Dec.  2017

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Understanding the Surface Temperature Cold Bias in CMIP5 AGCMs over the Tibetan Plateau

• The temperature biases of 28 CMIP5 AGCMs are evaluated over the Tibetan Plateau (TP) for the period 1979-2005. The results demonstrate that the majority of CMIP5 models underestimate annual and seasonal mean surface 2-m air temperatures (T as) over the TP. In addition, the ensemble of the 28 AGCMs and half of the individual models underestimate annual mean skin temperatures (T s) over the TP. The cold biases are larger in T as than in T s, and are larger over the western TP. By decomposing the T s bias using the surface energy budget equation, we investigate the contributions to the cold surface temperature bias on the TP from various factors, including the surface albedo-induced bias, surface cloud radiative forcing, clear-sky shortwave radiation, clear-sky downward longwave radiation, surface sensible heat flux, latent heat flux, and heat storage. The results show a suite of physically interlinked processes contributing to the cold surface temperature bias. Strong negative surface albedo-induced bias associated with excessive snow cover and the surface heat fluxes are highly anti-correlated, and the cancelling out of these two terms leads to a relatively weak contribution to the cold bias. Smaller surface turbulent fluxes lead to colder lower-tropospheric temperature and lower water vapor content, which in turn cause negative clear-sky downward longwave radiation and cold bias. The results suggest that improvements in the parameterization of the area of snow cover, as well as the boundary layer, and hence surface turbulent fluxes, may help to reduce the cold bias over the TP in the models.
摘要: 在28个CMIP5-AMIP试验的模式中, 大多数模式对高原气温的模拟存在着冷偏差, 多模式集合和半数以上的模式模拟的地表温度偏低, 主要特征为气温的冷偏差强于地表温度, 高原西部强于高原东部. 通过地表能量平衡分解法, 将地表温度的冷偏差定量分解为反照率反馈项、云辐射强迫项、晴空短波辐射项、晴空向下长波辐射项、感潜热项和地表热通量项. 结果表明, 这些分解项对冷偏差的贡献存在物理上相联系的过程, 积雪覆盖面积偏大引发的反照率反馈作用和晴空向下长波辐射强迫造成了地表温度模拟的冷偏差, 其物理过程是: 低温模式模拟的积雪覆盖面积偏大, 使得地表反照率增大, 地表吸收的短波辐射减少、地表感、潜热通量减少, 使得地表向大气输送的热量和水汽(尽管很小)偏少, 大气温度偏低、水汽含量减少, 导致晴空向下长波辐射减小, 地表温度偏冷. 鉴于积雪覆盖面积在模式模拟中的重要性, 有必要对积雪覆盖面积的参数化方案做出改进, 并提高地表湍流通量, 这可能会有助于减少模式模拟的地表温度冷偏差.

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Manuscript History

Manuscript revised: 02 June 2017
通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Understanding the Surface Temperature Cold Bias in CMIP5 AGCMs over the Tibetan Plateau

• 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
• 2. University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: The temperature biases of 28 CMIP5 AGCMs are evaluated over the Tibetan Plateau (TP) for the period 1979-2005. The results demonstrate that the majority of CMIP5 models underestimate annual and seasonal mean surface 2-m air temperatures (T as) over the TP. In addition, the ensemble of the 28 AGCMs and half of the individual models underestimate annual mean skin temperatures (T s) over the TP. The cold biases are larger in T as than in T s, and are larger over the western TP. By decomposing the T s bias using the surface energy budget equation, we investigate the contributions to the cold surface temperature bias on the TP from various factors, including the surface albedo-induced bias, surface cloud radiative forcing, clear-sky shortwave radiation, clear-sky downward longwave radiation, surface sensible heat flux, latent heat flux, and heat storage. The results show a suite of physically interlinked processes contributing to the cold surface temperature bias. Strong negative surface albedo-induced bias associated with excessive snow cover and the surface heat fluxes are highly anti-correlated, and the cancelling out of these two terms leads to a relatively weak contribution to the cold bias. Smaller surface turbulent fluxes lead to colder lower-tropospheric temperature and lower water vapor content, which in turn cause negative clear-sky downward longwave radiation and cold bias. The results suggest that improvements in the parameterization of the area of snow cover, as well as the boundary layer, and hence surface turbulent fluxes, may help to reduce the cold bias over the TP in the models.

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