Attribution of extreme climate events based on CMIP6 high-resolution global climate model: a case study of Guangdong Province, China

Published: 4 December 2023| Version 1 | DOI: 10.17632/ywnfcz2wc8.1


The data of the original data from, The dataset consists of historical simulation data from six models (GFDL-ESM4, CESM2, IPSL-CM6A-LR, MRI-ESM2-0, E3SM2-0, Fgots-G3) from 1995-2014 and one model (MIROC6) from 2081-2100 under ssp245 scenarios To simulate the data composition. The data contains two parts, temperature and precipitation, in degrees Celsius and millimeters per day, respectively, with the same spatial resolution of 100km. Each part of the historical data contains four scenarios: historical, hist-GHG, hist-nat, and hist-aer, which respectively represent the historical whole scenario data, the simulation data only under the influence of greenhouse gases, the simulation data only under the influence of natural factors, and the simulation data only under the influence of aerosols. The future data are ssp245, SSP245-GHG, SSP245-NAT, and SSP245-AER, which respectively represent the data under the corresponding future scenarios. The Cumulative distribution map folder is used to plot the cumulative distribution curve of extreme values. Therefore, the cumulative distribution Map folder is used to plot the cumulative distribution curve of extreme values. The cumulative distribution map folder is used to plot the cumulative distribution curve of extreme values. In the Cumulative distribution map folder, pr stands for the precipitation data folder and tas stands for the temperature data folder. The Cumulative Distribution Map contains data from 1995-2014 and 2081-2100 respectively. The data under these two time periods include historical, hist-GHG, hist-nat, hist-aer, and ssp245, SSP245-GHG, SSP245-NAT, and SSP245-AER respectively. For example, file represents the monthly average maximum data of precipitation in Guangdong Province during 1995-2014 simulated by CESM2 model under the influence of greenhouse gases only. The data in the Spatial distribution map folder are used to plot the spatial distribution maps of mean temperature and precipitation and extreme temperature and precipitation from 1995 to 2014, in which the extreme temperature and precipitation are represented by the maximum value and 90th percentile value, respectively, and are represented by max and m90. mean temperature and precipitation data are expressed as mean. The data range from 108.5-118.5 east and 19-26.5 north. In the Spatial distribution map folder, pr represents the precipitation data folder and tas represents the temperature data folder. In each folder, mean, m90 and max represent three kinds of processed data respectively. Among them, the file name containing CN05 is the actual observation data with a resolution of 25km, which is used for comparison with the simulation data of other models. For example, the file represents the 90th percentile data of precipitation at this latitude and longitude during 1995-2014 simulated by the CESM2 model.


Steps to reproduce

1. The model that accords with a requirement on the website to download data, It includes six daily precipitation and temperature data of GFDL-ESM4, CESM2, IPSL-CM6A-LR, MRI-ESM2-0, E3SM2-0 and FGOES-G3. 2. In the Linux system, cdo was used to process data in nc format, and the time, longitude and latitude of all data were cut. The instruction for cutting time was "cdo selyeear,1995/2014 input output"; Instructions for the cut out of the longitude and latitude "cdo sellonlatbox,108.5,118.5,19,26.5 input output". 3. Change the unit of temperature data to Celsius and the unit of precipitation data to mm/day. The instruction to change the temperature data unit is "cdo subc,273.15 input output"; The instruction to change the precipitation data unit is "cdo mulc,86400 input output". 4. Unify all data resolution of each model to 100km, and the instruction is "cdo remapbil,gridinfo input output". 5. Take the average, maximum and 90th percentile values of temperature and precipitation data for 20 years respectively to obtain the data in the Spatial distribution map folder. The instructions are "cdo timmean input output; cdo timmax input output; cdo timpctl,90 input -timmin input -timmax input output ". 6. For the data obtained in step 4, use the contour data of Guangdong Province, China, to cut the space. 7. By using the data obtained in Step 6, the Cumulative distribution map folder was obtained by taking the maximum values of each data in each space grid point in a month and calculating the average values. The instruction is "cdo fldmean -monmax input output".


Sun Yat-Sen University


Computer Science, Meteorology, Geography, Statistics