# europe_spai032 - Cuenca Vega de Cordono - Breitenmoser Tree Ring Chronology Data
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#		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
#--------------------
# Title
#	Study_Name: europe_spai032 - Cuenca Vega de Cordono - 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: Cuenca Vega de Cordono
#	Location:
#	Country: Spain
#	Northernmost_Latitude: 40.43
#	Southernmost_Latitude: 40.43
#	Easternmost_Longitude: -1.9
#	Westernmost_Longitude: -1.9
#	Elevation: 1440 m
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# Data_Collection
#	Collection_Name: europe_spai032B
#	Earliest_Year: 1846
#	Most_Recent_Year: 1983
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"sensitivity":"moisture"}{"T1":"3.44640135181"}{"T2":"14.2820353946"}{"M1":"0.0229032063164"}{"M2":"0.57128726716"}
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# Species
#	Species_Name: Austrian pine
#	Species_Code: PINI
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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
1846	0.915
1847	0.707
1848	0.79
1849	0.665
1850	0.832
1851	0.834
1852	0.733
1853	0.944
1854	1.219
1855	1.192
1856	1.14
1857	0.962
1858	1.071
1859	1.368
1860	0.909
1861	1.18
1862	0.983
1863	0.86
1864	1.253
1865	0.887
1866	1.182
1867	1.111
1868	0.839
1869	1.138
1870	1.017
1871	1.062
1872	0.643
1873	0.604
1874	0.597
1875	0.736
1876	0.767
1877	1.081
1878	1.056
1879	0.39
1880	0.785
1881	1.015
1882	0.836
1883	0.859
1884	0.677
1885	1.276
1886	1.224
1887	0.938
1888	1.026
1889	1.124
1890	0.77
1891	0.88
1892	1.046
1893	0.857
1894	0.788
1895	0.959
1896	0.733
1897	0.76
1898	0.683
1899	1.267
1900	0.709
1901	0.944
1902	1.338
1903	1.81
1904	1.073
1905	1.318
1906	1.222
1907	0.925
1908	1.376
1909	1.323
1910	1.796
1911	1.255
1912	1.115
1913	1.427
1914	1.752
1915	0.892
1916	1.214
1917	0.937
1918	0.957
1919	1.174
1920	1.13
1921	0.708
1922	0.635
1923	0.928
1924	0.683
1925	0.901
1926	1.378
1927	0.965
1928	0.987
1929	1.092
1930	1.076
1931	1.014
1932	1.411
1933	1.74
1934	0.872
1935	1.006
1936	1.229
1937	1.526
1938	1.225
1939	1.023
1940	1.859
1941	0.976
1942	0.71
1943	1.07
1944	0.993
1945	0.803
1946	0.578
1947	0.491
1948	0.732
1949	0.333
1950	0.635
1951	0.722
1952	1.185
1953	0.825
1954	0.932
1955	0.715
1956	0.856
1957	0.917
1958	0.693
1959	0.822
1960	1.08
1961	0.716
1962	0.704
1963	0.492
1964	1.044
1965	0.602
1966	0.809
1967	0.585
1968	0.702
1969	0.873
1970	0.897
1971	0.72
1972	0.597
1973	1.471
1974	1.196
1975	1.155
1976	1.321
1977	1.457
1978	1.233
1979	0.924
1980	1.23
1981	0.539
1982	1.174
1983	1.017