Abstract
This study focuses on the evaluation of daily precipitation and temperature climate indices and extremes simulated by an ensemble of 12 Regional Climate Model (RCM) simulations from the ARCTIC-CORDEX experiment with surface observations in the Canadian Arctic from the Adjusted Historical Canadian Climate Dataset. Five global reanalyses products (ERA-Interim, JRA55, MERRA, CFSR and GMFD) are also included in the evaluation to assess their potential for RCM evaluation in data sparse regions. The study evaluated the means and annual anomaly distributions of indices over the 1980–2004 dataset overlap period. The results showed that RCM and reanalysis performance varied with the climate variables being evaluated. Most RCMs and reanalyses were able to simulate well climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Overall performance was generally poor for indices related to cold extremes. Likewise, only a few RCM simulations and reanalyses were able to provide realistic simulations of precipitation extreme indicators. The multi-reanalysis ensemble provided superior results to individual datasets for climate indicators related to mean air temperature and hot extremes, but not for other indicators. These results support the use of reanalyses as reference datasets for the evaluation of RCM mean air temperature and hot extremes over northern Canada, but not for cold extremes and precipitation indices.
Highlights
Trends in temperature and precipitation and their extremes in observations and Global Climate Models (GCMs) have been a subject of extensive study over the past decade because of the potential impacts on human society and ecosystems (e.g. Alexander et al 2006; Kharin et al 2007, 2013; Sillmann and Roeckner 2008; Donat et al 2013; Sillmann et al 2013a, b)
In addition to limited surface observations, a key challenge in model evaluation is the scale mismatch (e.g. Booij 2002; Fowler et al 2005; Zhang et al 2011) between surface observations and climate models with resolutions ranging from ~25 to 50 km for the Regional Climate Models (RCMs) to ~100–300 km for the GCMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al 2012)
The CCCma-CanRCM4 simulations were provided on a rotated grid at two horizontal grid spacings, 0.44° and 0.22°, while the other model simulations were only available at the 0.44° horizontal resolution
Summary
Trends in temperature and precipitation and their extremes in observations and Global Climate Models (GCMs) have been a subject of extensive study over the past decade because of the potential impacts on human society and ecosystems (e.g. Alexander et al 2006; Kharin et al 2007, 2013; Sillmann and Roeckner 2008; Donat et al 2013; Sillmann et al 2013a, b). Zwiers et al 2013; Alexander 2016) especially over data sparse regions such as the Canadian Arctic which is the focus of this study. Different interpolation methods have been proposed in order to aggregate station information to the GCM scale. These methods can only be used with some confidence in regions with good spatial coverage. One approach for dealing with the scale-difference issue is to dynamically downscale GCM simulations using high-resolution RCM simulations. This has been shown to provide more realistic simulations of precipitation and precipitation extremes, with intensities and frequencies comparable to those recorded at surface stations (Chan et al 2013)
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