Historical Changes and Future Projections of Extreme Temperature and Precipitation along the Sichuan–Tibet Railway

川藏铁路沿线极端气温和降水变化及未来预估

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  • Corresponding author: Tianbao ZHAO, zhaotb@tea.ac.cn
  • Funds:

    Supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20020201); Breakthrough Project of Strategic Priority Program of Chinese Academy of Sciences (KFZD-SW-426); National Natural Science Foundation of China (41675094 and 41975115); Natural Science Foundation of Shaanxi Province (2021JQ-166); and Open Research Fund of Key Laboratory of the Loess Plateau Soil Erosion and Water Process and Control, Ministry of Water Resources of China (HTGY202002)

  • doi: 10.1007/s13351-021-0175-2

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  • Based on multiresource high-resolution in situ and satellite merged observations along with model simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), this study first investigated historical changes in extreme temperature and precipitation during the period of 1979–2018 in areas along the Sichuan–Tibet Railway, and then projected the future changes in the frequency and intensity of extreme temperature and precipitation under the RCP (Representative Concentration Pathway) 4.5 and 8.5 scenarios. This paper is expected to enhance our understanding of the spatiotemporal variability in the extreme temperature and precipitation along the Sichuan–Tibet Railway, and to provide scientific basis to advance the Sichuan–Tibet Railway construction and operation. The results show that temperatures in the Sichuan–Tibet region display a noticeable warming trend in the past 40 years, and the increase of minimum temperature is significantly higher than that of maximum temperature in the northwest of the region. Significant increase of precipitation is found mainly over the northwest of the Tibetan Plateau. Except for Lhasa and its surrounding areas, precipitation over other areas along the Sichuan–Tibet Railway shows no significant change in the past 40 years, as indicated in five datasets; however, precipitation along the railway has shown a remarkable decrease in the past 20 years in the TRMM satellite dataset. The warm days and nights have clearly increased by 6 and 5 day decade−1 for 1979–2019, while cold days and nights have markedly decreased by about 6.6 and 3.6 day decade−1, respectively. In the past 20 years, the areas with increased precipitation from very wet days and extremely wet days are mainly distributed to the north of the Sichuan–Tibet Railway, while in the areas along the railway itself, the very wet days and extremely wet days are decreasing. Under RCPs 4.5 and 8.5, the temperature in the Sichuan–Tibet region will increase significantly, and the frequency of extreme high (low) temperature events in the late 21st century (2070–2099) will greatly increase (decrease) by about 50%–80% (10%) compared with occurrences in the late 20th century (1970–1999). Meanwhile, the frequency of very wet days and extremely wet days in the Sichuan–Tibet region will increase by about 2%–19% and 2%–5%, respectively, and the areas along the Sichuan–Tibet Railway will be affected by more extreme high temperature and extreme precipitation events.

    基于高分辨率的台站观测与卫星遥感融合的多源观测信息与“协同区域气候降尺度试验”数值模拟试验结果,本文首先分析研究了近40年川藏铁路沿线地区极端气温和极端降水的时空分布演变规律,然后预估了未来两种“典型浓度路径”排放情景下(RCP4.5和8.5)川藏铁路沿线区域极端气温和极端降水发生频率和影响强度,以期为川藏铁路建设和运营提供科学依据。研究表明,近40年川藏地区整体呈现显著的增温趋势,川藏西北部最低气温的增幅明显高于最高气温;而降水主要是在青藏高原西北部呈现显著增加的趋势。川藏区域暖昼和暖夜的日数都呈现显著增加的趋势(+6d/10a和+5d/10a),而冷昼和冷夜的日数则显著减少(-6.6d/10a和-3.6d/10a)。尽管不同观测资料所揭示的极端降水变化存在一定的区域差异,但近20年强降水和极端强降水事件多主要发生在川藏铁路沿线以北地区,而在铁路沿线区域则是减少的趋势。在未来两种排放情景下,21世纪后期(2070–2099年)川藏地区均呈现显著增温的趋势,但极端高(低)温事件的发生频率较历史时期(1970–1999年)明显增加(减少)约50%–80%(10%);与此同时,未来川藏地区强降水和极端强降水事件的发生频率增加2%–19%和2%–5%。这意味着,在未来气候持续变暖情景下,川藏铁路沿线将可能遭受到更多极端高温和极端降水事件的影响。

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  • Fig. 1.  Topography of the Sichuan–Tibet region and the location of the Sichuan–Tibet Railway (black dotted line). The red frame indicates the area along the Sichuan–Tibet Railway.

    Fig. 2.  (a–c) The climatological means (°C) and (d–f) linear trends (°C decade−1) of the mean (Tmean), maximum (Tmax), and minimum (Tmin) temperatures derived from the CRU dataset over the Sichuan–Tibet region from 1979 to 2018. The stippled areas in (d–f) indicate that the trend is statistically significant at the 5% level.

    Fig. 3.  Time series of the mean (Tmean), maximum (Tmax), and minimum temperature (Tmin) anomalies derived from the CRU dataset averaged along the Sichuan–Tibet Railway for 1979–2018. S indicates the slope of the linear regression of the time series.

    Fig. 4.  Spatial distributions of multiyear (1979–2019) mean precipitation (mm) in the Sichuan–Tibet region from the six observational datasets: (a) GPCC, (b) CRU, (c) NOAA, (d) CPC, (e) APHRO, and (f) TRMM. Note that it is 1979–2015 mean for APHRO and 1998–2019 for TRMM.

    Fig. 5.  As in Fig. 4, but for the linear trends (mm decade−1) of annual precipitation. The stippled areas indicate that the trend is statistically significant at the 5% level.

