# southamerica_arge041 - Rio Horqueta Tucuman - 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/5190
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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: southamerica_arge041 - Rio Horqueta Tucuman - 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: Rio Horqueta Tucuman
#	Location:
#	Country: Argentina
#	Northernmost_Latitude: -27.13
#	Southernmost_Latitude: -27.13
#	Easternmost_Longitude: -65.85
#	Westernmost_Longitude: -65.85
#	Elevation: 1850 m
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# Data_Collection
#	Collection_Name: southamerica_arge041B
#	Earliest_Year: 1862
#	Most_Recent_Year: 1982
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"database":{"database1":"LMR","database2":"Breits"}} {"climateInterpretation":{"basis":"", "climateVariable":"T", "climateVariableDetail":"air", "interpDirection":"positive", "seasonality":"[-12, 1, 2]"}}{"VSLite_parameters":{"T1":"5.36229997285","T2":"21.1099189665","M1":"0.0222864617128","M2":"0.194766696304"}}
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# Species
#	Species_Name: Argentine walnut
#	Species_Code: JGAU
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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
1862	1.64
1863	0.993
1864	1.572
1865	1.056
1866	1.328
1867	1.413
1868	1.416
1869	0.982
1870	0.916
1871	0.892
1872	0.931
1873	0.927
1874	1.064
1875	1.132
1876	0.872
1877	1.104
1878	1.044
1879	0.777
1880	0.81
1881	1.029
1882	1.073
1883	1.305
1884	0.97
1885	1.336
1886	0.89
1887	0.734
1888	1.001
1889	0.703
1890	1.094
1891	0.756
1892	0.764
1893	0.975
1894	0.852
1895	0.862
1896	0.994
1897	1.389
1898	1.137
1899	1.595
1900	0.948
1901	1.16
1902	0.92
1903	0.358
1904	0.3
1905	0.252
1906	0.612
1907	0.798
1908	0.801
1909	0.832
1910	0.643
1911	1.414
1912	1.538
1913	0.831
1914	0.643
1915	0.867
1916	1.129
1917	1.526
1918	1.094
1919	1.159
1920	1.104
1921	0.702
1922	0.182
1923	0.566
1924	0.939
1925	1.034
1926	1.657
1927	1.069
1928	1.239
1929	1.249
1930	0.662
1931	0.168
1932	0.537
1933	0.57
1934	0.458
1935	0.992
1936	0.94
1937	1.414
1938	1.8
1939	1.268
1940	1.93
1941	1.765
1942	1.82
1943	1.004
1944	1.714
1945	1.259
1946	1.09
1947	1.589
1948	1.166
1949	1.483
1950	1.422
1951	1.26
1952	1.336
1953	1.264
1954	1.224
1955	1.076
1956	0.881
1957	1.263
1958	0.706
1959	0.442
1960	0.523
1961	0.639
1962	0.786
1963	0.22
1964	0.32
1965	0.372
1966	0.346
1967	0.69
1968	0.816
1969	0.917
1970	0.832
1971	0.92
1972	1.188
1973	1.051
1974	0.78
1975	1.235
1976	1.034
1977	0.917
1978	0.776
1979	0.224
1980	0.222
1981	0.212
1982	0.299