19 September 2014

Assessing Model Simulations of Deep Convection: A Novel Compositing Approach

Climate model graphs of deep convective events
Science

Model evaluation commonly involves assessing the ability of models to capture observed climatological features. However, realistic climatologies can be achieved despite a poor simulation of important but short-lived phenomena. In this study, DOE- funded researchers perform a more stringent assessment of models by testing their ability to capture the observed spatio-temporal evolution of atmospheric properties and radiation in association with deep convection.

Approach

The authors create composite deep convective events in observations and in three climate models at 3-hour resolution by averaging over many instances of intense rain rates. This allows for a focused and systematic evaluation of simulated deep convective events. Despite having a larger climatological mean upper tropospheric relative humidity, the models closely capture the satellite-derived moistening of the upper troposphere following convection. Although simulated outgoing longwave radiation anomalies associated with deep convection are in reasonable agreement with observations, large errors in simulated cloud ice water content and cloud fraction suggest that such agreement with satellite-retrieved data is achieved in part due to compensating errors.

Impact

This work advances model evaluation beyond simple comparisons of climatological maps to assess the short-lived but critical phenomenon of deep convection, which is a challenge for large scale models to simulate accurately. This study identifies important compensating errors on short timescales that may be masked in climatological comparisons.

Contact
Mark Zelinka
Lawrence Livermore National Laboratory (LLNL)
Publications
Johnston, MS, S Eliasson, P Eriksson, RM Forbes, A Gettelman, P Räisänen, and MD Zelinka.  2014.  "Diagnosing the Average Spatio-Temporal Impact of Convective Systems - Part 2: A model intercomparison using satellite data."  Atmospheric Chemistry and Physics 14: 8701-8721.  https://doi.org/10.5194/acp-14-8701-2014.
Acknowledgments

The work of M. D. Zelinka is supported by the Regional and Global Climate Modeling Program of the Office of Science at the US Department of Energy (DOE) and is performed under the auspices of the US DOE by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. The National Center for Atmospheric Research is sponsored by the US National Science Foundation. P. Eriksson is supported by the Swedish National Space Board. The TMPA data were provided by the NASA/Goddard Space Flight Center’s Mesoscale Atmospheric Processes Laboratory and PPS, which develop and compute the TMPA as a contribution to TRMM. In addition the CERES data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. The authors would also like to acknowledge the NASA CloudSat project, which provided the CloudSat–CALIPSO data set used in this project. We would especially like to thank the anonymous reviewers, whose insights and critique of the paper contributed greatly to its quality.