Near Real-time, 4-Dimensional, Weak Constraint

Data Assimilative Physical Modeling of the

California Current System (experimental)

with Observational Impact Assesments

 

Figures showing model output for date highlighted in calendar.

 

Sea Surface Height Sea Surface Temperature Sea Surface Salinity

 

Click on Calendar to change date of plots. Today's Date is .

 

 

Overview: This web-page presents infomation about and output from our ocean state estimate for the California Current System. We use a 4-dimensional, weak-constraint approach.

 

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 ocean state in which the model initial conditions have been modified. Several physical data sets are assimilated. AVISO Sea Level Anomalies and subtidally averaged NOAA 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. 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.

 

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).

What impact does any given observation have on the 4D-Var data-assimilative solution, or analysis? Observation impact calculations allow for the computation of the contribution of each individual observation that is assimilated during 4D-Var process to the increments in a given metric of interest, such as the eddy kinetic energy. The observations can be grouped by state variable (salinity, velocity, etc.), or measurement platform (glider, satellite, etc.), to show the aggregate effects of any type of observation. The metrics are time-averaged over each 4-day cycle, or analysis window. For more information on a given metric, click a figure below, or see a more detailed description here.


Eddy Kinetic Energy Vertical Transport
Isopycnal Depth Northward Transport
Energy Density

 

Eddy Kinetic Energy




The Eddy Kinetic Energy (EKE) represents the energy in the difference between the horizontal ocean velocities and some mean background flow state, in this case a monthly climatology. This energy metric, 0.5(u′2 + v′2), is then averaged in time over each 4-day assimilation window and vertically from the surface to 500 m depth. An example for one cycle is shown below. Finally, the EKE is averaged horizontally within the white box below, an area of high eddy energy in the CCS during summer and fall.





The EKE evolves from cycle to cycle, as shown in the top panel below. The bottom panel reveals changes, or increments, in the metric due to the 4D-Var assimilation. Two estimates of the increments are given, one from a direct differencing of the prior and posterior model solutions (blue), and the other from the summation of all the individual observation impacts (red).





The impacts for different groupings of observation type are shown in the figure at the top of the page.

 

Vertical Transport




The Vertical Transport represents the net volume flux through an imaginary horizontal surface at 40 m depth. The metric is derived from the vertical velocity at 40 m. A time-average of the velocity for one 4-day cycle is shown below. The velocity at each ROMS grid cell is multiplied by the cell's area, and then the flux for all cells in the central California coastal region (within 50 km of shore, outlined in black) is summed to form a scalar metric.





The Vertical Transport evolves from cycle to cycle, as shown in the top panel below. The bottom panel reveals changes, or increments, in the metric due to the 4D-Var assimilation. Two estimates of the increments are given, one from a direct differencing of the prior and posterior model solutions (blue), and the other from the summation of all the individual observation impacts (red).





The impacts for different groupings of observation type are shown in the figure at the top of the page.

 

Northward Transport




The Northward Transport represents the flux through an imaginary vertical surface extending along 37°N, from -127°E shoreward, and from the surface down to 500 m, or the bottom, whichever is encountered first. The metric is derived from the depth-averaged northward velocity at 37°N. A time-average of the velocity for one 4-day cycle is shown below. The northward velocity at each ROMS grid cell along 37°N is multiplied by the cell's width and thickness, and then the flux for all oceanic cells shoreward of -127°E is summed to form a scalar metric.





The Northward Transport evolves from cycle to cycle, as shown in the top panel below. The bottom panel reveals changes, or increments, in the metric due to the 4D-Var assimilation. Two estimates of the increments are given, one from a direct differencing of the prior and posterior model solutions (blue), and the other from the summation of all the individual observation impacts (red).





The impacts for different groupings of observation type are shown in the figure at the top of the page.

 

Isopycnal Depth




Density increases with depth in the ocean. The depth at which the density reaches a specific value can give insights into the ocean's circulation. The time average depth of the 1026 kg m-3 isopycnal for one 4-day cycle is shown below. This 2D map is averaged within the central California coastal region (within 50 km of shore, outlined in black) to form a scalar metric.





The Isopycnal Depth evolves from cycle to cycle, as shown in the top panel below. The bottom panel reveals changes, or increments, in the metric due to the 4D-Var assimilation. Two estimates of the increments are given, one from a direct differencing of the prior and posterior model solutions (blue), and the other from the summation of all the individual observation impacts (red).





The impacts for different groupings of observation type are shown in the figure at the top of the page.

 

Energy Density




The Energy Density represents the sum of the available gravitational potential and kinetic energy in the ocean. The metric is dominated by the available gravitational potential energy. This statistic gives the energy stored in the density structure of the ocean that could be converted into the kinetic energy of ocean currents. Near-surface values of the available potential energy for one 4-day cycle are shown below. The energy per unit volume is integrated over the entire model domain to give a scalar Energy Density metric.





The Energy Density evolves from cycle to cycle, as shown in the top panel below. The bottom panel reveals changes, or increments, in the metric due to the 4D-Var assimilation. Two estimates of the increments are given, one from a direct differencing of the prior and posterior model solutions (blue), and the other from the summation of all the individual observation impacts (red).





The impacts for different groupings of observation type are shown in the figure at the top of the page.

 

Access to the model fields is not currently available for this run.

 

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