# asia_russ071w - Sidorovsk - 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/4643 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_russ071w - Sidorovsk - 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: Sidorovsk # Location: # Country: Russia # Northernmost_Latitude: 66.67 # Southernmost_Latitude: 66.67 # Easternmost_Longitude: 82.33 # Westernmost_Longitude: 82.33 # Elevation: 15 m #-------------------- # Data_Collection # Collection_Name: asia_russ071wB # Earliest_Year: 1694 # 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":"5.42175540186","T2":"19.1000547336","M1":"0.0225977829842","M2":"0.241372265401"}} #-------------------- # Species # Species_Name: Siberian spruce # Species_Code: PCOB #-------------------- # 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 1694 1.05 1695 1.54 1696 1.421 1697 0.937 1698 0.921 1699 0.518 1700 0.768 1701 0.662 1702 0.695 1703 0.813 1704 0.812 1705 1.079 1706 0.925 1707 0.912 1708 1.105 1709 1.202 1710 1.073 1711 0.906 1712 1.068 1713 0.964 1714 0.694 1715 0.792 1716 1.028 1717 0.986 1718 0.918 1719 1.33 1720 0.777 1721 0.987 1722 1.001 1723 0.926 1724 0.964 1725 0.962 1726 0.684 1727 1.249 1728 1.147 1729 1.199 1730 1.285 1731 1.195 1732 0.835 1733 0.991 1734 0.808 1735 0.947 1736 0.706 1737 0.976 1738 0.81 1739 0.69 1740 0.564 1741 0.593 1742 0.542 1743 0.712 1744 0.8 1745 0.683 1746 1.042 1747 0.904 1748 1.129 1749 1.225 1750 0.986 1751 0.767 1752 0.748 1753 0.861 1754 1.024 1755 0.764 1756 0.805 1757 0.987 1758 0.858 1759 0.838 1760 0.54 1761 0.938 1762 1.078 1763 1.15 1764 0.913 1765 0.994 1766 1.109 1767 1.056 1768 0.808 1769 1.004 1770 1.002 1771 1.078 1772 0.897 1773 1.187 1774 1.082 1775 1.119 1776 0.867 1777 0.901 1778 0.888 1779 0.584 1780 0.819 1781 0.834 1782 0.862 1783 0.649 1784 1.526 1785 0.889 1786 1.238 1787 0.881 1788 0.682 1789 0.929 1790 1.054 1791 0.855 1792 0.682 1793 0.962 1794 1.078 1795 1.156 1796 1.329 1797 1.086 1798 1.039 1799 0.989 1800 0.852 1801 0.912 1802 0.942 1803 0.94 1804 0.977 1805 0.994 1806 0.851 1807 0.686 1808 0.787 1809 1.326 1810 1.225 1811 0.774 1812 0.81 1813 0.898 1814 1.086 1815 0.932 1816 0.906 1817 0.951 1818 0.745 1819 0.591 1820 0.629 1821 0.651 1822 0.819 1823 1.389 1824 0.773 1825 0.655 1826 1.002 1827 0.952 1828 0.925 1829 1.209 1830 0.64 1831 0.941 1832 1.21 1833 0.75 1834 0.979 1835 1.233 1836 1.05 1837 1.051 1838 1.325 1839 0.85 1840 0.948 1841 0.822 1842 1.34 1843 0.929 1844 1.194 1845 1.177 1846 1.101 1847 0.863 1848 1.04 1849 0.84 1850 0.823 1851 0.979 1852 0.896 1853 1.142 1854 0.962 1855 0.941 1856 1.274 1857 1.065 1858 1.256 1859 1.253 1860 1.125 1861 1.313 1862 0.909 1863 1.191 1864 0.892 1865 1.059 1866 0.897 1867 0.345 1868 1.127 1869 0.615 1870 1.108 1871 0.777 1872 0.951 1873 0.988 1874 0.882 1875 0.88 1876 0.867 1877 1.068 1878 1.385 1879 1.183 1880 1.177 1881 0.897 1882 0.716 1883 0.951 1884 0.769 1885 0.75 1886 1.182 1887 0.661 1888 0.617 1889 0.683 1890 0.872 1891 0.82 1892 1.181 1893 1.066 1894 1.335 1895 1.114 1896 1.134 1897 1.461 1898 1.386 1899 1.04 1900 1.337 1901 0.917 1902 1.124 1903 1.181 1904 1.067 1905 1.052 1906 1.134 1907 0.658 1908 1.277 1909 1.259 1910 1.169 1911 1.316 1912 1.064 1913 1.208 1914 1.001 1915 1.366 1916 0.809 1917 1.027 1918 1.243 1919 0.895 1920 1.15 1921 1.23 1922 1.278 1923 1.226 1924 1.075 1925 0.988 1926 1.443 1927 1.348 1928 1.47 1929 1.242 1930 1.166 1931 1.016 1932 0.805 1933 0.886 1934 0.732 1935 1.208 1936 1.273 1937 1.193 1938 1.159 1939 1.153 1940 1.171 1941 0.955 1942 1.489 1943 1.152 1944 1.244 1945 1.266 1946 1.173 1947 0.976 1948 1.476 1949 0.952 1950 1.301 1951 0.58 1952 0.943 1953 1.181 1954 1.081 1955 1.187 1956 1.212 1957 1.006 1958 0.934 1959 1.098 1960 0.734 1961 0.826 1962 0.846 1963 0.892 1964 0.821 1965 1.044 1966 0.641 1967 1.044 1968 0.832 1969 1.207 1970 1.061 1971 0.705 1972 0.767 1973 0.588 1974 0.559 1975 0.582 1976 0.734 1977 0.722 1978 0.734 1979 0.826 1980 0.628 1981 0.738 1982 0.722 1983 0.933 1984 0.926 1985 0.862 1986 0.764 1987 0.703 1988 0.66 1989 0.817 1990 0.742