The reemergence of the winter sea surface temperature tripole in the North Atlantic from ocean reanalysis data

Sukhonos, P. A.; Alexander, Michael A.

doi:10.1007/s00382-022-06581-x

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Furthermore, compared to many other regions like the North American West Coast, predictability for the East Coast is hindered by its weaker teleconnections to ENSO and other predictable climate modes at seasonal time scales. The seasonal variability of ocean temperature anomalies in the North Atlantic basin is strongly connected to the North Atlantic Oscillation (NAO), but this mode is only moderately predictable at seasonal time scales (with dynamical model skill generally requiring large ensembles; Smith et al, 2020), and the contribution of NAO to ocean temperature predictability involves features and processes that are often poorly simulated in coarse resolution models, including modulation of the Gulf Stream (Sanchez-Franks et al, 2016;Shin and Newman, 2021) and reemergence of anomalies from the previous winter sequestered below the mixed layer (Sukhonos and Alexander, 2023).…”

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Dynamically downscaled seasonal ocean forecasts for North American East Coast ecosystems

Ross,

Stock,

Koul

et al. 2024

Preprint

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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.

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confidence: 99%

Dynamically downscaled seasonal ocean forecasts for North American East Coast ecosystems

Ross,

Stock,

Koul

et al. 2024

Preprint

View full text Add to dashboard Cite

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Winter–to–winter recurrence of the tripole pattern of the sea surface temperature anomalies in the North Atlantic ocean and its interaction with the NAO

Sukhonos,

Alexander

2024

Clim Dyn

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Dynamically downscaled seasonal ocean forecasts for North American east coast ecosystems

Ross,

Stock,

Koul

et al. 2024

Ocean Sci.

View full text Add to dashboard Cite

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 US Large Marine Ecosystem, a region that global models have historically struggled to skillfully predict. The downscaled sea surface temperature (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 the SST anomaly prediction skill in the Northeast US region, it improved bottom temperature and sea surface salinity anomaly skill across many of the marine ecosystems along the North American east coast. Although improvements in anomaly prediction via downscaling were ubiquitous, the effects of downscaling on prediction bias were mixed. 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 US). 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 reemergence 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.

show abstract

The reemergence of the winter sea surface temperature tripole in the North Atlantic from ocean reanalysis data

Cited by 3 publications

References 35 publications

Dynamically downscaled seasonal ocean forecasts for North American East Coast ecosystems

Dynamically downscaled seasonal ocean forecasts for North American East Coast ecosystems

Winter–to–winter recurrence of the tripole pattern of the sea surface temperature anomalies in the North Atlantic ocean and its interaction with the NAO

Dynamically downscaled seasonal ocean forecasts for North American east coast ecosystems

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