Explore the vast range of titles available.

The frequent occurrence of extreme cold waves under climate change has attracted widespread attention. Based on the Japanese 55-year Reanalysis daily dataset from 1958 to 2021, we use a newly developed dynamic metric, the local finite-amplitude wave activity (LWA), to explore the precursory signals, outburst conditions, and key dynamic features of extreme cold waves over eastern China from the perspective of synoptic climatology. The statistical results show that approximately 40% of extreme cold waves have the following features. First, the formation of significant positive LWA anomalies over the Balkhash–Baikal region is an evident precursory signal, which is accompanied by significant cold surface air temperature anomalies that accumulate over mid- and high-latitude Eurasia. Second, the appearance of extreme positive LWA anomalies over the region east of Lake Baikal (ELB) is necessary for subsequent outbursts of extreme cold waves. These extreme positive LWA anomalies indicate the meridionally enhanced planetary trough over East Asia and advection of the accumulated cold air masses southeastward to eastern China. Third, the evident positive change in the LWA anomalies over the ELB is mainly attributable to the convergence of the zonal LWA flux due to the zonal wind in the eddy-free state and Stokes drift flux over the eastern area of the ELB and the convergence of the meridional eddy heat flux over the western area. This study demonstrates that the LWA could be used as a simple and feasible metric for monitoring and forecasting extreme cold waves.

Climate change (CC) is now a global challenge due to uncertainties on the drivers and the multifaceted nature of its impacts. It impacts many sectors such as agriculture, water supply, and global economies through temperature and precipitation, affecting many livelihoods. Although there are global, regional, and national studies on CC, their application to determine local CC occurence mitigation and adaptation measures is not ideal. Therefore, this study aimed to determine climate change trends in Lake Kyoga Basin using standardized precipitation and anomaly indexes. Short-term (39 years, 1981–2020) and long-term (59 years, 1961–2020) monthly data from eight strategic meteorological stations were acquired from the Uganda National Meteorological Authority and supplemented with satellite and model reanalysis climate datasets. Change in precipitation was determined by SPI-6, while SAI determined change in temperature. The Mann–Kendall test was used to determine the trend significance. Whereas two (Serere and Lira) long-term data stations showed significant changes in precipitation, all the short-term data stations showed a significant increasing trend. Decadal relative rainfall anomaly increased from 85.6–105 in 1981–1990 to 92.0–120.9 in 2011–2020, while mean temperature anomaly increased from 0.2–0.6 °C to 1.0–1.6 °C in the same period. The frequency of severe wet weather events was more than for dry weather events in many stations, indicating an increase in precipitation. Maximum, mean, and minimum temperatures increased, with resultant warmer nights. The findings showed that the Lake Kyoga basin is experiencing climate change, with both temperature and rainfall increasing spatially and temporarily. Climate change affects agriculture, which is the main economic activity, and causes the destruction of infrastructure from floods, landslides, and mudslides. The results of this study are helpful in pointing out climate change-affected areas, and hence for designing mitigation and adaption strategies for local communities by policy and decision-makers from relevant stakeholders.