# europe_lith016 - Sirguske - 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/8596
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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_lith016 - Sirguske - 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: Sirguske
#	Location:
#	Country: Lithuania
#	Northernmost_Latitude: 53.93
#	Southernmost_Latitude: 53.93
#	Easternmost_Longitude: 23.72
#	Westernmost_Longitude: 23.72
#	Elevation: 100 m
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# Data_Collection
#	Collection_Name: europe_lith016B
#	Earliest_Year: 1866
#	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":"4.74199739783","T2":"16.5098920939","M1":"0.0225705626132","M2":"0.456220894384"}}
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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
1866	0.92
1867	0.927
1868	0.864
1869	1.2
1870	0.994
1871	0.989
1872	1.451
1873	1.164
1874	0.949
1875	0.84
1876	1.518
1877	1.306
1878	0.891
1879	0.774
1880	0.841
1881	0.774
1882	0.999
1883	1.01
1884	0.404
1885	0.463
1886	0.847
1887	1.277
1888	1.344
1889	0.57
1890	0.782
1891	0.952
1892	1.023
1893	0.864
1894	0.767
1895	1.134
1896	0.938
1897	1.406
1898	1.171
1899	0.752
1900	0.442
1901	0.593
1902	0.883
1903	0.805
1904	0.995
1905	1.018
1906	1.054
1907	1.181
1908	1.302
1909	1.278
1910	1.311
1911	1.31
1912	1.075
1913	1.311
1914	1.164
1915	1.091
1916	1.629
1917	1.236
1918	1.477
1919	1.222
1920	0.661
1921	0.878
1922	1.096
1923	0.804
1924	0.442
1925	0.559
1926	0.753
1927	0.884
1928	1.004
1929	1.118
1930	0.689
1931	0.958
1932	0.956
1933	0.775
1934	1.042
1935	1.21
1936	1.541
1937	1.231
1938	1.07
1939	1.069
1940	0.839
1941	0.862
1942	0.881
1943	0.982
1944	1.286
1945	1.13
1946	1.177
1947	1.0
1948	1.079
1949	1.153
1950	1.264
1951	1.455
1952	1.288
1953	1.073
1954	1.037
1955	0.886
1956	0.748
1957	0.951
1958	0.991
1959	0.832
1960	0.91
1961	0.94
1962	0.875
1963	0.814
1964	0.624
1965	0.664
1966	0.877
1967	0.807
1968	0.73
1969	0.686
1970	0.831
1971	0.701
1972	0.874
1973	1.096
1974	0.969
1975	1.108
1976	0.754
1977	0.902
1978	1.028
1979	1.0
1980	0.954
1981	1.216
1982	0.942
1983	1.247
1984	0.825
1985	0.914
1986	1.157
1987	1.043
1988	1.245
1989	1.066
1990	1.12
1991	1.041
1992	0.587
1993	1.143
1994	0.896
1995	0.812
1996	0.936
1997	1.104
1998	0.919
1999	1.044
2000	0.787
2001	1.079
2002	1.083
2003	1.026
2004	0.998
2005	1.181
2006	0.676