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

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).

Natural variability vs forced signal in the 2015–2019 Central American drought

“…The SPEAR atmospheric model is run at different horizontal resolutions (atmosphere, land) in this paper: 0.5 • (SPEAR MED) and 1 • (SPEAR LO), and it has 33 atmospheric levels in the vertical. More details about SPEAR and the SPEAR large ensemble can be found in Delworth et al (2020), Lu et al (2020), Pascale et al (2020), andMurakami et al (2020). The SPEAR MED large ensemble is characterized by a horizontal grid-spacing that is finer than those of most other available large ensembles, which makes the SPEAR MED ensemble an unprecedented and unique tool to study regional climates.…”

Seasonal Prediction and Predictability of Regional Antarctic Sea Ice

“…The ICs used for the SPEAR_LO and SPEAR_MED predictions come from two separate assimilation experiments spanning 1990-2018. The ocean ICs come from an ocean data assimilation (ODA) system based on the SPEAR_LO model (Lu et al 2020). The ODA system uses an EAKF to assimilate daily SST from NOAA's OISST product and T/S profiles from Argo floats, expendable bathythermograph data (XBT), and tropical moorings.…”

“…This procedure applies the climatological increments obtained from a prior ODA run as 3D temperature and salinity tendency terms to the free-running ocean model. This technique reduces model drift and improves both assimilation accuracy and prediction skill in coupled model predictions of El Niño-Southern Oscillation (Lu et al 2020).…”

Are Multiseasonal Forecasts of Atmospheric Rivers Possible?

“…This difference might be associated with the spring predictability barrier, where the tropical ENSO signal is weak during its transition stage around late spring, or that 8-month ENSO predictability is not strongly reflected in tropical SST composites at these lags. Indeed, a previous study (Lu et al, 2020) has shown that SPEAR can skillfully predict ENSO 9 months in advance (see also Figures S7 and S8). We demonstrate in Figure S7 that SPEAR also skillfully predicts the IPO at these lead times, which supports that both ENSO and the IPO may be potential seasonal predictability sources for AR activity.…”

“…Simulations with 15 ensemble members are initialized each January 1, April 1, July 1, and October 1 from 1995 to 2018 and then run for 12 months. For initialization, an updated ocean data assimilation using ocean tendency adjustment (OTA; Lu et al, 2020) was developed for the MOM6 ocean model, which ingests data from NOAA's Optimum Interpolation SST (OISST), tropical buoys, and three-dimensional temperature and salinity profiles acquired from Argo (2020).…”