Abstract
Changes in temperature and precipitation extremes are examined in transient climate change simulations performed with the second-generation coupled global climate model of the Canadian Centre for Climate Modelling and Analysis. Three-member ensembles were produced for the time period 1990–2100 using the IS92a, A2, and B2 emission scenarios of the Intergovernmental Panel on Climate Change. The return values of annual extremes are estimated from a fitted generalized extreme value distribution with time-dependent location and scale parameters by the method of maximum likelihood. The L-moment return value estimates are revisited and found to be somewhat biased in the context of transient climate change simulations.
The climate response is of similar magnitude in the integrations with the IS92a and A2 emission scenarios but more modest for the B2 scenario. Changes in temperature extremes are largely associated with changes in the location of the distribution of annual extremes without substantial changes in its shape over most of the globe. Exceptions are regions where land and ocean surface properties change drastically, such as the regions that experience sea ice and snow cover retreat. Globally averaged changes in warm extremes are comparable to the corresponding changes in annual mean daily maximum temperature, while globally averaged cold extremes warm up faster than annual mean daily minimum temperature. There are considerable regional differences between the magnitudes of changes in temperature extremes and the corresponding annual means. Changes in precipitation extremes are due to changes in both the location and scale of the extreme value distribution and exceed substantially the corresponding changes in the annual mean precipitation. Generally speaking, the warmer model climate becomes wetter and hydrologically more variable. The probability of precipitation events that are considered extreme at the beginning of the simulations is increased by a factor of about 2 by the end of the twenty-first century.
The Distribution Analysis for Extreme Returns of Nikkei 225 Index: Based on the Extreme Value Distribution of GEV and GL
