Statistical prediction of non‐Gaussian climate extremes in urban areas based on the first‐order difference method

Abstract

Prediction of climate extremes is challenging, especially for non‐Gaussian extremes in urban areas where the majority of people live, since the Gaussian assumption used in linear regression is violated and the urbanization effect needs to be considered. In this study, the first‐order difference method is introduced to take these difficulties into account. Statistical prediction of the non‐Gaussian annual occurrence of hot days in downtown Hong Kong, which is highly urbanized, is used to illustrate this method. With the help of the first‐order difference of the annual occurrences, which follows a Gaussian distribution, the difference series is used as the predictant to find predictors and to construct a prediction model by using traditional linear regression. The difference is first predicted and is then added to the observed value at the preceding time to obtain the predicted annual occurrence. The historical urbanization effect is thus obtained directly from the observations at the preceding time. The prediction results are found desirable. The broad application potential and conditions in which this method should be used are also discussed.

Climate‐sensitivity of sugarcane yield in the southeastern Africa lowlands

“…With serial persistence ~2 years, thus an absolute correlation >0.35 is needed to achieve statistical significance above 90% confidence. Temporal trends were calculated by linear regression and assessed for variance with the seasonal cycle removed, following standard methods (Sneyers, 1990; Qian et al ., 2018).…”

Climate prediction of summer extreme precipitation frequency in the Yangtze River valley based on sea surface temperature in the southern Indian Ocean and ice concentration in the Beaufort Sea

“…The operational forecast skill of the summer precipitation over the MLYRV has been significantly enhanced by the year‐to‐year increment method, which is based on the quasi‐biennial oscillation of climate variables (Fan et al ., ). So far, this method has been proven to be effective in other aspects of extreme climate prediction, such as wintertime heavy snow, typhoon frequency over the western North Pacific, the number of landfalling tropical cyclones, the Atlantic storm frequency, and heat extremes (Fan, ; Fan and Wang, ; Fan, ; Fan and Tian, ; Qian et al ., ; Qian et al ., ). Comprehensive studies of quasi‐periodicities in the 2–3‐ and 3–4‐ year range of precipitation patterns or EPF in the Yangtze River have been conducted (Becker et al ., ; Hartmann et al ., ).…”

Interdecadal Variation of Spring Extreme High-Temperature Events in the Western Tianshan Mountains and Its Relationship with the Tropical SST

“…In eastern and southern China, the EHEs are afected by the western Pacifc subtropical high (WPSH) anomaly [19,20]. Te atmospheric circulation anomalies that contribute to the EHEs in China are related to several elements, including the tropical Pacifc sea surface temperature (SST) [18,21], El Niño-southern oscillation [22,23], Pacifc El Niño-like pattern [24], Atlantic SST [25], tropical Indian Ocean SST [26], and sea ice [27,28]. SST anomalies in the tropical Indian Ocean enhance latent heat to excite the Rossby wave train, resulting in the anomalies of the South Asian high and WPSH, and thus afecting the summer EHEs in eastern China [23].…”