An oceanographic characterizations of the Olympic Coast National Marine Sanctuary and Pacific Northwest: interpretive summary of ocean climate and regional processes through satellite remote sensing

This report presents the results of a two-year investigation and summary of oceanographic satellite data obtained from multiple operational data providers and sources, spanning years of operational data collection. Long-term summaries of Sea Surface Temperature (SST) and SST fronts, Sea Surface Height Anomalies (SSHA), surface currents, ocean color chlorophyll and turbidity, and winds are provided. Merged satellite oceanographic data revealed information on: (1) seasonal cycles and timing of transition periods; (2) linkages between seasonal effects (warming and cooling), upwelling processes and transport; and (3) nutrient/sediment sources, sinks, and physical limiting factors controlling surface response for Olympic Coast marine environments. These data and information can be used for building relevant hind cast models, ecological forecasts, and regional environmental indices (e.g. upwelling, climate, “hot spot”) on biological distribution and/or response in the PNW

Scientific and Technical Advisory Committee Review of the Chesapeake Bay Program Partnership’s Climate Change Assessment Framework and Programmatic Integration and Response Efforts

[From the Executive Summary] The following report presents a synthesis of reviewer responses from the Scientific and Technical Advisory Committee’s (STAC) panel on the Chesapeake Bay Program Partnership’s Climate Change Assessment Framework (CCAF) and Programmatic Integration and Response Efforts. The enclosed findings and recommendations are in response to the 16 questions delivered to the panel (Appendix A). In summary, given the current state of knowledge, the combination of using climate model projections and downscaling provides an acceptable baseline for estimating changing climate conditions for the Chesapeake Bay, and the panel finds the CCAF approach to be fundamentally sound. However, the panel members have a number of concerns pertaining primarily to the current lack of complete formal documentation on the details of the approach. In the responses to the questions that follow in the body of the report, the panel has outlined several areas where more details or further investigations are suggested and has also provided some specific recommendations for CBP consideration in regard to future use and application of the CCAF

Water clarity patterns in South Florida coastal waters and their linkages to synoptic-scale wind forcing

Temporal variability in water clarity for South Florida’s marine ecosystems was examined through satellite-derived light attenuation (Kd) coefficients, in the context of wind- and weather patterns. Reduced water clarity along Florida’s coasts is often the result of abrupt wind-resuspension events and other exogenous factors linked to frontal passage, storms, and precipitation. Kd data between 1998 and 2013 were synthesized to form a normalized Kd index (KDI) and subsequently compared with Self Organizing Map (SOM)-based wind field categorizations to reveal spatiotemporal patterns and their inter-relationships. Kd climatological maximums occur from October through December along southern sections of the West Florida Shelf (WFS) and from January through March along the Florida Straits. Spatial clusters of elevated Kd occur along 3 spatial domains: central WFS, southern WFS, and Florida Straits near the Florida Reef Tract, where intra-seasonal variability is the highest, and clarity patterns are associated with transitional wind patterns sequenced with cyclonic circulation. Temporal wind transitions from southerly to northerly, typically accompanying frontal passages, most often result in elevated Kd response. Results demonstrate the potential of using synoptic climatological analysis and satellite indices for tracking variability in water clarity and other indicators related to biological health

Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y., Brodie, S., Ortiz, I., Tommasi, D., Widlansky, M. J., Barrie, D., Capotondi, A., Cheng, W., Di Lorenzo, E., Edwards, C., Fiechter, J., Fratantoni, P., Hazen, E. L., Hermann, A. J., Kumar, A., Miller, A. J., Pirhalla, D., Buil, M. P., Ray, S., Sheridan, S. C., Subramanian, A., Thompson, P., Thorne, L., Annamalai, H., Aydin, K., Bograd, S. J., Griffis, R. B., Kearney, K., Kim, H., Mariotti, A., Merrifield, M., & Rykaczewski, R. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography, 183, (2020): 102307, doi:10.1016/j.pocean.2020.102307.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 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 marine ecosystem predictability, 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 verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.This study was supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program through grants NA17OAR4310108, NA17OAR4310112, NA17OAR4310111, NA17OAR4310110, NA17OAR4310109, NA17OAR4310104, NA17OAR4310106, and NA17OAR4310113. This paper is a product of the NOAA/MAPP Marine Prediction Task Force

The Development of a Non-linear Autoregressive Model with Exogenous Input (NARX) to Model Climate-water Clarity Relationships: Reconstructing a Historical Water Clarity Index for the Coastal Waters of the Southeastern USA

The coastal waters of the southeastern USA contain important protected habitats and natural resources that are vulnerable to climate variability and singular weather events. Water clarity, strongly affected by atmospheric events, is linked to substantial environmental impacts throughout the region. To assess this relationship over the long-term, this study uses an artificial neural network-based time series modeling technique known as non-linear autoregressive models with exogenous input (NARX models) to explore the relationship between climate and a water clarity index (KDI) in this area and to reconstruct this index over a 66-year period. Results show that synoptic-scale circulation patterns, weather types, and precipitation all play roles in impacting water clarity to varying degrees in each region of the larger domain. In particular, turbid water is associated with transitional weather and cyclonic circulation in much of the study region. Overall, NARX model performance also varies—regionally, seasonally and interannually—with wintertime estimates of KDI along the West Florida Shelf correlating to the actual KDI at r \u3e 0.70. Periods of extreme (high) KDI in this area coincide with notable El Niño events. An upward trend in extreme KDI events from 1948 to 2013 is also present across much of the Florida Gulf coast

Observational Needs Supporting Marine Ecosystems Modeling and Forecasting: From the Global Ocean to Regional and Coastal Systems

International audienceMany 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