Depth-dependent heat amplification across soil and atmosphere driven by soil moisture-temperature hypersensitive regime

Xihui Gu; Sixiang Wang; Yansong Guan; Xiang Zhang; Liangwei Li; Sijia Luo; Ruijie Wang; Jianfeng Li; Xinfeng Wu; Chuyu Luo; Dongdong Kong; Lunche Wang

doi:10.59717/j.xinn-geo.2026.100250

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Depth-dependent heat amplification across soil and atmosphere driven by soil moisture-temperature hypersensitive regime

1.
Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and information engineering, China University of Geosciences, Wuhan 430074, China
2.
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830000, China
3.
Shenzhen Research Institute, China University of Geosciences, Shenzhen 518063, China
4.
Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
5.
National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
6.
Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650000, China

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Corresponding author: guxh@cug.edu.cn
Received Date: November 24, 2025

Accepted Date: July 27, 2026

Published Online: July 29, 2026

The Innovation Geoscience https://doi.org/10.59717/j.xinn-geo.2026.100250 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Depth-dependent soil heat amplification outpace than near-surface air down to ~40 cm depth.
  
  Stronger soil heat amplification is driven by hypersensitive soil moisture-temperature coupling regime.
  
  Both soil heat amplification and its depth dependence are projected to persist in the future.

ABSTRACT

ABSTRACT

Record-breaking heat extremes are increasingly amplified by soil-atmosphere interactions, threatening ecosystems and agriculture. Yet the vertical heterogeneity of this amplification across air and multiple soil layers and potential mechanisms remain unclear. Here, we integrate multi-source datasets to quantify depth dependence of heat-extreme amplification (i.e., upper-bound rise and upper-tail widening) in soil and atmosphere worldwide over the past four decades. For annual upper-bound rise of air and soil temperatures, we find that soil heat extremes outpace near-surface air across western North America, South America, central Africa, Europe, northeastern Asia, and southern Australia, with this soil-over-air “outpacing” obviously detectable to ~40 cm soil depth. These hotspots also exhibit pronounced, depth-dependent upper-tail widening of soil temperature distributions, which diminishes with depth and becomes weaker than the air counterpart below ~40 cm. Amplification in soil exceeds that in atmosphere because long-term soil drying frequently triggers an unfamiliar hypersensitive soil moisture-temperature coupling regime: once soil moisture drops below a critical threshold, soil temperature responds to drying more rapidly and dramatically than air. Persistent drying also raises soil temperature variability through this hypersensitivity, thereby widening the upper tail of soil temperature distributions. Under the business-as-usual scenario, both soil heat amplification and its depth dependence are projected to persist. These findings imply ecological risk assessment and management must explicitly consider the depth reached by heat amplification, prioritizing the 0–40 cm biotic layer now and systematically extending to deeper soils.

