EnKF; isopycnal coordinate; reanalysis; SST assimilation; vertical localization; Global and Planetary Change; Environmental Science (miscellaneous); Pollution; Atmospheric Science; Management, Monitoring, Policy and Law
Abstract :
[en] Sea surface temperature (SST) observations are a critical data set for long-term climate reconstruction. However, their assimilation with an ensemble-based data assimilation method can degrade performance in the ocean interior due to spurious covariances. Assimilation in isopycnal coordinates can delay the degradation, but it remains problematic for long reanalysis. We introduce vertical localization for SST assimilation in the isopycnal coordinate. The tapering functions are formulated empirically from a large pre-industrial ensemble. We propose three schemes: 1) a step function with a small localization radius that updates layers from the surface down to the first layer for which insignificant correlation with SST is found, 2) a step function with a large localization radius that updates layers down to the last layer for which significant correlation with SST is found, and 3) a flattop smooth tapering function. These tapering functions vary spatially and with the calendar month and are applied to isopycnal temperature and salinity. The impact of vertical localization on reanalysis performance is tested in identical twin experiments within the Norwegian Climate Prediction Model (NorCPM) with SST assimilation over the period 1980–2010. The SST assimilation without vertical localization greatly enhances performance in the whole water column but introduces a weak degradation at intermediate depths (e.g., 2,000–4,000 m). Vertical localization greatly reduces the degradation and improves the overall accuracy of the reanalysis, in particular in the North Pacific and the North Atlantic. A weak degradation remains in some regions below 2,000 m in the Southern Ocean. Among the three schemes, scheme 2) outperforms schemes 1) and 3) for temperature and salinity.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Wang, Yiguo; Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Counillon, François; Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway ; Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
Barthélémy, Sébastien; Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
Barth, Alexander ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER)
Language :
English
Title :
Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model
This study was supported by the Research Council of Norway (Grant Nos. 301396 and 270061) and the Trond Mohn Foundation under project number BFS2018TMT01.
Adcroft A. Anderson W. Balaji V. Blanton C. Bushuk M. Dufour C. O. et al. (2019). The gfdl global ocean and sea ice model om4.0: model description and simulation features. J. Adv. Model. Earth Syst. 11, 3167–3211. 10.1029/2019MS001726
Ammann C. M. Meehl G. A. Washington W. M. Zender C. S. (2003). A monthly and latitudinally varying volcanic forcing dataset in simulations of 20th century climate. Geophys. Res. Lett. 30, 16875. 10.1029/2003GL016875
Balmaseda M. Anderson D. (2009). Impact of initialization strategies and observations on seasonal forecast skill. Geophys. Res. Lett. 36, 35561. 10.1029/2008GL035561
Bentsen M. Bethke I. Debernard J. B. Iversen T. Kirkevåg A. Seland O. et al. (2013). The norwegian earth system model, NorESM1-Part 1: description and basic evaluation of the physical climate. Geosci. Model Dev. 6, 687–720. 10.5194/gmd-6-687-2013
Bethke I. Wang Y. Counillon F. Keenlyside N. Kimmritz M. Fransner F. et al. (2021). Norcpm1 and its contribution to cmip6 dcpp. Geosci. Model Dev. 14, 7073–7116. 10.5194/gmd-14-7073-2021
Bethke I. Wang Y. Counillon F. Kimmritz M. Langehaug H. Bentsen M. et al. (2018). “Subtropical north atlantic preconditioning key to skillful subpolar gyre prediction,” in Second International Conference on Seasonal to Decadal Prediction, Boulder.
Billeau S. Counillon F. Keenlyside N. Bertino L. (2016). Impact of changing the assimilation cycle: centered vs. staggered, snapshot vs monthly averaged. NERSC Technical Report 400, Nansen Environmental and Remote Sensing Center.
