气温时序数据是全球和区域气候研究、水文模拟及生态系统评估中的关键基础数据。然而，现有市县气温数据序列普遍较短。这导致当前对区域尺度，尤其是市县等局部单元上的长期气温动态与趋势变化的评估仍存在不确定性。本研究利用中国气象驱动数据集（China Meteorological Forcing Dataset, CMFD）版本 1.7”，生成黄土高原1982-2020年历史气温数据产品，再从中提取出不同市县的历史气温数据，空间覆盖范围为北纬33.75°-41.25°、东经100.95°-114.55°。相较于其他数据集，本数据集的核心优势在于提供了1982-2020年共39年的长序列气温数据。这一完整的长时间序列有助于揭示局地气候对全球变化的响应细节，从而为市县尺度的长期气候风险评估、适应性规划提供坚实的数据基础。
\n本数据以Excel格式（*.xlsx）存储。数据文件中不同工作表为不同市县气温数据，时间分辨率为年，时间范围为1982-2020年。", "ds_source": "
中国气象驱动数据集（China Meteorological Forcing Dataset, CMFD）版本 1.7”。它是由中国科学院青藏高原研究所研制、专门针对中国陆面过程研究而开发的一套高时空分辨率格点气象数据集。该数据集通过融合中国气象局约700个地面观测站数据、多套卫星遥感产品以及国际再分析资料，生成了覆盖中国区域自1979年以来的长时间序列、时空连续的近地表气象要素驱动场。CMFD1.7提供了包括2米气温、地表气压、比湿、10米风速、向下短波与长波辐射以及降水率在内的7个关键变量，其空间分辨率高达0.1°（约10公里），时间分辨率为3小时，因其高质量和在中国区域的良好表现，已成为驱动各类陆面、水文及生态模型，以及进行区域气候分析的重要基准数据之一。
", "ds_process_way": "（1）采用覆盖全国范围的中国气象驱动数据集（CMFD）版本1.7的3小时近地表气温格点数据作为基础输入数据源，从中提取并裁剪出黄土高原地区不同市县（北纬33.75°–41.25°，东经100.95°–114.55°）的时空数据子集；（2）将高时间分辨率的网格数据聚合为日尺度数据，具体方法是对一天内8个3小时时次的瞬时气温（°C）值计算平均值，最终生成每个格点的日平均气温（°C）；（3）基于得到的日平均气温格点数据，在时间维度上逐年进行平均，计算出每个格点的年平均气温。随后，计算每个市县级行政单元内所有格点的平均值，最终生成每个市县的逐年平均气温序列；（4）通过对比区域内国家气象站观测的年气温数据，从平均偏差（MBE）、均方根误差（RMSE）和相关系数（R²）等多方面系统验证所生成的黄土高原不同市县年降水数据集的精度和可靠性。
", "ds_quality": "本研究采用平均偏差误差（MBE）、均方根误差（RMSE）与相关系数（R²）作为核心验证指标，系统评估了该气温数据与独立站点观测的一致性。
\n验证结果表明，该气温数据在黄土高原不同市县与站点数据表现出高一致性。该数据与站点数据的平均偏差误差（MBE）为0.04℃至0.15℃之间、均方根误差（RMSE）为0.16℃至0.33℃之间、相关系数（R²）为0.89至0.97。该气温数据在系统偏差、误差幅度、相关系数上能够更可靠地刻画黄土高原不同市县近地面气温的时间分布，适用于市县尺度的长期气候风险评估。
", "ds_acq_start_time": "1982-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "黄土高原", "ds_acq_lon_east": 114.53333333333333, "ds_acq_lat_south": 33.75, "ds_acq_lon_west": 100.95, "ds_acq_lat_north": 41.25, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 23855, "ds_files_count": 0, "ds_format": "*.xlsx", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "032e712b-4447-47f5-bc8f-03ed00e75926.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": "", "organization_id": "bf138922-7121-438c-8d1b-19d5f751c907", "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": 0, "publish_time": "2026-05-21 10:05:17", "last_updated": "2026-05-21 10:50:40", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.loess.db7333.2026", "i18n": { "en": { "title": "Annual Temperature Dataset for Different Cities and Counties on the Loess Plateau (1982–2020)", "ds_format": "*.xlsx", "ds_source": "The China Meteorological Forcing Dataset (CMFD) version 1.7 is a high spatiotemporal resolution gridded meteorological dataset specifically developed for land surface process studies over China. It was created by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences. By integrating ground-based observational data from approximately 700 stations of the China Meteorological Administration, multiple satellite remote sensing products, and international reanalysis datasets, it generates a long-term, spatiotemporally continuous near-surface meteorological forcing field covering China since 1979. CMFD version 1.7 provides seven key variables, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave and longwave radiation, and precipitation rate. It features a high spatial resolution of 0.1° (approximately 10 kilometers) and a temporal resolution of 3 hours. Due to its high quality and excellent performance over China, it has become a crucial benchmark dataset for driving various land surface, hydrological, and ecological models, as well as for conducting regional climate analysis.", "ds_quality": "
This study employed Mean Bias Error (MBE), Root Mean Square Error (RMSE), and the correlation coefficient (R²) as the core validation metrics to systematically evaluate the consistency between this temperature dataset and independent station observations.\r\n