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REFERENCES

[1]	Thompson V., Kennedy-Asser A.T., Vosper E., et al. (2022). The 2021 western North America heat wave among the most extreme events ever recorded globally. Sci. Adv. 8:eabm6860. DOI:10.1126/sciadv.abm6860 View in Article CrossRef Google Scholar
[2]	Fischer E.M., Sippel S. and Knutti R. (2021). Increasing probability of record-shattering climate extremes. Nat. Clim. Change 11:689−695. DOI:10.1038/s41558-021-01092-9 View in Article CrossRef Google Scholar
[3]	White R.H., Anderson S., Booth J.F., et al. (2023). The unprecedented Pacific Northwest heatwave of June 2021. Nat. Commun. 14:727. DOI:10.1038/s41467-023-36289-3 View in Article CrossRef Google Scholar
[4]	Gloege L., Kornhuber K., Skulovich O., et al. (2022). Land-atmosphere cascade fueled the 2020 Siberian heatwave. AGU Adv. 3:e2021AV000619. DOI:10.1029/2021AV000619 View in Article CrossRef Google Scholar
[5]	Fischer E.M., Seneviratne S.I., Vidale P.L., et al. (2007). Soil moisture-atmosphere interactions during the 2003 European summer heat wave. J. Clim. 20:5081−5099. DOI:10.1175/JCLI4288.1 View in Article CrossRef Google Scholar
[6]	Schumacher D.L., Keune J., Van Heerwaarden C.C., et al. (2019). Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 12:712−717. DOI:10.1038/s41561-019-0431-6 View in Article CrossRef Google Scholar
[7]	Bartusek S., Kornhuber K. and Ting M. (2022). 2021 North American heatwave amplified by climate change-driven nonlinear interactions. Nat. Clim. Change 12:1143−1150. DOI:10.1038/s41558-022-01520-4 View in Article CrossRef Google Scholar
[8]	Zhang Y. and Boos W.R. (2023). An upper bound for extreme temperatures over midlatitude land. Proc. Natl. Acad. Sci. USA 120:e2215278120. DOI:10.1073/pnas.2215278120 View in Article CrossRef Google Scholar
[9]	An N., Chen Y., Liao Z., et al. (2025). Trans-seasonal vegetation-land-atmosphere interactions explained record-breaking cascading extremes in the upper reaches of the Yangtze River. Geophys. Res. Lett. 52:e2024GL114165. DOI:10.1029/2024GL114165 View in Article CrossRef Google Scholar
[10]	Kornhuber K., Bartusek S., Seager R., et al. (2024). Global emergence of regional heatwave hotspots outpaces climate model simulations. Proc. Natl. Acad. Sci. 121:e2411258121. DOI:10.1073/pnas.2411258121 View in Article CrossRef Google Scholar
[11]	Ciais P., Reichstein M., Viovy N., et al. (2005). Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437:529−533. DOI:10.1038/nature03972 View in Article CrossRef Google Scholar
[12]	Schlenker W. and Roberts M.J. (2009). Nonlinear temperature effects indicate severe damages to U. S. crop yields under climate change. Proc. Natl. Acad. Sci. USA 106:15594−15598. DOI:10.1073/pnas.0906865106 View in Article CrossRef Google Scholar
[13]	Abatzoglou J.T. and Williams A.P. (2016). Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. USA 113:11770−11775. DOI:10.1073/pnas.1607171113 View in Article CrossRef Google Scholar
[14]	García-García A., Cuesta-Valero F.J., Miralles D.G., et al. (2023). Soil heat extremes can outpace air temperature extremes. Nat. Clim. Change 13:1237−1241. DOI:10.1038/s41558-023-01812-3 View in Article CrossRef Google Scholar
[15]	Fischer E.M., Beyerle U., Bloin-Wibe L., et al. (2023). Storylines for unprecedented heatwaves based on ensemble boosting. Nat. Commun. 14:4643. DOI:10.1038/s41467-023-40112-4 View in Article CrossRef Google Scholar
[16]	Miralles D.G., Teuling A.J., Van Heerwaarden C.C., et al. (2014). Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7:345−349. DOI:10.1038/ngeo2141 View in Article CrossRef Google Scholar
