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Dashan Wang
,
Xianwei Wang
,
Lin Liu
,
Dagang Wang
, and
Zhenzhong Zeng

Abstract

Urban areas demonstrate great influence on precipitation, yet the spatial clustering features of precipitation are still unclear over urban areas. This study quantitatively examines the spatial clustering of precipitation intensity in 130 urban-affected regions over mainland China during 2008–15 using a high-resolution merged precipitation product. Results show that the spatial heterogeneity patterns display diverse distribution and vary with precipitation intensity and urban sizes. Extreme and heavy precipitation has higher spatial heterogeneity than light precipitation over the urban-affected regions of grade 1 cities, and their mean Moran’s I are 0.49, 0.47, and 0.37 for the intensity percentiles of ≥95%, 75%–95%, and <75%, respectively. The urban signatures in the spatial clustering of precipitation extremes are observed in 37 cities (28%), mainly occurring in the Haihe River basin, the Yangtze River basin, and the Pearl River basin. The spatial clustering patterns of precipitation extremes are affected by the local dominant synoptic conditions, such as the heavy storms of convective precipitation in Beijing (Moran’s I = 0.47) and the cold frontal system in the Pearl River delta (Moran’s I = 0.78), resulting in large regional variability. The role of urban environments for the spatial clustering is more evident in wetter conditions [e.g., relative humidity (RH) > 75% over Beijing and RH > 85% over the Pearl River delta] and warmer conditions (T > 25°C over Beijing and T > 28°C over the Pearl River delta). This study highlights the urban modification on the spatial clustering of some precipitation extremes, and calls for precautions and adaptation strategies to mitigate the adverse effect of the highly clustered extreme rainfall events.

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Lihong Zhou
,
Zhenzhong Zeng
,
Cesar Azorin-Molina
,
Yi Liu
,
Jie Wu
,
Dashan Wang
,
Dan Li
,
Alan D. Ziegler
, and
Li Dong

Abstract

To investigate changes in global wind speed phenomena, we constructed homogenized monthly time series (1980–2018) for 4722 meteorological stations. Through examining monthly averaged wind speeds (MWS), we found that seasonal wind speed range (SWSR; calculated as the difference between maximum and minimum MWS) has declined significantly by 10% since 1980 (p < 0.001). This global SWSR reduction was primarily influenced by decreases in Europe (−19%), South America (−16%), Australia (−14%), and Asia (−13%), with corresponding rate reductions of −0.13, −0.08, −0.09, and −0.06 m s−1 decade−1, respectively (p < 0.01). In contrast, the SWSR in North America rose 3%. Important is that the decrease in SWSR occurred regardless of the stilling or reversal of annual wind speed. The shrinking SWSR in Australia and South America was characterized by continuous decreases in maximum MWS and increases in the minimum. For Europe and Asia, maximum and minimum MWS declined initially after 1980, followed by substantial increases in minimum MWS (about 2000 and 2012, respectively) that preserved the long-term reduction in the range. Most reanalysis products (ERA5, ERA-Interim, and MERRA-2) and climate model simulations (AMIP6 and CMIP6) fail to reproduce the observed trends. However, some ocean–atmosphere indices (seasonality characteristics) were correlated significantly with these trends, including the Western Hemisphere warm pool, East Atlantic pattern, Pacific decadal oscillation, and others. These findings are important for increasing the understanding of mechanisms behind wind speed variations that influence a multitude of other biogeophysical processes and the development of efficient wind power generation, now and in the future.

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