Estuarine Forecasts at Daily Weather to Subseasonal Time Scales. Issue 10 (30th September 2020)

• Record Type: Journal Article
• Authors: Ross, Andrew C.; Stock, Charles A.; Dixon, Keith W.; Friedrichs, Marjorie A. M.; Hood, Raleigh R.; Li, Ming; Pegion, Kathleen; Saba, Vincent; Vecchi, Gabriel A.
• Is Part Of: Earth and space science. Volume 7:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-30
• Subjects: Chesapeake Bay -- forecasting -- prediction -- subseasonal; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2020EA001179
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: Most present forecast systems for estuaries predict conditions for only a few days into the future. However, there are many reasons to expect that skillful estuarine forecasts are possible for longer time periods, including increasingly skillful extended atmospheric forecasts, the potential for lasting impacts of atmospheric forcing on estuarine conditions, and the predictability of tidal cycles. In this study, we test whether skillful estuarine forecasts are possible for up to 35 days into the future by combining an estuarine model of Chesapeake Bay with 35‐day atmospheric forecasts from an operational weather model. When compared with both a hindcast simulation from the same estuarine model and with observations, the estuarine forecasts for surface water temperature are skillful up to about 2 weeks into the future, and the forecasts for bottom temperature, surface and bottom salinity, and density stratification are skillful for all or the majority of the forecast period. Bottom oxygen forecasts are skillful when compared to the model hindcast, but not when compared with observations. We also find that skill for all variables in the estuary can be improved by taking the mean of multiple estuarine forecasts driven by an ensemble of atmospheric forecasts. Finally, we examine the forecasts in detail using two case studies of extreme events, and we discuss opportunities for improving the forecast skill. Plain Language Summary: This paper evaluates a suite of forecasts for Chesapeake Bay water temperature, salinity, and dissolved oxygen created using a numerical model. By comparing the model forecasts with observations, we show that the model forecasts for temperature and salinity are more accurate than reference forecasts of previously observed conditions or the long‐term mean; in other words, the forecasts are skillful. In general, the forecasts are skillful for at least 2 weeks into the future. Improvements to our forecasting system, such as predicting future river discharge into Chesapeake Bay, would likely improve the forecast skill even more. By showing that accurate, skillful forecasts are possible for a much longer time frame than previously considered, this paper takes an important step toward applying forecasts to improve water quality and fisheries management and to prepare for the impacts of extreme events like hurricanes and heat waves. Key Points: Skillful forecasts of estuarine temperature and salinity are possible beyond the few days typically considered Dissolved oxygen remains challenging to forecast Taking the mean of multiple forecasts driven by different atmospheric ensemble members improves the skill