Near Real-time, 4-Dimensional Variational Data Assimilative Physical and Biological Modeling of the California Current System (experimental)


Figures showing model output for


Sea Surface Height Sea Surface Temperature Sea Surface Salinity
Sea Surface Chlorophyll


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Overview: This web-page presents infomation about and output from our ocean state estimate for the California Current System. We use a 4-dimensional variational approach and estimate both physical and biological information. The assimilation is fully coupled, with physical data influencing biological fields and biological information contributing to adjustments in physical variables.


Our assimilation procedure currently runs over 4-day cycles. Every day, a data assimilation run is performed for the previous 4 days, producing an estimate of the physical and biological ocean state in which the model initial conditions have been modified. Several physical data sets are assimilated. AVISO Sea Level Anomalies and subtidally averaged tide-guage data provide information of the sea surface height. OSTIA Sea Surface Temperature and AQUARIUS Sea Surface Salinity provide additional sea surface information. Subsurface hydrography derives from several platforms: Argo buoys provide subsurface information broadly throughout our domain. In addition, glider lines in the central and southern California regions are supported by SCCOOS and CeNCOOS. Glider information off the Washington coast is supported by NANOOS. In addition, we assimilate sea surface chlorophyll estimates from the Modis Aqua platform into this coupled physical biological model. We are grateful to the agencies and individuals who have made this data available for use in this ocean state estimate.


We note that not all data mentioned above is generally assimilated into every cycle. Some platforms (e.g., Argo) do not necessarily collect information within our domain during every assimilation cycle. In addition, AQUARIUS data is presently only available in delayed-time mode and not in real-time. However, this information is useful for reanalyses, which we run occasionally to update our historical estimates.


Figures above present snapshots of surface ocean properties as produced by a numerical model, the Regional Ocean Modeling System (ROMS). The properties shown are (1) sea surface height (SSH), which varies about 1/2 meter within this domain; (2) sea surface temperature (SST), which usually shows a strong gradient between the warm subtropical waters to the southwest and cold subpolar waters to the north or cold upwelled waters along the coast; (3) sea surface salinity (SSS), which shows a typical CCS value of about 33 psu (practical salinity units which is close to g/kg), with modest variation north to south owing to a relative increase in precipitation to the north and evaporation to the south; and (4) sea surface chlorophyll-a concentration (SCHL, mg/m^3). Superposed on SST, SSS, and SCHL are surface velocity vectors which show the direction and relative intensity of the instantaneous circulation. Although only surface features are presented above, the model resolves the full 3-dimensional structure of the ocean extending from the surface to the ocean bottom as deep as 5000 m beneath the surface.
The California Current System (CCS) refers to the multiple oceanographic features of the northeast Pacific Ocean circulation off the U.S. west coast. It includes the broad equatorward surface motion that represents the easternmost portion of the North Pacific subtropical gyre spanning the breadth of the ocean basin, a narrow subsurface poleward flow (the California Undercurrent) often found between about 100 and 300 m depth along the continental slope, and occasionally a narrow surface nearshore countercurrent, seasonally varying in intensity and often strongest in fall. Superposed on these slowly varying motions are intense mesoscale eddies of tens to 100 km in scale that vary position on weekly to monthly time-scales, as well as smaller submesoscale and smaller motions that fluctuate on still shorter time-scales.


CCS Physical Model: The ocean circulation is modeled using the Regional Ocean Modeling System (ROMS). Our domain extends from midway down the Baja Peninsula to the southern tip of Vancouver Island at 1/10 degree (roughly 10 km) resolution, with 42 terrain-following levels resolving vertical structure in ocean properties. The model is forced at the surface by atmospheric fields produced by the Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS) which is run in near-real-time by the Naval Research Laboratory. Oceanic fields at the lateral boundaries are obtained from a larger, basin-scale data assimilative model, HYCOM. The model does not include freshwater forcing by rivers, and it neglects tidal motion. Ocean model fields are stored as daily snapshots at midnight GMT.


Biological Model: Biological fields are represented by a lower trophic level ecosystem model for the North Pacific Ocean, the North Pacific Ecosystem Model for Understanding Regional Oceanography, "NEMURO". NEMURO has eleven state variables: nitrate, ammonium, small and large phytoplankton biomass, small, large and predatory zooplankton biomass, particulate and dissolved organic nitrogen, particulate silica, and silicic acid concentration. In the upper water column where sunlight is plentiful, phytoplankton photosynthesize, taking up dissolved, inorganic nitrogen. Zooplankton graze on phytoplankton. In turn, live phytoplankton and zooplankton contribute to disolved nitrogen through excretion and respiration. Dead phytoplankton and zooplankton (and some grazed phytoplankton that is not assimilated into zooplankton biomass) contribute to decomposition, the slow remineralization processes where nitrogen returns to inorganic form. Silicon has its own cycle of uptake and remineralization. Although not explicitly present in the model, we calculate and present the total chlorophyll concentration of small and large phytoplankton biomass. All biological fields are transported and mixed by ocean currents and turbulence obtained from the physical model.
Despite tremendous advances in ocean circulation models over the last several decades, many sources of error exist and are in fact unavoidable when trying to accurately represent or predict the complex evolution of the true ocean state (i.e., the full physical structure of temperature, salinity and velocity fields). For example, the initial ocean state from which a simulation evolves is never known with 100% accuracy. In addition, ocean models themselves involve approximations of the true fluid dynamics for many reasons, such as limited resolution or imperfect representation of unresolved processes like small-scale turbulence. Finally, forcing fields (such as wind stress or heat flux) and conditions applied at the lateral (open) boundaries of the regional ocean model are themselves model solutions also with their own uncertainties.


