基于 2912 个主要来源于树年轮和历史文献的年分辨率代用序列，我们提出了一套自1700年以来每年（11 月至 10 月）覆盖整个亚洲的标准降水指数（SPI）重建，以及雨季（即西亚的11月至4月，其他地区的5月至10月）的标准降水指数重建，空间分辨率为 2.5∘。为了从其相连区域内具有同质降水机制和相似降水变率的可用代用指标中筛选出用于重建每个网格内 SPI 的最佳候选代用指标，我们开发了一种新方法，采用从仪器 SPI 数据中得出的网格位置依赖性划分。验证结果表明，这些重建对亚洲大部分地区是有效的。与校准时间前的实测降水量相比，对数据质量的评估表明，除了俄罗斯西部、东南亚沿海地区和日本北部的少数网格外，我们的重建具有较高的质量，可以显示大部分研究地区的降水变率。\n
数据集包括四种SPI重建： (1) 整个亚洲的 11-10月SPI重建，不使用与降水负相关的树环密度年代学和宽度年代学（11-10月SPIA版）；(2) 整个亚洲的 11-10月SPI重建，增加了与降水负相关的树环密度年代学和宽度年代学（11-10 月 SPI B 版）；(3) 亚洲热带外地区的雨季 SPI 重建（西亚地区为 11 月-4 月 SPI，其他地区为 5 月-10 月 SPI），未使用与降水负相关的树环密度年代学和宽度年代学（雨季 SPIA 版）； (4) 重建亚洲热带外地区的雨季 SPI（西亚地区为 11 月-4 月 SPI，其他地区为 5 月-10 月 SPI），增加了与降水负相关的树环密度年代学和宽度年代学（雨季 SPI B 版）。每个版本都存储在一个 NetCDF 文件（.nc）中，包含五个三维（经度×纬度×时间）变量，包括重建的 SPI、调整的判定系数（R2a）、验证 RE、验证 CE 和用于构建的代用指标数量（nPrx）。", "ds_source": "
在研究中，用于 SPI 重建的网格大小设定为 2.5° × 2.5°。用于校准的仪器数据是根据美国国家海洋和大气管理局陆地降水产品计算的 1948-2019 年 0.5° × 0.5°网格月SPI数据调整的，该数据通过 IRI/LDEO 气候数据图书馆下载（http://iridl.ldeo.columbia.edu/SOURCES/.IRI/.Analyses/.SPI/\n
亚洲及邻近陆地地区（东欧和阿拉斯加）共有 2912 个年分辨率代用序列可供重建，其中 2792 个来自树环，115 个来自历史文献，4 个来自冰芯，1 个来自石笋。值得注意的是，所有代用系列都有至少 20 条记录与 1948 年以来的仪器时段重叠，以确保有足够的样本量进行校准和验证，并有超过 30 条 1948 年之前的记录用于重建。树木年轮数据主要（2772）来自国际树木年轮数据库 （ITRDB），由世界古气候学数据中心（WDC-P，https://www.ncei.noaa.gov/products/paleoclimatology", "ds_process_way": "
由于研究区降水异质性气候类型众多，且单个代用指标的空间代表性对位置敏感，我们开发了一种新方法来确定代用指标搜索区域（以下称为 “搜索区域”），以重建每个网格中的 SPI。这种方法是根据降水系统空间模式中年际降水变化一致性的区域划分而制定的，以确保搜索区域中的代用指标能够很好地指示目标网格中的 SPI 变化。我们从用于 SPI 重建的各目标网格的 SPI 与研究区内其他网格的相关系数（CC）的空间格局来划分区域，该相关系数由仪器 SPI 数据计算得出。搜索区域的定义是目标网格周围的所有相连网格，其 CC 均超过 0.05 的显著性水平。", "ds_quality": "
与以往研究对亚洲季风区夏季（JJA 或 5-9 月）降水（或 PDSI）的三次重建相比，我们的5-10 月 SPI 重建在青藏高原南部至印度次大陆东部、东南亚大陆西部和中国西北部校准期的R2a比三次重建中最好的一次高10%。此外，我们的重建在蒙古、中亚和中国东部部分地区的R2a略高于其他重建。特别是在中国东部，我们重建的R2a比仅使用树环数据重建的R2a高出约 40%。这些改进不仅是因为增加了更多的代用数据（包括从中国历史文献中得到的DWI和最近发表的树环数据），而且还因为重建方法的发展，即通过GLDD方法从与目标网格SPI显著正相关的连接搜索区域中选择代用数据。\n
此外，我们的雨季 SPI 重建结果与之前研究的亚洲季风区四种重建结果之间的相关性图显示，大多数网格都通过了 0.01 的显著性水平。", "ds_acq_start_time": "1700-01-01 00:00:00", "ds_acq_end_time": "2000-12-31 00:00:00", "ds_acq_place": "亚洲区域", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 0.0, "ds_acq_lon_west": 20.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 121083401, "ds_files_count": 6, "ds_format": "Excel、nc", "ds_space_res": "2.5°", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "270cd587-4a55-4a00-b2ec-823938b23d68.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-05-22 09:01:34", "last_updated": "2025-05-29 11:22:01", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.01829", "i18n": { "en": { "title": "A dataset of standard precipitation index reconstructed from multi-proxies over Asia for the past 300 years", "ds_format": "Excel、nc", "ds_source": "
In the study, the grid size used for SPI reconstruction was set to 2.5 °× 2.5 °. The instrument data used for calibration is adjusted based on the 0.5 °× 0.5 ° grid monthly SPI data calculated from the National Oceanic and Atmospheric Administration's land precipitation products from 1948 to 2019, which can be downloaded from the IRI/LDEO Climate Data Library（ http://iridl.ldeo.columbia.edu/SOURCES/.IRI/.Analyses/.SPI/\n
There are a total of 2912 annual resolution proxy sequences available for reconstruction in Asia and adjacent land areas (Eastern Europe and Alaska), including 2792 from tree rings, 115 from historical literature, 4 from ice cores, and 1 from stalagmites. It is worth noting that all substitute series have at least 20 records overlapping with instrument time periods since 1948 to ensure sufficient sample size for calibration and validation, and over 30 records prior to 1948 for reconstruction. The tree ring data (2772) mainly comes from the International Tree Ring Database (ITRDB), which is compiled by the World Paleoclimatology Data Center (WDC-P), https://www.ncei.noaa.gov/products/paleoclimatology", "ds_quality": "
Compared with previous studies on the three reconstructions of summer precipitation (or PDSI) in the Asian monsoon region (JJA or May September), our May October SPI reconstruction showed a 10% increase in R2a during the calibration period from the southern Qinghai Tibet Plateau to the eastern Indian subcontinent, western Southeast Asia, and northwestern China, compared to the best of the three reconstructions. In addition, our reconstruction has slightly higher R2a than other reconstructions in parts of Mongolia, Central Asia, and eastern China. Especially in eastern China, the R2a we reconstructed is about 40% higher than the R2a reconstructed using only tree ring data. These improvements are not only due to the addition of more surrogate data (including DWI obtained from Chinese historical literature and recently published tree ring data), but also due to the development of reconstruction methods, which select surrogate data from the connection search area significantly positively correlated with the target grid SPI through the GLDD method.\n
