阿拉斯加冰川是全球冰川物质亏损最严重的区域，研究该区域长时间序列物质平衡对认识冰川对气候变化响应具有极其重要的意义，本数据重建了阿拉斯加区域9条冰川过去六十年月尺度物质平衡，开展物质平衡变化对比分析，揭示变化机制的异同及其关联性：基于新开发的冰川物质平衡重建模型，重建了阿拉斯加9条冰川1961-2023年月尺度冰川物质平衡，阐明阿拉斯加冰川物质平衡变化的空间异质性，并进行敏感性试验，揭示阿拉斯加冰川物质平衡空间变化的驱动机制。", "ds_source": "
实测冰川物质平衡数据来自于国际冰川监测网络（World Glacier Monitoring Service, WGMS, 网址：https://wgms.ch/)，用于驱动模型的气温和降水数据来自于欧洲天气预报中心的ERA5数据(ERA5 monthly averaged data on single levels from 1940 to present, 网址：https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-monthly-means?tab=overview)", "ds_process_way": "
开发了冰川物质平衡多参数自动率定模型：对原有冰川模型（Open Global Glacier Model， OGGM模型）进行改进开发，得到冰川物质平衡多参数率定模型（OGGM_MPAC: Multi-Parameter Automatic Calibration for OGGM），OGGM_MPAC自动率定的多参数包括：降水校正系数（Pf），冰川温度指数（μ）,偏差校正因子（ε），冰川开始消融阈值（Tmelt），气温海拔梯度（Tlap）和雨雪分离阈值（Tsolid）,其中原模型中只率定前三个参数，而后三个参数采用统一的默认值，这种设定不能很好的反映不同冰川之间分布环境的气候差异性，因此新模型增加了三个参数的率定。模型采用步长迭代法进行参数的自动率定，采用“留一法交叉验证”。", "ds_quality": "
采用均方根误差（RMSE）指标验证模拟冰川物质平衡与实测冰川物质平衡之间的误差，并采用2000-2020年测高法得到的这9条冰川物质平衡对模拟结果进行验证。\n
验证结果表明，在年尺度上（即年物质平衡）， 模拟值与实测值之间的均方根误差为0.68 m w.e/a， 在季节尺度上， 冬季物质平衡模拟值与实测值之间的RMSE为0.68， 夏季RMSE为1.07。因此，本数据集可作为评估该地区冰川对气候变化响应的可靠指标。", "ds_acq_start_time": "1961-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "阿拉斯加", "ds_acq_lon_east": -135.0, "ds_acq_lat_south": 50.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 72.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 3600785, "ds_files_count": 2, "ds_format": "*.pkl", "ds_space_res": "", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "36d3b8c6-40cc-4b52-ade6-4dacdff24288.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "53943799-d453-4bf2-a141-56c205c1355b", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-05-07 10:11:14", "last_updated": "2026-05-07 10:11:48", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7132.2026", "i18n": { "en": { "title": "Monthly Mass Balance Reconstruction Dataset for Representative Glaciers in Alaska from 1961 to 2023", "ds_format": "", "ds_source": "
The observed glacier mass balance data were sourced from the World Glacier Monitoring Service (WGMS, https://wgms.ch/). Meteorological forcing data (temperature and precipitation) were retrieved from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (ERA5 monthly averaged data on single levels from 1940 to present, https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-monthly-means?tab=overview).", "ds_quality": "
The Root Mean Square Error (RMSE) was employed to evaluate the discrepancy between the simulated and observed glacier mass balance. Furthermore, the simulation results were validated using mass balance data for these nine glaciers derived from altimetry measurements from 2000 to 2020.The validation results indicate that on an annual scale (annual mass balance), the RMSE between simulated and observed values is 0.68 m w.e/a. On a seasonal scale, the RMSE is 0.68 m w.e/a for the winter mass balance and 1.07 m w.e/a for the summer mass balance. Consequently, this dataset serves as a reliable indicator for assessing the response of glaciers in this region to climate change.", "ds_ref_way": "", "ds_abstract": "
Alaska glaciers are among the regions experiencing the most severe mass loss globally. Studying the long-term mass balance in this region is of critical importance for understanding glacial responses to climate change. This dataset reconstructs monthly-scale mass balance for nine glaciers in Alaska over the past six decades, conducts comparative analyses of mass balance variations, and reveals the similarities, differences, and underlying mechanisms of these changes. Using a newly developed glacier mass balance reconstruction model, the monthly mass balance of nine glaciers in Alaska from 1961 to 2023 has been reconstructed, clarifying the spatial heterogeneity of mass balance changes in Alaska glaciers. Sensitivity experiments were further conducted to uncover the driving mechanisms behind the spatial variations in mass balance across Alaska glaciers.", "ds_time_res": "", "ds_acq_place": "Alaska", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Developed a multi-parameter automatic calibration model for glacier mass balance: The original glacier model (Open Global Glacier Model, OGGM) was improved and developed to create the Glacier Mass Balance Multi-Parameter Calibration Model (OGGM_MPAC: Multi-Parameter Automatic Calibration for OGGM). The multi-parameters automatically calibrated by OGGM_MPAC include: precipitation correction factor (Pf), glacier temperature index (μ), bias correction factor (ε), glacier melt onset threshold (Tmelt), temperature lapse rate with altitude (Tlap), and rain-snow separation threshold (Tsolid). In the original model, only the first three parameters were calibrated, while the latter three were assigned uniform default values. This setting fails to adequately reflect the climatic differences in the distribution environments among various glaciers. Therefore, the new model incorporates calibration for these three additional parameters. The model employs a stepwise iterative method for automatic parameter calibration and utilizes \"leave-one-out cross-validation.\"", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0 (Creative Commons Attribution 4.0 International License)", "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": [ "冰川物质平衡", "重建", "多参数自动率定", "OGGM" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "美国、加拿大", "阿拉斯加" ], "ds_time_tags": [ 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 ], "ds_contributors": [ { "true_name": "李耀军", "email": "", "work_for": "国家青藏高原科学数据中心", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李耀军", "email": "", "work_for": "国家青藏高原科学数据中心", "country": "中国" } ], "ds_managers": [ { "true_name": "李耀军", "email": "", "work_for": "国家青藏高原科学数据中心", "country": "中国" } ], "category": "冰川" }