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The representation of soil moisture-atmosphere feedbacks across the Tibetan Plateau in CMIP6

Talib, J., Muller, O. V., Barton, E. J., Taylor, C. M. and Vidale, P. L. ORCID: (2023) The representation of soil moisture-atmosphere feedbacks across the Tibetan Plateau in CMIP6. Advances in Atmospheric Sciences, 40 (11). pp. 2063-2081. ISSN 1861-9533

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To link to this item DOI: 10.1007/s00376-023-2296-2


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:116386


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