Abstract
Since 2016, the Copernicus Marine Environment Monitoring Service (CMEMS) has produced and disseminated an ensemble of four global ocean reanalyses produced at eddy-permitting resolution for the period from 1993 to present, called GREP (Global ocean Reanalysis Ensemble Product). This dataset offers the possibility to investigate the potential benefits of a multi-system approach for ocean reanalyses, since the four reanalyses span by construction the same spatial and temporal scales. In particular, our investigations focus on the added value of the information on the ensemble spread, implicitly contained in the GREP ensemble, for temperature, salinity, and steric sea level studies. It is shown that in spite of the small ensemble size, the spread is capable of estimating the flow-dependent uncertainty in the ensemble mean, although proper re-scaling is needed to achieve reliability. The GREP members also exhibit larger consistency (smaller spread) than their predecessors, suggesting advancement with time of the reanalysis vintage. The uncertainty information is crucial for monitoring the climate of the ocean, even at regional level, as GREP shows consistency with CMEMS high-resolution regional products and complement the regional estimates with uncertainty estimates. Further applications of the spread include the monitoring of the impact of changes in ocean observing networks; the use of multi-model ensemble anomalies in hybrid ensemble-variational retrospective analysis systems, which outperform static covariances and represent a promising application of GREP. Overall, the spread information of the GREP product is found to significantly contribute to the crucial requirement of uncertainty estimates for climatic datasets.
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References
Balmaseda MA (2017) Editorial for ocean reanalysis intercomparison special issue. Clim Dyn 49:707. https://doi.org/10.1007/s00382-017-3813-8
Balmaseda MA, Trenberth KE, Källén E (2013) Distinctive climate signals in reanalysis of global ocean heat content. Geophys Res Lett 40:1754–1759. https://doi.org/10.1002/grl.50382
Balmaseda MA, Hernandez F, Storto A, Palmer MD, Alves O, Shi L, and Coauthors (2015) The ocean reanalyses intercomparison project (ORA-IP). J Oper Oceanogr 8(sup1):s80–s97. https://doi.org/10.1080/1755876X.2015.1022329
Blockley EW, Martin MJ, McLaren AJ, Ryan AG, Waters J, Lea DJ, Mirouze I, Peterson KA, Sellar A, Storkey D (2014) Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts. Geosci. Model Dev 7:2613–2638. https://doi.org/10.5194/gmd-7-2613-2014
Bouillon S, Morales Maqueda M, Legat V, Fichefet T (2009) An elastic–viscous–plastic sea ice model formulated on Arakawa B and C grids. Ocean Model 27:174–184
Brix H, Menemenlis D, Hill C, Dutkiewicz S, Jahn O, Wang D, Bowman K, Zhang H (2015) Using Green’s Functions to initialize and adjust a global, eddying ocean biogeochemistry general circulation model, Ocean Model. https://doi.org/10.1016/j.ocemod.2015.07.008
Burgers G, van Leeuwen PJ, Evensen G (1998) Analysis Scheme in the Ensemble Kalman Filter. Mon Weather Rev 126:1719–1724
Cabanes C, Grouazel A, von Schuckmann K, Hamon M, Turpin V, Coatanoan C, Paris F, Guinehut S, Boone C, Ferry N, de Boyer Montégut C, Carval T, Reverdin G, Pouliquen S, Traon L (2013) The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements. Ocean Sci 9:1–18. https://doi.org/10.5194/os-9-1-2013
Candille G, Talagrand O (2005) Evaluation of probabilistic prediction systems for a scalar variable. QJR Meteorol Soc 131:2131–2150. https://doi.org/10.1256/qj.04.71
Chandler RE (2013) Exploiting strength, discounting weakness: combining information from multiple climate simulators. Phil Trans R Soc A 371:20120388. https://doi.org/10.1098/rsta.2012.0388
