Fast and slow responses of Southern Ocean sea surface temperature to SAM in coupled climate models
We investigate how sea surface temperatures (SSTs) around Antarctica respond to the Southern Annular Mode (SAM) on multiple timescales. To that end we examine the relationship between SAM and SST within unperturbed preindustrial control simulations of coupled general circulation models (GCMs) included in the Climate Modeling Intercomparison Project phase 5 (CMIP5). We develop a technique to extract the response of the Southern Ocean SST (55°S–70°S) to a hypothetical step increase in the SAM index. We demonstrate that in many GCMs, the expected SST step response function is nonmonotonic in time. Following a shift to a positive SAM anomaly, an initial cooling regime can transition into surface warming around Antarctica. However, there are large differences across the CMIP5 ensemble. In some models the step response function never changes sign and cooling persists, while in other GCMs the SST anomaly crosses over from negative to positive values only 3 years after a step increase in the SAM. This intermodel diversity can be related to differences in the models’ climatological thermal ocean stratification in the region of seasonal sea ice around Antarctica. Exploiting this relationship, we use observational data for the time-mean meridional and vertical temperature gradients to constrain the real Southern Ocean response to SAM on fast and slow timescales.
KeywordsSouthern Ocean Southern Annular Mode Surface westerlies Atmosphere–ocean interaction CMIP5
The CMIP5 data for this study is available at the Earth System Grid Federation (ESGF) Portal (https://pcmdi9.llnl.gov/projects/esgf-llnl/). Y.K. received support from an NSF MOBY Grant, award #1048926. J.M., U.H., D.F., and M.M.H. were funded by the NSF FESD program, Grant Award #1338814. K.C.A. was supported by a James McDonnell Foundation Postdoctoral Fellowship and NSF Grant OCE-1523641. We would like to thank the World Climate Research Programme and the Working Group on Coupled Modelling, which is in charge of CMIP5. We extend our appreciation to the organizations that support and develop the CMIP infrastructure: the US Department of Energy through its Program for Climate Model Diagnosis and Intercomparison and the Global Organization for Earth System Science Portals. We thank the CMIP5 climate modeling groups for providing their numerical output. We express gratitude to Paul O’Gorman, Jan Zika, and an anonymous reviewer for their helpful comments and suggestions.
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