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Local climate regionalization of the Tibetan Plateau: A data-driven scale-dependent analysis

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Abstract

The Tibetan Plateau (TP) has experienced an overall rapid warming and moistening; however, the knowledge of TP climate regionalization and of its spatial-temporal variations is far behind the rapid climate change and subsequent environmental responses. To address the threats of frequent compound processes, like heatwaves or flash droughts, we analyze the interaction of atmospheric processes and climate subsystems, propose a novel data-driven climate diagnostics approach, and generate a time series of multi-scale local climate zonings, which characterize the spatial-temporal variations of TP climate and provides an in-depth understanding of TP climate change from 1979 to 2018. The interpretation of this data driven approach is supported by Holdridge’s life-zones, Budyko’s physical framework of geobotanical biomes driven by the surface fluxes of water supply (precipitation) and water demand (net radiation), and the Köppen phenomenological climate classification. Three different local climate patterns are identified: (i) The main driver of local climate change in the Qiangtang area (central-western TP) is shifting from water supply to demand dominance. (ii) In the Qaidam area (north-eastern TP), the humid region expands accompanied with a contracting arid region; this trend of warming and moistening expands from the east westwards. (iii) Hengduan Mountains area (south-eastern TP) becomes warmer and wetter but with frequent local climate variations.

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Data availability

The dataset of TP decadal multi-scale local climate regionalization generated by this study is available at [https://github.com/yuningf/TP_LocalClimateRegionalization]. The climate data used in this study is from China Meteorological Forcing Dataset (CMFD) (He et al. 2020) and the spatial constraint data of landform units are provided by Geomorphological Database of the People’s Republic of China (Cheng et al. 2011). The calculation of merge control parameter is realized by the ESP (estimation scale parameter) tool (Drǎguţ et al. 2010) and other methods are realized by eCognition software available at [https://cn.geospatial.trimble.com/products-and-solutions/trimble-ecognition]. Corresponding codes are available at [https://github.com/yuningf/Codes_LocalClimateRegionalization].

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Acknowledgements

The authors appreciate a reviewer’s insightful comments and the Editor in Chief for his careful handling of the editorial process.

Funding

The work was supported by Youth Research and Innovation Project – Young teachers research ability enhancement program (X23002), National Natural Science Foundation of China (41930650) and National Key Research and Development Program of China (2021YFE0117100).

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Authors and Affiliations

  1. School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Yuning Feng & Mingyi Du

  2. Institute of Remote Sensing and GIS, Peking University, Beijing, 100871, China

    Yuning Feng, Shihong Du & Xiuyuan Zhang

  3. Max Planck Institute for Meteorology, 20146, Hamburg, Germany

    Klaus Fraedrich

  4. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Weiming Cheng

Authors
  1. Yuning Feng
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  2. Shihong Du
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  4. Xiuyuan Zhang
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  5. Mingyi Du
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  6. Weiming Cheng
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Contributions

Yuning Feng, Shihong Du, Klaus Fraedrich and Xiuyuan Zhang contributed to the study conception and design. Material preparation and data collection were performed by Yuning Feng, Weiming Cheng and Mingyi Du. Analysis and investigation were performed by Yuning Feng, Klaus Fraedrich, Xiuyuan Zhang and Weiming Cheng. The first draft of the manuscript was written by Yuning Feng and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shihong Du.

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Feng, Y., Du, S., Fraedrich, K. et al. Local climate regionalization of the Tibetan Plateau: A data-driven scale-dependent analysis. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04916-8

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