David S. Trossman
Visiting Associate Research Scientist
University of Maryland-College Park
Earth System Science Interdisciplinary Center
Affiliate of NOAA STAR/NESDIS LSA
Adjunct Professor at Louisiana State University
email: david.s.trossman at noaa dot gov
https://orcid.org/0000-0001-8222-7214
Introduction
I'm an ocean scientist who generally develops methods to make the most of the imperfect numerical models/theories and the sparse/aliased observations we have of the ocean. My research has taken two trajectories: 1) I have studied the interactions between the ocean and other components of the Earth system by developing numerical models and running experiments and 2) I have advanced the reconstruction of the ocean’s historical conditions by understanding the information provided by various observing systems about difficult-to-observe quantities of interest. I currently apply these lines of work to the development of data products, forecasting systems, and enhancement of observing systems.
Research
Computational physical oceanography (parameterizing and understanding interactions at oceanic boundaries)
Ocean data science (combining information from theories, models, and observations to assess and design observing systems or improve ocean state estimates)
Education
University of Washington-Seattle: PhD in physical oceanography, 2011
University of Chicago: M.A. in public policy, 2008
Washington University in St. Louis: B.A in mathematics and physics, 2004; M.A. in physics, 2005
Current/Pending Procured Funding
E Hugo Berbery (CISESS lead), Paige Lavin (Primary lead), Deirdre Byrne (NOAA lead), David Trossman (CI scientist): CISESS: Further Development of New Methods for Determining Upper Ocean Heat Content. NOAA NESDIS ORS (2022-2023)
Robert Tyler (PI) and David Trossman (Co-I): Collaborative Research: Exploiting Geomagnetic Records to Describe Past and Present Ocean Variability; NSF Physical Oceanography #: 2048789 (2021-2024); this is funding myself
David Trossman (PI), Nathalie Zilberman (Co-I), Helen Pillar (Co-I): Collaborative Research: Using Models and Historical Data to Guide Effective Monitoring and Enhance Understanding of Deep Ocean Oxygen Variability; NSF Chemical Oceanography [pending for 2023-2026]; this will be funding Nemi Abomaye-Nimenibo at the University of Maryland-College Park's Department of Atmospheric and Oceanic Science
Bruce Howe (PI), ..., and David Trossman (Co-I): SMART Subsea Cables: Geophysics, Early Warning, Oceans, Vanuatu-New Caledonia to Global. Moore Foundation (2022-2025); this is funding Karen Renninger-Rojas at Louisiana State University's Department of Oceanography and Coastal Sciences
Current unfunded collaborations
Improve and understand how constraints on Arctic sea surface salinity influences other variables:
Train a machine learning algorithm on in situ data of salinity in the top 5 or 10 meters with satellite sea surface salinity and air-sea flux/forcing fields as predictors to estimate a bias-corrected sea surface salinity in the Arctic Ocean appropriate for data assimilation and determine what constraints these estimates provide to forecasts - see Trossman and Bayler (2022) for proof-of-concept
Sarah Hall of Global Science & Technology is doing this work
Estimate and understand deep ocean respiration rates:
Use the output of a biogeochemical ocean state estimate and estimates of mean ages to get oxygen utilization and respiration rates as well as determine the relative roles of DOC vs POC
Olivier Sulpis of Utrecht University is doing this work