Elevation correction of ERA-Interim temperature data in the Tibetan Plateau
Elevation correction of ERA-Interim temperature data in the Tibetan Plateau
复制标题
青藏高原ERA-Interim气温资料的高程改正
DOI：
10.1002/joc.4935
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Gao Lu
中科院分区：
文献类型：
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作者：
Gao Lu
近地面气温是气候变化的主要指标。由于青藏高原气象网稀疏，再分析作为大尺度观测的替代手段被广泛使用。然而，在青藏高原地区，ERA-Interim月2-m温度与观测值之间的平均偏差为−3.54 °C，均方根误差（RMSE）为4.31 °C，这表明ERA-Interim在局部尺度应用之前有必要进行校正。为了克服这一挑战，开发了一种强大的高程校正方法，以根据ERA-Interim内部垂直直减率缩小ERA-Interim 2° × 2°每月2 m温度数据。该方法针对1979年至2013年位于26个ERA-Interim网格单元中的80个气象站进行了验证。它还与其他四种校正方法进行了比较，这些方法使用不同的直减率方案，例如固定的月直减率，从气象站计算的地面直减率（在单个网格内或与相邻站点），以及ERA-Interim压力水平数据的三阶曲线函数。结果表明，利用ERA-Interim内部垂直直减率进行订正不仅可以显著降低ERA-Interim原始资料的偏差（89%）和RMSE（62%），而且可以很好地捕捉高原气候的年际变化。与其他四种方法相比，模拟的季节和年气温变暖趋势也很好。这种方法最大的优点是它不依赖于当地的气象站。因此，有可能外推ERA-中期温度数据的任何其他高山地区，没有测量存在。这项工作将有助于科学界确定最适当和最简单的方法，以缩小再分析温度数据的规模，用于现场或区域尺度的气候影响评估。
The near surface air temperature is the primary indicator for climate change. Reanalysis as the surrogates for large‐scale observations are widely used in the Tibetan Plateau because of the sparse meteorological network. However, an average bias of −3.54 °C and root‐mean‐square error (RMSE) of 4.31 °C were found between ERA‐Interim monthly 2‐m temperature and observation over the Tibetan Plateau, which indicated that a correction procedure for ERA‐Interim is necessary before local scale applications. To overcome this challenge, a robust elevation correction method is developed to downscale ERA‐Interim 2° × 2° monthly 2‐m temperature data based on ERA‐Interim internal vertical lapse rates. This method is validated against 80 meteorological stations from 1979 to 2013 located in 26 ERA‐Interim grid cells. It is also compared with other four correction methods, which are using different lapse rate schemes such as fixed monthly lapse rates, surface lapse rates calculated from the meteorological stations (within a single grid or with neighbouring sites), as well as a third‐order curvilinear function of ERA‐Interim pressure level data. The results indicate that the correction method using ERA‐Interim internal vertical lapse rates cannot only significantly reduce the bias (89%) and RMSE (62%) for the original ERA‐Interim data, but also capture the inter‐annual variations for the plateau‐wide climatology very well. The seasonal and annual temperature warming trends are also modelled encouragingly compared with other four methods. The strongest advantage of this method is that it is independent of local meteorological stations. Therefore, it is possible to extrapolate ERA‐Interim temperature data for any other high mountain areas where no measurements exist. This work will help the scientific community identify the most proper and easiest method to downscale reanalysis temperature data for climate impact assessments at the site or regional scale.