Most Systematic Errors in Climate Models Appear in Only a Few Days of Model Integration Revealed by the Transpose-AMIP Hindcasts
Better understanding of the systematic errors in climate models will improve their fidelity in simulating the mean state and variability of the current and future climate. However, this is challenging because nonlinear feedback processes in the climate system make it difficult to unambiguously identify causal relationships.
Scientists at Lawrence Livermore National Laboratory and several modeling centers around the world including National Center for Atmospheric Research, U. K. Met Office, Institut Pierre Simon Laplace, Météo-France, and Atmosphere and Ocean Research Institute, University of Tokyo, examine the correspondence between short- and long-term systematic errors in five atmospheric models. They compare the sixteen 5-day hindcast ensembles from the Transpose-AMIP II for the July-August 2009 (short-term), to the climate simulations from the CMIP5/AMIP for the June-August mean conditions of the years of 1979-2008 (long-term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach. Their analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by Day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first two to three days. However biases associated with model physics can have impacts on the large-scale states by Day 5, such as zonal winds, 2 meter temperature and sea level pressure. Their analysis further indicates a good correspondence between short- and long-term biases for these large-scale states.
Improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.
Lawrence Livermore National Laboratory
- Regional & Global Climate Modeling
We are grateful to the ECMWF for making their operational analyses available. The efforts of H.-Y. Ma, S. Xie, S. A. Klein, and J. S. Boyle were funded by the Regional and Global Climate Modeling and Atmospheric System Research programs of the U.S. Department of Energy as part of the Cloud-Associated Parameterizations Testbed. This work was performed under the auspices of the U.S. Department of Energy by LLNL under contract DE-AC52-07NA27344. The TAMIP work by S. Bony and S. Fermepin was supported by the FP7-ENV-2009-1 European project EUCLIPSE (#244067). The efforts of B. Medeiros and D. Williamson were partially supported by the Office of Science (BER), U.S. Department of Energy, Cooperative Agreement No. DE-FC02-97ER62402. The National Center for Atmospheric Research is sponsored by the National Science Foundation.