Jacox, M.G., M. A. Alexander, S. Siedlecki, K. Chen, Y.-O. Kwon, S. Brodie, I. Ortiz, D.
Tommasi, M. J. Widlansky, D. Barrie, A. Capotondi, W. Cheng, E. Di Lorenzo, C.
Edwards, J. Fiechter, P. Fratantoni, E. L. Hazen, A. J. Hermann, A. Kumar, A. J.
Miller, D. Pirhalla, M. Pozo Buil, S. Ray, S. C. Sheridan, A. Subramanian, P.
Thompson, L. Thorne, H. Annamalai, S. J. Bograd, R. B. Griffis, H. Kim, A. Mariotti,
M. Merrifield and R. Rykaczewski, 2019:
Seasonal-to-interannual prediction of
U.S. coastal marine ecosystems: Forecast methods, mechanisms of predictability,
and priority developments.
Progress in Oceanography,
Marine ecosystem forecasting is an area of active research and rapid development. Promise has
been shown for skillful prediction of physical, biogeochemical, and ecological variables on a
range of timescales, suggesting potential for forecasts to aid in the management of living marine
resources and coastal communities. However, the mechanisms underlying forecast skill in
marine ecosystems are often poorly understood, and many forecasts, especially for biological
variables, rely on empirical statistical relationships developed from historical observations. Here,
we review statistical and dynamical marine ecosystem forecasting methods and highlight
examples of their application along U.S. coastlines for seasonal-to-interannual (1-24 month)
prediction of properties ranging from coastal sea level to marine top predator distributions. We
then describe known mechanisms governing marine ecosystem predictability in these regions
and how they have been used in forecasts to date. These mechanisms include physical
atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing,
and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on
forecasting techniques and mechanisms underlying predictability in U.S. marine ecosystems, we
aim to facilitate forecast development and uptake by (i) identifying methods and processes that
can be exploited for development of skillful regional forecasts, (ii) informing priorities for
forecast development and validation, and (iii) improving understanding of conditional forecast
skill (i.e., a priori knowledge of whether a forecast is likely to be skillful).