在全球暖化背景下，北极地区植被绿化已是公认现象，此变化深刻影响着区域内的水与能量循环。基于此背景，本数据集旨在量化1982年至2015年间，北极显著绿化区域由陆地蒸散发（ET）变化所产生的降温效应。结果现实，ET过程在夏季对北极气候产生了重要的冷却效应，在显著绿化区域内，7月的长期平均降温为-0.27°C，8月为-0.20°C，两月平均则为-0.24°C 。该数据集是基于物理经验公式计算得出的衍生产品 ，其核心输入数据源自多个权威数据库。其中，关键的蒸散发（ET）数据采用了全球陆地蒸发阿姆斯特丹模型（GLEAM）、TerraClimate及Synthesized-ET三套产品的集合平均值，以降低单一数据源的不确定性 ；气温数据来源于英国气候研究中心（CRU）的时间序列数据，用于计算潜热通量和空气密度 ；而研究范围（即“显著绿化区域”）则通过北京大学GIMMS NDVI产品进行界定 。所有输入数据均被统一处理至10 km空间分辨率和月度时间分辨率 。为确保数据的可靠性，本研究对作为关键输入的ET数据进行了严格的精度评估。评估采用“点对像元”的验证策略，利用北极地区11个FLUXNET通量站点的实测数据作为地面真值，并使用相关系数（CC）、平均偏差（BIAS）和均方根误差（RMSE）等多个统计指标进行检验 。评估结果显示，三套ET产品的集合平均值在综合表现上最优，具有最高的相关系数（0.69）和最低的均方根误差（14.70 mm），因此被选为最终计算依据 。这一严谨的验证过程确保了输入数据的准确性，从而为本降温效应数据集的可信度提供了有力支撑。", "ds_source": "
关键的蒸散发（ET）数据采用了全球陆地蒸发阿姆斯特丹模型（GLEAM）、TerraClimate及Synthesized-ET三套产品的集合平均值，以降低单一数据源的不确定性；气温数据来源于英国气候研究中心（CRU）的时间序列数据，用于计算潜热通量和空气密度；而研究范围（即“显著绿化区域”）则通过北京大学GIMMS NDVI产品进行界定 。所有输入数据均被统一处理至10 km空间分辨率和月度时间分辨率 。", "ds_process_way": "
本研究采用了一套标准化的数据加工方法，旨在统一多源数据以进行综合分析 。首先，所有网格数据集均被处理至一个共同的面积可伸缩地球网格（EASE-Grid）上 。具体加工步骤根据数据源的特性有所不同：对于PKU GIMMS、TerraClimate和Synthesized-ET数据集，研究人员采用双线性插值法进行重投影和空间重采样 ；对于分辨率较高的GLEAM数据，则通过对其2x2的像元进行平均来聚合到10 km的网格.在数据质量控制方面，由于各网格化产品在发布前均经过严格质控，因此未发现缺失值或异常值 。而用于验证模型的站点观测数据，则预先剔除了缺失值，并采用四分位距法进行了异常值检测 。此外，为降低单一数据产品带来的不确定性，研究采用了集合平均的方法。最终用于分析的陆地蒸散发（ET）数据，是GLEAM、TerraClimate和Synthesized-ET三套产品在通过精度验证后计算出的算术平均值 ；同样地，植被指数（NDVI）也整合了两个不同版本的PKU GIMMS数据源 。这一系列预处理流程确保了所有变量在时空上的一致性和可比性，为后续的趋势分析和归因建模提供了可靠的数据基础。", "ds_quality": "
本研究的数据质量通过多层次的验证和不确定性控制措施得到保障。首先，研究所选用的基础网格化数据集，包括蒸散发（ET）、植被指数（NDVI）、气象数据（CRU, TerraClimate）等，均是在其发布前经过了严格的质量控制，因此数据本身完整，无缺失值或明显异常值 。其次，针对研究中最为关键的ET数据，研究团队开展了独立的精度验证 。验证采用“点对像元”的方法 ，以北极地区11个FLUXNET通量观测站点的实测数据为地面真值 ，使用相关系数（CC）、均方根误差（RMSE）等四个统计指标 ，对三套ET产品（GLEAM, TerraClimate, Synthesized-ET）及其集合平均值进行了系统评估 。评估结果表明，三套产品的集合平均值表现最佳，其相关系数最高（CC=0.69），均方根误差（RMSE=14.70 mm）和无偏均方根误差（ubRMSE=13.36 mm）最低 。这一量化评估证实了所用ET数据的可靠性。最后，为进一步降低单一数据源可能带来的不确定性，本研究在关键变量上采用了集合平均策略 。最终分析所使用的ET数据即为上述验证中表现最优的集合平均数据 。同时，在植被动态监测方面，研究选用了经过改良的PKU GIMMS NDVI产品，该产品有效消除了卫星轨道漂移和传感器退化的影响，精度更高 。通过以上严格的筛选、独立的验证以及不确定性控制策略，确保了本研究分析所用数据的整体高质量和可靠性。", "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": 2178880, "ds_files_count": 17, "ds_format": "Geotiff", "ds_space_res": "10km", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "cc2f7254-6297-4557-b0a4-4e7b093824b9.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" ], "quality_level": 3, "publish_time": "2026-05-13 17:29:30", "last_updated": "2026-05-13 17:29:30", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7152.2026", "i18n": { "en": { "title": "Dataset of the Temperature Effect of Arctic Land Evapotranspiration Variation", "ds_format": "Geotiff", "ds_source": "
The key evapotranspiration (ET) data were derived as the ensemble mean of three products—GLEAM (Global Land Evaporation Amsterdam Model), TerraClimate, and Synthesized-ET—to reduce the uncertainties associated with any single dataset. Temperature data were obtained from the CRU (Climatic Research Unit) time series and used to calculate latent heat flux and air density. The study extent, i.e., the “significantly greening areas,” was defined using the Peking University GIMMS NDVI product. All input data were harmonized to a spatial resolution of 10 km and a monthly temporal resolution.", "ds_quality": "
The data quality in this study was ensured through multi-level validation and uncertainty control measures. First, the foundational gridded datasets used—including evapotranspiration (ET), vegetation index (NDVI), and meteorological data (CRU, TerraClimate)—had all undergone strict quality control prior to release, and therefore were complete, with no missing or obviously erroneous values.Second, for the most critical ET data, an independent accuracy assessment was conducted. A “point-to-pixel” validation approach was employed, using in-situ measurements from 11 FLUXNET stations across the Arctic as ground truth. Four statistical metrics, including correlation coefficient (CC) and root mean square error (RMSE), were applied to systematically evaluate three ET products (GLEAM, TerraClimate, Synthesized-ET) as well as their ensemble mean. The results indicated that the ensemble mean outperformed the individual products, achieving the highest correlation (CC = 0.69) and the lowest RMSE (14.70 mm) and unbiased RMSE (ubRMSE = 13.36 mm), confirming the reliability of the ET data used.Finally, to further reduce uncertainties associated with individual datasets, an ensemble averaging strategy was adopted for key variables. The ET data used in the final analysis correspond to the ensemble mean that performed best in the validation. For vegetation dynamics monitoring, the study employed the refined PKU GIMMS NDVI product, which effectively corrected for satellite orbital drift and sensor degradation, resulting in higher accuracy.Through rigorous data selection, independent validation, and uncertainty control strategies, this study ensured the overall high quality and reliability of the datasets used for analysis.", "ds_ref_way": "", "ds_abstract": "
