Dr. Mark D. Zelinka – Mentor Cloud Processes Research Group, Lawrence Livermore National Laboratory

Dr. Xiaoqing Wu – Co-Mentor Department of Geological and Atmospheric Sciences, Iowa State University


Global climate model predictions of future warming vary substantially due to uncertainties in how clouds respond, through their impact on Earth’s energy budget. Since it is not possible to evaluate how clouds changed as the planet warmed over the industrial era, it is useful to look at short-term cloud feedbacks operating on inter-annual timescales. Because cloud feedbacks on inter-annual timescales are highly correlated with those on climate change timescales, evaluating models’ cloud feedbacks on short timescales may help constrain climate sensitivity. Here, a novel cloud radiative kernel technique was used to detail the short-term cloud feedback in the Community Atmosphere Model version 5 (CAM5) and in a suite of satellite cloud observations. Whereas past studies indicated that models’ short-term tropical cloud feedbacks tend to be too positive, we found that the model closely matched observations. However, in agreement with previous work, we found that the tropical high cloud amount feedback is too large in models. We also found that the simulated total net high cloud feedback resembles the net high cloud amount feedback, but the observed net high cloud feedback more resembles the high cloud altitude and optical depth components. For low clouds, estimated inversion strength (EIS) was shown to be a strong indicator of the cloud amount feedback, and observations have a more positive optical depth feedback than the models. Finally, constraining meteorology using hindcast simulations improved regional and global aspects of the simulated feedbacks. Our results strongly suggest that high clouds in the model are too optically thick leading to biases in regional high cloud amount feedback, but future work is needed to quantify to what extent.

Copyright Owner

Scott W. Feldman

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