Sisi Chen’s research while affiliated with Nanjing University of Information Science & Technology and other places

Global warming exacerbates the increase of soil moisture drought by accelerating the water cycle, posing potential threats to food security and ecological sustainability. The design of drought prevention and mitigation policies should be based on the reliable detection of the future change signal in droughts, so it is critical to know when the signal can be detected (Time of Emergence, ToE) in the background noise of the climate system. While the ToE framework has been successfully applied for temperature‐related signal detection, the ToE for changes in drought has not been well studied. Based on 66 Coupled Model Intercomparison Project Phase 6 model ensemble members under four Shared Socio‐economic Pathways, we conduct a global ToE analysis of seasonal soil moisture drought characteristics and discuss the impact of different warming levels. Six subregions with robust increase in soil moisture droughts are identified. For drought frequency, most of the subregion's ToE is centered around 2080, however for drought intensity it is much earlier and can even reach around 2040 in AMZ. For drought frequency and drought intensity, approximately 14%–22% and 47%–49% of global land areas would reach ToE in 21st century. The global land areas with ToE of increasing droughts would increase by at least 1/5 when global warming level is kept to 2°C rather than 1.5°C above pre‐industrial conditions. This suggests that limiting global warming can significantly delay the emergence time of increases in seasonal soil moisture droughts, allowing additional adaptation time for the drought‐related sectors.

Understanding and quantifying drought projection uncertainty at regional scales is critical for climate adaptations and mitigations. The model uncertainty has been well represented by multi-model ensemble through the implementation of Coupled Model Intercomparison Projects (CMIPs). However, the uncertainty from internal variability is usually quantified by statistical fitting due to insufficient initial-condition ensembles for each global climate model (GCM), resulted in an underestimation of the uncertainty. In this study, Single Model Initial-condition Large Ensembles (SMILEs) that represent internal variability based on GCMs with different initial conditions, are combined with CMIP5 and CMIP6 GCMs to separate the uncertainty of seasonal soil drought projection over China. All three datasets show that internal variability dominates uncertainty for the near-term drought projection, and the internal variability uncertainty is exceeded by model uncertainty for the long-term projection. By using SMILEs as a benchmark, we revisit the method from Hawkins and Sutton (2009; hereafter, HS09) and find that this method performs well for drought projection at national scale over China. For drought projections at regional scale, however, HS09 method underestimates the uncertainty of internal variability for drought frequency, duration and intensity by 27%–54%, 15%–47% and 16%–31%, respectively. Our study highlights the importance of the selected approach for addressing the internal variability in the near-term projection of regional extremes and related adaptations.

Seasonal drought occurrences are found to increase across different regions over China under global warming, but with large uncertainties among models. With ten selected Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models and seven CMIP6 models according to their performances in reproducing historical drought trends (p < 0.1), here we show that future seasonal soil moisture (SM) droughts over China projected by CMIP6 models are less frequent than that by CMIP5 models. We find national mean seasonal drought frequency is projected to increase by 28 ± 4% based on CMIP5 models at 1.5 °C global warming level, but only increase by 18 ± 6% based on CMIP6 models and 12 ± 4% based on land surface model ensemble simulations driven by downscaled CMIP5 models. Compared with CMIP6, CMIP5 projection suggests larger increase in precipitation but also larger increase in evapotranspiration, leading to more frequent seasonal SM droughts. Comparing the results at 3 C global warming level with those at 1.5 °C, drought frequency over China will increase further by 10 ± 4%, but drought duration will decrease by 6 ± 4%, suggesting more frequent seasonal SM droughts with shorter durations will occur in a warming future. The future increase in China drought frequency will reduce from 12%-45% based on selected climate models to 3%-27% based on all available models (30 CMIP5 models and 31 CMIP6 models), which indicates that the model selection is critical for future drought projection. Nevertheless, CMIP6 still projects less frequent seasonal SM droughts than CMIP5 even without any model discriminations.