Seasonal-to-interannual prediction of U.S. coastal marine ecosystems: Forecast methods, mechanisms of predictability, and priority developments
Michael Jacox, NOAA Southwest Fisheries Science Center, La Jolla, CA, United States, Michael A Alexander, NOAA Physical Sciences Laboratory, Boulder, United States, Samantha Siedlecki, University of Connecticut, Department of Marine Sciences, Groton, United States, Ke Chen, Woods Hole Oceanographic Inst, Woods Hole, United States, Young-Oh Kwon, Woods Hole Oceanographic Institution, Woods Hole, United States, Stephanie Brodie, University of New South Wales, School of Biological, Earth and Environmental Sciences, Monterey, United States, Ivonne Ortiz, University of Washington, JISAO, Seattle, WA, United States, Desiree Tommasi, Princeton University, Atmospheric and Oceanic Sciences, Princeton, NJ, United States, Matthew J Widlansky, University of Hawaii at Manoa, CIMAR, Honolulu, United States, Daniel Barrie, NOAA Climate Program Office, Silver Spring, MD, United States, Antonietta Capotondi, Cooperative Institute for Research in Environmental Sciences, Boulder, United States, Wei Cheng, Univ of Washington, Seattle, United States, Emanuele Di Lorenzo, Georgia Inst Tech, Earth and Atmospheric Sciences, Atlanta, GA, United States, Christopher A Edwards, University of California Santa Cruz, Santa Cruz, CA, United States, Jerome Fiechter, University of California Santa Cruz, Ocean Sciences, Santa Cruz, United States, Paula Sue Fratantoni, NOAA NMFS, Northeast Fisheries Science Center, Woods Hole, MA, United States, Elliott L. Hazen, NOAA Southwest Fisheries Science Center, Environmental Research Division, Monterey, United States, Albert J Hermann, University of Washington, Cooperative Institute for Climate, Ocean, and Ecosystem Studies, Seattle, United States, Arun Kumar, NOAA/NCEP, College Park, MD, United States, Arthur J Miller, University of California San Diego, La Jolla, CA, United States, Douglas Pirhalla, National Ocean Service, Silver Spring, MD, United States, Mercedes Pozo Buil, Georgia Institute of Technology Main Campus, School of Earth and Atmospheric Sciences, Atlanta, United States, Sulagna Ray, Princeton University, Program in Atmospheric and Oceanic Sciences, Princeton, NJ, United States, Scott C Sheridan, Kent State University Kent Campus, Kent, OH, United States, Aneesh Subramanian, University of Colorado Boulder, Boulder, United States, Philip R Thompson, JIMAR, University of Hawaiʻi at Mānoa, Honolulu, United States, Lesley H Thorne, Stony Brook University, School of Marine and Atmospheric Sciences, Stony Brook, NY, United States, H Annamalai, University of Hawai’i at Mānoa, International Pacific Research Center, Honolulu, United States, Steven J. Bograd, National Oceanic and Atmospheric Administration, Southwest Fisheries Science Center, Monterey, United States, Roger Griffis, NOAA, National Marine Fisheries Service, Silver Spring, MD, United States, Hyemi Kim, Stony Brook University, Stony Brook, NY, United States, Annarita Mariotti, OSTP, Washington D.C., DC, United States, Mark A Merrifield, Scripps Institution of Oceanography, La Jolla, United States and Ryan R Rykaczewski, University of South Carolina, Columbia, SC, United States
Abstract:
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).
