Climate Dynamics

, Volume 50, Issue 7–8, pp 2605–2635 | Cite as

Alleviating tropical Atlantic sector biases in the Kiel climate model by enhancing horizontal and vertical atmosphere model resolution: climatology and interannual variability

Article

Abstract

We investigate the quality of simulating tropical Atlantic (TA) sector climatology and interannual variability in integrations of the Kiel climate model (KCM) with varying atmosphere model resolution. The ocean model resolution is kept fixed. A reasonable simulation of TA sector annual-mean climate, seasonal cycle and interannual variability can only be achieved at sufficiently high horizontal and vertical atmospheric resolution. Two major reasons for the improvements are identified. First, the western equatorial Atlantic westerly surface wind bias in spring can be largely eliminated, which is explained by a better representation of meridional and especially vertical zonal momentum transport. The enhanced atmospheric circulation along the equator in turn greatly improves the thermal structure of the upper equatorial Atlantic with much reduced warm sea surface temperature (SST) biases. Second, the coastline in the southeastern TA and steep orography are better resolved at high resolution, which improves wind structure and in turn reduces warm SST biases in the Benguela upwelling region. The strongly diminished wind and SST biases at high atmosphere model resolution allow for a more realistic latitudinal position of the intertropical convergence zone. Resulting stronger cross-equatorial winds, in conjunction with a shallower thermocline, enable a rapid cold tongue development in the eastern TA in boreal spring. This enables simulation of realistic interannual SST variability and its seasonal phase locking in the KCM, which primarily is the result of a stronger thermocline feedback. Our findings suggest that enhanced atmospheric resolution, both vertical and horizontal, could be a key to achieving more realistic simulation of TA climatology and interannual variability in climate models.

Keywords

Tropical Atlantic SST bias Benguela Climate modelling Resolution GCM biases 

Notes

Acknowledgements

We thank two anonymous reviewers for their thoughtful comments. This work was supported by the Bundesministerium für Bildung und Forschung grant SACUS (03G0837A) and EU FP7/2007–2013 under Grant agreement no. 603521, project PREFACE. Model integrations were performed at the Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen and the Rechenzentrum der Universität Kiel.

Supplementary material

382_2017_3760_MOESM1_ESM.pdf (399 kb)
Fig. S1 Regression of SST on ATL3 SST anomalies (°C/°C). Stippling denotes 95% significance level. a) HadISST (1982-2009), b) NOAA-OISST (1982-2009), c) T42 L31 (L), d) T159 L31 (M), e) T159 L62 (M-V), f) T255 L62 (H-V) (PDF 398 KB)
382_2017_3760_MOESM2_ESM.pdf (267 kb)
Fig. S2 Regression of 10m winds (vectors, m/s per °C) and total precipitation (shading, mm/day per °C) on ATL3 SST for uncoupled simulations. Stippling denotes 95% significance level, only significant vectors depicted. a) T42 L31 L(A), b) T159 L31 M(A), c) T159 L62 M-V(A) (PDF 266 KB)
382_2017_3760_MOESM3_ESM.pdf (72 kb)
Fig. S3 Lines denote green: T42 L31 (L/L(A)), red: T159 L31 (M/M(A)), blue: T159 L62 (M-V/M-V(A)), purple: T255 L62 (H-V), black crossed: observations. a) zonal wind at 850hPa (m/s) in WTA (40°W-10°W, 3°S-3°N) for coupled models and b) for uncoupled models, obs: ERA-interim, c) standard deviation (STD) of 23°C isotherm depth in ATL3 (20°W-0°W, 3°S-3°N) for coupled models, obs: SODA (PDF 72 KB)
382_2017_3760_MOESM4_ESM.pdf (461 kb)
Fig. S4 Regression of net surface short wave radiation (SW, a-c) and latent heat flux (LH, d-f) anomalies on ATL3 SST anomalies (W/m2 per °C) for uncoupled simulations a/d) T42 L31 L(A), b/e) T159 L31 M(A), c/f) T159 L62 M-V(A).. Stippling denotes 95% significance level (PDF 461 KB)
382_2017_3760_MOESM5_ESM.pdf (636 kb)
Fig. S5 Regression of upper ocean temperature (averaged over 3°S-3°N) on ATL3 SST (°C/°C). Stippling denotes 95% significance level. a) T42 L31 (L), b) T159 L31 (M), c) T159 L62 (M-V), d) T255 L62 (H-V), e) SODA (1958-2001) (PDF 635 KB)
382_2017_3760_MOESM6_ESM.pdf (59 kb)
Fig. S6 Seasonally stratified Bjerknes index. Abbreviations are as in Fig. 18. Lines denote: black crossed: SODA (1958-2001), green: T42 L31 (L), red: T159 L31 (M), blue: T159 L62 (M-V), purple: T255 L62 (H-V) (PDF 59 KB)