Chevallier M, Smith GC, Dupont F, Lemieux J-F, Forget G, Fujii Y, Hernandez F, Msadek R, Peterson KA, Storto A, Toyoda T, Valdivieso M, Vernieres G, Zuo H, Balmaseda M, Chang Y-S, Ferry N, Garric G, Haines K, Keeley S, Kovach RM, Kuragano T, Masina S, Tang Y, Tsu**o H, Wang X (2017) Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project. Clim Dyn 49:1107–1136. https://doi.org/10.1007/s00382-016-2985-y
Crosnier L, Le Provost C (2007) Inter-comparing five forecast operational systems in the North Atlantic and Mediterranean basins: The MERSEA-strand1 methodology. J Mar Syst 65:354–375. https://doi.org/10.1016/j.jmarsys.2005.01.003
de Boisséson E, Balmaseda MA, Mayer M (2017) Ocean heat content variability in an ensemble of twentieth century ocean reanalyses. Clim Dyn. https://doi.org/10.1007/s00382-017-3845-0
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thépaut J-N, Vitart F (2011) The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597
Fortin V, Abaza M, Anctil F, Turcotte R (2014) Why should ensemble spread match the RMSE of the ensemble mean? J Hydrometeorol 15:1708–1713
Fujii Y, Cummings J, Xue Y, Schiller A, Lee T, Balmaseda MA, Rémy E, Masuda S, Brassington G, Alves O, Cornuelle B, Martin M, Oke P, Smith G, Yang X (2015) Evaluation of the tropical pacific observing system from the ocean data assimilation perspective. QJR Meteorol Soc 141:2481–2496. https://doi.org/10.1002/qj.2579
Garric G, Parent L, Greiner E, Drévillon M, Hamon M, Lellouche JM, Régnier C, Desportes C, Le Galloudec O, Bricaud C, Drillet Y, Hernandez F, Dubois C, Le Traon P-Y (2018) Performance and quality assessment of the global ocean eddy-permitting physical reanalysis GLORYS2V4. Operational Oceanography serving Sustainable Marine Development. Proceedings of the Eight EuroGOOS International Conference. 3–5 October 2017, Bergen, Norway. E. Buch, V. Fernandez, G. Nolan and D. Eparkhina (Eds.) EuroGOOS. Brussels, Belgium. 2018. ISBN:978-2-9601883-3-2. 516
Good SA, Martin MJ, Rayner NA (2013) EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J Geoph Res 118:6704–6716. https://doi.org/10.1002/2013JC009067
Griffies S, Greatbatch R (2012) Physical processes that impact the evolution of global mean sea level in ocean climate models. Ocean Model 51:37–72
Guinehut S, Dhomps A-L, Larnicol G (2012) High resolution 3-D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci 8:845–857. https://doi.org/10.5194/os-8-845-2012
Hamill TM, C. Snyder (2000) A hybrid ensemble kalman filter—3D variational analysis scheme. Mon Wea Rev 128:2905–2919, https://doi.org/10.1175/1520-0493(2000)128%3C2905:AHEKFV%3E2.0.CO;2
Hanna E et al (2013) Ice-sheet mass balance and climate change. Nature 498:51–59
Hart RE, Grumm RH (2001) Using normalized climatological anomalies to rank synoptic-scale events objectively. Mon Weather Rev 129(9):2426–2442
Hernandez F, Bertino L, Brassington G, Chassignet E, Cummings J, Davidson F, Drévillon M, Garric G, Kamachi M, Lellouche J-M, Mahdon R, Martin MJ, Ratsimandresy A, Regnier C (2009) Validation and intercomparison studies within GODAE. Oceanography 22(3):128–143. https://doi.org/10.5670/oceanog.2009.71
Hernandez F, Blockley E, Brassington GB, Davidson F, Divakaran P, Drévillon M et al. (2015) Recent progress in performance evaluations and near real-time assessment of operational ocean products, J Oper Oceanogr 8(2):s221–s238, https://doi.org/10.1080/1755876X.2015.1050282
Hu Z-Z, Kumar A (2015) Influence of availability of TAO data on NCEP ocean data assimilation systems along the equatorial Pacific. J Geophys Res Oceans 120:5534–5544. https://doi.org/10.1002/2015JC010913
