Uncertainty analysis of global meteorological drought in CMIP6 projections

Qing Niu, Dunxian She (), Jun Xia, Qin Zhang, Yu Zhang and Tianyue Wang
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Qing Niu: Wuhan University
Dunxian She: Wuhan University
Jun Xia: Wuhan University
Qin Zhang: Wuhan University
Yu Zhang: Beijing Normal University
Tianyue Wang: Wuhan University

Climatic Change, 2025, vol. 178, issue 4, No 17, 23 pages

Abstract: Abstract Accurate projection of future droughts is crucial for effective planning and adaptation strategies. However, the reliability of futural projection is challenged by uncertainties related to model, scenario, and internal variability, and it is essential to evaluate how these uncertainties affect global and regional drought projections. In this study, we quantify uncertainty in future drought events from 2015 to 2100 under different scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) considering CMIP6 data. Globally, model uncertainty is the dominant factor in the early 21st-century drought projections, while scenario uncertainty becomes the dominant factor toward the end of the century. At the regional scale, the contributions of different sources of uncertainty vary significantly. Uncertainty associated with the drought duration, frequency and severity is similar. In the long-term, the maximum proportion of model uncertainty, scenario uncertainty and internal variability uncertainty is higher than the minimum ones by 40%, 60%, and 30%, respectively, between the highest and lowest regions in the long-term. These different sources of uncertainty evolve over time, with the rate of change varying across regions. Mediterranean and Central America experience faster changes, while North America and Africa exhibit slower changes. Our study underscores the importance of regional-scale research to account for spatial disparities in uncertainty and emphasizes the necessity for region-specific strategies in planning for and adapting to future drought projections.

Keywords: Drought projection; Model uncertainty; Scenario uncertainty; Internal variability uncertainty; Regional scale (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10584-025-03919-2

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