Tomoshige Inoue

Comparison of the Impact of Global Climate Changes and Urbanization on Summertime Future Climate in the Tokyo Metropolitan Area

AbstractIn this study, the impact of global climate change and anticipated urbanization over the next 70 years is estimated with regard to the summertime local climate in the Tokyo metropolitan area (TMA), whose population is already near its peak now. First, five climate projections for the 2070s calculated with the aid of general circulation models (GCMs) are used for dynamical downscaling experiments to evaluate the impact of global climate changes using a regional climate model. Second, the sensitivity of future urbanization until the 2070s is examined assuming a simple developing urban scenario for the TMA. These two sensitivity analyses indicate that the increase in the surface air temperature from the 1990s to the 2070s is about 2.0°C as a result of global climate changes under the A1B scenario in the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios (SRES) and about 0.5°C as a result of urbanization. Considering the current urban heat island intensity (UHII) of 1.0°...

Application of Cluster Analysis to Climate Model Performance Metrics

The overall performance of general circulation models is often investigated on the basis of the synthesis of anumberofscalar performancemetricsofindividualmodelsthatmeasurethereproducibilityofdiverseaspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulatingacertainaspectoftheclimateissometimesrelatedcloselytothatinsimulatinganotheraspect,which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysistogroup43performancemetrics. Two clusteringmethods, theK-means and the Wardmethods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.