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Abstract Evaluation methods for Regional Climate Models (RCMs) commonly rely on point comparisons with observed meteorological fields, which provide limited understanding of the spatial and temporal representation of important factors affecting urban areas in models. These factors are not only complex but also difficult to differentiate, which complicates their analysis. This study thus develops an innovative approach using Empirical Orthogonal Function (EOF) analysis to compare urban heat island and precipitation patterns in RCM simulations with those from observations, taking advantage of the capacity of the method for data disaggregation. The method was tested on summer daily maximum and minimum temperature (T_maxand T_min) and precipitation (P) in the Chicago Metro Area (CMA). Using observed data, the EOF analysis on temperature consistently produced coherent patterns that reflect known impacts of urban environments on climate and weather. EOF evaluation of corresponding 4-km WRF simulations against observations confirmed a strong warm bias (~3°C) for simulated T_minin the urban area, as observed in point comparisons against stations; further analysis, however, suggested that the shape and time behavior of the urban pattern were well represented. EOF analysis on T_max, which showed no problems in the point comparison, revealed important differences in shape (urban area of influence on temperatures) and time [Principal Components (PC) correlation of −0.5] for the urban pattern between datasets, suggesting the need for model improvements. Results showed no systematic urban effects on summer P for the CMA for observations or simulations, but analysis of winter patterns suggested a possible urban enhancement on P over the city. 

Nutrient runoff from agricultural regions of the midwestern U.S. corn belt has degraded water quality in many inland and coastal water bodies such as the Great Lakes and Gulf of Mexico. Under current climate, observational studies have shown that winter cover crops can reduce dissolved nitrogen and phosphorus losses from row-cropped agricultural watersheds, but performance of cover crops in response to climate variability and climate change has not been systematically evaluated. Using the Soil & Water Assessment Tool (SWAT) model, calibrated using multiple years of field-based data, we simulated historical and projected future nutrient loss from two representative agricultural watersheds in northern Indiana, USA. For 100% cover crop coverage, historical simulations showed a 31–33% reduction in nitrate (NO3−) loss and a 15–23% reduction in Soluble Reactive Phosphorus (SRP) loss in comparison with a no-cover-crop baseline. Under climate change scenarios, without cover crops, projected warmer and wetter conditions strongly increased nutrient loss, especially in the fallow period from Oct to Apr when changes in infiltration and runoff are largest. In the absence of cover crops, annual nutrient losses for the RCP8.5 2080s scenario were 26–38% higher for NO3−, and 9–46% higher for SRP. However, the effectiveness of cover crops also increased under climate change. For an ensemble of 60 climate change scenarios based on CMIP5 RCP4.5 and RCP8.5 scenarios, 19 out of 24 ensemble-mean simulations of future nutrient loss with 100% cover crops were less than or equal to historical simulations with 100% cover crops, despite systematic increases in nutrient loss due to climate alone. These results demonstrate that planting winter cover crops over row-cropped land areas constitutes a robust climate change adaptation strategy for reducing nutrient losses from agricultural lands, enhancing resilience to a projected warmer and wetter winter climate in the midwestern U.S.