Sulagna Ray

I am a Physical Oceanographer seeking to enhance our understanding of the global climate/ocean variability using observations and climate models. My research has focused on the largest global climate variability – El Niño and Southern Oscillation (ENSO), trying to understand its variation from past observational records, like ocean reanalyses, reconstructed records, as well as how we simulate it in current generation of climate models. Currently my research focuses on the seasonal predictability of the North-Western US coast using downscaling approach in the regional model –ROMS. The temperature off the Washington-Oregon shelf is affected by ENSO conditions in the tropical Pacific via teleconnections, coastally trapped waves from south, California Undercurrent, beside the local winds and alongshore pressure gradients. On longer timescales advection from the West Pacific could have some influence on the bottom conditions. My research investigates how the various processes essentially works to maintain the bottom water conditions off Washington-Oregon shelf, and how well can we predict them on seasonal timescale.

Current climate models not only vary widely in simulating ENSO characteristics, but also struggle to simulate the mean state of the tropical Pacific, which is essential to set in ENSO conditions and wide teleconnection patterns globally.  A dominant issue plaguing most of the current generation of climate models is the common climatological biases, like – excessive cold equatorial cold tongue that extends too far west in the equatorial Pacific, too warm coastal waters along the Peru coast, to name a few. My research built comprehensive diagnostics towards understanding these biases thereby contributing to model developers with solutions on how to fix them.

CMIP models are used to provide decadal predictions of global and regional climate patterns to address the needs of different stake-holders. Given the differences among existing climate models in simulating and predicting the global climate, it is also essential to address the reliability of such predictions issued from each such modelling systems. Hence, I assessed the initialized ocean state in IPSLCM5A (one of the CMIP5 models) globally as well as regionally, that is used to launch decadal forecasts. The prospect of reliable initialized decadal predictions would heavily rely on whether the initialized ocean state reconstructs the past observed variability.


Selected Publications:

Ray, S., A.T. Wittenberg, S. M. Griffies, and F. Zeng: Understanding the Equatorial Pacific Cold Tongue Time-Mean heat budget, Part I: Diagnostic framework.  accepted in J. Climate. paper

Ray, S., A.T. Wittenberg, S. M. Griffies, and F. Zeng:Understanding the Equatorial Pacific Cold Tongue Time-Mean heat budget, Part II: Evaluation of the GFDL-FLOR coupled GCM.  accepted in J. Climate.paper

Ray, S., D. Swingeduow, J. Mignot, E. Guilyardi, 2015: Effect of surface restoring on subsurface variability in a climate model during 1949-2005. Climate Dynamics. 44: 2333-2349. DOI 10.1007/s00382-014-2358-3.paper

Ray, S., and B. Giese, 2012: Historical changes in El Niño and La Niña characteristics in an ocean reanalysis. J. Geophys. Res., 117, C11007, doi:10.1029/2012JC008031.paper

Giese, B., and S. Ray, 2011: El Niño variability in simple ocean data assimilation (SODA), 1871-2008. J. Geophys. Res., 116, C02024, doi:10.1029/2010JC006695.paper

Giese, B., G. Compo, N. Slowey, P. Sardeshmukh, J. Carton, S. Ray and J. Whitaker, 2010: The 1918/19 El Niño , Bull. Amer.Met.Soc. 91, 2 177-183.paper