# europe_germ061 - Laerchenbogen - 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/5270
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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_germ061 - Laerchenbogen - 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: Laerchenbogen
#	Location:
#	Country: Germany
#	Northernmost_Latitude: 48.95
#	Southernmost_Latitude: 48.95
#	Easternmost_Longitude: 11.93
#	Westernmost_Longitude: 11.93
#	Elevation: 460 m
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# Data_Collection
#	Collection_Name: europe_germ061B
#	Earliest_Year: 1864
#	Most_Recent_Year: 1996
#	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":"3.95310205048","T2":"15.5114695486","M1":"0.0229812098175","M2":"0.525948759825"}}
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# Species
#	Species_Name: silver fir
#	Species_Code: ABAL
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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
1864	0.885
1865	0.631
1866	0.816
1867	0.843
1868	0.625
1869	0.807
1870	0.625
1871	1.0
1872	0.626
1873	1.053
1874	0.938
1875	0.994
1876	0.929
1877	0.8
1878	0.957
1879	0.949
1880	0.882
1881	1.06
1882	1.07
1883	0.974
1884	0.937
1885	0.825
1886	0.789
1887	0.886
1888	0.99
1889	0.971
1890	1.179
1891	1.166
1892	1.056
1893	0.838
1894	0.826
1895	0.973
1896	0.928
1897	1.118
1898	1.095
1899	0.985
1900	0.958
1901	1.007
1902	0.983
1903	1.048
1904	0.875
1905	0.751
1906	1.067
1907	0.906
1908	0.89
1909	1.014
1910	1.053
1911	0.94
1912	1.068
1913	1.047
1914	1.174
1915	0.914
1916	1.22
1917	0.915
1918	0.929
1919	1.05
1920	1.114
1921	0.684
1922	0.805
1923	0.998
1924	0.968
1925	1.025
1926	1.57
1927	1.584
1928	1.237
1929	0.647
1930	0.904
1931	1.19
1932	1.319
1933	1.285
1934	0.943
1935	1.021
1936	1.123
1937	1.258
1938	1.127
1939	1.127
1940	0.613
1941	0.986
1942	0.856
1943	1.038
1944	1.036
1945	1.093
1946	1.488
1947	1.122
1948	0.876
1949	1.13
1950	0.788
1951	1.004
1952	0.955
1953	1.131
1954	1.247
1955	1.321
1956	0.774
1957	1.117
1958	1.062
1959	1.27
1960	0.765
1961	1.09
1962	0.845
1963	1.027
1964	0.917
1965	0.942
1966	0.823
1967	0.74
1968	0.718
1969	0.816
1970	0.657
1971	0.652
1972	0.6
1973	0.694
1974	0.381
1975	0.444
1976	0.186
1977	0.274
1978	0.45
1979	0.373
1980	0.574
1981	0.511
1982	0.338
1983	0.774
1984	0.843
1985	0.997
1986	0.877
1987	0.921
1988	0.995
1989	1.015
1990	1.301
1991	1.447
1992	1.661
1993	1.421
1994	1.377
1995	1.102
1996	1.055