Accurate surface air temperature (T_2m) data are key to investigating eco-hydrological responses to global warming. Because of sparse in-situ observations, T_2m datasets from atmospheric reanalysis or multi-source observation-based land data assimilation system (LDAS) are widely used in research over alpine regions such as the Tibetan Plateau (TP). It has been found that the warming rate of T_2m over the TP accelerates during the global warming slowdown period of 1998–2013, which raises the question of whether the reanalysis or LDAS datasets can capture the warming feature. By evaluating two global LDASs, five global atmospheric reanalysis datasets, and a high-resolution dynamical downscaling simulation driven by one of the global reanalysis, we demonstrate that the LDASs and reanalysis datasets underestimate the warming trend over the TP by 27%–86% during 1998–2013. This is mainly caused by the underestimations of the increasing trends of surface downward radiation and nighttime total cloud amount over the southern and northern TP, respectively. Although GLDAS2.0, ERA5, and MERRA2 reduce biases of T_2m simulation from their previous versions by 12%-94%, they do not show significant improvements in capturing the warming trend. The WRF dynamical downscaling dataset driven by ERA-Interim shows a great improvement, as it corrects the cooling trend in ERA-Interim to an observation-like warming trend over the southern TP. Our results indicate that more efforts are needed to reasonably simulate the warming features over the TP during the global warming slowdown period, and the WRF dynamical downscaling dataset provides more accurate T_2m estimations than its driven global reanalysis dataset ERA-Interim for producing LDAS products over the TP.

中文翻译：

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全球土地数据同化系统和大气再分析低估了1998-2013年青藏高原的变暖趋势

准确的地表气温（T _2m）数据是调查全球变暖的生态水文响应的关键。由于稀疏的原地观测，来自大气再分析或基于多源观测的土地数据同化系统（LDAS）的T _2m数据集被广泛用于诸如青藏高原（TP）的高山地区的研究。已经发现，升温速度为T _2m在1998-2013年全球变暖放缓期间，TP的速度加快，这引发了一个问题，即重新分析或LDAS数据集是否可以捕获变暖特征。通过评估两个全球LDAS，五个全球大气再分析数据集以及由其中一个全球再分析驱动的高分辨率动态降尺度模拟，我们证明了LDAS和再分析数据集在TP期间低估了TP的变暖趋势27％–86％。 1998–2013。这主要是由于分别低估了南部和北部TP的地面向下辐射和夜间总云量的增加趋势。尽管GLDAS2.0，ERA5和MERRA2减少了T _2m的偏差从以前的版本进行了12％-94％的模拟，它们在捕获变暖趋势方面并未显示出明显的改进。由ERA-Interim驱动的WRF动态降尺度数据集显示出很大的改进，因为它将ERA-Interim的降温趋势校正为南部TP的类似观测的变暖趋势。我们的结果表明，在全球变暖放缓期间，需要付出更多的努力来合理模拟TP上的变暖特征，并且WRF动态降尺度数据集提供了比其驱动的全球再分析数据集ERA-Interim更准确的T _2m估计值，以便在TP。