Modeling the biological response to the eddy-resolved circulation in the California Current Arthur J. Miller SIO, La Jolla, CA John R. Moisan NASA GSFC/Wallops Bruce D. Cornuelle SIO, La Jolla, CA Douglas J. Neilson SIO, La Jolla, CA How do we fit/assimilate the physical data? 1. We use a strong constraint 4DVAR approach (  adjust only initial state, boundary conditions and physical forcing). 2. Minimize weighted misfits (difference between observations and model data) and correction to initial state. 3. Reduce size of problem by limiting corrections to largest/eddy space scales projecting the initial error on basis functions. 4. Determine the sensitivity matrix with set of perturbation runs of the non-linear model, invert it assuming linearity, test degree of non-linearity and repeat process iteratively. Why are these fits useful? The resulting time-dependent 3D reconstruction of the evolving flow fields can be used in three ways. * Diagnose eddy dynamics - baroclinic/barotropic instabilities - coastline/topographic influences - atmospheric forcing effects * Determine predictive timescales - deep ocean eddies - shelf/slope eddies - atmospheric-forced surface fields * Assess ecosystem response - fit part of biology controlled by physics - diagnose ecosystem balances - determine ecosystem predictive timescales 2) Data Assimilation and Independent Verification (figure above): In situ temperature and salinity are fitted by the numerical model using a strong constraint assimilation method. The comparison of CalCOFI observations and model hindcast/fits averages for the period 23 Jan to 14 Feb are shown in Column A and B. Independent verification/skill is assessed using T/P and SeaWIFs satellite data (Column C). The T/P tracks are superimposed on the model SSHa. The white portion of the track labels regions where the RMS between model and satellite are higher than the error bars. The ecosystem model is initialized and driven using the physical fields without any data assimilation. Model chlorophyll is consistent with the spatial structure evident in SeaWiFS with high values along the coast. Off the coast, horizontal advection and vertical mixing by the eddies contribute to the generation of high values of model surface Chl-a. 1) ROMS physical and ecosystem model: Simulating seasonal cycle statistics Long term mean (left) and variance (right) maps of the physical and ecosystem model compare well with SeaWiFS maps of Chlorophyll (upper plots) and CalCOFI in situ measurements (lower plots). This is a necessary step before using the model for data assimilation. ModelObs. Model Obs. AVVISO TOPEX/ERS SeaWIFS [  M N/m 3 ] [m] E1 E2 E1 SSHa Satellite Chl-a Satellite Satellite Observations [m] [  M N/m 3 ] E1 E2 Model Hindcast E1 “SSHa” Chl-a In Situ SSHa Model Chl-a Model CalCOFI In Situ Observations Data Assimilation Independent verification (A) (B) (C) ABSTRACT - Over fifty years of hydrographic and other physical and biological data have been collected by the California Cooperative Oceanic Fisheries Investigations (CalCOFI) in the California Current System. The coarse sampling (70 km), however, has precluded definitive study of the dynamics controlling eddies in the system. In recent years, additional data from ADCP upper ocean currents, satellite altimetry of sea level, ocean color by SeaWiFS and other variables has been contemporaneously sampled. The goal of this study is to combine these datasets with a physical- biological ocean model (strong constraint data assimilation) to resolve the mesoscale dynamics, understand ecosystem processes and eventually to assess predictive timescales. February 98 : Model Velocity [cm/sec] Poleward Coastal Current April 98 : Model Velocity [cm/sec] Southward Coastal Current 3) Forecasting Ocean Current The circulation pattern observed during the CalCOFI cruise was characterized by a strong coastal northward current. The low-salinity core of the California Current was located unusually far offshore. Two month later in the April 98 cruise the jet of the California Current was found inshore and the coastal countercurrent was absent and replaced by a southward flow. Here we present a model simulation in this same period. After inverting for the initial condition for the month of February 98 we integrated the model forward and forced it using weekly wind stresses from the Leetmaa Ocean Analysis (from CDC/NOAA). Qualitatively the model captured the changes in the current as shown in the figure In April there was a well developed southward current. Model Hindcast Model Forecast 4) Conclusions The physical model fit is largely consistent with the mesoscale dynamics in the Southern California Bight region of the CCS. The dynamical fit reduces the model-data misfit variance of temperature and salinity by 49% during the 23 Jan to 14 Feb 1998 period. As an independent verification, the spatial structure in T/P compares well with the model (within errorbars) both inside the CalCOFI data domain as well as north of this area. Spatial differences are noticeable along the coast in the SCB region where the model shows an anomalous narrow poleward flow that T/P is unable to resolve. The 3D NPZD ecosystem is initialized and driven only by the 3D time-dependent physical fields obtained by the fit. The spatial structure of the CHL-a is consistent with both in situ (CalCOFI) and satellite (SeaWiFS) observations. The forecast of alongshore current reversal observed in the Southern California Bight during April 1998 has been qualitatively simulated using observed initial conditions from February 1998, evolving boundary conditions from NCEP's ocean analysis, and atmospheric forcing from NCEP's atmospheric analysis. Publications Di Lorenzo, E., A.J. Miller, D.J. Neilson, B.D. Cornuelle, and J.R. Moisan, 2004: Modeling observed California Current mesoscale eddies and the ecosystem response, International Journal of Remote Sensing, 25 (7-8), Moore, A., H. Arango, E. Di Lorenzo, B. D. Cornuelle, A. J. Miller, D. and J. Neilson, 2003: A Comprehensive Ocean Prediction and Analysis System Based on the Tangent Linear and Adjoint of a Regional Ocean Model, Ocean Modeling, 7, Emanuele Di Lorenzo Georgia Institute of Technology, Atlanta, GA 30332