References

  1. Adler RF, Huffman GJ, Chang A et al (2003) The version-2 global precipitation climatology project (gpcp) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167. doi: 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2 CrossRefGoogle Scholar
  2. Ashfaq M, Skinner CB, Diffenbaugh NS (2011) Influence of SST biases on future climate change projections. Clim Dyn 36:1303–1319. doi: 10.1007/s00382-010-0875-2 CrossRefGoogle Scholar
  3. Bachèlery M-L, Illig S, Dadou I (2015) Interannual variability in the South-East Atlantic Ocean, focusing on the Benguela upwelling system: remote versus local forcing. J Geophys Res Ocean. doi: 10.1002/2015JC011168 Google Scholar
  4. Bellomo K, Clement AC, Mauritsen T et al (2015) The influence of cloud feedbacks on equatorial atlantic variability. J Clim 28:2725–2744. doi: 10.1175/JCLI-D-14-00495.1 CrossRefGoogle Scholar
  5. Biasutti M, Sobel AH, Kushnir Y (2006) AGCM precipitation biases in the tropical Atlantic. J Clim 19:935–958. doi: 10.1175/JCLI3673.1 CrossRefGoogle Scholar
  6. Brandt P, Funk A, Hormann V et al (2011) Interannual atmospheric variability forced by the deep equatorial Atlantic Ocean. Nature 473:497–500. doi: 10.1038/nature10013 CrossRefGoogle Scholar
  7. Breugem W-P, Hazeleger W, Haarsma RJ (2006) Multimodel study of tropical Atlantic variability and change. Geophys Res Lett 33:1–5. doi: 10.1029/2006GL027831 CrossRefGoogle Scholar
  8. Caniaux G, Giordani H, Redelsperger J-L et al (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 CrossRefGoogle Scholar
  9. Carton JA, Giese BS (2008) A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon Weather Rev 136:2999–3017. doi: 10.1175/2007MWR1978.1 CrossRefGoogle Scholar
  10. Chang P, Yamagata T, Schopf P et al (2006) Climate fluctuations of tropical coupled systems—the role of ocean dynamics. J Clim 19:5122–5174. doi: 10.1175/JCLI3903.1 CrossRefGoogle Scholar
  11. Chang C-Y, Carton J a., Grodsky S a., 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. doi: 10.1175/JCLI4047.1 CrossRefGoogle Scholar
  12. Chang C-Y, Nigam S, Carton J a (2008) Origin of the springtime westerly bias in equatorial atlantic surface winds in the community atmosphere model version 3 (CAM3) simulation. J Clim 21:4766–4778. doi: 10.1175/2008JCLI2138.1 CrossRefGoogle Scholar
  13. Compo GP, Whitaker JS, Sardeshmukh PD et al (2011) The Twentieth century reanalysis project. Q J R Meteorol Soc 137:1–28. doi: 10.1002/qj.776 CrossRefGoogle Scholar
  14. Davey M, Huddleston M, Sperber K et al (2002) STOIC: a study of coupled model climatology and variability in tropical ocean regions. Clim Dyn 18:403–420. doi: 10.1007/s00382-001-0188-6 CrossRefGoogle Scholar
  15. Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. doi: 10.1002/qj.828 CrossRefGoogle Scholar
  16. Delworth TL, Rosati A, Anderson W et al (2012) Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. J Clim 25:2755–2781. doi: 10.1175/JCLI-D-11-00316.1 CrossRefGoogle Scholar
  17. Deppenmeier A-L, Haarsma RJ, Hazeleger W (2016) The Bjerknes feedback in the tropical Atlantic in CMIP5 models. Clim Dyn 47:2691–2707. doi: 10.1007/s00382-016-2992-z CrossRefGoogle Scholar
