Eastern U.S. Continental Shelf Carbon Budget: Integrating Models, Data Assimilation, and Analysis

Eileen Hofmann, Old Dominion University, hofmann@ccpo.odu.edu (Presenting)
Jean-Noel Druon, NASA Goddard Space Flight Center, jean-noel.druon@libero.it
Katja Fennel, Dalhousie University, katja.fennel@dal.ca
Marjorie Friedrichs, Virginia Institute of Marine Science, marjy@vims.edu
Dale Haidvogel, Rutgers University, dale@imcs.marine.rutgers.edu
Cindy Lee, Stony Brook University, cindylee@notes.cc.sunysb.edu
Antonio Mannino, NASA Goddard Space Flight Center, antonio.mannino-1@nasa.gov
Charles McClain, NASA Goddard Space Flight Center, charles.r.mcclain@nasa.gov
Raymond Najjar, Pennsylvania State University, najjar@essc.psu.edu
John O'reilly, NOAA/NMFS Narragansett Laboratory, jay.oreilly@noaa.gov
David Pollard, Pennsylvania State University, pollard@essc.psu.edu
Michael Previdi, Lamont-Doherty Earth Observatory, mprevidi@ldeo.columbia.edu
Sybil Seitzinger, Rutgers University, sybil@imcs.marine.rutgers.edu
John Siewert, Pennsylvania State University, jsiewert79@gmail.com
Sergio Signorini, SAIC, sergio@simbios.gsfc.nasa.gov
John Wilkin, Rutgers University, wilkin@marine.rutgers.edu

The U.S. Eastern Continental Shelf Carbon Budget (USECoS) Program is seeking to understand how carbon is introduced into the eastern U.S. continental shelf environment, how it is transformed and transported while resident on the shelf, and its ultimate fate. Our approach combines remote sensing data, especially ocean color imagery, a synthesis of in situ measurements, a coupled ocean biogeochemistry-carbon-circulation model configured for the Mid-Atlantic Bight and South Atlantic Bight regions of the U.S. eastern continental shelf, and data assimilation studies. An important and active part of the USECoS project has been evaluation of the simulations by comparison with in situ and satellite-derived data using a suite of statistical approaches of escalating rigor, including comparisons of spatial distributions, means, variance, two-dimensional histograms and other skill assessment methods, such as Taylor/Target diagrams which reveal seasonal timing/phase relationships. This diversity of model skill assessment methods has helped identify seasons and regions where model improvements are required. In addition, a one-dimensional data assimilative model has provided the basis for quantitative assessment of model processes, which furthers the development of a model with improved skill. Results from the model, the approaches used to evaluate model skill, and the process that the USECoS team has used to integrate results from different disciplines and expertise are presented. A basic conclusion is that the iterative approach used in the USECoS research program resulted in a stronger program that is yielding results that likely would not have been achieved otherwise.