3D-var assimilation of GTS observation with the gravity wave drag scheme improves summer high resolution climate simulation over the Tibetan Plateau
3D-var assimilation of GTS observation with the gravity wave drag scheme improves summer high resolution climate simulation over the Tibetan Plateau
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GTS观测与重力波拖曳方案的3D-var同化改进了青藏高原夏季高分辨率气候模拟
DOI：
10.1007/s00382-021-05720-0
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发表时间：
2021-03
期刊：
影响因子：
4.6
通讯作者：
Mai Xiaoping
中科院分区：
文献类型：
--
作者：
Xie Qian;Yang Yi;Qiu Xiaobin;Ma Yuanyuan;Lai Anwei;Lin Erliang;Mai Xiaoping
青藏高原是世界上地形最复杂的地区之一，由于缺乏定量观测资料，各种气候模式对青藏高原的模拟存在着不同的偏差。本文利用三维变分资料同化（3D-Var）方法，对新的重力波拖曳（GWD）方案和全球电信系统（GTS）观测资料的循环同化进行了一个夏季一个月的6 km动力降尺度模拟。更新后的GWD方案提供了更好的模拟结果，地面和垂直风，温度和湿度。GWD方案对GTS观测资料的3D-Var循环同化进一步改善了高层大气风场的预报，但增强了TP上空的温度冷偏差，后者可能与同化后地表感热和潜热通量减少有关。值得注意的是，它在日降水量的空间分布和时间变化方面表现得更好，从而有效地减少了降水的湿偏差，特别是对东部TP。这得益于资料同化对不同降水类别的模拟更加准确，特别是对小雨（1-10 mm/天）的模拟。降水偏少的机制是GTS资料同化后，低纬海洋季风气流水汽输送减弱，青藏高压减弱，环流场更加精确，垂直上升速度减小。该研究为建立更高时空分辨率的TP降尺度数据集提供了指导。
The Tibetan Plateau (TP) is one of the most complicated orographic regions worldwide, and due to the lack of quantitative observations, the different simulation biases are still existing via various climate models over the TP. In this study, a one-summer-month 6-km dynamical downscaling simulation is conducted to evaluate the improvement of the new gravity wave drag (GWD) scheme and cycled assimilation of observations from the Global Telecommunications System (GTS) by the three-dimensional variational data assimilation (3D-Var) method. The updated GWD scheme provides better simulation results for surface and vertical winds, temperature, and humidity. 3D-Var cycled assimilation of GTS observations with the GWD scheme further improves the wind forecasting at the upper atmosphere levels but enhances temperature cold bias over the TP, and the latter may be partly related to less surface sensible and latent heat flux after assimilation. Notably, it obviously performs better at the spatial distribution and temporal variation of daily precipitation, thus effectively reduces the precipitation wet bias, especially for the eastern TP. This benefits from the more accurate simulation of different precipitation categories by data assimilation, especially for the light rain (1–10 mm/day). The mechanism of less precipitation wet bias is that assimilation of GTS observation results in weaker monsoon flow water vapor transport from low-latitude oceans, weaker Tibetan High with more accurate circulation fields and less upward vertical velocity. This research may provide guidance for establishing a downscaling dataset of higher spatiotemporal resolution for the TP.
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