Evaluation and analysis of daily atmospheric circulation patterns of the 500 HPA pressure field over the southwestern USA

Daily atmospheric circulation patterns (CPʹs) are defined and analyzed on the basis of the 500 hPa pressure field for the purpose of describing and then later generating local hydroclimatological variables such as precipitation under possible climate change over the southwestern USA. To obtain the CPʹs we first use a so-called objective or automated clustering method, namely, principal component analysis (PCA) coupled with K-means clustering algorithm. To obtain a set of CP types that are more distinguishable and so more useful for our purpose we follow two possible ways: (1) reduce subjectively the number of CPʹs from PCA coupled with K-means clustering analysis by aggregating the types on the basis of the precipitation producing characteristics, (2) perform K-means clustering analysis with fewer types. Thus we have three different cluster systems: original types from the result of PCA coupled with K-means clustering (8 or 9 types depending on the season), types from the K-means clustering analysis with fewer types (5 or 6 types in each season) and the subjectively aggregated types (3 types in any seasons). We compare them from the point of view of information content for precipitation modeling. An analysis is made for these types for comparison: statistical properties of these patterns are evaluated and analyzed using first observed data, and then General Circulation Model (GCM) outputs for 1 × CO2 and 2 × CO2 scenarios to estimate climate change effects. On the basis of the historical circulation pattern catalogue and observed precipitation data in Arizona, simple calculations are provided to find the “precipitation-producing” types of each system in each season. Three indices are evaluated using the same observed precipitation data from ten Arizona stations in order to measure objectively the information content of each type in the three cluster systems. It turns out that the larger number of types in a given season gives higher information content as we expected.