  18. Deser C, Capotondi A, Saravanan R, Phillips AS (2006) Tropical pacific and Atlantic climate variability in CCSM3. J Clim 19:2451–2481. doi: 10.1175/JCLI3759.1 CrossRefGoogle Scholar
  19. DeWitt DG (2005) Diagnosis of the tropical Atlantic near-equatorial SST bias in a directly coupled atmosphere-ocean general circulation model. Geophys Res Lett 32:1–4. doi: 10.1029/2004GL021707 CrossRefGoogle Scholar
  20. Ding H, Greatbatch RJ, Latif M, Park W (2015a) The impact of sea surface temperature bias on equatorial Atlantic interannual variability in partially coupled model experiments. Geophys Res Lett 42:5540–5546. doi: 10.1002/2015GL064799 CrossRefGoogle Scholar
  21. Ding H, Keenlyside N, Latif M et al (2015b) The impact of mean state errors on equatorial Atlantic interannual variability in a climate model. J Geophys Res Ocean 120:1133–1151. doi: 10.1002/2014JC010384 CrossRefGoogle Scholar
  22. Doi T, Tozuka T, Sasaki H et al (2007) Seasonal and interannual variations of oceanic conditions in the Angola Dome. J Phys Oceanogr 37:2698–2713. doi: 10.1175/2007JPO3552.1 CrossRefGoogle Scholar
  23. 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 25:5494–5511. doi: 10.1175/JCLI-D-11-00360.1 CrossRefGoogle Scholar
  24. Florenchie P, Reason CJC, Lutjeharms JRE et al (2004) Evolution of interannual warm and cold events in the southeast Atlantic ocean. J Clim 17:2318–2334. doi: 10.1175/1520-0442(2004)017<2318:EOIWAC>2.0.CO;2 CrossRefGoogle Scholar
  25. Giannini A, Saravanan R, Chang P (2003) Oceanic forcing of sahel rainfall on interannual to interdecadal time scales. Science 302(80):1027–1030. doi: 10.1126/science.1089357 CrossRefGoogle Scholar
  26. Graham FS, Brown JN, Langlais C et al (2014) Effectiveness of the Bjerknes stability index in representing ocean dynamics. Clim Dyn 43:2399–2414. doi: 10.1007/s00382-014-2062-3 CrossRefGoogle Scholar
  27. Guinehut S, Coatanoan C, Dhomps A-L et al (2009) On the use of satellite Altimeter data in Argo quality control. J Atmos Ocean Technol 26:395–402. doi: 10.1175/2008JTECHO648.1 CrossRefGoogle Scholar
  28. Hagemann S, Arpe K, Roeckner E (2006) Evaluation of the hydrological cycle in the ECHAM5 model. J Clim 19:3810–3827. doi: 10.1175/JCLI3831.1 CrossRefGoogle Scholar
  29. Harlaß J, Latif M, Park W (2015) Improving climate model simulation of tropical Atlantic sea surface temperature: the importance of enhanced vertical atmosphere model resolution. Geophys Res Lett 42:2401–2408. doi: 10.1002/2015GL063310 CrossRefGoogle Scholar
  30. Hormann V, Brandt P (2009) Upper equatorial Atlantic variability during 2002 and 2005 associated with equatorial Kelvin waves. J Geophys Res 114:C03007. doi: 10.1029/2008JC005101 CrossRefGoogle Scholar
  31. Hourdin F, Găinuşă-Bogdan A, Braconnot P et al (2015) Air moisture control on ocean surface temperature, hidden key to the warm bias enigma. Geophys Res Lett. doi: 10.1002/2015GL066764 Google Scholar