Assimilation Approach The solution shown on this page applies the Incremental, Strong-Constraint 4-D Variational Assimilation (4DVar) method. This approach finds changes to the initial ocean state during an assimilation cycle that minimize a cost function representing the sum of squared model-data differences and squared deviations of a background model ocean state. This method can also expand the control-space to include changes in the surface forcing and lateral boundary conditions, though such changes are not included in the shown solution. This approach is referred to as incremental as it determines increments to the background ocean state that are assumed to be small. The phrase strong-constraint means that errors in ocean dynamics are neglected (i.e., model dynamics are applied as a strong constraint). Biological model Biological fields in the ocean (e.g., sea surface chlorophyll) are not well represnted by Gaussian statistics. Rather, they exhibit a typically positive skew, and are bounded by zero at the low end. As a result, standard assimilation methods that assume that errors are Gaussian distributed are not optimal for assimilating this data. We carry out the biological assimilation using a lognormal version of 4DVar. Not only are the statistics of the variables more accurately represented by this approach, but field estimates are also positive definite, as are real biological concentrations in the ocean.
Access to the model fields is available using OPeNDAP on the following Thredds Data Server:


Many people are responsible for various aspects of ROMS and the overall data assimilation code. Major contributors to UCSC data assimilation projects are listed below.

Andrew M. Moore


Hernan Arango


Hajoon Song


Milena Veneziani


Nicole Goebel

Christopher A. Edwards


Patrick Drake


Paul Mattern


Jerome Fiechter


Gregoire Broquet

We are grateful to Dan Rudnick (UCSD/SIO) and Craig Lee (UW) for providing access to their west coast glider data for assimilation.


James Doyle (NRL) has provided considerable assistance in our use of the COAMPS atmospheric fields.


We are also indebted to Brian Powell (UH) and John Wilkin (Rutgers) who have written and kindly shared outstanding scripts to carry out various ROMS-related operations.
References: More information on the CCS model, data assimilation system and related studies in the following:
  1. Veneziani, M., C. A. Edwards, J. D. Doyle, and D. Foley (2009), A central California coastal ocean modeling study: 1. Forward model and the influence of realistic versus climatological forcing, J. Geophys. Res., 114, C04015, doi:10.1029/2008JC004774.
  2. Veneziani ,M., C. A. Edwards and A. M. Moore (2009). A central California coastal ocean modeling study: 2. Adjoint sensitivities to local and remote forcing mechanisms. J Geophys Res, 114, doi:10.1029/2008JC004775.
  3. Broquet G., C. A. Edwards, A. M. Moore, B. S. Powell, M. Veneziani and J. D. Doyle, (2009), Application of 4D-Variational data assimilation to the California Current System, Dyn. Atmos. Oceans, doi:10.1016/j.dynatmoce.2009.03.001.
  4. Broquet G., A. M. Moore, H. G. Arango, C. A. Edwards, and B. S. Powell (2009), Ocean state and surface forcing correction using the ROMS-IS4DVAR data assimilation system, Mercator Ocean Quarterly Newsletter, Mercator Ocean Quarterly Newsletter, 34, pp. 5-13.
  5. Broquet, G., A. M. Moore, H. G. Arango, and C.A. Edwards (2010), Corrections to ocean surface forcing in the California Current System using 4D variational data assimilation, Ocean Mod. 36, doi:10.1016/j.ocemod.2010.10.005.
  6. Moore, A.M., Arango, H.G., Broquet, G., Powell, B.S., Zavala-Garay, J., Weaver, A.T., in press-a. The regional ocean modeling system (ROMS) 4-dimensional variational data assimilation systems. I: System overview and formulation. Prog. Oceanogr. doi:10.1016/j.pocean.2011.05.004.
  7. Moore, A.M., Arango, H.G., Broquet, G., Edwards, C.A., Veneziani, M., Powell, B.S., Foley, D., Doyle, J.D., Costa, D., Robinson, P., in press b. The regional ocean modeling system (ROMS) 4-dimensional variational data assimilation systems. II: Performance and application to the California current system. Prog. Oceanogr.doi:10.1016/j.pocean.2011.05.003.
  8. Moore, A.M., Arango, H.G., Broquet, G., Edwards, C.A., Veneziani, M., Powell, B.S., Foley, D., Doyle, J.D., Costa, D., Robinson, P., in press-b. The regional ocean modeling system (ROMS) 4-dimensional variational data assimilation systems: III Observation impact and observation sensitivity in the California Current system. Prog. Oceanogr. doi:10.1016/j.pocean.2011.05.005.


This web-page and the near-real-time ocean state estimation system is supported by the National Oceanographic and Atmospheric Administration (NOAA) through a grant from the Central and Northern California Ocean Observing System (CeNCOOS).


We gratefully acknowledge financial support for various elements of this data assimilative system by the National Oceanographic Partnership Program (NOPP) , the Office of Naval Research (ONR), the National Oceanographic and Atmospheric Administration (NOAA) , the National Science Foundation (NSF), and the Gordon and Betty Moore Foundation .


The National Oceanographic Partnership Program   Office of Naval Research   National Science Foundation   National Oceanographic and Atmospheric Administration

Central and Northern California Ocean Observing System   Gordon and Betty Moore Foundation