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CAS FGOALS-f3-H and CAS FGOALS-f3-L outputs for the high-resolution model intercomparison project simulation of CMIP6
CAS FGOALS-f3-H and CAS FGOALS-f3-L outputs for the high-resolution model intercomparison project simulation of CMIP6

Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project (PAMIP) were carried out by the model group of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L). Eight groups of experiments forced by different combinations of the sea surface temperature (SST) and sea ice concentration (SIC) for pre-industrial, present-day, and future conditions were performed and published. The time-lag method was used to generate the 100 ensemble members, with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period. All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.

Created Time:2022-11-08
CAS FGOALS-f3-H and CAS FGOALS-f3-L outputs for the high-resolution model intercomparison project simulation of CMIP6(2015-2050)
CAS FGOALS-f3-H and CAS FGOALS-f3-L outputs for the high-resolution model intercomparison project simulation of CMIP6(2015-2050)

CAS FGOALS-f3-H, with a 0.25° horizontal resolution, and CAS FGOALS-f3-L, with a 1° horizontal resolution, were forced by the standard external conditions, and two coordinated sets of simulations were conducted for 1950–2014 and 2015–50 with the Experiment IDs of ‘highresSST-present’ and ‘highresSST-future’, respectively. The model outputs contain multiple time scales including the required hourly mean, three-hourly mean, six-hourly transient, daily mean, and monthly mean datasets.

Created Time:2022-11-08
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020

The monthly High spatial resolution human Thermal Index Collection (HiTIC-Monthly) includes near-surface air temperature (SAT) and 11 commonly used human-perceived temperature indices: indoor Apparent Temperature (ATin), outdoor shaded Apparent Temperature (ATout), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is stacked by year, and each stack is composed of 12 monthly images in the NetCDF format. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”. If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via zhangh573@mail2.sysu.edu.cn, Dr. Ming Luo via luom38@mail.sysu.edu.cn, or Dr. Yongquan Zhao via zhaoyq66@mail.sysu.edu.cn. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in: Zhang H., Luo M., Zhao Y., et al., 2022. HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020. Earth System Science Data (submitted for consideration for publication). https://doi.org/10.5194/essd-2022-257

Created Time:2022-11-07
Data set of spatial and temporal distribution of water resources in Indus from 1998 to 2017
Data set of spatial and temporal distribution of water resources in Indus from 1998 to 2017

This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.

Created Time:2022-10-23
SMAP soil moisture and vegetation optical depth product using MCCA (2015-2022)
SMAP soil moisture and vegetation optical depth product using MCCA (2015-2022)

Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.

Created Time:2022-03-04
Daily cloud-free snow cover products for Tibetan Plateau from 2002 to 2021
Daily cloud-free snow cover products for Tibetan Plateau from 2002 to 2021

Based on long-term series Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products, daily snow cover products without data gaps at 500 m spatial resolution over the Tibetan Plateau from 2002 to 2021 were generated by employing a Hidden Markov Random Field (HMRF) modeling technique. This HMRF framework optimally integrates spectral, spatiotemporal, and environmental information together, which not only fills data gaps caused by frequent clouds, but also improves the accuracy of the original MODIS snow cover products. In particular, this technology incorporates solar radiation as an environmental contextual information to improve the accuracy of snow identification in mountainous areas. Validation with in situ observations and snow cover derived from Landsat-8 OLI images revealed that these new snow cover products achieved an accuracy of 98.31% and 92.44%, respectively. Specifically, the accuracy of the new snow products is higher during the snow transition period and in complex terrains with higher elevations as well as sunny slopes. These gap-free snow cover products effectively improve the spatiotemporal continuity and the low accuracy in complex terrains of the original MODIS snow products, and is thus the basis for the study of climate change and hydrological cycling in the TP.

Created Time:2022-02-25
Codes of the global daily-scale soil moisture fusion dataset based on Triple Collocation Analysis (2011-2018)
Codes of the global daily-scale soil moisture fusion dataset based on Triple Collocation Analysis (2011-2018)

This data set is a code file set of TCA (triple collision analysis) algorithm, which is used to generate the global daily-scale soil moisture fusion dataset from 2011 to 2018.

Created Time:2022-02-12
The water level observation of lakes on the Tibetan Plateau (2010-2017)
The water level observation of lakes on the Tibetan Plateau (2010-2017)

The water level observation data set of lakes on the Tibetan Plateau contains the daily variations of water levels for three lakes: Zhari Namco, Bamco and Dawaco. The lake water level was obtained by a HOBO water level gauge (U20-001-01) installed on the lakeshore, then corrected using the barometer installed on the shore or pressure data of nearby weather stations, and then the real water level changes were obtained. The accuracy was less than 0.5 cm. The items of this data set are as follows: Daily variation data of water level in Zhari Namco from 2009 to 2014; Daily variation data of water level in Bamco from 2013 to 2014; Daily variation data of water level in Dawaco from 2013 to 2014. Water level, unit: m.

Created Time:2018-07-21

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