A Novel Technique to Diagnose and Partition Cloud Feedbacks
As the planet warms due to greenhouse gases, clouds and their effects on the planet’s energy budget are likely to change, with the potential to amplifying or diminish the amount of warming that arises from greenhouse gases. It is well established that these so-called cloud feedbacks lie at the heart of uncertainty in future projections of climate change due to greenhouse gases, and reliable methods that provide insight into cloud feedbacks are necessary for progress.
A recent study supported by DOE developed a new technique for computing cloud feedbacks that allows for a clear partitioning of the feedbacks into contributions from 49 different cloud types segregated by cloud top altitude and reflectivity. The authors developed cloud radiative kernels, which quantify the effect of each cloud type on top-of-atmosphere radiation fluxes. Computation of the cloud feedback requires simply multiplying these kernels by the change in the amount of each of the 49 cloud types as the planet warms. Rather than inferring which clouds are responsible for changes in the planet’s energy budget, this new technique allows direct attribution of radiation changes to the cloud types responsible. Spatial structures and globally integrated cloud feedbacks computed with this new technique agree remarkably well with an independent estimate computed with the current benchmark method.
The authors found that the global and annual mean model-simulated cloud feedback is dominated by contributions from medium thickness cloud changes, but thick cloud changes cause the rapid transition of cloud feedback values from positive in midlatitudes to negative poleward of 50°S and 70°N. High cloud changes are the dominant contributor to longwave (LW) cloud feedback, but because their LW and shortwave (SW) impacts are in opposition, they contribute less to the net cloud feedback than do the positive contributions from low cloud changes. Finally, clouds high in the atmosphere induce a wider range of LW and SW cloud feedbacks across models than do low clouds, a perhaps surprising result given the community’s focus on low cloud feedback.
The authors computed "cloud radiative kernels" that can be used to efficiently and accurately calculate cloud feedback, while providing highly detailed information about the cloud types responsible for the cloud feedback. Because it can be applied across models, the technique provides a quantification of the robust and non-robust aspects of cloud feedback in climate models at an unprecedented level of detail. Among the authors' scientific findings was the attribution of high clouds in causing large model-to-model disagreement in shortwave and longwave cloud feedbacks.
We acknowledge the international modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CFMIP multimodel dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. We thank Karen Shell and one anonymous reviewer for detailed critiques of this manuscript, Brian Soden and Karen Shell for providing radiative kernels, Rick Hemler for providing additional gfdl_mlm2_1 model output, Rob Wood, Chris Bretherton, and Robert Pincus for useful discussion and suggestions for improvement, and Marc Michelsen for computer support. This research was supported by the Regional and Global Climate Modeling Program of the Office of Science at the U. S. Department of Energy and by NASA Grant NNX09AH73G at the University of Washington. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.