Downscaling and uncertainty analysis of future concurrent long-duration dry and hot events in China

Yang, Yi; Tang, Jianping

doi:10.1007/s10584-023-03481-9

Cited by 6 publications

(1 citation statement)

References 88 publications

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“…Therefore, this study adopted the widely used Bias Correction and Spatial Disaggregation method (Wood et al, 2002(Wood et al, , 2004 to correct the bias of GCM, and downscale the original grid to 0.25°× 0.25°resolution. It has been indicated that the BCSD downscaling method has good applicability in China and is a suitable method to reduce simulation errors (Wang & Chen, 2013;Yang & Tang, 2023). Additionally, the BCSD method is not model-dependent and has shown improvement in the simulation of various GCMs.…”

Section: Climate Data and Future Climate Scenariosmentioning

confidence: 99%

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

Liu

Zhang

et al. 2023

River

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Projection 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 (midfuture) 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[CO 2 ] 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.

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Section: Climate Data and Future Climate Scenariosmentioning

confidence: 99%

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

Liu

Zhang

et al. 2023

River

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Future changes in extremes across China based on NEX-GDDP-CMIP6 models

Baogang,

Linxiao,

Hongyu

et al. 2024

Clim Dyn

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Ensemble modeling of extreme seasonal temperature trends in Iran under socio-economic scenarios

Kamangar,

Ahmadi,

Rabiei-Dastjerdi

et al. 2024

Nat Hazards

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In climate science, ensemble modeling has emerged as a powerful tool for addressing the uncertainties inherent in individual climate models. This approach generates more robust and reliable predictions by harnessing the collective insights of multiple models. Nonetheless, the method of combining these models to derive an ensemble model remains an open question. To this end, the objectives of this research are twofold: (i) to introduce and evaluate the weighted average-correlation ensemble model for projecting minimum and maximum temperatures in Iran, and (ii) to assess near-term (2021–2040) trends across 95 synoptic stations using socio-economic scenarios derived from five models: GFDL-ESM4, MPI-ESM1-2-HR, IPSL-CM6A-LR, MRI-ESM2, and UKESM1-0-LL. The ensemble technique effectively reduces the Root Mean Square Error (RMSE) (1/3 − 1/10) associated with the individual models. The predicted values for the minimum temperature are more similar to the actual data than the maximum temperature. The results also indicate a significant increase in the minimum temperature compared to the maximum temperature during the base period. The distribution of the maximum temperature across the country is influenced mainly by its latitude. In contrast, the distribution of the minimum temperature is influenced by both the country’s major altitudes and latitudes. Surveys also indicate that, compared to the base period, there is an increasing trend in temperature for winter, spring, and autumn, while a decrease is observed during the summer. Notably, the increase in temperature is more pronounced during winter.

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Downscaling and uncertainty analysis of future concurrent long-duration dry and hot events in China

Cited by 6 publications

References 88 publications

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

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

Future changes in extremes across China based on NEX-GDDP-CMIP6 models

Ensemble modeling of extreme seasonal temperature trends in Iran under socio-economic scenarios

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