# europe_fran014 - L'Orgere B - 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/5109 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: europe_fran014 - L'Orgere B - 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: L'Orgere B # Location: # Country: France # Northernmost_Latitude: 45.22 # Southernmost_Latitude: 45.22 # Easternmost_Longitude: 6.68 # Westernmost_Longitude: 6.68 # Elevation: 2100 m #-------------------- # Data_Collection # Collection_Name: europe_fran014B # Earliest_Year: 1770 # Most_Recent_Year: 1973 # 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":"6.79692491983","T2":"16.5549208055","M1":"0.0220722905054","M2":"0.412597796363"}} #-------------------- # 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 1770 0.921 1771 0.956 1772 0.989 1773 1.138 1774 1.099 1775 1.09 1776 1.009 1777 1.164 1778 1.18 1779 1.046 1780 1.092 1781 1.448 1782 0.945 1783 1.127 1784 1.011 1785 0.763 1786 0.822 1787 0.852 1788 0.899 1789 0.903 1790 0.9 1791 1.007 1792 0.81 1793 0.733 1794 1.028 1795 1.065 1796 1.154 1797 1.088 1798 1.112 1799 1.19 1800 1.307 1801 1.271 1802 1.099 1803 1.114 1804 0.921 1805 0.927 1806 0.879 1807 1.315 1808 1.087 1809 0.766 1810 0.864 1811 1.096 1812 1.091 1813 0.741 1814 0.973 1815 0.832 1816 0.758 1817 0.739 1818 0.975 1819 1.09 1820 1.062 1821 1.238 1822 1.102 1823 1.231 1824 0.843 1825 0.797 1826 0.918 1827 0.854 1828 0.977 1829 1.046 1830 0.92 1831 1.193 1832 0.878 1833 0.822 1834 1.182 1835 0.889 1836 0.624 1837 0.876 1838 0.869 1839 0.892 1840 0.945 1841 1.074 1842 1.344 1843 1.068 1844 0.805 1845 0.903 1846 1.266 1847 1.047 1848 1.387 1849 0.899 1850 0.883 1851 1.031 1852 0.975 1853 1.173 1854 0.998 1855 0.902 1856 1.094 1857 1.106 1858 0.804 1859 1.306 1860 0.821 1861 0.927 1862 0.913 1863 1.139 1864 1.122 1865 0.807 1866 0.987 1867 1.154 1868 1.173 1869 1.393 1870 0.836 1871 1.096 1872 0.924 1873 0.93 1874 0.879 1875 0.966 1876 1.066 1877 0.925 1878 0.907 1879 0.743 1880 0.464 1881 0.687 1882 0.824 1883 0.905 1884 1.004 1885 0.855 1886 0.703 1887 0.777 1888 0.56 1889 0.918 1890 0.859 1891 0.625 1892 0.852 1893 1.111 1894 0.884 1895 0.808 1896 0.821 1897 0.948 1898 1.229 1899 0.86 1900 0.81 1901 0.643 1902 0.818 1903 1.011 1904 1.109 1905 1.033 1906 0.862 1907 1.043 1908 0.998 1909 0.743 1910 0.888 1911 1.087 1912 0.849 1913 0.725 1914 1.058 1915 0.992 1916 1.016 1917 0.901 1918 0.877 1919 0.939 1920 0.84 1921 0.814 1922 0.784 1923 1.141 1924 0.986 1925 1.065 1926 0.902 1927 1.106 1928 0.954 1929 1.049 1930 0.931 1931 1.028 1932 1.341 1933 1.109 1934 0.856 1935 0.995 1936 1.075 1937 1.157 1938 1.016 1939 1.142 1940 1.064 1941 1.15 1942 0.9 1943 1.128 1944 0.955 1945 0.687 1946 1.017 1947 1.107 1948 0.709 1949 1.082 1950 0.967 1951 1.191 1952 1.365 1953 1.012 1954 0.908 1955 1.087 1956 0.837 1957 0.945 1958 1.393 1959 1.084 1960 1.097 1961 1.065 1962 1.136 1963 0.843 1964 1.102 1965 0.953 1966 1.06 1967 1.163 1968 1.145 1969 1.296 1970 1.182 1971 1.436 1972 0.999 1973 1.149