Bleck R. (2002). An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Model. 4, 55–88. 10.1016/S1463-5003(01)00012-9
Bleck R. Rooth C. Hu D. Smith L. T. (1992). Salinity-driven thermocline transients in a wind- and thermohaline-forced isopycnic coordinate model of the North Atlantic. J. Phys. Oceanogr. 22, 1486–1505. 10.1175/1520-0485(1992)022<1486:SDTTIA>2.0.CO;2
Bozec A. Lozier M. S. Chassignet E. P. Halliwell G. R. (2011). On the variability of the mediterranean outflow water in the north atlantic from 1948 to 2006. J. Geophys. Res. Oceans 116, 7191. 10.1029/2011JC007191
Brune S. Nerger L. Baehr J. (2015). Assimilation of oceanic observations in a global coupled Earth system model with the SEIK filter. Ocean Model. 96, 254–264. 10.1016/j.ocemod.2015.09.011
Carrassi A. Bocquet M. Bertino L. Evensen G. (2018). Data assimilation in the geosciences: an overview of methods, issues, and perspectives. Wiley Interdisc. Rev. Clim. Change 9, e535. 10.1002/wcc.535
Carri,ó D. S. Bishop C. H. Kotsuki S. (2021). Empirical determination of the covariance of forecast errors: an empirical justification and reformulation of hybrid covariance models. Q. J. R. Meteorol. Soc. 147, 2033–2052. 10.1002/qj.4008
Carter L. McCave I. N. Wiliams M. J. M. (2008). “Circulation and water masses of the southern ocean: A review,” in Developments in Earth and Environmental Sciences, Vol. 8, eds F. Florindo and M. Siegert (Elsevier), 85–114. 10.1016/S1571-9197(08)00004-9
Counillon F. Bertino L. (2009). Ensemble optimal interpolation: multivariate properties in the gulf of mexico. Tellus A 61, 296–308. 10.1111/j.1600-0870.2008.00383.x
Counillon F. Bethke I. Keenlyside N. Bentsen M. Bertino L. Zheng F. (2014). Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment. Tellus A 66, 1–21. 10.3402/tellusa.v66.21074
Counillon F. Keenlyside N. Bethke I. Wang Y. Billeau S. Shen M. L. et al. (2016). Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model. Tellus A 68, 1–17. 10.3402/tellusa.v68.32437
Craig A. P. Vertenstein M. Jacob R. (2012). A new flexible coupler for earth system modeling developed for CCSM4 and CESM1. Int. J. High Perform. Comput. Appl. 26, 31–42. 10.1177/1094342011428141
Dee D. P. Uppala S. M. Simmons A. J. Berrisford P. Poli P. Kobayashi S. et al. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597. 10.1002/qj.828
Evensen G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dyn. 53, 343–367. 10.1007/s10236-003-0036-9
Eyring V. Bony S. Meehl G. A. Senior C. A. Stevens B. Stouffer R. J. et al. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958. 10.5194/gmd-9-1937-2016
Gaspari G. Cohn S. E. (1999). Construction of correlatin functions in two and three dimensions. Q. J. R. Meteorol. Soc., 125, 723–757. 10.1002/qj.49712555417
Gavart M. Mey P. D. (1997). Isopycnal eofs in the azores current region: a statistical tool fordynamical analysis and data assimilation. J. Phys. Oceanogr. 27, 2146–2157. 10.1175/1520-0485(0)027<2146:IEITAC>2.0.CO;2
Gent P. R. Danabasoglu G. Donner L. J. Holland M. M. Hunke E. C. Jayne S. R. et al. (2011). The community climate system model version 4. J. Clim. 24, 4973–4991. 10.1175/2011JCLI4083.1
Goddard L. Kumar A. Solomon A. Smith D. Boer G. Gonzalez P. et al. (2013). A verification framework for interannual-to-decadal predictions experiments. Clim. Dyn. 40, 245–272. 10.1007/s00382-012-1481-2
Halem M. Dlouhy R. (1984). Observing system simulation experiments related to space-borne lidar wind profiling. part 1: Forecast impacts of highly idealized observing systems. Res. Rev. 1983, 19840013984.