    Fig. 6.  Time series of annual precipitation anomaly percentages (%) derived from the six observational datasets averaged along the Sichuan–Tibet Railway for 1979–2019 (1979–2015 for APHRO and 1998–2019 for TRMM). S indicates the slope of the linear regression of the time series.

    Fig. 7.  The estimated linear trends (day decade−1) of extreme cold and warm events based on the CPC daily observation data in the Sichuan–Tibet region from 1979 to 2019. (a) TX90: warm days; (b) TN90: warm nights; (c) TX10: cold days; and (d) TN10: cold nights. The stippled areas indicate that the trend is statistically significant at the 5% level.

    Fig. 8.  Time series of extreme cold and warm events along the Sichuan–Tibet Railway based on the CPC daily observational data. (a) TX90 (warm days) and TN90 (warm nights); (b) TX10 (cold days) and TN10 (cold nights). S indicates the slope of the linear regression of the time series.

    Fig. 9.  The estimated linear trends (mm decade−1) of annual precipitation for (a–c) very wet days (R95p) and (d–f) extremely wet days (R99p) in the Sichuan–Tibet region based on the daily observational data of (a, d) CPC, (b, e) APHRO, and (c, f) TRMM for 1979–2019, 1979–2015, and 1998–2019, respectively. The stippled areas indicate that the trend is statistically significant at the 5% level.

    Fig. 10.  Variations in extreme precipitation along the Sichuan–Tibet Railway based on the daily precipitation data from CPC, APHRO, and TRMM for 1979–2019, 1979–2015, and 1998–2019, respectively. (a) and (b) show the annual precipitation anomalies (mm) for very wet days (R95p) and extremely wet days (R99p); (c) and (d) show the frequency of very wet days (R95p) and extremely wet days (R99p).

    Fig. 11.  Spatial distributions of multiyear mean Tmax, Tmin, and precipitation in the Sichuan–Tibet region from the CRU data and the multimodel ensemble mean (MEM) of the regional climate models (RCMs) during 1970–2005. The differences between the CRU data and the multimodel ensemble mean are also shown. The stippled areas in (c, f, i) indicate that all simulations agree on the sign of the differences.

    Fig. 12.  Time series of the annual anomaly of (a) Tmax (°C), (b) Tmin (°C), and (c) precipitation (%) derived from the CRU data and MEM of the RCMs averaged along the Sichuan–Tibet Railway for 1970–2005. Blue shadings show the ranges of the individual model simulations.

    Fig. 13.  Frequency changes of extreme cold and warm events in the future period (2070–2099) relative to the historical period (1970–1999) in the Sichuan–Tibet region under different emission scenarios (RCP 4.5 and 8.5) based on the ensemble mean of the dynamic downscaling results from CORDEX high-resolution RCMs. (a) and (b) show the frequency changes of extreme high temperature events, and (c) and (d) refer to the frequency changes of extreme low temperature events. Extreme high (low) temperature events are defined by calculating the number of days when the extreme temperature in the future period exceeds (falls below) the threshold value of the 90th (10th) percentile in the historical period. The stippled areas indicate that all simulations agree on the sign of the frequency changes.

    Fig. 14.  As in Fig. 13, but for frequency changes (%) of extreme precipitation events. (a) and (b) are the frequency changes of very wet days, while (c) and (d) are the frequency changes of extremely wet days. The stippled areas indicate that all simulations agree on the sign of the frequency changes.

    Fig. 15.  The probability density function (PDF) distribution of temperature and precipitation changes along the Sichuan–Tibet Railway: (a) Tmax, (b) Tmin, and (c) precipitation. Black lines indicate the historical period (1970–1999), and red and blue lines respectively indicate the future period (2070–2099) under RCP 4.5 and RCP 8.5 scenarios. The anomalies of temperature are normalized at each grid box by using the standard deviation of the 1970–1999 period before regional averaging.

    Table 1.  Basic information on the observational data used in this study

    DatasetHorizontal
    resolution
    Temporal
    resolution
    Temporal coverageVariableData source
    CRU-TS v4.040.5°Monthly1901–presentPrecipitation; mean, maximum, and minimum temperatureshttps://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.04/
    GPCC0.25°Monthly1901–presentPrecipitationhttps://opendata.dwd.de/climate_environment/GPCC/html/fulldata-monthly_v2018_doi_download.html
    CPC0.5°Daily1979–presentPrecipitation; maximum and minimum temperatureshttps://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html
    NOAA-PRECL0.5°Monthly1979–presentPrecipitationhttps://psl.noaa.gov/data/gridded/data.precl.html
    APHRO0.25°Daily1951–2015Precipitationhttps://www.chikyu.ac.jp/precip/english/
    TRMM 3B42-v70.25°Daily1998–presentPrecipitationhttps://disc.gsfc.nasa.gov/datasets/TRMM_3B42_Daily_7/summary
    Download: Download as CSV

    Table 2.  Basic information on the six regional climate simulation experiments used in this study

    No.Regional climate model (RCM)Driving modelHorizontal resolutionExperiment outputData source
    1REMO2015HadGEM2-ES0.44° × 0.44°Historical, RCP 4.5, and RCP 8.5http://www.remo-rcm.de
    2MPI-ESM-LR
    3NorESM1-M
    4CCLM5-0-2CNRM-CM50.22° × 0.22°http://cordex.clm-community.eu
    5HadGEM2-ES
    6MPI-ESM-LR
    Download: Download as CSV

