A reanalysis of the Chilean ocean circulation: preliminary results for the region between 20[degrees]S to 40[degrees]S.
In the following we briefly outline the development and preliminary results of a new reanalysis of the historical state of Chile's coastal ocean, named the Chilean Ocean State Estimate (COSE). The region covered by COSE is shown in Figure 1. While various global ocean state estimates now exist (e.g., SODA, Carton & Giese, 2008; ECCO Forget et al., 2015; GODAS Behringer 1998; HYCOM, Chassignet et al., 2006), their resolution is, in most cases too coarse to represent coastal zone processes well. And although new eddy resolving reanalysis products are available (e.g., HYCOM, the Operational Mercator Global Ocean
Analysis and Forecast system (Lellouche & Regnier, 2016) the global focus necessarily rests developers' attention from regional details. For example, the 1/12o resolution HY COM reanalysis uniquely provides a 20 year record that resolves well the upwelling and instability processes that dominate mesoscale variability in central Chile, but suffers from an occasional spurious surface intensified onshore flow at 30oS resulting from a numerical artifact of the atmospheric model used to provide the wind field (Alan Wallcraft, pers. comm.) (Fig. 2). Such a localized error may be acceptable at the global scale of HYCOM, but obviously it may compromise the utility of the HYCOM reanalysis for applications in central Chile. The COSE regional reanalysis presented here was intended to correct such limitations of global reanalyses, providing a state estimate that improves the representation of the coastal zone, but with the aim of nesting smoothly within the global HY COM reanalysis.
Estimating the state of a system by combining observations and models, commonly referred to as data assimilation (Wunch, 2006), is an example of a control problem--how should the control variables of the model (i.e., surface fluxes, boundary and initial conditions) be set so that the trajectory of the model passes within the confidence intervals of the observations? Solving the problem amounts to minimizing a quadratic cost function that penalizes the model-observation misfit and departures from the prior estimate of the system variables, subject to the constraint that the solution must remain physically consistent. In COSE, the observations used are satellite ocean surface temperature and height; prior estimates of the system state are taken form the HYCOM reanalysis; and the Rutgers version of the Regional Ocean Modelling System (ROMS; Shchepetkin & McWilliams, 2005) imposes physical correctness of the solution. Besides being widely used for coastal ocean simulations, the existence of tangent linear and adjoint versions of ROMS permits implementation of the computationally efficient "Lagrange multiplier" or "4DVAR" method (Wunsch, 2006). Unlike common data assimilation schemes such as Optimal Interpolation or 3DVAR, the 4DVAR scheme retains the desirable quality of allowing error statistics to evolve, as in the optimal full Kalman filter, without the need to update error covariance matrices. (In COSE the error covariance's are estimated from the variance in the HYCOM reanalysis). This efficiency means that 4DVAR can allow tractable near optimal data assimilation in the full space of the model. The Ensemble Kalman Filter (EnKF) shares similar properties (Lorenc, 2003). In the ROMS implementation of 4DVAR (Moore et al., 2004, 2011), nonlinearity of the underlying physics is dealt with by incrementally minimizing a linearized cost function via the Kalman gain. 4DVAR ROMS remains computationally demanding, even for moderately sized problems. Each 3 day analysis window of COSE took approximately 2.5 h walltime to run on eight threads of a 2.3 GHz Intel i7.
The ROMS configuration used in COSE is typical of those employed in other eastern boundary current systems. For computational efficiency the region is divided into two domains (Fig. 1). Here results from the northern domain only are presented. Both domains employ a horizontal resolution of 1/120 and 30 vertical levels. Surface heat and momentum fluxes are determined through the ROMS "bulk flux" option, fed by NCEP heat flux (Kalnay et al., 1996) and CCMP winds (Atlas et al., 2011). NCEP reanalysis data were obtained from the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www. esrl.noaa.gov/psd/. At the open boundaries standard Flather and Chapman conditions are respectively applied to barotropic velocity and surface elevation, while the baroclinic velocity component is clamped to the corresponding values from the HYCOM reanalysis. The time step for the baroclinic mode was 15 min.
