Contributions by C. A. Edwards & C.V. Lewis CIMT Meeting June 5-6, 2006 Emphasize Integration of CIMT data 2 Parts –Visualization –Ecosystem component

Edwards & Lewis (part 1) Graphical synthesis of CIMT data for web distribution HF Radar Wind stress SST Ship survey T/S/F profiles Ocean color Underway data

CIMT Cruise, May 2004 Temperature Fluorescence SST Color

August 2003

Jan 2004 Temperature River flow Salinity

CIMT data reveals iron limitation on coastal ecosystem –Hypothesized to induce alongshore variability in production Present published ecosystem models of California Current System do not explicitly include iron We developed an ecosystem model based on this hypothesis –Explicitly includes iron –Tested model in simplified system to evaluate its potential for alongshore variability Edwards & Lewis (part 2) Modeling Synthesis of CIMT results

MOTIVATION SeaWiFS Chlorophyll Monthly Average (May)

Climatology

Conceptual Ecosystem Model Based on Franks et al. (1986), Edwards et al. (2000), Edwards et al. (2002) Integrates observations by Bruland, Chavez, and Kudela Simple NPZ model with Iron –Dissolved –Cellular

Hypothesize that distribution derives from iron limitation idealized 2D bathymetries –Pt. Sur –Davenport –Pescadero Investigates COAMPS wind stress from three locations Examine net production Promising sensitivity to iron Bathymetry

IOOS Modeling and Analysis Objectives (1) Improve, develop, test, and validate operational models; (2) Produce accurate estimates of current states of marine systems (e.g., estimates of the distributions of core variables); (3) Develop data assimilating techniques to initialize and update models for more accurate forecasts of state changes; and (4) Optimize the observing subsystem (e.g., observing system simulation experiments). Models include …dynamical models based on first principles (e.g. storm surge models, numerical ecosystem models in both Lagrangian and Eulerian frames of reference), or coupled models of the biological and non-biological components of the marine ecosystem (e.g. coupled atmosphere-ocean-wave-sediment- biogeochemistry and ecosystem models).

IOOS Modeling and Analysis Objectives (1) Improve, develop, test, and validate operational models; (2) Produce accurate estimates of current states of marine systems (e.g., estimates of the distributions of core variables); (3) Develop data assimilating techniques to initialize and update models for more accurate forecasts of state changes; and (4) Optimize the observing subsystem (e.g., observing system simulation experiments). Models include …dynamical models based on first principles (e.g. storm surge models, numerical ecosystem models in both Lagrangian and Eulerian frames of reference), or coupled models of the biological and non-biological components of the marine ecosystem (e.g. coupled atmosphere-ocean-wave-sediment- biogeochemistry and ecosystem models).

Contributions by C. A. Edwards & C.V. Lewis CIMT Meeting June 5-6, )Improve predictions of climate change and weather and their effects on coastal communities and the nation; 2)Improve the safety and efficiency of maritime operations; 3)Mitigate the effects of natural hazards more effectively; 4)Improve national and homeland security; 5)Reduce public health risks; 6)Protect and restore healthy coastal ecosystems more effectively; 7)Enable the sustained use of ocean and coastal resources. IOOS Goals