Spatial distribution of global mean annual temperature simulated by multi-model ensemble under different climate scenarios (2006-2100)

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.

0 2022-10-23

Glacier meteorological data from Wetern Pamir in Tajikstan (2021)

Tajikistan West Pamir Glacier Meteorological Station (38°3′15″N, 72°16′52″E, 3730m), the station is the Urumqi Desert Meteorological Institute of the China Meteorological Administration and the Tajikistan National Academy of Sciences for Water Issues, Water Energy and Ecology The Institute and the Tajikistan Hydrological and Meteorological Service are jointly constructed. Observation data includes hourly meteorological elements (average wind direction (°), average wind speed (m/s), wind direction at maximum wind speed (°), maximum wind speed (m/s), average temperature (°C), maximum Air temperature (°C), minimum air temperature (°C), average relative humidity (%), minimum relative humidity (%), average atmospheric pressure (hPa), maximum atmospheric pressure (hPa), minimum atmospheric pressure (hPa)). The data period is from December 10, 2020 to October 13, 2021 Meteorological observation data can provide important basic data for studying the relationship between climate change, glaciers and water resources in the West Pamir Mountains, and provide important data for the economic construction of the lower reaches of the Amu Darya River Basin in Tajikistan.

0 2022-05-23

Time series data set of annual average temperature of external dynamic environmental factors in Sanjiang Basin (2000-2020)

Temperature is one of the important external dynamic environmental factors. Collect the daily temperature data of 20 meteorological observation stations in the typical area of Sanjiang River Basin in the study area, including Wudaoliang, Tuotuohe River, qumalai, Naqu, Yushu, Dingqing, Changdu, Batang, Derong and Lijiang. Process the collected data through screening, elimination and classification calculation, and obtain the time series data set of annual average temperature external dynamic environmental factors in key areas of the study area from 2000 to 2020. Through this data set, It can reflect the change law and trend of annual average temperature in key areas of Sanjiang River Basin from 2000 to 2020, and master the change of temperature, the external dynamic factor affecting the landslide on the Qinghai Tibet Plateau.

0 2022-03-20

Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Subalpine shrub, 2020

This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from Janurary 1to December 31, 2020. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018/8/31 10:30. Moreover, suspicious data were marked in red.

0 2021-06-08

Downscaling simulations of future temperature based on CMIP5 outputs in Heihe river basin (2011-2100)

Based on the downscaling temperature result data in the historical period of CMIP5 (Coupled Model Intercomparison Project Phase 5), the future multi-year average temperature in the three periods of 2011-2040, 2041-2070, and 2071-2100 was predicted. Under the scenarios of rcp2.6, rcp4.5, and rcp8.5, the method of combining ordinary least squares regression with HASM (High Accuracy Surface Modeling Method) was used to downscaling simulate and predict, and the 1km downscaling results of the multi-year average temperature in the three scenarios of 2011-2040, 2041-2070 and 2071-2100 were obtained.

0 2020-03-02