A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

doi:10.1007/s40333-024-0053-8

Journal of Arid Land

2024, Vol. 16

Issue (2): 195-219 DOI: 10.1007/s40333-024-0053-8

Research article

A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

LIU Xinyu¹^,²^,³, LI Xuemei¹^,²^,³^,^*(

), ZHANG Zhengrong¹^,²^,³, ZHAO Kaixin¹^,²^,³, LI Lanhai⁴^,⁵

¹Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
²National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
³Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
⁴State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
⁵Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China

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Abstract

Climate warming profoundly affects hydrological changes, agricultural production, and human society. Arid and semi-arid areas of China are currently displaying a marked trend of warming and wetting. The Chinese Tianshan Mountains (CTM) have a high climate sensitivity, rendering the region particularly vulnerable to the effects of climate warming. In this study, we used monthly average temperature and monthly precipitation data from the CN05.1 gridded dataset (1961-2014) and 24 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to assess the applicability of the CMIP6 GCMs in the CTM at the regional scale. Based on this, we conducted a systematic review of the interannual trends, dry-wet transitions (based on the standardized precipitation index (SPI)), and spatial distribution patterns of climate change in the CTM during 1961-2014. We further projected future temperature and precipitation changes over three terms (near-term (2021-2040), mid-term (2041-2060), and long-term (2081-2100)) relative to the historical period (1961-2014) under four shared socio-economic pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). It was found that the CTM had experienced significant warming and wetting from 1961 to 2014, and will also experience warming in the future (2021-2100). Substantial warming in 1997 was captured by both the CN05.1 derived from interpolating meteorological station data and the multi-model ensemble (MME) from the CMIP6 GCMs. The MME simulation results indicated an apparent wetting in 2008, which occurred later than the wetting observed from the CN05.1 in 1989. The GCMs generally underestimated spring temperature and overestimated both winter temperature and spring precipitation in the CTM. Warming and wetting are more rapid in the northern part of the CTM. By the end of the 21^st century, all the four SSP scenarios project warmer and wetter conditions in the CTM with multiple dry-wet transitions. However, the rise in precipitation fails to counterbalance the drought induced by escalating temperature in the future, so the nature of the drought in the CTM will not change at all. Additionally, the projected summer precipitation shows negative correlation with the radiative forcing. This study holds practical implications for the awareness of climate change and subsequent research in the CTM.

Key words： climate change Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) shared socio-economic pathway (SSP) scenarios standardized precipitation index (SPI) Chinese Tianshan Mountains

Received: 31 August 2023 Published: 29 February 2024

Corresponding Authors: ^*LI Xuemei (E-mail: lixuemei@mail.lzjtu.cn)

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	LIU Xinyu
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	LI Lanhai

Cite this article:

LIU Xinyu, LI Xuemei, ZHANG Zhengrong, ZHAO Kaixin, LI Lanhai. A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains. Journal of Arid Land, 2024, 16(2): 195-219.

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http://jal.xjegi.com/10.1007/s40333-024-0053-8 OR http://jal.xjegi.com/Y2024/V16/I2/195

Fig. 1 Overview of the Chinese Tianshan Mountains (CTM) based on the digital elevation model (DEM) derived from the 90 m resolution Shuttle Radar Topography Mission (SRTM) dataset. The DEM data were downloaded from the Geospatial Data Cloud (http://www.gscloud.cn).

Table 1 Basic details of the 24 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6)

Table 2 Climatic moisture categories based on the standardized precipitation index (SPI)

Fig. 2 Scatter plot of the relationship of the correlation coefficients between monthly average temperature and monthly precipitation after bias correlation for the 24 global climate models (GCMs) and multi-model ensemble (MME)

Table 3 Quantitative evaluation results of monthly average temperature and monthly precipitation simulations after bias correlation from the 24 GCMs

Fig. 3 Temporal variations in annual average temperature (a) and annual precipitation (b) from the MME and CN05.1 during 1961-2014

Fig. 4 Increase rates of temperature and precipitation simulated from the 24 GCMs, CN05.1, and MME during 1961-2014

Fig. 5 Portrait diagrams showing the intra-annual distribution of the cumulative departure (CD) values of temperature and precipitation between the GCMs and CN05.1 during 1961-2014 at different time scales

Fig. 6 Temporal variations of standardized precipitation index (SPI) based on the CN05.1 (a1, b1, c1, and d1) and MME (a2, b2, c2, and, d2) during 1961-2014 at the 3-, 12-, 24-, and 60-month time scales. CN05.1_SP13, CN05.1_SPI12, CN05.1_SPI24, and CN05.1_SPI60 denote the SPI values at the 3-, 12-, 24-, and 60-month time scales, respectively, based on the CN05.1. MME_SPI3, MME_SPI12, MME_SPI24, and MME_SPI60 denote the SPI values at the 3-, 12-, 24-, and 60-month time scales, respectively, based on the MME. The SPI with values between -1.0 (the red dotted line) and 1.0 (the blue dotted line) indicates that the dry and wet conditions are normal in the region.

