# europe_aust109 - Bierhaeuslberg - 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/5056
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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_aust109 - Bierhaeuslberg - 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: Bierhaeuslberg
#	Location:
#	Country: Austria
#	Northernmost_Latitude: 48.12
#	Southernmost_Latitude: 48.12
#	Easternmost_Longitude: 16.25
#	Westernmost_Longitude: 16.25
#	Elevation: 350 m
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# Data_Collection
#	Collection_Name: europe_aust109B
#	Earliest_Year: 1850
#	Most_Recent_Year: 1995
#	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":"4.2301761157","T2":"15.4032116706","M1":"0.0228494255688","M2":"0.568226825964"}}
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# Species
#	Species_Name: Austrian pine
#	Species_Code: PINI
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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
1850	1.009
1851	1.021
1852	0.928
1853	1.135
1854	1.111
1855	1.039
1856	1.07
1857	0.755
1858	0.825
1859	0.94
1860	0.886
1861	1.044
1862	1.052
1863	0.835
1864	1.31
1865	0.928
1866	1.11
1867	1.028
1868	0.841
1869	0.709
1870	0.983
1871	1.326
1872	1.028
1873	0.936
1874	0.977
1875	0.857
1876	1.072
1877	0.924
1878	1.27
1879	1.136
1880	0.831
1881	1.061
1882	1.123
1883	0.934
1884	1.338
1885	1.051
1886	1.002
1887	0.817
1888	0.896
1889	0.654
1890	0.915
1891	1.081
1892	1.208
1893	0.899
1894	1.151
1895	1.054
1896	0.965
1897	1.454
1898	1.223
1899	0.969
1900	0.809
1901	0.601
1902	1.129
1903	1.408
1904	0.839
1905	0.972
1906	1.119
1907	0.662
1908	0.682
1909	0.667
1910	0.949
1911	0.845
1912	1.367
1913	1.117
1914	1.294
1915	0.907
1916	1.498
1917	0.572
1918	1.05
1919	1.264
1920	1.482
1921	0.858
1922	0.515
1923	0.873
1924	0.711
1925	1.027
1926	1.502
1927	1.159
1928	0.918
1929	0.752
1930	0.65
1931	0.958
1932	1.08
1933	0.7
1934	0.564
1935	0.784
1936	1.057
1937	0.816
1938	1.043
1939	1.162
1940	1.122
1941	1.606
1942	1.24
1943	1.202
1944	0.878
1945	0.57
1946	0.728
1947	0.617
1948	0.55
1949	0.719
1950	0.651
1951	0.984
1952	0.854
1953	1.033
1954	0.928
1955	0.98
1956	0.674
1957	0.77
1958	1.124
1959	1.506
1960	1.157
1961	1.187
1962	0.905
1963	0.81
1964	0.764
1965	1.33
1966	1.604
1967	1.129
1968	0.697
1969	0.983
1970	1.204
1971	0.932
1972	1.43
1973	1.471
1974	0.877
1975	1.171
1976	0.56
1977	0.598
1978	0.901
1979	0.75
1980	0.832
1981	0.801
1982	1.264
1983	0.838
1984	0.848
1985	1.112
1986	0.84
1987	0.775
1988	0.614
1989	1.107
1990	1.062
1991	1.165
1992	0.937
1993	0.552
1994	1.451
1995	1.357