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Florida State Univ, COAPS, Tallahassee, FL 32306 USA
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- PublicationRestrictedJRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)(2018)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ; ;We present a new surface-atmospheric dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently used in the framework of the Coordinated Ocean-ice Reference Experiments (COREs) and the Ocean Model Intercomparison Project (OMIP). A major improvement in JRA55-do is the refined horizontal grid spacing (∼ 55 km) and temporal interval (3 hr). The data production method for JRA55-do essentially follows that of the CORE dataset, whereby the surface fields from an atmospheric reanalysis are adjusted relative to reference datasets. To improve the adjustment method, we use high-quality products derived from satellites and from several other atmospheric reanalysis projects, as well as feedback on the CORE dataset from the ocean modelling community. Notably, the surface air temperature and specific humidity are adjusted using multi-reanalysis ensemble means. In JRA55-do, the downwelling radiative fluxes and precipitation, which are affected by an ambiguous cloud parameterisation employed in the atmospheric model used for the reanalysis, are based on the reanalysis products. This approach represents a notable change from the CORE dataset, which imported independent observational products. Consequently, the JRA55-do dataset is more self-contained than the CORE dataset, and thus can be continually updated in near real-time. The JRA55-do dataset extends from 1958 to the present, with updates expected at least annually. This paper details the adjustments to the original JRA-55 fields, the scientific rationale for these adjustments, and the evaluation of JRA55-do. The adjustments successfully corrected the biases in the original JRA-55 fields. The globally averaged features are similar between the JRA55-do and CORE datasets, implying that JRA55-do can suitably replace the CORE dataset for use in driving global ocean–sea-ice models.117 2 - PublicationOpen AccessAn assessment of the Indian Ocean mean state and seasonal cycle in a suite of interannual CORE-II simulations(2020)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;We present an analysis of annual and seasonal mean characteristics of the Indian Ocean circulation and water masses from 16 global ocean-sea-ice model simulations that follow the Coordinated Ocean-ice Reference Experiments (CORE) interannual protocol (CORE-II). All simulations show a similar large-scale tropical current system, but with differences in the Equatorial Undercurrent. Most CORE-II models simulate the structure of the Cross Equatorial Cell (CEC) in the Indian Ocean. We uncover a previously unidentified secondary pathway of northward cross-equatorial transport along 75 °E, thus complementing the pathway near the Somali Coast. This secondary pathway is most prominent in the models which represent topography realistically, thus suggesting a need for realistic bathymetry in climate models. When probing the water mass structure in the upper ocean, we find that the salinity profiles are closer to observations in geopotential (level) models than in isopycnal models. More generally, we find that biases are model dependent, thus suggesting a grouping into model lineage, formulation of the surface boundary, vertical coordinate and surface salinity restoring. Refinement in model horizontal resolution (one degree versus ¼ degree) does not significantly improve simulations, though there are some marginal improvements in the salinity and barrier layer results. The results in turn suggest that a focus on improving physical parameterizations (e.g. boundary layer processes) may offer more near-term advances in Indian Ocean simulations than refined grid resolution.92 6 - PublicationOpen AccessNorth Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean states(2014-01)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;Danabasoglu, G.; NCAR, Boulder, CO USA ;Yeager, S. G.; NCAR, Boulder, CO USA ;Bailey, D.; NCAR, Boulder, CO USA ;Behrens, E.; GEOMAR, Helmholtz Ctr Ocean Res, Kiel, Germany ;Bentsen, M.; Uni Res