# asia_russ036w - Pinega - Breitenmoser Tree Ring Chronology Data #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite Publication, and Online_Resource and date accessed when using these data. # If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed. # # # Online_Resource: # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/24611 # # Original_Source_URL:https://www.ncdc.noaa.gov/paleo/study/4592 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_russ036w - Pinega - Breitenmoser Tree Ring Chronology Data #-------------------- # Investigators # Investigators: Breitenmoser, P.; Bronnimann, S.; Frank, D. #-------------------- # Description_and_Notes # Description: Data from Breitenmoser 2014 Journal of past Climate supplementary, see publication for ARSTAN standardization details #-------------------- # Publication # Authors: Breitenmoser, P.; Bronnimann, S.; Frank, D. # Published_Date_or_Year: 2014-03-11 # Published_Title: Forward modelling of tree-ring width and comparison with a global network of tree-ring chronologies # Journal_Name: Climate of the Past # Volume: 10 # Edition: # Issue: # Pages: 437-449 # DOI: 10.5194/cp-10-437-2014 # Online_Resource: www.clim-past.net/10/437/2014/ # Full_Citation: # Abstract: We investigate relationships between climate and tree-ring data on a global scale using the process-based Vaganov–Shashkin Lite (VSL) forward model of tree-ring width formation. The VSL model requires as inputs only latitude, monthly mean temperature, and monthly accumulated precipitation. Hence, this simple, process-based model enables ring-width simulation at any location where monthly climate records exist. In this study, we analyse the growth response of simulated tree rings to monthly climate conditions obtained from the CRU TS3.1 data set back to 1901. Our key aims are (a) to assess the VSL model performance by examining the relations between simulated and observed growth at 2287 globally distributed sites, (b) indentify optimal growth parameters found during the model calibration, and (c) to evaluate the potential of the VSL model as an observation operator for data-assimilation-based reconstructions of climate from tree-ring width. The assessment of the growth-onset threshold temperature of approximately 4–6 C for most sites and species using a Bayesian estimation approach complements other studies on the lower temperature limits where plant growth may be sustained. Our results suggest that the VSL model skilfully simulates site level treering series in response to climate forcing for a wide range of environmental conditions and species. Spatial aggregation of the tree-ring chronologies to reduce non-climatic noise at the site level yielded notable improvements in the coherence between modelled and actual growth. The resulting distinct and coherent patterns of significant relationships between the aggregated and simulated series further demonstrate the VSL model’s ability to skilfully capture the climatic signal contained in tree-ring series. Finally, we propose that the VSL model can be used as an observation operator in data assimilation approaches to reconstruct past climate. #-------------------- # Authors: Anderson, D.M., Tardif, R., Horlick, K., Erb, M.P., Hakim, G.J., Noone, D., Perkins, W.A., and E. Steig # Published_Date_or_Year: 2018 # Published_Title: Additions to the last millennium reanalysis multi-proxy database # Journal_Name: Data Science Journal # Volume: # Edition: # Issue: # Pages: # Report_Number: # DOI: # Online_Resource: # Full_Citation: Anderson, D.M., Tardif, R., Horlick, K., Erb, M.P., Hakim, G., J., Noone, D., Perkins, W.A., and E. Steig, submitted. Additions to the last millennium reanalysis multi-proxy database. Data Science Journal. # Abstract: Progress in paleoclimatology increasingly occurs via data syntheses. We describe additions to a collection prepared for use in paleoclimate state estimation, specifically the Last Millennium Reanalysis (LMR). The 2290 additional series include 2152 tree ring chronologies and 138 other series. They supplement the collection used previously and together form a database titled LMRdb 1.0.0. The additional data draws from lake core, ice core, coral, speleothem, and tree ring archives, using published data primarily from the NOAA Paleoclimatology archive and a set of tree ring width chronologies standardized from raw International Tree Ring Data Bank ring width series. In contrast to many previous paleo compilations, the data were not selected (screened) on the basis of their environmental correlation, multi-century length, or other attributes. The inclusion of proxies sensitive to moisture and other environmental variables expands their use in data assimilation. A preliminary calibration using linear regression with mean annual temperature reveals characteristics of the proxy series and their relationship to temperature, as well as the noise and error characteristics of the records. The additional records are structured as individual files in the NOAA Paleoclimatology format and archived at NOAA Paleoclimatology (Anderson et al. 2018) and will continue to be improved and expanded as part of the LMR Project. The additions represent a four-fold increase in the number of records available for assimilation, provide expanded geographic coverage, and add additional proxy variables. Applications include data assimilation, proxy system model development, and paleoclimate reconstruction using climate field reconstruction and other methods. #------------------ # Funding_Agency # Funding_Agency_Name: Swiss National Science Foundation # Grant: #-------------------- # Funding_Agency_Name: National Science Foundation # Grant:AGS-1304263 # Funding_Agency_Name: National Oceanic and Atmospheric Administration # Grant:NA14OAR4310176 #------------------ # Site_Information # Site_Name: Pinega # Location: # Country: Russia # Northernmost_Latitude: 64.92 # Southernmost_Latitude: 64.92 # Easternmost_Longitude: 42.5 # Westernmost_Longitude: 42.5 # Elevation: 230 m #-------------------- # Data_Collection # Collection_Name: asia_russ036wB # Earliest_Year: 1687 # Most_Recent_Year: 1990 # Time_Unit: y_ad # Core_Length: # Notes: {"database":{"database1":"LMR","database2":"Breits"}} {"climateInterpretation":{"basis":"", "climateVariable":"T", "climateVariableDetail":"air", "interpDirection":"positive", "seasonality":"[6, 7, 8]"}}{"VSLite_parameters":{"T1":"6.19112671341","T2":"19.0392007708","M1":"0.0223474707375","M2":"0.313658759759"}} #-------------------- # Species # Species_Name: Scots pine # Species_Code: PISY #-------------------- # Chronology: # # # #-------------------- # Variables # # Data variables follow that are preceded by ## in columns one and two. # Data line variables format: Variables list, one per line, shortname-tab-longname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) # ##age age, , ,years AD, , , , ,N ##trsgi tree ring