Global patterns of directed soil moisture influence on air temperature

Guo, Xiaohang; Han, Jichong; Shao, Huaiyong; Zhu, Xinyan; Sun, Xiaofei; Ling, Long; Chen, Zimeng; Liu, Gang

doi:10.5194/egusphere-2026-2866

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https://doi.org/10.5194/egusphere-2026-2866

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15 Jun 2026

| 15 Jun 2026

Status: this preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).

Global patterns of directed soil moisture influence on air temperature

Xiaohang Guo, Jichong Han, Huaiyong Shao, Xinyan Zhu, Xiaofei Sun, Long Ling, Zimeng Chen, and Gang Liu

Abstract. Soil moisture regulation of near-surface air temperature is essential for improving global climate prediction skill. Yet conventional statistical methods are often insufficient to establish causality, and the dominant pathways of this coupling remain poorly understood at the global scale. Here we combine multiple datasets with the information flow method to quantify the effects of soil moisture on temperature. We find that significant soil moisture–temperature causality exists over about 37 % of the global land area. This causality is dominated by the evaporative and sensible heat pathways, affecting 33.5 % and 18.5 % of land area, respectively, while both pathways co-occur over an additional 7.6 % of land area. In addition, these strong causal relationships are mainly driven by soil moisture variability, while most CMIP6 models tend to overestimate the likelihood of such coupling. Our study identifies the main characteristics of land–atmosphere interactions from a causal perspective and provides an important scientific basis for improving land-surface processes in future climate models.

Received: 18 May 2026 – Discussion started: 15 Jun 2026

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Xiaohang Guo, Jichong Han, Huaiyong Shao, Xinyan Zhu, Xiaofei Sun, Long Ling, Zimeng Chen, and Gang Liu

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RC1:
'Comment on egusphere-2026-2866', Anonymous Referee #1, 21 Jul 2026 reply
General Comments:
The manuscript addresses an interesting topic by applying the Liang–Kleeman information flow framework to investigate soil moisture–temperature coupling at the global scale. The analysis is comprehensive, examining the problem from different angles, including coupling pathways, seasonality, reanalysis vs GCMs, among others. However, several aspects of the manuscript organization and interpretation should be improved before publication.
The manuscript would benefit from a clearer discussion of its scientific contribution relative to the existing literature. Several findings, such as, the stronger coupling in regions with high soil moisture variability, or the persistence of coupling in deeper soil layers, are generally expected and consistent with well-established knowledge. The authors should better position their results with respect to previous studies (e.g., compare the identified hotspots with previous work) and explicitly state the added value of applying the Liang–Kleeman framework over existing approaches.

The organization of Sections 3–5 is somewhat inadequate. Subsections 4.1 and 4.2 primarily present results and would fit better within Section 3 (Results) rather than in Section 4 (Discussion). The actual discussion starts in Sections 4.3 and 4.4, which could either remain as the Discussion section or be merged with Section 5 into a combined Discussion and Conclusions. Moreover, the current Conclusion section is rather limited and does not address the gaps identified in the introduction (e.g. L38-41).

Specific Comments:
Section 2.1: Please report the spatial resolution of ERA5.

Please include Table S1 (list of GCMs) in the main manuscript. It would also be useful to report the native resolution of each GCM and the soil depth represented by “mrsos”.

L72: Please clarify what is meant by “model structural diversity”.

The manuscript should explain how the different soil depths among ERA5, GLEAM, and the CMIP models are handled to perform a fair comparison.

L93–101: The description of the Liang–Kleeman information flow framework should be revised for clarity. There are several grammatical and notation inconsistencies (e.g., incorrect capitalization after equations and identation). In addition, using (T) for both temperature and the information flow (Eq. 1) is potentially confusing and should be clarified or replaced with less ambiguous notation. Lastly, it would be helpful to briefly explain the physical interpretation of each term of Eq. 1 and the possible range of the index.

L165 and L259: Although significant coupling persists in the second soil layer, Fig. S1 shows a marked reduction in both spatial extent and strength with increasing depth. The discussion should better reflect this overall weakening rather than support that “deeper soil moisture layers continue to exert significant control on temperature…”.

L182: The discussion highlights Australia as presenting a strong SSM->SH relationship, whereas Fig. 2b shows relatively weak coupling there. Other regions seem to exhibit much stronger signals.

Fig. 2: Please clarify whether the analysis corresponds to the top soil layer.

The spatial patterns in Figs. 1 and 2 are difficult to reconcile. Figure 1 indicates that significant SSM→T coupling is confined to relatively limited regions, whereas the individual pathways in Fig. 2 appear significant over much larger areas. In particular, the terrestrial legs (SSM→ET and SSM→SH) are broadly consistent with Fig. 1, whereas the atmospheric legs (ET→T and SH→T) extend across most continents. Since Fig. 2 is intended to explain the mechanisms behind Fig. 1, I would expect greater spatial consistency. Would masking the atmospheric maps to regions with significant terrestrial coupling, or using a different color scale or class intervals, improve the interpretation?

Fig. 3: Panel (c) This is an interesting figure, but the probability values are difficult to distinguish. The meaning of the circle sizes should be explained in the caption, and a discrete color scale (e.g., 5% intervals) would facilitate interpretation. Similar suggestions apply to Figs. S6–S7 and Fig. 7. Panel (d): Why are the soil moisture variability percentile bins defined using unequal intervals?

L216: The relationship between soil moisture variability and coupling strength is largely expected, since temporal variability is a prerequisite for soil moisture to influence temperature. This result therefore seems to reinforce the existing understanding rather than represent a novel finding and should be discussed in the context of previous literature. In addition, the manuscript repeatedly refers to land-atmosphere “feedbacks”, although only the directed influence of soil moisture on temperature is evaluated; but the reciprocal atmospheric influence required to characterize a feedback is not analyzed.

Fig. 4: It would be helpful to include the global ERA5-Land map as a visual reference.

L268–277: I disagree with the interpretation because the discussion overlooks the opposite seasons of the two hemispheres. The results indicate stronger coupling during the warm season of each hemisphere, rather than during the boreal warm season alone. A version of Fig. 5 separated by hemisphere could help illustrate this point.

Section 5 concludes that deeper soil moisture exerts more persistent influence on temperature. Given that deeper layers generally exhibit longer memory, this conclusion appears expected. The discussion should better distinguish between expected behavior and genuinely new insights.

Technical Comments:
L164: I believe the correct reference is Fig. 1b rather than Fig. 1a.

Fig. 1: Please add latitude–longitude gridlines in the map to facilitate the link with panel (a).

Supplementary figures are not presented in the order in which they are first cited in the manuscript. For example, Fig. S10 is discussed before Figs. S2 and S3.

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Citation: https://doi.org/10.5194/egusphere-2026-2866-RC1

Xiaohang Guo, Jichong Han, Huaiyong Shao, Xinyan Zhu, Xiaofei Sun, Long Ling, Zimeng Chen, and Gang Liu

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https://doi.org/10.5194/egusphere-2026-2866-supplement

Xiaohang Guo, Jichong Han, Huaiyong Shao, Xinyan Zhu, Xiaofei Sun, Long Ling, Zimeng Chen, and Gang Liu

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Short summary

We quantify how soil moisture influences near-surface air temperature using a causal information-flow method. Significant soil moisture–temperature causality appears across about 37 % of global land, mainly through evapotranspiration and sensible heat pathways. The coupling is driven more by soil moisture variability than by mean soil moisture, while CMIP6 models tend to overestimate its likelihood and spatial extent.


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