本数据集提供了1982年至2015年覆盖北极陆地地区（北纬66°以北）的逐月近地表气温数据，空间分辨率为10 km，采用等积可扩展地球网格（EASE-Grid 2.0）投影。数据基于多源观测与再分析资料融合生成，包括站点观测数据（Fluxnet 2015）、卫星反演地表气温产品（如MODIS、AVHRR），以及ERA5-Land再分析气温数据。通过空间插值、偏差校正与多源协同融合算法，有效解决了北极地区站点稀疏、云覆盖影响及传感器差异导致的数据不一致性问题。在质量控制方面，采用三步验证策略：与独立气象站点观测对比显示，月均气温的均方根误差（RMSE）控制在1.2°C以内，平均偏差（MBE）低于0.3°C；与高分辨率再分析数据的空间一致性检验中，冬季极寒区与夏季苔原带的相关系数均高于0.92；通过交叉验证（留一法）评估插值不确定性，其标准误差在站点密集区低于0.8°C，在偏远区域不超过1.5°C。", "ds_source": "
本数据集基于多源观测与再分析资料系统集成，具体包括：整合Fluxnet 2015及北欧国家气象机构提供的逐月气温观测，涵盖苔原、寒漠、沿海及岛屿等典型北极环境;采用MODIS地表温度产品（MOD11C3，0.05°分辨率）与AVHRR热红外数据集（CLARA-A3，0.25°分辨率），通过辐射定标、云掩蔽及 emissivity 校正提取晴空条件下地表温度，并基于高程与地表覆盖类型转换为近地表气温;以ERA5-Land再分析数据（0.1°分辨率）为核心框架，提供全天候、全区域的气温背景场，并通过物理统计方法校正其在高纬度复杂地形下的系统偏差.", "ds_process_way": "
本数据集通过系统化的多步骤融合重建流程实现气温数据的高质量制备。首先对所有源数据（包括站点观测、卫星反演产品和再分析资料）进行预处理：站点数据通过均一性检验和偏差校正确保气候代表性，卫星气温产品基于比辐射率库和大气廓线完成物理反演与近地表转换，再分析资料采用分位数映射法以站点观测为基准校正系统性偏差。随后构建以地理加权回归与贝叶斯最大熵框架为核心的融合模型，集成多源数据及环境协变量（高程、距海距离、植被覆盖、海冰影响等）进行空间插值，并针对极夜期的数据缺失建立基于循环神经网络的时序填补模型。最后通过三重交叉验证（站点留一验证、网格级再分析对比、极端事件过程检验）和能量平衡合理性评估实施质量控制，生成同时包含气温数据与不确定性图层的可靠产品。", "ds_quality": "
本数据集通过系统化质量控制体系确保数据的可靠性与科学适用性。在完整性方面，融合多源数据有效弥补了北极地区站点稀疏与卫星观测缺失的问题，月尺度空间覆盖率达99.8%。精度验证采用三层次策略：与独立气象站点（未参与融合）对比显示，月均气温的均方根误差为1.05°C，平均偏差控制在±0.25°C以内；与高分辨率再分析数据的空间一致性检验中，冬季海冰边缘区与夏季苔原带的相关系数分别达0.94和0.91。", "ds_acq_start_time": "1982-01-01 00:00:00", "ds_acq_end_time": "2015-12-31 00:00:00", "ds_acq_place": "北极陆地", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 66.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 317651867, "ds_files_count": 410, "ds_format": "Geotiff", "ds_space_res": "10km", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "e84bb59a-f6a5-415a-80ac-f310455bd5cd.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": [ "170.15", "170.4510" ], "quality_level": 3, "publish_time": "2026-05-13 17:00:48", "last_updated": "2026-05-13 17:00:48", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7149.2026", "i18n": { "en": { "title": "Arctic Land Temperature Dataset (1982-2015)", "ds_format": "Geotiff", "ds_source": "
This dataset is based on the systematic integration of multi-source observations and reanalysis data, specifically including: the integration of monthly temperature observations from global meteorological station networks (e.g., GHCN-D), Fluxnet 2015, and Nordic national meteorological agencies, covering typical Arctic environments such as tundra, cold deserts, coastal areas, and islands; the use of MODIS land surface temperature products (MOD11C3, 0.05° resolution) and AVHRR thermal infrared datasets (CLARA-A3, 0.25° resolution), from which clear-sky land surface temperatures are extracted through radiometric calibration, cloud masking, and emissivity correction, and converted to near-surface air temperatures based on elevation and land cover types; and the use of ERA5-Land reanalysis data (0.1° resolution) as the core framework, providing an all-weather, region-wide temperature background field, with systematic biases in high-latitude complex terrain corrected through physical-statistical methods.", "ds_quality": "
This dataset ensures data reliability and scientific applicability through a systematic quality control system. In terms of completeness, the integration of multi-source data effectively compensates for the issues of sparse station coverage and missing satellite observations in the Arctic region. The monthly spatial coverage reaches 99.8%. Accuracy validation employs a three-tiered strategy: comparisons with independent meteorological stations (not involved in the fusion process) show a root mean square error of 1.05°C for monthly average temperature, with the mean bias controlled within ±0.25°C; spatial consistency tests with high-resolution reanalysis data reveal correlation coefficients of 0.94 and 0.91 for the winter sea ice edge zone and the summer tundra belt, respectively.", "ds_ref_way": "", "ds_abstract": "
