GFDL's SPEAR Seasonal Prediction System: Initialization and Ocean Tendency Adjustment (OTA) for Coupled Model Predictions

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Feiyu Lu, Matthew J. Harrison, Anthony Rosati, Thomas L. Delworth, Xiaosong Yang, William F. Cooke, Liwei Jia, Colleen McHugh, Nathaniel C. Johnson, Mitchell Bushuk, Yongfei Zhang, Alistair Adcroft
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2020
• Subjects: seasonal prediction; data assimilation; coupled model; SPEAR; model bias; model initialization; Physical geography; GB3-5030; Oceanography; GC1-1581; Sea ice
• Online Access: https://doi.org/10.1029/2020MS002149; https://doaj.org/article/09f23caa6e744f68bee36f34b3b174ce

Abstract 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 the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction 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 ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).