# europe_swed324 - Skuleskogen - 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/6138 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: europe_swed324 - Skuleskogen - 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: Skuleskogen # Location: # Country: Sweden # Northernmost_Latitude: 62.33 # Southernmost_Latitude: 62.33 # Easternmost_Longitude: 18.48 # Westernmost_Longitude: 18.48 # Elevation: 250 m #-------------------- # Data_Collection # Collection_Name: europe_swed324B # Earliest_Year: 1700 # Most_Recent_Year: 1998 # 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":"3.83046711509","T2":"13.9318919856","M1":"0.0227576812114","M2":"0.538183493094"}} #-------------------- # 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 1700 1.202 1701 0.966 1702 0.922 1703 1.125 1704 0.675 1705 0.687 1706 0.827 1707 0.751 1708 0.922 1709 0.902 1710 1.195 1711 0.815 1712 1.328 1713 1.08 1714 1.403 1715 1.678 1716 1.054 1717 1.128 1718 1.12 1719 0.943 1720 1.161 1721 1.074 1722 1.18 1723 1.155 1724 1.152 1725 1.055 1726 0.602 1727 0.81 1728 0.738 1729 0.8 1730 0.915 1731 1.42 1732 1.026 1733 1.069 1734 1.097 1735 0.856 1736 0.806 1737 0.755 1738 0.872 1739 0.734 1740 0.476 1741 0.347 1742 0.485 1743 0.754 1744 0.935 1745 0.717 1746 0.774 1747 0.681 1748 0.882 1749 0.67 1750 0.87 1751 0.978 1752 1.324 1753 1.124 1754 1.091 1755 1.023 1756 1.017 1757 1.303 1758 1.207 1759 0.946 1760 0.915 1761 1.068 1762 1.801 1763 1.423 1764 1.113 1765 0.958 1766 0.931 1767 0.796 1768 0.63 1769 0.745 1770 0.767 1771 0.584 1772 0.695 1773 0.708 1774 0.646 1775 1.447 1776 1.547 1777 1.73 1778 1.616 1779 1.442 1780 1.105 1781 0.481 1782 0.729 1783 0.688 1784 0.99 1785 0.856 1786 0.736 1787 0.867 1788 0.772 1789 0.912 1790 0.549 1791 0.812 1792 1.022 1793 1.284 1794 1.1 1795 0.56 1796 0.92 1797 0.981 1798 0.872 1799 1.272 1800 1.066 1801 1.133 1802 0.966 1803 0.845 1804 1.233 1805 1.702 1806 1.391 1807 1.095 1808 0.871 1809 1.055 1810 0.837 1811 0.98 1812 1.199 1813 0.864 1814 1.074 1815 1.004 1816 0.855 1817 1.185 1818 1.195 1819 1.199 1820 0.998 1821 0.859 1822 0.548 1823 0.889 1824 0.802 1825 1.018 1826 1.087 1827 1.271 1828 1.314 1829 1.195 1830 1.019 1831 0.514 1832 0.319 1833 0.669 1834 0.498 1835 0.409 1836 0.722 1837 0.71 1838 0.872 1839 0.681 1840 0.684 1841 0.496 1842 0.639 1843 0.771 1844 0.768 1845 0.797 1846 0.89 1847 0.539 1848 0.594 1849 0.658 1850 0.915 1851 0.951 1852 0.788 1853 0.475 1854 0.769 1855 0.965 1856 1.103 1857 1.208 1858 1.18 1859 0.835 1860 1.066 1861 0.515 1862 0.55 1863 0.722 1864 0.851 1865 0.956 1866 1.166 1867 0.864 1868 1.214 1869 0.92 1870 0.958 1871 0.735 1872 0.762 1873 0.806 1874 0.55 1875 0.86 1876 0.935 1877 1.068 1878 0.994 1879 0.91 1880 0.715 1881 0.705 1882 1.069 1883 0.886 1884 0.928 1885 0.966 1886 0.986 1887 0.856 1888 0.766 1889 1.228 1890 1.244 1891 0.745 1892 0.993 1893 0.871 1894 0.764 1895 0.805 1896 1.127 1897 0.809 1898 0.849 1899 0.774 1900 0.828 1901 1.14 1902 0.746 1903 0.744 1904 0.921 1905 1.142 1906 1.019 1907 1.155 1908 0.97 1909 0.818 1910 0.892 1911 0.957 1912 1.046 1913 1.161 1914 1.003 1915 1.112 1916 1.027 1917 1.034 1918 1.198 1919 1.367 1920 1.079 1921 1.406 1922 1.789 1923 1.404 1924 1.567 1925 1.52 1926 1.117 1927 1.052 1928 1.139 1929 1.524 1930 1.045 1931 0.867 1932 0.968 1933 0.703 1934 0.73 1935 0.718 1936 0.675 1937 1.002 1938 0.798 1939 0.846 1940 0.596 1941 0.989 1942 0.943 1943 0.908 1944 1.31 1945 1.235 1946 1.344 1947 1.496 1948 1.138 1949 1.157 1950 1.188 1951 1.204 1952 1.343 1953 1.534 1954 1.692 1955 0.949 1956 1.277 1957 1.54 1958 1.361 1959 0.84 1960 0.821 1961 0.805 1962 1.245 1963 0.933 1964 1.186 1965 1.109 1966 0.809 1967 1.077 1968 0.767 1969 0.438 1970 0.822 1971 1.051 1972 1.365 1973 0.977 1974 1.201 1975 1.187 1976 1.04 1977 0.961 1978 1.039 1979 1.171 1980 1.264 1981 1.017 1982 1.135 1983 1.226 1984 1.287 1985 1.192 1986 1.031 1987 1.23 1988 0.942 1989 0.876 1990 1.211 1991 1.368 1992 0.587 1993 0.76 1994 0.89 1995 0.765 1996 0.914 1997 0.697 1998 0.828