Decadal prediction of summer extreme high temperatures in Eurasian mid-high latitudes using on Recurrent Neural Networks
The frequency of extreme high temperature events has increased against the backdrop of global war-ming,posing serious risks to natural ecosystems,socio-economic development,and human safety.The Eurasian mid-high latitudes,or core regions of the Belt and Road area,feature fragile ecological environments highly sus-ceptible to climate change,with limited adaptive capacities to extreme weather events.In recent decades,the fre-quent occurrence of extreme high-temperature events in these latitudes has resulted in tens of thousands of fatali-ties and billions of dollars in economic losses.Accurate prediction of extreme high temperatures in this region,es-pecially on a decadal scale,is urgently needed by governmental decision-makers to effectively address climate change and promote sustainable development.This paper assesses the decadal predictive skill of current state-of-the-art dynamical models(CMIP6 DCPP)for summer extreme high temperatures in the Eurasian mid-high lati-tude region.We utilize the anomaly correlation coefficient(ACC)to assess the model's skill in capturing the ob-served variability phase and the mean-square skill score(MSSS)as a deterministic verification metric sensitive to amplitude errors.By comparing DCPP hindcasts(initialization)with historical simulations(external forcing),we examine the sources of predictive skill.The evaluation results show that multi-model ensemble average(MME)exhibits high predictive skill for the region south of 60°N(South Eurasia,SEA),accurately forecasting its linear growth trend and prominent decadal variability during 1968-2008.However,MME shows almost no predictive skill for the decadal variability of extreme high temperatures in the North Eurasia(NEA)region,only forecasting a linear growth trend lower than observed.To improve decadal predictive skills,we developed a three-layer recur-rent neural network(RNN).This model utilizes the large-sample model predictions of 86 initial fields as input,with training and testing periods of 1968-2007 and 2008-2022,respectively.Significant improvements in ex-treme high temperature skills in NEA and SEA during test period of 2008-2020 were observed in the RNN mod-el.The ACC skills of NEA and SEA in RNN are 0.86 and 0.83,respectively,compared to-0.61 and-0.03 in MME.Meanwhile,the MSSSs of NEA and SEA in RNN are 0.37 and 0.52,whereas they are-1.1 and-0.94 in MME,respectively.Real-time forecasts from RNN indicate that extreme high temperatures in the SEA region will continue to rise from 2021 to 2026,with a record-breaking event in 2026.Mean while,the NEA region is predicted to experience anomalously fewer events in 2022,followed by fluctuating increases.A comparison of the perform-ance of various input sizes in RNN reveals that large sample sizes are necessary for the RNN model.Additionally,incorporating additional predictors with significant physical mechanisms for extreme high-temperature events may further enhance decadal prediction skills,warranting further investigation.Nevertheless,this study provides new in-sights into current decadal prediction of extreme climate,offering promising scientific support for governmental decision-makers in addressing climate change.
extreme high temperatureDCPPdecadal climate predictionRecurrent Neural Network
万方数据
维普
