# asia_nepa040 - TilaNala - 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/5409 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_nepa040 - TilaNala - 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: TilaNala # Location: # Country: Nepal # Northernmost_Latitude: 29.05 # Southernmost_Latitude: 29.05 # Easternmost_Longitude: 81.5 # Westernmost_Longitude: 81.5 # Elevation: 2080 m #-------------------- # Data_Collection # Collection_Name: asia_nepa040B # Earliest_Year: 1739 # Most_Recent_Year: 1979 # 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":"3.92468787608","T2":"16.8581822381","M1":"0.0229647039756","M2":"0.549280152642"}} #-------------------- # 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 1739 1.467 1740 1.259 1741 1.03 1742 1.243 1743 1.192 1744 0.947 1745 0.977 1746 0.941 1747 1.003 1748 1.001 1749 1.335 1750 1.256 1751 1.229 1752 1.026 1753 0.895 1754 1.045 1755 0.577 1756 0.639 1757 0.295 1758 0.313 1759 0.57 1760 0.709 1761 0.677 1762 0.748 1763 0.837 1764 1.268 1765 1.253 1766 1.128 1767 0.97 1768 1.08 1769 1.162 1770 1.129 1771 1.22 1772 1.355 1773 1.274 1774 0.935 1775 0.819 1776 0.743 1777 0.745 1778 0.83 1779 0.772 1780 1.145 1781 1.14 1782 1.174 1783 0.934 1784 1.172 1785 1.576 1786 1.383 1787 1.522 1788 0.754 1789 0.933 1790 0.666 1791 0.575 1792 0.673 1793 0.861 1794 1.177 1795 1.03 1796 1.399 1797 1.012 1798 1.222 1799 0.818 1800 1.107 1801 0.941 1802 0.769 1803 0.828 1804 0.917 1805 1.118 1806 1.198 1807 1.258 1808 1.369 1809 1.532 1810 1.024 1811 1.007 1812 1.011 1813 1.005 1814 1.102 1815 0.861 1816 0.355 1817 0.565 1818 0.316 1819 0.411 1820 0.7 1821 0.518 1822 1.192 1823 1.217 1824 1.12 1825 1.349 1826 1.035 1827 1.102 1828 1.033 1829 1.028 1830 1.41 1831 1.127 1832 0.979 1833 1.084 1834 1.091 1835 1.142 1836 0.77 1837 0.831 1838 1.159 1839 1.208 1840 1.203 1841 1.261 1842 1.186 1843 1.334 1844 1.039 1845 1.152 1846 1.131 1847 0.195 1848 0.537 1849 0.748 1850 1.414 1851 1.683 1852 1.688 1853 1.299 1854 1.211 1855 0.842 1856 0.748 1857 0.953 1858 0.993 1859 1.279 1860 1.02 1861 0.95 1862 0.977 1863 0.886 1864 0.875 1865 1.153 1866 1.249 1867 0.843 1868 0.974 1869 1.238 1870 1.121 1871 0.982 1872 0.897 1873 0.477 1874 0.659 1875 0.707 1876 0.77 1877 1.05 1878 1.796 1879 1.176 1880 1.002 1881 0.982 1882 1.13 1883 0.434 1884 0.524 1885 0.79 1886 1.051 1887 0.822 1888 0.776 1889 0.859 1890 0.582 1891 0.755 1892 0.522 1893 0.391 1894 0.536 1895 0.424 1896 0.367 1897 0.816 1898 0.833 1899 0.773 1900 0.939 1901 1.025 1902 1.297 1903 0.937 1904 0.993 1905 0.677 1906 0.728 1907 0.997 1908 0.806 1909 0.746 1910 0.776 1911 1.087 1912 1.195 1913 1.349 1914 1.672 1915 1.838 1916 0.941 1917 1.074 1918 0.9 1919 0.939 1920 1.038 1921 0.808 1922 0.8 1923 0.951 1924 0.721 1925 0.726 1926 0.703 1927 0.853 1928 1.163 1929 1.159 1930 1.197 1931 1.218 1932 0.979 1933 1.332 1934 0.989 1935 0.563 1936 0.773 1937 0.836 1938 0.916 1939 0.768 1940 0.758 1941 0.973 1942 1.114 1943 0.956 1944 0.82 1945 0.935 1946 0.965 1947 1.055 1948 0.997 1949 1.04 1950 0.95 1951 1.024 1952 1.1 1953 0.704 1954 0.935 1955 0.906 1956 1.019 1957 0.974 1958 0.964 1959 1.011 1960 0.83 1961 1.056 1962 0.884 1963 0.892 1964 0.894 1965 0.904 1966 1.054 1967 0.935 1968 0.883 1969 1.188 1970 1.077 1971 1.333 1972 1.052 1973 1.779 1974 1.363 1975 1.049 1976 1.102 1977 1.201 1978 1.361 1979 1.486