# europe_fran6 - Ste. Baume (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/5113
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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_fran6 - Ste. Baume (forest) - Breitenmoser Tree Ring Chronology Data
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# 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 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:
#--------------------
#	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: Ste. Baume (forest)
#	Location:
#	Country: France
#	Northernmost_Latitude: 43.32
#	Southernmost_Latitude: 43.32
#	Easternmost_Longitude: 5.73
#	Westernmost_Longitude: 5.73
#	Elevation: 750 m
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# Data_Collection
#	Collection_Name: europe_fran6B
#	Earliest_Year: 1840
#	Most_Recent_Year: 1982
#	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":"5.20167826701","T2":"16.1774479611","M1":"0.0223238404044","M2":"0.503707774386"}}
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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
1840	0.431
1841	1.098
1842	0.972
1843	1.149
1844	1.071
1845	1.04
1846	1.151
1847	0.709
1848	1.01
1849	0.758
1850	1.051
1851	1.288
1852	1.342
1853	1.138
1854	1.244
1855	0.941
1856	0.971
1857	1.059
1858	0.904
1859	0.956
1860	0.775
1861	0.85
1862	0.896
1863	0.993
1864	0.963
1865	0.842
1866	0.936
1867	0.878
1868	0.992
1869	0.779
1870	0.597
1871	0.87
1872	0.995
1873	0.615
1874	0.749
1875	0.892
1876	0.976
1877	0.877
1878	0.884
1879	0.702
1880	1.07
1881	0.882
1882	0.863
1883	0.935
1884	1.037
1885	1.068
1886	0.803
1887	0.55
1888	0.793
1889	0.828
1890	0.99
1891	1.043
1892	0.925
1893	0.866
1894	0.878
1895	1.033
1896	1.079
1897	1.174
1898	1.026
1899	0.937
1900	0.796
1901	0.618
1902	0.748
1903	0.937
1904	0.941
1905	0.904
1906	0.848
1907	0.829
1908	0.567
1909	0.75
1910	1.138
1911	1.349
1912	1.484
1913	1.421
1914	1.359
1915	1.304
1916	0.979
1917	0.746
1918	0.917
1919	1.196
1920	1.23
1921	0.865
1922	0.775
1923	1.156
1924	1.07
1925	1.145
1926	1.312
1927	1.149
1928	1.076
1929	1.141
1930	1.435
1931	1.067
1932	1.6
1933	1.659
1934	1.242
1935	1.235
1936	1.039
1937	1.111
1938	1.226
1939	1.194
1940	1.37
1941	1.324
1942	1.016
1943	1.252
1944	1.102
1945	0.841
1946	0.766
1947	0.515
1948	0.869
1949	1.247
1950	0.994
1951	0.96
1952	0.962
1953	1.038
1954	0.83
1955	1.027
1956	0.902
1957	1.011
1958	1.193
1959	1.173
1960	1.039
1961	1.32
1962	0.856
1963	1.183
1964	0.961
1965	0.55
1966	0.869
1967	0.944
1968	0.999
1969	1.16
1970	0.419
1971	0.425
1972	0.551
1973	0.414
1974	0.449
1975	0.57
1976	0.592
1977	0.614
1978	0.421
1979	0.511
1980	0.838
1981	0.812
1982	0.73