# asia_nepa022 - Kalingchok - 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/3779 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_nepa022 - Kalingchok - 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: Kalingchok # Location: # Country: Nepal # Northernmost_Latitude: 27.45 # Southernmost_Latitude: 27.45 # Easternmost_Longitude: 86.05 # Westernmost_Longitude: 86.05 # Elevation: 3720 m #-------------------- # Data_Collection # Collection_Name: asia_nepa022B # Earliest_Year: 1754 # Most_Recent_Year: 1978 # 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":"4.9553450246","T2":"15.0309933134","M1":"0.0224358061068","M2":"0.43668645248"}} #-------------------- # 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 1754 0.699 1755 0.623 1756 0.69 1757 0.689 1758 0.849 1759 1.037 1760 0.844 1761 0.763 1762 0.769 1763 0.901 1764 1.049 1765 1.055 1766 1.071 1767 0.986 1768 0.942 1769 0.936 1770 0.817 1771 0.911 1772 0.898 1773 0.959 1774 0.967 1775 0.894 1776 0.836 1777 0.905 1778 0.946 1779 0.884 1780 0.88 1781 0.848 1782 0.792 1783 0.783 1784 0.949 1785 0.89 1786 0.976 1787 0.919 1788 0.839 1789 0.908 1790 0.776 1791 0.71 1792 0.751 1793 0.929 1794 1.326 1795 1.252 1796 1.391 1797 1.371 1798 1.364 1799 1.075 1800 1.177 1801 1.147 1802 1.126 1803 1.209 1804 0.978 1805 0.923 1806 1.004 1807 0.908 1808 0.921 1809 0.995 1810 0.985 1811 0.79 1812 0.603 1813 0.515 1814 0.776 1815 0.634 1816 0.681 1817 0.577 1818 0.594 1819 0.559 1820 0.626 1821 0.754 1822 0.692 1823 0.842 1824 1.174 1825 1.308 1826 1.282 1827 1.361 1828 1.057 1829 0.774 1830 0.619 1831 0.652 1832 0.669 1833 0.906 1834 0.826 1835 0.945 1836 0.966 1837 0.909 1838 0.861 1839 0.868 1840 0.941 1841 0.864 1842 0.841 1843 0.822 1844 0.844 1845 0.892 1846 1.059 1847 1.2 1848 1.115 1849 1.058 1850 1.286 1851 1.38 1852 1.068 1853 1.207 1854 1.262 1855 1.247 1856 1.317 1857 1.518 1858 1.452 1859 1.155 1860 1.159 1861 1.336 1862 1.029 1863 1.144 1864 1.04 1865 1.15 1866 1.158 1867 1.025 1868 0.709 1869 0.769 1870 0.841 1871 1.118 1872 1.114 1873 1.191 1874 1.052 1875 0.905 1876 0.958 1877 0.859 1878 0.991 1879 0.849 1880 0.88 1881 1.074 1882 0.89 1883 0.962 1884 1.106 1885 1.17 1886 0.995 1887 0.761 1888 0.892 1889 1.196 1890 1.086 1891 1.209 1892 1.464 1893 1.127 1894 1.104 1895 0.857 1896 1.078 1897 1.146 1898 1.028 1899 1.064 1900 1.197 1901 0.869 1902 0.881 1903 0.766 1904 1.026 1905 0.561 1906 0.32 1907 0.475 1908 0.886 1909 0.914 1910 0.953 1911 1.001 1912 0.863 1913 0.886 1914 1.114 1915 0.81 1916 0.841 1917 1.076 1918 1.097 1919 1.131 1920 1.443 1921 1.387 1922 1.11 1923 0.97 1924 1.346 1925 1.192 1926 0.847 1927 0.895 1928 0.908 1929 0.982 1930 1.203 1931 1.62 1932 1.19 1933 1.147 1934 1.238 1935 1.198 1936 1.023 1937 1.016 1938 0.924 1939 0.814 1940 1.038 1941 1.239 1942 1.167 1943 0.902 1944 0.744 1945 0.679 1946 0.687 1947 0.905 1948 0.808 1949 0.697 1950 0.785 1951 0.986 1952 0.878 1953 0.557 1954 0.629 1955 0.746 1956 0.739 1957 1.027 1958 1.211 1959 0.913 1960 0.867 1961 0.802 1962 0.882 1963 0.872 1964 0.884 1965 0.744 1966 1.007 1967 0.876 1968 0.544 1969 0.583 1970 0.602 1971 0.619 1972 0.438 1973 0.524 1974 0.61 1975 0.797 1976 0.697 1977 0.982 1978 0.721