# europe_spai019 - Riscopal - 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/3288 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: europe_spai019 - Riscopal - 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: Riscopal # Location: # Country: Spain # Northernmost_Latitude: 40.78 # Southernmost_Latitude: 40.78 # Easternmost_Longitude: -4.0 # Westernmost_Longitude: -4.0 # Elevation: 1600 m #-------------------- # Data_Collection # Collection_Name: europe_spai019B # Earliest_Year: 1734 # Most_Recent_Year: 1988 # 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":"4.46299133711","T2":"15.1793202173","M1":"0.0229880934536","M2":"0.489680071867"}} #-------------------- # Species # Species_Name: Austrian pine # Species_Code: PINI #-------------------- # 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 1734 1.029 1735 1.162 1736 0.961 1737 1.157 1738 1.292 1739 1.175 1740 1.011 1741 1.044 1742 0.789 1743 1.066 1744 1.257 1745 0.74 1746 0.869 1747 0.588 1748 0.926 1749 0.931 1750 0.83 1751 0.925 1752 0.749 1753 0.812 1754 0.85 1755 0.666 1756 0.825 1757 1.159 1758 1.087 1759 0.75 1760 1.032 1761 1.219 1762 1.39 1763 1.337 1764 0.962 1765 1.141 1766 1.157 1767 0.797 1768 0.752 1769 0.449 1770 0.526 1771 0.474 1772 0.669 1773 0.943 1774 0.979 1775 0.871 1776 0.953 1777 0.695 1778 0.781 1779 1.023 1780 0.911 1781 1.102 1782 0.911 1783 1.199 1784 1.022 1785 0.939 1786 1.027 1787 1.094 1788 1.546 1789 0.999 1790 0.986 1791 1.109 1792 1.326 1793 1.216 1794 1.235 1795 1.368 1796 1.2 1797 0.892 1798 0.902 1799 1.046 1800 1.183 1801 1.163 1802 0.954 1803 0.653 1804 1.225 1805 1.301 1806 0.736 1807 0.977 1808 0.798 1809 1.086 1810 1.122 1811 1.459 1812 0.964 1813 1.177 1814 1.359 1815 1.534 1816 1.095 1817 1.068 1818 1.014 1819 1.126 1820 0.849 1821 1.198 1822 1.033 1823 0.974 1824 0.758 1825 1.188 1826 1.235 1827 0.986 1828 1.137 1829 0.984 1830 1.11 1831 0.992 1832 0.724 1833 0.674 1834 0.953 1835 0.875 1836 1.028 1837 1.077 1838 0.739 1839 0.815 1840 0.596 1841 1.0 1842 0.937 1843 1.02 1844 0.985 1845 0.978 1846 1.226 1847 0.881 1848 0.876 1849 1.147 1850 1.174 1851 0.934 1852 0.918 1853 1.018 1854 0.976 1855 0.76 1856 0.937 1857 0.981 1858 0.88 1859 1.067 1860 0.755 1861 1.085 1862 0.755 1863 0.753 1864 1.028 1865 0.844 1866 0.983 1867 0.851 1868 1.102 1869 1.147 1870 0.631 1871 0.887 1872 0.794 1873 0.772 1874 0.909 1875 0.889 1876 0.631 1877 0.823 1878 1.036 1879 0.577 1880 0.9 1881 1.291 1882 1.051 1883 1.002 1884 0.958 1885 1.655 1886 1.57 1887 1.095 1888 1.24 1889 1.347 1890 0.991 1891 1.2 1892 1.033 1893 1.239 1894 0.988 1895 1.138 1896 1.162 1897 1.131 1898 1.002 1899 1.102 1900 0.619 1901 0.771 1902 1.022 1903 1.321 1904 1.16 1905 1.076 1906 1.036 1907 0.922 1908 0.828 1909 0.971 1910 1.081 1911 1.13 1912 1.201 1913 0.919 1914 1.371 1915 0.959 1916 0.899 1917 0.751 1918 0.818 1919 1.012 1920 1.064 1921 0.854 1922 0.801 1923 1.014 1924 0.796 1925 1.182 1926 1.473 1927 0.95 1928 0.914 1929 1.294 1930 1.376 1931 1.025 1932 1.484 1933 1.681 1934 0.683 1935 0.692 1936 0.999 1937 1.212 1938 0.874 1939 0.715 1940 1.395 1941 0.953 1942 0.563 1943 0.757 1944 0.825 1945 0.809 1946 0.549 1947 0.728 1948 0.852 1949 0.779 1950 0.992 1951 1.047 1952 1.239 1953 1.083 1954 0.826 1955 0.984 1956 0.978 1957 1.093 1958 1.066 1959 0.976 1960 0.986 1961 0.943 1962 0.704 1963 0.659 1964 0.83 1965 0.498 1966 0.652 1967 0.624 1968 0.644 1969 0.761 1970 0.854 1971 0.749 1972 0.613 1973 0.862 1974 0.668 1975 0.706 1976 0.909 1977 1.26 1978 1.204 1979 0.793 1980 1.225 1981 0.909 1982 1.015 1983 1.267 1984 1.212 1985 0.922 1986 0.578 1987 1.185 1988 1.18