Comparisons of Statistical Downscaling Methods for Air Temperature Over the Qilian Mountains
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AbstractAir temperature is an important indicator of climate change, as well as for understanding changes in hydrology, ecology, and other natural systems. However, meteorological stations that provide reliable temperature observations are usually sparse in areas of complex terrain, thus limiting our ability to quantify high spatial resolution temperature variability in these regions. Here, we apply three statistical downscaling methods to daily air temperature output from the sixth Coupled Model Intercomparison Project (CMIP6), validated with 22 meteorological stations over the Qilian Mountains. Based on different downscaling methods, we find RMSE and MAE are reduced as much as 5966%, with the ratio of RMSE and MAE to the annual average temperature of stations decreasing from 147.9% to 61.0% and 143.3% to 64.7%, respectively, depending on the method. Compared to the original data, annual temperature based on the best downscaling methods differed by 2.853.61C during the historical 18502014 period and, for the 20152100 projections, by 2.133.30C for SSP1-2.6, 2.133.29C for SSP2-4.5, 2.113.24C for SSP3-7.0, and 2.123.23C for SSP5-8.5. The downscaled annual air temperatures show a warming trend ranging 0.150.22C/10yr for the historical experiment, 0.08C0.14C/10yr for SSP1-2.6, 0.240.35C/10yr for SSP2-4.5, 0.430.63C/10yr for SSP3-7.0, and 0.520.76C/10yr for SSP5-8.5 in the Qilian Mountain. These results indicate that the accuracy of the downscaled temperatures is improved compared to the original data. However, we also find that, compared with the downscaled data, the original projections have been overestimated.
