IMPERVIOUS SURFACE
; ENERGY-CONSUMPTION
; URBAN
; EMISSIONS
; CLIMATE
; ISLAND
; CITY
; PATTERNS
; RELEASE
; BALANCE
WOS学科分类:
Multidisciplinary Sciences
WOS研究方向:
Science & Technology - Other Topics
英文摘要:
Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human-environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970-2050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16 W/m(2) in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.
1.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China 2.Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, Yangling 712100, Shaanxi, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Univ Gothenburg, Dept Earth Sci, Reg Climate Grp, Box 460, S-40530 Gothenburg, Sweden 5.Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Shaanxi, Peoples R China
Recommended Citation:
Jin, Kai,Wang, Fei,Chen, Deliang,et al. A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series[J]. SCIENTIFIC DATA,2019-01-01,6