Opportunity at National Oceanic & Atmospheric Administration (NOAA)
Seasonal-to-Interannual Statistical Forecasting on the Northeast US Shelf
National Marine Fisheries Service, Northeast Fisheries Science Center
||Woods Hole, MA 02543
The Northeast US Shelf Large Marine Ecosystem supports some of the most commercially valuable fisheries in the world and has experienced dramatic ecosystem change in response to fishing pressure, climate variability and climate change, the combined effects of which create a challenge for fisheries stock assessment in this region. Incorporating physical environmental variables into stock assessment population models and subsequent forecasts could improve model performance and reduce uncertainty in future population size. This project aims to develop and test a new statistical seasonal-to-interannual prediction system for ocean temperatures on the NES, specifically tailored to the needs of NOAA Fisheries stock assessments. The primarily goals will be: (1) to use previously described statistical relationships linking shelf ocean temperature to various indicators of basin-scale and local variability, e.g. North Atlantic Oscillation, Gulf Stream path, and coastal sea-level, to develop a prediction system at the 3–36 month time scale, (2) to evaluate this system in the context of selected stock assessments executed by NOAA Fisheries, and (3) to clarify the dynamical basis for the statistical relationships using ocean hindcast models and coupled ocean-atmosphere models.
This work will involve collaboration with an interdisciplinary team of physical oceanographers and fisheries scientists from the NOAA Northeast Fisheries Science Center (NEFSC), the Woods Hole Oceanographic Institution (WHOI), and Stony Brook University.
North Atlantic; Ocean Prediction; Physical Oceanography; Fisheries; Climate Variability; Ocean Models
Open to U.S. citizens, permanent residents and non-U.S. citizens
Open to Postdoctoral and Senior applicants
Postdoctoral and Senior Associates will receive an appropriately higher stipend based on the
number of years of experience past their PhD.