Evaluating Surface Variables Simulated by the North American Regional Climate Change Assessment Program over the Great Lakes Region

Lingli He

doi:10.4172/2332-2594.1000160

Abstract

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.

Highlights

The Great Lakes region, as the largest fresh water body in the world, is regarded as a major resource for water usages and plays an important role in the U.S economy [1,2]
The Global Land Data Assimilation System (GLDAS) has the largest magnitude of latent heat (LH) year long, Weather Research and Forecasting Model (WRFG)-CGCM3 and WRFG-National Center Environmental Prediction (NCEP) tend to underestimate LH for the summer time, Canadian Regional Climate Model version 4 (CRCM)-CGCM3 and CRCM-NCEP tend to underestimate LH for both the summer and winter time
The simulation results of WRFG-CGCM3 and WRFGNCEP are nearly identical

Summary

Introduction

The Great Lakes region, as the largest fresh water body in the world, is regarded as a major resource for water usages (e.g., drinking, irrigation, shipping, ecological habits, hydropower, and recreation) and plays an important role in the U.S economy [1,2]. The Great Lakes region might be susceptible to the effect of global warming, as the changes of climate condition could influence the surface energy partition and water cycle, further affecting Great Lakes water level [3,4]. Various assessments on the potential impacts of global climate change on the Great Lakes region have been focused on climate projections from General Circulation Models (GCMs) [7,8]. GCMs usually have a resolution of 200-300 km, which is inadequate to resolve the spatial details (e.g., topography, vegetation, soils, lakes, and shorelines) and unable to adequately represent regional assessments. Regional Climate Models (RCMs), which provides finer spatial resolution than GCMs, provide one possible solution to resolve regional variability. Same principles of physics, chemistry, and fluid dynamics are employed in RCMs or GCMs, different formulations, parameterizations, and boundary conditions in models lead to different projections [9,10,11]

Methods

Results

Conclusion