# europe_ital001 - Camosciara E M.Te Amaro - 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/2752 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: europe_ital001 - Camosciara E M.Te Amaro - 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: Camosciara E M.Te Amaro # Location: # Country: Italy # Northernmost_Latitude: 41.77 # Southernmost_Latitude: 41.77 # Easternmost_Longitude: 13.82 # Westernmost_Longitude: 13.82 # Elevation: 1550 m #-------------------- # Data_Collection # Collection_Name: europe_ital001B # Earliest_Year: 1786 # Most_Recent_Year: 1987 # 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":"5.34567670143","T2":"15.1039884384","M1":"0.0220532842519","M2":"0.21633470753"}} #-------------------- # 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 1786 0.992 1787 0.75 1788 0.791 1789 0.882 1790 0.943 1791 1.05 1792 1.032 1793 1.148 1794 1.233 1795 1.159 1796 1.05 1797 1.168 1798 0.953 1799 0.961 1800 0.994 1801 0.826 1802 0.826 1803 0.882 1804 0.837 1805 0.643 1806 0.602 1807 0.641 1808 0.864 1809 0.918 1810 0.936 1811 0.805 1812 0.981 1813 1.058 1814 1.069 1815 0.979 1816 0.94 1817 0.885 1818 0.949 1819 0.903 1820 0.812 1821 0.798 1822 0.531 1823 0.465 1824 0.726 1825 0.849 1826 0.844 1827 0.943 1828 0.925 1829 0.805 1830 1.013 1831 1.522 1832 1.331 1833 0.993 1834 1.234 1835 1.078 1836 1.202 1837 1.176 1838 1.097 1839 0.958 1840 0.892 1841 1.199 1842 1.147 1843 1.433 1844 1.167 1845 1.24 1846 1.25 1847 1.22 1848 1.298 1849 1.133 1850 1.117 1851 0.992 1852 1.114 1853 1.19 1854 1.249 1855 1.171 1856 0.955 1857 1.32 1858 1.264 1859 1.44 1860 1.197 1861 0.877 1862 0.957 1863 1.187 1864 1.201 1865 1.244 1866 1.128 1867 0.931 1868 0.913 1869 1.149 1870 1.122 1871 1.286 1872 1.426 1873 1.38 1874 1.056 1875 1.088 1876 1.38 1877 1.21 1878 1.123 1879 0.766 1880 0.696 1881 0.947 1882 1.07 1883 0.982 1884 0.939 1885 0.996 1886 0.814 1887 0.882 1888 0.86 1889 0.824 1890 0.901 1891 0.755 1892 0.849 1893 1.097 1894 1.282 1895 1.202 1896 0.964 1897 1.288 1898 1.328 1899 1.031 1900 1.033 1901 1.005 1902 1.143 1903 1.106 1904 0.822 1905 0.896 1906 0.879 1907 0.817 1908 0.997 1909 1.143 1910 1.059 1911 0.925 1912 0.98 1913 0.924 1914 0.861 1915 0.672 1916 0.538 1917 0.785 1918 0.648 1919 0.638 1920 0.728 1921 0.608 1922 0.48 1923 0.771 1924 0.974 1925 0.883 1926 1.008 1927 0.974 1928 0.691 1929 0.72 1930 1.041 1931 0.822 1932 0.89 1933 0.983 1934 0.905 1935 0.966 1936 1.112 1937 0.853 1938 0.741 1939 0.914 1940 0.966 1941 0.968 1942 0.911 1943 0.922 1944 0.798 1945 0.876 1946 0.803 1947 0.788 1948 0.988 1949 1.161 1950 1.06 1951 0.851 1952 0.988 1953 1.104 1954 1.028 1955 0.812 1956 1.192 1957 0.637 1958 0.609 1959 1.002 1960 0.835 1961 0.989 1962 1.144 1963 1.053 1964 1.181 1965 1.142 1966 1.356 1967 1.189 1968 1.397 1969 1.447 1970 1.508 1971 1.314 1972 0.922 1973 0.873 1974 0.799 1975 0.757 1976 0.599 1977 0.774 1978 0.81 1979 0.907 1980 0.874 1981 1.008 1982 0.716 1983 0.845 1984 0.825 1985 1.049 1986 0.858 1987 1.011