# northamerica_usa_va020 - Brush Mountain Jefferson National Forest - Breitenmoser Tree Ring Chronology Data
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#		World Data Center for Paleoclimatology, Boulder
#				and
#		NOAA Paleoclimatology Program
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# 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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# Online_Resource: https://www.ncdc.noaa.gov/paleo/study/24611
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# Original_Source_URL:https://www.ncdc.noaa.gov/paleo/study/3679
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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_va020 - Brush Mountain Jefferson National Forest - Breitenmoser Tree Ring Chronology Data
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# Investigators
#	Investigators:  Breitenmoser, P.; Bronnimann, S.; Frank, D.
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# Description_and_Notes
#	Description: Data from Breitenmoser 2014 Journal of past Climate supplementary, see publication for ARSTAN standardization details
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# 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 VaganovÃÂ¢ÃÂÃÂShashkin 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 4ÃÂ¢ÃÂÃÂ6 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 modelÃÂ¢ÃÂÃÂs 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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#	Authors: Anderson, D.M., Tardif, R., Horlick, K., Erb, M.P., Hakim, G.J., Noone, D., Perkins, W.A., and E. Steig
#	Published_Date_or_Year: 2018
#	Published_Title: Additions to the last millennium reanalysis multi-proxy database
#	Journal_Name: Data Science Journal
#	Volume:
#	Edition:
#	Issue:
#	Pages:
#	Report_Number:
#	DOI:
#	Online_Resource:
#	Full_Citation: Anderson, D.M., Tardif, R., Horlick, K., Erb, M.P., Hakim, G., J., Noone, D., Perkins, W.A., and E. Steig, submitted. Additions to the last millennium reanalysis multi-proxy database. Data Science Journal.
#	Abstract: Progress in paleoclimatology increasingly occurs via data syntheses. We describe additions to a collection prepared for use in paleoclimate state estimation, specifically the Last Millennium Reanalysis (LMR).  The 2290 additional series include 2152 tree ring chronologies and 138 other series.  They supplement the collection used previously and together form a database titled LMRdb 1.0.0. The additional data draws from lake core, ice core, coral, speleothem, and tree ring archives, using published data primarily from the NOAA Paleoclimatology archive and a set of tree ring width chronologies standardized from raw International Tree Ring Data Bank ring width series. In contrast to many previous paleo compilations, the data were not selected (screened) on the basis of their environmental correlation, multi-century length, or other attributes. The inclusion of proxies sensitive to moisture and other environmental variables expands their use in data assimilation.  A preliminary calibration using linear regression with mean annual temperature reveals characteristics of the proxy series and their relationship to temperature, as well as the noise and error characteristics of the records. The additional records are structured as individual files in the NOAA Paleoclimatology format and archived at NOAA Paleoclimatology (Anderson et al. 2018) and will continue to be improved and expanded as part of the LMR Project.  The additions represent a four-fold increase in the number of records available for assimilation, provide expanded geographic coverage, and add additional proxy variables.  Applications include data assimilation, proxy system model development, and paleoclimate reconstruction using climate field reconstruction and other methods.
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# Funding_Agency
#	Funding_Agency_Name: Swiss National Science Foundation
#	Grant:
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#	Funding_Agency_Name: National Science Foundation
#	Grant:AGS-1304263
#	Funding_Agency_Name: National Oceanic and Atmospheric Administration
#	Grant:NA14OAR4310176
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# Site_Information
#	Site_Name: Brush Mountain Jefferson National Forest
#	Location:
#	Country: United States
#	Northernmost_Latitude: 37.25
#	Southernmost_Latitude: 37.25
#	Easternmost_Longitude: -80.38
#	Westernmost_Longitude: -80.38
#	Elevation: 823 m
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# Data_Collection
#	Collection_Name: northamerica_usa_va020B
#	Earliest_Year: 1869
#	Most_Recent_Year: 1992
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"database":{"database1":"LMR","database2":"Breits"}} {"climateInterpretation":{"basis":"", "climateVariable":"M", "climateVariableDetail":"air", "interpDirection":"positive", "seasonality":"[6, 7, 8]"}}{"VSLite_parameters":{"T1":"4.26267764339","T2":"15.3637288071","M1":"0.0228211589456","M2":"0.603269676857"}}
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# Species
#	Species_Name: Table Mountain pine
#	Species_Code: PIPU
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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
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age	trsgi
1869	0.875
1870	0.94
1871	0.952
1872	1.147
1873	1.067
1874	1.039
1875	1.111
1876	1.101
1877	0.866
1878	1.261
1879	0.909
1880	1.049
1881	0.57
1882	0.929
1883	0.62
1884	0.932
1885	0.571
1886	0.712
1887	0.752
1888	0.923
1889	1.141
1890	1.145
1891	1.038
1892	0.909
1893	1.064
1894	0.943
1895	0.916
1896	1.001
1897	1.013
1898	1.091
1899	0.993
1900	0.735
1901	0.861
1902	0.731
1903	1.197
1904	1.011
1905	0.935
1906	0.824
1907	1.027
1908	1.345
1909	1.307
1910	1.152
1911	0.861
1912	1.394
1913	1.187
1914	0.947
1915	1.041
1916	1.53
1917	0.956
1918	1.391
1919	1.086
1920	1.293
1921	0.771
1922	0.799
1923	0.851
1924	1.322
1925	0.827
1926	0.717
1927	1.108
1928	1.131
1929	1.49
1930	0.605
1931	0.932
1932	0.627
1933	0.959
1934	0.781
1935	0.873
1936	0.657
1937	1.055
1938	1.293
1939	1.223
1940	1.003
1941	0.68
1942	1.025
1943	1.184
1944	0.622
1945	1.008
1946	1.159
1947	1.309
1948	1.763
1949	1.504
1950	1.494
1951	1.21
1952	1.037
1953	1.232
1954	0.912
1955	0.948
1956	1.056
1957	1.132
1958	1.097
1959	0.89
1960	0.876
1961	1.068
1962	1.178
1963	1.178
1964	0.935
1965	1.38
1966	0.819
1967	1.52
1968	1.187
1969	1.033
1970	0.834
1971	0.677
1972	0.756
1973	0.72
1974	1.115
1975	1.083
1976	1.23
1977	1.104
1978	0.618
1979	0.595
1980	0.738
1981	0.582
1982	0.716
1983	0.419
1984	0.428
1985	0.426
1986	0.478
1987	0.49
1988	0.74
1989	1.241
1990	1.203
1991	1.055
1992	0.568