Weather Pattern Classification to Represent the Urban Heat Island in Present and Future Climate

Abstract

classification of weather patterns (WP) is derived that is tailored to best represent situations relevant for the urban heat island (UHI). Three different types of k-means-based cluster methods are conducted. The explained cluster variance is used as a measure for the quality. Several variables of the 700-hPa fields from the 40-yr ECMWF Re-Analysis (ERA-40) were tested for the classification. The variables as well as the domain for the clustering are chosen in a way to explain the variability of the UHI as best as possible. It turned out that the combination of geopotential height, relative humidity, vorticity, and the 1000?700-hPa thickness is best suited. To determine the optimal cluster number k several statistical measures are applied. Except for autumn (k = 12) an optimal cluster number of k = 7 is found. The WP frequency changes are analyzed using climate projections of two regional climate models (RCM). Both RCMs, the Regional Model (REMO) and Climate Limited-Area Model (CLM), are driven with the A1B simulations from the global climate model ECHAM5. Focusing on the periods 2036?65 and 2071?2100, no change can be found of the frequency for the anticyclonic WP when compared with 1971?2000. Since these WPs are favorable for the development of a strong UHI, the frequency of strong UHI days stays the same for the city of Hamburg,Germany. For other WPs changes can be found for both future periods. At the end of the century, a large increase (17%?40%) in the frequency of the zonal WP and a large decrease (20%?26%) in the southwesterly WP are projected.