Capotondi, A., M. Jacox, C. Bowler, M. Kavanaugh, P. Lehodey, D. Barrie, S. Brodie, S.
Chaffron, W. Cheng, D. Faggiani Dias, D. Eveillard, L. Guidi, D. Iudicone, N.
Lovenduski, J. A. Nye, I. Ortiz, D. E. Pirhalla, M. Pozo Buil, V. Saba, S. C. Sheridan, S.
Siedlecki, A. Subramanian, C. De Vargas, E. Di Lorenzo, S. C. Doney, A. J.
Hermann, T. Joyce, M. Merrifield, A. J. Miller, F. Not and S. Pesant, 2019:
Observational needs supporting marine ecosystems modeling and forecasting.
Frontiers in Marine Science, sub judice.
Many coastal areas host rich marine ecosystems and are also centers of economic activities,
including fishing, shipping and recreation. Due to the socioeconomic and ecological importance
of these coastal areas, predicting relevant indicators of the ecosystem state on sub-seasonal to
interannual timescales is gaining increasing attention. Depending on the application, forecasts may
be sought for variables and indicators spanning physics (e.g., sea level, temperature, currents),
chemistry (e.g., nutrients, oxygen, pH), and biology (from viruses to top predators). Many
components of the marine ecosystem are known to be influenced by leading modes of climate
variability, which provide a physical basis for predictability. However, prediction capabilities
remain limited by the lack of a clear understanding of the processes involved, as well as by
insufficient observations for forecast initialization and verification. The situation is further
complicated by the influence of climate change on ocean conditions along coastal areas, including
sea level rise, increased stratification, and shoaling of oxygen minimum zones. A global,
standardized ocean observing system is vital to all aspects of marine forecasting: statistical and/or
dynamical model development, forecast initialization, and forecast validation, each of which has
different observational requirements. Here, we identify and describe the key requirements for an
observational network that will be needed to facilitate improved process understanding, as well as
for sustaining operational ecosystem forecasting. We also describe new holistic observational
approaches that have the potential to support and expand ecosystem modeling and forecasting