Examining interactions between large-scale land cover and land use change and regional climate in areas undergoing dynamic land transformations, like the Brazilian Cerrado, is crucial for understanding tradeoffs between human needs and ecosystem services. Yet regional climate models often do not include accurate land cover data of these complex landscapes. We use National Center for Atmospheric Research’s Weather Research and Forecasting (WRF) model coupled to the Noah-Multiparameterization (Noah-MP) land surface model to run 10-year climate simulations across Brazil to assess (1) whether an accurate, regionally validated land cover data set with two, new agricultural land cover classifications improves model simulation results; (2) the ability of Noah-MP’s dynamic vegetation option to model vegetation growth; and (3) the sensitivity of the model output to scale. The results of the simulations with the updated land surface perform better over intensive agricultural areas for precipitation, evapotranspiration, and temperature, especially during the wet-to-dry season transition months. Evapotranspiration is overestimated during the start of the rainy season across all model simulations, which is likely due to the soil moisture model. We also find that using the Noah-MP dynamic vegetation significantly degrades agricultural leaf area index phenology simulations in Brazilian agricultural regions. Lastly, improving the model’s resolution did not improve model output when compared to observational data. Incorporating more accurate representations of the landscape into regional climate models is essential for quantifying potential changes in climatological seasonality in dynamic, human-modified regions and making informed land use decisions.

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Copyright © 2018, AMER GEOPHYSICAL UNION. This article first appeared in Journal of Geophysical Research: Atmospheres 123:10 (2018), 5163-5176.

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