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.
Temporal coverage Variable Data source CRU-TS v4.04 0.5° Monthly 1901–present Precipitation; mean, maximum, and minimum temperatures https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.04/ GPCC 0.25° Monthly 1901–present Precipitation https://opendata.dwd.de/climate_environment/GPCC/html/fulldata-monthly_v2018_doi_download.html CPC 0.5° Daily 1979–present Precipitation; maximum and minimum temperatures https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html NOAA-PRECL 0.5° Monthly 1979–present Precipitation https://psl.noaa.gov/data/gridded/data.precl.html APHRO 0.25° Daily 1951–2015 Precipitation https://www.chikyu.ac.jp/precip/english/ TRMM 3B42-v7 0.25° Daily 1998–present Precipitation https://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 model Horizontal resolution Experiment output Data source 1 REMO2015 HadGEM2-ES 0.44° × 0.44° Historical, RCP 4.5, and RCP 8.5 http://www.remo-rcm.de 2 MPI-ESM-LR 3 NorESM1-M 4 CCLM5-0-2 CNRM-CM5 0.22° × 0.22° http://cordex.clm-community.eu 5 HadGEM2-ES 6 MPI-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.
Variable Index Indicator Definition Unit Extreme
TX90 Warm days Days with Tmax > 90th percentile of the same day during the study period day TN90 Warm nights Days with Tmin > 90th percentile of the same day during the study period day TX10 Cold days Days with Tmax < 10th percentile of the same day during the study period day TN10 Cold nights Days with Tmin < 10th percentile of the same day during the study period day Extreme
R95p Precipitation from very wet days Annual total precipitation when daily precipitation > 95th percentile of the same day during the study period* mm R99p Precipitation from extremely wet days Annual 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.
|Temporal coverage||Variable||Data source|
|CRU-TS v4.04||0.5°||Monthly||1901–present||Precipitation; mean, maximum, and minimum temperatures||https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.04/|
|CPC||0.5°||Daily||1979–present||Precipitation; maximum and minimum temperatures||https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html|