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Climate Dynamics

, Volume 43, Issue 11, pp 3123–3145 | Cite as

Diagnosing southeast tropical Atlantic SST and ocean circulation biases in the CMIP5 ensemble

  • Zhao Xu
  • Ping ChangEmail author
  • Ingo Richter
  • Who Kim
  • Guanglin Tang
Article

Abstract

Warm sea-surface temperature (SST) biases in the southeastern tropical Atlantic (SETA), which is defined by a region from 5°E to the west coast of southern Africa and from 10°S to 30°S, are a common problem in many current and previous generation climate models. The Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble provides a useful framework to tackle the complex issues concerning causes of the SST bias. In this study, we tested a number of previously proposed mechanisms responsible for the SETA SST bias and found the following results. First, the multi-model ensemble mean shows a positive shortwave radiation bias of ~20 W m−2, consistent with models’ deficiency in simulating low-level clouds. This shortwave radiation error, however, is overwhelmed by larger errors in the simulated surface turbulent heat and longwave radiation fluxes, resulting in excessive heat loss from the ocean. The result holds for atmosphere-only model simulations from the same multi-model ensemble, where the effect of SST biases on surface heat fluxes is removed, and is not sensitive to whether the analysis region is chosen to coincide with the maximum warm SST bias along the coast or with the main SETA stratocumulus deck away from the coast. This combined with the fact that there is no statistically significant relationship between simulated SST biases and surface heat flux biases among CMIP5 models suggests that the shortwave radiation bias caused by poorly simulated low-level clouds is not the leading cause of the warm SST bias. Second, the majority of CMIP5 models underestimate upwelling strength along the Benguela coast, which is linked to the unrealistically weak alongshore wind stress simulated by the models. However, a correlation analysis between the model simulated vertical velocities and SST biases does not reveal a statistically significant relationship between the two, suggesting that the deficient coastal upwelling in the models is not simply related to the warm SST bias via vertical heat advection. Third, SETA SST biases in CMIP5 models are correlated with surface and subsurface ocean temperature biases in the equatorial region, suggesting that the equatorial temperature bias remotely contributes to the SETA SST bias. Finally, we found that all CMIP5 models simulate a southward displaced Angola–Benguela front (ABF), which in many models is more than 10° south of its observed location. Furthermore, SETA SST biases are most significantly correlated with ABF latitude, which suggests that the inability of CMIP5 models to accurately simulate the ABF is a leading cause of the SETA SST bias. This is supported by simulations with the oceanic component of one of the CMIP5 models, which is forced with observationally derived surface fluxes. The results show that even with the observationally derived surface atmospheric forcing, the ocean model generates a significant warm SST bias near the ABF, underlining the important role of ocean dynamics in SETA SST bias problem. Further model simulations were conducted to address the impact of the SETA SST biases. The results indicate a significant remote influence of the SETA SST bias on global model simulations of tropical climate, underscoring the importance and urgency to reduce the SETA SST bias in global climate models.

Keywords

Southeast tropical Atlantic SST bias Coupled general circulation model Ocean circulation 

Notes

Acknowledgments

We are grateful to Paquita Zuidema and C. Roberto Mechoso for their insightful comments, which improved this paper significantly. This research is supported by the U.S. National Science Foundation Grants, OCE-1334707 and AGS-1067937, and Department of Energy Grant DE-SC0006824, as well as National Oceanic and Atmospheric Administration Grant NA11OAR4310154. PC acknowledges the support from the National Science Foundation of China (41028005, 40921004 and 40930844).

