# europe_spai043 - Gudar Villarluengo - 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/4257
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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_spai043 - Gudar Villarluengo - 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: Gudar Villarluengo
#	Location:
#	Country: Spain
#	Northernmost_Latitude: 40.63
#	Southernmost_Latitude: 40.63
#	Easternmost_Longitude: -0.48
#	Westernmost_Longitude: -0.48
#	Elevation: 1500 m
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# Data_Collection
#	Collection_Name: europe_spai043B
#	Earliest_Year: 1841
#	Most_Recent_Year: 1985
#	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":"4.45468025237","T2":"17.8423779269","M1":"0.0223917533645","M2":"0.461335025153"}}
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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
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age	trsgi
1841	1.329
1842	0.914
1843	1.18
1844	0.778
1845	1.055
1846	1.104
1847	0.62
1848	0.734
1849	0.527
1850	0.587
1851	0.744
1852	0.779
1853	1.05
1854	1.13
1855	0.741
1856	1.155
1857	1.099
1858	1.06
1859	0.976
1860	0.882
1861	1.024
1862	1.089
1863	1.15
1864	1.241
1865	1.366
1866	1.074
1867	0.747
1868	1.157
1869	1.445
1870	0.796
1871	1.159
1872	1.328
1873	0.917
1874	1.026
1875	1.053
1876	0.779
1877	0.789
1878	0.858
1879	0.522
1880	1.059
1881	1.065
1882	0.728
1883	0.693
1884	0.968
1885	1.264
1886	1.188
1887	0.863
1888	1.004
1889	1.333
1890	0.907
1891	0.775
1892	1.075
1893	1.004
1894	0.894
1895	0.899
1896	1.122
1897	0.978
1898	0.806
1899	1.174
1900	1.091
1901	1.063
1902	0.928
1903	1.174
1904	1.111
1905	1.039
1906	0.983
1907	0.815
1908	1.345
1909	0.789
1910	0.888
1911	1.334
1912	0.838
1913	0.8
1914	1.816
1915	1.579
1916	0.779
1917	0.821
1918	0.752
1919	0.774
1920	1.062
1921	0.77
1922	0.873
1923	1.096
1924	0.608
1925	0.656
1926	1.068
1927	1.194
1928	0.908
1929	1.184
1930	1.043
1931	0.508
1932	1.154
1933	1.265
1934	0.883
1935	1.289
1936	1.112
1937	1.483
1938	1.584
1939	1.18
1940	1.187
1941	0.827
1942	0.856
1943	1.211
1944	0.971
1945	0.784
1946	0.778
1947	1.019
1948	0.854
1949	0.719
1950	0.562
1951	1.243
1952	1.446
1953	0.782
1954	0.778
1955	0.912
1956	1.015
1957	1.043
1958	1.525
1959	1.973
1960	1.783
1961	0.648
1962	1.091
1963	0.944
1964	1.516
1965	0.828
1966	0.983
1967	0.637
1968	0.831
1969	0.684
1970	0.581
1971	0.585
1972	0.784
1973	1.047
1974	0.726
1975	0.745
1976	0.867
1977	1.619
1978	1.002
1979	0.816
1980	1.116
1981	0.846
1982	0.911
1983	0.539
1984	0.783
1985	0.536