5A.3 Tropical SSTs: The Boon and Bane of Climate Predictability around the Globe

Tuesday, 8 January 2019: 11:00 AM
North 121BC (Phoenix Convention Center - West and North Buildings)
Prashant D. Sardeshmukh, CIRES, Boulder, CO; and S. I. Shin, G. P. Compo, and C. McColl

Much of the potential for global climate predictability from seasonal to multidecadal time scales rests on the predictability of tropical SSTs. Model misrepresentations of the magnitudes and patterns of tropical SST changes on these time scales thus have important consequences. The sensitivities of remote teleconnections to SST errors at different tropical locations may be estimated from the global responses to prescribed localized SST anomalies in atmospheric models. Such investigations yield a “Fuzzy Green’s Function” of the global atmospheric response to tropical SST forcing, and have been performed by prescribing 42 regularly spaced localized tropical SST patches as anomalous boundary conditions in the NCAR and ECHAM5 models. The dominant EOF patterns of the 42 responses, and the relative magnitudes with which they are excited by the individual patches, determine the dominant pairs of response and forcing (formally, the left and right singular vectors of the Green’s function operator). The dominant SST patterns can be interpreted both as major sources of regional climate predictability and as patterns of tropical SST forecast errors to which the errors in remote regions are most sensitive.

The dominant SST sensitivity pattern (which is different for every season) has a very different structure from the dominant ENSO pattern of observed SST variability, and has the largest magnitude but opposite signs in the western and eastern halves of the Indo-Pacific warm pool. This make it important for models to predict SSTs accurately in this region, which they currently do not. Specifically, on the seasonal scale, the NMME models used for seasonal predictions extend the predicted central Pacific SST anomalies during ENSO events too far westward into the warm pool. On decadal and longer scales, the CMIP5 models underestimate the magnitude and misrepresent the spatial variation of tropical SST changes, and hence the magnitude of the atmospheric circulation changes (including changes in weather extremes) in most regions of the globe. By underestimating the changes in SST gradients, the models exaggerate the relatively robust regional thermodynamic aspects of the changes on these time scales over the equally important dynamic circulation aspects that are much less robust and spuriously weak in the models.

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