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Are atmospheric biases responsible for the tropical Atlantic SST biases in the CNRM-CM5 coupled model?

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Abstract

In this study, the CNRM-CM5 model is shown to simulate too warm SSTs in the tropical Atlantic as most state-of-the-art CMIP5 models. The warm bias develops within 1 or 2 months in decadal experiments initialised in January using an observationally derived state. To better quantify the role of the atmospheric biases in initiating this warm SST bias, several sensitivity experiments have been performed. In a first set of experiments, the surface solar net heat flux sent to the ocean model is academically corrected over the southeastern tropical Atlantic Ocean. This correction locally reduces the warm SST bias by more than 50 % with some remote impacts over equatorial regions. In contrast, the solar heat flux correction has locally little impact on the spring cooling. A second set of experiments quantifies the role of surface winds, using a nudging technique. When applied in a narrow equatorial region, the wind correction mainly improves the SST annual cycle amplitude along the Equator. It promotes not only the spring cooling along the Equator in preconditioning the mixed-layer depth but also in the southeastern Atlantic along the African coast. These local and remote effects are attributed to the more realistic representation of the oceanic equatorial circulation, driven by corrected winds. These results are consistent with those reported by Wahl et al. (Clim Dyn 36:891–906, 2011) in a very similar study with the Kiel Climate Model. The solar and wind biases have comparable effects in their study, although the importance of off-equatorial winds is less clear in our study. Diagnosing the wind energy flux provides a physical understanding of the equatorial region. When combining the corrections of both the equatorial wind and the southeastern solar heat flux, no obvious feedback between them is evidenced. The present study also emphasizes the need to consider two time-scales, the annual mean and the seasonal cycle, as well as two regions, the equatorial and the southeastern Atlantic regions, to comprehensively address the Atlantic SST bias. As pointed out in Richter (Clim Dyn, doi:10.1007/s00382-012-1624-5, 2013), the need to improve the atmospheric component of the CNRM-CM model is emphasized, even though strong positive coupling feedbacks are highlighted.

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References

  • Balmaseda M, Mogensen K, Molteni F, Weaver A (2010) The NEMOVAR-COMBINE ocean re-analysis. COMBINE Technical report No. 1. Available from http://www.combine-project.eu/Technical-Reports.1668.0.html

  • Barnier B et al (1995) Thermal forcing for a global ocean circulation model using a three-year climatology of ECMWF analyses. J. Mar. Sys. 6:363–380

    Article  Google Scholar 

  • Batté L, Déqué M (2011) Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts. Tellus-A 63:283–299. doi:10.1111/j.1600-0870.2010.00493.x

    Article  Google Scholar 

  • Belamari S (2005) Report on uncertainty estimates of an optimal bulk formulation for surface turbulent fluxes. MERSEA IP Deliverable, D.4.1.2, 29

  • Bourlès B, Lumpkin R, McPhaden MJ, Hernandez F, Nobre P, Campos E, Yu L, Planton S, Busalacchi AJ, Moura AD, Servain J, Trotte J (2008) The PIRATA program: history, accomplishments, and future directions. Bull Am Meteorol Soc 89(8):1. doi:10.1175/2008BAMS2462.1

    Article  Google Scholar 

  • Breugem W-P, Chang P, Jang CJ, Mignot J, Hazeleger W (2008) Barrier layers and tropical Atlantic SST biases in coupled GCMs. Tellus A 60:885–897. doi:10.1111/j.1600-0870.2008.00343.x

    Article  Google Scholar 

  • Busalacchi AJ, Picaut J (1983) Seasonal variability from a model of the tropical Atlantic ocean. J Phys Oceanogr 13:1564–1588

    Article  Google Scholar 

  • Caniaux G, Giordani H, Redelsperger JL, Guichard F, Key E, Wade M (2011) Coupling between the Atlantic cold tongue and the West African monsoon in boreal spring and summer. J Geophys Res 116:C04003. doi:10.1029/2010JC006570

    Google Scholar 

  • Césana G, Chepfer H (2012) How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models. Geophys. Res. Letters 39:L20803. doi:10.1029/2012GL053153

    Google Scholar 

  • Chang CY, Carton JA, Grodsky SA, Nigam S (2007) Seasonal climate of the tropical Atlantic sector in the NCAR Community Climate System Model 3: error structure and probable causes of errors. J Clim 20:1053–1070

    Article  Google Scholar 

  • De Boyer-Montégut C, Madec G, Fisher AS, Lazar A, Iudicone D (2004) Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology. J Geophys Res 109:C12003. doi:10.1029/2004JC002378

    Article  Google Scholar 

  • Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quat. J. of Roy. Met. Soc. 137:553–597. doi:10.1002/qj.828

    Article  Google Scholar 

  • DeSzoeke SP, Fairall CW, Wolfe DE, Bariteau L, Zuidema P (2010) Surface flux observations on the southeastern tropical Pacific Ocean and attribution of SST errors in coupled ocean–atmosphere models. J. Climate 23:4152–4174