Under the context of global warming, vegetation greening in the Arctic is a well-recognized phenomenon, profoundly affecting regional water and energy cycles. Against this backdrop, the present dataset aims to quantify the cooling effect induced by changes in terrestrial evapotranspiration (ET) over significantly greening areas in the Arctic from 1982 to 2015. The results indicate that ET processes exerted an important cooling effect on Arctic climate during summer. Within the significantly greening regions, the long-term mean cooling in July was -0.27°C, in August -0.20°C, and the average for the two months was -0.24°C.This dataset is a derived product calculated based on physical empirical formulas, with core input data sourced from multiple authoritative databases. Specifically, the key ET data were obtained as the ensemble mean of three products—GLEAM (Global Land Evaporation Amsterdam Model), TerraClimate, and Synthesized-ET—to reduce uncertainties associated with any single dataset. Temperature data were obtained from the CRU (Climatic Research Unit) time series, used to calculate latent heat flux and air density. The study extent, i.e., the “significantly greening areas,” was defined using the Peking University GIMMS NDVI product. All input data were harmonized to a spatial resolution of 10 km and a monthly temporal resolution.To ensure data reliability, the ET data, as a key input, underwent a strict accuracy evaluation. A “point-to-pixel” validation strategy was applied, using in-situ measurements from 11 FLUXNET stations across the Arctic as ground truth. Multiple statistical metrics—including correlation coefficient (CC), mean bias (BIAS), and root mean square error (RMSE)—were used for assessment. The results showed that the ensemble mean of the three ET products performed best overall, exhibiting the highest correlation coefficient (0.69) and the lowest RMSE (14.70 mm), and was thus selected as the final basis for calculations. This rigorous validation process ensured the accuracy of the input data, providing strong support for the reliability of the cooling effect dataset.", "ds_time_res": "", "ds_acq_place": "Arctic Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
This study employed a standardized data processing framework to harmonize multiple data sources for integrated analysis. First, all gridded datasets were projected onto a common, area-scalable EASE-Grid. Specific processing procedures varied according to the characteristics of each data source: for the PKU GIMMS, TerraClimate, and Synthesized-ET datasets, bilinear interpolation was applied for reprojection and spatial resampling; for the higher-resolution GLEAM dataset, a 2×2 pixel aggregation was performed to produce a 10 km grid.Regarding data quality control, all gridded products had undergone rigorous quality checks prior to release, and no missing or anomalous values were found. The in-situ observational data used for model validation were preprocessed by removing missing values and applying the interquartile range method for outlier detection.To reduce uncertainties associated with individual datasets, an ensemble averaging approach was adopted. The final terrestrial evapotranspiration (ET) data used for analysis were obtained as the arithmetic mean of the three products (GLEAM, TerraClimate, and Synthesized-ET) after accuracy validation. Similarly, the vegetation index (NDVI) was derived by integrating two versions of the PKU GIMMS dataset.This preprocessing workflow ensured temporal and spatial consistency and comparability across all variables, providing a reliable data foundation for subsequent trend analysis and attribution modeling.", "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" ], "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" }