# northamerica_usa_ia018 - Cedar Cliff Farm - 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: northamerica_usa_ia018 - Cedar Cliff Farm - 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: Cedar Cliff Farm
#	Location:
#	Country: United States
#	Northernmost_Latitude: 43.0
#	Southernmost_Latitude: 43.0
#	Easternmost_Longitude: -95.42
#	Westernmost_Longitude: -95.42
#	Elevation: 411 m
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# Data_Collection
#	Collection_Name: northamerica_usa_ia018B
#	Earliest_Year: 1852
#	Most_Recent_Year: 1977
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"sensitivity":"moisture"}{"T1":"4.00664432154"}{"T2":"15.4781100187"}{"M1":"0.0223491025246"}{"M2":"0.533171566973"}
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# Species
#	Species_Name: burr oak
#	Species_Code: QUMA
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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
1852	0.682
1853	1.17
1854	1.101
1855	0.774
1856	0.877
1857	1.051
1858	0.854
1859	1.144
1860	0.741
1861	0.773
1862	0.838
1863	0.798
1864	0.639
1865	0.634
1866	0.822
1867	0.795
1868	0.642
1869	0.921
1870	0.616
1871	0.49
1872	0.69
1873	0.675
1874	0.598
1875	0.841
1876	0.831
1877	0.921
1878	0.939
1879	1.018
1880	0.843
1881	0.943
1882	0.554
1883	0.41
1884	0.504
1885	0.996
1886	0.687
1887	1.086
1888	1.092
1889	0.932
1890	0.908
1891	1.081
1892	1.488
1893	1.065
1894	0.776
1895	0.842
1896	1.326
1897	0.85
1898	0.978
1899	0.981
1900	1.0
1901	1.117
1902	1.017
1903	1.61
1904	1.565
1905	1.264
1906	1.473
1907	1.412
1908	1.597
1909	1.407
1910	1.019
1911	0.837
1912	1.321
1913	1.152
1914	1.016
1915	1.254
1916	1.618
1917	1.114
1918	0.906
1919	0.977
1920	1.014
1921	0.973
1922	0.663
1923	0.919
1924	0.822
1925	0.525
1926	0.679
1927	1.162
1928	1.071
1929	0.805
1930	0.808
1931	0.562
1932	1.098
1933	0.978
1934	0.924
1935	0.955
1936	0.589
1937	0.895
1938	0.998
1939	1.096
1940	1.257
1941	1.431
1942	1.325
1943	1.093
1944	0.747
1945	0.949
1946	0.633
1947	0.613
1948	0.742
1949	1.001
1950	1.05
1951	1.076
1952	1.022
1953	0.852
1954	0.889
1955	0.813
1956	0.666
1957	1.108
1958	0.841
1959	0.853
1960	0.9
1961	0.711
1962	1.021
1963	0.755
1964	0.828
1965	0.915
1966	0.941
1967	1.031
1968	0.721
1969	1.313
1970	1.003
1971	1.178
1972	1.194
1973	1.05
1974	1.107
1975	1.312
1976	0.807
1977	0.759