Climate Dynamics

, Volume 51, Issue 5–6, pp 1819–1840 | Cite as

Origin of the warm eastern tropical Atlantic SST bias in a climate model

  • E. ExarchouEmail author
  • C. Prodhomme
  • L. Brodeau
  • V. Guemas
  • F. Doblas-Reyes


The substantial warm sea surface temperature bias in the eastern Tropical Atlantic reported in most CMIP5 climate simulations with various models, in particular along the coast of Namibia and Angola, remains an issue in more recent and CMIP6-ready versions of climate models such as EC-Earth. A complete and original set of experiments with EC-Earth3.1 is performed to investigate the causes and mechanisms responsible for the emergence and persistence of this bias. The fully-developed bias is studied in a historical experiment that has reached quasi-equilibrium, while retrospective prediction experiments are used to highlight the development/growth from an observed initial state. Prediction experiments are performed at both low and high resolution to assess the possible dependence of the bias on horizontal resolution. Standalone experiments with the ocean and the atmosphere components of EC-Earth are also analyzed to separate the respective contributions of the ocean and atmosphere to the development of the bias. EC-Earth3.1 exhibits a bias similar to that reported in most climate models that took part in CMIP5. The magnitude of this bias, however, is weaker than most CMIP5 models by few degrees. Increased horizontal resolution only leads to a minor reduction of the bias in EC-Earth. The warm SST bias is found to be the result of an excessive solar absorption in the ocean mixed layer, which can be linked to the excessive solar insolation due to unrealistically low cloud cover, and the absence of spatial and temporal variability of the biological productivity in the ocean component. The warm SST bias is further linked to deficient turbulent vertical mixing of cold water to the mixed layer. Our study points at a need for better representation of clouds in the vicinity of eastern boundaries in atmosphere models, and better representation of solar penetration and turbulent mixing in the ocean models in order to eliminate the Tropical Atlantic biases.


Seasonal prediction Tropical Atlantic Model biases Global climate models 



We would like to acknowledge the anonymous reviewer who provided constructive comments that led to a considerable improvement of the manuscript. We would also like to thank Aurore Voldoire and Anna-Lena Deppenmeier for the useful discussion, and Yann Planton for providing the code for implementing the tendency diagnostics in NEMO. This research has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under grant agreements 308378 (SPECS), 603521 (PREFACE) and the Horizon 2020 EU program under grand agreements 641727 (PRIMAVERA). We acknowledge RES and ECMWF for awarding access to supercomputing facilities in the Barcelona Supercomputing Center in Spain and the ECMWF Supercomputing Center in the UK, through the HiResClim and SPESICCF projects, recpectively. We acknowledge the work of the developers of the s2dverification R-based package ( The visualization of some of the figures was done with the NCAR Command Language (NCL, Version 6.3.0, 2016, Boulder, Colorado: UCAR/NCAR/CISL/TDD,

Supplementary material

382_2017_3984_MOESM1_ESM.pdf (70 kb)
Figure S1: Forecast biases in the four components of the area-averaged surface heat fluxes over the CAB region (positive is downwards, units are W/m2) for the LR-Hind experiment with respect to TropFlux. Sensible heat fluxes are shown in green, latent in blue, longwave in brown, shortwave in red, and net heat fluxes in black, for the the summer months (May-August) in the top row and winter months (November-February) in the bottom row. The grey line is the SST bias (with respect to ERAi), and its values are shown in the right Y axis, in °C. The biases are calculated as the difference between LR-Hind and the reference dataset (TropFlux or ERAi) in their time mean data over 17 years (1993-2009) for each day in May (left) and November (right) (PDF 69 KB)
382_2017_3984_MOESM2_ESM.pdf (84 kb)
Figure S2: Climatological meridional surface wind speed vas (in m/s, time averages between 1999-2009), and zonally averaged between 25°S-5°S, for DFS4.3 and QuikSCAT (PDF 84 KB)
382_2017_3984_MOESM3_ESM.pdf (68 kb)
Figure S3: Forecast biases in the four components of the meridionally averaged (between 18°S-5°S) surface heat fluxes (positive is downwards, units are W/m2) for the LR-Hind experiment with respect to OAFlux (as in Fig 15) (PDF 68 KB)


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© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Barcelona Supercomputing Center (BSC), Earth SciencesBarcelonaSpain
  2. 2.Centre National de Recherche Meteorologique, Meteo-FranceToulouseFrance
  3. 3.ICREA, Pg. Lluís Companys 23BarcelonaSpain

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