# asia_nepa037 - Rara Goan - 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/3794 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_nepa037 - Rara Goan - 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: Rara Goan # Location: # Country: Nepal # Northernmost_Latitude: 29.35 # Southernmost_Latitude: 29.35 # Easternmost_Longitude: 82.05 # Westernmost_Longitude: 82.05 # Elevation: 3000 m #-------------------- # Data_Collection # Collection_Name: asia_nepa037B # Earliest_Year: 1747 # Most_Recent_Year: 1979 # 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":"2.80717536768","T2":"11.241846021","M1":"0.0228446925028","M2":"0.634163965262"}} #-------------------- # Species # Species_Name: Himalayan spruce # Species_Code: PCSM #-------------------- # 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 1747 0.675 1748 0.78 1749 0.71 1750 0.71 1751 0.836 1752 0.982 1753 0.973 1754 1.205 1755 1.159 1756 1.032 1757 0.947 1758 0.785 1759 1.049 1760 0.976 1761 1.078 1762 1.105 1763 0.93 1764 0.989 1765 1.124 1766 0.922 1767 0.962 1768 0.968 1769 1.105 1770 0.925 1771 1.031 1772 0.845 1773 1.173 1774 0.875 1775 1.026 1776 1.211 1777 0.905 1778 0.929 1779 0.688 1780 0.804 1781 0.885 1782 0.646 1783 0.792 1784 0.515 1785 0.699 1786 0.725 1787 0.647 1788 0.733 1789 0.718 1790 1.101 1791 1.165 1792 0.989 1793 0.646 1794 0.812 1795 0.836 1796 1.294 1797 1.24 1798 0.919 1799 1.209 1800 0.882 1801 0.945 1802 1.085 1803 1.186 1804 0.718 1805 0.812 1806 0.971 1807 0.828 1808 1.036 1809 0.831 1810 0.797 1811 0.99 1812 1.035 1813 0.583 1814 1.144 1815 1.189 1816 0.923 1817 1.227 1818 1.119 1819 1.083 1820 0.925 1821 0.735 1822 1.039 1823 0.941 1824 1.056 1825 1.197 1826 0.884 1827 1.21 1828 1.277 1829 1.39 1830 1.482 1831 0.906 1832 1.059 1833 0.734 1834 0.755 1835 1.42 1836 1.026 1837 0.835 1838 1.214 1839 1.291 1840 1.006 1841 1.419 1842 0.8 1843 0.905 1844 1.043 1845 1.109 1846 1.071 1847 0.98 1848 0.969 1849 0.573 1850 0.758 1851 0.994 1852 1.295 1853 1.108 1854 1.163 1855 1.352 1856 1.001 1857 0.814 1858 0.696 1859 1.102 1860 0.78 1861 0.91 1862 1.19 1863 1.338 1864 1.389 1865 1.019 1866 1.142 1867 0.996 1868 1.024 1869 0.76 1870 0.96 1871 0.843 1872 1.114 1873 0.734 1874 0.765 1875 0.553 1876 0.718 1877 1.036 1878 1.305 1879 1.051 1880 1.191 1881 0.938 1882 0.979 1883 0.975 1884 0.892 1885 1.198 1886 1.419 1887 1.077 1888 1.379 1889 1.226 1890 0.985 1891 1.188 1892 0.395 1893 1.139 1894 0.982 1895 1.156 1896 1.031 1897 1.047 1898 0.658 1899 1.137 1900 1.277 1901 1.034 1902 1.079 1903 1.024 1904 0.904 1905 1.069 1906 0.829 1907 1.268 1908 0.588 1909 0.642 1910 0.785 1911 0.991 1912 1.02 1913 0.992 1914 0.992 1915 1.009 1916 0.859 1917 1.099 1918 0.774 1919 0.925 1920 1.085 1921 0.573 1922 0.887 1923 0.978 1924 1.12 1925 1.183 1926 1.025 1927 0.724 1928 1.066 1929 0.926 1930 0.954 1931 0.996 1932 0.776 1933 1.163 1934 0.979 1935 0.682 1936 1.004 1937 1.097 1938 1.197 1939 1.131 1940 0.907 1941 0.688 1942 0.807 1943 1.002 1944 0.906 1945 0.779 1946 0.965 1947 0.945 1948 0.789 1949 0.944 1950 1.118 1951 1.09 1952 1.048 1953 0.564 1954 0.458 1955 0.922 1956 0.887 1957 0.996 1958 0.512 1959 0.724 1960 0.793 1961 0.794 1962 0.972 1963 1.285 1964 1.11 1965 1.134 1966 0.805 1967 0.591 1968 0.767 1969 0.76 1970 0.9 1971 0.838 1972 0.936 1973 1.21 1974 0.824 1975 1.182 1976 1.327 1977 1.27 1978 1.465 1979 1.512