Seasonal-to-interannual prediction of U.S. coastal marine ecosystems: Forecast methods, mechanisms of predictability, and priority developments
Michael Jacox1, Michael A Alexander2, Samantha Siedlecki3, Ke Chen4, Young-Oh Kwon5, Stephanie Brodie6, Ivonne Ortiz7, Desiree Tommasi8, Matthew J Widlansky9, Daniel Barrie10, Antonietta Capotondi11, Wei Cheng12, Emanuele Di Lorenzo13, Christopher A Edwards14, Jerome Fiechter15, Paula Sue Fratantoni16, Elliott L. Hazen1, Albert J Hermann17, Arun Kumar18, Arthur J Miller19, Douglas Pirhalla20, Mercedes Pozo Buil21, Sulagna Ray22, Scott C Sheridan23, Aneesh Subramanian24, Philip R Thompson25, Lesley H Thorne26, H Annamalai27, Steven J. Bograd1, Roger Griffis28, Hyemi Kim29, Annarita Mariotti30, Mark A Merrifield31 and Ryan R Rykaczewski32, (1)NOAA Southwest Fisheries Science Center, Ecosystem Science Division, Monterey, United States, (2)NOAA Physical Sciences Laboratory, Boulder, United States, (3)University of Connecticut, Department of Marine Sciences, Groton, United States, (4)Woods Hole Oceanographic Institution, Physical Oceanography, Woods Hole, United States, (5)Woods Hole Oceanographic Institution, Woods Hole, United States, (6)University of New South Wales, School of Biological, Earth and Environmental Sciences, Monterey, United States, (7)University of Washington, JISAO, Seattle, WA, United States, (8)Princeton University, Atmospheric and Oceanic Sciences, Princeton, NJ, United States, (9)University of Hawaii at Manoa, CIMAR, Honolulu, HI, United States, (10)NOAA Climate Program Office, Silver Spring, MD, United States, (11)University of Colorado, CIRES, Boulder, United States, (12)University of Washington, School of Oceanography, Seattle, United States, (13)Georgia Inst Tech, Earth and Atmospheric Sciences, Atlanta, United States, (14)University of California Santa Cruz, Santa Cruz, CA, United States, (15)University of California Santa Cruz, Ocean Sciences, Santa Cruz, United States, (16)NOAA NMFS, Northeast Fisheries Science Center, Woods Hole, MA, United States, (17)University of Washington, Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES), Seattle, United States, (18)NOAA/NCEP, College Park, MD, United States, (19)University of California San Diego, La Jolla, CA, United States, (20)National Ocean Service, Silver Spring, MD, United States, (21)University of California, Santa Cruz, Institute of Marine Sciences, Santa Cruz, United States, (22)Princeton University, Program in Atmospheric and Oceanic Sciences, Princeton, NJ, United States, (23)Kent State University Kent Campus, Kent, OH, United States, (24)University of Colorado Boulder, Boulder, United States, (25)CIMAR, University of Hawaiʻi at Mānoa, Department of Oceanography, Honolulu, United States, (26)Stony Brook University, School of Marine and Atmospheric Sciences, Stony Brook, United States, (27)University of Hawai’i at Mānoa, International Pacific Research Center, Honolulu, United States, (28)NOAA, National Marine Fisheries Service, Silver Spring, MD, United States, (29)Ewha Womans University, Department of Science Education, Seoul, South Korea, (30)NOAA, Silver Spring, United States, (31)Scripps Institution of Oceanography, La Jolla, United States, (32)University of South Carolina, Columbia, 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).
