional formulation. The role of cloud feedbacks in controlling climate sensitivity is also reflected more clearly in the new formulation.

Are equilibrium climate sensitivity and the associated radiative feedbacks a constant property of the climate system, or do they change with forcing magnitude and base climate? Using the radiative kernel technique, feedbacks and climate sensitivity are evaluated in a fully coupled general circulation model (GCM) for three successive doublings of carbon dioxide starting from present-day concentrations. Climate sensitivity increases by 23% between the first and third CO 2 doublings. Increases in the positive water vapor and cloud feedbacks are partially balanced by a decrease in the positive surface albedo feedback and an increase in the negative lapse rate feedback. Feedbacks can be decomposed into a radiative flux change and a climate variable response to temperature change. The changes in water vapor and Planck feedbacks are due largely to changes in the radiative response with climate state. Higher concentrations of greenhouse gases and higher temperatures lead to more absorption and emission of longwave radiation. Changes in cloud feedbacks are dominated by the climate response to temperature change, while the lapse rate and albedo feedbacks combine elements of both. Simulations with a slab ocean model (SOM) version of the GCM are used to verify whether an SOM-GCM accurately reproduces the behavior of the fully coupled model. Although feedbacks differ in magnitude between model configurations (with differences as large as those between CO 2 doublings for some feedbacks), changes in feedbacks between CO 2 doublings are consistent in sign and magnitude in the SOM-GCM and the fully coupled model.

Climate feedbacks vary strongly among climate models and continue to represent a major source of uncertainty in estimates of the response of climate to anthropogenic forcings. One method to evaluate feedbacks in global climate models is the radiative kernel technique, which is well suited for model intercomparison studies because of its computational efficiency. However, the usefulness of this technique is predicated on the assumption of linearity between top-of-atmosphere (TOA) radiative fluxes and feedback variables, limiting its application to simulations of small climate perturbations, where nonlinearities can be neglected. This paper presents an extension of the utility of this linear technique to large forcings, using global climate model simulations forced with CO 2 concentrations ranging from 2 to 8 times present-day values. Radiative kernels depend on the model's radiative transfer algorithm and climate base state. For large warming, kernels based on the present-day climate significantly underestimate longwave TOA flux changes and somewhat overestimate shortwave TOA flux changes. These biases translate to inaccurate feedback estimates. It is shown that a combination of present-day kernels and kernels computed using a large forcing climate base state leads to significant improvement in the approximation of TOA flux changes and increased reliability of feedback estimates. While using present-day kernels results in a climate sensitivity that remains constant, using the new kernels shows that sensitivity increases significantly with each successive doubling of CO 2 concentrations.