Fig. 7 Spatial distribution of the rates of change in annual average temperature (a1 and b1) and annual precipitation (a2 and b2) based on the CN05.1 and MME during 1961-2014. The dotted areas indicate that the change trends of temperature and precipitation are significant at the P<0.05 level, and the lined areas indicate that the change trends are significant at the P<0.01 level.

Fig. 8 Temporal variations in annual average temperature (a) and annual precipitation (b) during 2021-2100 under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios compared to the values from the CN05.1 in the historical period (1961-2014). The colored shaded areas indicate the range of simulated fluctuations for the individual models under a given scenario, and the solid lines indicate the simulated mean value after averaging over multiple models under a given scenario. SSP, shared socio-economic pathway.

Table 4 Rates of change in annual average temperature and annual precipitation during 2021-2100 under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios

Fig. 9 Magnitude of increasing of monthly average temperature (a) and monthly precipitation (b) during 2021-2100 under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios relative to the historical period

Fig. 10 Violin plot showing the trends and distribution of annual average temperature (a) and annual precipitation (b) in near-term (2021-2040), mid-term (2041-2060), and long-term (2081-2100) under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The violin shape indicates the distributional characteristics of annual average temperature and annual precipitation, with the width representing the degree of concentration. Black dots indicate the data points of annual average temperature and annual precipitation in different years. Box boundaries indicate the 25^th and 75^th percentiles across years, and whiskers below and above the box indicate the 10^th and 90^th percentiles, respectively. The horizontal line within each box indicates the median of data points.

Fig. 11 Spatial distribution of the rate of change in annual average temperature increase (warming) in near-term (2021-2040), mid-term (2041-2060), and long-term (2081-2100) under the SSP1-2.6 (a1-a3), SSP2-4.5 (b1-b3), SSP3-7.0 (c1-c3), and SSP5-8.5 (d1-d3) scenarios. SSP1-2.6_Near, SSP2-4.5_Near, SSP3-7.0_Near, and SSP5-8.5_Near mean SSPs in near-term; SSP1-2.6_Mid, SSP2-4.5_Mid, SSP3-7.0_Mid, and SSP5-8.5_Mid mean SSPs in mid-term; SSP1-2.6_Long, SSP2-4.5_Long, SSP3-7.0_Long, and SSP5-8.5_Long mean SSPs in long-term. The dotted areas indicate that the change trends of temperature are significant at the P<0.05 level, and the lined areas indicate that the change trends are significant at the P<0.01 level.

Fig. 12 Spatial distribution of the rate of change in annual precipitation in near-term (2021-2040), mid-term (2041-2060), and long-term (2081-2100) under the SSP1-2.6 (a1-a3), SSP2-4.5 (b1-b3), SSP3-7.0 (c1-c3), and SSP5-8.5 (d1-d3) scenarios. The red dotted areas indicate that the change trends of precipitation are significant at the P<0.05 level, and the lined areas indicate that the change trends are significant at the P<0.01 level.

Fig. 13 Temporal variations of the SPI values based on the MME during 2021-2100 at the 12- and 60-month time scales under the SSP1-2.6 (a1 and a2), SSP2-4.5 (b1 and b2), SSP3-7.0 (c1 and c2), and SSP5-8.5 (d1 and d2) scenarios. SSP1-2.6_SPI12, SSP2-4.5_SPI12, SSP3-7.0_SPI12, and SSP5-8.5_SPI12 denote the SPI values at the 12-month time scale under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. SSP1-2.6_SPI60, SSP2-4.5_SPI60, SSP3-7.0_SPI60, and SSP5-8.5_SPI60 denote the SPI values at the 60-month time scale under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. The SPI with values between -1.0 (the red dotted line) and 1.0 (the blue dotted line) indicates that the drought and wet conditions are normal in the region.


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