[17]	Loikith P.C., Neelin J.D., Meyerson J., et al. (2018). Short warm-side temperature distribution tails drive hot spots of warm temperature extreme increases under near-future warming. J. Clim. 31:9469−9487. DOI:10.1175/JCLI-D-17-0878.1 View in Article CrossRef Google Scholar
[18]	McKinnon K.A., Simpson I.R. and Williams A.P. (2024). The pace of change of summertime temperature extremes. Proc. Natl. Acad. Sci. USA 121:e2406143121. DOI:10.1073/pnas.2406143121 View in Article CrossRef Google Scholar
[19]	Wang M., Guo X., Zhang S., et al. (2022). Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate. Nat. Commun. 13:5514. DOI:10.1038/s41467-022-33278-w View in Article CrossRef Google Scholar
[20]	Peng Z., Van Der Heijden M.G.A., Liu Y., et al. (2025). Agricultural subsoil microbiomes and functions exhibit lower resistance to global change than topsoils in Chinese agroecosystems. Nat. Food 6:375−388. DOI:10.1038/s43016-024-01106-7 View in Article CrossRef Google Scholar
[21]	Wu L., Zhang Y., Guo X., et al. (2022). Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat. Microbiol. 7:1054−1062. DOI:10.1038/s41564-022-01147-3 View in Article CrossRef Google Scholar
[22]	Li J., Pei J., Fang C., et al. (2024). Drought may exacerbate dryland soil inorganic carbon loss under warming climate conditions. Nat. Commun. 15:617. DOI:10.1038/s41467-024-44895-y View in Article CrossRef Google Scholar
[23]	Pregitzer K.S., King J.S., Burton A.J., et al. (2000). Responses of tree fine roots to temperature. New Phytol. 147:105−115. DOI:10.1046/j.1469-8137.2000.00689.x View in Article CrossRef Google Scholar
[24]	Lembrechts J.J., Van Den Hoogen J., Aalto J., et al. (2022). Global maps of soil temperature. Glob. Change Biol. 28:3110−3144. DOI:10.1111/gcb.16060 View in Article CrossRef Google Scholar
[25]	Balsamo G., Albergel C., Beljaars A., et al. (2015). ERA-Interim/Land: A global land surface reanalysis data set. Hydrol. Earth Syst. Sci. 19:389−407. DOI:10.5194/hess-19-389-2015 View in Article CrossRef Google Scholar
[26]	Liang X., Jiang L., Pan Y., et al. (2020). A 10-yr global land surface reanalysis interim dataset (CRA-Interim/Land): Implementation and preliminary evaluation. J. Meteorol. Res. 34:101−116. DOI:10.1007/s13351-020-9083-0 View in Article CrossRef Google Scholar
[27]	Tetzlaff A., Bourgeois Q., Stöckli R., et al. (2024). CM SAF land surface temperature dataset from Meteosat first and second generation - edition 2 (SUMET ed. 2). DOI:10.5676/EUM_SAF_CM/LST_METEOSAT/V002 View in Article Google Scholar
[28]	Cornes R.C., Van Der Schrier G., Van Den Besselaar E.J.M., et al. (2018). An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123:9391−9409. DOI:10.1029/2017JD028200 View in Article CrossRef Google Scholar
[29]	Rohde R., Muller R., Jacobsen R., et al. (2013). Berkeley Earth temperature averaging process. Geoinformatics Geostat. Overv. 1:1000103. DOI:10.4172/2327-4581.1000103 View in Article CrossRef Google Scholar
[30]	Wang D., Wang A. and Wang Z. (2023). A multilayer daily high-resolution gridded homogenized soil temperature over continental China. Int. J. Climatol. 43:2015−2030. DOI:10.1002/joc.7959 View in Article CrossRef Google Scholar
[31]	Xu Y., Gao X., Shen Y., et al. (2009). A daily temperature dataset over China and its application in validating a RCM simulation. Adv. Atmos. Sci. 26:763−772. DOI:10.1007/s00376-009-9029-z View in Article CrossRef Google Scholar
[32]	Hersbach H., Bell B., Berrisford P., et al. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146:1999−2049. DOI:10.1002/qj.3803 View in Article CrossRef Google Scholar
[33]	Gelaro R., McCarty W., Suárez M.J., et al. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30:5419−5454. DOI:10.1175/JCLI-D-16-0758.1 View in Article CrossRef Google Scholar
