Office of Coast Survey / CSDL Sensitivity Analysis of Temperature and Salinity from a Suite of Numerical Ocean Models for the Chesapeake Bay Lyon Lanerolle 1,2, Aaron J. Bever 3 and Marjorie A. M Friedrichs 4 1 NOAA/NOS/OCS/Coast Survey Development Laboratory,1315 East-West Highway, Silver Spring, MD; 2 Earth Resources Technology (ERT) Inc.,6100 Frost Place, Suite A, Laurel, MD; 3 Delta Modeling Associates, Inc., San Francisco, CA ; 4 Virginia Institute of Marine Science, The College of William & Mary, Gloucester Point, VA. U.S. IOOS Coastal Ocean Modeling Testbed 24 January th Symposium on the Coastal Environment 92 nd Annual American Meteorological Society Meeting

Office of Coast Survey / CSDL Introduction and Motivation Physical component of Numerical Ocean models generate water elevations, currents, T and S Water quality models and ecological models/applications rely primarily on T and S (from the physical model) Expect “best” water quality predictions to result from the “best” T and S predictions (relative to observations) Therefore attempt to:  examine predicted T, S sensitivity to various model parameters  optimize the predictions for T, S from models  examine how different models compare with observations and each other  employ “best” T, S predictions for water quality forecasting

Office of Coast Survey / CSDL US IOOS Coastal Ocean Modeling Testbed Focus on Estuarine Dynamics and Modeling component Ideal candidate is Chesapeake Bay:  Extensive data sets available (in time and space)  Several numerical ocean model applications available Ocean models available for Testbed:  CBOFS (NOAA/NOS/CSDL-CO-OPS, Lyon Lanerolle et al.)  ChesROMS (U-Md/UMCES, Wen Long et al.)  UMCES ROMS (U-Md/UMCES, Ming Li, Yun Li)  CH3D (CBP, Ping Wang; USACE, Carl Cerco)  EFDC (William & Mary/VIMS, Jian Shen and Harry Wang) Observed data – Chesapeake Bay Program (CBP) Simulation period – 2004 calendar year (2005 is similar)

Office of Coast Survey / CSDL Model-Observation Comparison Metrics Metric used is the Normalized Target Diagram (Jolliff et al. 2009) m’ = m - M, o’ = o - O σ o - SD of obs. Model skill is distance from origin (origin = perfect model-obs. fit) Graphical versus numerical approach more informative unbiased RMSD [sign(σ m - σ o )· {Σ (m’-o’) 2 / N} 1/2 ] / σ o Bias [(M-O) / σ o ] +1 Overestimates RMSD Overestimates mean

Office of Coast Survey / CSDL Chesapeake Bay Program Comparison Stations Model(s)-Observation comparisons were made at 28 CBP stations Stations covered lower, mid, upper Bay, Bay axis and tributaries

Office of Coast Survey / CSDL Model Calibration / Parameter Sensitivity (using CBOFS) Bottom T Bottom S Maximum S stratificationDepth of max. S strat. Greatest sensitivity

Office of Coast Survey / CSDL Global Errors Kachemak Bay Upper Cook Inlet Nests Bottom T Bottom S Errors were computed by considering all (28) stations at all depths and for full year T - CBOFS best with accurate mean and error is in overestimated RMSD – EFDC and ChesROMS underestimate RMSD and latter underestimates mean S – EFDC, CH3D best but have opposite RMSD error; former underestimates mean - again, errors show greater spread and larger magnitude than for T

Office of Coast Survey / CSDL Geographical Error Dependence (T) Bay axis errors plotted as a function of station latitude Errors are for bottom T No strong dependence on geography (lower-, mid-, upper-bay) – small error spread Different models have different skill characteristics (over/under estimation of mean and RMSD)

Office of Coast Survey / CSDL Geographical Error Dependence (S) Errors are for bottom S Unlike T, errors show greater spread 3 ROMS models similar, have largest errors and greatest in upper Bay CH3D, EFEC smaller errors, evenly spread and less geographical dependence

Office of Coast Survey / CSDL Value-Based Error Dependence (T) Errors for bottom T plotted as the observed mean value itself Models show similar trends with UMCES ROMS and CBOFS showing slight improvements over others Generally, warmer T values have smaller errors – as seen by UMCES ROMS

Office of Coast Survey / CSDL Value-Based Error Dependence (S) Bottom S errors show greater spread than for T Error characteristics from models are similar except UMCES ROMS – full underestimation of mean No consistent value-based error dependence in any of the models

Office of Coast Survey / CSDL Seasonal Error Dependence (T) Errors for bottom T plotted as a function of month in 2004 Spread in errors seen for all models – EFDC the most; warmer months have smaller errors CBOFS is most accurate and errors well balanced CH3D – overestimates mean, ChesROMS – underestimates mean EFDC – largest errors during latter half of year

Office of Coast Survey / CSDL Seasonal Error Dependence (S) Bottom S errors show less spread than for T Different error characteristics in each model 3 ROMS models show similarity – overestimation of RMSD and underestimation of mean (except CBOFS) CH3D – underestimates RMSD EFDC – underestimates mean and under- and over- estimates RMSD

Office of Coast Survey / CSDL Conclusions Inferences for 2004, 2005 similar - so focused on 2004 Bottom S was the most sensitive variable and was used as a proxy Model calibration/sensitivity study showed CBOFS was not significantly sensitive to parameter variation Global T, S errors – no drastic differences between different model predictions (although some were relatively better) Geographical error dependence – ROMS models had largest errors in upper Bay; CH3D, EFDC less geographically dependent Value-based error dependence – warmer T values have smaller errors; no discernible error trends for S Seasonal error dependence – T from ROMS models are similar and CBOFS has best error balance (mean/RMSD); for S, models show different error characteristics with under/over estimation of mean/RMSD in each Target Diagrams proved to be an invaluable and straightforward metric for studying T and S model-observation differences