# europe_swit180 - Brigels GR Scatle - 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/2735
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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_swit180 - Brigels GR Scatle - 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: Brigels GR Scatle
#	Location:
#	Country: Switzerland
#	Northernmost_Latitude: 46.78
#	Southernmost_Latitude: 46.78
#	Easternmost_Longitude: 9.05
#	Westernmost_Longitude: 9.05
#	Elevation: 1600 m
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# Data_Collection
#	Collection_Name: europe_swit180B
#	Earliest_Year: 1873
#	Most_Recent_Year: 1999
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"database":{"database1":"LMR","database2":"Breits"}} {"climateInterpretation":{"basis":"", "climateVariable":"T", "climateVariableDetail":"air", "interpDirection":"positive", "seasonality":"[6, 7, 8]"}}{"VSLite_parameters":{"T1":"5.91294071674","T2":"17.0984361745","M1":"0.0228726570172","M2":"0.391571068078"}}
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# Species
#	Species_Name: Norway spruce
#	Species_Code: PCAB
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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
1873	1.398
1874	1.183
1875	1.287
1876	1.304
1877	1.274
1878	1.255
1879	1.177
1880	1.453
1881	1.712
1882	1.364
1883	1.35
1884	1.234
1885	1.041
1886	1.039
1887	1.129
1888	0.953
1889	1.088
1890	0.923
1891	0.928
1892	1.08
1893	1.132
1894	1.267
1895	1.365
1896	1.212
1897	1.292
1898	1.068
1899	1.015
1900	1.1
1901	1.103
1902	1.033
1903	1.114
1904	1.331
1905	1.152
1906	1.045
1907	1.012
1908	1.164
1909	0.785
1910	0.994
1911	1.034
1912	0.855
1913	0.915
1914	1.065
1915	1.099
1916	1.055
1917	1.176
1918	0.824
1919	1.014
1920	1.003
1921	1.004
1922	0.88
1923	1.036
1924	0.998
1925	1.016
1926	1.062
1927	1.134
1928	1.012
1929	0.918
1930	0.864
1931	1.111
1932	1.085
1933	0.785
1934	0.706
1935	0.807
1936	0.882
1937	0.887
1938	0.804
1939	0.888
1940	0.929
1941	0.933
1942	0.867
1943	0.714
1944	0.819
1945	0.908
1946	0.959
1947	1.181
1948	0.789
1949	1.141
1950	0.991
1951	1.07
1952	1.267
1953	1.117
1954	0.845
1955	1.044
1956	0.873
1957	0.801
1958	0.908
1959	0.69
1960	0.848
1961	0.955
1962	0.857
1963	0.8
1964	0.868
1965	0.778
1966	0.882
1967	0.896
1968	0.876
1969	0.922
1970	0.811
1971	0.611
1972	0.717
1973	0.87
1974	0.66
1975	0.797
1976	0.726
1977	0.967
1978	0.957
1979	0.958
1980	0.592
1981	0.686
1982	0.817
1983	0.942
1984	0.918
1985	1.102
1986	0.813
1987	0.871
1988	1.032
1989	1.153
1990	1.101
1991	1.078
1992	0.918
1993	1.151
1994	1.124
1995	1.069
1996	0.841
1997	0.925
1998	1.07
1999	1.253