[A-OS09] Climate variability and predictability on subseasonal to multidecadal timescales

[AOS09-07] GFDL's SPEAR Seasonal Prediction

★Invited Papers

*Feiyu Lu^1,2, Matt Harrison², Anthony Rosati^2,3, Tom Delworth², Xiaosong Yang², William Cooke^2,3, Liwei Jia^3,2, Colleen McHugh^2,4, Nathaniel Johnson², Mitchell Bushuk^2,3, Yongfei Zhang^1,2, Alistair Adcroft^1,2 (1.Princeton University, 2.NOAA/GFDL, 3.UCAR, 4.SAIC)

Keywords:seasonal prediction, bias correction, SPEAR, data assimilation

The next-generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of NOAA. SPEAR is an effort to develop a seamless system for prediction and research across timescales. The ensemble-based ocean data assimilation (ODA) system is updated for Modular Ocean Model version 6 (MOM6), the ocean component of SPEAR. In this talk, we will describe the updated ODA system and discuss our choice of the coupled model initialization scheme for seasonal predictions. A bias-reduction scheme called Ocean Tendency Adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ocean data assimilation as 3-dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially SST forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño-Southern Oscillation (ENSO).