[34]	Martens B., Miralles D.G., Lievens H., et al. (2017). GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10:1903−1925. DOI:10.5194/gmd-10-1903-2017 View in Article CrossRef Google Scholar
[35]	Dirmeyer P.A., Gao X., Zhao M., et al. (2006). GSWP-2: Multimodel analysis and implications for our perception of the land surface. Bull. Am. Meteorol. Soc. 87:1381−1398. DOI:10.1175/BAMS-87-10-1381 View in Article CrossRef Google Scholar
[36]	Poggio L., de Sousa L.M., Batjes N.H., et al. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil 7:217–240. DOI:10.5194/soil-7-217-2021 View in Article Google Scholar
[37]	Smith A.B., Walker J.P., Western A.W., et al. (2012). The murrumbidgee soil moisture monitoring network data set. Water Resour. Res. 48:2012WR011976. DOI:10.1029/2012WR011976 View in Article CrossRef Google Scholar
[38]	Pastorello G., Trotta C., Canfora E., et al. (2020). The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7:225. DOI:10.1038/s41597-020-0534-3 View in Article CrossRef Google Scholar
[39]	Rodell M., Houser P.R., Jambor U., et al. (2004). The Global land data assimilation system. Bull. Am. Meteorol. Soc. 85:381−394. DOI:10.1175/BAMS-85-3-381 View in Article CrossRef Google Scholar
[40]	Wang L. and Chen W. (2014). Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation. Atmos. Sci. Lett. 15:1−6. DOI:10.1002/asl2.454 View in Article CrossRef Google Scholar
[41]	Frieler K., Volkholz J., Lange S., et al. (2024). Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the inter-sectoral impact model intercomparison project (ISIMIP3a). Geosci. Model Dev. 17:1−51. DOI:10.5194/gmd-17-1-2024 View in Article CrossRef Google Scholar
[42]	Yokohata T., Kinoshita T., Sakurai G., et al. (2020). MIROC-INTEG-LAND version 1: A global biogeochemical land surface model with human water management, crop growth, and land-use change. Geosci. Model Dev. 13:4713−4747. DOI:10.5194/gmd-13-4713-2020 View in Article CrossRef Google Scholar
[43]	Guimberteau M., Zhu D., Maignan F., et al. (2018). ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: Model description and validation. Geosci. Model Dev. 11:121–163. DOI:10.5194/gmd-11-121-2018 View in Article Google Scholar
[44]	Cucchi M., Weedon G.P., Amici A., et al. (2020). WFDE5: Bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12:2097−2120. DOI:10.5194/essd-12-2097-2020 View in Article CrossRef Google Scholar
[45]	Slivinski L.C., Compo G.P., Whitaker J.S., et al. (2019). Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteorol. Soc. 145:2876−2908. DOI:10.1002/qj.3598 View in Article CrossRef Google Scholar
[46]	Lange S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12:3055–3070. DOI:10.5194/gmd-12-3055-2019 View in Article Google Scholar
[47]	O’Neill B.C., Tebaldi C., Van Vuuren D.P., et al. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9:3461−3482. DOI:10.5194/gmd-9-3461-2016 View in Article CrossRef Google Scholar
[48]	Guan Y., Gu X., Slater L.J., et al. (2023). Increase in ocean-onto-land droughts and their drivers under anthropogenic climate change. npj Clim. Atmos. Sci. 6:195. DOI:10.1038/s41612-023-00523-y View in Article CrossRef Google Scholar
[49]	Gutjahr O., Putrasahan D., Lohmann K., et al. (2019). Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP). Geosci. Model Dev. 12:3241–3281. DOI:10.5194/gmd-12-3241-2019 View in Article Google Scholar
[50]	Padrón R.S., Gudmundsson L., Decharme B., et al. (2020). Observed changes in dry-season water availability attributed to human-induced climate change. Nat. Geosci. 13:477−481. DOI:10.1038/s41561-020-0594-1 View in Article CrossRef Google Scholar
