final_program_abstracts[1]

11 IMSC Session Program
No title provided
Wednesday - Poster Session 5
Carlos Gaitan
The coarse-resolution Canadian Coupled General Circulation Model (CGCM 3.1)
running the SRES A2, and the SRES A1B simulations (Nakicenovic, N. et al, 2000)
was downscaled using a variety of linear and nonlinear machine learning/statistical
methods (e.g. linear regression, principal component analysis, artificial neural
networks) to obtain daily station values of maximum temperature (TMAX), and
minimum temperature (TMIN) for five weather stations located on Vancouver Island.
The downscaled data were analyzed for variability and extremes events. In addition,
based on previous work by STARDEX (Climatic Research Unit, 2005), extreme
indices were calculated, and comparisons in variability were made between values of
these indices observed in the base climate period (1961-2000), and values projected
for the 21st century. The results show that the downscaled data series were able to
capture higher variances than the NCEP/NCAR Reanalysis, although these variances
are smaller than weather station/observed ones.
Abstracts 203