    Table 3.  Definitions of the six extreme climate indices chosen for this study

    VariableIndexIndicatorDefinitionUnit
    Extreme
    temperature
    TX90Warm daysDays with Tmax > 90th percentile of the same day during the study periodday
    TN90Warm nightsDays with Tmin > 90th percentile of the same day during the study periodday
    TX10Cold daysDays with Tmax < 10th percentile of the same day during the study periodday
    TN10Cold nightsDays with Tmin < 10th percentile of the same day during the study periodday
    Extreme
    precipitation
    R95pPrecipitation from very wet daysAnnual total precipitation when daily precipitation > 95th percentile of the same day during the study period*mm
    R99pPrecipitation from extremely wet daysAnnual total precipitation when daily precipitation > 99th percentile of the same day during the study period*mm
    * The study period of CPC, APHRO, and TRMM 3B42-V7 is 1979–2019, 1979–2015, and 1998–2019, respectively.
    Download: Download as CSV
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Historical Changes and Future Projections of Extreme Temperature and Precipitation along the Sichuan–Tibet Railway

    Corresponding author: Tianbao ZHAO, zhaotb@tea.ac.cn
  • 1. College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100
  • 2. Key Laboratory of Regional Climate–Environment Research for East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 3. Department of Lower Atmosphere Observation Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 4. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 5. Key Laboratory of Cloud–Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
Funds: Supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20020201); Breakthrough Project of Strategic Priority Program of Chinese Academy of Sciences (KFZD-SW-426); National Natural Science Foundation of China (41675094 and 41975115); Natural Science Foundation of Shaanxi Province (2021JQ-166); and Open Research Fund of Key Laboratory of the Loess Plateau Soil Erosion and Water Process and Control, Ministry of Water Resources of China (HTGY202002)

Abstract: 

Based on multiresource high-resolution in situ and satellite merged observations along with model simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), this study first investigated historical changes in extreme temperature and precipitation during the period of 1979–2018 in areas along the Sichuan–Tibet Railway, and then projected the future changes in the frequency and intensity of extreme temperature and precipitation under the RCP (Representative Concentration Pathway) 4.5 and 8.5 scenarios. This paper is expected to enhance our understanding of the spatiotemporal variability in the extreme temperature and precipitation along the Sichuan–Tibet Railway, and to provide scientific basis to advance the Sichuan–Tibet Railway construction and operation. The results show that temperatures in the Sichuan–Tibet region display a noticeable warming trend in the past 40 years, and the increase of minimum temperature is significantly higher than that of maximum temperature in the northwest of the region. Significant increase of precipitation is found mainly over the northwest of the Tibetan Plateau. Except for Lhasa and its surrounding areas, precipitation over other areas along the Sichuan–Tibet Railway shows no significant change in the past 40 years, as indicated in five datasets; however, precipitation along the railway has shown a remarkable decrease in the past 20 years in the TRMM satellite dataset. The warm days and nights have clearly increased by 6 and 5 day decade−1 for 1979–2019, while cold days and nights have markedly decreased by about 6.6 and 3.6 day decade−1, respectively. In the past 20 years, the areas with increased precipitation from very wet days and extremely wet days are mainly distributed to the north of the Sichuan–Tibet Railway, while in the areas along the railway itself, the very wet days and extremely wet days are decreasing. Under RCPs 4.5 and 8.5, the temperature in the Sichuan–Tibet region will increase significantly, and the frequency of extreme high (low) temperature events in the late 21st century (2070–2099) will greatly increase (decrease) by about 50%–80% (10%) compared with occurrences in the late 20th century (1970–1999). Meanwhile, the frequency of very wet days and extremely wet days in the Sichuan–Tibet region will increase by about 2%–19% and 2%–5%, respectively, and the areas along the Sichuan–Tibet Railway will be affected by more extreme high temperature and extreme precipitation events.

川藏铁路沿线极端气温和降水变化及未来预估

基于高分辨率的台站观测与卫星遥感融合的多源观测信息与“协同区域气候降尺度试验”数值模拟试验结果,本文首先分析研究了近40年川藏铁路沿线地区极端气温和极端降水的时空分布演变规律,然后预估了未来两种“典型浓度路径”排放情景下(RCP4.5和8.5)川藏铁路沿线区域极端气温和极端降水发生频率和影响强度,以期为川藏铁路建设和运营提供科学依据。研究表明,近40年川藏地区整体呈现显著的增温趋势,川藏西北部最低气温的增幅明显高于最高气温;而降水主要是在青藏高原西北部呈现显著增加的趋势。川藏区域暖昼和暖夜的日数都呈现显著增加的趋势(+6d/10a和+5d/10a),而冷昼和冷夜的日数则显著减少(-6.6d/10a和-3.6d/10a)。尽管不同观测资料所揭示的极端降水变化存在一定的区域差异,但近20年强降水和极端强降水事件多主要发生在川藏铁路沿线以北地区,而在铁路沿线区域则是减少的趋势。在未来两种排放情景下,21世纪后期(2070–2099年)川藏地区均呈现显著增温的趋势,但极端高(低)温事件的发生频率较历史时期(1970–1999年)明显增加(减少)约50%–80%(10%);与此同时,未来川藏地区强降水和极端强降水事件的发生频率增加2%–19%和2%–5%。这意味着,在未来气候持续变暖情景下,川藏铁路沿线将可能遭受到更多极端高温和极端降水事件的影响。

    • The Sichuan–Tibet Railway starts at Chengdu, Sichuan Province, in the east, travels west through Ya’an, Kangding, Changdu, Nyingchi, and Shannan, and finally reaches Lhasa in the Tibet Autonomous Region, after a total journey length of about 1600 km. The Sichuan–Tibet Railway is a key project in China’s 13th Five-Year Plan and an important infrastructure for its western development strategy. The construction of the Sichuan–Tibet Railway has been a century’s dream of the Chinese people, and has great strategic and military significance. After completion, it will be one of the most important railway links to Tibet, which is of great significance in promoting the leapfrog development of the Tibetan economy and society.