References

  1. Bretherton CS, Uttal T, Fairall CW, Yuter SE, Weller RA, Baumgardner D, Comstock K, Wood R (2004) The EPIC 2001 stratocumulus study. Bull Am Meteorol Soc 85:967–977CrossRefGoogle Scholar
  2. Carton JA, Giese BS (2008) A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Mon Weather Rev 136:2999–3017CrossRefGoogle Scholar
  3. Carton JA, Giese BS, Grodsky V (2005) Sea level rise and the warming of the oceans in the Simple Ocean Data Assimilation (SODA) ocean reanalysis. J Geophys Res 110. doi: 10.1029/2004JC002817
  4. Chang C-Y, 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 20:1053–1070CrossRefGoogle Scholar
  5. Chassignet EP et al (2007) The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system. J Mar Syst 65:60–83CrossRefGoogle Scholar
  6. Colberg F, Reason CJC (2006) A model study of the Angola Benguela Frontal Zone: sensitivity to atmospheric forcing. Geophys Res Lett 33. doi: 10.1029/2006GL027463
  7. Colbo K, Weller R (2007) The variability and heat budget of the upper ocean under the Chile-Peru stratus. J Mar Res 65:607–637CrossRefGoogle Scholar
  8. Dai AC (2006) Precipitation characteristics in eighteen coupled climate models. J Clim 9:4605–4630CrossRefGoogle Scholar
  9. Danabasoglu G, Bates SC, Briegleb BP, Jayne SR, Jochum M, Large WG, Yeager SG (2012) The CCSM4 ocean component. J Clim 25:1361–1389CrossRefGoogle Scholar
  10. Davey MK et al (2002) STOIC: a study of coupled model climatology and variability in tropical ocean regions. Clim Dyn 18:403–420CrossRefGoogle Scholar
  11. de Szoeke SP, Xie S-P (2008) The tropical eastern Pacific seasonal cycle: assessment of errors and mechanisms in IPCC AR4 coupled ocean–general circulation models. J Clim 21:2573–2590CrossRefGoogle Scholar
  12. de Szoeke 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 Clim 23:4152–4174CrossRefGoogle Scholar
  13. de Szoeke SP, Yuter S, Mechem D, Fairall CW, Burleyson CD, Zuidema P (2012) Observations of stratocumulus clouds and their effects on the Eastern Pacific surface heat budget along 20°S. J Clim 25:8542–8567. doi: 10.1175/JCLI-D-11-00618.1 CrossRefGoogle Scholar
  14. Deser C, Phillips AS, Alexander MA (2010) Twentieth century tropical sea surface temperature trends revisited. Geophys Res Lett 37. doi: 10.1029/2010GL043321
  15. DeWitt DG (2005) Diagnosis of the tropical Atlantic near-equatorial SST bias in a directly coupled atmosphere-ocean general circulation model. Geophys Res Lett 32Google Scholar
  16. Doi T, Vecchi GA, Rosati AJ, Delworth TL (2012) Biases in the Atlantic ITCZ in seasonal–interannual variations for a coarse- and a high-resolution coupled climate model. J Clim 25:5494–5511CrossRefGoogle Scholar
  17. Fennel W, Tucker T, Schimdt M, Mohrholz V (2012) Response of the Benguela upwelling systems to spatial variations in the wind stress. Cont Shelf Res 45:65–77CrossRefGoogle Scholar
  18. Garreaud RD, Muñoz RC (2005) The low-level jet off the west coast of subtropical South America: structure and variability. Mon Weather Rev 133:2246–2261CrossRefGoogle Scholar
  19. Grodsky SA, Carton JA, Nigam S, Okumura YM (2012) Tropical Atlantic biases in CCSM4. J Clim 25:3684–3701CrossRefGoogle Scholar
  20. Hu Z-Z, Huang B, Pegion K (2008) Low cloud errors over the southeastern Atlantic in the NCEP CFS and their association with lower-tropospheric stability and air-sea interaction. J Geophys Res 113. doi: 10.1029/2007JD009514
  21. Huang B (2004) Remotely forced variability in the tropical Atlantic Ocean. Clim Dyn 23:133–152CrossRefGoogle Scholar
  22. Huang B, Hu Z–Z, Jha B (2007) Evolution of model systematic errors in the Tropical Atlantic Basin from coupled climate hindcasts. Clim Dyn 28:661–682CrossRefGoogle Scholar
  23. Kirtman BP et al (2012) Impact of ocean model resolution on CCSM climate simulations. Clim Dyn 39:1303–1328. doi: 10.1007/s00382-012-1500-3 CrossRefGoogle Scholar
  24. Large WG, Danabasoglu G (2006) Attribution and impacts of upper-ocean biases in CCSM3. J Clim 19:2325–2346CrossRefGoogle Scholar
  25. Large WG, Yeager SG (2004) Diurnal to decadal global forcing for ocean and sea–ice models: the data sets and climatologies. Technical reportGoogle Scholar
  26. Large WG, Yeager SG (2008) The global climatology of an interannually varying air–sea flux data set. Clim Dyn 33:341–364CrossRefGoogle Scholar
  27. Lass HU, Schmidt M, Mohrholz V, Nausch G (2000) Hydrographic and current measurements in the area of the Angola–Benguela front. J Phys Oceanogr 30:2589–2609CrossRefGoogle Scholar
  28. Ma C–C, Mechoso CR, Robertson AW, Arakawa A (1996) Peruvian stratus clouds and the tropical pacific circulation: a coupled ocean–atmosphere GCM study. J Clim 9:1635–1645CrossRefGoogle Scholar