  32. Huang B, Hu Z-Z (2007) Cloud-SST feedback in southeastern tropical Atlantic anomalous events. J Geophys Res 112:1–19. doi: 10.1029/2006JC003626 CrossRefGoogle Scholar
  33. 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 CrossRefGoogle Scholar
  34. Huffman GJ, Bolvin DT, Nelkin EJ et al (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55. doi: 10.1175/JHM560.1 CrossRefGoogle Scholar
  35. Jin F-F, Kim ST, Bejarano L (2006) A coupled-stability index for ENSO. Geophys Res Lett 33:L23708. doi: 10.1029/2006GL027221 CrossRefGoogle Scholar
  36. Kato S, Loeb NG, Rose FG et al (2013) Surface irradiances consistent with ceres-derived top-of-atmosphere shortwave and longwave irradiances. J Clim 26:2719–2740. doi: 10.1175/JCLI-D-12-00436.1 CrossRefGoogle Scholar
  37. Keenlyside N, Latif M (2007) Understanding equatorial Atlantic interannual variability. J Clim 20:131–142. doi: 10.1175/JCLI3992.1 CrossRefGoogle Scholar
  38. Krebs M, Biastoch A, Böning CW, Latif M (2017) Understanding Benguela upwelling system warm sea surface temperature bias in a forced ocean model.Google Scholar
  39. Large WG, Danabasoglu G (2006) Attribution and Impacts of Upper-Ocean Biases in CCSM3. J Clim 19:2325–2346. doi: 10.1175/JCLI3740.1 CrossRefGoogle Scholar
  40. Li G, Xie S-P (2012) Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys Res Lett. doi: 10.1029/2012GL053777 Google Scholar
  41. Lübbecke JF, McPhaden MJ (2013) A comparative stability analysis of Atlantic and Pacific Niño modes*. J Clim 26:5965–5980. doi: 10.1175/JCLI-D-12-00758.1 CrossRefGoogle Scholar
  42. Lübbecke JF, Böning CW, Keenlyside N, Xie S-P (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 CrossRefGoogle Scholar
  43. 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 9:1635–1645. doi: 10.1175/1520-0442(1996)009<1635:PSCATT>2.0.CO;2 CrossRefGoogle Scholar
  44. Madec G (2008) NEMO ocean engine. Note du Pole de modelisation, Institut Pierre-Simon Laplace (IPSL) No 27, FranceGoogle Scholar
  45. Meynadier R, de Coëtlogon G, Leduc-Leballeur M et al (2015) Seasonal influence of the sea surface temperature on the low atmospheric circulation and precipitation in the eastern equatorial Atlantic. Clim Dyn. doi: 10.1007/s00382-015-2892-7 Google Scholar
  46. Mitchell TP, Wallace JM (1992) The annual cycle in Equatorial convection and sea surface temperature. J Clim 5:1140–1156. doi: 10.1175/1520-0442(1992)005<1140:TACIEC>2.0.CO;2 CrossRefGoogle Scholar
  47. Nnamchi HC, Li J, Kucharski F et al (2015) Thermodynamic controls of the Atlantic Niño. Nat Commun 6:8895. doi: 10.1038/ncomms9895 CrossRefGoogle Scholar
  48. Okumura YM, Xie S-P (2004) Interaction of the Atlantic equatorial cold tongue and the African monsoon*. J Clim 17:3589–3602. doi: 10.1175/1520-0442(2004)017<3589:IOTAEC>2.0.CO;2 CrossRefGoogle Scholar
  49. Park W, Keenlyside N, Latif M et al (2009) Tropical pacific climate and Its response to global warming in the Kiel climate model. J Clim 22:71–92. doi: 10.1175/2008JCLI2261.1 CrossRefGoogle Scholar
  50. Patricola CM, Chang P (2016) Structure and dynamics of the Benguela low-level coastal jet. Clim Dyn. doi: 10.1007/s00382-016-3479-7 Google Scholar
