Document Type
Article
Department/Program
Virginia Institute of Marine Science
Publication Date
10-1-2020
Journal
Earth and Space Science
Volume
7
Issue
10
First Page
e2020EA001179
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.
DOI
doi: 10.1029/2020EA001179
Keywords
Chesapeake Bay, forecasting, subseasonal, estuarine environment
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ross, Andrew C.; Stock, Charles A.; Dixon, Keith W.; Friedrichs, Marjorie A.M.; and et al, Estuarine Forecasts at Daily Weather to Subseasonal Time Scales (2020). Earth and Space Science, 7(10), e2020EA001179.
doi: 10.1029/2020EA001179
Supporting Information S1
