Evaluation of GCMs historical simulations of monthly and seasonal climatology over Bolivia

  • Azar M. Abadi
  • Robert Oglesby
  • Clinton Rowe
  • Rachindra Mawalagedara


Bolivia is a low-latitude, developing country at grave risk to the deleterious effects of human-induced climate changes. Due to the complexity of the topography in Bolivia, it is difficult to capture future impacts of the climate change on the regional scale with the coarse resolution of current GCMs. A robust strategy has been developed to dynamically downscale the GCM outputs to a more appropriate temporal and spatial resolution for impact studies. Prior to downscaling, however, evaluation of the GCMs used to provide large-scale forcing is a necessary step to ensure physically meaningful results from regional climate models. This study represents the first part of a broader project on evaluating climate change impacts over Bolivia. We examined precipitation, temperature, wind patterns and moisture transport to evaluate the performance of eight CMIP5 GCMs in simulating the continental and regional climate patterns. Phenomena including the seasonal and monthly positions of the intertropical convergence zone, South Atlantic convergence zone, Bolivian high, Chaco low and South American low-level jet, were analyzed. Our results confirm that, in general, all the GCMs do reasonably well in simulating the basic patterns of the variables with some discrepancies in magnitude across models, especially in the regional scale. Some models outperform the others for the variables and the region of our interest. Finally, the results of this research will help improve quantifying the uncertainty range of further regional downscaling outputs.



This work was initially supported by the Ministry of Environment and Water (MMAyA) of the Plurinational State of Bolivia (contract MMAyA/PPCR no 117/2012 to RO and CR). We acknowledge further support from the Interamerican Development Bank (to RO and CR) for the development of tools and techniques used in this research, the Daugherty Water for Food Global Institute (postdoctoral support to RM), and the UNL Holland Computing Center. We also acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Earth and Atmospheric SciencesUniversity of Nebraska-LincolnLincolnUSA
  2. 2.Department of Geological and Atmospheric SciencesIowa State UniversityAmesUSA

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