Estuarine Forecasts at Daily Weather to Subseasonal Time Scales

Andrew C. Ross, Charles A. Stock, Keith W. Dixon, Marjorie A.M. Friedrichs, Raleigh R. Hood, Ming Li, Kathleen Pegion, Vincent Saba, Gabriel A. Vecchi

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Article numbere2020EA001179
JournalEarth and Space Science
Issue number10
StatePublished - Oct 1 2020

All Science Journal Classification (ASJC) codes

  • Environmental Science (miscellaneous)
  • General Earth and Planetary Sciences


  • Chesapeake Bay
  • forecasting
  • prediction
  • subseasonal


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