# europe_ital016 - Monte Mattone - 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: # # Original_Source_URL: # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: europe_ital016 - Monte Mattone - 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. #-------------------- # Funding_Agency # Funding_Agency_Name: Swiss National Science Foundation # Grant: #-------------------- # Site_Information # Site_Name: Monte Mattone # Location: # Country: Italy # Northernmost_Latitude: 41.78 # Southernmost_Latitude: 41.78 # Easternmost_Longitude: 14.03 # Westernmost_Longitude: 14.03 # Elevation: 1550 m #-------------------- # Data_Collection # Collection_Name: europe_ital016B # Earliest_Year: 1860 # Most_Recent_Year: 1980 # Time_Unit: y_ad # Core_Length: # Notes: {"sensitivity":"temperature"}{"T1":"3.64740685157"}{"T2":"13.3473607183"}{"M1":"0.0224657043623"}{"M2":"0.353297086618"} #-------------------- # 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 1860 0.847 1861 0.816 1862 0.869 1863 0.933 1864 0.833 1865 0.795 1866 0.914 1867 0.998 1868 1.145 1869 1.166 1870 1.135 1871 1.292 1872 1.265 1873 1.321 1874 1.157 1875 1.041 1876 1.354 1877 1.278 1878 1.058 1879 0.678 1880 0.802 1881 0.857 1882 1.121 1883 1.048 1884 1.22 1885 1.344 1886 1.122 1887 1.014 1888 0.889 1889 0.84 1890 0.966 1891 1.095 1892 1.153 1893 1.345 1894 1.129 1895 0.872 1896 0.838 1897 1.416 1898 1.018 1899 0.964 1900 0.826 1901 1.067 1902 1.077 1903 1.026 1904 0.86 1905 0.87 1906 0.936 1907 0.925 1908 0.967 1909 1.043 1910 1.063 1911 0.955 1912 1.16 1913 1.074 1914 1.224 1915 1.071 1916 0.828 1917 0.774 1918 0.556 1919 0.598 1920 0.86 1921 0.691 1922 0.512 1923 0.789 1924 0.835 1925 0.803 1926 0.899 1927 1.042 1928 0.767 1929 0.609 1930 1.036 1931 0.802 1932 0.827 1933 0.873 1934 0.895 1935 1.124 1936 1.279 1937 0.969 1938 0.942 1939 1.08 1940 0.999 1941 1.061 1942 0.96 1943 0.936 1944 0.744 1945 0.67 1946 0.786 1947 0.859 1948 0.961 1949 1.379 1950 1.194 1951 0.773 1952 0.949 1953 1.342 1954 1.042 1955 0.951 1956 1.009 1957 0.761 1958 0.657 1959 1.066 1960 1.303 1961 1.241 1962 0.873 1963 0.765 1964 0.935 1965 1.029 1966 1.088 1967 0.997 1968 0.939 1969 1.115 1970 1.18 1971 1.062 1972 0.95 1973 0.978 1974 0.94 1975 0.929 1976 0.877 1977 1.426 1978 1.163 1979 1.136 1980 0.962