Hunke EC, Lipscomb WH, Turner AK, Jeffery N, Elliott SM (2013) CICE: the Los Alamos Sea Ice Model, Documentation and Software, Version 5.0. Los Alamos National Laboratory Tech. Rep. LA-CC-06-012. http://oceans11.lanl.gov/trac/CICE
Jackson L, Peterson KA, Roberts C, Wood R (2016) Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening. Nat Geosci 9:518–522. https://doi.org/10.1038/ngeo2715
Johnson G, Chambers D (2013) Ocean bottom pressure seasonal cycles and decadal trends from GRACE Release-05: ocean circulation implications. J Geophys Res 118:4228–4240
Josey SA, Yu L, Gulev S, ** X, Tilinina N, Barnier B, Brodeau L (2014) Unexpected impacts of the Tropical Pacific array on reanalysis surface meteorology and heat fluxes. Geophys Res Lett 41:6213–6220. https://doi.org/10.1002/2014GL061302
Karspeck AR, Stammer D, Köhl A, Danabasoglu G, Balmaseda M, Smith DM, Fujii Y, Zhang S, Giese B, Tsu**o H, Rosati A (2017) Comparison of the Atlantic meridional overturning circulation between 1960 and 2007 in six ocean reanalysis products. Clim Dyn 49:957–982. https://doi.org/10.1007/s00382-015-278
Krishnamurti TN, Kishtawal CM, Shin DW, Williford CE (2000) Multi-model superensemble forecasts for weather and seasonal climate. J Clim 13:4196–4216. https://doi.org/10.1175/1520-0442(2000)013%3C4196:MEFFWA%3E2.0.CO;2
Laloyaux P, Balmaseda M, Dee D, Mogensen K, Janssen P (2015) A coupled data assimilation system for climate reanalyses. Q J R Meteorol Soc 142:65–78
Large WG, Yeager SG (2004) Diurnal to decadal global forcing for ocean and sea-ice models: the data sets and flux climatologies, NCAR Technical report NCAR/TN-460, Boulder, Colorado, USA
Le Traon PY, Nadal F, Ducet N (1998) An improved map** method of multisatellite altimeter data. J Atmos Oceanic Technol 15:522–534, https://doi.org/10.1175/1520-0426(1998)015%3C0522:AIMMOM%3E2.0.CO;2
Le Traon PY et al. (2017) The copernicus marine environmental monitoring service: main scientific achievements and future prospects. Mercator Ocean Journal (Special Issue CMEMS), pp 2–101. http://www.mercator-ocean.fr/en/science-publications/mercator-ocean-journal/mercator-ocean-journal-56-special-issue-cmems
Lea DJ, Drecourt J-P, Haines K, Martin MJ (2008) Ocean altimeter assimilation with observational- and model-bias correction. QJR Meteorol Soc 134:1761–1774. https://doi.org/10.1002/qj.320
Locarnini RA, Mishonov AV, Antonov JI, Boyer TP, Garcia HE, Baranova OK, Zweng MM, Paver CR, Reagan JR, Johnson DR, Hamilton M, Seidov D (2013) World Ocean Atlas 2013, Volume 1: Temperature. S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 73, 40 pp
Loeb NG et al (2009) Towards optimal closure of the earth’s top-of-atmosphere radiation budget. J Clim 22:748–766
Lorenc AC (1986) Analysis methods for numerical weather prediction. QJR Meteorol Soc 112:1177–1194. https://doi.org/10.1002/qj.49711247414
Lorenc AC (2003) The potential of the ensemble Kalman filter for NWP—a comparison with 4D-Var. QJR Meteorol Soc 129:3183–3203. https://doi.org/10.1256/qj.02.132
Madec G, Imbard M (1996) A global ocean mesh to overcome the North Pole singularity. Clim Dyn 12:381–388. https://doi.org/10.1007/BF00211684
Madec G, the NEMO team (2012) “NEMO ocean engine”. Note du Pole de modélisation de l’Institut Pierre-Simon Laplace, France, No 27 ISSN No 1288–1619
Marbà N, Jordà G, Agustí S, Girard C, Duarte CM (2015) Footprints of climate change on Mediterranean Sea biota. Front Mar Sci 2:56. https://doi.org/10.3389/fmars.2015.00056
Masina S, Storto A (2017) Reconstructing the recent past ocean variability: status and perspective. J Mar Res 75:727–764. https://doi.org/10.1357/002224017823523973
