# southamerica_arge056 - Rio Foyel - 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.
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# Online_Resource:
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# Original_Source_URL:
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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: southamerica_arge056 - Rio Foyel - 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.
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# Funding_Agency
#	Funding_Agency_Name: Swiss National Science Foundation
#	Grant:
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# Site_Information
#	Site_Name: Rio Foyel
#	Location:
#	Country: Argentina
#	Northernmost_Latitude: -41.67
#	Southernmost_Latitude: -41.67
#	Easternmost_Longitude: -71.42
#	Westernmost_Longitude: -71.42
#	Elevation: 1200 m
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# Data_Collection
#	Collection_Name: southamerica_arge056B
#	Earliest_Year: 1851
#	Most_Recent_Year: 1982
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"sensitivity":"temperature"}{"T1":"6.11755039159"}{"T2":"14.2120645517"}{"M1":"0.0225842641955"}{"M2":"0.462816279509"}
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# Species
#	Species_Name: cipres de las Guaytecas
#	Species_Code: PLUV
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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
1851	0.952
1852	0.864
1853	1.268
1854	0.88
1855	1.156
1856	0.965
1857	0.988
1858	0.913
1859	0.93
1860	0.698
1861	0.792
1862	0.81
1863	0.965
1864	0.795
1865	0.778
1866	0.877
1867	0.91
1868	0.815
1869	1.115
1870	0.842
1871	0.762
1872	0.933
1873	1.101
1874	0.983
1875	1.052
1876	1.027
1877	1.114
1878	1.08
1879	1.21
1880	0.814
1881	0.922
1882	0.991
1883	0.961
1884	1.017
1885	1.006
1886	0.795
1887	1.037
1888	1.01
1889	1.091
1890	0.91
1891	0.778
1892	0.783
1893	0.862
1894	0.792
1895	0.874
1896	1.127
1897	1.03
1898	0.71
1899	0.844
1900	1.317
1901	1.211
1902	1.168
1903	1.034
1904	1.147
1905	1.148
1906	0.801
1907	1.006
1908	1.139
1909	1.106
1910	0.961
1911	1.028
1912	0.881
1913	1.134
1914	1.302
1915	1.385
1916	1.78
1917	1.309
1918	1.137
1919	1.286
1920	1.149
1921	1.107
1922	0.959
1923	0.966
1924	0.814
1925	0.92
1926	0.907
1927	1.255
1928	0.955
1929	0.871
1930	0.828
1931	0.748
1932	0.529
1933	0.599
1934	0.798
1935	0.9
1936	0.996
1937	0.839
1938	0.847
1939	0.721
1940	0.769
1941	0.82
1942	0.697
1943	0.806
1944	0.745
1945	0.846
1946	1.02
1947	1.209
1948	1.172
1949	1.106
1950	1.152
1951	1.267
1952	1.377
1953	1.111
1954	0.994
1955	0.912
1956	0.606
1957	0.821
1958	0.592
1959	0.799
1960	0.828
1961	0.743
1962	1.068
1963	0.819
1964	0.977
1965	1.125
1966	0.986
1967	1.071
1968	0.978
1969	1.0
1970	1.124
1971	0.8
1972	1.069
1973	0.941
1974	0.914
1975	0.917
1976	0.761
1977	0.943
1978	1.19
1979	0.787
1980	0.619
1981	0.712
1982	0.915