# asia_indi011 - Tuni - 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/2799 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_indi011 - Tuni - 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: Tuni # Location: # Country: India # Northernmost_Latitude: 30.83 # Southernmost_Latitude: 30.83 # Easternmost_Longitude: 77.43 # Westernmost_Longitude: 77.43 # Elevation: 1800 m #-------------------- # Data_Collection # Collection_Name: asia_indi011B # Earliest_Year: 1858 # Most_Recent_Year: 1988 # 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":"5.00387360519","T2":"16.358182349","M1":"0.0226086964851","M2":"0.410725509596"}} #-------------------- # Species # Species_Name: chir pine # Species_Code: PIRO #-------------------- # 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 1858 0.853 1859 1.01 1860 1.149 1861 0.918 1862 0.848 1863 0.987 1864 1.061 1865 1.09 1866 0.908 1867 0.91 1868 0.642 1869 0.8 1870 1.237 1871 0.729 1872 0.735 1873 0.854 1874 1.222 1875 1.054 1876 1.248 1877 1.058 1878 1.702 1879 0.854 1880 1.228 1881 1.068 1882 1.179 1883 1.017 1884 1.285 1885 1.302 1886 1.274 1887 0.834 1888 0.742 1889 0.885 1890 0.621 1891 1.239 1892 0.911 1893 1.094 1894 1.143 1895 0.953 1896 1.003 1897 1.279 1898 0.962 1899 0.632 1900 1.211 1901 1.14 1902 0.717 1903 0.789 1904 1.191 1905 0.815 1906 0.962 1907 0.922 1908 0.697 1909 0.983 1910 0.761 1911 0.886 1912 1.064 1913 1.065 1914 1.289 1915 1.123 1916 0.607 1917 1.154 1918 0.841 1919 1.109 1920 1.119 1921 0.899 1922 0.855 1923 1.022 1924 0.999 1925 0.971 1926 1.198 1927 1.322 1928 1.179 1929 0.784 1930 0.988 1931 0.954 1932 0.558 1933 1.16 1934 1.186 1935 0.923 1936 0.949 1937 0.869 1938 0.696 1939 0.681 1940 0.829 1941 0.348 1942 0.671 1943 0.842 1944 0.918 1945 0.847 1946 0.973 1947 0.589 1948 0.837 1949 0.795 1950 0.986 1951 0.956 1952 0.848 1953 0.694 1954 0.918 1955 0.866 1956 0.811 1957 0.981 1958 0.838 1959 0.756 1960 0.533 1961 0.769 1962 0.994 1963 1.096 1964 1.189 1965 1.399 1966 1.434 1967 1.379 1968 1.483 1969 1.363 1970 0.929 1971 1.524 1972 1.147 1973 1.052 1974 0.901 1975 0.861 1976 0.865 1977 1.193 1978 1.106 1979 1.545 1980 1.206 1981 0.897 1982 0.761 1983 1.233 1984 1.195 1985 0.445 1986 0.965 1987 0.782 1988 1.301