An Innovative Approach to Improve Assessment of Climate Model Clouds: Scientists show the latest Community Atmospheric Model (CAM5) significantly improves cloud simulations
Large uncertainty in simulations of future climate is largely due to difficulty in representing clouds and their feedback processes in GCMs. Although the abundance of cloud-observing satellites provides opportunities to understand and quantify climate model cloud biases, this process has been long hampered by poor model-observational comparison techniques. A diagnostic tool, known as the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP), has been recently developed through a close collaboration among a group of scientists worldwide to permit a meaningful comparison between model-simulated clouds and the corresponding satellite observations. Scientists at Lawrence Livermore National Laboratory (LLNL) have played a leading role in developing COSP and worked with NCAR scientists in using COSP to assess cloud simulations by the latest Community Atmosphere Models (CAM4/CAM5). Their results have been summarized in an article that was published by Journal of Climate Community Earth System Model Special Issue.
Researchers examined the COSP outputs from 10-year CAM4/5 AMIP runs. The produced COSP outputs with CAM4/5 that contain a variety of cloud property and cloud fraction diagnostics were compared with their corresponding satellite observations.
Results from the global study show that CAM5 with advanced physics significantly reduces the long-standing errors in simulated clouds through the increase of total cloud fraction, decrease of optically thick clouds, and increase of mid-level clouds in comparison with its earlier version, CAM4. Despite the improvements in CAM5, both CAM versions have cloud and precipitation deficiencies. CAM clouds were also evaluated in select climatically important regions. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are known to explain inter-model differences in cloud feedbacks and climate sensitivity.
This study presents the first comprehensive global evaluation of CAM cloud biases using the cutting-edge satellite observations and demonstrates that simulator-facilitated evaluation of cloud properties can robustly expose large and at times radiatively compensating climate model cloud biases. It illustrates the advantages of using multiple satellite observations and simulators to evaluate model cloud biases. The satellite simulator strategy removes much of the ambiguity in climate model cloud evaluation and guides a more rapid model improvement. It will ultimately help to reduce uncertainty in climate predictions.
This study presents the first comprehensive global evaluation of CAM cloud biases using the cutting-edge satellite observations and demonstrates that simulator-facilitated evaluation of cloud properties can robustly expose large and at times radiatively compensating climate model cloud biases. It illustrates the advantages of using multiple satellite observations and simulators to evaluate model cloud biases. The satellite simulator strategy removes much of the ambiguity in climate model cloud evaluation, and guides a more rapid model improvement. It will ultimately help to reduce uncertainty in climate predictions.
Steve A. Klein, Yuying Zhang, and James Boyle were supported by Earth System Modeling and Regional and Global Climate Programs of the Office of Science at the U.S. Department of Energy and their contributions to this work were performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DEAC52- 07NA27344.