ABSTRACT
Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models. In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies.
ABSTRACT
Winter windstorms are known to be among the most dangerous and loss intensive natural hazards in Europe. In order to gain a better understanding of their variability and driving mechanisms, this study analyses the temporal variability which is often referred to as serial or seasonal clustering. This is realized by developing a statistical model relating the winter storm counts to known teleconnection patterns affecting European weather and climate conditions (e.g., North Atlantic Oscillation [NAO], Scandinavian pattern [SCA], etc.). The statistical model is developed via a stepwise Poisson regression approach that is applied to windstorm counts and large-scale indices retrieved from the ERA-20C reanalysis. Significant large-scale drivers accountable for the inter-annual variability of storms for several European regions are identified and compared. In addition to the SCA and the NAO which are found to be the essential drivers for most areas within the European domain, other teleconnections (e.g., East Atlantic pattern) are found to be more significant for the inter-annual variability in certain regions. Furthermore, the statistical model allows an estimation of the expected number of storms per winter season and also whether a season has the characteristic of being what we define an active or inactive season. The statistical model reveals high skill particularly over British Isles and central Europe; however, even for regions with less frequent storm events (e.g., southern and eastern Europe) the model shows adequate positive skill. This feature could be of specific interest for the actuarial sector.
ABSTRACT
Cholera is one of the most important climate sensitive diseases in Nigeria that pose a threat to public health because of its fatality and endemic nature. This study aims to investigate the influences of meteorological and socioeconomic factors on the spatiotemporal variability of cholera morbidity and mortality in Nigeria. Stepwise multiple regression and generalised additive models were fitted for individual states as well as for three groups of the states based on annual precipitation. Different meteorological variables were analysed, taking into account socioeconomic factors that are potentially enhancing vulnerability (e.g. absolute poverty, adult literacy, access to pipe borne water). Results quantify the influence of both climate and socioeconomic variables in explaining the spatial and temporal variability of the disease incidence and mortality. Regional importance of different factors is revealed, which will allow further insight into the disease dynamics. Additionally, cross validated models suggest a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.