Masina S, Storto A, Ferry N, Valdivieso M, Haines K, Balmaseda M, Zuo H et al (2017) An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Clim Dyn 49:813–841. https://doi.org/10.1007/s00382-015-2728-5
Masson D, Knutti R (2011) Climate model genealogy. Geophys Res Lett 38:L08703. https://doi.org/10.1029/2011GL046864
Mayer M, Haimberger L, Pietschnig M, Storto A (2016), Facets of Arctic energy accumulation based on observations and reanalyses 2000–2015, Geophys Res Lett. https://doi.org/10.1002/2016GL070557
Meehl GA, Boer GJ, Covey C, Latif M, Stouffer RJ (1997) Intercomparison makes for a better climate model. Eos Trans AGU 78(41):445–451. https://doi.org/10.1029/97EO00276
Megann A, Storkey D, Aksenov Y, Alderson S, Calvert D, Graham T, Hyder P, Siddorn J, Sinha B (2014) GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications. Geosci Model Dev 7:1069–1092. https://doi.org/10.5194/gmd-7-1069-2014
Mirouze I, Blockley EW, Lea DJ, Martin MJ, Bell MJ (2016) A multiple length scale correlation operator for ocean data assimilation. Tellus A: Dyn Meteorol Oceanogr 68:1. https://doi.org/10.3402/tellusa.v68.29744
Nerem R, Chambers D, Choe C, Mitchum G (2010) estimating mean sea level change from the TOPEX and jason altimeter missions. Mar Geodesy 33:435–446
Oddo P, Storto A, Dobricic S, Russo A, Lewis C, Onken R, Coelho E (2016) A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models. Ocean Sci 12:1137–1153. https://doi.org/10.5194/os-12-1137-2016
Ota Y, Derber JC, Kalnay E, Miyoshi T (2013) Ensemble-based observation impact estimates using the NCEP GFS.Tellus A65, https://doi.org/10.3402/tellusa.v65i0.20038
Palmer MD, Roberts CD, Balmaseda M, Chang Y-S, Chepurin G, Ferry N, Fujii Y, Good SA, Guinehut S, Haines K, Hernandez F, Köhl A, Lee T, Martin MJ, Masina S, Masuda S, Peterson KA, Storto A, Toyoda T, Valdivieso M, Vernieres G, Wang O, Xue Y (2017) Ocean heat content variability and change in an ensemble of ocean reanalyses. Clim Dyn 49:909–930. https://doi.org/10.1007/s00382-015-2801-0
Penduff T, Juza M, Brodeau L, Smith GC, Barnier B, Molines J-M, Treguier A-M, Madec G (2010) Impact of global ocean model resolution on sea-level variability with emphasis on interannual time scales. Ocean Sci 6:269–284. https://doi.org/10.5194/os-6-269-2010
Penny SG, Behringer DW, Carton JA, Kalnay E (2015) A Hybrid Global Ocean Data Assimilation System at NCEP. Mon Wea Rev 143:4660–4677. https://doi.org/10.1175/MWR-D-14-00376.1
Potter GL, Carriere L, Hertz J, Bosilovich M, Duffy D, Lee T, Williams DN (2018) Enabling reanalysis research using the collaborative reanalysis technical environment (CREATE). Bull Amer Meteor Soc. In press, https://doi.org/10.1175/BAMS-D-17-0174.1
Rae JGL, Hewitt HT, Keen AB, Ridley JK, West AE, Harris CM, Hunke EC, Walters DN (2015) Development of the global sea ice 6.0 CICE configuration for the Met Office Global Coupled model. Geosci Model Dev 8:2221–2230. https://doi.org/10.5194/gmd-8-2221-2015
Rainwater S, Hunt BR (2013) Ensemble data assimilation with an adjusted forecast spread. Tellus A: Dyn Meteorol Oceanogr 65(1):19929. https://doi.org/10.3402/tellusa.v65i0.19929
Raynaud L, Berre L, Desroziers G (2008) Spatial averaging of ensemble-based background-error variances. Q J R Meteorol Soc 134:1003–1014
Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407. https://doi.org/10.1029/2002JD002670, D14
Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20:5473–5496. https://doi.org/10.1175/2007JCLI1824.1
Riser SC et al. (2016) Fifteen years of ocean observations with the global Argo array. Nat Clim Change 6:145–150. https://doi.org/10.1038/nclimate2872
Ryan AG, Regnier C, Divakaran P, Spindler T, Mehra A, Smith GC, Davidson F, Hernandez F, Maksymczuk J, Liu Y (2015) GODAE OceanView Class 4 forecast verification framework: global ocean inter-comparison. J Oper Oceanogr 8(sup1):s98–s111. https://doi.org/10.1080/1755876X.2015.1022330
