The Representation of Soil Moisture-Atmosphere Feedbacks across the Tibetan Plateau in CMIP6
The Representation of Soil Moisture-Atmosphere Feedbacks across the Tibetan Plateau in CMIP6
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DOI：
10.1007/s00376-023-2296-2
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发表时间：
2023-08
影响因子：
5.8
通讯作者：
J. Talib;O. Müller;E. J. Barton;C. Taylor;P. Vidale
中科院分区：
文献类型：
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作者：
J. Talib;O. Müller;E. J. Barton;C. Taylor;P. Vidale
青藏高原的热力过程在区域和全球尺度上影响着大气条件。鉴于此，之前的工作表明，土壤水分驱动的表面通量变化会反馈到大气中。虽然土壤水分是大气可预测性的一个来源，但没有研究评估土壤水分-大气耦合对大气环流模式（GCMs）中TP的影响。在这项研究中，我们使用几种分析技术来评估土壤水分-大气耦合CMIP 6模拟，包括：瞬时耦合指数;分析通量和大气行为在干旱期间;和量化的偏好对流干燥的土壤。通过这些指标，我们分区反馈到他们的大气和陆地components.Consistent与以前的全球研究，我们得出的结论是实质性的模型间差异的土壤水分-大气耦合的表示，大多数模型低估了这样的反馈。集中在干旱分析，大多数模型低估了增加感热在降雨不足的时期。例如，与观测值相比，模式平均偏差在异常感热通量上小了10 W m−2（约25%）。干旱期感热通量不足导致大气响应较弱。我们还发现，大多数GCM未能捕捉到土壤水分和深对流之间的负反馈。CMIP 6实验中反馈的模拟效果不佳表明，预测模型也很难利用土壤水分驱动的可预测性。为了提高陆地-大气反馈的代表性，不仅需要大气建模，而且还需要地面过程的发展，因为我们发现降雨偏差和耦合指数之间的关系很弱。
Thermal processes on the Tibetan Plateau (TP) influence atmospheric conditions on regional and global scales. Given this, previous work has shown that soil moisture–driven surface flux variations feed back onto the atmosphere. Whilst soil moisture is a source of atmospheric predictability, no study has evaluated soil moisture–atmosphere coupling on the TP in general circulation models (GCMs). In this study, we use several analysis techniques to assess soil moisture-atmosphere coupling in CMIP6 simulations including: instantaneous coupling indices; analysis of flux and atmospheric behaviour during dry spells; and a quantification of the preference for convection over drier soils. Through these metrics we partition feedbacks into their atmospheric and terrestrial components.Consistent with previous global studies, we conclude substantial inter-model differences in the representation of soil moisture–atmosphere coupling, and that most models underestimate such feedbacks. Focusing on dry spell analysis, most models underestimate increased sensible heat during periods of rainfall deficiency. For example, the model-mean bias in anomalous sensible heat flux is 10 W m−2(≈25%) smaller compared to observations. Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response. We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection. The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture–driven predictability. To improve the representation of land–atmosphere feedbacks requires developments in not only atmospheric modelling, but also surface processes, as we find weak relationships between rainfall biases and coupling indexes.