# northamerica_usa_oh004 - Buffalo Beats North  Ridgetop Forest Site - 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/3921
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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_oh004 - Buffalo Beats North  Ridgetop Forest Site - 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: Buffalo Beats North  Ridgetop Forest Site
#	Location:
#	Country: United States
#	Northernmost_Latitude: 39.45
#	Southernmost_Latitude: 39.45
#	Easternmost_Longitude: -82.15
#	Westernmost_Longitude: -82.15
#	Elevation: 314 m
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# Data_Collection
#	Collection_Name: northamerica_usa_oh004B
#	Earliest_Year: 1866
#	Most_Recent_Year: 1995
#	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":"4.3134965551","T2":"15.6394560815","M1":"0.0233277945442","M2":"0.601142413397"}}
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# Species
#	Species_Name: white oak
#	Species_Code: QUAL
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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
1866	1.041
1867	1.011
1868	0.941
1869	1.087
1870	0.655
1871	0.544
1872	0.661
1873	0.618
1874	0.497
1875	0.863
1876	0.679
1877	0.789
1878	0.567
1879	0.625
1880	0.557
1881	0.582
1882	0.75
1883	0.91
1884	0.714
1885	0.584
1886	0.777
1887	0.787
1888	0.609
1889	0.861
1890	0.684
1891	0.698
1892	0.625
1893	0.468
1894	0.422
1895	0.309
1896	0.486
1897	0.629
1898	0.397
1899	0.461
1900	0.315
1901	0.474
1902	0.4
1903	0.487
1904	0.336
1905	0.285
1906	0.321
1907	0.568
1908	0.684
1909	0.548
1910	0.678
1911	0.406
1912	0.929
1913	1.028
1914	0.771
1915	1.599
1916	1.686
1917	1.55
1918	1.121
1919	1.166
1920	1.616
1921	1.381
1922	1.618
1923	1.868
1924	1.906
1925	1.036
1926	1.531
1927	1.697
1928	1.587
1929	1.401
1930	1.124
1931	1.095
1932	1.558
1933	1.085
1934	1.243
1935	1.663
1936	0.857
1937	1.471
1938	1.68
1939	1.214
1940	1.067
1941	1.608
1942	1.671
1943	1.863
1944	0.997
1945	1.374
1946	1.334
1947	1.21
1948	1.234
1949	1.077
1950	1.484
1951	1.205
1952	1.013
1953	0.938
1954	0.606
1955	0.929
1956	0.902
1957	0.935
1958	1.127
1959	0.757
1960	1.069
1961	1.021
1962	0.917
1963	0.858
1964	0.677
1965	0.656
1966	0.634
1967	0.728
1968	1.094
1969	0.928
1970	0.811
1971	1.041
1972	1.033
1973	0.922
1974	1.133
1975	1.015
1976	1.127
1977	0.623
1978	0.827
1979	1.002
1980	1.219
1981	0.9
1982	0.932
1983	0.905
1984	0.64
1985	0.879
1986	0.742
1987	0.769
1988	0.53
1989	0.88
1990	0.781
1991	0.51
1992	0.78
1993	0.847
1994	0.681
1995	0.91