Schroeder K, Chiggiato J, Josey SA, Borghini M, Aracri S, Sparnocchia S (2017) Rapid response to climate change in a marginal sea. Sci Rep 7:4065. https://doi.org/10.1038/s41598-017-04455-5
Shi L, Alves O, Wedd R, Balmaseda MA, Chang Y, Chepurin G, Ferry N, Fujii Y, Gaillard F, Good SA, Guinehut S, Haines K, Hernandez F, Lee T, Palmer M, Peterson KA, Masuda S, Storto A, Toyoda T, Valdivieso M, Vernieres G, Wang X, Yin Y (2017) An assessment of upper ocean salinity content from the Ocean Reanalyses Inter-comparison Project (ORA-IP). Clim Dyn 49:1009–1029. https://doi.org/10.1007/s00382-015-2868-7
Simoncelli S, Fratianni C, Pinardi N, Grandi A, Drudi M, Oddo P, Dobricic S (2014) Mediterranean Sea physical reanalysis (MEDREA 1987–2015). Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/medsea_reanalysis_phys_006_004
Simoncelli S, Masina S, Axell L, Liu Y, Salon S, Cossarini G, Bertino L, **e J, Samuelsen A, Levier B et al (2016) MyOcean Regional Reanalyses: Overview of Reanalyses Systems and Main Results. Mercator Ocean Journal n.54: Special Issue on Main Outcomes of the MyOcean2 and MyOcean Follow-on projects. https://www.mercator-ocean.fr/wp-content/uploads/2016/03/JournalMO-54.pdf
Stammer D, Balmaseda M, Heimbach P, Köhl A, Weaver A (2016) Ocean data assimilation in support of climate applications: status and perspectives. Ann Rev Mar Sci 8:491–518. https://doi.org/10.1146/annurev-marine-122414-034113
Steiger J (1980) Tests for comparing elements of a correlation matrix. Psychological Bull 87:245–251
Storto A (2016) Variational quality control of hydrographic profile data with non-Gaussian errors for global ocean variational data assimilation systems. Ocean Model 104:2016, 226–241. https://doi.org/10.1016/j.ocemod.2016.06.011
Storto A, Masina S (2016a) C-GLORSv5: an improved multipurpose global ocean eddy-permitting physical reanalysis. Earth Syst Sci Data 8:679–696. https://doi.org/10.5194/essd-8-679-2016
Storto A, Masina S (2017) Objectively estimating the temporal evolution of accuracy and skill in a global ocean reanalysis. Met Apps 24:101–113. https://doi.org/10.1002/met.1609
Storto A, Dobricic S, Masina S, Di Pietro P (2011) Assimilating along-track altimetric observations through local hydrostatic adjustments in a global ocean reanalysis system. Mon Weather Rev 139:738–754. https://doi.org/10.1175/2010MWR3350.1
Storto A, Masina S, Dobricic S (2013) Ensemble spread-based assessment of observation impact: application to a global ocean analysis system. QJR Meteorol Soc 139:1842–1862. https://doi.org/10.1002/qj.2071
Storto A, Masina S, Dobricic S (2014) Estimation and impact of non-uniform horizontal correlation length-scales for global ocean physical analyses. J Atmos Ocean Tech 31:2330–2349. https://doi.org/10.1175/JTECH-D-14-00042.1
Storto A, Masina S, Navarra A (2016b) Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982–2012) and its assimilation components. Q J Roy Meteorol Soc 142:738–758. https://doi.org/10.1002/qj.2673
Storto A, Yang C, Masina S (2016c) Sensitivity of global ocean heat content from reanalyses to the atmospheric reanalysis forcing: A comparative study. Geophys Res Lett 43:5261–5270. https://doi.org/10.1002/2016GL068605
Storto A, Masina S, Balmaseda M, Guinehut S, Xue Y, Szekely T (2017) Steric sea level variability (1993–2010) in an ensemble of ocean reanalyses and objective analyses. Clim Dyn 49(3):709–729. https://doi.org/10.1007/s00382-015-2554-9
Storto A, Oddo P, Cipollone A, Mirouze I, Lemieux-Dudon B (2018) Extending an oceanographic variational scheme to allow for affordable hybrid and four-dimensional data assimilation. Ocean Modeling Ocean Modelling 128:67–86,. https://doi.org/10.1016/j.ocemod.2018.06.005
