CMIP6 is the sixth climate model comparison plan organized by the World Climate Research Program (WCRP). Original data from https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 。 This dataset contains four SSP scenarios of Scenario MIP in CMIP6. (1) SSP126: Upgrade of RCP2.6 scenario based on SSP1 (low forcing scenario) (radiation forcing will reach 2.6W/m2 in 2100). (2) SSP245: Upgrade of RCP4.5 scenario based on SSP2 (moderate forcing scenario) (radiation forcing will reach 4.5 W/m2 in 2100). (3) SSP370: New RCP7.0 emission path based on SSP3 (medium forcing scenario) (radiation forcing will reach 7.0 W/m2 in 2100). (4) SSP585: Upgrade the RCP8.5 scenario based on SSP5 (high forcing scenario) (SSP585 is the only SSP scenario that can make the radiation forcing reach 8.5 W/m2 in 2100). Using GRU data to correct the post-processing deviation of the original CMIP data, the post-processing data set of monthly precipitation (pr) and temperature (tas) estimates from 2046-2065 was obtained, with a reference period of 1985-2014.
YE Aizhong
The data set is a numerical simulation data set based on CESM2.1.3 mode. The data set is global multi scenario monthly climate data. The spatial resolution is f19_ G17 atmosphere/land is 1.9x2.5 degrees, from January 2015 to December 2010, and the data is in NETCDF format. The data set includes historical data from 1850-2014 (referred to as Hist for short) and SSP scenarios (SSP126, SSP245, SSP370, SSP585). Each scenario includes three sets of climate data (default emission data CMIP6 (referred to as CMIP6 for short), China's carbon neutral CNCN (referred to as CNCN for short) CO2 emissions, and China's CH4 and N2O changes with CNCN, which are further used to drive the CESM (referred to as CNCNext for short)), The data set contains a geospatial range of - 90 ° N – 90 ° N and - 180 ° E – 180 ° E.
LI Longhui
Based on the historical daily maximum temperature data and reanalysis data set of stations, a daily maximum temperature statistical downscaling model based on first-order autoregressive and multiple linear regression models is developed. Driven by the IPCC cmip6 scenario data of the global climate model (cnrm-cm6-1), the statistical downscaling model predicts the number of five heat wave indexes (heat wave events) of 65 stations in Central Asia from 2015 to 2100 (HWM), heat wave frequency (HWF), heat wave intensity (HWM), maximum duration of heat wave (HWD), heat wave amplitude (HWA)). Finally, the heat wave change scenario data sets of 65 stations in Central Asia under four emission scenarios (ssp126, ssp245, ssp370, ssp585) from 2015 to 2100 were obtained.
FAN Lijun
To understand the potential impact of projected climate changes on the vulnerable agriculture in Central Asia (CA) in the future, six agroclimatic indicators are calculated based on the 9km-resolution dynamical downscaled results of three different global climate models and a high-resolution projection dataset of agroclimatic indicators over CA is produced. These indicators are growing season length (GSL, days), biologically effective degree days (BEDD, ℃), frost days (FD, days), summer days (SU, days), warm spell duration index (WSDI, days), and tropical nights (TR, days). The periods are 1986-2005 and 2031-2050. The spatial resolution is 0.1°. As all the indicators except WSDI are defined with absolute temperature thresholds and particularly sensitive to the systematics biases in the model data, the quantile mapping (QM) method is applied to correct the simulated temperature. Results show the QM method largely reduces the biases in all the indicators. GSL, SU, WSDI, and TR will significantly increase over CA and FD will decrease. However, changes in BEDD are spatially heterogeneous, with the increases in northern CA and the mountainous areas and decreases in the southern and middle part of the plain areas. This dataset can be applied for assessing the future risks in the local agriculture for climate changes and will be beneficial to adaption and mitigation actions for food security in this region.
