# asia_russ093w - Murmashi - 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/4547 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_russ093w - Murmashi - 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: Murmashi # Location: # Country: Russia # Northernmost_Latitude: 68.77 # Southernmost_Latitude: 68.77 # Easternmost_Longitude: 32.8 # Westernmost_Longitude: 32.8 # Elevation: 140 m #-------------------- # Data_Collection # Collection_Name: asia_russ093wB # Earliest_Year: 1728 # Most_Recent_Year: 1992 # 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":"6.69056602891","T2":"20.1082217452","M1":"0.0223408164916","M2":"0.234877544645"}} #-------------------- # 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 1728 0.836 1729 1.112 1730 0.97 1731 0.789 1732 0.808 1733 0.762 1734 0.525 1735 0.962 1736 0.934 1737 0.819 1738 1.181 1739 1.146 1740 0.817 1741 0.787 1742 0.861 1743 0.825 1744 0.797 1745 0.664 1746 1.016 1747 0.846 1748 0.828 1749 1.112 1750 1.028 1751 0.87 1752 1.019 1753 1.081 1754 1.365 1755 1.294 1756 1.512 1757 1.382 1758 1.172 1759 1.206 1760 1.072 1761 1.183 1762 1.265 1763 1.115 1764 0.916 1765 1.257 1766 1.071 1767 1.11 1768 1.165 1769 0.8 1770 0.777 1771 0.942 1772 0.969 1773 0.756 1774 1.082 1775 1.186 1776 0.962 1777 1.227 1778 1.092 1779 1.0 1780 1.396 1781 0.961 1782 1.05 1783 1.1 1784 1.12 1785 1.047 1786 0.769 1787 0.804 1788 0.927 1789 0.78 1790 0.539 1791 0.617 1792 0.879 1793 0.828 1794 0.732 1795 0.739 1796 0.863 1797 0.75 1798 0.792 1799 1.095 1800 1.043 1801 0.945 1802 1.172 1803 0.921 1804 1.051 1805 1.29 1806 0.541 1807 0.93 1808 1.024 1809 1.104 1810 0.817 1811 0.708 1812 0.711 1813 0.706 1814 0.579 1815 0.612 1816 0.547 1817 0.626 1818 0.89 1819 0.902 1820 0.726 1821 0.839 1822 0.999 1823 1.33 1824 1.248 1825 1.019 1826 1.845 1827 1.473 1828 1.067 1829 1.415 1830 1.336 1831 1.451 1832 1.137 1833 1.022 1834 1.064 1835 0.825 1836 0.731 1837 0.381 1838 0.632 1839 0.58 1840 0.93 1841 0.724 1842 0.781 1843 0.621 1844 0.704 1845 1.058 1846 1.006 1847 0.874 1848 0.871 1849 1.326 1850 1.222 1851 1.438 1852 1.639 1853 1.607 1854 1.617 1855 1.54 1856 1.751 1857 1.51 1858 1.565 1859 1.241 1860 1.249 1861 1.148 1862 0.835 1863 0.868 1864 1.234 1865 1.092 1866 0.884 1867 0.936 1868 0.818 1869 0.969 1870 0.864 1871 0.694 1872 0.482 1873 1.002 1874 0.664 1875 0.726 1876 0.963 1877 0.83 1878 0.754 1879 0.747 1880 0.646 1881 0.644 1882 0.616 1883 0.726 1884 0.808 1885 1.226 1886 1.286 1887 1.06 1888 0.859 1889 0.983 1890 1.548 1891 1.083 1892 0.487 1893 0.722 1894 1.002 1895 0.916 1896 1.062 1897 0.895 1898 1.442 1899 1.135 1900 0.519 1901 1.126 1902 0.84 1903 0.381 1904 0.56 1905 0.571 1906 0.537 1907 0.42 1908 0.337 1909 0.474 1910 0.244 1911 0.265 1912 0.45 1913 0.395 1914 0.658 1915 0.721 1916 0.753 1917 0.724 1918 0.734 1919 0.916 1920 0.69 1921 0.842 1922 0.935 1923 1.008 1924 1.084 1925 1.492 1926 0.995 1927 1.47 1928 0.995 1929 0.834 1930 1.517 1931 1.426 1932 1.31 1933 1.132 1934 1.451 1935 1.481 1936 1.195 1937 1.229 1938 1.363 1939 1.078 1940 0.864 1941 1.347 1942 0.891 1943 0.584 1944 0.804 1945 0.968 1946 0.754 1947 0.854 1948 1.008 1949 0.973 1950 1.032 1951 0.952 1952 1.164 1953 1.193 1954 1.332 1955 1.064 1956 1.363 1957 1.443 1958 1.114 1959 1.045 1960 1.455 1961 0.803 1962 0.852 1963 0.673 1964 1.469 1965 1.014 1966 0.916 1967 1.062 1968 0.904 1969 0.631 1970 1.307 1971 1.248 1972 1.141 1973 1.337 1974 1.175 1975 1.224 1976 1.217 1977 1.24 1978 0.977 1979 1.174 1980 1.132 1981 1.265 1982 0.964 1983 1.165 1984 1.277 1985 1.209 1986 0.783 1987 0.711 1988 1.021 1989 1.006 1990 0.957 1991 1.008 1992 1.119