Impact of Having Realistic Tropical Cyclone Frequency on Ocean Heat Content & Transport Forecasts

Tuesday, 19 April 2016: 1:30 PM
Ponce de Leon C (The Condado Hilton Plaza)
Shaoqing N. Zhang, NOAA/GFDL, Princeton, NJ

When observations are assimilated into a high-resolution coupled model, a traditional scheme that preferably projects observations to correct large scale background tends to filter out small scale cyclones. Here we separately process the large scale background and small scale perturbations with low-resolution observations for reconstructing historical cyclone statistics in a cyclone-permitting model. We show that by maintaining the interactions between small scale perturbations and successively-corrected large scale background, a model can successfully retrieve the observed cyclone statistics that in return improve estimated ocean states. The improved ocean initial conditions together with the continuous interactions of cyclones and background flows are expected to reduce model forecast errors.

Then we examine two sets of high-resolution coupled model forecasts starting from no-tropical cyclone (TC) and correct-TC-statistics initial conditions to understand the role of TC events on climate prediction. While the model with no-TC initial conditions can quickly spin up TCs within a week, the initial conditions with a correct TC distribution can produce more accurate forecast of sea surface temperature up to one and half months and maintain larger ocean heat content up to 6 months due to enhanced mixing from continuous interactions between initialized and forecasted TCs and the evolving ocean states. The TC-enhanced tropical ocean mixing strengthens the meridional heat transport in the Southern Hemisphere driven primarily by Southern Ocean surface Ekman fluxes but weakens the Northern Hemisphere poleward transport in this model. This study suggests a future plausible initialization procedure for seamless weather-climate prediction when individual convection-permitting cyclone initialization is incorporated into this TC-statistics-permitting framework.

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