standardized growth index, tree ring, ,percent relative to mean growth, , Tree Rings, , ,N # #-------------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: nan # age trsgi 1687 1.688 1688 1.441 1689 1.525 1690 1.315 1691 1.316 1692 1.462 1693 1.319 1694 1.563 1695 1.413 1696 1.059 1697 1.059 1698 0.89 1699 0.707 1700 0.541 1701 0.569 1702 0.575 1703 0.524 1704 0.694 1705 0.828 1706 0.932 1707 0.784 1708 0.909 1709 0.577 1710 0.599 1711 0.775 1712 0.69 1713 0.66 1714 0.692 1715 0.689 1716 0.549 1717 0.65 1718 0.821 1719 0.694 1720 0.794 1721 0.859 1722 0.546 1723 0.831 1724 1.076 1725 1.079 1726 1.531 1727 1.25 1728 1.159 1729 0.9 1730 0.834 1731 0.721 1732 0.738 1733 0.727 1734 0.731 1735 0.78 1736 0.794 1737 0.753 1738 0.875 1739 0.808 1740 0.805 1741 0.867 1742 1.01 1743 1.095 1744 1.084 1745 1.186 1746 1.0 1747 0.84 1748 0.81 1749 0.862 1750 0.942 1751 1.014 1752 0.904 1753 1.268 1754 1.361 1755 1.39 1756 1.393 1757 1.408 1758 1.34 1759 1.153 1760 1.35 1761 1.295 1762 1.468 1763 1.242 1764 1.195 1765 1.097 1766 1.109 1767 1.449 1768 1.348 1769 1.068 1770 0.834 1771 1.141 1772 0.853 1773 0.794 1774 1.208 1775 1.089 1776 1.002 1777 0.943 1778 0.814 1779 0.775 1780 0.932 1781 0.811 1782 1.002 1783 0.992 1784 0.975 1785 1.075 1786 0.906 1787 0.895 1788 0.98 1789 0.85 1790 0.901 1791 1.096 1792 1.113 1793 1.165 1794 1.017 1795 1.288 1796 1.473 1797 1.435 1798 1.102 1799 1.119 1800 1.087 1801 0.978 1802 1.174 1803 0.881 1804 1.02 1805 1.254 1806 0.809 1807 0.923 1808 0.943 1809 1.168 1810 0.731 1811 0.834 1812 1.011 1813 0.429 1814 0.572 1815 0.609 1816 0.405 1817 0.359 1818 0.5 1819 0.582 1820 0.51 1821 0.488 1822 0.543 1823 0.709 1824 0.689 1825 0.601 1826 0.776 1827 0.797 1828 0.795 1829 0.96 1830 1.071 1831 1.01 1832 1.026 1833 1.018 1834 0.878 1835 0.869 1836 0.592 1837 0.623 1838 0.505 1839 0.63 1840 0.526 1841 0.695 1842 1.05 1843 0.973 1844 1.039 1845 1.111 1846 1.214 1847 1.41 1848 1.319 1849 1.469 1850 1.626 1851 1.9 1852 1.446 1853 1.351 1854 1.117 1855 1.031 1856 1.078 1857 0.93 1858 0.773 1859 1.071 1860 1.084 1861 1.076 1862 0.762 1863 0.485 1864 1.22 1865 0.888 1866 0.87 1867 0.825 1868 0.697 1869 0.941 1870 0.875 1871 0.733 1872 0.613 1873 0.782 1874 0.783 1875 0.813 1876 0.89 1877 0.877 1878 1.152 1879 0.962 1880 0.878 1881 1.112 1882 1.08 1883 1.07 1884 1.238 1885 1.444 1886 1.216 1887 1.048 1888 1.222 1889 1.283 1890 1.831 1891 1.59 1892 0.896 1893 1.326 1894 0.885 1895 0.952 1896 1.15 1897 0.88 1898 1.191 1899 1.087 1900 0.901 1901 1.047 1902 1.407 1903 0.736 1904 1.199 1905 1.355 1906 1.229 1907 1.425 1908 1.198 1909 1.384 1910 1.147 1911 1.115 1912 1.213 1913 1.006 1914 1.345 1915 1.26 1916 1.15 1917 1.247 1918 1.042 1919 0.997 1920 1.068 1921 1.254 1922 1.343 1923 1.161 1924 0.922 1925 1.348 1926 0.945 1927 1.02 1928 0.842 1929 0.902 1930 0.884 1931 0.784 1932 1.163 1933 1.06 1934 1.103 1935 1.124 1936 1.028 1937 1.223 1938 1.187 1939 1.178 1940 1.293 1941 1.122 1942 0.895 1943 1.245 1944 1.07 1945 1.091 1946 0.954 1947 0.915 1948 1.096 1949 1.103 1950 1.115 1951 1.228 1952 1.1 1953 0.923 1954 1.244 1955 1.118 1956 1.075 1957 1.34 1958 1.055 1959 0.925 1960 0.882 1961 0.818 1962 0.532 1963 0.483 1964 0.9 1965 0.82 1966 0.879 1967 0.801 1968 0.781 1969 0.46 1970 0.787 1971 0.728 1972 0.572 1973 0.711 1974 0.976 1975 0.788 1976 0.711 1977 0.63 1978 0.711 1979 0.538 1980 0.785 1981 1.1 1982 0.932 1983 0.896 1984 1.027 1985 0.791 1986 0.676 1987 0.671 1988 0.852 1989 0.873 1990 0.882