# northamerica_usa_ca596 - Wolverton Sequoia National Park - 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.
#
#
# Online_Resource:
#
# Original_Source_URL:
#
# Description/Documentation lines begin with #
# Data lines have no #
#
# Archive: Tree Rings
#--------------------
# Contribution_Date
#	Date: 2016-01-07
#--------------------
# Title
#	Study_Name: northamerica_usa_ca596 - Wolverton Sequoia National Park - 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.
#--------------------
# Funding_Agency
#	Funding_Agency_Name: Swiss National Science Foundation
#	Grant:
#--------------------
# Site_Information
#	Site_Name: Wolverton Sequoia National Park
#	Location:
#	Country: United States
#	Northernmost_Latitude: 36.6
#	Southernmost_Latitude: 36.6
#	Easternmost_Longitude: -118.7
#	Westernmost_Longitude: -118.7
#	Elevation: 2475 m
#--------------------
# Data_Collection
#	Collection_Name: northamerica_usa_ca596B
#	Earliest_Year: 1880
#	Most_Recent_Year: 1990
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"sensitivity":"moisture"}{"T1":"4.53222391006"}{"T2":"16.4581628934"}{"M1":"0.0222961476975"}{"M2":"0.41971821061"}
#--------------------
# Species
#	Species_Name: sugar pine
#	Species_Code: PILA
#--------------------
# Chronology:
#
#
#
#--------------------
# Variables
#
# 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)
#
##age	age, , ,years AD, , , , ,N
##trsgi	tree ring standardized growth index, tree ring, ,percent relative to mean growth, , Tree Rings, , ,N
#
#--------------------
# Data:
# Data lines follow (have no #)
# Data line format - tab-delimited text, variable short name as header
# Missing Values: nan
#
age	trsgi
1880	0.759
1881	0.972
1882	0.901
1883	0.939
1884	0.874
1885	0.972
1886	0.864
1887	0.998
1888	0.984
1889	0.951
1890	0.968
1891	1.093
1892	0.988
1893	1.018
1894	1.143
1895	1.121
1896	1.027
1897	1.162
1898	1.04
1899	0.939
1900	1.027
1901	1.128
1902	1.097
1903	1.125
1904	1.181
1905	1.033
1906	1.075
1907	1.135
1908	1.128
1909	1.056
1910	1.028
1911	0.955
1912	0.965
1913	1.054
1914	1.02
1915	1.022
1916	0.96
1917	0.756
1918	0.923
1919	0.965
1920	0.965
1921	0.851
1922	0.809
1923	0.974
1924	0.667
1925	0.964
1926	1.007
1927	0.891
1928	1.012
1929	0.829
1930	0.899
1931	0.943
1932	0.817
1933	0.821
1934	1.155
1935	0.974
1936	1.008
1937	1.084
1938	1.241
1939	1.253
1940	1.236
1941	1.052
1942	1.176
1943	1.284
1944	1.01
1945	1.028
1946	1.166
1947	1.095
1948	0.829
1949	0.897
1950	1.028
1951	0.992
1952	0.729
1953	0.858
1954	1.019
1955	0.947
1956	0.908
1957	1.054
1958	1.151
1959	1.013
1960	1.024
1961	0.677
1962	0.77
1963	0.833
1964	0.923
1965	0.807
1966	1.008
1967	1.018
1968	0.998
1969	1.012
1970	0.894
1971	0.966
1972	0.958
1973	1.01
1974	1.21
1975	1.065
1976	1.14
1977	0.915
1978	1.01
1979	0.904
1980	1.187
1981	1.029
1982	1.119
1983	1.076
1984	1.057
1985	1.073
1986	1.194
1987	0.858
1988	0.965
1989	0.9
1990	0.929