      The area along the Sichuan–Tibet Railway includes some of the most complex terrains in the world. The Tibetan Plateau, at the highest altitude in the world; the Hengduan Mountain area; and the Sichuan basin constitute the main features of the landform in the Sichuan–Tibet region. The high altitude and complex terrain result in strong and complex mountain atmospheric systems (Wu et al., 2013), which can easily cause extreme weather and climate disasters, and seriously threaten the construction and operation of the Sichuan–Tibet Railway. For example, strong wind and wind shear can cause damage to railway bridge structures and running vehicles (Gawthorpe, 1994); heavy precipitation can aggravate mountain disasters such as ice avalanche, landslide, and debris flow (Li S. S. et al., 2020); and extreme low temperatures and large temperature differences can seriously damage railway subgrade and facilities (Chinowsky et al., 2019).

      In recent years, some progress has been made in research on extreme climate events in the Sichuan and Tibet regions. Sun et al. (2017) analyzed the trend of extreme precipitation events in Sichuan from 1971 to 2014 using daily precipitation data. Their results showed that precipitation in Sichuan showed a weak downward trend in the past 40 years, and that there appeared a trend of warming and drying in many areas, except for the western Sichuan highland. This result is basically consistent with the conclusion of Huang et al. (2014). As for the Tibetan Plateau, which is a sensitive and key area of global climate change, the annual average temperature rise rate has been about twice that of the global average since the 1980s (Yao, 2019). The annual average maxi-mum and minimum temperatures also show significant increasing trends, which are very notable in some regions (Niu et al., 2004; Ding and Zhang, 2008). Higher warming intensifies the water cycle (Allen and Ingram, 2002; Overland and Wang, 2010; You et al., 2011; Herring et al., 2015), and extreme precipitation events may become more frequent and more intense. Studies also reveal that precipitation extremes over the Tibetan Plateau have shown an increasing trend in recent years (You et al., 2008). However, due to the inconsistency in observational data periods and site selection, research conclusions vary from one another. For example, based on the daily precipitation observational data from 71 meteorological stations, You et al. (2008) found that extreme precipitation increased in the north and south of the central and eastern Tibetan Plateau, and decreased in the central region, from 1961 to 2005. However, Cao et al. (2019) found that summer precipitation showed a decreasing trend in the central and eastern Tibetan Plateau during 1961–2014, while the other regions showed an increasing trend. Nevertheless, most of the above studies concentrated on the central and eastern regions of the Tibetan Plateau, while the areas along the Sichuan–Tibet Railway, which have a great impact on railway construction and operation, are not considered as a whole.

      Compared with extreme climate changes in the historical period, the prediction of future climate extremes has drawn more attention in recent years (Boo et al., 2006; Mei et al., 2018). The prediction results of several climate models under the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios (SRES; Jiang et al., 2012) indicate that the number of heatwave days on the Tibetan Plateau will increase by more than 10 times by the end of the 21st century, and that warm nights will increase by more than 4 times; however, frost days will show a decreasing trend. Under the background of significant warming, the intensity and frequency of extreme precipitation will increase—for instance, maximum 5-day precipitation will increase by ~25%–45%, and precipitation intensity by ~10%–26%. Zhou et al. (2014) analyzed the long-term changes of extreme weather and climate events in Southwest China, including the Tibetan Plateau, in the late 21st century (2081–2100) using the experimental results of 24 models from the 5th phase of the Coupled Model Intercomparison Project (CMIP5). Their results showed that extreme events related to high temperature will increase, with a greater increase under the Representative Concentration Pathway (RCP) 8.5 scenario than the RCP 4.5 scenario, while extreme events related to low temperature will decrease, with a greater decrease under RCP 8.5 than RCP 4.5. The extreme events related to precipitation show an increasing trend, and the increase under RCP 8.5 is greater than that under RCP 4.5. The above studies provide strong theoretical support for dealing with regional extreme climate changes in the future. However, as already stated, complex terrain factors in the Sichuan–Tibet region lead to extremely complex climate changes, so in this case there are still large errors in the simulation of extreme climate by using global climate models.

      In view of this, this paper uses high-resolution multisource temperature and precipitation data derived from meteorological stations and satellite remote sensing monitoring, as well as the Coordinated Regional Climate Downscaling Experiment (CORDEX), to analyze and study the large-scale climate change background covering the whole area along the Sichuan–Tibet Railway, focusing on extreme weather and climate events as well as their future changes that could affect the construction and operation of the Sichuan–Tibet Railway. The results of this study can therefore provide theoretical basis and application support for the smooth construction and future safe operation of the Sichuan–Tibet Railway.

      The organization of this paper is as follows. Section 2 describes the observational data, model simulations, and analysis methods used in this study. Climatological means and trends of temperature and precipitation, extremes of temperature and precipitation, and their future projections under different emission scenarios are described in Section 3. A summary and concluding remarks are given in Section 4.

