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Human influences on regional temperature change – comparing adjacent plains of China and Russia
Author(s) -
Du Haibo,
He Hong S.,
Wu Zhengfang,
Wang Lei,
Zong Shengwei,
Liu Jie
Publication year - 2017
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4888
Subject(s) - climatology , china , environmental science , geography , physical geography , geology , archaeology
Land‐use and land‐cover change ( LUCC , such as the conversion of natural landscape to agricultural land) and human activities ( HAs ) are the most important human influences ( HIs ) on regional climate. Studying such influences is fundamental to understanding anthropogenic climate change. Our objectives were to quantify contributions of HI on regional temperature change and demonstrate that HI may cause abrupt climate change. We quantified the contribution of HI on regional temperature by comparing the seasonal temperature changes between a human‐dominated plain and an adjoining natural plain using the daily maximum ( T max ) and minimum ( T min ) temperature data from 12 meteorological stations. We quantified the contribution of HA on regional temperature change using winter weather data because snow cover in both plains masked the effects of LUCC . HI caused decreases in T max and increases in T min , and an increase in mean temperature. HA contributed to the reduction in the diurnal temperature range, whereas LUCC caused an increase in the range. The abrupt temperature change was in accord with the abrupt changes in agricultural land area and population. The HI led to an abrupt change in T max 3 years earlier (1985) than the recognized abrupt climate change in the Northern Hemisphere (1988). The synthetic effects of HI ( LUCC and HA ) evidently altered the change trends and accelerated abrupt changes in regional temperature, which affected the regional climate. Our findings can elucidate regional climate patterns and improve the accuracy of downscaling general circulation models.