The global high-resolution simulated near sea surface temperature precipitation SST data set from 1990 to 2020 is from the latest cmip6 project. Cmip6 is the sixth climate model comparison program organized by the world climate research project (WCRP). Original data source: https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 。 The data set includes the global near ocean surface temperature (TMP), precipitation (PR) and sea surface temperature (TOS). The air temperature and precipitation data include the rectangular combination of shared social economic path (SSP) and representative concentration path (RCP) of four different experimental scenarios of scenario MIP in cmip6. (1) Ssp126: upgrade rcp2.6 scenario based on ssp1 (low forcing scenario) (radiation forcing will reach 2.6w/m2 in 2100). (2) Ssp245: upgrade rcp4.5 scenario based on SSP2 (moderate forcing scenario) (radiation forcing will reach 4.5 w / m2 in 2100). (3) Ssp370: a new rcp7.0 emission path based on ssp3 (medium forcing scenario) (radiation forcing will reach 7.0 w / m2 in 2100). (4) Ssp585: upgrade rcp8.5 scenario based on ssp5 (high forcing scenario) (ssp585 is the only SSP scenario that can make radiation forcing reach 8.5 w / m2 in 2100). SST data provides ssp126 scenario data.
YE Aizhong
The atmospheric and oceanic thermal conditions over the Indian Ocean-Third Pole (Qinghai-Tibet Plateau) are important for affecting the Asian monsoon activity and pan-Third Pole climate. At seasonal and interannual timescales, the meridional atmospheric and oceanic heat sources are closely related to Indian monsoon, Bay of Bengal monsoon, and the sea surface temperature (SST) mode in the tropical Indian Ocean. Therefore, we calculate and establish the meridional atmospheric and oceanic heat sources dataset for the Indian Ocean-Third pole section. In order to obtain the horizontal distribution of atmospheric heating rate on each pressure level, we use the inverse algorithm from Yanai et al. (1973): Q_1=c_p [∂T/∂t+V ⃑∙∇T+(p/p_0 )^κ ω ∂θ/∂p] Q_1 is the atmospheric apparent heat source, which can be affected by temperature local variation, temperature advection and potential temperature vertical variation. T, θ, V ⃑, and ω respectively represent the temperature, potential temperature, horizontal wind vector, and vertical velocity. p_0=1013.25hPa. κ=R/c_p, R and c_p are the gas constant and specific heat of dry air at constant pressure respectively, κ≈0.286。 Based on the ERA5 Atmospheric Reanalysis data from 2000 to 2019, we calculate the monthly meridional (along 60°E, 70°E, 80°E, 90°E) atmospheric heating rate (unit: K/s) for the Indian Ocean-Third pole section (30°S-60°N) with horizontal resolution of 1°×1° and vertical range of 1000-100hPa at 27 levels. With reference to Hall and Bryden (1982), the vertical Ocean Heat Transport (OHT) at given longitudes can be calculated by the following formula: OHT=∮_(Θ=Θ_i)▒∫_(z_b)^(z_0)▒〖ρ_0 c_p (θ-θ_r ) 〗∙udz Where ρ_0, c_p, θ, θ_r, and u represent the density, specific heat, capacity potential temperature, reference temperature (0℃), and zonal velocity of sea water respectively. z_0 and z_b are the depths of sea surface and sea floor. Based on the CMEMS (Copernicus Marine Service) Oceanic Reanalysis data from 2000 to 2019, we calculate the monthly meridional (along 60°E, 70°E, 80°E, 90°E) OHT (eastward positive, unit: PW(1015W)) over the Indian Ocean-Third pole region (30°S-30°N) with horizontal resolution of 1°×1° and vertical range from sea surface to sea floor at a depth of about 5900m on 75 levels. This dataset can reflect the close relationship between meridional atmospheric and oceanic thermal conditions of Indo-Tibetan Plateau region and Indian monsoon, Bay of Bengal monsoon, and SST mode over tropical Indian Ocean. For example, from the monthly evolution of meridional atmospheric heating rate along 70°E for the Indian Ocean-Third pole section (Figure 1), the atmospheric heat source area above the tropical southern Indian Ocean gradually advances northward from Marth to May. In particular, from May to June, this tropical atmospheric heat source area moves to the tropical northern Indian Ocean with its intensity strengthened and scope expanded, at the same time, the Indian summer monsoon onsets. For instance, from the monthly evolution of meridional atmospheric heating rate along 90°E for the Indian Ocean-Third pole region (Figure 2), we can see that the atmospheric heat source area above the tropical Indian Ocean expands to the south of Qinghai-Tibet Plateau and increases significantly from April to June, coinciding with the onset and northward advance of the Bay of Bengal monsoon. Another example, from the monthly evolution of meridional OHT along 60°E and 90°E for the Indian Ocean-Third pole section (Figures 3 and 4), it can be found the ocean heat at the equatorial Indian Ocean subsurface transports from west to east, and its position is very close to the Equatorial undercurrent. And this subsurface OHT intensity in the west is obviously higher than that in the east, which is related to the wind-thermocline-SST feedback mechanism. It is also worth noting that this subsurface OHT is strong in spring (March-May), weakens in summer, and significantly strengthens in late autumn and early winter (October-December), interacting with the development and formation of Indian Ocean Dipole.
LI Delin , XIAO Ziniu, ZHAO Liang
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from August 31 to December 24, 2018. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m 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, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), 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), infrared temperature (IRT_1 and IRT_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_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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.
Li Xiaoyan
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