  51. Patricola CM, Li M, Xu Z et al (2012) An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model. Clim Dyn 39:2443–2463. doi: 10.1007/s00382-012-1320-5 CrossRefGoogle Scholar
  52. Philander SGH, Pacanowski RC (1981) The oceanic response to cross-equatorial winds (with application to coastal upwelling in low latitudes). Tellus 33:201–210. doi: 10.1111/j.2153-3490.1981.tb01744.x CrossRefGoogle Scholar
  53. Polo I, Lazar A, Rodriguez-Fonseca B, Arnault S (2008) Oceanic Kelvin waves and tropical Atlantic intraseasonal variability: 1. Kelvin wave characterization. J Geophys Res 113:C07009. doi: 10.1029/2007JC004495 CrossRefGoogle Scholar
  54. Rayner NA (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407. doi: 10.1029/2002JD002670 CrossRefGoogle Scholar
  55. Repelli CA, Nobre P (2004) Statistical prediction of sea-surface temperature over the tropical Atlantic. Int J Climatol 24:45–55. doi: 10.1002/joc.982 CrossRefGoogle Scholar
  56. Reynolds RW, Smith TM, Liu C et al (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20:5473–5496. doi: 10.1175/2007JCLI1824.1 CrossRefGoogle Scholar
  57. Richter I, Xie S-P (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 CrossRefGoogle Scholar
  58. Richter I, Xie S-P, Wittenberg AT, Masumoto Y (2011) 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
  59. Richter I, Xie S-P, Behera SK et al (2012) Equatorial Atlantic variability and its relation to mean state biases in CMIP5. Clim Dyn 42:171–188. doi: 10.1007/s00382-012-1624-5 CrossRefGoogle Scholar
  60. Richter I, Behera SK, Doi T et al (2014) What controls equatorial Atlantic winds in boreal spring? Clim Dyn 43:3091–3104. doi: 10.1007/s00382-014-2170-0 CrossRefGoogle Scholar
  61. Risien CM, Chelton DB (2008) A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data. J Phys Oceanogr 38:2379–2413. doi: 10.1175/2008JPO3881.1 CrossRefGoogle Scholar
  62. Roeckner E, Baeuml G, Bonventura L et al (2003) The atmospheric general circulation model ECHAM5. PART I: model description, report 349. Max Planck Institute for Meteorology, HamburgGoogle Scholar
  63. Servain J, Picaut J, Merle J (1982) Evidence of remote forcing in the equatorial Atlantic ocean. J Phys Oceanogr 12:457–463. doi: 10.1175/1520-0485(1982)012<0457:EORFIT>2.0.CO;2 CrossRefGoogle Scholar
  64. Small RJ, Bacmeister J, Bailey D et al (2014) A new synoptic-scale resolving global climate simulation using the community earth system model. J Adv Model Earth Syst 6:1065–1094. doi: 10.1002/2014MS000363 CrossRefGoogle Scholar
  65. Small RJ, Curchitser E, Hedstrom K et al (2015) The Benguela upwelling system: quantifying the sensitivity to resolution and coastal wind representation in a global climate model*. J Clim 28:9409–9432. doi: 10.1175/JCLI-D-15-0192.1 CrossRefGoogle Scholar
  66. Steinig S, Harlaß J, Latif M, Park W (2017) Influence of tropical model biases on Sahel rainfall in the Kiel Climate Model (in prep)Google Scholar
