# asia_russ157w - Taksimo dry - 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/4676 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_russ157w - Taksimo dry - 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: Taksimo dry # Location: # Country: Russia # Northernmost_Latitude: 56.33 # Southernmost_Latitude: 56.33 # Easternmost_Longitude: 114.67 # Westernmost_Longitude: 114.67 # Elevation: 510 m #-------------------- # Data_Collection # Collection_Name: asia_russ157wB # Earliest_Year: 1736 # Most_Recent_Year: 1996 # Time_Unit: y_ad # Core_Length: # Notes: {"database":{"database1":"LMR","database2":"Breits"}} {"climateInterpretation":{"basis":"", "climateVariable":"M", "climateVariableDetail":"air", "interpDirection":"positive", "seasonality":"[6, 7, 8]"}}{"VSLite_parameters":{"T1":"4.90292237485","T2":"14.9886049163","M1":"0.0227518495101","M2":"0.513612593564"}} #-------------------- # 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 1736 1.058 1737 1.098 1738 0.942 1739 0.974 1740 1.118 1741 1.045 1742 0.95 1743 0.996 1744 1.021 1745 1.098 1746 0.788 1747 0.98 1748 1.098 1749 1.074 1750 0.814 1751 0.856 1752 0.974 1753 1.208 1754 0.951 1755 0.873 1756 0.936 1757 0.917 1758 0.894 1759 0.964 1760 1.257 1761 1.164 1762 1.019 1763 1.13 1764 1.165 1765 1.104 1766 0.949 1767 0.859 1768 0.994 1769 1.125 1770 1.131 1771 0.967 1772 1.088 1773 1.266 1774 0.941 1775 1.09 1776 1.188 1777 0.887 1778 0.903 1779 1.033 1780 0.96 1781 0.957 1782 0.833 1783 0.818 1784 0.593 1785 0.695 1786 0.743 1787 0.82 1788 0.781 1789 0.918 1790 0.694 1791 0.861 1792 1.013 1793 1.033 1794 0.698 1795 0.842 1796 1.009 1797 0.984 1798 1.113 1799 1.122 1800 1.116 1801 1.206 1802 1.215 1803 1.192 1804 0.81 1805 1.04 1806 1.01 1807 0.925 1808 1.201 1809 1.038 1810 1.143 1811 0.971 1812 0.895 1813 1.048 1814 1.088 1815 1.108 1816 1.132 1817 0.754 1818 0.991 1819 1.011 1820 0.986 1821 0.93 1822 1.033 1823 0.957 1824 0.999 1825 1.001 1826 0.994 1827 1.194 1828 1.179 1829 1.164 1830 1.269 1831 1.239 1832 1.075 1833 0.917 1834 0.801 1835 0.902 1836 0.82 1837 0.827 1838 0.599 1839 0.816 1840 0.773 1841 0.888 1842 0.894 1843 0.995 1844 0.968 1845 0.854 1846 0.854 1847 0.493 1848 0.775 1849 0.934 1850 0.985 1851 1.206 1852 0.949 1853 0.86 1854 1.072 1855 1.05 1856 1.363 1857 1.067 1858 1.046 1859 1.108 1860 0.555 1861 1.156 1862 0.9 1863 1.18 1864 0.916 1865 0.872 1866 1.032 1867 1.127 1868 0.914 1869 0.944 1870 1.031 1871 1.109 1872 1.389 1873 1.476 1874 1.242 1875 0.996 1876 1.124 1877 1.021 1878 1.042 1879 1.104 1880 1.068 1881 0.931 1882 1.029 1883 1.302 1884 1.248 1885 1.217 1886 1.194 1887 0.923 1888 0.857 1889 0.928 1890 0.616 1891 0.914 1892 0.714 1893 0.925 1894 0.996 1895 0.839 1896 0.835 1897 0.877 1898 0.878 1899 0.98 1900 1.135 1901 0.81 1902 0.556 1903 0.707 1904 0.812 1905 0.952 1906 0.987 1907 0.801 1908 0.865 1909 0.91 1910 0.522 1911 0.791 1912 0.855 1913 0.833 1914 1.082 1915 0.937 1916 0.811 1917 0.769 1918 1.122 1919 0.848 1920 1.03 1921 1.286 1922 0.885 1923 1.065 1924 0.719 1925 1.112 1926 1.479 1927 1.21 1928 1.047 1929 1.399 1930 1.194 1931 1.158 1932 1.214 1933 1.299 1934 1.19 1935 1.29 1936 1.116 1937 1.325 1938 1.143 1939 0.641 1940 0.744 1941 0.844 1942 1.044 1943 0.647 1944 1.028 1945 0.669 1946 0.884 1947 0.874 1948 0.957 1949 1.09 1950 1.228 1951 1.222 1952 1.493 1953 1.339 1954 1.282 1955 1.176 1956 1.187 1957 1.119 1958 0.963 1959 1.131 1960 0.831 1961 1.234 1962 1.402 1963 1.144 1964 1.099 1965 0.979 1966 1.084 1967 0.905 1968 0.991 1969 0.846 1970 0.993 1971 0.964 1972 0.874 1973 0.783 1974 0.877 1975 0.876 1976 0.976 1977 0.9 1978 0.974 1979 0.977 1980 0.988 1981 0.932 1982 1.082 1983 1.138 1984 0.967 1985 0.7 1986 0.893 1987 0.471 1988 0.513 1989 0.936 1990 0.819 1991 0.909 1992 0.907 1993 1.2 1994 1.094 1995 0.961 1996 1.221