Climate Dynamics

, Volume 41, Issue 11, pp 3127-3143

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

How well can CMIP5 simulate precipitation and its controlling processes over tropical South America?

  • Lei YinAffiliated withDepartment of Geological Sciences, The University of Texas at Austin Email author 
  • , Rong FuAffiliated withDepartment of Geological Sciences, The University of Texas at Austin
  • , Elena ShevliakovaAffiliated withNOAA/Geophysical Fluid Dynamics LaboratoryDepartment of Ecology and Evolutionary Biology, Princeton University
  • , Robert E. DickinsonAffiliated withDepartment of Geological Sciences, The University of Texas at Austin


Underestimated rainfall over Amazonia was a common problem for the Coupled Model Intercomparison Project phase 3 (CMIP3) models. We investigate whether it still exists in the CMIP phase 5 (CMIP5) models and, if so, what causes these biases? Our evaluation of historical simulations shows that some models still underestimate rainfall over Amazonia. During the dry season, both convective and large-scale precipitation is underestimated in most models. GFDL-ESM2M and IPSL notably show more pentads with no rainfall. During the wet season, large-scale precipitation is still underestimated in most models. In the dry and transition seasons, models with more realistic moisture convergence and surface evapotranspiration generally have more realistic rainfall totals. In some models, overestimates of rainfall are associated with the adjacent tropical and eastern Pacific ITCZs. However, in other models, too much surface net radiation and a resultant high Bowen ratio appears to cause underestimates of rainfall. During the transition season, low pre-seasonal latent heat, high sensible flux, and a weaker influence of cold air incursions contribute to the dry bias. About half the models can capture, but overestimate, the influences of teleconnection. Based on a simple metric, HadGEM2-ES outperforms other models especially for surface conditions and atmospheric circulation. GFDL-ESM2M has the strongest dry bias presumably due to its overestimate of moisture divergence, induced by overestimated ITCZs in adjacent oceans, and reinforced by positive feedbacks between reduced cloudiness, high Bowen ratio and suppression of rainfall during the dry season, and too weak incursions of extratropical disturbances during the transition season.


Amazon precipitation Rainfall seasonality CMIP5 models Climate variability ET SST indices