Dynamically downscaled seasonal ocean forecasts for North American East Coast ecosystems
Andrew C. Ross,Charles A. Stock,Vimal Koul,Thomas L. Delworth,Feiyu Lu,Andrew Wittenberg,and Michael A. Alexander
Abstract. Using a 1/12° regional model of the Northwest Atlantic Ocean (MOM6-NWA12), we downscale an ensemble of retrospective seasonal forecasts from a 1° global forecast model. To evaluate whether downscaling improved the forecast skill for surface temperature and salinity and bottom temperature, the global and downscaled forecasts are compared with each other and with a reference forecast of persistence using anomaly correlation. Both sets of forecasts are also evaluated on the basis of mean bias and ensemble spread. We find that downscaling significantly improved the forecast skill for monthly sea surface temperature anomalies in the Northeast U.S. Large Marine Ecosystem, a region that global models have historically struggled to predict skillfully. The downscaled SST predictions for this region were also more skillful than the persistence baseline across most initialization months and lead times. Although some of the SST prediction skill in this region stems from the recent, rapid warming trend, prediction skill above persistence is generally maintained after removing the contribution of the trend, and patterns of skill suggestive of predictable processes are also preserved. While downscaling mainly improved SST skill in the Northeast U.S. region, it improved bottom temperature and sea surface salinity skill across many of the marine ecosystems along the North American East Coast. Downscaling generally reduced the mean surface salinity biases found in the global model, particularly in regions with sharp salinity gradients (the Northern Gulf of Mexico and the Northeast U.S.). In some cases, however, downscaling amplified the surface and bottom temperature biases found in the global predictions. We discuss several processes that are better resolved in the regional model and contribute to the improved skill, including the autumn re-emergence of temperature anomalies and advection of water masses by coastal currents. Overall, the results show that a downscaled, high resolution model can produce improved seasonal forecast skill by representing fine-scale processes that drive predictability.
Received: 13 Feb 2024 – Discussion started: 21 Feb 2024
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The paper provides an extensive study on downscaled retrospective forecast in the Northwest Atlantic Ocean from GFDL global model using a 1/12 configuration based on MOM6, previously designed and assessed by the Authors in another dedicated paper.
The methodology used for assessing the forecast is very interesting and quite comprehensive as well as the process-oriented analysis, supported by discussed results.
Andrew C. Ross,Charles A. Stock,Vimal Koul,Thomas L. Delworth,Feiyu Lu,Andrew Wittenberg,and Michael A. Alexander
Data sets
Model output for "Dynamically downscaled seasonal ocean forecasts for North American East Coast ecosystems"Andrew C. Ross, Charles A. Stock, Vimal Koul, Thomas L. Delworth, Feiyu Lu, Andrew Wittenberg, and Michael A. Alexander https://doi.org/10.5281/zenodo.10642294
Model code and software
Model source code for "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)"Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins https://doi.org/10.5281/zenodo.7893349
Andrew C. Ross,Charles A. Stock,Vimal Koul,Thomas L. Delworth,Feiyu Lu,Andrew Wittenberg,and Michael A. Alexander
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In this paper, we use a high resolution regional ocean model to downscale seasonal ocean forecasts from GFDL’s SPEAR model. We find that the downscaled model has significantly higher prediction skill in many cases.
In this paper, we use a high resolution regional ocean model to downscale seasonal ocean...