This dataset takes the glacier area in western China as the research area, based on existing glacier mass balance observation data, and uses the internationally mainstream OGGM glacier dynamics model to simulate glacier changes in the main mountain ranges of the Qinghai Tibet Plateau. In the SSP1-2.6 scenario with relatively ideal emissions, the glaciers in the main mountain ranges around the Qinghai Tibet Plateau still show a strong retreat state. Even in the Kunlun Mountains region with the slightest retreat, the reserves and area will only be 64% and 60% by the end of this century, respectively. The Qilian Mountains region has the strongest decline in reserves and area, with reserves and area remaining at 63% and 75% respectively by 2050, while by the end of this century, the Qilian Mountains glacier reserves will only be 34% and 42%. The glaciers in the Qilian Mountains, Kunlun Mountains, Karakoram Mountains and the Himalayas show relatively consistent reserves and area change trends in 2020 – 2050 and 2050 – 2100, while the glaciers in the Tianshan Mountains show a relatively strong reserves change trend in 2020 – 2050, and a relatively slow reserves change trend in 2050 – 2100.
| collect time | 1950/01/01 - 2100/12/31 |
|---|---|
| collect place | Qilian Mountains, Kunlun Mountains, Tianshan Mountains, Karakoram Mountains, Himalayas |
| altitude | 3700.0m - 8200.0m |
| data size | 4.7 GiB |
| Coordinate system |
1. SSP1.26 Meteorological Prediction Data: The SSP scenario meteorological prediction data comes from climate prediction based on the BCC-CSM2-MR model under the SSP-RCP scenario (Su et al., 2021). Su, B., et al. (2021), Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China, Atmospheric Research, 250, 105375.
2. Glacier cataloging data: RGI 6.0( https://www.glims.org/RGI/rgi60_dl.html ).
1. Prepare meteorological dataset: gridded monthly scale meteorological and precipitation data.
2. Determine the glacier range in the simulation area: The OGGM model is a modular glacier model written in Python, and communication between modules is achieved through interfaces. Write script files based on self simulated requirements and interface with the requirements.
3. Run script file: Run the written script file and output the result
4. Extract data: OGGM outputs the annual scale storage, area, and length information of a single glacier in NC format, and extracts the required glacier change results in a certain region by processing the NC format file.
The model continuously adjusts the temperature sensitivity factor to achieve the best match with the measured material balance, and the simulation error is determined to be within 10%.
This work is licensed under a
Creative
Commons Attribution 4.0 International License.
| # | title | file size |
|---|---|---|
| 1 | OGGM.zip | 4.7 GiB |
| 2 | _ncdc_meta_.json | 5.9 KiB |
Qilian Mountains Kunlun Mountains Tianshan Mountains Karakoram Mountains Himalayas
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