    2.   Data and methods
    • Due to the complex topography and lack of surface observational data in the Sichuan–Tibet Railway region, it is best to study the extreme climate characteristics of this area with the help of high-resolution satellite remote sensing monitoring information. However, the existing data of this kind [such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM)] have a relatively short time span, which makes it difficult for use in the study of long-term variations of extreme climate. Therefore, we collected high-resolution multisource data based on meteorological station observations and satellite remote sensing retrievals to reveal the characteristics and evolution of extreme climate changes along the Sichuan–Tibet Railway. These observational data include: the latest version of the climate dataset (CRU-TS v4.04; Jones and Osborn, 2020) from the Climatic Research Unit (CRU) of the University of East Anglia in the UK; the latest version of the global high-resolution grid precipitation dataset from the Global Precipitation Climatology Centre (GPCC) in Germany (Schneider et al., 2017); the monthly land precipitation reconstruction dataset from the NOAA (NOAA-PRECL; Chen et al., 2002); and the NOAA’s global precipitation Climate Prediction Center (CPC) dataset (Xie et al., 2007; Chen et al., 2008), derived from daily observation and satellite remote sensing retrieval. In addition, Japan’s Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) data (abbreviated as APHRO; Yatagai et al., 2009, 2012) and multisatellite precipitation analysis products from the Tropical Rainfall Measuring Mission (TRMM) 3B42-V7 (Huffman et al., 2007, 2010) were also collected. Basic information about the observational data used in this study is shown in Table 1.

      DatasetHorizontal
      resolution
      Temporal
      resolution
      Temporal coverageVariableData source
      CRU-TS v4.040.5°Monthly1901–presentPrecipitation; mean, maximum, and minimum temperatureshttps://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.04/
      GPCC0.25°Monthly1901–presentPrecipitationhttps://opendata.dwd.de/climate_environment/GPCC/html/fulldata-monthly_v2018_doi_download.html
      CPC0.5°Daily1979–presentPrecipitation; maximum and minimum temperatureshttps://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html
      NOAA-PRECL0.5°Monthly1979–presentPrecipitationhttps://psl.noaa.gov/data/gridded/data.precl.html
      APHRO0.25°Daily1951–2015Precipitationhttps://www.chikyu.ac.jp/precip/english/
      TRMM 3B42-v70.25°Daily1998–presentPrecipitationhttps://disc.gsfc.nasa.gov/datasets/TRMM_3B42_Daily_7/summary

      Table 1.  Basic information on the observational data used in this study

    • According to the simulation and prediction results from the CMIP, the frequency and intensity of extreme weather and climate events show a tendency to increase in many regions of the world under the background of future climate warming (Guo et al., 2017); in particular, the results indicate that high-temperature heatwaves and extremely heavy precipitation events will pose a serious threat to human living environments and socially sustainable development. Therefore, predicting future climate changes, especially extreme climate changes along the Sichuan–Tibet Railway, is of great significance for the operation of the railway and for disaster prevention and mitigation. However, due to the low resolution of gene-ral circulation models, it is impossible to describe the characteristics of the underlying surface in detail, and so these models cannot be used directly for the simulation and prediction of regional climate changes along the railway. Therefore, we collected six regional climate simulation experiments with high resolution (0.22° and 0.44°) from the REMO2015 (Remedio et al., 2019; Top et al., 2020) and CCLM5-0-2 (Zhu et al., 2019) of the CORDEX to predict future climate changes, especially the frequency of extreme climate events, along the Sichuan–Tibet Railway. The output information of the simulation experiments used in this paper are shown in Table 2.

      No.Regional climate model (RCM)Driving modelHorizontal resolutionExperiment outputData source
      1REMO2015HadGEM2-ES0.44° × 0.44°Historical, RCP 4.5, and RCP 8.5http://www.remo-rcm.de
      2MPI-ESM-LR
      3NorESM1-M
      4CCLM5-0-2CNRM-CM50.22° × 0.22°http://cordex.clm-community.eu
      5HadGEM2-ES
      6MPI-ESM-LR

      Table 2.  Basic information on the six regional climate simulation experiments used in this study

    • The climate extreme indices used in this study are based on the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI), which is recommended by the Commission for Climatology of the World Meteorological Organization (CCI/WMO) and the Climate Variability and Predictability Programme (CLIVAR) of the World Climate Research Programme (WCRP). The climate extreme indices defined by this method have been widely used (Vincent et al., 2005; Alexander et al., 2006; Moberg et al., 2006; Zhang et al., 2011; Li C. X. et al., 2020). The indices selected for extreme temperature include warm days (TX90), warm nights (TN90), cold days (TX10), and cold nights (TN10). The indices for extreme precipitation include very wet days (R95p) and extremely wet days (R99p). The definitions of the six extreme climate indicators are given in Table 3.

      VariableIndexIndicatorDefinitionUnit
      Extreme
      temperature
      TX90Warm daysDays with Tmax > 90th percentile of the same day during the study periodday
      TN90Warm nightsDays with Tmin > 90th percentile of the same day during the study periodday
      TX10Cold daysDays with Tmax < 10th percentile of the same day during the study periodday
      TN10Cold nightsDays with Tmin < 10th percentile of the same day during the study periodday
      Extreme
      precipitation
      R95pPrecipitation from very wet daysAnnual total precipitation when daily precipitation > 95th percentile of the same day during the study period*mm
      R99pPrecipitation from extremely wet daysAnnual total precipitation when daily precipitation > 99th percentile of the same day during the study period*mm
      * The study period of CPC, APHRO, and TRMM 3B42-V7 is 1979–2019, 1979–2015, and 1998–2019, respectively.

      Table 3.  Definitions of the six extreme climate indices chosen for this study

      To eliminate the influence of auto-correlation on the estimation of long-term trends in the time series, the Mann–Kendall Tau-b trend test (Kendall, 1975; Gilbert, 1987) and Sen’s slope estimation method (Helsel and Hirsch, 2002) were used in this study. At the same time, in order to analyze climate changes along the Sichuan–Tibet Railway in a more accurate way, we selected ~28°–32°N, ~89°–106°E (as shown by the red frame in Fig. 1) as the regional average.

