本数据集以中国西部冰川区作为研究区，以现有冰川物质平衡观测数据为基础，利用国际主流的OGGM冰川动力模型，模拟了青藏高原主要山脉冰川变化。在排放相对最为理想的SSP1-2.6情景下，青藏高原周边主要山脉的冰川依然呈现强烈的退缩状态，即使退缩最为轻微的昆仑山区域，储量和面积至本世纪末也仅剩64%和60%。储量和面积退缩最为强烈的是祁连山区域，至2050年储量和面积分别剩余63%和75%，而到了本世纪末祁连山冰川储量面积仅剩34%和42%。祁连山、昆仑山、喀喇昆仑和喜马拉雅山脉冰川在2020 – 2050和2050 – 2100两个阶段变现出相对一致的储量和面积变化趋势，而天山冰川则在2020 – 2050呈现较为强烈的储量变化趋势，而在2050 – 2100呈现相对较为减缓的储量变化趋势。", "ds_source": "
1. SSP1.26气象预估数据：SSP情景气象预估数据来自于SSP-RCP情景下基于BCC-CSM2-MR模式的气候预估（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.\n
2. 冰川编目数据：RGI 6.0 (https://www.glims.org/RGI/rgi60_dl.html)。", "ds_process_way": "
1. 制备气象数据集：格网化月尺度气象、降水数据。\n
2. 确定模拟区域冰川范围：OGGM模型是由python编写的模块化冰川模型，各模块之间通过接口实现通信。基于自身模拟需求编写脚本文件，对接需求的接口。 \n
3. 运行脚本文件：运行编写的脚本文件，输出结果\n
4. 提取数据：OGGM输出为NC格式的单个冰川年尺度的储量、面积和长度信息，通过处理NC格式文件提取需要的某区域冰川变化结果。", "ds_quality": "
模型通过不断调整温度敏感性因子以达到与实测物质平衡最佳匹配，模拟误差确定在10%以内。", "ds_acq_start_time": "1950-01-01 00:00:00", "ds_acq_end_time": "2100-12-31 00:00:00", "ds_acq_place": "祁连山,昆仑山,天山,喀喇昆仑山,喜马拉雅山", "ds_acq_lon_east": 104.0, "ds_acq_lat_south": 26.0, "ds_acq_lon_west": 67.0, "ds_acq_lat_north": 45.0, "ds_acq_alt_low": 3700.0, "ds_acq_alt_high": 8200.0, "ds_share_type": "login-access", "ds_total_size": 5012399751, "ds_files_count": 2, "ds_format": "", "ds_space_res": null, "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "7fb56bd0-21f2-498f-83ba-11c4ab5fa637.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "9de89acc-5714-4927-aba3-ac88067dff8a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-06-14 09:11:15", "last_updated": "2025-04-29 16:10:09", "protected": true, "protected_to": "2026-06-14 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6518.2024", "i18n": { "en": { "title": "Dataset of historical Change and Projection under SSP1 Scenario of Glaciers in the Major Mountain Ranges of Tibet Plateau ", "ds_format": "", "ds_source": "
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.\n
2. Glacier cataloging data: RGI 6.0（ https://www.glims.org/RGI/rgi60_dl.html ）.", "ds_quality": "
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%.", "ds_ref_way": "", "ds_abstract": "
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.
", "ds_time_res": "年", "ds_acq_place": "Qilian Mountains, Kunlun Mountains, Tianshan Mountains, Karakoram Mountains, Himalayas", "ds_space_res": "", "ds_projection": "", "ds_process_way": "1. Prepare meteorological dataset: gridded monthly scale meteorological and precipitation data.\n
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. \n
3. Run script file: Run the written script file and output the result\n
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.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "冰川", "高亚洲" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "祁连山", "昆仑山", "天山", "喀喇昆仑山", "喜马拉雅山" ], "ds_time_tags": [ 1950, 2100 ], "ds_contributors": [ { "true_name": "杜文涛", "email": "duwentao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "陈记祖", "email": "chenjizu900120@qq.com", "work_for": " 中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "徐强强", "email": "yangchengde@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李博文", "email": "libowen@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "杜文涛", "email": "duwentao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "陈记祖", "email": "chenjizu900120@qq.com", "work_for": " 中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "徐强强", "email": "yangchengde@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李博文", "email": "libowen@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "陈记祖", "email": "chenjizu900120@qq.com", "work_for": " 中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冰川" }