Modeling the biological response to the eddy-resolved circulation in the California CurrentEmanuele Di Lorenzo

GaTech, La Jolla, 92093 CAArthur J. Miller

SIO, La Jolla, 92093 CAJohn R. MoisanNASA GSFC/Wallops

Bruce D. CornuelleSIO, La Jolla, 92093 CA

Douglas J. NeilsonSIO, 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.

web site: http://ono.ucsd.edu

A-B

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SST and SL variance transitionCCS core inshore shift where curl differential = 0

poleward flow differential

A Stronger Wind Stress Curl Weaker Upwelling Favorable Winds

B Stronger Upwelling Favorable Winds Weaker Wind Stress Curl

Wind Forcing Scenarios

Temperature Variance

Free Surface elevation Variance

BA A-B Difference field

Mean Free Surface elevation (14 year mean)

Ocean Model Response to Wind Forcing Scenarios

1) ROMS physical and ecosystem model: Simulating seasonal cycle statisticsLong 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).

2) Impacts of interannual, decadal and longer-term changes in atmospheric forcingAn 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.

CalCOFI Chl-a (in situ obs.)

How do we fit the physical data? (inverse method strategy)

1. Adjust only initial state, boundary conditions and physical forcing2. Minimize weighted misfits and correction to initial state 3. Reduce size of problem by limiting corrections to largest/eddy space scales using basis functions4. 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 

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. 

CalCOFI Dynamic Height (obs.)

SeaWiFS Chl-a (satellite obs.)Chl-a (model average)

Average Fields (23 Jan 1998 – 14 Feb 1998) Free-Surface (model average)

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.

Model Obs.

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PublicationsMiller, 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, 87-97Moisan, 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.