      Figure 1.  Topography of the Sichuan–Tibet region and the location of the Sichuan–Tibet Railway (black dotted line). The red frame indicates the area along the Sichuan–Tibet Railway.

    3.   Results
    • In order to reveal the characteristics and evolution of extreme climate changes along the Sichuan–Tibet Railway, we first studied the spatial distribution and long-term changes of temperature and precipitation in the whole Sichuan–Tibet region in the past 40 years. Figure 2 shows the spatial distribution of multiyear (1979–2018) mean states and linear trends of mean, maximum, and minimum temperatures in the Sichuan–Tibet region, based on the latest global high-resolution grid climate dataset (CRU-TS v4.04). It can be seen that the temperature is higher in the southeast of the region and lower in the northwest, and the daily temperature range is larger (about 5–10°C) along the Sichuan–Tibet Railway. From the perspective of long-term changes, in the past 40 years, the temperature has been rising in the Sichuan–Tibet region, especially in the northwest, and the increase of minimum temperature is obviously higher than that of the maximum temperature. However, for the areas along the Sichuan–Tibet Railway (Fig. 3), the increasing ranges of the maximum and minimum temperatures are basically the same, at about 0.2°C decade−1.

      Figure 2.  (a–c) The climatological means (°C) and (d–f) linear trends (°C decade−1) of the mean (Tmean), maximum (Tmax), and minimum (Tmin) temperatures derived from the CRU dataset over the Sichuan–Tibet region from 1979 to 2018. The stippled areas in (d–f) indicate that the trend is statistically significant at the 5% level.

      Figure 3.  Time series of the mean (Tmean), maximum (Tmax), and minimum temperature (Tmin) anomalies derived from the CRU dataset averaged along the Sichuan–Tibet Railway for 1979–2018. S indicates the slope of the linear regression of the time series.

      We collected six sets of precipitation observational data with high resolution to analyze and study the regional precipitation distribution and its long-term change along the Sichuan–Tibet Railway, and to objectively indicate as far as possible the uncertainty caused by the differences among the datasets. Compared with the temperature results, the distribution of precipitation in the Sichuan–Tibet region has more distinct regional terrain characteristics. These data reflect that the high precipitation value areas are mainly centralized in the Sichuan basin and its surrounding areas, with some small precipitation centers also distributed along the Sichuan–Tibet Railway, but there are obvious differences among different datasets (Fig. 4). In contrast, the satellite remote sensing data (such as TRMM 3B42-v7) combined with station observational data can better describe the detailed characteristics of precipitation distribution affected by the local complex terrain (Fig. 4f). In the past 40 years, the precipitation has shown a noticeably increasing trend in the northwest of the Tibetan Plateau (Figs. 5a, c–f). However, except for Lhasa and its surrounding areas, there is no obvious change in the precipitation in other areas along the Sichuan–Tibet Railway (Figs. 5ac). Moreover, in the TRMM dataset, from the perspective of the past 20 years, the precipitation along the Sichuan–Tibet Railway has decreased significantly (Fig. 5f). From the regional average along the Sichuan–Tibet Railway, the interannual variation of precipitation described by multisource data shows good consistency (Fig. 6). In the past 40 years, the precipitation has increased by ~1%–4% decade−1, but has decreased by 2.6% decade−1 since 1998 (Fig. 6).

      Figure 4.  Spatial distributions of multiyear (1979–2019) mean precipitation (mm) in the Sichuan–Tibet region from the six observational datasets: (a) GPCC, (b) CRU, (c) NOAA, (d) CPC, (e) APHRO, and (f) TRMM. Note that it is 1979–2015 mean for APHRO and 1998–2019 for TRMM.

      Figure 5.  As in Fig. 4, but for the linear trends (mm decade−1) of annual precipitation. The stippled areas indicate that the trend is statistically significant at the 5% level.

      Figure 6.  Time series of annual precipitation anomaly percentages (%) derived from the six observational datasets averaged along the Sichuan–Tibet Railway for 1979–2019 (1979–2015 for APHRO and 1998–2019 for TRMM). S indicates the slope of the linear regression of the time series.

    • Based on the CPC daily observational data, we further analyzed the spatial and temporal distribution characteristics of extreme cold and warm climate events in the Sichuan–Tibet region in the past 40 years, and revealed the long-term variations of high and low temperature events along the Sichuan–Tibet Railway. As shown in Fig. 7, warm days and nights in the Sichuan–Tibet region significantly increased, while cold days and nights markedly decreased. This is basically consistent with the trends of extreme cold and warm events in the middle and high latitudes of the Northern Hemisphere under the background of global warming. It should be noted that the urban heat island effect in Lhasa has a significant aggravating effect on the increase (decrease) of warm days and nights (cold days and nights), while the opposite effect occurs in Chengdu. On average, the impact of glo-bal warming is extremely significant along the Sichuan–Tibet Railway. From 1979 to 2019, warm days and nights increased by about 6 and 5 day decade−1, respectively, while cold days and nights decreased by about 6.6 and 3.6 day decade−1 (Fig. 8).

      Figure 7.  The estimated linear trends (day decade−1) of extreme cold and warm events based on the CPC daily observation data in the Sichuan–Tibet region from 1979 to 2019. (a) TX90: warm days; (b) TN90: warm nights; (c) TX10: cold days; and (d) TN10: cold nights. The stippled areas indicate that the trend is statistically significant at the 5% level.