[51]	Berg A., Sheffield J., Milly P.C.D. (2017). Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44:236−244. DOI:10.1002/2016GL071921 View in Article CrossRef Google Scholar
[52]	Balesdent J., Basile-Doelsch I., Chadoeuf J., et al. (2018). Atmosphere-soil carbon transfer as a function of soil depth. Nature 559:599−602. DOI:10.1038/s41586-018-0328-3 View in Article CrossRef Google Scholar
[53]	Fan Y., Miguez-Macho G., Jobbágy E.G., et al. (2017). Hydrologic regulation of plant rooting depth. Proc. Natl. Acad. Sci. USA 114:10572−10577. DOI:10.1073/pnas.1712381114 View in Article CrossRef Google Scholar
[54]	Miralles D.G., Van Den Berg M.J., Teuling A.J., et al. (2012). Soil moisture-temperature coupling: A multiscale observational analysis. Geophys. Res. Lett. 39:2012GL053703. DOI:10.1029/2012GL053703 View in Article CrossRef Google Scholar
[55]	Hsu H., Dirmeyer P.A., Seo E. (2024). Exploring the mechanisms of the soil moisture-air temperature hypersensitive coupling regime. Water Resour. Res. 60:e2023WR036490. DOI:10.1029/2023WR036490 View in Article CrossRef Google Scholar
[56]	Zhang W., Zhou T., Wu P. (2024). Anthropogenic amplification of precipitation variability over the past century. Science 385:427−432. DOI:10.1126/science.adp0212 View in Article CrossRef Google Scholar
[57]	Zhong Z., He B., Chen H.W., et al. (2023). Reversed asymmetric warming of sub-diurnal temperature over land during recent decades. Nat. Commun. 14:7189. DOI:10.1038/s41467-023-43007-6 View in Article CrossRef Google Scholar
[58]	Wang H., Liu J., Klaar M., et al. (2024). Anthropogenic climate change has influenced global river flow seasonality. Science 383:1009−1014. DOI:10.1126/science.adi9501 View in Article CrossRef Google Scholar
[59]	Ribes A., Planton S. and Terray L. (2013). Application of regularised optimal fingerprinting to attribution. Part I: Method, properties and idealised analysis. Clim. Dyn. 41:2817−2836. DOI:10.1007/s00382-013-1735-7 View in Article CrossRef Google Scholar
[60]	Seneviratne S.I., Corti T., Davin E.L., et al. (2010). Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Sci. Rev. 99:125−161. DOI:10.1016/j.earscirev.2010.02.004 View in Article CrossRef Google Scholar
[61]	Wu J., Feng Y., Li L., et al. (2024). Earth greening mitigates hot temperature extremes despite the effect being dampened by rising CO₂. One Earth 7:100−109. DOI:10.1016/j.oneear.2023.12.003 View in Article CrossRef Google Scholar
[62]	Vereecken H., Amelung W., Bauke S.L., et al. (2022). Soil hydrology in the earth system. Nat. Rev. Earth Environ. 3:573−587. DOI:10.1038/s43017-022-00324-6 View in Article CrossRef Google Scholar
[63]	Roesch C.M., Fons E., Ballinger A.P., et al. (2025). Decreasing aerosols increase the European summer diurnal temperature range. npj Clim. Atmos. Sci. 8:47. DOI:10.1038/s41612-025-00922-3 View in Article CrossRef Google Scholar
[64]	Jiang Y., Yang X.-Q., Liu X., et al. (2020). Impacts of wildfire aerosols on global energy budget and climate: The role of climate feedbacks. J. Clim. 33:3351−3366. DOI:10.1175/JCLI-D-19-0572.1 View in Article CrossRef Google Scholar
[65]	Wankmüller F.J.P., Delval L., Lehmann P., et al. (2024). Global influence of soil texture on ecosystem water limitation. Nature 635:631−638. DOI:10.1038/s41586-024-08089-2 View in Article CrossRef Google Scholar
[66]	Piao S., Nan H., Huntingford C., et al. (2014). Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5:5018. DOI:10.1038/ncomms6018 View in Article CrossRef Google Scholar
[67]	Ge J., Liu Q., Zan B., et al. (2022). Deforestation intensifies daily temperature variability in the northern extratropics. Nat. Commun. 13:5955. DOI:10.1038/s41467-022-33622-0 View in Article CrossRef Google Scholar