QIU Yuan QIU Yuan
1. The total number is the unified number of the survey year, such as 17-001 (the first survey point in 2017), and the field number is the single field number. 2. Time: Beijing time at the time of measurement, such as: 13:25, August 1, 2017 (13:25, August 1, 2017). 3. Geographical location: the longitude and latitude of the measuring point, such as 29.6584101.0884 (29.6584 ° n, 101.0884 ° E), which is measured by Garmin 63sc GPS in the field. 4. Altitude: the absolute altitude of the measuring point, such as 4500m (4500m above sea level), is measured by Garmin 63sc GPS in the field with an accuracy of 1m. 5. Measured vegetation coverage (%): measured in the field with quadrat (1000 m * 1000 m). 6. Atmospheric pressure: measured by dph-103 intelligent digital temperature and humidity barometer in the field, such as 651.7kpa, accuracy: 0.1 kPa. 7. Air temperature: measured by dph-103 intelligent digital temperature, humidity and barometer in the field, such as 15.61 ℃, accuracy: 0.01 ℃. 8. Relative humidity: measured by dph-103 intelligent digital temperature, humidity and barometer in the field, such as 79.1%, accuracy: 0.1%. 9. Relative oxygen content: measured by td400-sh-o2 portable oxygen detector in the field, such as 20.16%, accuracy: 0.01%. Among them, the altitude of sampling points 17-001 to 17-065 is measured by Garmin Oregon 450 GPS with an accuracy of 1 m; The atmospheric pressure is measured by Casio prg-130gc barometer with an accuracy of 5 HPA; The relative oxygen content is measured by cy-12c digital oxygen meter, with a range of 0-50.0%, a resolution of 0.1% and an accuracy of ± 1%.
SHI Peijun
According to the data of three future scenarios of CMIP5 (RCP2.6、RCP4.5、RCP8.5), the spatial variation characteristics and temporal variation trend of the global mean annual air temperature from 2006 to 2100 are analyzed. Under rcp2.6 scenario, the mean annual air temperature shows an increasing trend, with the growth rate ranging from 0.0 ° c/decade to 0.2 ° c/decade (P<0.05), the growth in high latitude regions is faster, ranging from 0.1 ° c/decade to 0.2 ° C / decade. Based on the spatial and temporal characteristics of the mean annual air temperature in the northern hemisphere in the 21st century, under different scenarios, the mean annual air temperature shows a warming trend, and the high latitudes show a more sensitive and rapid growth.
NIU Fujun
The Qinghai-Tibet Engineering Corridor runs from Golmud to Lhasa. It passes through the core region of the Qinghai-Tibet Plateau and is an important passage connecting the interior and Tibet. As the primary parameter in the surface energy balance, the land surface temperature represents the degree of energy and water exchange between the earth and the atmosphere, and is widely used in the research of climatology, hydrology and ecology. The annual average surface land temperature is obtained by using the four day and night observations of Aqua and Terra. Therefore, the 8-day land surface temperature synthesis products MOD11A2 and MYD11A2 with a resolution of 1km were downloaded first, and then the data were batch projected by MRT (MODIS Reprojection Tool). Finally, the annual average MODIS land surface temperature data after 2010 was calculated by IDL.
NIU Fujun
(1) Data content: the annual mean Northern Annular mode index and the Northern Annular mode index from 1500 to 2000; (2) Data source and processing method: this data is independently produced by the author. It is based on PAGES2k data set and reconstructed by machine learning model (random forest, extreme tree, Light GBM and catboost). (3) Data quality description: the data set has high consistency with multiple instrumental data during the observed period, and the reconstruction is better. The data can be used to study the change and mechanism of the main atmospheric circulation in the northern and southern hemispheres on multiple time scales (interannual, interdecadal and multidecadal).
YANG Jiao
The triple pole aerosol type data product is an aerosol type result obtained through a series of data pre-processing, quality control, statistical analysis and comparative analysis processes by comprehensively using MEERA 2 assimilation data and active satellite CALIPSO products. The key of the aerosol type fusion algorithm is to judge the aerosol type of CALIPSO. During the data fusion of aerosol type, the final aerosol type data (12 types in total) and quality control results in the three polar regions are obtained according to the types and quality control of CALIPSO aerosol types and referring to MERRA 2 aerosol types. The data product fully considers the vertical and spatial distribution of aerosols, and has a high spatial resolution (0.625 ° × 0.5 °) and time resolution (month).
