Project No: 16308722

Title: Joint probability of concurrent hot and dry extremes realized by convection-permitting projections based on multi-RCMs and multi-scenarios

Principal Investigator: Prof. Eun-Soon IM

Co-Investigator: Prof. Hyun-Han KWON, Prof. Chi-yung Francis TAM, Prof. Zhongfeng XU


The severity and frequency of compound extremes are expected to increase with the acceleration in global warming. The negative impacts of compound extremes on different sectors often extend beyond a single event, which suggests that the future impacts of a warmer climate may vary from those of previous eras. In particular, southeastern China has witnessed concurrent hot and dry extremes increasing over the few decades. Their foreseeable intensification will aggravate the economic losses and people’s well-being. While the profound effects of concurrent extremes have promoted strong demand for reliable future projections at local levels, most studies have focused primarily on univariate analysis of single extremes using coarse-grid global climate model (GCM) projections. However, univariate analysis may not appropriately account for the compounding effects that are exacerbated or dampened because of the dependence structure of climate drivers. Furthermore, the high-resolution downscaled climate data will provide added value in better representing the region-specific climate impacts of global warming.

This proposed project will integrate the convection-permitting (CP) regional climate modeling and multivariate statistical method to assess the joint probability of concurrent hot and dry extremes in China under different pathways of future emission scenarios. Two regional climate models (RCMs) will produce the CP projections (4 km) over southeastern China. To ensure the representation of fine-scale future projections, two RCMs will be driven by Coupled Model Intercomparison Project Phase 6 (CMIP6) GCM forcings, whose mean and variance biases are corrected and the long-term nonlinear trend is fitted to the multi-CMIP6 ensemble mean. A pseudo global warming experiment will also be conducted by combining the mean climate state from historical reanalysis data with the CMIP6-based warming signal to enhance the understanding of thermodynamic processes and the effects of transient variations on the compound extremes. A four-dimensional copula incorporating time-varying nonstationary parameters estimated using a hierarchical Bayesian model will be developed to fully comprehend joint dependency of concurrent extremes and identify the joint geographical occurrence of compound events at multiple locations. The new scientific discoveries, such as the process-based added value of CP projections and the difference between the univariate and multivariate assessment, will greatly advance the understanding of compound climate extremes. In addition, the proposed climate modeling system, statistical analysis framework, and multiple CP projections customized over China will be useful in a wide variety of applications.