# europe_finl064 - Pallasmaja - 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/2841 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: europe_finl064 - Pallasmaja - 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: Pallasmaja # Location: # Country: Finland # Northernmost_Latitude: 68.0 # Southernmost_Latitude: 68.0 # Easternmost_Longitude: 24.2 # Westernmost_Longitude: 24.2 # Elevation: 290 m #-------------------- # Data_Collection # Collection_Name: europe_finl064B # Earliest_Year: 1693 # Most_Recent_Year: 1983 # 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":"6.66427946443","T2":"19.937589385","M1":"0.0224102918807","M2":"0.218645850749"}} #-------------------- # 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 1693 1.109 1694 1.102 1695 0.729 1696 0.612 1697 0.939 1698 1.086 1699 0.948 1700 0.994 1701 0.973 1702 1.107 1703 0.974 1704 1.027 1705 0.827 1706 1.011 1707 1.194 1708 1.051 1709 0.637 1710 0.844 1711 0.896 1712 0.829 1713 0.852 1714 0.773 1715 0.82 1716 0.718 1717 0.736 1718 0.916 1719 0.738 1720 0.881 1721 0.829 1722 0.838 1723 1.033 1724 1.062 1725 1.274 1726 0.968 1727 1.066 1728 0.777 1729 0.956 1730 1.152 1731 0.849 1732 0.974 1733 1.08 1734 0.577 1735 0.847 1736 1.063 1737 0.822 1738 1.237 1739 1.34 1740 0.958 1741 0.847 1742 0.929 1743 0.966 1744 1.12 1745 0.823 1746 1.111 1747 0.834 1748 1.068 1749 1.045 1750 1.009 1751 0.822 1752 1.046 1753 1.044 1754 1.14 1755 1.515 1756 1.421 1757 1.349 1758 1.138 1759 1.315 1760 1.339 1761 0.999 1762 1.096 1763 1.106 1764 0.911 1765 0.861 1766 1.104 1767 0.836 1768 0.815 1769 0.653 1770 0.834 1771 0.742 1772 0.853 1773 0.837 1774 1.047 1775 1.257 1776 1.077 1777 1.163 1778 1.171 1779 1.141 1780 1.297 1781 0.848 1782 1.034 1783 0.887 1784 1.055 1785 1.405 1786 0.942 1787 1.01 1788 1.122 1789 1.305 1790 0.864 1791 1.035 1792 1.054 1793 0.904 1794 1.163 1795 1.214 1796 1.163 1797 1.177 1798 1.421 1799 1.314 1800 0.928 1801 1.091 1802 1.142 1803 0.964 1804 1.083 1805 1.118 1806 0.568 1807 0.965 1808 1.098 1809 1.028 1810 0.894 1811 0.912 1812 0.733 1813 0.598 1814 0.819 1815 0.748 1816 0.803 1817 0.984 1818 1.257 1819 1.223 1820 0.775 1821 0.539 1822 0.725 1823 1.075 1824 0.966 1825 0.814 1826 1.453 1827 1.355 1828 1.018 1829 1.483 1830 1.31 1831 1.553 1832 1.085 1833 0.992 1834 0.952 1835 0.819 1836 1.003 1837 0.377 1838 0.919 1839 0.815 1840 1.056 1841 0.85 1842 0.804 1843 0.986 1844 0.935 1845 1.145 1846 0.834 1847 0.91 1848 0.784 1849 0.876 1850 0.93 1851 1.189 1852 1.345 1853 1.169 1854 1.478 1855 1.125 1856 0.999 1857 0.816 1858 1.036 1859 0.856 1860 0.769 1861 0.988 1862 0.691 1863 0.65 1864 0.829 1865 0.837 1866 0.721 1867 0.711 1868 0.732 1869 0.732 1870 0.765 1871 0.752 1872 0.701 1873 1.011 1874 0.6 1875 0.617 1876 0.736 1877 0.697 1878 0.782 1879 0.837 1880 0.686 1881 0.638 1882 0.99 1883 1.023 1884 0.858 1885 0.897 1886 0.862 1887 1.081 1888 0.671 1889 0.912 1890 1.125 1891 0.991 1892 0.766 1893 0.852 1894 1.147 1895 1.229 1896 1.127 1897 0.956 1898 1.403 1899 0.998 1900 0.732 1901 1.309 1902 0.816 1903 0.519 1904 0.73 1905 0.836 1906 0.999 1907 0.888 1908 1.124 1909 0.929 1910 0.811 1911 0.907 1912 1.363 1913 1.103 1914 1.265 1915 1.298 1916 1.271 1917 1.04 1918 1.115 1919 0.852 1920 0.927 1921 1.157 1922 1.293 1923 1.331 1924 1.2 1925 1.493 1926 1.165 1927 1.232 1928 0.934 1929 0.744 1930 1.228 1931 1.074 1932 0.951 1933 0.978 1934 1.393 1935 1.102 1936 0.905 1937 1.245 1938 0.994 1939 0.894 1940 0.806 1941 1.105 1942 0.922 1943 0.854 1944 0.889 1945 0.888 1946 0.707 1947 0.964 1948 1.02 1949 0.975 1950 0.948 1951 0.841 1952 0.83 1953 1.033 1954 1.196 1955 0.978 1956 0.814 1957 0.99 1958 0.711 1959 0.977 1960 1.022 1961 0.632 1962 0.72 1963 0.655 1964 1.105 1965 0.769 1966 0.691 1967 1.06 1968 0.894 1969 0.908 1970 1.134 1971 0.902 1972 0.963 1973 1.182 1974 0.966 1975 1.023 1976 1.425 1977 1.076 1978 0.956 1979 1.296 1980 1.047 1981 0.871 1982 0.998 1983 1.274