# europe_finl065 - Pisa - 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/2843 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: europe_finl065 - Pisa - 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: Pisa # Location: # Country: Finland # Northernmost_Latitude: 66.32 # Southernmost_Latitude: 66.32 # Easternmost_Longitude: 25.15 # Westernmost_Longitude: 25.15 # Elevation: 160 m #-------------------- # Data_Collection # Collection_Name: europe_finl065B # Earliest_Year: 1734 # 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":"5.0065520244","T2":"17.0514746851","M1":"0.0230960820703","M2":"0.393237727486"}} #-------------------- # 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 1734 0.845 1735 0.742 1736 0.541 1737 0.433 1738 0.429 1739 0.487 1740 0.443 1741 0.362 1742 0.459 1743 0.52 1744 0.665 1745 0.663 1746 0.617 1747 0.549 1748 0.626 1749 0.536 1750 0.449 1751 0.45 1752 0.746 1753 0.779 1754 0.918 1755 1.078 1756 1.014 1757 1.123 1758 0.944 1759 0.873 1760 0.901 1761 0.86 1762 0.85 1763 0.789 1764 0.807 1765 0.813 1766 0.771 1767 0.48 1768 0.405 1769 0.472 1770 0.643 1771 0.616 1772 0.79 1773 1.097 1774 1.23 1775 1.131 1776 0.939 1777 1.031 1778 1.444 1779 1.516 1780 1.471 1781 1.215 1782 1.333 1783 1.294 1784 1.552 1785 1.784 1786 1.514 1787 1.41 1788 1.674 1789 1.756 1790 1.241 1791 1.449 1792 1.384 1793 1.389 1794 1.457 1795 1.24 1796 1.07 1797 0.858 1798 1.438 1799 1.237 1800 0.952 1801 1.305 1802 1.237 1803 1.353 1804 1.55 1805 1.393 1806 0.873 1807 1.131 1808 0.763 1809 0.875 1810 1.015 1811 1.106 1812 0.914 1813 0.907 1814 1.126 1815 1.279 1816 1.326 1817 1.404 1818 1.75 1819 1.663 1820 1.131 1821 0.912 1822 1.201 1823 1.211 1824 1.087 1825 1.184 1826 1.543 1827 1.261 1828 0.877 1829 0.997 1830 0.983 1831 1.162 1832 0.962 1833 1.042 1834 1.301 1835 0.951 1836 1.115 1837 0.85 1838 1.006 1839 1.094 1840 1.037 1841 0.751 1842 0.922 1843 1.016 1844 0.936 1845 1.007 1846 0.927 1847 0.683 1848 0.856 1849 1.059 1850 1.125 1851 1.256 1852 1.221 1853 1.127 1854 1.144 1855 1.132 1856 0.897 1857 0.926 1858 1.083 1859 0.873 1860 0.815 1861 1.02 1862 0.654 1863 0.675 1864 0.79 1865 0.92 1866 0.9 1867 0.841 1868 0.974 1869 0.805 1870 0.894 1871 0.998 1872 1.034 1873 1.164 1874 0.962 1875 1.049 1876 1.118 1877 1.103 1878 0.973 1879 1.016 1880 0.487 1881 0.537 1882 0.863 1883 0.96 1884 0.841 1885 0.913 1886 1.103 1887 1.181 1888 0.858 1889 1.006 1890 1.138 1891 0.844 1892 0.701 1893 0.629 1894 0.809 1895 0.934 1896 1.014 1897 0.782 1898 0.867 1899 0.75 1900 0.634 1901 1.056 1902 0.639 1903 0.586 1904 0.622 1905 0.622 1906 0.674 1907 0.748 1908 0.913 1909 0.989 1910 0.805 1911 0.905 1912 0.832 1913 0.794 1914 0.88 1915 1.173 1916 1.193 1917 1.04 1918 0.754 1919 0.734 1920 0.829 1921 1.138 1922 1.4 1923 1.332 1924 1.039 1925 1.241 1926 0.758 1927 0.815 1928 0.655 1929 0.745 1930 0.989 1931 0.829 1932 0.847 1933 0.711 1934 1.021 1935 0.876 1936 0.783 1937 0.932 1938 0.977 1939 0.679 1940 0.754 1941 1.018 1942 0.844 1943 1.01 1944 0.789 1945 0.518 1946 0.519 1947 1.113 1948 1.06 1949 1.063 1950 1.008 1951 0.789 1952 0.725 1953 1.0 1954 1.166 1955 0.83 1956 0.632 1957 1.005 1958 0.89 1959 1.025 1960 1.02 1961 0.847 1962 0.877 1963 0.784 1964 1.285 1965 1.072 1966 1.035 1967 0.995 1968 1.072 1969 1.024 1970 1.097 1971 0.814 1972 0.846 1973 1.123 1974 1.021 1975 1.058 1976 1.217 1977 1.042 1978 0.989 1979 1.204 1980 1.223 1981 0.994 1982 0.833 1983 1.012