The dataset is from the transient experiment TRN40ka in Zhang et al (2021, Nature Geoscience), spanning 40ka-32ka BP with changing orbital parameters. For detailed description of experimental design, please refer to the original paper. Model details: COSMOS (ECHAM5-JSBACH-MPI-OM), a comprehensive fully coupled atmosphere–ocean general circulation model (AOGCM), is used to generate the dataset. The atmospheric model ECHAM5, complemented by the land surface component JSBACH, is used at T31 resolution (∼3.75°), with 19 vertical layers. The ocean model MPI-OM, including sea-ice dynamics that is formulated using viscous-plastic rheology, has a resolution of GR30 (3°×1.8°) in the horizontal, with 40 uneven vertical layers.
ZHANG Xu
The data includes the daily mean value of stable isotope δ18O in precipitation, the air temperature and precipitation amounts in Bomi in 2008; the precipitation samples are collected by Bomi meteorological station, and the stable isotope of precipitation is measured at the Laboratoire des Sciences du Climat et de l’Environnement, France., The δ18O amounts were measured by equilibration on a MAT-252 mass spectrometer, with an analytical precision of 0.05‰. The air temperatures and precipitation amounts were recorded for each precipitation events at Bomi meteorological stations, through the average of the observed temperature before and after the precipitation event, and through the total precipitation amount for each event. The data study has been published in the Journal of Climate, entitled Precipitation Water Stable Isotopes in the South Tibetan Plateau: Observations and Modeling.
GAO Jing
Effective evaluation of future climate change, especially prediction of future precipitation, is an important basis for formulating adaptation strategies. This data is based on the RegCM4.6 model, which is compatible with multi-model and different carbon emission scenarios: CanEMS2 (RCP 45 and RCP85), GFDL-ESM2M (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), HadGEM2-ES (RCP2.6, RCP4.5 And RCP8.5), IPSL-CM5A-LR (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), MIROC5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The future climate data (2007-2099) has 21 sets, with a spatial resolution at 0.25 degrees and the temporal resolution at 3 hours (or 6 hours), daily and yearly scales.
PAN Xiaoduo, ZHANG Lei
The data set is the daily precipitation stable isotope data (δ 18O, δ D, d-excess) from Satkhira, Barisal and sylhet3 stations in Bangladesh from 2017 to 2018. The data set was collected by Bangladesh Atomic Energy Commission (BAEC) and measured by picarro l2130i wavelength scanning cavity ring down spectrometer in the Key Laboratory of environment and surface processes, Institute of Qinghai Tibet Plateau, Chinese Academy of Sciences. Sampling location and time of three observation points: Satkhira ：2017.03.11-2018.07.16 Barisal：2017.03.05-2018.07.02 Sylhet : 2017.02.20-2018.09.04
GAO Jing
Precipitation stable isotopes (2H and 18O) are adequately understood on their climate controls in the Tibetan Plateau, especially the north of Himalayas via about 30 years’ studies. However, knowledge of controls on precipitation stable isotopes in Nepal (the south of Himalayas), is still far from sufficient. This study described the intra-seasonal and annual variations of precipitation stable isotopes at Kathmandu, Nepal from 10 May 2016 to 21 September 2018 and analysed the possible controls on precipitation stable isotopes. All samples are located in Kathmandu, the capital of Nepal (27 degrees north latitude, 85 degrees east longitude), with an average altitude of about 1400 m. Combined with the meteorological data from January 1, 2001 to September 21, 2018, the values of precipitation (P), temperature (T) and relative humidity (RH) are given.
GAO Jing
The stable oxygen isotope ratio (δ 18O) in precipitation is a comprehensive tracer of global atmospheric processes. Since the 1990s, efforts have been made to study the isotopic composition of precipitation at more than 20 stations located on the TP of the Tibetan Plateau, which are located at the air mass intersection between westerlies and monsoons. In this paper, we establish a database of monthly precipitation δ 18O over the Tibetan Plateau and use different models to evaluate the climate control of precipitation δ 18O over TP. The spatiotemporal pattern of precipitation δ 18O and its relationship with temperature and precipitation reveal three different domains, which are respectively related to westerly wind (North TP), Indian monsoon (South TP) and their transition.
GAO Jing
This dataset is the monthly precipitation data of China, with a spatial resolution of 0.0083333 ° (about 1km) and a time range of 1901.1-2021.12. The data format is NETCDF, i.e.. Nc format. This dataset is generated in China through the Delta spatial downscaling scheme based on the global 0.5 ° climate dataset released by CRU and the global high-resolution climate dataset released by WorldClim. In addition, 496 independent meteorological observation point data are used for verification, and the verification results are reliable. This data set covers the main land areas in China (including Hong Kong, Macao and Taiwan), excluding islands and reefs in the South China Sea. In order to facilitate storage, the data are all int16 type and stored in nc files, with precipitation units of 0.1mm. NC data can be mapped using ArcMAP software; Matlab software can also be used for extraction processing. Matlab has released the function to read and store nc files. The read function is ncread, and switch to the nc file storage folder. The statement is expressed as: ncread ('XXX.nc ',' var ', [i j t], [leni lenj lent]), where XXX.nc is the file name, and is the string required' '; Var is from XXX The variable name read in NC. If it is a string, '' is required; i. J and t are the starting row, column and time of the read data respectively, and leni, lenj and lent i are the length of the read data in the row, column and time dimensions respectively. In this way, this function can be used to read in any region and any time period in the study area. There are many commands about NC data in the help of Matlab, which can be viewed. WGS84 is recommended for data coordinate system.
PENG Shouzhang
The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
MA Yaoming
Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm, and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation, mean and time trend derived from LZU had a higher correlation with that produced by the CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP) - Asian Monsoon Project, (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change.