  29. Mechoso CR, Wood R (2010) An abbreviated history of VOCALS. CLIVAR Exchanges, 53. International CLIVAR Project Office, Southampton, pp 3–5Google Scholar
  30. Mechoso CR et al (1995) The seasonal cycle over the tropical Pacific in general circulation models. Mon Weather Rev 123:2825–2838CrossRefGoogle Scholar
  31. Mechoso et al (2014) Ocean–cloud–atmosphere–land interaction in the southeastern Pacific: the VOCALS program. Bull Am Meteorol Soc 95:357–375. doi: 10.1175/BAMS-D-11-00246.1 CrossRefGoogle Scholar
  32. Muñoz RC, Garreaud RD (2005) Dynamics of the low-level jet off the west coast of subtropical South America. Mon Weather Rev 133:3661–3677CrossRefGoogle Scholar
  33. Nicholson SE (2010) A low-level jet along the Benguela coast, an integral part of the Benguela current ecosystem. Clim Change 99:613–624. doi: 10.1007/s10584-009-9678-z CrossRefGoogle Scholar
  34. Nigam S (1997) The annual warm to cold phase transition in the eastern equatorial Pacific: diagnosis of the role of stratus cloud-top cooling. J Clim 10:2447–2467CrossRefGoogle Scholar
  35. Patricola CM, Li M, Xu Z, Chang P, Saravanan R, Hsieh J-S (2011) An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model. Clim Dyn 39:2443–2463CrossRefGoogle Scholar
  36. Penven P, Echevin V, Pasapera J, Colas F, Tam V (2005) Average circulation, seasonal cycle, and mesoscale dynamics of the Peru Current system: a modeling approach. J Geophys Res Oceans 110. doi: 10.1029/2005JC002945
  37. Peterson RG, Stramma L (1991) Upper-level circulation in the south Atlantic ocean. Prog Oceanogr 26:1–73CrossRefGoogle Scholar
  38. Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20:5473–5496CrossRefGoogle Scholar
  39. Richter I, Xie S-P (2008) On the origin of equatorial Atlantic biases in coupled general circulation models. Clim Dyn 31:587–598CrossRefGoogle Scholar
  40. Richter I, Mechoso CR, Robertson AW (2008) What determines the position and intensity of the South Atlantic anticyclone in austral winter? An AGCM study. J Clim 21:214–229CrossRefGoogle Scholar
  41. Richter I, Xie S-P, Wittenberg AT, Masumoto Y (2012a) 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 CrossRefGoogle Scholar
  42. Richter I, Xie S-P, Behera SK, Doi T, Masumoto Y (2012b) Equatorial Atlantic variability and its relation to mean state biases in CMIP5. Clim Dyn. doi: 10.1007/s00382-012-1624-5
  43. Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057CrossRefGoogle Scholar
  44. Seo H, Jochum V, Murtugudde R, Miller AJ (2006) Effect of ocean mesoscale variability on the mean state of tropical Atlantic climate. Geophys Res Lett 33Google Scholar
  45. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498CrossRefGoogle Scholar
  46. Toniazzo T, Mechoso CR, Shaffrey L, Slingo JM (2009) Upper-ocean heat budget and ocean eddy transport in the south-east Pacific in a high resolution coupled model. Clim Dyn 35:1309–1329. doi: 10.1007/s00382-009-0703-8 CrossRefGoogle Scholar
  47. Toniazzo T, Woolnough S (2013) Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts. Clim Dyn. doi: 10.1007/s00382-013-1691-2
  48. Wacongne S, Piton B (1992) The near-surface circulation in the northeastern corner of the South Atlantic Ocean. Deep Sea Research Part A. Oceanogr Res Pap 39:1273–1298CrossRefGoogle Scholar
  49. Wahl S, Latif M, Park W, Keenlyside N (2009) On the tropical Atlantic SST warm bias in the Kiel Climate Model. Clim Dyn 36:891–906CrossRefGoogle Scholar
  50. Xu Z, Li M, Chang P (2013) Oceanic origins of biases in southeast tropical Atlantic. Clim Dyn. doi: 10.1007/s00382-013-1901-y
  51. Yamagata T, Iizuka S (1995) Simulation of the tropical thermal domes in the Atlantic-a Seasonal Cycle. J Phys Oceanogr 25:2129–2140CrossRefGoogle Scholar
  52. Yu JY, Mechoso CR (1999) A discussion on the errors in the surface heat fluxes simulated by a coupled GCM. J Clim 12:416–426CrossRefGoogle Scholar
  53. Yu LS, Weller RA, Sun BM (2004) Mean and variability of the WHOI daily latent and sensible heat fluxes at in situ flux measurement sites in the Atlantic Ocean. J Clim 17:2096–2118CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Zhao Xu
    • 1
    • 2
  • Ping Chang
    • 2
    • 3
    Email author
  • Ingo Richter
    • 4
    • 5
  • Who Kim
    • 2
  • Guanglin Tang
    • 3
  1. 1.Physical Oceanography LaboratoryOcean University of ChinaQingdaoChina
  2. 2.Department of OceanographyTexas A&M UniversityCollege StationUSA
  3. 3.Department of Atmospheric SciencesTexas A&M UniversityCollege StationUSA
  4. 4.Research Institute for Global ChangeJAMSTECYokohamaJapan
  5. 5.Application LaboratoryJAMSTECYokohamaJapan

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