The validation results demonstrate a high degree of consistency between this temperature data and station observations across various cities and counties on the Loess Plateau. The dataset exhibits a Mean Bias Error (MBE) ranging from 0.04 °C to 0.15 °C, a Root Mean Square Error (RMSE) between 0.16 °C and 0.33 °C, and a correlation coefficient (R²) from 0.89 to 0.97 when compared to the station data. With its reliable performance in systematic bias, error magnitude, and spatial representativeness, this temperature data can more accurately characterize the spatiotemporal distribution of near-surface air temperature in different cities and counties of the Loess Plateau. It is, therefore, well-suited for long-term climate risk assessment at the city and county scale.", "ds_ref_way": "", "ds_abstract": "
Temperature time series data are fundamental and crucial for global and regional climate research, hydrological modeling, and ecosystem assessments. However, existing temperature data series for cities and counties are generally short in duration. This introduces uncertainty in assessing long-term temperature dynamics and trends at regional scales, particularly for local administrative units such as cities and counties. To address this, the present study utilizes version 1.7 of the China Meteorological Forcing Dataset (CMFD) to generate a historical air temperature data product for the Loess Plateau covering the period from 1982 to 2020. Historical temperature data for various cities and counties were then extracted from this product. The spatial coverage of the dataset spans from 33.75°N to 41.25°N in latitude and 100.95°E to 114.55°E in longitude. Compared to other datasets, the core advantage of this product lies in its provision of a long-term temperature data series spanning 39 years from 1982 to 2020. This complete, extended temporal record facilitates the detailed analysis of local climate responses to global change, thereby providing a solid data foundation for long-term climate risk assessment and adaptation planning at the city and county scale.\r\n
The dataset is stored in Excel format (*.xlsx). Different worksheets contain air temperature data of various cities and counties, with an annual temporal resolution spanning from 1982 to 2020.", "ds_time_res": "", "ds_acq_place": "Loess Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
(1) The 3-hourly near-surface air temperature gridded data from the nationwide China Meteorological Forcing Dataset (CMFD) version 1.7 is adopted as the foundational input data source. A spatiotemporal data subset for various cities and counties within the Loess Plateau region (33.75°N–41.25°N, 100.95°E–114.55°E) is extracted and cropped from this source. (2) The high-temporal-resolution gridded data is aggregated to a daily scale. The specific method involves calculating the average of the eight 3-hourly instantaneous air temperature (°C) values within each day, ultimately generating the daily mean air temperature (°C) for each grid point. (3) Based on the obtained daily mean air temperature gridded data, an annual average is computed for each grid point by averaging across the time dimension year by year. Subsequently, the average value of all grid points within each city/county-level administrative unit is calculated, ultimately generating an annual mean air temperature time series for each city and county. (4) The accuracy and reliability of the generated annual air temperature dataset for different cities and counties on the Loess Plateau are systematically validated from multiple aspects—including Mean Bias Error (MBE), Root Mean Square Error (RMSE), and the correlation coefficient (R²)—by comparing it with observed annual air temperature data from national meteorological stations within the region.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 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": [ "气温", "长时序" ], "ds_subject_tags": [ "大气科学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "延安市", "兰州市", "天水市", "庆阳市", "西吉县", "泾阳县", "北吴起县", "绥德县", "衡山县" ], "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, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "气象" }