Statistical techniques;
Statistics;
Time series;
Model output statistics;
REGIONAL CLIMATE MODEL;
SEASONAL PRECIPITATION;
EXTREMES;
STATISTICS;
CONVECTION;
RAINFALL;
D O I:
10.1175/JCLI-D-14-00360.1
中图分类号:
P4 [大气科学(气象学)];
学科分类号:
0706 ;
070601 ;
摘要:
As a consequence of the increase in atmospheric greenhouse gas concentrations, potential changes in both precipitation occurrence and intensity may lead to several consequences for Earth's environment. It is therefore relevant to estimate these changes in order to anticipate their consequences. Many studies have been published on precipitation changes based on climate simulations. These studies are almost always based on time slices; precipitation changes are estimated by comparing two 30-yr windows. To this extent, it is commonly assumed that nonstationary processes are not significant for such a 30-yr slice. Thus, it frees the investigator to statistically model nonstationary processes. However, using transient runs instead of time slices surely leads to more accurate analysis since more data are taken into account. Therefore, the aim of the present study was to develop a transient probabilistic model for describing simulated daily precipitation from the Canadian Regional Climate Model (CRCM) in order to investigate precipitation evolution over North America. Changes to both the occurrence and intensity of precipitation are then assessed from a continuous time period. Extreme values are also investigated with the transient run; a new methodology using the models for precipitation occurrence and intensity was developed for achieving nonstationary frequency analysis. The results herein show an increase in both precipitation occurrence and intensity for most parts of Canada while a decrease is expected over Mexico. For the continental United States, a decrease in both occurrence and intensity is expected in summer but an increase is expected in winter.