# asia_russ125w - Alakurtti - 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/4307 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_russ125w - Alakurtti - 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: Alakurtti # Location: # Country: Russia # Northernmost_Latitude: 66.98 # Southernmost_Latitude: 66.98 # Easternmost_Longitude: 30.25 # Westernmost_Longitude: 30.25 # Elevation: 200 m #-------------------- # Data_Collection # Collection_Name: asia_russ125wB # Earliest_Year: 1704 # 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":"3.70936616721","T2":"16.8583199938","M1":"0.0225146600759","M2":"0.451757269018"}} #-------------------- # 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 1704 0.895 1705 1.191 1706 1.111 1707 1.236 1708 1.074 1709 0.735 1710 0.956 1711 1.064 1712 0.811 1713 0.793 1714 0.958 1715 1.243 1716 1.075 1717 1.043 1718 1.09 1719 0.893 1720 0.887 1721 0.969 1722 1.005 1723 0.998 1724 1.077 1725 1.331 1726 1.367 1727 1.458 1728 1.423 1729 1.687 1730 1.615 1731 1.312 1732 1.178 1733 0.924 1734 0.8 1735 0.904 1736 1.006 1737 1.063 1738 1.398 1739 1.267 1740 1.116 1741 0.86 1742 0.983 1743 0.928 1744 0.943 1745 0.815 1746 1.108 1747 0.978 1748 0.904 1749 0.73 1750 0.861 1751 0.805 1752 0.86 1753 1.022 1754 1.268 1755 1.225 1756 1.114 1757 1.133 1758 0.999 1759 1.092 1760 1.065 1761 0.974 1762 0.966 1763 1.017 1764 0.909 1765 0.943 1766 0.938 1767 0.788 1768 0.835 1769 0.764 1770 0.763 1771 0.793 1772 0.794 1773 0.595 1774 0.725 1775 0.912 1776 0.768 1777 0.805 1778 0.91 1779 0.65 1780 0.649 1781 0.576 1782 0.562 1783 0.654 1784 0.576 1785 0.661 1786 0.475 1787 0.496 1788 0.718 1789 0.444 1790 0.186 1791 0.308 1792 0.479 1793 0.42 1794 0.341 1795 0.355 1796 0.583 1797 0.639 1798 0.731 1799 0.906 1800 0.7 1801 0.839 1802 0.813 1803 0.88 1804 1.001 1805 1.08 1806 0.869 1807 1.246 1808 1.252 1809 1.138 1810 0.982 1811 1.0 1812 0.982 1813 0.914 1814 0.862 1815 0.883 1816 1.041 1817 1.149 1818 1.369 1819 1.405 1820 1.358 1821 1.479 1822 1.47 1823 1.565 1824 1.483 1825 1.166 1826 1.744 1827 1.829 1828 1.406 1829 1.594 1830 1.347 1831 1.312 1832 1.155 1833 1.267 1834 1.227 1835 1.035 1836 1.042 1837 0.706 1838 0.849 1839 0.819 1840 1.03 1841 0.944 1842 0.992 1843 1.092 1844 1.054 1845 1.187 1846 1.173 1847 1.183 1848 1.073 1849 1.412 1850 1.381 1851 1.499 1852 1.295 1853 1.121 1854 1.323 1855 1.287 1856 1.089 1857 0.946 1858 0.994 1859 0.938 1860 0.887 1861 0.93 1862 0.83 1863 0.748 1864 0.841 1865 0.772 1866 0.652 1867 0.596 1868 0.752 1869 0.711 1870 0.819 1871 0.681 1872 0.696 1873 0.756 1874 0.774 1875 0.927 1876 0.95 1877 0.875 1878 0.782 1879 0.77 1880 0.671 1881 0.673 1882 0.82 1883 0.758 1884 0.646 1885 0.812 1886 0.946 1887 0.789 1888 0.604 1889 0.808 1890 0.885 1891 0.734 1892 0.557 1893 0.577 1894 0.597 1895 0.707 1896 0.769 1897 0.546 1898 0.804 1899 0.785 1900 0.641 1901 0.717 1902 0.664 1903 0.468 1904 0.586 1905 0.57 1906 0.449 1907 0.524 1908 0.504 1909 0.526 1910 0.402 1911 0.428 1912 0.603 1913 0.548 1914 0.698 1915 0.807 1916 0.718 1917 0.658 1918 0.684 1919 0.808 1920 0.844 1921 1.079 1922 1.23 1923 1.101 1924 1.147 1925 1.202 1926 0.925 1927 1.161 1928 1.001 1929 0.886 1930 1.165 1931 1.072 1932 1.151 1933 1.13 1934 1.517 1935 1.196 1936 1.072 1937 1.218 1938 0.961 1939 1.161 1940 1.14 1941 1.189 1942 1.044 1943 0.999 1944 0.932 1945 0.855 1946 0.667 1947 0.885 1948 1.033 1949 0.868 1950 1.129 1951 0.988 1952 0.975 1953 1.151 1954 1.338 1955 1.36 1956 1.306 1957 1.493 1958 1.41 1959 1.445 1960 1.797 1961 1.178 1962 1.262 1963 1.13 1964 1.747 1965 1.321 1966 1.082 1967 1.296 1968 1.374 1969 1.035 1970 1.233 1971 0.996 1972 1.09 1973 1.208 1974 0.903 1975 1.216 1976 1.472 1977 1.422 1978 1.217 1979 1.518 1980 1.275 1981 1.037 1982 1.179 1983 1.178 1984 0.947 1985 0.955 1986 0.903 1987 0.858 1988 0.938 1989 0.936 1990 0.899 1991 0.864 1992 0.775