# asia_nepa038 - Banal-Salme - Breitenmoser Tree Ring Chronology Data
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#		World Data Center for Paleoclimatology, Boulder
#				and
#		NOAA Paleoclimatology Program
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# 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.
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# Online_Resource:
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# Online_Resource: https://www.ncdc.noaa.gov/paleo/study/24611
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# Original_Source_URL:https://www.ncdc.noaa.gov/paleo/study/3761
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# Description/Documentation lines begin with #
# Data lines have no #
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# Archive: Tree Rings
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# Contribution_Date
#	Date: 2016-01-07
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# Title
#	Study_Name: asia_nepa038 - Banal-Salme - Breitenmoser Tree Ring Chronology Data
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# Investigators
#	Investigators:  Breitenmoser, P.; Bronnimann, S.; Frank, D.
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# Description_and_Notes
#	Description: Data from Breitenmoser 2014 Journal of past Climate supplementary, see publication for ARSTAN standardization details
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# 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 VaganovÃÂ¢ÃÂÃÂShashkin 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 4ÃÂ¢ÃÂÃÂ6 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 modelÃÂ¢ÃÂÃÂs 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.
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#	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.
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# 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
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# Site_Information
#	Site_Name: Banal-Salme
#	Location:
#	Country: Nepal
#	Northernmost_Latitude: 28.03
#	Southernmost_Latitude: 28.03
#	Easternmost_Longitude: 85.07
#	Westernmost_Longitude: 85.07
#	Elevation: 3115 m
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# Data_Collection
#	Collection_Name: asia_nepa038B
#	Earliest_Year: 1769
#	Most_Recent_Year: 1996
#	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":"6.19332698012","T2":"17.3485561476","M1":"0.0220320245373","M2":"0.391545895493"}}
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# Species
#	Species_Name: silver fir
#	Species_Code: ABSB
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# Chronology:
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# Variables
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# 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)
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##age	age, , ,years AD, , , , ,N
##trsgi	tree ring standardized growth index, tree ring, ,percent relative to mean growth, , Tree Rings, , ,N
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# Data:
# Data lines follow (have no #)
# Data line format - tab-delimited text, variable short name as header
# Missing Values: nan
#
age	trsgi
1769	0.879
1770	0.8
1771	0.929
1772	1.059
1773	0.967
1774	0.899
1775	0.958
1776	0.945
1777	0.958
1778	0.99
1779	0.902
1780	0.83
1781	0.901
1782	1.02
1783	0.907
1784	0.998
1785	1.087
1786	1.186
1787	1.088
1788	1.128
1789	1.064
1790	0.928
1791	0.951
1792	1.008
1793	0.99
1794	1.202
1795	1.322
1796	1.575
1797	1.272
1798	1.159
1799	1.335
1800	1.386
1801	1.244
1802	1.386
1803	1.102
1804	0.93
1805	0.81
1806	0.846
1807	0.783
1808	0.844
1809	0.826
1810	0.911
1811	0.864
1812	0.873
1813	0.771
1814	0.784
1815	0.88
1816	0.957
1817	0.916
1818	0.709
1819	0.627
1820	0.831
1821	0.907
1822	0.811
1823	0.747
1824	1.094
1825	1.023
1826	1.148
1827	1.262
1828	1.002
1829	0.848
1830	0.937
1831	0.945
1832	1.053
1833	0.982
1834	0.686
1835	0.811
1836	1.012
1837	0.976
1838	0.642
1839	0.446
1840	0.698
1841	0.837
1842	0.829
1843	0.681
1844	0.773
1845	0.992
1846	0.665
1847	0.734
1848	1.036
1849	0.776
1850	0.872
1851	1.013
1852	0.751
1853	0.926
1854	0.895
1855	1.117
1856	1.244
1857	1.436
1858	1.138
1859	0.817
1860	0.974
1861	1.225
1862	1.474
1863	1.501
1864	1.051
1865	1.04
1866	0.903
1867	0.92
1868	0.892
1869	0.845
1870	0.712
1871	0.857
1872	0.89
1873	0.944
1874	0.688
1875	0.615
1876	0.811
1877	0.873
1878	1.271
1879	0.782
1880	0.743
1881	1.074
1882	1.002
1883	1.08
1884	1.026
1885	1.115
1886	0.917
1887	0.785
1888	1.15
1889	1.277
1890	0.751
1891	1.028
1892	1.173
1893	0.853
1894	0.875
1895	0.939
1896	1.076
1897	0.634
1898	0.797
1899	0.992
1900	1.403
1901	1.002
1902	1.3
1903	1.27
1904	1.315
1905	1.347
1906	1.086
1907	0.964
1908	1.393
1909	1.011
1910	1.198
1911	1.424
1912	1.139
1913	1.007
1914	1.005
1915	0.983
1916	1.103
1917	1.263
1918	1.248
1919	1.098
1920	0.967
1921	0.914
1922	0.914
1923	1.04
1924	1.186
1925	0.946
1926	0.791
1927	1.022
1928	0.872
1929	0.83
1930	1.145
1931	0.919
1932	0.656
1933	0.851
1934	1.051
1935	0.794
1936	0.688
1937	0.854
1938	0.626
1939	0.565
1940	0.577
1941	0.623
1942	0.746
1943	0.942
1944	0.855
1945	1.165
1946	0.814
1947	0.871
1948	0.886
1949	0.737
1950	0.779
1951	1.0
1952	0.801
1953	0.709
1954	1.13
1955	0.874
1956	0.901
1957	1.162
1958	1.159
1959	0.796
1960	0.695
1961	0.662
1962	0.755
1963	0.97
1964	1.015
1965	0.722
1966	1.011
1967	0.991
1968	1.042
1969	1.368
1970	0.907
1971	0.923
1972	1.335
1973	0.911
1974	0.418
1975	1.131
1976	1.495
1977	1.894
1978	1.153
1979	1.366
1980	1.131
1981	1.179
1982	1.347
1983	1.265
1984	1.093
1985	1.116
1986	1.126
1987	1.138
1988	0.968
1989	0.729
1990	0.79
1991	0.943
1992	0.684
1993	0.876
1994	0.925
1995	0.609
1996	0.74