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Article Type:
Research Article

Spatiotemporal Temperature Variability over the Tibetan Plateau: Altitudinal Dependence Associated with the Global Warming Hiatus

Danlu Cai Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

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Qinglong You Key Laboratory of Meteorological Disaster of Ministry of Education, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Klaus Fraedrich Max-Planck-Institute for Meteorology, Hamburg, Germany

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Yanning Guan Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

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Print Publication:
01 Feb 2017
DOI:
https://doi.org/10.1175/JCLI-D-16-0343.1
Page(s):
969–984
Restricted access

Abstract

The recent slowdown in global warming has initiated a reanalysis of temperature data in some mountainous regions for understanding the consequences and impact that a hiatus has on the climate system. Spatiotemporal temperature variability is analyzed over the Tibetan Plateau because of its sensitivity to climate change with a station network updated to 2014, and its linkages to remote sensing–based variability of MODIS daytime and nighttime temperature are investigated. Results indicate the following: 1) Almost all stations have experienced a notable warming in the time interval 1961–2014, with most obvious warming in winter, which depends on the selected time intervals. 2) There is no clear shift from a predominant warming to a near stagnation during the most recent period (2001–present). 3) Uniform altitudinal dependence of temperature change trends could not be confirmed for all regions, time intervals, and seasons, but sometimes an altitude threshold around 3 km is apparent. 4) Most of the meteorological stations are associated with MODIS temperature warming pixels, and thus regional cooling is missing when considering only the locations of meteorological stations. In summarizing, previous studies based on station observations do not provide a complete picture for the temperature change over the Tibetan Plateau. Remote sensing–based analyses have the potential to find early signals of regional climate changes and assess the impact of global climate changes in complex regional, seasonal, and altitudinal environments.

Corresponding author e-mails: Yanning Guan, guanyn@radi.ac.cn; Danlu Cai, caidl@radi.ac.cn

Abstract

The recent slowdown in global warming has initiated a reanalysis of temperature data in some mountainous regions for understanding the consequences and impact that a hiatus has on the climate system. Spatiotemporal temperature variability is analyzed over the Tibetan Plateau because of its sensitivity to climate change with a station network updated to 2014, and its linkages to remote sensing–based variability of MODIS daytime and nighttime temperature are investigated. Results indicate the following: 1) Almost all stations have experienced a notable warming in the time interval 1961–2014, with most obvious warming in winter, which depends on the selected time intervals. 2) There is no clear shift from a predominant warming to a near stagnation during the most recent period (2001–present). 3) Uniform altitudinal dependence of temperature change trends could not be confirmed for all regions, time intervals, and seasons, but sometimes an altitude threshold around 3 km is apparent. 4) Most of the meteorological stations are associated with MODIS temperature warming pixels, and thus regional cooling is missing when considering only the locations of meteorological stations. In summarizing, previous studies based on station observations do not provide a complete picture for the temperature change over the Tibetan Plateau. Remote sensing–based analyses have the potential to find early signals of regional climate changes and assess the impact of global climate changes in complex regional, seasonal, and altitudinal environments.

Corresponding author e-mails: Yanning Guan, guanyn@radi.ac.cn; Danlu Cai, caidl@radi.ac.cn
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