Change in diurnal temperature range on the Tibetan plateau in the last 40 years and its influencing factors

The Tibetan Plateau (TP) is a sensitive area for climate change, with its temperature affected by global warming. However, the variation in the diurnal temperature range (DTR) remains unclear. Therefore, using observational data from 115 stations and ERA5 reanalysis data for TP and the surrounding regions from 1980 to 2022, this study explores the change in DTR and its causes. It also examines temperature simulations of TP in 2000-2021. The results show that: (1) From 1980 to 2022, both the annual mean maximum temperature (Tmax) and minimum temperature (Tmin) in TP show an upward trend, increasing by 0.33 °C/10a and 0.47 °C/10a respectively. The annual mean DTR exhibits a downward trend, decreasing by -0.14 °C/10a. Moreover, the rates of change for Tmax and Tmin are highest during winter, at 0.47 °C/10a and 0.59 °C/10a, respectively. The DTR declines most rapidly in summer, at - 0.17 °C/10a. The current temperature trend in TP is expected to persist. (2) During 1980-2022, TCC, SMOIS and Rnl values in TP are higher in the east than in the west, with a significant increase in the western region. The rates of increase surpass 0.005/10a for TCC, 0.005 m<sup>3</sup>·m<sup>-3</sup>/10a for SMOIS, and 0.5 W·m<sup>-2</sup>/10a for Rnl. Rns is higher in the west and lower in the east, with significant increases in the east, reaching up to 2.5 W·m<sup>-2</sup>/10a. In summer, the rates of change for all four factors in the western part of TP pass the significance test of α = 95%. (3) Simulations based on the QTP-HRAD data are generally lower than observed values. Simulation results indicate that the annual mean Tmax and Tmin in TP are warming at rates over 0.6 °C/10a. The DTR is decreasing at rates below - 0.6 °C/10a in the eastern regions of TP. During autumn, Tmax and Tmin exhibit the most pronounced changes with increases exceeding 1.6 °C/10a, while DTR drops most sharply in summer, with decreases below - 1.6 °C/10a. (4) Analysis of observational and ERA5 reanalysis data from 1980 to 2022 reveals that the three temperature variables in TP are negatively correlated with TCC, SMOIS, and Rnl, and positively correlated with Rns. Validation using QTP-HRAD data from 2000 to 2021 shows that SMOIS negatively correlates with Tmax in the northern region and positively with Tmin in the southern region, and negatively with DTR. Rnl and Rns exhibit negative correlations with all three temperature variables and positive correlations respectively. This study enhances understanding of the impacts of climate change on TP and the surrounding areas.

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Publication Details

Journal
Scientific Reports
Published
2026-03-13
DOI
https://doi.org/10.1038/s41598-026-43200-9
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Change in diurnal temperature range on the Tibetan plateau in the last 40 years and its influencing factors

Xiaohang Wen, Maoshan Li, Mei Chen, Xian Zhu et al.
Scientific Reports
Climate variability and models
article

Change in diurnal temperature range on the Tibetan plateau in the last 40 years and its influencing factors

Xiaohang Wen, Maoshan Li, Mei Chen, Xian Zhu, Juan Feng
article en

Abstract

The Tibetan Plateau (TP) is a sensitive area for climate change, with its temperature affected by global warming. However, the variation in the diurnal temperature range (DTR) remains unclear. Therefore, using observational data from 115 stations and ERA5 reanalysis data for TP and the surrounding regions from 1980 to 2022, this study explores the change in DTR and its causes. It also examines temperature simulations of TP in 2000-2021. The results show that: (1) From 1980 to 2022, both the annual mean maximum temperature (Tmax) and minimum temperature (Tmin) in TP show an upward trend, increasing by 0.33 °C/10a and 0.47 °C/10a respectively. The annual mean DTR exhibits a downward trend, decreasing by -0.14 °C/10a. Moreover, the rates of change for Tmax and Tmin are highest during winter, at 0.47 °C/10a and 0.59 °C/10a, respectively. The DTR declines most rapidly in summer, at - 0.17 °C/10a. The current temperature trend in TP is expected to persist. (2) During 1980-2022, TCC, SMOIS and Rnl values in TP are higher in the east than in the west, with a significant increase in the western region. The rates of increase surpass 0.005/10a for TCC, 0.005 m<sup>3</sup>·m<sup>-3</sup>/10a for SMOIS, and 0.5 W·m<sup>-2</sup>/10a for Rnl. Rns is higher in the west and lower in the east, with significant increases in the east, reaching up to 2.5 W·m<sup>-2</sup>/10a. In summer, the rates of change for all four factors in the western part of TP pass the significance test of α = 95%. (3) Simulations based on the QTP-HRAD data are generally lower than observed values. Simulation results indicate that the annual mean Tmax and Tmin in TP are warming at rates over 0.6 °C/10a. The DTR is decreasing at rates below - 0.6 °C/10a in the eastern regions of TP. During autumn, Tmax and Tmin exhibit the most pronounced changes with increases exceeding 1.6 °C/10a, while DTR drops most sharply in summer, with decreases below - 1.6 °C/10a. (4) Analysis of observational and ERA5 reanalysis data from 1980 to 2022 reveals that the three temperature variables in TP are negatively correlated with TCC, SMOIS, and Rnl, and positively correlated with Rns. Validation using QTP-HRAD data from 2000 to 2021 shows that SMOIS negatively correlates with Tmax in the northern region and positively with Tmin in the southern region, and negatively with DTR. Rnl and Rns exhibit negative correlations with all three temperature variables and positive correlations respectively. This study enhances understanding of the impacts of climate change on TP and the surrounding areas.

Scientific Reports
Sun Yat-sen University (CN), Yunnan University (CN), Chengdu University of Information Technology (CN), Beijing Normal University (CN), Gansu Meteorological Bureau (CN), Wuzhou University (CN), Yunnan Center for Disease Control And Prevention (CN)
Chengdu University of Information Technology, Chengdu University
Climate action
Openalex Percentile: Top 31%
Climate variability and models
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