# asia_indi003 - Khillanmarg - 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/3569 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_indi003 - Khillanmarg - 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: Khillanmarg # Location: # Country: India # Northernmost_Latitude: 35.08 # Southernmost_Latitude: 35.08 # Easternmost_Longitude: 74.33 # Westernmost_Longitude: 74.33 # Elevation: 3125 m #-------------------- # Data_Collection # Collection_Name: asia_indi003B # Earliest_Year: 1752 # Most_Recent_Year: 1980 # 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":"3.68664844209","T2":"15.3467566719","M1":"0.0231730620978","M2":"0.508236822962"}} #-------------------- # Species # Species_Name: Himalayan silver fir # Species_Code: ABPI #-------------------- # 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 1752 1.109 1753 0.698 1754 0.7 1755 0.685 1756 0.801 1757 0.787 1758 0.805 1759 1.178 1760 1.228 1761 1.267 1762 0.971 1763 1.053 1764 1.242 1765 1.278 1766 1.488 1767 1.393 1768 1.124 1769 1.502 1770 1.25 1771 1.144 1772 1.078 1773 1.055 1774 0.745 1775 0.977 1776 1.011 1777 0.983 1778 0.918 1779 1.257 1780 0.813 1781 0.699 1782 0.766 1783 0.941 1784 1.033 1785 0.912 1786 0.9 1787 1.142 1788 1.312 1789 1.529 1790 1.302 1791 1.256 1792 1.264 1793 1.108 1794 0.934 1795 1.272 1796 1.489 1797 1.377 1798 1.397 1799 1.419 1800 1.479 1801 1.105 1802 0.633 1803 0.758 1804 0.83 1805 0.886 1806 0.809 1807 0.801 1808 0.832 1809 0.688 1810 0.923 1811 0.95 1812 1.04 1813 1.101 1814 1.231 1815 1.068 1816 1.48 1817 1.298 1818 1.198 1819 1.093 1820 0.898 1821 0.97 1822 0.71 1823 0.399 1824 0.418 1825 0.544 1826 0.675 1827 0.697 1828 0.89 1829 1.169 1830 0.832 1831 0.628 1832 0.668 1833 0.541 1834 0.725 1835 0.87 1836 1.198 1837 1.313 1838 1.35 1839 1.105 1840 1.225 1841 1.135 1842 1.174 1843 1.157 1844 0.99 1845 1.107 1846 1.045 1847 1.073 1848 1.185 1849 0.952 1850 0.985 1851 1.221 1852 1.003 1853 1.093 1854 1.175 1855 1.054 1856 1.059 1857 0.964 1858 1.089 1859 0.919 1860 0.86 1861 0.674 1862 0.761 1863 0.775 1864 0.828 1865 0.927 1866 0.96 1867 1.062 1868 1.281 1869 1.208 1870 1.24 1871 1.149 1872 1.042 1873 1.038 1874 1.264 1875 1.169 1876 1.256 1877 1.092 1878 1.174 1879 1.165 1880 0.94 1881 0.997 1882 0.999 1883 1.062 1884 1.22 1885 1.006 1886 0.911 1887 0.889 1888 0.931 1889 0.987 1890 0.911 1891 1.033 1892 0.879 1893 1.07 1894 1.436 1895 1.101 1896 1.202 1897 1.029 1898 1.061 1899 1.006 1900 1.049 1901 1.016 1902 0.971 1903 1.094 1904 1.371 1905 1.255 1906 1.088 1907 1.042 1908 1.138 1909 1.023 1910 0.964 1911 0.885 1912 0.887 1913 0.878 1914 0.891 1915 0.681 1916 0.646 1917 0.782 1918 0.841 1919 0.829 1920 0.713 1921 0.63 1922 0.935 1923 0.906 1924 1.075 1925 0.817 1926 0.738 1927 0.693 1928 0.528 1929 0.644 1930 0.955 1931 1.18 1932 1.343 1933 1.107 1934 0.934 1935 1.075 1936 0.994 1937 0.834 1938 0.949 1939 0.821 1940 0.936 1941 0.876 1942 0.809 1943 0.927 1944 0.991 1945 0.907 1946 0.827 1947 0.72 1948 1.0 1949 1.124 1950 1.111 1951 1.079 1952 1.105 1953 1.171 1954 1.168 1955 0.876 1956 0.72 1957 0.762 1958 1.112 1959 1.095 1960 1.008 1961 0.992 1962 1.012 1963 0.91 1964 0.868 1965 1.028 1966 1.08 1967 1.135 1968 0.925 1969 0.835 1970 0.883 1971 0.794 1972 0.764 1973 0.773 1974 0.699 1975 0.825 1976 0.845 1977 1.122 1978 0.846 1979 0.732 1980 0.721