Projection of future dry‐wet evolution in Northwest China and its uncertainty attribution analysis

Abstract

AbstractProjection of future dry‐wet evolution is essential for making long‐term regional climate adaptation strategies. In this study, the projection of regional dry‐wet evolution is conducted with a careful consideration on uncertainty attribution. The Evaporative Stress Index (ESI) is adopted due to its physical mechanisms for taking evaporative demand into account. A three‐dimensional framework is constructed for quantifying the range of uncertainty of the ESI in which six Global Climate Models (GCMs) in Coupled Model Intercomparison Project 6 (CMIP6), three latest Shared Socioeconomic Pathway (SSP) scenarios, and six Potential Evapotranspiration Models (PETMs) are used. The framework provides 108 different ESI simulations for two future periods: 2041–2070 (mid‐future) and 2071–2100 (far‐future). An agglomerative‐hierarchical clustering method and the Analysis of Variance methodology are employed to evaluate the relative contribution of each uncertainty source. The region of Northwest China is used as a case to illustrate the effectiveness of the proposed framework. The results indicate that most of the parts in Northwest China would experience dry mitigation in both mid‐future and far‐future periods. Projected ESI by PM[CO2] model and ACCESS‐ESM1‐5 suggestes a higher tendency for dry mitigation. Hierarchical clustering analysis of the 108 sets of ESI predictions indicate that most clusters are dominated by GCM forcing, and one cluster is dominated by the SSP1‐2.6 scenario. Furthermore, GCM‐related uncertainty′s relative contribution to the total projection uncertainty is the greatest, with an average value of 49.98% in the far future (i.e., 2071–2100 s). Although the contribution of SSP uncertainty is smaller (21.68%−28.43%), it increases in far‐future over mid‐future. The case study indicates that the large scale ensemble prediction of ESI and its uncertainty analysis provide a more comprehensive data set on climate change and help water managers to gain in‐depth understanding of future trends of drought projections.