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Research Article Open Access
The Great Lakes Region as important resources for water usages plays an important role in the U.S. economy. As the area might be susceptible to global warming, well-informed decisions in response to the possible global warming effects depend on accurate regional assessments by climate models such as Regional Climate Models (RCMs). Four historical RCM runs from the North American Regional Climate Change Assessment Program (NARCCAP) were chosen to study the reliability of simulated land surface variables such as latent heat, sensible heat, surface air temperature, soil moisture, and runoff. The Global Land Data Assimilation System (GLDAS) was used as a truth dataset to evaluate the biases of the RCM results. The comparisons of the monthly climatology of the energy components and water budget components simulated by the RCMs and GLDAS showed that, latent heat and skin air temperature by RCMs were close to the truth data, large biases were identified for sensible heat and runoff values. Specifically, the Weather Research and Forecasting Model (WRFG) model, which used the same Noah land scheme as in GLDAS, showed positive biases of down-welling radiation, sensible heat, and surface air temperature. The Canadian Regional Climate Model version 4 (CRCM) model was found to have lower soil water content, larger snow amount, and more snow melt than the truth data. The results from this study provide a certain degree of confidence for other studies concerning the Great Lakes region to interpret the future predictions of latent heat and air temperatures by the NARCCAP project. Meanwhile, caution should be taken to review and utilize the simulated results related to soil moisture or runoff. This study also provides insights and direction for RCM model developers to further refine related modeling parameterizations.
Climate change, Great Lakes, RCM, GCM, North American regional climate change assessment program (NARCCAP), Global land data assimilation system (GLDAS), Climate Change, ELNINO Effect