# europe_spai052 - Burgos San Zanordil - 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
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# Title
#	Study_Name: europe_spai052 - Burgos San Zanordil - 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: Burgos San Zanordil
#	Location:
#	Country: Spain
#	Northernmost_Latitude: 42.83
#	Southernmost_Latitude: 42.83
#	Easternmost_Longitude: -3.17
#	Westernmost_Longitude: -3.17
#	Elevation: 880 m
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# Data_Collection
#	Collection_Name: europe_spai052B
#	Earliest_Year: 1851
#	Most_Recent_Year: 1985
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"sensitivity":"temperature"}{"T1":"3.06326598087"}{"T2":"15.0512646724"}{"M1":"0.0220411740965"}{"M2":"0.514697491945"}
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# Species
#	Species_Name: Scots pine
#	Species_Code: PISY
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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	0.682
1852	1.418
1853	1.267
1854	1.076
1855	1.03
1856	1.014
1857	1.093
1858	0.823
1859	0.984
1860	0.987
1861	0.804
1862	1.222
1863	0.981
1864	1.018
1865	1.107
1866	1.544
1867	1.289
1868	0.908
1869	1.453
1870	0.786
1871	0.882
1872	0.345
1873	0.688
1874	0.716
1875	0.863
1876	1.063
1877	1.173
1878	1.378
1879	0.891
1880	1.28
1881	1.171
1882	0.641
1883	0.844
1884	1.11
1885	1.511
1886	1.057
1887	0.842
1888	0.904
1889	0.927
1890	1.065
1891	1.022
1892	0.945
1893	0.838
1894	0.854
1895	0.692
1896	0.671
1897	1.015
1898	0.55
1899	0.721
1900	0.762
1901	0.968
1902	1.173
1903	1.196
1904	1.021
1905	1.146
1906	0.987
1907	0.759
1908	0.886
1909	0.827
1910	1.032
1911	0.823
1912	1.357
1913	1.558
1914	1.425
1915	0.962
1916	1.406
1917	1.261
1918	0.827
1919	0.92
1920	1.26
1921	1.01
1922	0.784
1923	1.294
1924	0.845
1925	0.813
1926	1.187
1927	1.338
1928	1.002
1929	0.656
1930	0.369
1931	0.582
1932	0.805
1933	0.887
1934	0.794
1935	0.846
1936	1.483
1937	1.374
1938	0.549
1939	0.77
1940	1.264
1941	1.201
1942	0.762
1943	0.585
1944	0.667
1945	0.782
1946	0.57
1947	0.887
1948	1.065
1949	1.203
1950	1.051
1951	1.284
1952	1.386
1953	1.173
1954	0.776
1955	0.966
1956	0.785
1957	0.855
1958	1.215
1959	1.731
1960	1.043
1961	1.307
1962	0.978
1963	0.626
1964	0.806
1965	0.632
1966	1.012
1967	1.128
1968	1.079
1969	0.886
1970	1.177
1971	1.278
1972	0.588
1973	0.768
1974	0.952
1975	0.89
1976	0.874
1977	1.675
1978	0.957
1979	1.039
1980	1.001
1981	0.448
1982	0.521
1983	1.009
1984	0.715
1985	0.662