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The six temperature regions of Norway with their respective observed mean temperature in 1961–1990 in °C, mean temperature bias (°C) per region, bias of the 0.05 quantile temperature (°C) per region and bias of the 0.95 quantile temperature (°C) per region
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Results from a first-time employment of the WRF regional climate model to climatological simulations in Europe are presented.
The ERA-40 reanalysis (resolution 1°) has been downscaled to a horizontal resolution of 30 and 10km for the period of 1961–1990.
This model setup includes the whole North Atlantic in the 30km domain and spectral nudging is u...
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... to the precipitation regions discussed in Sect. 3.3.3, the Norwegian meteorological office has defined 6 different temperature regions (Hanssen-Bauer and Førland 2000). These regions can be seen in Fig. 9 with their 30-year mean observed temperatures. The west coast is the warmest region (5), followed by the eastern part of the country (6). Average temperatures drop the further north the regions are located, with the region 3 in the northern inland as the coldest. The regional biases are calculated the same way as in the case of ...
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... Average temperatures drop the further north the regions are located, with the region 3 in the northern inland as the coldest. The regional biases are calculated the same way as in the case of precipitation. The WRF simu- lations are outperforming the ENSEMBLES models when looking at the regional mean temperature bias (upper right panel of the Fig. 9). The WRF simulations reproduce the regional differences quite well with best agreement in the regions 4 and 6 and the weakest agreement in the regions 1 and 2. The 30-km WRF simulation performs better than the 10-km simulation in all regions with an average difference of almost 0.5°C. The ENSEMBLES mean is underestimat- ing the ...
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... but are generally colder. The WRF model, instead, seems to be more independent of the driving data and is able to keep the bias low in all regions. This could be due to the larger domain size used in the WRF simulation indicating that it is an asset and also the spectral nudging procedure used above the boundary layer. The lower two panels of Fig. 9 show the 0.05 and 0.95 quantile temperatures describing the extremely low and extremely high temperatures. Both WRF simulations are in good agreement with the observed extreme temperatures and outperform the ENSEMBLES mean or the ERA-40 reanalysis. There are no large differences between the regions in the extremely high temperatures ...
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... There are several reasons to believe that the higher probability of daily rainfall exceeding 30.0 mm d −1 simulated by WRF is realistic. First, being a high-resolution model, WRF can better resolve the mesoscale topography and convective rainfall process, which coarse-resolution models tend to smooth out (Heikkilä et al., 2011;Yu et al., 2015). Second, the interpolation of station data to form the observational gridded dataset causes underestimation in the frequency of extremes compared to the original station gauges. ...
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Deserts cover a vast area of the world’s land surface, but the study of desert climate has been impeded by the lack of ground meteorological observations. In recent years, the climate reanalysis products provide an important data source to investigate climate change in observation-limited areas. However, their accuracy in desert regions has been poorly investigated. Here, we evaluated the performance of the latest ERA5 reanalysis datasets for the climatic conditions over the deserts of northern China (DNC), including temperature and precipitation variations, climate extremes, and detection skills of daily precipitation. The results show that ERA5 well captures the observed pattern of annual and seasonal temperatures, as well as the warming trend during the past four decades in DNC, compared to the ground observations. However, both annual and seasonal precipitations are greatly overestimated over DNC, and large uncertainties exist in precipitation trends. In addition, the variability of interannual precipitation, precipitation intensity, maximum 1-day precipitation, and the number of continuous dry days are underestimated by ERA5. The bias of precipitation estimates may be traced to the overestimation of rainfall occurrence in ERA5. Comparing to other reanalysis (such as MERRA2, NCEP/NCAR, and NOAA-20C), the ERA5 outperforms them for precipitation and precipitation extremes over DNC, although its performance is not as good as gridded gauge-based precipitation datasets (such as CPC). Overall, this study provides references for the use of ERA5 over DNC and facilitates the applications of ERA5 over other observation-limited desert regions.
