Publications

Spera, SA; Winter, JM; Chipman, JW (2018). Evaluation of Agricultural Land Cover Representations on Regional Climate Model Simulations in the Brazilian Cerrado. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 123(10), 5163-5176.

Abstract
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. Plain Language Summary Humans are the largest drive of landscape change globally. One region that exemplifies this change is Brazil's Cerradoover half of it has been cleared for agriculture. Farmers in the region depend on a stable rainy season to cultivate crops like soy and corn, but, clearing Cerrado for agriculture may disturb regional climate and affect precipitation. The first step to assessing these impacts is determining whether a more accurate land surface improves simulation results and where the model still needs to be improved. We use the Weather Research and Forecasting (WRF) model to run 10-year-long climate simulations across Brazil with both the default U.S. Geological Survey land cover map and an updated land cover map with two new agricultural categories. Our results show that using an updated map improves model results over regions of intensive agriculture, especially in the dry-to-wet-season transition months. All simulation results show an overestimation in evapotranspiration rates and a cold bias during the rainy season. These biases seem to be the result of WRF's soil-moisture model. Understanding both these interactions and how we can use climate models to better study them is essential for making informed land use decisions.

DOI:
10.1029/2017JD027989

ISSN:
2169-897X