CCS 13-Year Historical Reanalysis


Figures showing model output for


Sea Surface Height Sea Surface Temperature Sea Surface Salinity


Click on Calendar to change date of plots. Reanalysis runs from Jan. 1, 1999 to Dec. 29, 2010.


Select Model Output



Methodology: Our sequential assimilation procedure uses 8-day overlapping analysis cycles spanning the period 1999-2010. The start date of each cycle corresponds to day 4 (the mid-point) of the previous cycle, and the prior circulation estimate (also referred to as the background) is taken to be the posterior (also referred to as the analysis) from the previous assimilation cycle on day 4. During each cycle, the model initial conditions, surface forcing, and open bondary conditions are modified. The following data sets are assimilated: daily AVISO Sea Level Anomalies, Sea Surface Temperature from the AVHRR/PathFinder, AMSR-E, GOES and MODIS-Terra satellite platforms, and all available in-situ hydrographic observations from the EN3 (version 2a) data set available from the U.K. Meteorological Office. (The version of the data to which the corrections of Levitus et al. (2009) have been applied to the XBT and MBT temperatures was used here for the period 1980-2008. After 2008 the uncorrected data were assimilated.) The in-situ observations are from various platforms, including Argo profiling floats, shipboard CTDs, XBTs, MBTs and tagged marine mammals. Observations are subjected to quality control based on departures from the prior before they are accepted for assimilation.


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; and (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. Superposed on SST and SSS 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.

CCS and Model

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 Model: The ocean circulation is modeled using the Regional Ocean Modeling System (ROMS). Our domain extends from midway down the Baja Peninsula to 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 from the Coupled Ocean Atmosphere Mesoscale Prediction Systems (COAMPS), run at NRL, Monterey. For the historical reanalyses, we apply oceanographic fields at the (open) lateral boundaries that are taken from the Simpla Ocean Data Assimilation (SODA) product which is a global data assimilative model. 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.

Data Assimilation

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 Incremental, dual, Strong-Constraint 4-D Variational Assimilation method. This approach finds changes to the initial ocean state, surface forcing, and open boundary conditions. It employs the indirect representer method during an assimilation cycle that minimizes a cost function representing the sum of squared model-data differences and squared deviations of the model state from a background model ocean state. 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).

Model Output

Access to the model fields is available using OPeNDAP on the following Thredds Data Server:



Access to the model diagnostic 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


Jerome Fiechter

Christopher A. Edwards


Emilie Neveu


Patrick Drake

We are also indebted to Brian Powell (UH) and John Wilkin (Rutgers) who have written and kindly shared outstanding computer scripts.




We are monitoring several fundamental fields within subregions of the domain to ensure the model does not drift to unreasonable values over the course of the 31-year run. Time series of various diagnostic quantities averaged over the sub-regions shown in the map to the right are shown in the figures below. Light blue: subregion 1; green: subregion 2; orange: subregion 3; and red: subregion 4. Specifically, the spatially averaged surface kinetic energy (Figure 1), surface relative enstrophy (Figure 2), sea surface height (Figure 3), sea surface salinity (Figure 4), and sea surface temperature (Figure 5) are shown. The red curves in Figures 1-5 show time series computed from the posterior fields, while the black curves are computed from the prior. Figure 6 shows the percentage of in situ hydrographic observations that are rejected by the background quality control procedure during each data assimilation cycle. Figures 7 and 8 show respectively the maximum and minimum deviations of SST from the observations during each assimilation cycle, and Figure 9 indicates the geographical location of the observations where the maximum deviations occur, as well as the size of the resulting deviation. Figures 10-14 show time series of the same diagnostic quantities mentioned above computed from the posterior circulation estimates of the data assimilation sequence (red curves) and a run of the model spanning the same period without data assimilation but using the same surface forcing fields (black curves). Figure 15 shows time series of the prior (blue) and posterior (red) values of the 4D-Var inner-loop cost function for each data assimilation cycle. The green crosses show the posterior values of the outer-loop cost function (i.e. those based on the posterior circulation estimate from the non-linear model). In addition the T-S characteristics of the full water column of both the prior and posterior circulation estimates are also shown (Figures 16-19). The greyscale points are the T-S scatter from ROMS, over three different depth ranges, while the colored points are the T-S scatter from the World Ocean Atlas (WOA09).
subregions map

Figure 1
Kinetic Energy
Figure 2
Relative Enstrophy
Figure 3
Sea Surface Height
Figure 4
Sea Surface Salinity
Figure 5
Sea Surface Temperature
Figure 6
% Rejected Observations
Figure 7
Max Deviation of SST from Obs.
Figure 8
Min Deviation of SST from Obs.
Figure 9
Locations of Max Deviations of SST
Figure 10
Kinetic Energy vs. ROMS Climatology
Figure 11
Relative Enstrophy vs. ROMS Climatology
Figure 12
Sea Surface Height vs. ROMS Climatology
Figure 13
Sea Surface Salinity vs. ROMS Climatology
Figure 14
Sea Surface Temperature vs. ROMS Climatology
Figure 15
Cost Function

Please select year and month for T-S diagrams

Select TS year
Select TS month

Figure 16
T-S Entire Domain
Figure 17
T-S Entire Domain vs. Climatology
Figure 18
T-S Subregions
Figure 19
T-S Subregions vs. Climatology


References: More information on the CCS model, data assimilation system and related studies in the following:
  1. 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.
  2. 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.
  3. 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.
  4. Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia and A. V. Mishonov (2009), Geophysical Research Letters, 36, L07608, doi:10.1029/2008GL037155.
  5. Moore, A.M., Arango, H.G., Broquet, G., Powell, B.S., Zavala-Garay, J., Weaver, A.T., 2011a. 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.
  6. 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., 2011b. 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.
  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., 2011c. 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.
  8. 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.
  9. 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.


This web-page and the CCS Historical Reanalysis ocean state estimation system is supported by the National Science Foundation (NSF).


National Science Foundation