  67. Stevens B, Duan J, McWilliams JC et al (2002) Entrainment, Rayleigh friction, and boundary layer winds over the tropical Pacific. J Clim 15:30–44. doi: 10.1175/1520-0442(2002)015<0030:ERFABL>2.0.CO;2 CrossRefGoogle Scholar
  68. Stockdale TN, Balmaseda M a., Vidard A (2006) Tropical Atlantic SST prediction with coupled ocean-atmosphere GCMs. J Clim 19:6047–6061. doi: 10.1175/JCLI3947.1 CrossRefGoogle Scholar
  69. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi: 10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  70. Toniazzo T, Woolnough S (2013) Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts. Clim Dyn 43:2889–2913. doi: 10.1007/s00382-013-1691-2 CrossRefGoogle Scholar
  71. Tozuka T, Doi T, Miyasaka T et al (2011) Key factors in simulating the equatorial Atlantic zonal sea surface temperature gradient in a coupled general circulation model. J Geophys Res 116:1–12. doi: 10.1029/2010JC006717 CrossRefGoogle Scholar
  72. Uppala SM, Kaallberg PW, Simmons AJ et al (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012. doi: 10.1256/qj.04.176 CrossRefGoogle Scholar
  73. Voldoire A, Claudon M, Caniaux G et al (2014) Are atmospheric biases responsible for the tropical Atlantic SST biases in the CNRM-CM5 coupled model? Clim Dyn 43:2963–2984. doi: 10.1007/s00382-013-2036-x CrossRefGoogle Scholar
  74. 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. doi: 10.1007/s00382-009-0690-9 CrossRefGoogle Scholar
  75. Wan X, Chang P, Jackson CS et al (2011) Plausible effect of climate model bias on abrupt climate change simulations in Atlantic sector. Deep Sea Res Part II Top Stud Oceanogr 58:1904–1913. doi: 10.1016/j.dsr2.2010.10.068 CrossRefGoogle Scholar
  76. Wentz FJ, Scott J, Hoffman R et al (2015) Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, Version 2.0. Remote Sensing Systems, Santa Rosa, CAGoogle Scholar
  77. Xie S-P (1994) On the genesis of the equatorial annual cycle. J Clim 7:2008–2013. doi: 10.1175/1520-0442(1994)007<2008:OTGOTE>2.0.CO;2 CrossRefGoogle Scholar
  78. Xie S-P, Carton JA (2004) Tropical Atlantic variability: Patterns, mechanisms, and impacts. In: Wang C, Xie S-P, Carton JA (eds) Earth climate: the ocean-atmosphere interaction. Geophysical monograph. AGU, Washington, DC, pp 121–142Google Scholar
  79. Xu Z, Chang P, Richter I et al (2014a) Diagnosing southeast tropical Atlantic SST and ocean circulation biases in the CMIP5 ensemble. Clim Dyn 43:3123–3145. doi: 10.1007/s00382-014-2247-9 CrossRefGoogle Scholar
  80. Xu Z, Li M, Patricola CM, Chang P (2014b) Oceanic origin of southeast tropical Atlantic biases. Clim Dyn 43:2915–2930. doi: 10.1007/s00382-013-1901-y CrossRefGoogle Scholar
  81. Yu L, Jin X, 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, pp 64. Woods Hole. MassachusettsGoogle Scholar
  82. Zermeño-Diaz DM, Zhang C (2013) Possible root causes of surface westerly biases over the equatorial Atlantic in Global climate models. J Clim 26:8154–8168. doi: 10.1175/JCLI-D-12-00226.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.GEOMAR Helmholtz Centre for Ocean Research KielKielGermany
  2. 2.Kiel UniversityKielGermany

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