Analysis of lower-boundary climate factors contributing to the summer heatwave frequency over eastern Europe using a machine-learning model

A machine-learning (ML) model, the light gradient boosting machine (LightGBM), was constructed to simulate the variation in the summer (June–July–August) heatwave frequency (HWF) over eastern Europe (HWF_EUR) and to analyze the contributions of various lower-boundary climate factors to the HWF_EUR variation. The examined lower-boundary climate factors were those that may contribute to the HWF_EUR variation—namely, the sea surface temperature, soil moisture, snow-cover extent, and sea-ice concentration from the simultaneous summer, preceding spring, and winter. These selected climate factors were significantly correlated to the summer HWF_EUR variation and were used to construct the ML model. Both the hindcast simulation of HWF_EUR for the period 1981–2020 and its real-time simulation for the period 2011–2020, which used the constructed ML model, were investigated. To evaluate the contributions of the climate factors, various model experiments using different combinations of the climate factors were examined and compared. The results indicated that the LightGBM model had comparatively good performance in simulating the HWF_EUR variation. The sea surface temperature made more contributions to the ML model simulation than the other climate factors. Further examination showed that the best ML simulation was that which used the climate factors in the preceding winter, suggesting that the lower-boundary conditions in the preceding winter may be critical in forecasting the summer HWF_EUR variation.摘要本文使用LightGBM机器学习模型模拟了欧洲东部夏季热浪频率的变化, 并分析了多个底边界层气候因子的贡献. 所选取的气候因子包括前期冬季, 前期春季以及同期夏季的下垫面海温, 土壤湿度, 积雪以及海冰. 分析结果说明LightGBM模型能够较好的模拟出欧洲东部夏季热浪频率的变化, 其中海温因子对模拟的贡献最大. 进一步的分析研究显示, 使用前期冬季的气候因子进行的模拟可以获得最佳模拟结果, 意味着前期冬季的下垫面气候因子可能对夏季欧洲东部热浪频率变化的预报能起到关键作用.