Albertella, A.

Bosch, W.

Janjic Pfander, Tijana

Rummel, R.

Savcenko, R.

Schröter, Jens

English

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Preliminary results on impact of combining GRACE and GOCEdata on ocean circulation estimatesT. Janjic´ (1), J. Schro¨ter (1), A. Albertella (2), R. Savcenko (3) and W.Bosch (3), R.Rummel (2)(1) Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany, (2) Institute for Astronomical and physicalGeodesy, TU Munich, Munich, Germany (3) Deutsches Geodtisches Forschungsinstitut, Munich, Germany.1 IntroductionThe geodetic approach for obtaining the dynamical ocean topog-raphy (DOT), that combines the multi-mission-altimeter dataand the GRACE/GOCE gravity fields, requires that both fieldsbe spectrally consistent. The spectral consistency is achieved byfiltering applied to the sea surface and the geoid. Combiningthe GRACE and GOCE data, considerably shorter filter lengthresolving more DOT details can be used.In this work we present the geodetic DOT obtained by usingJekeli-Wahr filter corresponding to 241 km, 121 km, 97 kmand 81 km halfwidths for the GRACE/GOCE based gravityfield model GOCO01S [5]. Further we assimilate satellite mea-surements of DOT into the global finite-element ocean model(FEOM) corresponding to 241 km, 121 km and 97 km halfwidths.The effects of gained resolution in the data fields are investigatedas well as the effects on other ocean fields obtained as the resultof the assimilation of these data.2 Geodetic DOT−60˚ −60˚−30˚ −30˚0˚ 0˚30˚ 30˚60˚ 60˚−1.5−1−0.5−0.5000.50.511−1.5 m −1.0 m −0.5 m 0.0 m 0.5 m 1.0 m 1.5 mMean DOT obtained using Jekeli-Wahr filter with half width of121 km and by combining GRACE and GOCE gravity data.3 Increase in resolution of geodeticDOTThe difference between geodetic DOTs. Left upper: obtainedform GRACE data only and by combining GRACE and GOCEgravity data. Both filtered using Jekeli-Wahr filter with halfwidth of 241 km. Right upper: Filtered up to degree 241 kmand 121 km. Left lower: Filtered up to degree 121 km and97 km. Right lower: Filtered up to degree 97 km and 81 km.4 Increase in variability of DOTTime varying DOT data are assimilated every 10 days. Thetemporal variability of this data set clearly shows all major eddyfields.Left: Difference in variance between half width 241 km and121 km filtering, Right: between half width 121 km and 97 km.5 Better resolved South AtlanticThe use of only satellite data starting with half width of 121 kmshow fine space scales that were previously poorly resolved withhalf width of 241 km. On the mean DOT as well on velocitiesfigures below superimposed are location of the fronts from [4].6 Geostrophic velocities in SouthernOceanGeostrophic velocities calculated from DOT filtered to Left up-per: half width of 241 km. Right upper: 121 km. Left lower:97 km. Right lower: 81 km.7 Assimilation of dataThe study was performed by the Finite-Element Ocean circula-tion Model (FEOM) [6, 1] configured on a global almost regulartriangular mesh with the spatial resolution of 1.5◦. Details of thedata assimilation algorithm are described in [2] for assimilationof the geodetic DOT using GRACE data only.Here we focus only on modifications to the algorithm that needto be done in order to take into account higher resolution data.In [2] it was shown that the optimal influence region is a circlewith a radius of 900 km (cutoff length) for observations that arefiltered to half width 241 km and that optimal covariance for lo-calization of ensemble Kalman filter algorithm approximates wella Gaussian with length scale of 246 km. For the data filtered upto 121 km experiments were performed using same specification,as well as a localization function with length scale of 123 km(450 km cutoff).Evolution of RMS error of SSH for the world ocean. The blacklines with bullets represent the 10-day model forecasts, while thedotted gray lines correspond to the analysis. Upper: RMS errorfor assimilation of data filtered up to half width of 121 km and lo-calization function that correspond to Gaussian with length scale123 km (450 km cutoff). Lower: Localization function corre-sponds to Gaussian with length scale 246 km (900 km cutoff).The difference between geodetic DOTs as a result of assimilation,Left: for assimilation of data with the half width of 241 and121 km. Right: and the difference in results for assimilation ofdata with 121 km and 97 km.Data assimilation scheme corrects all the ocean fields, althoughonly geodetic DOT is assimilated. Radar altimetry cannot beused for those regions where the sea-ice coverage exceeds a cer-tain percentage during the entire year, as well as for ice shelvesand near-coastal zones. Therefore it is interesting to comparethe assimilation results in the area which is not well observed.Temperature and velocity at 800 m depth. Left Upper: Com-posite of ARGO data from 1999 to 2009 is used (courtesy ofDr.O.Klatt, AWI). Right Upper: For model only without dataassimilation. Left Lower: As result of assimilation of geodeticDOT filtered up to 241 km Right Lower: As result of assimila-tion of geodetic DOT filtered up to 121 km.8 Conclusion• DOT with much finer space scales, that were previously poorlyresolved, is obtained by combining GRACE and GOCE grav-ity field data.• Fine space scale structures are particularly visible in the areasof strong currents. Here we show South Atlantic and SouthernOcean example only.• Results of assimilation into the global finite element oceanmodel, shows similar increase in the resolution of DOT ob-tained as seen in the data.• Further comparison of the results with independent ARGOdata set shows positive impact of increased resolution in Wed-dell Sea area.References[1] S. Danilov and G. Kivman and J. Schro¨ter (2004): A finite-element ocean model: principles and evaluation,Ocean Modelling, 6, 125–150.[2] T. Janjic´, J. Schro¨ter, A. Albertella, W. Bosch, R. Rummel, R. Savcenko, J. Schwabe, M.Scheinert: Assimilation of geodetic dynamic ocean topography using ensemble based Kalman filter, J. of Geodynamics,submitted.[3] L. Nerger and L., W. Hiller and J. Schro¨ter (2005): PDAF - the Parallel Data Assimilation Framework:Experiences with Kalman filtering. In W. Zwieflhofer and G. Mozdzynski, editors, Use of High Performance Computing inMeteorology - Proceedings of the 11. ECMWF Workshop, pages 63–83. World Scientific.[4] Orsi, A.H., T. Whitworth III, and W.D. Nowlin Jr. (1995): On the meridional extent and fronts of theAntarctic Circumpolar Current, Deep-Sea Research, 42, 641-673.[5] R. Pail and H. Goiginger and W.-D. Schuh and E. Ho¨ck and Brockmann, J. M., Fecher, T., Gruber, T., Mayer-Grr, T., Kusche, J., Ja¨ggi, A., Rieser, D. (2010): Combined satellite gravity field model GOCO01S derived from GOCEand GRACE, Geophysical Research Letters, 37, DOI: 10.1029/2010GL044906.[6] Q. Wang and S. Danilov and J. Schro¨ter (2008): Ocean circulation Model based on triangular prismatic elementswith application in studying the effect of topography representation, J. Geophys. Res, 113, C05015.

Preliminary results on impact of combining GRACE and GOCE data on ocean circulation estimates

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