Hamill T. M. Snyder C. (2000). A hybrid ensemble kalman filter-3d variational analysis scheme. Mon. Weather Rev. 128, 2905–2919. 10.1175/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2
Hamill T. M. Whitaker J. S. Snyder C. (2001). Distance-dependent filtering of background error covariance estimates in an ensemble kalman filter. Mon. Weather Rev. 129, 2776–2790. 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2
Holland M. M. Bailey D. A. Briegleb B. P. Light B. Hunke E. (2012). Improved sea ice shortwave radiation physics in CCSM4: the impact of melt ponds and aerosols on arctic sea ice. J. Clim. 25, 1413–1430. 10.1175/JCLI-D-11-00078.1
Houtekamer P. L. Mitchell H. L. (2001). A sequential ensemble kalman filter for atmospheric data assimilation. Mon. Weather Rev. 129, 123–137. 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
Hurtt G. C. Chini L. P. Frolking S. Betts R. A. Feddema J. Fischer G. et al. (2011). Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Chang. 109, 117. 10.1007/s10584-011-0153-2
Kalnay E. Kanamitsu M. Kistler R. Collins W. Deaven D. Gandin L. et al. (1996). The ncep/ncar 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471. 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
Karspeck A. R. Yeager S. Danabasoglu G. Hoar T. Collins N. Raeder K. et al. (2013). An ensemble adjustment kalman filter for the CCSM4 ocean component. J. Clim. 26, 7392–7413. 10.1175/JCLI-D-12-00402.1
Kimmritz M. Counillon F. Smedsrud L. H. Bethke I. Keenlyside N. Ogawa F. et al. (2019). Impact of ocean and sea ice initialisation on seasonal prediction skill in the arctic. J. Adv. Model. Earth Syst. 11, 4147–4166. 10.1029/2019MS001825
Kirkevåg A. Iversen T. Seland Ø. Hoose C. Kristjánsson J. E. Struthers H. et al. (2013). Aerosol-climate interactions in the norwegian earth system-noresm1-m. Geosci. Model Dev. 6, 207–244. 10.5194/gmd-6-207-201328781391
Koul V. Tesdal J. Bersch M. Hatun H. Brune S. Borchert L. et al. (2020). Unraveling the choice of the north atlantic subpolar gyre index. Sci Rep 10, 1005. 10.1038/s41598-020-57790-531969636
Laloyaux P. de Boisseson E. Balmaseda M. Bidlot J.-R. Broennimann S. Buizza R. et al. (2018a). Cera-20c: a coupled reanalysis of the twentieth century. J. Adv. Model. Earth Syst. 10, 1172–1195. 10.1029/2018MS001273
Laloyaux P. Frolov S. Ménétrier B. Bonavita M. (2018b). Implicit and explicit cross-correlations in coupled data assimilation. Q. J. R. Meteorol. Soc. 144, 1851–1863. 10.1002/qj.3373
Lamarque J.-F. Bond T. C. Eyring V. Granier C. Heil A. Klimont Z. et al. (2010). Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application. Atmospheric Chem. Phys. 10, 7017–7039. 10.5194/acp-10-7017-2010
Lawrence D. M. Oleson K. W. Flanner M. G. Thornton P. E. Swenson S. C. Lawrence P. J. et al. (2011). Parameterization improvements and functional and structural advances in version 4 of the community land model. J. Adv. Model. Earth Syst. 3, M03001. 10.1029/2011MS000045
Lean J. Rottman G. Harder J. Kopp G. (2005). Sorce contributions to new understanding of global change and solar variability. Solar Phys. 230, 27–53. 10.1007/s11207-005-1527-2
Ménétrier B. Auligné T. (2015). Optimized localization and hybridization to filter ensemble-based covariances. Mon. Weather Rev. 143, 3931–3947. 10.1175/MWR-D-15-0057.1
Ménétrier B. Montmerle T. Michel Y. Berre L. (2015a). Linear filtering of sample covariances for ensemble-based data assimilation. Part i: optimality criteria and application to variance filtering and covariance localization. Mon. Weather Rev. 143, 1622–1643. 10.1175/MWR-D-14-00157.1
Ménétrier B. Montmerle T. Michel Y. Berre L. (2015b). Linear filtering of sample covariances for ensemble-based data assimilation. Part ii: application to a convective-scale nwp model. Mon. Weather Rev. 143, 1644–1664. 10.1175/MWR-D-14-00156.1
Mignac D. Tanajura C. a,. S Santana a,. N. Lima L. N. Xie J. (2015). Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean. Ocean Sci. 11, 195–213. 10.5194/os-11-195-2015
Miyoshi T. Kondo K. Imamura T. (2014). The 10,240-member ensemble kalman filtering with an intermediate agcm. Geophys. Res. Lett. 41, 5264–5271. 10.1002/2014GL060863
Mulholland D. P. Laloyaux P. Haines K. Balmaseda M. A. (2015). Origin and impact of initialization shocks in coupled atmosphere-ocean forecasts. Mon. Weather Rev. 143, 4631–4644. 10.1175/MWR-D-15-0076.1
Murphy A. H. (1988). Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Weather Rev. 116, 2417–2424. 10.1175/1520-0493(1988)116<2417:SSBOTM>2.0.CO;2
O'Kane T. J. Sandery P. A. Kitsios V. Sakov P. Chamberlain M. A. Collier M. A. et al. (2021). Cafe60v1: a 60-year large ensemble climate reanalysis. Part i: system design, model configuration, and data assimilation. J. Clim. 34, 5153–5169. 10.1175/JCLI-D-20-0974.1
Oleson K. W. Lawrence D. M. Bonan G. B. Flanner M. G. Kluzek E. Lawrence P. J. et al. (2010). Technical Description of version 4.0 of the Community Land Model (CLM). Technical Report. NCAR/TN-478+STR. National Center for Atmospheric Research, Boulder, Colorado, USA.