      Figure 8.  Time series of extreme cold and warm events along the Sichuan–Tibet Railway based on the CPC daily observational data. (a) TX90 (warm days) and TN90 (warm nights); (b) TX10 (cold days) and TN10 (cold nights). S indicates the slope of the linear regression of the time series.

      For extreme precipitation, we also used several sets of high-resolution daily precipitation observational data to analyze the long-term change of precipitation for very (extremely) wet days and the frequency of very (extremely) wet days. It is clear that the spatial distributions of the linear trends of annual precipitation for very wet days and extremely wet days are highly inconsistent (Fig. 9), reflecting the obvious regional and local differences. For the past 20 years, the areas with increased precipitation for very wet days and extremely wet days are mainly distributed to the north of the Sichuan–Tibet Railway, while the areas along the railway showed a decreasing trend (Figs. 9c, f). Similarly, differences also exist in terms of the interannual variation characteristics and trends of extreme precipitation averaged along the railway line as revealed by different datasets (Figs. 10a, b). According to the statistical results, the number of heavy precipitation events along the railway is on average about 4–8 days a year, with the highest occurrence of 11 days a year; the number of extremely heavy precipitation events is on average about 0.5–1.5 days a year, with the highest up to 4 days (Figs. 10c, d).

      Figure 9.  The estimated linear trends (mm decade−1) of annual precipitation for (a–c) very wet days (R95p) and (d–f) extremely wet days (R99p) in the Sichuan–Tibet region based on the daily observational data of (a, d) CPC, (b, e) APHRO, and (c, f) TRMM for 1979–2019, 1979–2015, and 1998–2019, respectively. The stippled areas indicate that the trend is statistically significant at the 5% level.

      Figure 10.  Variations in extreme precipitation along the Sichuan–Tibet Railway based on the daily precipitation data from CPC, APHRO, and TRMM for 1979–2019, 1979–2015, and 1998–2019, respectively. (a) and (b) show the annual precipitation anomalies (mm) for very wet days (R95p) and extremely wet days (R99p); (c) and (d) show the frequency of very wet days (R95p) and extremely wet days (R99p).

    • Before using regional climate models (RCMs) to predict the future changes of extreme climate events over the area covering the Sichuan–Tibet Railway, it is necessary to assess the performance of the CORDEX outputs in simulating the historical climatic changes at first. Figure 11 displays spatial distributions of the climatological mean Tmax, Tmin, and precipitation from the CRU data and the multimodel ensemble mean (MEM) of the RCMs in the Sichuan–Tibet region from 1970 to 2005, as well as the mean differences between them. It is clear that the MEM can reproduce well main features of the mean state and its spatial variations in the observed Tmax, Tmin, and precipitation, with pattern correlations of 0.95, 0.96, and 0.70, respectively. Even for individual simulations, their climatological mean patterns are highly correlated with their observed counterpart, with pattern correlations of 0.94–0.96, 0.94–0.97, and 0.41–0.77, respectively. The MEM underestimates the observations by −9 to −3°C over most areas for Tmax, and the differences between the MEM and the observations in Tmin are relatively small. For precipitation, the MEM overestimates the observations over most areas, especially over Southeast Tibet. For long-term changes (Fig. 12), the MEM can basically reproduce the variations of the observations, and the observations are well within the ranges of individual model simulations, especially for Tmin.

      Figure 11.  Spatial distributions of multiyear mean Tmax, Tmin, and precipitation in the Sichuan–Tibet region from the CRU data and the multimodel ensemble mean (MEM) of the regional climate models (RCMs) during 1970–2005. The differences between the CRU data and the multimodel ensemble mean are also shown. The stippled areas in (c, f, i) indicate that all simulations agree on the sign of the differences.

      Figure 12.  Time series of the annual anomaly of (a) Tmax (°C), (b) Tmin (°C), and (c) precipitation (%) derived from the CRU data and MEM of the RCMs averaged along the Sichuan–Tibet Railway for 1970–2005. Blue shadings show the ranges of the individual model simulations.

      Finally, based on the dynamic downscaling results from six CORDEX high-resolution RCMs, we predicted future climate change, especially the frequency of extreme climate events, along the Sichuan–Tibet Railway. The results show that under both the high (RCP 8.5) and middle–low (RCP 4.5) emission scenarios, there will be a significant warming trend in the Sichuan–Tibet region. As for the frequency of extreme cold and warm events, extreme high temperature events in the late 21st century (2070–2099) are predicted to increase by about 50%–80% compared with the historical period (1970–1999), while extreme cold events are predicted to decrease by about 10%. More extreme high temperature events will affect the Sichuan–Tibet Railway (Fig. 13). Under the RCP 4.5 and 8.5 emission scenarios, the frequency of extreme precipitation in the Sichuan–Tibet region will also increase in the future. The frequency of very wet days will increase by about 2%–19% compared with the historical period, and the frequency of extremely wet days will increase by 2%–5% (more than 10% in some areas). The eastern part of the Sichuan–Tibet Railway will be affected by more extreme precipitation events (Fig. 14).

      Figure 13.  Frequency changes of extreme cold and warm events in the future period (2070–2099) relative to the historical period (1970–1999) in the Sichuan–Tibet region under different emission scenarios (RCP 4.5 and 8.5) based on the ensemble mean of the dynamic downscaling results from CORDEX high-resolution RCMs. (a) and (b) show the frequency changes of extreme high temperature events, and (c) and (d) refer to the frequency changes of extreme low temperature events. Extreme high (low) temperature events are defined by calculating the number of days when the extreme temperature in the future period exceeds (falls below) the threshold value of the 90th (10th) percentile in the historical period. The stippled areas indicate that all simulations agree on the sign of the frequency changes.

