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:
From the global ocean
to regional and coastal systems.
Frontiers in Marine Science, 6, 623.
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 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 physical and biological 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. Observations are
thus 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, which may be also specific to the study region. Here, we use examples from United
States (U.S.) coastal applications to identify and describe the key requirements for an observational
network that is needed to facilitate improved process understanding, as well as for sustaining
operational ecosystem forecasting. We also describe new holistic observational approaches, e.g.,
approaches based on acoustics, inspired by Tara Oceans or by landscape ecology, which have the
potential to support and expand ecosystem modeling and forecasting activities by bridging global
and local observations.