Reanalysis temperature products are important datasets for temperature estimates over high-elevation areas with few meteorological stations. In this study, surface 2 m air temperature data from 17 meteorological stations from 1979 to 2017 in the Qilian Mountains (QLM) are used for comparison with the newest reanalysis product: ERA5-Land derived from the European Centre for Medium-Range Weather Forecasts (ECMWF). In general, the ERA5-Land temperature product can reproduce the observation variation at different time scales very well. A high monthly correlation coefficient that ranges from 0.978 to 0.998 suggests that ERA5-Land reanalysis temperature could capture the observations very well. However, attention should be paid before using ERA5-Land at individual sites because of the average root-mean-square-error (RMSE) of 2.2°C of all stations. The biases between ERA5-Land temperature and observations are mainly caused by the elevation differences between ERA5-Land grid points and meteorological sites. The annual mean temperature shows a significant warming trend (0.488°C/decade) from 1979 to 2017 based on the observations. ERA5-Land reanalysis temperature captures the increasing trend very well (0.379°C/decade). The biggest positive warming trends of observations and ERA5-Land are both found in summer with values of 0.574°C/decade and 0.496°C/decade, respectively. We suggest that ERA5-Land generally reproduces the temperature trend very well for observations and is reliable for scientific research over the QLM.

An increase in extreme temperature events could have a significant impact on terrestrial ecosystems. Reanalysis temperature data are an important data set for extreme temperature estimation in mountainous areas with few meteorological stations. The ability of ERA5-Land reanalysis data to capture the extreme temperature index published by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated by using the observational data from 17 meteorological stations in the Qilian Mountains (QLM) during 1979–2017. The results show that the ERA5-Land reanalysis temperature data can capture well for the daily maximum temperature, two warm extremes (TXx and TX90p) and one cold extreme (FD0) in the QLM. ERA5-Land’s ability to capture temperature extremes is best in summer and worst in spring and winter. In addition, ERA5-Land can capture trends in all extreme temperature indices except the daily temperature range (DTR). The main bias of ERA5-Land is due to the difference in elevation between the ground observation station and the ERA5-Land grid point. The simulation accuracy of ERA5-Land increases with the decrease of elevation difference. The results can provide a reference for the study of local extreme temperature by using reanalysis data.

The applicability of reanalysis data has been widely addressed in climate and hydrology studies over the past two decades. In this study, we analyzed spatiotemporal variations in ERA-Interim temperature data from four climate zones within Gansu Province from 1979 to 2017 by using linear regression model and Mann-Kendall mutation test. Results showed that: (1) The highest temperature was found in the subtropical monsoon climate zone, and the lowest in the plateau mountain climate zone. Temperatures in high-elevation regions were lower than those in low-elevation regions; (2) The annual mean temperature increased across Gansu Province from 1979 to 2017. The lowest warming rates of annual mean, annual maximum, and annual minimum temperatures were found in the subtropical monsoon climate zone, and these were 0.334, 0.300, and 0.336 °C/10a, respectively. The highest warming rates of annual mean and annual minimum temperature were found in the temperate monsoon climate zone, and these were 0.420 and 0.464 °C/10a, respectively. The highest warming rate of annual maximum temperature was found in the temperate continental climate zone (0.471 °C/10a); (3) The Mann-Kendall analysis showed that the mutation times of annual mean temperature of the subtropical monsoon, temperate monsoon, and temperate continental climate zones in Gansu Province were all in 1997. The mutation times of annual maximum temperature were found in the subtropical monsoon climate zone (1997) and temperate monsoon climate zone (1997). The mutation times of annual minimum temperature were found in the temperate continental climate zone (1997) and plateau mountain climate zone (1994). ERA-Interim reanalysis data are reliable for capturing mutation time of temperature, especially in the high-elevation areas with rare meteorological station. This study can provide a reference when analyzing climate change at different climatic zones using reanalysis data.

In this study, 2 m air temperature data from 24 meteorological stations in the Qilian Mountains (QLM) are examined to evaluate ERA-Interim reanalysis temperature data derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the period of 1979–2017. ERA-Interim generally captures the monthly, seasonal, and annual variation very well. High daily correlations ranging from 0.956 to 0.996 indicate that ERA-Interim captures the daily temperature observations very well. However, an average root-mean-square error (RMSE) of ±2.7°C of all stations reveals that ERA-Interim should not be directly applied at individual sites. The biases are mainly attributed to the altitude differences between ERA-Interim grid points and stations. The positive trend (0.457°C/decade) is significant over the Qilian Mountains based on the 1979–2017 observations. ERA-Interim captures the warming trend very well with an increase rate of 0.384°C/decade. The observations and ERA-Interim both show the largest positive trends in summer with the values of 0.552°C/decade and 0.481°C/decade, respectively. We conclude that in general ERA-Interim captures the trend very well for observed 2 m air temperatures and ERA-Interim is generally reliable for climate change research over the Qilian Mountains.

Air temperature is the primary indicator of climate change. Reanalysis temperature products are important datasets for temperature estimates over high-elevation areas with few meteorological stations. However, they contain biases in observations, so a bias correction is required to enhance the accuracy of modeling predictions. In this study, we used the temperature lapse-rate method to correct ERA-Interim reanalysis-temperature data in the Qilian Mountains of China from 1979 to 2017. These temperature lapse rates were based on observations (Γ_Obs) and on model internal vertical lapse rates derived from different ERA-Interim pressure levels (Γ_ERA). The results showed that the temperature lapse rates in warm periods were larger than those in cold periods. Both the original and corrected ERA-Interim temperature can significantly capture the warming trend exhibited by observations. In general, the temperature lapse rate method was reliable for correcting ERA-interim reanalysis-temperature data. Although Γ_Obs performed best in bias correction, it depends heavily on the density of ground observation stations and is not appropriate for remote areas with a low data coverage. Correction methods based on Γ_ERA were shown to be reliable for bias correction, and will be especially applicable to mountainous areas with few observation stations. Our results contribute to the improvement of quality of data products and enhance the accuracy of modeling of climate change effects and risks to the environment and human health.

Warming in mountainous regions has experienced obvious elevation dependence (the warming rate of air temperature is amplified with elevation), which accelerates the melting of ice and snow, affecting glacier size and mass, and water resources in mountainous regions. Here, we used ERA5-Land monthly averaged datasets from 1979 to 2019 to analyze the elevation-dependent warming (EDW) variability, driving factor, and its implications to water resources in the Qilian Mountains (QLM). Results showed that: (1) Annual mean temperature shows a significant increasing trend in the QLM from 1979 to 2019 (0.31 °C/10a; p < 0.01), and the warming rate of annual mean temperature increased with elevation in the QLM in general. For seasonal scales, the most obvious EDW was found in summer. In the past 41 years, snow cover and snow depth showed a slight decreasing trend. There was a significant negative correlation between temperature and snow cover, which can explain that enhanced regional warming has caused decreases in snow cover and snow depth, largely controlling the pattern of EDW on the QLM. EDW has significant implications for water resources over QLM, especially above 5,000 m. Our study can provide a reference in understanding the importance of EDW to water resources in mountainous areas.