[68]	Xie S.-P., Miyamoto A., Zhang P., et al. (2025). What made 2023 and 2024 the hottest years in a row. npj Clim. Atmos. Sci. 8:117. DOI:10.1038/s41612-025-01006-y View in Article CrossRef Google Scholar
[69]	Song F., Dong H., Wu L., et al. (2025). Hot season gets hotter due to rainfall delay over tropical land in a warming climate. Nat. Commun. 16:2188. DOI:10.1038/s41467-025-57501-6 View in Article CrossRef Google Scholar
[70]	Tuttle S. and Salvucci G. (2016). Empirical evidence of contrasting soil moisture-precipitation feedbacks across the United States. Science 352:825−828. DOI:10.1126/science.aaa7185 View in Article CrossRef Google Scholar
[71]	Albrecht B.A. (1989). Aerosols, cloud microphysics, and fractional cloudiness. Science 245:1227−1230. DOI:10.1126/science.245.4923.1227 View in Article CrossRef Google Scholar
[72]	Wang Y., Mao J., Hoffman F.M., et al. (2022). Quantification of human contribution to soil moisture-based terrestrial aridity. Nat. Commun. 13:6848. DOI:10.1038/s41467-022-34071-5 View in Article CrossRef Google Scholar
[73]	Norris J.R., Allen R.J., Evan A.T., et al. (2016). Evidence for climate change in the satellite cloud record. Nature 536:72−75. DOI:10.1038/nature18273 View in Article CrossRef Google Scholar
[74]	Chung E.-S. and Soden B.J. (2017). Hemispheric climate shifts driven by anthropogenic aerosol-cloud interactions. Nat. Geosci. 10:566−571. DOI:10.1038/ngeo2988 View in Article CrossRef Google Scholar
[75]	Zhang X., Gu X., Li L., et al. (2026). Drying soil moisture dominates enhancing summer soil moisture-temperature coupling under climate change. Geophys. Res. Lett. 53:e2025GL119826. DOI:10.1029/2025GL119826 View in Article CrossRef Google Scholar
[76]	Xu H., Lian X., Slette I.J., et al. (2022). Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons. Nat. Commun. 13:4093. DOI:10.1038/s41467-022-31826-y View in Article CrossRef Google Scholar
[77]	Lin Z., Zhang R., Tang J., et al. (2011). Effects of high soil water content and temperature on soil respiration. Soil Sci. 176:150. DOI:10.1097/SS.0b013e31820d1d76 View in Article CrossRef Google Scholar
[78]	Kan Z.-R., Li Z., Amelung W., et al. (2025). Soil carbon accrual and crop production enhanced by sustainable subsoil management. Nat. Geosci. 18:631−638. DOI:10.1038/s41561-025-01720-5 View in Article CrossRef Google Scholar
[79]	Pei J., Li J., Luo Y., et al. (2025). Patterns and drivers of soil microbial carbon use efficiency across soil depths in forest ecosystems. Nat. Commun. 16:5218. DOI:10.1038/s41467-025-60594-8 View in Article CrossRef Google Scholar
[80]	Soong J.L., Phillips C.L., Ledna C., et al. (2020). CMIP5 models predict rapid and deep soil warming over the 21st century. J. Geophys. Res. Biogeosci. 125:e2019JG005266. DOI:10.1029/2019JG005266 View in Article CrossRef Google Scholar

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Gu X., Wang S., Guan Y., et al. (2026). Depth-dependent heat amplification across soil and atmosphere driven by soil moisture-temperature hypersensitive regime. The Innovation Geoscience 4:100250. https://doi.org/10.59717/j.xinn-geo.2026.100250

Gu X., Wang S., Guan Y., et al. (2026). Depth-dependent heat amplification across soil and atmosphere driven by soil moisture-temperature hypersensitive regime. The Innovation Geoscience 4:100250. https://doi.org/10.59717/j.xinn-geo.2026.100250

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Depth-dependent heat amplification across soil and atmosphere driven by soil moisture-temperature hypersensitive regime

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