      Figure 14.  As in Fig. 13, but for frequency changes (%) of extreme precipitation events. (a) and (b) are the frequency changes of very wet days, while (c) and (d) are the frequency changes of extremely wet days. The stippled areas indicate that all simulations agree on the sign of the frequency changes.

      As far as the regional average of the Sichuan–Tibet Railway is concerned, for either maximum temperature or minimum temperature, its probability density function (PDF) is distributed with increasing mean and variance at the same time (Figs. 15a, b), which means that the probability of extremely warm events will increase in the future, while the probability of extremely cold events will decrease. In comparison, the variation of precipitation PDF in the future is mainly reflected in the larger variance (Fig. 15c), indicating that the probability of occurrence of heavy rainfall and drought events will increase.

      Figure 15.  The probability density function (PDF) distribution of temperature and precipitation changes along the Sichuan–Tibet Railway: (a) Tmax, (b) Tmin, and (c) precipitation. Black lines indicate the historical period (1970–1999), and red and blue lines respectively indicate the future period (2070–2099) under RCP 4.5 and RCP 8.5 scenarios. The anomalies of temperature are normalized at each grid box by using the standard deviation of the 1970–1999 period before regional averaging.

    4.   Summary and concluding remarks
    • Based on high-resolution multisource temperature and precipitation data derived from meteorological stations, satellite remote sensing monitoring, and the CORDEX experiments, the background of large-scale climate changes in the areas along the Sichuan–Tibet Railway is systematically analyzed, with a focus on the spatiotemporal distribution and trends of extreme weather and climate events affecting the construction and operation of the railway. The future changes of temperature and precipitation extremes are also predicted. The purpose of this study is to provide theoretical basis and application support for the smooth construction and safe operation of the Sichuan–Tibet Railway in the future. The main conclusions are as follows.

      The spatial distribution of temperature in the Sichuan–Tibet region is characterized by high temperature in the southeast and low temperature in the northwest. The daily temperature range in the areas along the Sichuan–Tibet Railway is relatively large (about 5–10°C). In the past 40 years, the temperature has shown a significant warming trend and, especially in the northwest of Sichuan–Tibet, the increase of the minimum temperature is obviously higher than that of the maximum temperature. However, for the areas along the Sichuan–Tibet Railway, the range of increase in both maximum and minimum temperatures is basically the same (about 0.2°C decade−1). Compared with temperature, the distribution of precipitation shows more regional terrain characteristics in the Sichuan–Tibet region. The high value areas of precipitation are mainly distributed in the Sichuan basin and its surrounding areas, but there are great differences among different datasets. Precipitation presents a significant increasing trend in the northwest of the Tibetan Plateau in the past 40 years. Except for Lhasa and its surrounding areas, there is no significant change in precipitation over other areas along the Sichuan–Tibet Railway. Moreover, in the TRMM dataset, from the perspective of the past 20 years, precipitation has been decreasing significantly along the Sichuan–Tibet Railway.

      Warm days and nights in the Sichuan–Tibet region increased significantly, while cold days and nights decreased. On average, along the Sichuan–Tibet Railway, warm days and nights increased by 6 and 5 day decade−1 for 1979–2019, while cold days and nights decreased by about 6.6 and 3.6 day decade−1 respectively. For extreme precipitation changes shown by different observational datasets, the trends of precipitation for very wet days and extremely wet days are extremely inconsistent in the spatial distribution, reflecting the obvious regional and local differences. In the past 20 years, the areas with increased precipitation from very (extremely) wet days are mainly concentrated to the north of the Sichuan–Tibet Railway, while it is decreasing in the areas along the railway; there are also differences in the interannual variation characteristics and trends of the regional average extreme precipitation along the railway.

      Based on the dynamic downscaling results from six CORDEX high-resolution regional climate models, the future changes of extreme climate events along the Sichuan–Tibet Railway are predicted. It is shown that there will be a significant warming trend in the Sichuan–Tibet region under different emission scenarios (RCP 4.5 and 8.5). In the late 21st century (2070–2099), the frequency of extreme high temperature events will increase by about 50%–80% compared with the historical period (1970–1999), while the frequency of extreme cold events will decrease by about 10%. The Sichuan–Tibet Railway will be affected by more extreme high temperature events. This result is basically consistent with the conclusion of Jiang et al. (2012). Under the medium and high emission scenarios, the frequency of extreme precipitation in the Sichuan–Tibet region will also increase in the future. The frequency of very wet days will increase by about 2%–19% compared with the historical period, and the frequency of extremely wet days will increase by approximately 2%–5%. The eastern part of the Sichuan–Tibet Railway will be affected by more extreme precipitation events. The probability density distribution analysis shows that the probability of extremely warm events will increase and the probability of extremely cold events will decrease in the future along the Sichuan–Tibet Railway, while the probability of extreme heavy precipitation and drought events will increase.

      It is worth emphasizing that there are uncertainties in the future projections. We know that the raw simulations for the future period were downscaled from the global climate models (GCMs). However, the future RCPs of GCMs can differ among the models because of differing model structures, lateral boundary conditions, initial land surface conditions, sea surface temperatures, and emission scenarios (Seth et al., 2007; Hawkins and Sutton, 2009; Peng and Li, 2018).

      Finally, it needs to be emphasized again that due to the inconsistency of observational data periods and site selection, the conclusions in this study may not be consistent based on different datasets. This is also a very important uncertainty factor that may lead to the inaccurate prediction in the future.

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