# europe_germ060 - Falkenfels - 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/5265 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: europe_germ060 - Falkenfels - 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: Falkenfels # Location: # Country: Germany # Northernmost_Latitude: 48.98 # Southernmost_Latitude: 48.98 # Easternmost_Longitude: 12.57 # Westernmost_Longitude: 12.57 # Elevation: 420 m #-------------------- # Data_Collection # Collection_Name: europe_germ060B # Earliest_Year: 1864 # Most_Recent_Year: 1997 # 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":"4.08174962342","T2":"16.096591842","M1":"0.0226130927516","M2":"0.510696862189"}} #-------------------- # Species # Species_Name: silver fir # Species_Code: ABAL #-------------------- # 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 1864 1.044 1865 0.924 1866 1.212 1867 1.226 1868 0.913 1869 1.096 1870 0.993 1871 1.171 1872 0.903 1873 1.266 1874 0.967 1875 0.903 1876 0.965 1877 0.946 1878 0.934 1879 1.137 1880 0.745 1881 1.134 1882 1.191 1883 1.138 1884 1.093 1885 0.8 1886 0.666 1887 0.808 1888 0.793 1889 0.842 1890 1.014 1891 0.898 1892 0.793 1893 0.613 1894 0.864 1895 1.131 1896 1.041 1897 1.214 1898 1.068 1899 1.018 1900 0.868 1901 0.938 1902 1.027 1903 0.956 1904 1.0 1905 0.752 1906 1.004 1907 0.767 1908 0.912 1909 0.819 1910 0.905 1911 0.858 1912 0.749 1913 0.966 1914 1.119 1915 0.756 1916 0.9 1917 1.014 1918 0.847 1919 1.14 1920 0.827 1921 0.648 1922 0.847 1923 0.955 1924 0.774 1925 0.955 1926 1.324 1927 1.171 1928 1.386 1929 0.647 1930 0.725 1931 1.049 1932 1.445 1933 1.471 1934 1.162 1935 1.251 1936 1.02 1937 1.23 1938 0.923 1939 1.087 1940 0.515 1941 0.942 1942 0.735 1943 0.794 1944 0.829 1945 0.801 1946 1.002 1947 1.296 1948 1.02 1949 1.022 1950 0.849 1951 1.063 1952 0.882 1953 0.884 1954 1.036 1955 1.412 1956 0.84 1957 1.391 1958 1.065 1959 1.241 1960 0.951 1961 1.373 1962 0.958 1963 1.129 1964 1.015 1965 0.958 1966 0.796 1967 0.824 1968 0.668 1969 0.896 1970 0.711 1971 0.81 1972 0.668 1973 0.757 1974 0.266 1975 0.484 1976 0.269 1977 0.166 1978 0.139 1979 0.227 1980 0.387 1981 0.463 1982 0.507 1983 0.913 1984 0.943 1985 0.705 1986 1.047 1987 0.984 1988 1.301 1989 1.059 1990 1.224 1991 1.233 1992 1.298 1993 1.062 1994 1.44 1995 1.397 1996 1.392 1997 2.029