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498
Toyoda T, Fujii Y, Kuragano T, Kamachi M, Ishikawa Y, Masuda S, Sato K, Awaji T, Hernandez F, Ferry N, Guinehut S, Martin M, Peterson KA, Good SA, Valdivieso M, Haines K, Storto A, Masina S, Köhl A, Zuo H, Balmaseda M, Yin Y, Li Shi O, Alves G, Smith Y-S, Chang G, Vernieres X, Wang G, Forget P, Heimbach O, Wang I, Fukumori T, Lee (2017) Intercomparison and validation of the mixed layer depth fields of global ocean syntheses. Clim Dyn 49:753–773. https://doi.org/10.1007/s00382-015-2637-7
Trenberth KE, Fasullo JT, von Schuckmann K, Cheng L (2016) Insights into earth’s energy imbalance from multiple sources. J Clim 29:7495–7505. https://doi.org/10.1175/JCLI-D-16-0339.1
Valdivieso M,K, Haines M, Balmaseda Y-S, Chang M, Drevillon N, Ferry Y, Fujii A, Köhl,A. Storto,T, Toyoda Xang,J, Waters Y, Xue Y, Yin B, Barnier F, Hernandez A, Kumar T, Lee S, Masina K (2017) An assessment of air–sea heat fluxes from ocean and coupled reanalyses. Clim Dyn 49:983–1008. https://doi.org/10.1007/s00382-015-2843-3
Vancoppenolle M, Fichefet T, Goosse H, Bouillon S, Madec G, Morales Maqueda MA (2009) Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1. Model description and validation. Ocean Model 27(1–2):33–53
von Schuckmann K, Le Traon P-Y, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik L-A et al. (2017) The copernicus marine environment monitoring service ocean state report, J Oper Oceanogr, 9, Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, s235-s320
von Schuckmann K et al. (2018) Copernicus marine service ocean state report. J Oper Oceanogr 11:s1–s142. https://doi.org/10.1080/1755876X.2018.1489208
Wang X, Snyder C, Hamill TM (2007) On the Theoretical Equivalence of Differently Proposed Ensemble–3DVAR Hybrid Analysis Schemes. Mon Wea Rev 135:222–227. https://doi.org/10.1175/MWR3282.1
Wunsch C (2016) Global ocean integrals and means, with trend implications. Ann Rev Mar Sci 8:1–33. https://doi.org/10.1146/annurev-marine-122414-034040
Xue Y, Huang B, Hu Z-Z, Kumar A, Wen C, Behringer D, Nadiga S (2011) An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Climate dynamics 37:2511–2539
Xue Y, Wen C, Kumar A et al (2017) A real-time ocean reanalyses intercomparison project in the context of tropical pacific observing system and ENSO monitoring. Clim Dyn 49:3647. https://doi.org/10.1007/s00382-017-3535-y
Yamaguchi M, Lang STK, Leutbecher M, Rodwell MJ, Radnoti G, Bormann N (2016) Observation-based evaluation of ensemble reliability. QJR Meteorol Soc 142:506–514. https://doi.org/10.1002/qj.2675
Yang C, Masina S, Bellucci A, Storto A (2016) The rapid warming of the North Atlantic Ocean in the Mid-1990s in an eddy-permitting ocean reanalysis (1982–2013). J Clim 29:5417–5430. https://doi.org/10.1175/JCLI-D-15-0438.1
Yang C, Masina S, Storto A (2017) Historical ocean reanalyses (1900–2010) using different data assimilation strategies. QJR Meteorol Soc 143:479–493. https://doi.org/10.1002/qj.2936
Zuo H, Balmaseda MA, de Boisseson E, Hirahara S, Chrust M, de Rosnay P (2017a) A generic ensemble generation scheme for data assimilation and ocean analysis. ECMWF Tech Memo 795, 46 pp, European Centre for Medium-Range Weather Forecasts, Reading, UK. https://www.ecmwf.int/en/elibrary/technical-memoranda
Zuo H, Balmaseda MA, Mogensen K (2017b) The new eddy-permitting ORAP5 ocean reanalysis: description, evaluation and uncertainties in climate signals. Clim Dyn 49:791. https://doi.org/10.1007/s00382-015-2675-1
Zuo H, Balmaseda MA, Mogensen K, Tietsche S (2018) OCEAN5: the ECMWF ocean reanalysis system ORAS5 and its real-time analysis component, ECMWF technical memorandum p 823
