# asia_nepa009 - Bhule Pokari - 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/3765 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_nepa009 - Bhule Pokari - 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: Bhule Pokari # Location: # Country: Nepal # Northernmost_Latitude: 27.43 # Southernmost_Latitude: 27.43 # Easternmost_Longitude: 86.28 # Westernmost_Longitude: 86.28 # Elevation: 3600 m #-------------------- # Data_Collection # Collection_Name: asia_nepa009B # Earliest_Year: 1696 # Most_Recent_Year: 1998 # 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":"5.53672180595","T2":"21.134074382","M1":"0.022316112392","M2":"0.252094981115"}} #-------------------- # Species # Species_Name: silver fir # Species_Code: ABSB #-------------------- # 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 1696 1.126 1697 0.907 1698 0.789 1699 1.012 1700 1.051 1701 1.118 1702 1.181 1703 1.023 1704 1.214 1705 1.672 1706 1.29 1707 0.986 1708 1.137 1709 1.148 1710 1.022 1711 0.919 1712 1.195 1713 0.963 1714 0.906 1715 0.851 1716 0.971 1717 1.074 1718 1.196 1719 1.039 1720 1.044 1721 0.992 1722 1.01 1723 1.208 1724 1.154 1725 0.888 1726 0.82 1727 0.88 1728 1.045 1729 0.892 1730 0.822 1731 0.755 1732 0.717 1733 0.669 1734 0.804 1735 0.912 1736 1.079 1737 0.902 1738 0.822 1739 0.885 1740 0.885 1741 0.744 1742 0.643 1743 0.728 1744 1.027 1745 1.01 1746 1.068 1747 1.163 1748 1.587 1749 1.238 1750 1.021 1751 1.307 1752 1.219 1753 1.203 1754 1.368 1755 1.067 1756 1.019 1757 0.794 1758 0.879 1759 0.961 1760 0.86 1761 0.826 1762 0.986 1763 1.03 1764 0.818 1765 0.939 1766 1.101 1767 1.038 1768 0.773 1769 0.705 1770 0.682 1771 0.816 1772 0.815 1773 0.825 1774 0.883 1775 1.0 1776 0.882 1777 0.865 1778 0.954 1779 1.189 1780 1.238 1781 1.186 1782 1.033 1783 0.885 1784 0.728 1785 0.694 1786 0.888 1787 0.806 1788 0.9 1789 1.064 1790 1.018 1791 0.799 1792 0.782 1793 0.815 1794 1.1 1795 1.005 1796 1.045 1797 0.986 1798 1.093 1799 1.133 1800 1.404 1801 1.288 1802 1.441 1803 1.55 1804 1.366 1805 0.9 1806 0.811 1807 0.716 1808 0.879 1809 0.877 1810 0.751 1811 0.673 1812 0.598 1813 0.517 1814 0.608 1815 0.509 1816 0.382 1817 0.284 1818 0.285 1819 0.241 1820 0.382 1821 0.38 1822 0.424 1823 0.637 1824 0.742 1825 0.724 1826 0.804 1827 0.976 1828 0.824 1829 0.597 1830 0.498 1831 0.508 1832 0.5 1833 0.556 1834 0.556 1835 0.639 1836 0.768 1837 0.871 1838 0.829 1839 0.786 1840 0.869 1841 0.774 1842 0.891 1843 1.006 1844 0.994 1845 1.144 1846 1.294 1847 0.982 1848 1.062 1849 1.073 1850 1.19 1851 1.253 1852 1.04 1853 1.028 1854 0.903 1855 0.93 1856 1.025 1857 0.926 1858 1.063 1859 0.944 1860 0.936 1861 1.036 1862 1.117 1863 1.08 1864 0.803 1865 0.944 1866 0.852 1867 0.932 1868 0.922 1869 1.209 1870 1.138 1871 1.245 1872 1.188 1873 1.245 1874 1.127 1875 1.461 1876 1.531 1877 1.248 1878 1.291 1879 1.563 1880 1.391 1881 1.293 1882 1.366 1883 1.438 1884 1.258 1885 1.233 1886 1.068 1887 0.873 1888 1.031 1889 1.573 1890 1.394 1891 1.259 1892 1.249 1893 1.103 1894 1.096 1895 0.985 1896 1.14 1897 1.25 1898 1.279 1899 1.158 1900 1.347 1901 1.4 1902 1.389 1903 1.08 1904 1.211 1905 0.729 1906 0.583 1907 0.652 1908 0.901 1909 1.031 1910 1.057 1911 1.372 1912 1.247 1913 1.134 1914 1.101 1915 0.99 1916 0.956 1917 0.953 1918 0.99 1919 1.143 1920 1.292 1921 1.477 1922 1.212 1923 1.228 1924 1.895 1925 1.484 1926 0.943 1927 0.832 1928 0.954 1929 1.075 1930 1.336 1931 1.632 1932 1.159 1933 1.016 1934 1.255 1935 1.319 1936 1.174 1937 1.073 1938 1.154 1939 0.962 1940 1.125 1941 1.134 1942 1.209 1943 1.098 1944 1.06 1945 1.23 1946 1.082 1947 1.047 1948 0.949 1949 0.805 1950 0.797 1951 0.918 1952 1.171 1953 0.925 1954 1.096 1955 1.132 1956 1.056 1957 1.12 1958 1.615 1959 1.143 1960 0.89 1961 0.761 1962 0.692 1963 0.835 1964 0.919 1965 0.664 1966 0.557 1967 0.306 1968 0.231 1969 0.363 1970 0.447 1971 0.471 1972 0.493 1973 0.654 1974 0.792 1975 0.878 1976 0.864 1977 1.145 1978 0.598 1979 0.553 1980 0.558 1981 0.681 1982 0.697 1983 0.73 1984 0.576 1985 0.694 1986 0.717 1987 0.82 1988 0.874 1989 0.774 1990 0.84 1991 0.852 1992 0.717 1993 0.631 1994 0.548 1995 0.589 1996 0.822 1997 0.805 1998 0.845