Assessment of the wind power density over South America simulated by CMIP6 models in the present and future climate

Abstract

Expanding the South American renewable energy matrix to ensure more sustainable socio-economic development, mitigate the climate change effects, and meet the targets set in the Paris Agreement is crucial. Hence, this study sought to estimate South America’s wind speed and wind power density alterations projected by eight global climate models (GCMs) from Coupled Model Intercomparison Project – Phase 6 (CMIP6). To this end, we applied statistical downscaling and bias correction to the GCMs outputs through the Quantile Delta Mapping method and assessed the projected changes in wind power in future climate under the Shared Socioeconomic Pathways (SSPs) SSP2-4.5 and SSP5-8.5 emission scenarios. ERA5 reanalysis data from 1995–2014 validated the models’ historical simulations. The CMIP6 multimodel ensemble indicated an approximate 25–50% increase in wind power density in sectors such as Northeast and South Brazil and growing wind power in regions such as Argentine Patagonia, northern Venezuela, and portions of Uruguay, Bolivia, and Paraguay. Estimates of the wind power growth for the 21st century in those regions reiterated their potential performance in the historical period. For the SSP5-8.5 emission scenario, the ensemble projections indicated even more favorable wind power conditions in the sectors mentioned. However, individual projections of wind intensity anomalies obtained by each ensemble member showed a large spread among the GCMs, evidencing the uncertainties associated with the prospects of change in wind power on the continent. Furthermore, this study has presented a first analysis of CMIP6 projections for South American wind power generation, providing relevant information to the energy sector decision-makers.

South American Monsoon Lifecycle Projected by Statistical Downscaling with CMIP6-GCMs

“…The models were selected in January 2022 under the R&D project 00403-0054/2022. As described in Ferreira et al [48,49], the performance of 50 CMIP6-GCMs in representing the mean state of the SA climate was analyzed with the methodology of ranking analysis [50]. Monthly data (from 1995 to 2014) of air temperature at 2 m and precipitation of these models were used.…”

“…As the BCSD methodology is described in Ferreira et al [48,49], here we provide only a summary. For obtaining a dataset with an intermediate horizontal resolution, initially, the daily precipitation from CMIP6-GCMs is spatially disaggregated, i.e., remapped to a grid of 0.5 • × 0.5 • , which is the same as that from the reference dataset (CPC), as mentioned in the previous section.…”