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Homogenization of surface temperature data in High Mountain Asia through comparison of reanalysis data and station observations

机译:通过比较分析数据和台站观测资料比较亚洲高山地区地表温度数据的均质化

摘要

High-quality temperature estimates with good spatio-temporal coverage are necessary for completely understanding the influences of warming climate on cryosphere and hydrological systems in High Mountain Asia (HMA). In this study, we compare reanalysis temperature data from ERA-Interim and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) with station observations in HMA during 1979-2011. The results indicate that although reanalysis temperature data can capture the warming trends in HMA, the trend magnitudes are clearly underestimated by reanalysis data. In particular, the increase in summer temperature cannot be identified from the NCEP/NCAR reanalysis. For ERA-Interim, temperature increases are underestimated in the north and northwest of HMA; for NCEP/NCAR, the warming magnitudes show evident biases in the Pamir, Himalayas, and southeastern Tibetan Plateau. Considering that high-frequency signals and periodical fluctuations among the three datasets are in good agreement, and based on the wavelet transform method, the low-frequency component decomposed from the temperature time series of ERA-Interim and NCEP/NCAR reanalyses is adjusted by that derived from station observations. The resulting homogenized reanalysis temperature data show much better spatio-temporal consistency with station data. The differences in monthly and annual temperature anomalies between station and homogenized ERA-Interim and NCEP/NCAR reanalysis data become more convergent. The homogenized temperature time series are better correlated with station data at annual and seasonal timescales.
机译:要完全了解变暖的气候对亚洲高山(HMA)冰冻圈和水文系统的影响,需要具有良好时空覆盖范围的高质量温度估算。在这项研究中,我们将ERA-临时组织和国家环境预测中心/国家大气研究中心(NCEP / NCAR)的再分析温度数据与1979-2011年HMA的站点观测进行了比较。结果表明,尽管重新分析温度数据可以捕获HMA的变暖趋势,但重新分析数据明显低估了趋势幅度。特别是,不能从NCEP / NCAR重新分析中识别出夏季气温的升高。对于ERA-Interim,HMA北部和西北部的温度升高被低估了。对于NCEP / NCAR,在帕米尔,喜马拉雅山和青藏高原东南部,变暖幅度显示出明显的偏差。考虑到三个数据集之间的高频信号和周期性波动是很好的一致性,并且基于小波变换方法,通过调整ERA-Interim和NCEP / NCAR的温度时间序列分解得到的低频分量可以得到调整。从台站观测中得出。所得的均质化再分析温度数据显示与站台数据具有更好的时空一致性。站点和均匀化的ERA-Interim以及NCEP / NCAR再分析数据之间月度和年度温度异常的差异变得越来越收敛。均质温度时间序列在年度和季节时间尺度上与台站数据更好地相关。

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