北极通量观测数据本数据集基于 FLUXNET2015 数据框架，针对北极地区通量站点的月度聚合数据进行了提取与构建。FLUXNET2015 是全球通量网络的标准化数据产品，汇集了来自不同区域通量网络一致质量控制下的涡相关通量观测，包括 CO₂、潜热和感觉热等物质和能量交换及辅助气象变量，并经过统一的数据处理流程生成多种时间分辨率的数据产品（如日、周、月、年）FLUXNET。在原始 FLUXNET2015 处理中，基于半小时或小时数据通过 gap-filling 和 QA/QC 流程生成月度（MM）分辨率的聚合数据，包含经过质量控制的通量和驱动变量，以及相应的不确定性和质量标记。本数据集选取北极生态系统范围内的通量观测站点（ 北极圈内站点）对应的 月度汇总产品，包括关键通量变量及其辅助环境因子，数据字段遵循 FLUXNET2015 标准变量命名和质量控制策略。所有站点的月度数据均采用统一格式，便于不同站点之间的比较分析与时空合成研究。", "ds_source": "
数据来源于全球涡相关通量观测网络 FLUXNET2015 数据集，该数据集汇集了来自不同区域通量网络采用一致处理流程的地–大气界面通量观测数据，包括碳通量、水通量和能量通量及其辅助气象变量。FLUXNET2015 由多个地区网络协调生成，经过统一的数据质量控制、缺失值填补和变量派生处理，形成了多个标准时间分辨率的产品（如半小时、日、月、年）供科研使用，其中月度产品按照统一处理流程对原始高频数据进行聚合和质量检查，保留了关键通量变量和质量标记信息，以便跨站点的比较与综合分析。基于 FLUXNET2015 数据库提取了北极区域通量观测站点的月度通量数据集，涵盖站点级别的 CO2、蒸散、能量交换等关键生态系统过程变量。所有样本数据按照 FLUXNET2015 的 SUBSET/FULLSET 规范统一整理，确保了变量定义与质量控制与原始数据一致性，同时保留了月度时间分辨率信息，适合用于北极区域碳–水–能量循环的季节性分析及与其他多源数据的联合研究。", "ds_process_way": "
数据集的加工基于 FLUXNET2015 数据处理体系，在原始通量站点提供的半小时或小时级数据基础上，通过统一的质量控制、缺失值填补与时间聚合流程生成月度级产品。首先，对原始通量和环境变量进行了严格的质量控制，包括时间戳一致性检测、变量间一致性检查以及不合理值剔除等预处理步骤，以保证不同站点数据的一致性和可比性。随后，采用常用的 Marginal Distribution Sampling（MDS）方法对缺失的观测数据进行 gap-filling，该方法通过寻找与缺失时段具有类似气象条件的观测记录来填补缺失值，从而最大程度保留原始数据特征，同时可选地结合重分析数据（如 ERA-Interim）进行更长期缺失的填补，以提高长期连续性和空间一致性。", "ds_quality": "
数据质量控制是确保本北极月度通量站点数据集科学性与可比性的核心环节。本数据集继承了 FLUXNET2015 统一的数据质量控制体系，该体系通过严格的 QA/QC 流程对各站点原始高频通量和气象观测数据进行一致性检查与处理，目的是尽可能降低数据测量误差、缺失值和处理步骤引入的偏差。FLUXNET2015 在质量控制过程中对每个变量提供了质量标记（QC）和不确定性量化指标，用于区分原始测量值与 gap-filled 值，以及表示不同质量等级的 gap-fill 结果，如高质量填补、中等质量或较低质量填补等，这些标记在生成最终的月度产品时进行了聚合统计，可用于识别高质量观测或潜在不确定性较大的数据段。质量标记在月度等粗时间尺度上表示该时间段内原始测量与高质量 gap-fill 的比率，对后续分析和解释提供必要的参考。FLUXNET2015 的处理流水线还采用了重新分析资料下填长时间段缺失、能量平衡调整和多方法过滤技术，从多层面提高了数据的完整性和一致性。不同站点和变量的不确定性估计、质量标记和处理说明可在 FULLSET 或 SUBSET 产品中查阅，这些信息为数据使用者提供了评估数据可靠性和适用条件的重要依据。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2014-12-31 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": 13497, "ds_files_count": 2, "ds_format": "csv", "ds_space_res": "", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "1ef87845-fc7b-432a-bdf1-956a468e2cb8.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": 3, "publish_time": "2026-05-13 17:11:07", "last_updated": "2026-05-13 17:11:07", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7157.2026", "i18n": { "en": { "title": "Arctic flux observation data", "ds_format": "csv", "ds_source": "
The data is sourced from the global eddy covariance flux observation network FLUXNET2015 dataset. This dataset integrates observations of land–atmosphere interface fluxes from different regional flux networks using consistent processing procedures. It includes carbon fluxes, water fluxes, energy fluxes, and auxiliary meteorological variables. FLUXNET2015 is generated through the coordination of multiple regional networks and undergoes unified data quality control, gap-filling, and variable derivation processes, resulting in standardized products at various temporal resolutions (e.g., half-hourly, daily, monthly, and annual) for scientific research. Among these, the monthly products are aggregated from high-frequency raw data following a standardized workflow and undergo quality checks, preserving key flux variables and quality flag information to facilitate cross-site comparisons and integrated analyses. Based on the FLUXNET2015 database, a monthly flux dataset for Arctic flux observation sites was extracted, encompassing key ecosystem process variables at the site level, such as CO2, evapotranspiration, and energy exchange. All sample data were uniformly organized according to the SUBSET/FULLSET specifications of FLUXNET2015, ensuring consistency in variable definitions and quality control with the original data, while preserving monthly temporal resolution information. This makes the dataset suitable for seasonal analyses of carbon–water–energy cycles in the Arctic region and for integrated studies with other multi-source data.", "ds_quality": "