Boundary conditions are typically troublesome in regional ocean models due to the unavoidable mismatch between the calculated internal and imposed external fields. Such boundary artifacts can influence the internal solution and are undesirable here as we wish the COSE circulation to merge smoothly into the HY COM reanalysis. This problem is dealt with to some extent through the inclusion of a 10 grid cell wide sponge layer on each open boundary, in which the eddy viscosity is raised linearly towards the open boundaries. In addition, by including boundary values in the control vector, and hence allowing them to be adjusted a posteriori, the assimilation should also tend to suppress boundary artifacts. To aid in this, velocities within the sponge layer were constrained with the HYCOM reanalysis. In effect this forces the assimilation to attempt to match the COSE and HYCOM solutions close to the open boundaries. An example of the reduction of rim currents in COSE is shown in Figure 3. This method is conceptually similar to direct nudging, but with the great advantage of preserving the physical consistency of the solution.
The observational products used to constrain the state estimate are daily 1/12o sea surface temperature (SST) from GHRSST (Merchant et al., 2014) and 5daily absolute sea surface height (SSH) mapped to a 1/40 grid from AVISO (http://www.aviso.altimetry.fr/). Additional sources of observations such as hydrographic casts are being assimilated in an updated reanalysis that is presently in development.
The assimilation significantly reduces the mismatch to the observations, relative to both the unconstrained ROMS simulation and the HY COM reanalyzes (Fig. 4). The median RMS errors in the COSE reanalysis for the year 2008 are 0.33 and 0.36 for SST and SSH, respectively, compared to 0.56 and 0.48 for the HYCOM reanalysis, and to 0.49 and 0.48 for the unconstrained ROMS simulation. It may be appreciated in figure 4 that some examples exist, predominantly during summer, where both COSE and ROMS perform worse than HYCOM. These cases correspond to the development of boundary artifacts towards the end of some assimilation cycles. As a result, the standard deviation in the hindcast error is substantially lower The errors do not overly influence the internal solution, but nonetheless it is hoped that the second generation reanalysis will reduce such errors.
It may be appreciated that COSE completely removes the spurious a geostrophic current present in HY COM at 30[degrees]S (Fig. 2). However, the artificial current is also absent from the unconstrained ROMS simulation, consistent with the origin being the Gibbs phenomenon in the wind product used in HY COM, and hence is not due to the assimilation. The added value of the assimilation is that it provide a solution that is consistent with the observations and reduces inconsistencies at the open boundaries.
Because only surface observations were assimilated, vertical structure differs little between COSE and unconstrained ROMS. Both, however, differ significantly from HYCOM. A cross-shore transect at 26[degrees]S (Fig. 5) illustrates that COSE and unconstrained ROMS sustain a more intense and coastal Poleward Under current (PUC) and a deeper mixed layer than HY COM. Figure 6 displays the mean correction made by COSE to the unconstrained ROMS solution in the surface velocity and temperature. The effect of the assimilation of SST and SSH observations can be seen to, in general, reduce the off-shore temperature gradient associated with coastal upwelling and weaken the mean northwards and off-shore surface flow. This suggests that the unconstrained ROMS model overestimates the intensity of the coastal upwelling circulation. The inadequately resolved wind field near to shore of satellite wind products such as CCMP, is likely to be the cause of this persistent error in the unconstrained ROMS simulation (Capet et al., 2004). Given the essential similarity of the majority of ROMS simulations of the south east Pacific ROMS, the overestimation of coastal upwelling may be a systematic deficiency in these simulations. Most variance in the corrections to velocity occur close to the open boundaries, owing to assimilation's attempts to constrain boundary currents. The entire COSE for both domains and the period 1993-2012 will be available for use by all interested users.
Received: 1 November 2015; Accepted: 5 September 2016
This work was financed by F ONDECYT grant 1131103 and through the Project ICM-CCM RC130004, supported by the Fondo de Innovacion para la Competitividad, of Chile's Ministerio de Economia, Fomento y Turismo.
Atlas, R., R.N. Hoffman, J. Ardizzone, S.M. Leidner, J.C. Jusem, D.K. Smith & D. Gombos. 2011. A crosscalibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Am. Meteor. Soc., 92, 157-174. doi: 10.1175/2010BAMS2946.1.
Behringer, D.W., M. Ji & A. Leetmaa. 1998. An improved coupled model for ENSO prediction and implications for ocean initialization. Part I. The ocean data assimilation system. Mon. Weather Rev., 126: 1013-1021. Capet, X., P. Marchesiello & J. McWilliams. 2004. Upwelling response to coastal wind profiles. Geophys. Res. Lett., 31, 13 L13309. doi: 10.1029/2004GL 020303.
