A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series

被引:48
作者
Jin, Kai [1 ]
Wang, Fei [1 ,2 ,3 ]
Chen, Deliang [4 ]
Liu, Huanhuan [5 ]
Ding, Wenbin [1 ]
Shi, Shangyu [2 ,3 ]
机构
[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
基金
欧盟地平线“2020”;
关键词
IMPERVIOUS SURFACE; ENERGY-CONSUMPTION; URBAN; EMISSIONS; CLIMATE; ISLAND; CITY; PATTERNS; RELEASE; BALANCE;
D O I
10.1038/s41597-019-0143-1
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
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.
引用
收藏
页数:14
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