Estimation of Regional Inter-Annual Variability in a 40-Member Ensemble of the CCSM3 and Three Observation Grids over Québec.
Thursday, 18 December 2014
Assessing the regional natural variability of the climate is crucial to better describe, understand and estimate climate change. The capability of GCMs to estimate the natural variability of the meteorological observations is of upmost importance to assess the changes in extreme events such as floods or droughts. In this study, the inter-annual variability of the three variables precipitation, minimum and maximum temperature (tasmin and tasmax), was derived from gridded data sets over the meridional area of Québec, Eastern Canada, with a focus on the catchment of the Saguenay Fjord. Gridded observation data sets, derived from meteorological station measurements, furthermore strongly depend on the applied gridding algorithms. To address the uncertainty associated to this issue, an ensemble of three gridded data sets, based on different interpolation methods, was applied for the period 1970 - 1999. The inter-annual variability of a 40-member ensemble of the Community Climate System Model Version 3 (CCSM3), by the National Center for Atmospheric Research, was compared to the variability in these three observation grids. To compare the 40 members with the observations, the linear trend was removed in all data sets, hence the variability in the observations should be fully covered by the CCSM3 members, as only 30 years of observations are available, compared to 2400 years of modeled data (40 members x 60 years transient run from 2000 - 2059), which more likely include extremes with lower recurrence rates, than the 30 year data sets. Variability was assessed for different spatial scales, starting from the 10km grid of the observations to the ~ 320km grid of the GCM. Furthermore, various temporal scales were analyzed to determine the number of years required to properly capture inter-annual variability. The performed analyses show, that the CCSM3 ensemble estimates all variations in the observations for tasmax and precipitation, however there seems to be a slight underestimation of tasmin for the researched area. Furthermore, the regional patterns of inter-annual variability vary strongly in the three observation datasets.