Ltd, Uni Climate, Bergen, Norway ;Bi, D.; CSIRO, Ctr Australian Weather & Climate Res, Melbourne, Australia ;Biastoch, A.; GEOMAR, Helmholtz Ctr Ocean Res, Kiel, Germany ;Boening, C.; GEOMAR, Helmholtz Ctr Ocean Res, Kiel, Germany ;Bozec, A.; Florida State Univ, COAPS, Tallahassee, FL 32306 USA ;Canuto, V. M.; NASA, Goddard Inst Space Studies, New York, NY 10025 USA ;Cassou, C.; CERFACS, Toulouse, France ;Chassignet, E.; Florida State Univ, COAPS, Tallahassee, FL 32306 USA ;Coward, A. C.; NOCS, Southampton, Hants, England ;Danilov, S.; Alfred Wegener Inst Polar & Marine Res AWI, Bremerhaven, Germany ;Diansky, N.; Russian Acad Sci, Inst Numer Math, Moscow, Russia ;Drange, H.; Univ Bergen, Bergen, Norway ;Farneti, R.; Abdus Salaam Int Ctr Theoret Phys, Trieste, Italy ;Fernandez, E.; CERFACS, Toulouse, France ;Fogli, P. G.; Ctr Euro Mediterraneo Cambiamenti Climatici CMCC, Bologna, Italy ;Forget, G.; MIT, Cambridge, MA 02139 USA ;Fujii, Y.; Japan Meteorol Agcy, MRI, Tsukuba, Ibaraki, Japan ;Griffies, S. M.; NOAA, GFDL, Princeton, NJ USA ;Gusev, A.; Russian Acad Sci, Inst Numer Math, Moscow, Russia ;Heimbach, P.; MIT, Cambridge, MA 02139 USA ;Howard, A.; CUNY Medgar Evers Coll, Brooklyn, NY 11225 USA ;Jung, T.; Alfred Wegener Inst Polar & Marine Res AWI, Bremerhaven, Germany ;Kelley, M.; NOAA, GFDL, Princeton, NJ USA ;Large, W. G.; NCAR, Boulder, CO USA ;Leboissetier, A.; NASA, Goddard Inst Space Studies, New York, NY 10025 USA ;Lu, J.; Florida State Univ, COAPS, Tallahassee, FL 32306 USA ;Madec, G.; CNRS IRD UPMC, IPSL LOCEAN, Paris, France ;Marsland, S. J.; CSIRO, Ctr Australian Weather & Climate Res, Melbourne, Australia CSIRO, Bur Meteorol, Melbourne, Australia ;Masina, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italia ;Navarra, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italia ;Nurser, A. J. G.; NOCS, Southampton, Hants, England ;Pirani, A.; Natl Oceanog Ctr, Int CLIVAR Project Off, Southampton, Hants, England ;Salas y Melia, D.; CNRM, Toulouse, France ;Samuels, B. L.; NOAA, GFDL, Princeton, NJ USA ;Scheinert, M.; GEOMAR, Helmholtz Ctr Ocean Res, Kiel, Germany ;Sidorenko, D.; Alfred Wegener Inst Polar & Marine Res AWI, Bremerhaven, German ;Treguier, A.; IUEM, CNRS Ifremer IRD UBO, UMR 6523, Lab Phys Oceans, Plouzane, France ;Tsujino, H.; Japan Meteorol Agcy, MRI, Tsukuba, Ibaraki, Japan ;Uotila, P.; CSIRO, Ctr Australian Weather & Climate Res, Melbourne, Australia ;Valcke, S.; CERFACS, Toulouse, France ;Voldoire, A.; CNRM, Toulouse, France ;Wangi, Q.; Alfred Wegener Inst Polar & Marine Res AWI, Bremerhaven, Germany; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; Simulation characteristics from eighteen global ocean–sea-ice coupled models are presented with a focus on the mean Atlantic meridional overturning circulation (AMOC) and other related fields in the North Atlantic. These experiments use inter-annually varying atmospheric forcing data sets for the 60- 1 Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site year period from 1948 to 2007 and are performed as contributions to the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II). The protocol for conducting such CORE-II experiments is summarized. Despite using the same atmospheric forcing, the solutions show significant differences. As most models also differ from available observations, biases in the Labrador Sea region in upper-ocean potential temperature and salinity distributions, mixed layer depths, and sea-ice cover are identified as contributors to differences in AMOC. These differences in the solutions do not suggest an obvious grouping of the models based on their ocean model lineage, their vertical coordinate representations, or surface salinity restoring strengths. Thus, the solution differences among the models are attributed primarily to use of different subgrid scale parameterizations and parameter choices as well as to differences in vertical and horizontal grid resolutions in the ocean models. Use of a wide variety of sea-ice models with diverse snow and sea-ice albedo treatments also contributes to these differences. Based on the diagnostics considered, the majority of the models appear suitable for use in studies involving the North Atlantic, but some models require dedicated development effort.480 271