# europe_lith017 - Selema - 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/8595
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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_lith017 - Selema - 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
#--------------------
# 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: Selema
#	Location:
#	Country: Lithuania
#	Northernmost_Latitude: 54.68
#	Southernmost_Latitude: 54.68
#	Easternmost_Longitude: 23.5
#	Westernmost_Longitude: 23.5
#	Elevation: 60 m
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# Data_Collection
#	Collection_Name: europe_lith017B
#	Earliest_Year: 1870
#	Most_Recent_Year: 2006
#	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":"6.00376277834","T2":"17.4188819321","M1":"0.0217000221456","M2":"0.481539427533"}}
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# Species
#	Species_Name: European larch
#	Species_Code: LADE
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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
1870	0.963
1871	1.015
1872	1.323
1873	1.099
1874	0.765
1875	0.892
1876	1.162
1877	1.098
1878	0.965
1879	1.267
1880	1.397
1881	1.001
1882	0.912
1883	1.02
1884	1.138
1885	0.573
1886	1.056
1887	0.795
1888	0.512
1889	0.424
1890	0.591
1891	0.698
1892	0.877
1893	0.873
1894	1.286
1895	1.222
1896	0.905
1897	0.785
1898	1.073
1899	0.941
1900	0.862
1901	1.017
1902	1.101
1903	1.284
1904	1.076
1905	1.328
1906	0.951
1907	1.119
1908	1.069
1909	0.863
1910	0.962
1911	0.904
1912	0.697
1913	1.005
1914	0.789
1915	0.811
1916	1.169
1917	0.8
1918	0.948
1919	1.182
1920	1.139
1921	1.192
1922	1.687
1923	1.173
1924	1.556
1925	1.069
1926	1.398
1927	0.762
1928	0.889
1929	1.228
1930	1.151
1931	1.323
1932	1.241
1933	1.038
1934	1.308
1935	1.226
1936	1.444
1937	0.877
1938	0.952
1939	0.831
1940	0.804
1941	0.76
1942	0.973
1943	1.299
1944	1.472
1945	0.849
1946	1.262
1947	1.059
1948	0.991
1949	1.317
1950	1.551
1951	1.354
1952	0.584
1953	0.818
1954	0.365
1955	0.578
1956	0.504
1957	0.624
1958	0.735
1959	0.745
1960	0.704
1961	1.014
1962	1.159
1963	1.437
1964	0.724
1965	0.859
1966	1.03
1967	0.792
1968	1.191
1969	1.246
1970	1.388
1971	0.96
1972	1.048
1973	0.722
1974	0.622
1975	0.691
1976	0.456
1977	0.762
1978	0.724
1979	0.863
1980	0.677
1981	1.125
1982	0.791
1983	1.052
1984	0.68
1985	1.308
1986	1.608
1987	1.203
1988	1.381
1989	0.669
1990	1.19
1991	1.017
1992	0.492
1993	1.162
1994	0.814
1995	0.328
1996	0.776
1997	0.851
1998	0.518
1999	0.93
2000	0.786
2001	0.96
2002	0.661
2003	1.051
2004	1.037
2005	1.662
2006	0.825