In addition, the correlation graph between our rainy season SPI reconstruction results and the four reconstruction results previously studied in the Asian monsoon region shows that most grids passed the significance level of 0.01.", "ds_ref_way": "", "ds_abstract": "
Based on 2912 annual resolution proxy sequences mainly derived from tree rings and historical literature, we propose a set of standard precipitation index (SPI) reconstructions covering the entire Asia every year (November to October) since 1700, as well as standard precipitation index reconstructions for the rainy season (i.e. November to April in West Asia and May to October in other regions), with a spatial resolution of 2.5 ∘. In order to screen the best candidate proxy indicators for reconstructing SPI within each grid from available proxy indicators with homogeneous precipitation mechanisms and similar precipitation variability in their connected areas, we developed a new method that uses grid position dependency partitioning obtained from instrument SPI data. The verification results indicate that these reconstructions are effective for most regions in Asia. Compared with the measured precipitation before calibration time, the evaluation of data quality shows that, except for a few networks in western Russia, coastal areas of Southeast Asia, and northern Japan, our reconstruction has high quality and can display precipitation variability in most of the study areas.\n
The dataset includes four types of SPI reconstructions: (1) SPI reconstructions for the entire Asia from November to October, without using tree ring density chronology and width chronology that are negatively correlated with precipitation (November October SPIA version); (2) The SPI reconstruction of the entire Asia from November to October has added tree ring density chronology and width chronology negatively correlated with precipitation (SPI B version from November to October); (3) The reconstruction of rainy season SPI in non tropical regions of Asia (November April SPI in West Asia and May October SPI in other regions) did not use tree ring density chronology and width chronology negatively correlated with precipitation (rainy season SPIA version); (4) Reconstructing the rainy season SPI in extra tropical regions of Asia (November April SPI in West Asia and May October SPI in other regions), adding tree ring density chronology and width chronology negatively correlated with precipitation (rainy season SPI B version). Each version is stored in a NetCDF file (. nc) containing five three-dimensional (longitude x latitude x time) variables, including reconstructed SPI, adjusted decision coefficients (R2a), validation RE, validation CE, and the number of surrogate indicators used for construction (nPrx).
", "ds_time_res": "年", "ds_acq_place": "Asia Region", "ds_space_res": "2.5°", "ds_projection": "", "ds_process_way": "Due to the diverse climate types of precipitation heterogeneity in the study area and the sensitivity of spatial representativeness of individual proxy indicators to location, we have developed a new method to determine the proxy indicator search area (hereinafter referred to as the \"search area\") to reconstruct the SPI in each grid. This method is developed based on the regional division of interannual precipitation variation consistency in the spatial model of the precipitation system, to ensure that the proxy indicators in the search area can effectively indicate the SPI changes in the target grid. We divide the regions based on the spatial pattern of the correlation coefficient (CC) between the SPI of each target grid used for SPI reconstruction and other grids within the study area, which is calculated from instrument SPI data. The definition of the search area is all connected grids around the target grid, whose CC exceeds the significance level of 0.05.", "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": [ "标准降水指数", "降水重建", "树木年轮数据", "亚洲季风" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "亚洲区域" ], "ds_time_tags": [ 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 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 ], "ds_contributors": [ { "true_name": "郑景云", "email": "zhengjy@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": "郑景云", "email": "zhengjy@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": "郑景云", "email": "zhengjy@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "category": "大气本底" }