GFDL's SPEAR Seasonal Prediction System: Initialization and Ocean Tendency Adjustment (OTA) for Coupled Model Predictions. (25th November 2020)

• Record Type: Journal Article
• Authors: Lu, Feiyu; Harrison, Matthew J.; Rosati, Anthony; Delworth, Thomas L.; Yang, Xiaosong; Cooke, William F.; Jia, Liwei; McHugh, Colleen; Johnson, Nathaniel C.; Bushuk, Mitchell; Zhang, Yongfei; Adcroft, Alistair
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-25
• Subjects: seasonal prediction -- data assimilation -- coupled model -- SPEAR -- model bias -- model initialization; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2020MS002149
• Languages: English
• ISSNs: 1942-2466

Abstract

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). Plain Language Summary: Dynamic seasonal prediction systems employ global climate models to predict climate variations on monthly to seasonal time scales. A new state‐of‐the‐art seasonal prediction system has been developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The prediction models are based on GFDL's new component models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The new seasonal prediction system includes new ways to apply observational information to initialize the model simulations, including an updated data assimilation system for the Modular Ocean Model Version 6 (MOM6). Bias correction is applied to the coupled dynamic models to reduce prediction bias, namely, the gap between model‐simulated and real‐world climatology. Preliminary seasonal prediction experiments demonstrate reduced errors in the predicted climate state globally, as well as improved prediction skill for applications such as El Niño–Southern Oscillation (ENSO). Key Points: The next‐generation SPEAR seasonal prediction system uses the recently developed coupled general circulation model at GFDL The updated ocean data assimilation system for the MOM6 ocean model produces ocean state estimation and initial conditions for predictions The new coupled model and ocean tendency adjustment (OTA) reduce model forecast drift and improve seasonal prediction skill