全球地表温度（ST）数据集是全球气候变化研究的基础。美国国家海洋和大气管理局 NCEI、美国国家航空航天局 GISS、英国气象局哈德利中心和 UEA CRU 以及伯克利地球等机构的不同小组已经开发了多个全球地表温度数据集。本研究提出了一个新的全球 ST 数据集，名为 “中国合并表面温度（CMST）”。CMST 是将中国陆地表面气温（C-LSAT1.3）与扩展重建海面温度第 5 版（ERSSTv5）的海面温度（SST）数据合并而成。C-LSAT 和 ERSSTv5 的合并显示了延伸至高纬度的高空间覆盖率，并且与极地地区的多数据集平均值参考更加一致。比较表明，从 1900 年到 2017 年，CMST 与其他现有全球 ST 数据集在全球、半球和区域尺度上的年际变化、十年变化和长期趋势是一致的。CMST 数据集可用于全球气候变化评估、监测和探测。", "ds_source": "
地表气温数据：C-LSAT1.0数据集处理了自1900年以来的SAT数据，共14个数据源，包括3个全球数据源（CRUTEM 4.6、GHCNv3和BEST）、南极研究科学委员会的3个区域数据源、欧洲气候评估和数据集（ECA&D）的日数据集和大阿尔卑斯地区历史仪器气候表面时间序列（HISTALP）、2004）、欧洲气候评估和数据集（ECA&D）的每日数据集、大阿尔卑斯地区历史仪器气候表面时间序列（HISTALP），以及来自中国、美国、俄罗斯、加拿大、澳大利亚、韩国、日本和越南的八个国家数据源。\n
海面温度数据：SST 数据集。", "ds_process_way": "
C-LSAT1.3 和 ERSST 的合并过程：\n
1. C-LSAT 和 ERSSTv5 计算了每个网格框中与 1961-1990 年基期有关的异常值。\n
2.对于海洋-陆地边界部分，陆地和海洋面积的比例 这具体过程如下：\n
a.将陆地 （C-LSAT1.3） 和海洋数据缩小到分辨率。海洋数据的分辨率为，分布在四个网格中.土地数据的分辨率为，分布在 25 个网格中.\n
b.使用海洋-陆地掩码文件将全局所有网格区分为陆地或海洋。\n
c.海洋网格数据和陆地网格数据通过海洋-陆地掩模拼接，得到全球 ST 网格数据。\n
d.计算网格的平均地表温度异常值（STA）。", "ds_quality": "
C-LSAT1.3 和 ERSSTv5 合并时的空间覆盖范围大于将 C-LSAT1.3 与 HadSST3 合并时，尤其是在极地地区。此外，前者（将 C-LSAT1.3 与 ERSSTv5 合并，命名为 CMST）是在空间分布和时间变化方面也更胜一筹。\n
CMST中的LSAT使用了高质量的C-LSAT1.3。超过4900个车站 被添加到C-LSAT1.0的先前版本（Xu et al.， 2018）中，该版本具有进一步扩大了数据覆盖率。新增的车站主要是来自 ISTI 数据集。CMST 中的 SST 将 ERSSTv5 与海洋一起使用来自最新 ICOADS R3.0 的数据，并包含多种类型的观察。与其他现有的全球ST数据集相比，CMST 增加对全球陆地和海洋表面的整体覆盖。\n
全球和中低纬度地区CMST的时间序列为与其他合并的数据集一致，包括年际和年代际时间尺度。在 NH 和 SH 的高纬度地区， 温度趋势的差异通常较大，CMST的趋势代表了主要的长期气候变化。因此，CMST 1900-2017年的气温趋势总体上与其他数据集，并被证明是全球气候变化的新有用工具研究。", "ds_acq_start_time": "1900-01-01 00:00:00", "ds_acq_end_time": "2018-12-01 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 5466025, "ds_files_count": 2, "ds_format": "txt", "ds_space_res": "", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "e33af632-7ffb-41c4-91ce-46986c15d022.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "99c0a56f-14cb-4cfc-a9a1-bb4b8d16a658", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-07-26 17:03:18", "last_updated": "2026-01-14 09:50:57", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.PANGAEA.DB6656.2024", "i18n": { "en": { "title": "A new merge of global surface temperature datasets since the start of the 20th century", "ds_format": "txt", "ds_source": "
Surface temperature data: The C-LSAT1.0 dataset processed SAT data since 1900, with a total of 14 data sources, including 3 global data sources (CRUTEM 4.6, GHCNv3, and BEST), 3 regional data sources from the Scientific Committee on Antarctic Research, daily datasets from the European Climate Assessment and Dataset (ECA&D) and the Alpine Historical Instrument Climate Surface Time Series (HISTALP), 2004, daily datasets from the European Climate Assessment and Dataset (ECA&D), the Alpine Historical Instrument Climate Surface Time Series (HISTALP), as well as eight national data sources from China, the United States, Russia, Canada, Australia, South Korea, Japan, and Vietnam.\n
Sea surface temperature data: SST dataset.", "ds_quality": "
The spatial coverage of the merger of C-LSAT1.3 and ERSSTv5 is greater than that of the merger of C-LSAT1.3 and HadSST3, especially in polar regions. In addition, the former (merging C-LSAT1.3 with ERSSTv5 and naming it CMST) is also superior in terms of spatial distribution and temporal variation.\n