Data quality control is a core step in ensuring the scientific validity and comparability of this Arctic monthly flux site dataset. This dataset adopts the unified data quality control framework of FLUXNET2015, which applies rigorous QA/QC procedures to conduct consistency checks and processing on the original high-frequency flux and meteorological observations from each site. The objective is to minimize errors arising from data measurement, missing values, and processing steps. During the quality control process, FLUXNET2015 provides quality flags (QC) and uncertainty quantification metrics for each variable. These are used to distinguish between original measured values and gap-filled values, as well as to indicate gap-filling results of different quality levels (e.g., high-quality filling, moderate-quality filling, or lower-quality filling). These flags are aggregated and summarized during the generation of the final monthly products, enabling the identification of high-quality observations or data segments with potentially higher uncertainty. At coarser temporal scales such as monthly, the quality flags indicate the proportion of original measurements versus high-quality gap-filled data within the given period, providing essential reference information for subsequent analysis and interpretation.The FLUXNET2015 processing pipeline also incorporates techniques such as gap-filling with reanalysis data for longer missing periods, energy balance adjustment, and multi-method filtering. These approaches enhance data completeness and consistency across multiple dimensions. Uncertainty estimates, quality flags, and processing notes for different sites and variables can be accessed in the FULLSET or SUBSET products. This information offers critical guidance for data users in evaluating the reliability and applicable conditions of the data.", "ds_ref_way": "", "ds_abstract": "
This dataset is constructed based on the FLUXNET2015 data framework, with extraction and compilation of monthly aggregated data from flux tower sites in the Arctic region. FLUXNET2015 is a standardized global flux network data product that integrates eddy covariance flux observations from various regional flux networks under consistent quality control. It includes measurements of material and energy exchanges such as CO₂, latent heat, and sensible heat, along with auxiliary meteorological variables. These data undergo unified processing workflows to generate products at multiple temporal resolutions (e.g., daily, weekly, monthly, and annual). In the original FLUXNET2015 processing, monthly (MM) aggregated data are derived from half-hourly or hourly data through gap-filling and QA/QC procedures. These monthly data include quality-controlled flux and driver variables, along with corresponding uncertainty estimates and quality flags.This dataset specifically selects monthly aggregated products from flux observation sites within Arctic ecosystems (sites located inside the Arctic Circle). It includes key flux variables and their associated environmental factors, adhering to the FLUXNET2015 standard variable naming conventions and quality control protocols. All site-specific monthly data are provided in a uniform format to facilitate comparative analysis and spatiotemporal synthesis across different sites.", "ds_time_res": "", "ds_acq_place": "Arctic Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The processing of the dataset is based on the FLUXNET2015 data processing framework. Starting from the half-hourly or hourly data provided by the original flux sites, monthly-level products are generated through unified quality control, gap-filling, and temporal aggregation procedures. First, rigorous quality control is applied to the original flux and environmental variables, including preprocessing steps such as timestamp consistency checks, inter-variable consistency validation, and removal of unreasonable values. This ensures the consistency and comparability of data across different sites. Subsequently, the commonly used Marginal Distribution Sampling (MDS) method is employed to fill gaps in missing observational data. This method fills missing values by identifying observational records from periods with similar meteorological conditions, thereby preserving the characteristics of the original data to the greatest extent. Additionally, reanalysis data (e.g., ERA-Interim) can be optionally incorporated to fill longer-term gaps, enhancing temporal continuity and spatial consistency.", "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": [ "北极", "通量站", "气温", "辐射" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北极" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014 ], "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" }