# asia_russ104w - Krasnovishersk - 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/4481 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_russ104w - Krasnovishersk - 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: Krasnovishersk # Location: # Country: Russia # Northernmost_Latitude: 60.38 # Southernmost_Latitude: 60.38 # Easternmost_Longitude: 57.12 # Westernmost_Longitude: 57.12 # Elevation: 140 m #-------------------- # Data_Collection # Collection_Name: asia_russ104wB # Earliest_Year: 1747 # Most_Recent_Year: 1991 # 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":"4.06344045488","T2":"15.4447799543","M1":"0.0226098286039","M2":"0.523532259583"}} #-------------------- # 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 1747 0.862 1748 1.046 1749 0.919 1750 0.744 1751 0.807 1752 1.088 1753 1.099 1754 0.982 1755 0.994 1756 1.24 1757 1.019 1758 0.648 1759 0.602 1760 0.549 1761 0.676 1762 0.737 1763 0.739 1764 0.746 1765 0.854 1766 0.388 1767 0.661 1768 0.678 1769 0.872 1770 0.759 1771 0.776 1772 0.803 1773 0.787 1774 0.694 1775 0.519 1776 0.57 1777 0.729 1778 0.452 1779 0.684 1780 0.919 1781 1.258 1782 0.802 1783 0.588 1784 1.004 1785 0.759 1786 0.687 1787 0.69 1788 0.91 1789 0.541 1790 0.692 1791 0.46 1792 0.442 1793 0.579 1794 0.638 1795 0.595 1796 0.577 1797 0.511 1798 0.627 1799 0.529 1800 0.689 1801 0.749 1802 0.438 1803 0.449 1804 0.644 1805 0.813 1806 0.742 1807 0.81 1808 0.919 1809 0.996 1810 0.892 1811 1.033 1812 0.925 1813 0.908 1814 0.671 1815 0.683 1816 0.723 1817 0.809 1818 0.747 1819 0.79 1820 0.752 1821 0.663 1822 0.747 1823 0.898 1824 0.922 1825 0.741 1826 0.879 1827 0.834 1828 0.769 1829 0.864 1830 0.816 1831 0.61 1832 0.592 1833 0.753 1834 0.816 1835 0.763 1836 0.696 1837 0.722 1838 0.902 1839 0.985 1840 0.721 1841 0.806 1842 0.859 1843 0.924 1844 0.878 1845 0.888 1846 1.141 1847 1.132 1848 0.978 1849 1.135 1850 1.172 1851 1.08 1852 0.915 1853 0.93 1854 1.129 1855 1.381 1856 1.724 1857 1.507 1858 1.851 1859 2.128 1860 1.439 1861 1.435 1862 1.195 1863 1.456 1864 1.356 1865 1.321 1866 1.508 1867 1.541 1868 1.899 1869 1.713 1870 1.589 1871 1.522 1872 1.422 1873 1.058 1874 0.89 1875 1.109 1876 1.208 1877 1.15 1878 1.637 1879 1.613 1880 1.637 1881 1.32 1882 1.277 1883 1.105 1884 1.36 1885 1.235 1886 1.061 1887 1.019 1888 1.341 1889 1.621 1890 0.863 1891 0.984 1892 1.191 1893 0.885 1894 0.995 1895 0.961 1896 0.786 1897 0.919 1898 1.042 1899 0.816 1900 1.003 1901 1.1 1902 1.119 1903 1.207 1904 0.828 1905 0.892 1906 1.377 1907 1.222 1908 0.935 1909 1.075 1910 1.196 1911 0.932 1912 0.847 1913 0.937 1914 0.745 1915 0.822 1916 0.643 1917 0.849 1918 0.994 1919 1.002 1920 0.914 1921 0.88 1922 0.951 1923 0.748 1924 0.943 1925 0.921 1926 1.062 1927 1.269 1928 0.967 1929 1.007 1930 0.949 1931 0.998 1932 0.953 1933 0.715 1934 0.745 1935 0.728 1936 0.565 1937 0.625 1938 0.612 1939 0.754 1940 0.597 1941 0.485 1942 0.631 1943 0.493 1944 0.527 1945 0.608 1946 0.719 1947 0.596 1948 0.878 1949 0.919 1950 1.04 1951 0.988 1952 0.716 1953 0.742 1954 0.762 1955 0.669 1956 0.879 1957 0.96 1958 0.796 1959 1.062 1960 0.832 1961 0.583 1962 0.734 1963 0.653 1964 0.884 1965 0.848 1966 0.793 1967 0.756 1968 0.901 1969 1.042 1970 0.948 1971 1.036 1972 1.087 1973 0.905 1974 1.148 1975 0.755 1976 1.1 1977 0.961 1978 0.948 1979 1.59 1980 1.514 1981 1.498 1982 0.915 1983 1.169 1984 1.178 1985 1.015 1986 1.047 1987 1.158 1988 0.84 1989 0.758 1990 0.793 1991 0.933