Carton, J.A. & B.S. Giese. 2008. A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Mon. Weather Rev., 136: 2999-3001.
Chassignet, E.P., H.E. Hurlburt, O.M. Smedstad, G.R. Halliwell, P.J. Hogan, A.J. Wallcraft, R. Baraille & R. Bleck. 2006. The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system. J. Mar. Syst., pp. 6083.
Forget, G., J.M. Campin, P. Heimbach, C.N. Hill, R.M. Ponte & C. Wunsch. 2015. ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geosci. Model Dev., 8: 3653-3743. doi:10.5194/gmdd-8-3653-2015.
Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin & M. Iredell et al., 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteor. Soc., 77(3): 437-470.
Lellouche, J.M. & C. Regnier. 2016. [http://marine. copernicus.eu/documents/PUM/CMEMS-GLO-PUM001-002.pdf]. Reviewed: 15 October 2015.
Lorenc, A.C. 2003. The potential of the 3ensemble Kalman filter for NWP--a comparison with 4D-Var. Q. J.R. Meteorol. Soc., 129: 3183-3203.
Merchant, C.J., J. Mittaz & G.K. Corlett. 2014. Climate data assessment framework. GHRSST Document, CDR-TAG_CDAF v 1.0.5.
Moore, A.M., H.G. Arango, A.J. Miller, B.D. Cornuelle, E. Di Lorenzo & D.J. Neilson. 2004. A comprehensive ocean prediction and analysis system based on the tangent linear and adjoint components of a regional ocean model. Ocean Model., 7: 227-258.
Moore, A.M., H.G. Arango, G. Broquet, B.S. Powell, A.T. Weaver & J. Zavala-Garay. 2011. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilations systems, Part I--System overview and formulation. Prog. Oceanogr., 91: 34-49.
Shchepetkin, A.F. & J.C. McWilliams. 2005. The regional ocean modeling system: a split-explicit, free-surface, topography following coordinates ocean model. Ocean Model., 9: 347-404.
Wunsch, C. 2006. Discrete inverse and state estimation problems: with geophysical fluid applications Cambridge University Press, Cambridge, 371 pp.
Christopher M. Aiken
Centro de Conservacion Marina, Pontificia Universidad Catolica de Chile Santiago, Chile
Corresponding author: Christopher Aiken (email@example.com)
Corresponding editor: Nelson Silva
Caption: Figure 1. The two domains comprising the Chilean Ocean State Estimate (COSE). This study presents results from the northern domain only.
Caption: Figure 2. Correction of the spurious velocity structure found at 30[degrees]S in the HYCOM reanalysis in unconstrained ROMS and COSE. The panels show the mean for the year 2008 in eastwards (u, upper row) and northwards (v, lower row) surface velocity. Contours of the standard deviation in each velocity component are overlaid.
Caption: Figure 3. An example of the partial suppression of boundary artifacts by the assimilation. The surface northwards (v) and eastwards (u) velocity from the unconstrained ROMS prior estimate (central column) and COSE (right column) is superimposed upon the HYCOM reanalysis (left column) that contributes the boundary conditions in each case.
Caption: Figure 4. RMS error in surface temperature (above) and sea surface elevation (below) of the HYCOM reanalysis (blue), the ROMS prior estimate (green) and COSE (red) for the year 2008. The mean, median and standard deviations are given in the upper right of each panel.
Caption: Figure 5. Cross-shore transects at 26[degrees]S of, from top to bottom, northwards velocity in HYCOM, in unconstrained ROMS and in COSE, and of the temperature difference to CoSE of HYCOM and of ROMS.
Caption: Figure 6. Mean (shaded) and standard deviation (contours) of the correction to the unconstrained ROMS solution for surface velocity and temperature in COSE.
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||Short communication|
|Author:||Aiken, Christopher M.|
|Publication:||Latin American Journal of Aquatic Research|
|Date:||Mar 1, 2017|
|Previous Article:||Utility of five SSR markers for genetic diversity and paternity exclusion analysis in the Patagonian toothfish.|
|Next Article:||New record of Pherecardia striata (Polychaeta: Amphinomidae) from Easter Island, Chile.|