ENSO simulation in coupled ocean-atmosphere models: are the current models better?
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Maintaining a multi-model database over a generation or more of model development provides an important framework for assessing model improvement. Using control integrations, we compare the simulation of the El Niño/Southern Oscillation (ENSO), and its extratropical impact, in models developed for the 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report with models developed in the late 1990s [the so-called Coupled Model Intercomparison Project-2 (CMIP2) models]. The IPCC models tend to be more realistic in representing the frequency with which ENSO occurs, and they are better at locating enhanced temperature variability over the eastern Pacific Ocean. When compared with reanalyses, the IPCC models have larger pattern correlations of tropical surface air temperature than do the CMIP2 models during the boreal winter peak phase of El Niño. However, for sea-level pressure and precipitation rate anomalies, a clear separation in performance between the two vintages of models is not as apparent. The strongest improvement occurs for the modelling groups whose CMIP2 model tended to have the lowest pattern correlations with observations. This has been checked by subsampling the multi-century IPCC simulations in a manner to be consistent with the single 80-year time segment available from CMIP2. Our results suggest that multi-century integrations may be required to statistically assess model improvement of ENSO. The quality of the El Niño precipitation composite is directly related to the fidelity of the boreal winter precipitation climatology, highlighting the importance of reducing systematic model error. Over North America distinct improvement of El Niño forced boreal winter surface air temperature, sea-level pressure, and precipitation rate anomalies to occur in the IPCC models. This improvement is directly proportional to the skill of the tropical El Niño forced precipitation anomalies.
KeywordsRoot Mean Square Difference Pattern Correlation CMIP2 Model Large Pattern Correlation Precipitation Rate Anomaly
We thank Drs Ben Santer, Karl Taylor, and Jim Boyle for helpful conversations regarding sampling issues. We wish to thank Matt Collins and one anonymous reviewer for helpful comments and suggestions. We acknowledge the international modelling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, US Department of Energy. This work was performed under the auspices of the US Department of Energy Office of Science, Climate Change Prediction Program by University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48.
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