# asia_indi002 - Gulmarg - 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/3567 # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Tree Rings #-------------------- # Contribution_Date # Date: 2016-01-07 #-------------------- # Title # Study_Name: asia_indi002 - Gulmarg - 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: Gulmarg # Location: # Country: India # Northernmost_Latitude: 35.08 # Southernmost_Latitude: 35.08 # Easternmost_Longitude: 74.3 # Westernmost_Longitude: 74.3 # Elevation: 2740 m #-------------------- # Data_Collection # Collection_Name: asia_indi002B # Earliest_Year: 1798 # Most_Recent_Year: 1980 # 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":"3.42761284165","T2":"17.059269436","M1":"0.0223344281971","M2":"0.339492264299"}} #-------------------- # 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 1798 0.95 1799 0.95 1800 1.136 1801 1.063 1802 0.966 1803 0.915 1804 0.799 1805 0.809 1806 0.77 1807 0.89 1808 0.749 1809 0.731 1810 0.9 1811 0.877 1812 0.808 1813 0.862 1814 1.012 1815 1.089 1816 1.148 1817 0.901 1818 1.012 1819 1.072 1820 1.383 1821 1.082 1822 1.071 1823 0.728 1824 0.689 1825 0.638 1826 0.785 1827 0.699 1828 0.882 1829 0.93 1830 0.963 1831 0.661 1832 0.933 1833 0.849 1834 1.161 1835 1.192 1836 1.333 1837 1.371 1838 1.166 1839 0.928 1840 1.091 1841 0.93 1842 0.946 1843 1.018 1844 1.139 1845 0.756 1846 0.807 1847 0.79 1848 0.901 1849 0.946 1850 0.865 1851 0.99 1852 0.777 1853 0.941 1854 1.035 1855 1.057 1856 1.44 1857 1.066 1858 1.086 1859 1.055 1860 1.042 1861 0.604 1862 0.612 1863 0.522 1864 0.734 1865 1.0 1866 0.904 1867 0.76 1868 0.814 1869 0.864 1870 0.928 1871 0.655 1872 0.835 1873 0.915 1874 0.888 1875 0.653 1876 1.0 1877 0.88 1878 1.238 1879 1.193 1880 1.076 1881 0.831 1882 0.942 1883 0.841 1884 0.89 1885 0.652 1886 0.915 1887 0.925 1888 1.176 1889 1.153 1890 1.023 1891 1.01 1892 1.218 1893 1.433 1894 1.818 1895 1.563 1896 1.46 1897 1.188 1898 1.081 1899 1.141 1900 0.988 1901 1.08 1902 1.068 1903 1.085 1904 1.327 1905 1.151 1906 1.078 1907 1.094 1908 1.18 1909 1.147 1910 1.119 1911 0.894 1912 1.04 1913 0.988 1914 0.984 1915 0.773 1916 0.86 1917 0.906 1918 0.959 1919 0.975 1920 0.914 1921 0.99 1922 1.073 1923 1.155 1924 1.032 1925 1.095 1926 0.955 1927 1.127 1928 0.887 1929 0.856 1930 1.021 1931 1.241 1932 1.608 1933 1.167 1934 1.008 1935 1.287 1936 1.32 1937 1.058 1938 1.073 1939 0.905 1940 0.969 1941 0.864 1942 0.983 1943 1.078 1944 1.068 1945 0.966 1946 1.041 1947 0.992 1948 1.3 1949 1.46 1950 1.176 1951 1.195 1952 1.099 1953 0.883 1954 0.996 1955 0.71 1956 0.662 1957 0.627 1958 0.892 1959 0.939 1960 0.996 1961 1.077 1962 0.868 1963 0.662 1964 0.681 1965 0.699 1966 0.728 1967 0.711 1968 0.51 1969 0.577 1970 0.677 1971 0.673 1972 0.597 1973 0.69 1974 0.74 1975 0.671 1976 0.674 1977 0.976 1978 1.297 1979 0.892 1980 1.005