Zweng MM, Reagan JR, Antonov JI, Locarnini RA, Mishonov AV, Boyer TP, Garcia HE, Baranova OK, Johnson DR, D.Seidov MM (2013) World Ocean Atlas 2013, Volume 2: Salinity. S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 74, 39 pp
Acknowledgements
Data from the reanalyses presented in this work are available from the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). Part of this work was supported by the EOS COST Action (“Evaluation of Ocean Synthesis”, http://eos-cost.eu/) through its Short Term Scientific Missions program. The full C-GLORS dataset is available at http://c-glors.cmcc.it. This work has received funding from the Copernicus Marine Environment Monitoring Service (CMEMS). The EN4 subsurface ocean temperature and salinity data were quality-controlled and distributed by the U.K. Met Office. The authors declare no conflicts of interest. We are grateful to four anonymous reviewers for their help in improving the quality of the manuscript.
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Appendix 1: Assessment of GREP
Appendix 1: Assessment of GREP
The GREP product has been extensively validated and the main outcomes are included in the CMEMS QUality Information Document (QUID), available at http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-026.pdf. Here, we focus only on skill score statistics of the monthly mean fields of temperature and salinity, which are the dataset used throughout this work. We base our assessment on the so-called GODAE OceanView CLASS4 metrics (Ryan et al. 2015), i.e. observation based statistics that estimate the reanalysis accuracy in observation space, inherited by the MERSEA project (Crosnier and Le Provost 2007) and then adopted by GODAE near real-time inter-comparison exercises (Hernandez et al. 2009). In accordance with disseminated data availability, we use monthly mean data for extracting the model equivalents from the GREP reanalyses. For sake of comparison, we introduce three observation-only products that do not make use of any dynamical model—also referred to as objective analyses, OA, with their ensemble mean (OA-EM)—and the World Ocean Atlas 2013 (WOA13) monthly climatology (Locarnini et al. 2013; Zweng et al. 2013) for the whole period 1955–2012. In particular, regarding the OAs, we calculated CLASS4 metrics for the Met Office EN4 objective analyses (Good et al. 2013), the Ifremer CORA objective analyses (Cabanes et al. 2013) and the CLS ARMOR objective analyses (Guinehut et al. 2012), within the GREP period. While there exists a large number of validation metrics (see e.g. Hernandez et al. 2015, for a detailed discussion), the goal of this exercise is to provide a basic assessment of the performances of the GREP-EM temperature and salinity through commonly adopted observation-based skill score metrics.
The Met Office EN4 (v4.2.0) observational data are used for this evaluation. EN4 includes profiles of MBTs and XBTs, Argo floats, CTDs, moorings and sea-mammal data. Only observations flagged as “good” are used. Note that these data are not independent strictly speaking, as they are assimilated by three out of four reanalyses and one objective analysis. Observational dataset may indeed differ notably because of different data sub-sampling, quality control procedure, and correction procedures (e.g. XBT fall rate corrections), especially before the Argo floats deployment. Therefore, the assessment presented hereafter has obvious limitations and serves only the purpose to verify how close to a reference dataset the reanalysis ensemble mean is, rather than quantifying the accuracy of either the reanalyses or objective analyses.