ZHAO Chuanfeng
The Qinghai-Tibet Plateau is the source of many major rivers in Asia, providing essential water for hundreds of millions of people, and is known as the "Water Tower of Asia". The main source of water recharge for the Asian Water Tower is precipitation from the Tibetan Plateau, of which the Tibetan Plateau vortex (TPV) is one of the important precipitation-producing systems on the Tibetan Plateau. Due to the complex topography of the Tibetan Plateau and the lack of observational data, there are still many gaps in the understanding of the climatic and structural characteristics of the TPVs and their formation and change mechanisms. This dataset uses multiple sets of reanalysis data and objective identification methods to obtain a long time series TPVs dataset, including the location, radius, intensity, life history, and movement path and other characteristics. The reanalysis datasets used in the dataset are: NCEP1 (NCEP/NCAR), NCEP2 (NCEP/DOE), ERA-Interim, ERA-40, ERA-5, CFSR, MERRA2, JRA55, NCEP FNL, CRA40, etc. NCEP1 and NCEP2 have lower resolution and the obtained highland low vortices are not applicable as climate feature analysis.
LIN Zhiqiang , LIN Zhiqiang, GUO Weidong GUO Weidong
This data is the plant diversity and distribution data of the chnab005 grid on the Qinghai Tibet Plateau, including the Chinese name, Latin name, latitude and longitude, altitude, collection number, number of molecular materials, number of specimens, administrative division, small place, collector, collection time and creator of the plants in this grid. This data is obtained from e-Science website（ http://ekk.kib.ac.cn/web/index/#/ ）And partially complete the identification. This data has covered the list of plants in this flora and the specific distribution information. This data can be used not only to study the floristic nature of this region, but also to explore the horizontal and vertical gradient pattern of plants in this region. What is different from last year is that the grid with the most scientific research data this year has changed, which may be affected by the epidemic or the environment.
DENG Tao
As a powerful heat source, the Tibetan Plateau (TP) affects the onset, advance and retreat of the Asian monsoon, and the interaction between the westerly belt and the monsoon belt. In order to study the variation of TP thermal effect and its influence on the surrounding climate, the basic data related to TP heat source are needed. This data set is composed of monthly basic heat source data of the TP and its surrounding areas calculated from reanalysis data, and its horizontal range covers 40°E-180° and 20°S-80°N. The spatial resolution is 2.5 ° x2.5 °, and the datasets mainly included ERA5 and NCEP/NCAR reanalysis data.
LI Qingquan
The Tibetan Plateau (TP) is the largest glacier enrichment area in the middle and low latitudes except the South Arctic and Greenland. The solid water body glaciers and liquid water bodies lakes and rivers together form the Asian Water Tower. The thermal and dynamic effects of the TP and their variability are one of the main driving forces for the TP to affect the Asian monsoon and global atmospheric circulation anomalies. To study the thermal properties of the TP itself and its feedback effect, it is necessary to use the results of climate model experiments to carry out the 100-year historical examination of the TP and its surrounding areas and the future 100-year prediction (temperature, precipitation, radiation, etc.). This dataset consists of grid point temperature, precipitation, radiation and other data of the TP and its surrounding areas. Its horizontal range covers 40 ° E-180 °, 20 ° S-80 ° N, and the time resolution includes annual and seasonal average. The data are based on the results of the BCC-CSM2-MR model test conducted by the National Climate Center of China in the Coupled Model Intercomparison Project Phase 6 (CMIP6), including historical, SSP126, SSP245, SSP370, and SSP585 experiments. According to the bilinear interpolation method, the data are uniformly interpolated to the resolution level of 1 ° x1 °. The data can provide basic information on regional climate and water cycle changes for the second TP investigation period, provide reference for the field investigation results, and study the possible change mechanism.
LI Qingquan
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