This dataset provides monthly near-surface air temperature data for Arctic land areas (north of 60°N) from 1982 to 2015, with a spatial resolution of 10 km and projected using the Equal-Area Scalable Earth Grid (EASE-Grid 2.0). The data are generated through the fusion of multi-source observations and reanalysis data, including station observations (Fluxnet 2015), satellite-derived land surface temperature products (such as MODIS and AVHRR), and ERA5-Land reanalysis temperature data. By employing spatial interpolation, bias correction, and multi-source collaborative fusion algorithms, the dataset effectively addresses data inconsistencies caused by sparse station coverage, cloud cover interference, and sensor discrepancies in the Arctic region.In terms of quality control, a three-step validation strategy was implemented: comparison with independent meteorological station observations showed that the root mean square error (RMSE) of monthly average temperature was controlled within 1.2°C, with a mean bias error (MBE) below 0.3°C; spatial consistency tests with high-resolution reanalysis data demonstrated correlation coefficients above 0.92 in both winter extreme cold regions and summer tundra zones; cross-validation (leave-one-out method) was used to assess interpolation uncertainty, yielding standard errors below 0.8°C in station-dense areas and no more than 1.5°C in remote regions.", "ds_time_res": "", "ds_acq_place": "Arctic Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
This dataset achieves high-quality temperature data preparation through a systematic multi-step fusion and reconstruction process. First, all source data—including station observations, satellite-retrieved products, and reanalysis datasets—undergo preprocessing: station data are homogenized and bias-corrected to ensure climate representativeness; satellite-derived land surface temperature products are physically inverted and converted to near-surface temperatures using an emissivity library and atmospheric profiles; and reanalysis data are calibrated for systematic biases relative to station observations using quantile mapping. Subsequently, a fusion model centered on geographically weighted regression and a Bayesian maximum entropy framework is constructed, integrating multi-source data and environmental covariates (elevation, distance to sea, vegetation cover, sea ice influence, etc.) for spatial interpolation. For data gaps during polar nights, a time-series imputation model based on recurrent neural networks is established. Finally, a three-fold cross-validation approach (leave-one-station-out validation, grid-level reanalysis comparison, and extreme event process verification) coupled with energy balance rationality assessment is implemented for quality control. This process yields a reliable product that includes both temperature data and an uncertainty layer.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/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": [ "多源数据", "植被", "陆地蒸散发", "温度效应" ], "ds_subject_tags": [ "大气科学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北极陆地" ], "ds_time_tags": [ 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 ], "ds_contributors": [ { "true_name": "俞琳飞", "email": "yulf.20b@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" }, { "true_name": "冷国勇", "email": "lenggy@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "俞琳飞", "email": "yulf.20b@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "俞琳飞", "email": "yulf.20b@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "category": "极地" }