The LSAT in CMST uses high-quality C-LSAT1.3. More than 4900 stations were added to the previous version of C-LSAT1.0 (Xu et al., 2018), which further expanded data coverage. The newly added stations are mainly from the ISTI dataset. The SST in CMST combines ERSSTv5 with ocean data from the latest ICOADS R3.0 and includes multiple types of observations. Compared to other existing global ST datasets, CMST increases overall coverage of land and ocean surfaces worldwide.\n
The time series of CMST in global and mid low latitude regions are consistent with other merged datasets, including interannual and decadal time scales. In the high latitude regions of NH and SH, the difference in temperature trends is usually significant, and the trend of CMST represents the main long-term climate change. Therefore, the temperature trend of CMST from 1900 to 2017 is generally consistent with other datasets and has been proven to be a new and useful tool for studying global climate change.", "ds_ref_way": "", "ds_abstract": "
The Global Surface Temperature (ST) dataset is the foundation of global climate change research. Different teams from the National Oceanic and Atmospheric Administration (NCEI), National Aeronautics and Space Administration (GISS), Met Office Hadley Centre and UEA CRU, as well as Berkeley Earth, have developed multiple global surface temperature datasets. This study proposes a new global ST dataset called \"China Consolidated Surface Temperature (CMST)\". CMST is a fusion of China's land surface temperature (C-LSAT1.3) and extended reconstructed sea surface temperature (ERSSTv5) sea surface temperature (SST) data. The merger of C-LSAT and ERSSTv5 shows high spatial coverage extending to high latitudes, and is more consistent with the average reference of multiple datasets in polar regions. Comparison shows that from 1900 to 2017, the interannual, decadal, and long-term trends of CMST are consistent with other existing global ST datasets at the global, hemisphere, and regional scales. The CMST dataset can be used for global climate change assessment, monitoring, and detection.
", "ds_time_res": "月", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The merger process of C-LSAT1.3 and ERSST:\n
C-LSAT and ERSSTv5 calculated the outliers related to the 1961-1990 base period in each grid box.\n
2. For the ocean land boundary, the specific process of the ratio of land and ocean area is as follows:\n
a. Reduce land (C-LSAT1.3) and ocean data to resolution. The resolution of ocean data is distributed in four grids The resolution of land data is distributed across 25 grids\n
b. Use the ocean land mask file to distinguish all global grids as either land or sea.\n
c. The ocean grid data and land grid data are concatenated through ocean land masks to obtain global ST grid data.\n
d. Calculate the average surface temperature anomaly (STA) of the grid.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "CMST", "全球", "地表温度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 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 ], "ds_contributors": [ { "true_name": "李庆祥", "email": "liqingx5@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李庆祥", "email": "liqingx5@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "李庆祥", "email": "liqingx5@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "category": "其他" }