Modeling the biological response to the eddy-resolved circulation in the California Current

1. A-B A-B Wind Forcing Scenarios AStronger Wind Stress Curl Weaker Upwelling Favorable Winds Biogeochemical Model * Phytoplankton * Zooplankton * Ammonia * Nitrate * Small Detrital Nitrogen * Large Detrital Nitrogen * Chlorophyll * Total Inorganic Carbon * Total Alkalinity * Large Detrital Carbon * Small Detrital Carbon * Oxygen Source/sink coupling terms computed following Fasham et al. strategy. Model is constrained with SeaWiFS surface chlorophyll, sub-surface CalCOFI data (chlorophyll, nitrate, and bulk/acoustic zooplankton), and other available data. How do we fit the physical data? (inverse method strategy) 1. Adjust only initial state, boundary conditions and physical forcing 2. Minimize weighted misfits and correction to initial state  3. Reduce size of problem by limiting corrections to largest/eddy space scales using 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 BStronger Upwelling Favorable Winds Weaker Wind Stress Curl Average Fields (23 Jan 1998 – 14 Feb 1998) CalCOFI Dynamic Height (obs.) Free-Surface (model average) CalCOFI Chl-a (in situ obs.) Chl-a (model average) SeaWiFS Chl-a (satellite obs.) Modeling the biological response to the eddy-resolved circulation in the California Current John R. Moisan NASA GSFC/Wallops Emanuele Di Lorenzo GaTech, La Jolla, 92093 CA Bruce D. Cornuelle SIO, La Jolla, 92093 CA Douglas J. Neilson SIO, La Jolla, 92093 CA Arthur J. Miller SIO, La Jolla, 92093 CA Motivation Coupled physical-biogeochemical models are used to answer questions about the variability of temperature, circulation and biogeochemistry in the California Current System. Rigorous model testing strategies are being employed in fitting the models to observations using both (a) statistical and (b) deterministic criteria. 2) Impacts of interannual, decadal and longer-term changes in atmospheric forcing An ecosystem can be altered by both changes in mean flows and by changes in eddy statistics. The sensitivity of our physical-biogeochemical model to large-scale changes in atmospheric forcing is being investigated by driving the model with two different atmospheric wind fields (A is the product from the Leetmaa Ocean Analysis and B is the da Silva COADS analysis) shown at the right. The model simulations clearly suggest that the statistics of the eddies strongly depend on the large-scale structure of the atmospheric forcing along the coast. Stronger upwelling winds increase the variance of the free surface height in the coastal region (delimited by the red line) but decrease the variance in the SST field. Analysis of the wind stress curl reveals a strong link with the variability of the California Current core and the offshore extent of the upwelling front. In these two wind forcing scenarios, we are studying the relationship between mean flows, wind stress curl variance and alongshore wind stress variance in establishing the patterns of eddy statistics and the consequent biological activity. The biogeochemical response to these different oceanic forcings is now being investigated with the ecosystem models. 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). A-B Obs. 3) Deterministic fits of the physical and biological CalCOFI observations Over fifty years of hydrographic and other physical and biological data have been collected by CalCOFI in theSouthern California Bight. The coarse sampling (70 km), however, has precluded definitive study of the dynamics controlling eddies and the ecosystem in the region. 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. This research is aimed at using these data to (a) test model dynamics, (b) understand ecosystem processes and eventually (c) assess predictive timescales. Model skill is quantified by the model-data mismatch (rms error) during a fitting interval. The physical fields are used to drive a 3D NPZD-type model constrained by sub-surface chlorophyll-a (Chla), nitrate and bulk zooplankton from CalCOFI and surface Chl-a from SeaWiFS. A-B Model poleward flow differential SST and SL variance transition CCS core inshore shift where curl  differential = 0 Ocean Model Response to Wind Forcing Scenarios B A Obs. Model A-B Difference field Model Obs. Mean Free Surface elevation (14 year mean) Free Surface elevation Variance Model Obs. Model Obs. Temperature Variance Publications Miller, A.J., E. Di Lorenzo, D.J. Neilson, B. Cornuelle, and J.R. Moisan, 2000: Modeling CalCOFI observations during El Nino: Fitting physics and biology. CalCOFI Reports, 41, 87-97 Moisan, J., T. Moisan, and M. Abbott, 2001: Modeling the effect of temperature on phytoplankton growth. Ecological Modeling, sub judice. Di Lorenzo, E., A.J. Miller, D.J. Neilson, B.D. Cornuelle, and J.R. Moisan, 2001: Modeling observed California Current mesoscale eddies and the ecosystem response, International Journal of Remote Sensing, sub judice. web site: http://ono.ucsd.edu