    Article  Google Scholar 

  • Fennig K, Bakan S, Grassl H, Klepp C-P, Schulz J (2006) Hamburg Ocean atmosphere parameters and fluxes from satellite data—HOAPS II—monthly mean., electronic publication, WDCC, doi:10.1594/WDCC/HOAPS2_MONTHLY

  • Foltz GR, Grodsky SA, Carton JA, McPhaden MJ (2003) Seasonal mixed layer heat budget of the tropical Atlantic Ocean. J Geophys Res 108:3146. doi:10.1029/2002JC001584

    Article  Google Scholar 

  • Giordani H, Caniaux G (2011) Diagnosing vertical motion in the equatorial Atlantic. Ocean Dyn 61(12):1995–2018

    Article  Google Scholar 

  • Giordani H, Caniaux G, Voldoire A (2013) Intraseasonal mixed layer heat budget in the equatorial Atlantic during the cold tongue development in 2006. J. of Geophys. Res. Oceans 118:1–22. doi:10.1029/2012JC008280

    Article  Google Scholar 

  • Grodsky SA, Carton JA, Nigam S, Okumura YM (2012) Tropical Atlantic biases in CCSM4. J Clim 25:3684–3701. doi:10.1175/JCLI-D-11-00315.1

    Article  Google Scholar 

  • Guilyardi E, Cai WJ, Collins M, Fedorov A, Jin FF, Kumar A, Sun DZ, Wittenberg A (2012) New strategies for evaluating ENSO processes in climate models. Bull. Ame. Met. Soc. 93:235–238

    Article  Google Scholar 

  • Hu Z, Huang B (2007) Physical processes associated with the tropical Atlantic SST gradient during the anomalous evolution in the southeastern ocean. J Clim 20:3366–3378

    Article  Google Scholar 

  • Hu ZZ, Huang B, Hou YT, Wang W, Yang F, Stan C, Schneider EK (2011) Sensitivity of tropical climate low-level clouds in the NCEP climate forecast system. Clim Dyn 36:1795–1811. doi:10.1007/s00382-010-0797-z

    Article  Google Scholar 

  • Huang B, Hu ZZ, Jha B (2007) Evolution of model systematic errors in the tropical Atlantic basin from coupled climate hindcasts. Clim Dyn 28:661–682. doi:10.1007/S00382-006-0223-8

    Article  Google Scholar 

  • Intergovernmental Panel on Cliamte Change (2007) Climate change 2007: the physical science basis. In: Solomon S (ed) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  • Joly M, Voldoire A (2010) Role of the Gulf of Guinea in the interannual variability of the West African monsoon: what do we learn from CMIP3 coupled simulations ? Int J Clim 30(12):1843–1856. doi:10.1002/joc.2026

    Google Scholar 

  • Klein et al (2013) Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. J Geophys Res Atm 118:1329–1342. doi:10.1002/jgrd.50141

    Article  Google Scholar 

  • Lin J-L (2007) The double-ITCZ problem in IPCC AR4 coupled GCMs: ocean–atmosphere feedback analysis. J Clim 20:4497–4525

    Article  Google Scholar 

  • Lubbecke JF, Boning CW, Keenlyside NS, Xie SP (2010) On the connection between Benguela and equatorial Atlantic niños and the role of the south Atlantic anticyclone. J Geophys Res 115:C09015. doi:10.1029/2009JC005964

    Google Scholar 

  • Moore D, Hisard P, McCreary JP, Merlo J, O’Brien JJ, Picaut J, Vestraete JM, Wunsch C (1978) Equatorial adjustment in th eastern Atlantic. Geophys Res Lett 5:637–640

    Article  Google Scholar 

  • Peixoto JP and Oort AH (1992) Physics of Climate, American Institute of Physics Ed

  • 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(D14):4407. doi:10.1029/2002JD002670

    Article  Google Scholar 

  • Richter I, Xie S (2008) On the origin of equatorial Atlantic biases in coupled general circulation models. Clim Dyn 31:587–598. doi:10.1007/s00382-008-0364-z

    Article  Google Scholar 

  • Richter I, Xie S-P, Wittenberg AT, Masumoto Y (2012) Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation. Clim Dyn 38:985–1001. doi:10.1007/s00382-011-1038-9

    Article  Google Scholar 

  • Richter I, Xie S-P, Behera SK, Doi T, Masumoto Y (2013) Equatorial Atlantic variability and its relation to mean state biases in CMIP5, Clim. Dyn., doi:10.1007/s00382-012-1624-5, online

  • Roehrig R, Bouniol D, Guichard F, Hourdin F and Redelsperger J-L (2013) The present and future of the West African monsoon: a process-oriented assessment of CMIP5 simulations along the AMMA transect, J. of Climate, under revision