HUANG Wei, ZHAO Hong
The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.
YANG Kun, HE Jie, WENJUN TANG , LU Hui, QIN Jun , CHEN Yingying, LI Xin
The GAME/Tibet project conducted a short-term pre-intensive observing period (PIOP) at the Amdo station in the summer of 1997. From May to September 1998, five consecutive IOPs were scheduled, with approximately one month per IOP. More than 80 scientific workers from China, Japan and South Korea went to the Tibetan Plateau in batches and carried out arduous and fruitful work. The observation tests and plans were successfully completed. After the completion of the IOP in September, 1998, five automatic weather stations (AWS), one Portable Atmospheric Mosonet (PAM), one boundary layer tower and integrated radiation observatory (Amdo) and nine soil temperature and moisture observation stations have been continuously observed to date and have obtained extremely valuable information for 8 years and 6 months consecutively (starting from June 1997). The experimental area is located in Nagqu, in northern Tibet, and has an area of 150 km × 200 km (Fig. 1), and observation points are also established in D66, Tuotuohe and the Tanggula Mountain Pass (D105) along the Qinghai-Tibet Highway. The following observation stations (sites) are set up on different underlying surfaces including plateau meadows, plateau lakes, and desert steppe. (1) Two multidisciplinary (atmosphere and soil) observation stations, Amdo and NaquFx, have multicomponent radiation observation systems, gradient observation towers, turbulent flux direct measurement systems, soil temperature and moisture gradient observations, radiosonde, ground soil moisture observation networks and multiangle spectrometer observations used as ground truth values for satellite data, etc. (2) There are six automatic weather stations (D66, Tuotuohe, D105, D110, Nagqu and MS3608), each of which has observations of wind, temperature, humidity, pressure, radiation, surface temperature, soil temperature and moisture, precipitation, etc. (3) PAM stations (Portable Automated Meso - net) located approximately 80 km north and south of Nagqu (MS3478 and MS3637) have major projects similar to the two integrated observation stations (Amdo and NaquFx) above and to the wind, temperature and humidity turbulence observations. (4) There are nine soil temperature and moisture observation sites (D66, Tuotuohe, D110, WADD, NODA, Amdo, MS3478, MS3478 and MS3637), each of which has soil temperature measurements of 6 layers and soil moisture measurement of 9 layers. (5) A 3D Doppler Radar Station is located in the south of Nagqu, and there are seven encrypted precipitation gauges in the adjacent (within approximately 100 km) area. The radiation observation system mainly studies the plateau cloud and precipitation system and serves as a ground true value station for the TRMM satellite. The GAME-Tibet project seeks to gain insight into the land-atmosphere interaction on the Tibetan Plateau and its impact on the Asian monsoon system through enhanced observational experiments and long-term monitoring at different spatial scales. After the end of 2000, the GAME/Tibet project joined the “Coordinated Enhanced Observing Period (CEOP)” jointly organized by two international plans, GEWEX (Global Energy and Water Cycle Experiment) and CL IVAR (Climate Change and Forecast). The Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau of the Global Coordinated Enhanced Observation Program (CEOP) has been started. The data set contains POP data for 1997 and IOP data for 1998. Ⅰ. The POP data of 1997 contain the following. 1. Precipitation Gauge Network (PGN) 2. Radiosonde Observation at Naqu 3. Analysis of Stable Isotope for Water Cycle Studies 4. Doppler radar observation 5. Large-Scale Hydrological Cycle in Tibet (Link to Numaguchi's home page) 6. Portable Automated Mesonet (PAM) [Japanese] 7. Ground Truth Data Collection (GTDC) for Satellite Remote Sensing 8. Tanggula AWS (D105 station in Tibet) 9. Syamboche AWS (GEN/GAME AWS in Nepal) Ⅱ. The IOP data of 1998 contain the following. 1. Anduo （1） PBL Tower, 2） Radiation, 3） Turbulence SMTMS 2. D66 （1） AWS （2） SMTMS （3） GTDC （4) Precipitation 3. Toutouhe （1） AWS （2） SMTMS （3 ）GTDC 4. D110 （1） AWS （2） SMTMS (3) GTDC (4) SMTMS 5. MS3608 （1） AWS （2） SMTMS （3） Precipitation 6. D105 （1） Precipitation (2) GTDC 7. MS3478(NPAM) （1） PAM （2） Precipitation 8. MS3637 （1） PAM （2） SMTMS （3） Precipitation 9. NODAA （1） SMTMS (2) Precipitation 10. WADD （1） SMTMS （2） Precipitation （3） Barometricmd 11. AQB （1） Precipitation 12. Dienpa (RS2) （1） Precipitation 13. Zuri （1） Precipitation （2） Barometricmd 14. Juze （1） Precipitation 15. Naqu hydrological station （1） Precipitation 16. MSofNaqu （1） Barometricmd 16. Naquradarsite （1）Radar system （2） Precipitation 17. Syangboche [Nepal] （1） AWS 18. Shiqu-anhe （1） AWS （2） GTDC 19. Seqin-Xiang （1） Barometricmd 20. NODA （1）Barometricmd （2） Precipitation （3) SMTMS 21. NaquHY （1） Barometricmd （2） Precipitation 22. NaquFx(BJ) （1） GTDC（2) PBLmd (3) Precipitation 23. MS3543 （1） Precipitation 24. MNofAmdo （1） Barometricmd 25. Mardi （1） Runoff 26. Gaize （1） AWS （2） GTDC （3） Sonde A CD of the data GAME-Tibet POP/IOP dataset cd （vol. 1) GAME-Tibet POP/IOP dataset cd （vol. 2)
MA Yaoming
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