Abstract
Global climate models (GCMs) play a vital role in understanding climate variability and estimating climate change at global and regional scales. Therefore, it becomes crucial to have an appropriate evaluation strategy for evaluating these models. A lot of work has been done to evaluate the ENSO simulations of different GCMs. However, they do not consider how well a GCM simulates the impact of ENSO all over the globe. Therefore, in this work, we used this criteria to evaluate the Coupled Model Intercomparison Project (CMIP5) GCMs. We found that the global impact of ENSO in CNRM-CM5, GFDL-CM3, and CESM-FASTCHEM is highly similar to that of observations.
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Acknowledgements
This work was supported by NSF grants IIS-0905581 and IIS-1029771. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.
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Agrawal, S., Rehberger, T., Liess, S., Atluri, G., Kumar, V. (2015). Evaluation of Global Climate Models Based on Global Impacts of ENSO. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_10
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DOI: https://doi.org/10.1007/978-3-319-17220-0_10
Publisher Name: Springer, Cham
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