Rapid attribution prototype for extreme high temperature events in China
Ying Sun, Dongqian Wang, Ting Hu, Xuebin Zhang
Abstract
Understanding the causes of extreme events and projecting their future changes are essential for society's adaptation to climate change. Although several entities have proposed different prototypes and frameworks for conducting rapid event attribution, these have not been extensively tailored to the Chinese context, highlighting a significant gap. This study introduces a probability-based event attribution prototype for climate variables with strong signals. It incorporates risk ratios conditional on external forcing and evaluates model performance to adjust for bias. We aimed to develop an initial prototype for a quasi-operational rapid attribution system of extreme events at the China Meteorological Administration using an inexpensive and computationally efficient approach. Real-time event analyses were conducted using observational data from weather stations, while the factual and counterfactual worlds relied on pre-calculated model simulations from the sixth phase of the Coupled Model Intercomparison Programme (CMIP6). To ensure the reliability of event attribution, the simulations were adjusted using an optimal-fingerprinting method so the model simulated responses could best match the observation. As demonstrated using extreme summer high temperatures as an indicator, the prototype is applicable to rapid attribution of both historical and future events. Before the 1980s, external forcing had a negligible effect on the extreme high temperature with various return periods; however, human influence has since significantly increased their occurrence probability. Notably, regions experiencing rapid warming, such as Northwest China and the Tibetan Plateau, exhibit the most substantial responses to human influence. Projected future scenarios indicate a considerable rise in risk ratios, with record-breaking temperature events in the current climate state expected to become the norm by the 2060s in most regions. All these confirm the practical applicability of the prototype method. Effectively communicating these attribution findings to policymakers and the general public poses a significant challenge.