First, we validate the use of monthly means for the observation misfit statistics, which might seem inappropriate due to the higher than monthly temporal resolution of the model fields actually used as background in the data assimilation systems. To simplify, we focus on one product only, cglo, to verify the impact of sub-monthly variability on the skill scores during an observation-rich period. Figure S5 compares the cglo RMSE timeseries for two periods (1993–1998 and 2010–2015), of either daily or monthly mean fields of temperature and salinity. For both variables, differences are small, generally because the spatial representativeness error dominates the RMSE budget, rather than the temporal representativeness error. During the early period 1993–1998, differences are always less than 5% and 10% for salinity and temperature, respectively. During the recent period, the relative difference is of the order of 2% for salinity with occasional peaks in 2013–2014 up to 8%. Differences in temperature are slightly larger, with average value equals to 11% and peaking up to 22% in 2014. The behavior with time of the curves is almost identical, which indicates, together with the small differences, that the use of monthly means does not compromise the statistics. Note also that relative differences are greater during the latest years—in spite of smaller absolute values of RMSE, linked to the much denser observational sampling in both time and space.
In Fig. 10, we show yearly values (1993–2015) of the global RMSE statistics for temperature and salinity in the top 700 m and in the layer 700–2000 m, for the GREP-EM, OA-EM, and the WOA13 monthly climatology, along with monthly number of observations (green bars). Dashed lines represent for GREP and OA separately the ensemble average of the RMSE timeseries from individual members. The RMSE of the ensemble mean always outperforms the ensemble mean of the RMSEs, especially for the first decade and the deep layer. That confirms the effectiveness of the ensemble approach within data-sparse regions or periods.
To clearly identify the skill scores behavior, Fig. 11 shows the differences between the average RMSE of the individual ORAs (AVE) and the RMSE of the ensemble mean (black), between RMSE of OA-EM and RMSE of GREP-EM (blue) and between RMSE of WOA13 and RMSE of GREP-EM (red). Positive values indicate that GREP-EM outperforms the other timeseries.
In the upper ocean, salinity skill scores are characterized by OA-EM errors smaller than GREP-EM up to around 2010. This suggests that the lack of in-situ observations is crucial for salinity RMSE. For temperature, WOA13 RMSE is significantly larger than GREP-EM and OA-EM, which present very similar behavior within the top 700 m layer. In the deeper ocean from 700 to 2000 m, OA-EM accuracy is higher than GREP-EM for the first simulated years, until 2000; then GREP-EM outperforms the ensemble of objective analyses. Figure S6 and S7 show similar analysis as Fig. 10 but for the three latitudinal bands Southern Extra-Tropics (60°S–20°S), Tropics (20°S–20°N) and Northern Extra-Tropics (20°N–60°N). The figures suggest that for temperature, especially in the Tropics, the reanalyses significantly outperform the OA-EM ensemble. Salinity skill scores of GREP-EM in the Southern Extra-Tropics, particularly in the upper ocean, are worse than OA-EM and the WOA13 climatology during the first decade of the reanalysis.
Overall, within data sparse periods, OA-EM generally shows the smallest errors, likely due to the use of climatology background within the objective analyses, while GREP-EM behaves at least as good as the objective analyses after 2000, when the deployment of Argo floats started. For the deep ocean, the added value of Argo floats after 2006, able to constrain the heat content evolution, results in better scores for GREP-EM than OA-EM. Note however that different time periods imply different spatial sampling of the observations—notably before Argo deployment the RMSE is representative mostly of the skill in the Northern Hemisphere—and should be considered accordingly.
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Storto, A., Masina, S., Simoncelli, S. et al. The added value of the multi-system spread information for ocean heat content and steric sea level investigations in the CMEMS GREP ensemble reanalysis product. Clim Dyn 53, 287–312 (2019). https://doi.org/10.1007/s00382-018-4585-5
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DOI: https://doi.org/10.1007/s00382-018-4585-5