Ott E. Hunt B. R. Szunyogh I. Zimin A. V. Kostelich E. J. Corazza M. et al. (2004). A local ensemble kalman filter for atmospheric data assimilation. Tellus A 56, 415–428. 10.3402/tellusa.v56i5.1446228839201
Price J. F. Baringer M. O. Lueck R. G. Johnson G. C. Ambar I. Parrilla G. et al. (1993). Mediterranean outflow mixing and dynamics. Science 259, 1277–1282. 10.1126/science.259.5099.127717732247
Rayner N. A. Parker D. E. Horton E. B. Folland C. K. Alexander L. V. Rowell D. P. et al. (2003). Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmosph. 108, 2670. 10.1029/2002JD002670
Richter I. (2015). Climate model biases in the eastern tropical oceans: causes, impacts and ways forward. Wiley Interdisc. Rev. Clim. Change 6, 345–358. 10.1002/wcc.338
Sakov P. Bertino L. (2010). Relation between two common localisation methods for the EnKF. Comput. Geosci. 15, 225–237. 10.1007/s10596-010-9202-6
Sakov P. Counillon F. Bertino L. Lisæter K. A. Oke P. R. Korablev A. (2012). TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Sci. 8, 633–656. 10.5194/os-8-633-2012
Sakov P. Oke P. R. (2008). A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters. Tellus A 60, 361–371. 10.1111/j.1600-0870.2007.00299.x
Srinivasan A. Chassignet E. P. Bertino L. Brankart J. M. Brasseur P. Chin T. M. et al. (2011). A comparison of sequential assimilation schemes for ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM): twin experiments with stati c forecast error covariances. Ocean Model. 37, 85–111. 10.1016/j.ocemod.2011.01.006
Taylor K. E. Stouffer R. J. Meehl G. A. (2012). An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498. 10.1175/BAMS-D-11-00094.135859545
van Vuuren D. P. Edmonds J. Kainuma M. Riahi K. Thomson A. Hibbard K. et al. (2011). The representative concentration pathways: an overview. Clim Change 109, 5. 10.1007/s10584-011-0148-z
Vertenstein M. Craig T. Middleton A. Feddema D. Fischer C. (2012). CESM1.0.3 User Guide. Available online at: http://www.cesm.ucar.edu/models/cesm1.0/cesm/cesm_doc_1_0_4/ug.pdf (accessed January 23, 2015).
Wang Y. Counillon F. Bertino L. (2016). Alleviating the bias induced by the linear analysis update with an isopycnal ocean model. Q. J. R. Meteorol. Soc. 142, 1064–1074. 10.1002/qj.2709
Wang Y. Counillon F. Bethke I. Keenlyside N. Bocquet M. Shen M.-L. (2017). Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation. Ocean Model. 114, 33–44. 10.1016/j.ocemod.2017.04.007
Wang Y. Counillon F. Keenlyside N. Svendsen L. Gleixner S. Kimmritz M. et al. (2019). Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF. Clim. Dyn. 53, 5777–5797. 10.1007/s00382-019-04897-9
Wang Y.-M. Lean J. L. Sheeley Jr N. R. (2005). Modeling the sun's magnetic field and irradiance since 1713. Astrophys. J. 625, 522–538. 10.1086/429689
Weber R. J. T. Carrassi A. Doblas-Reyes F. J. (2015). Linking the anomaly initialization approach to the mapping paradigm: a proof-of-concept study. Mon. Weather Rev. 143, 4695–4713. 10.1175/MWR-D-14-00398.1
Zhang S. Harrison M. J. Rosati A. Wittenberg A. (2007). System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon. Weather Rev. 135, 3541–3564. 10.1175/MWR3466.1
Zuo H. Balmaseda M. A. Tietsche S. Mogensen K. Mayer M. (2019). The ECMWF operational ensemble reanalysis-analysis system for ocean and sea ice: a description of the system and assessment. Ocean Sci. 15, 779–808. 10.5194/os-15-779-2019