  • Rossow WB, Walker AW, Beuschel DE and Roiter MD (1996) International Satellite Cloud Climatology Project (ISCCP) Documentation of New Cloud Datasets. WMO/TD-No. 737, World Meteorological Organization, 115 pp

  • Seo H, Jochum M, Murtugudde R, Miller AJ (2006) Effect of ocean mesoscale variability on the mean state of tropical Atlantic climate. Geophys Res Lett 33:L09606. doi:10.1029/2005GL025651

    Article  Google Scholar 

  • Su et al (2013) Diagnosis of regime-dependent cloud simulation errors in CMIP5 models using “A-Train” satellite observations and reanalysis data. J Geophys Res Atm 118:2762–2780. doi:10.1029/2012JD018575

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93:485–498. doi:10.1175/BAMS-D-11-00094

    Article  Google Scholar 

  • Toniazzo T and Woolnough S (2013) Development of warm SST errors in the southeastern tropical Atlantic in CMIP5 decadal hindcasts, Clim Dyn, doi:10.1007/s00382-013-1691-2

  • Voldoire A, Sanchez-Gomez AE, Salas y Mélia D, Decharme B, Cassou C, Sénési S, Valcke S, Beau I, Alias A, Chevallier M, Déqué M, Deshayes J, Douville H, Fernandez E, Madec G, Maisonnave E, Moine M-P, Planton S, Saint-Martin D, Szopa S, Tyteca S, Alkama R, Belamari S, Braun A, Coquart L, Chauvin F (2013) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn. doi:10.1007/s00382-011-1259-y

  • Wade M, Caniaux G, DuPenhoat Y (2011) Variability of the mixed layer heat budget in the Eastern Equatorial Atlantic during 2005-2007 as inferred using Argo floats. J Geophys Res 116:C08006. doi:10.1029/2010JC006683

    Google Scholar 

  • Wahl S, Latif M, Park W, Keenlyside N (2011) On the tropical Atlantic SST warm bias in the Kiel climate model. Clim Dyn 36:891–906

    Article  Google Scholar 

  • Weill A, Eymard L, Caniaux G, Hauser D, Planton S, Dupuis H, Brut A, Guerin C, Nacass P, Butet A, Cloché S, Pedreros R, Durand P, Bourras D, Giordani H, Lachaud G, Bouhours G (2003) Toward a better determination of turbulent air-sea fluxes from several experiments. J Clim 16:600–618

    Article  Google Scholar 

  • Yu L, Jin X, and Weller RA (2008) Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution, OAFlux Project Technical Report. OA-2008-01, 64 pp. Woods Hole. Massachusetts

  • Zebiac SE (1993) Air-sea interaction in the equatorial Atlantic region. J Clim 6:1567–1586

    Article  Google Scholar 

  • Zermeño D, Zhang C (2013) Possible root causes of the surface westerly bias over the equatorial Atlantic in Atmospheric Global Climate Model. J Clim. doi:10.1175/JCLI-D-12-00226.1

    Google Scholar 

  • Zhang W, Jin F–F (2012) Improvements in the CMIP5 simulations of ENSO-SSTA meridional width. Geophys Res Lett 39:L23704. doi:10.1029/2012GL053588

    Google Scholar 

  • Zhang T, Stackhouse PW, Gupta SK, Cox SJ, Mikovitz JC, Hinkelman LM (2013) The validation of the GEWEX SRB surface shortwave flux data products using BSRN measurements: a systematic quality control, production and application approach. J Quant Spectrosc Radiat Transf. doi:10.1016/j.jqsrt.2012.10.004

    Google Scholar 

  • Zheng Y, Shinoda T, Lin J-L, Kiladis GN (2011) Sea surface temperature biases under the stratus cloud deck in the southeast Pacific Ocean in 19 IPCC AR4 coupled general circulation models. J Clim 24:4139–4164

    Article  Google Scholar 

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Acknowledgments

The authors wish to thank C. Cassou and E. Sanchez for providing the CNRM-CM5 decadal simulations and for helpful discussions. Thanks to the two anonymous reviewers for their helpful comments. The authors also thank the global ocean heat flux and evaporation products that were provided by the WHOI OAFlux project (http://oaflux.whoi.edu) funded by the NOAA Climate Observations and Monitoring (COM) program.

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Correspondence to A. Voldoire.

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This paper is a contribution to the special issue on tropical Atlantic variability and coupled model climate biases that have been the focus of the recently completed Tropical Atlantic Climate Experiment (TACE), an international CLIVAR program (http://www.clivar.org/organization/atlantic/tace). This special issue is coordinated by William Johns, Peter Brandt, and Ping Chang, representatives of the TACE Observations and TACE Modeling and Synthesis working groups.

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Voldoire, A., Claudon, M., Caniaux, G. et al. Are atmospheric biases responsible for the tropical Atlantic SST biases in the CNRM-CM5 coupled model?. Clim Dyn 43, 2963–2984 (2014). https://doi.org/10.1007/s00382-013-2036-x

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