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Indian Ocean mean state biases and IOD behaviour in the CMIP6 multimodel ensemble

Authors

Gler,  Marimel
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Turner,  Andrew
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Hirons,  Linda
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Wainwright,  Caroline
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Marzin,  Charline
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Gler, M., Turner, A., Hirons, L., Wainwright, C., Marzin, C. (2023): Indian Ocean mean state biases and IOD behaviour in the CMIP6 multimodel ensemble, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-4736


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021144
Abstract
The Indian Ocean Dipole (IOD) is the main coupled mode of interannual variability in the equatorial Indian Ocean. Despite its socio-economic importance, the Indian Ocean region suffers large biases in weather and climate models used for seasonal forecasts and climate projections. In this study, the performance of 42 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) in reproducing the observed climate over the Indian Ocean is examined. We investigate whether the ability of a model to capture characteristics of the IOD and simulate IOD teleconnection patterns is related to its representation of the mean state. Skill metrics are calculated to quantify precipitation biases in the mean state during Boreal summer (JJA) in models from the Atmospheric Model Intercomparison Project (AMIP) and 20th-century historical all-forcings experiments. Cluster analysis is performed to determine whether biases in the seasonal cycle during JJA impact the response of the atmosphere to the IOD. The IOD behaviour in the AMIP and coupled models is assessed and the response of the atmospheric circulation to IOD forcing is examined by performing regression analysis. The next phase of this work will be to examine the development of systematic errors in the Indian Ocean in a seasonal forecasting system. The work will contribute to our understanding of Indian Ocean biases in weather and climate models, and their likely sources, and thus the wider implications for predictability of the IOD.