# southamerica_arge103 - CastaÃÂ±o Overo 3 - 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/5143
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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_arge103 - CastaÃÂ±o Overo 3 - 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:
#--------------------
#	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: CastaÃÂ±o Overo 3
#	Location:
#	Country: Argentina
#	Northernmost_Latitude: -41.15
#	Southernmost_Latitude: -41.15
#	Easternmost_Longitude: -71.8
#	Westernmost_Longitude: -71.8
#	Elevation: 1600 m
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# Data_Collection
#	Collection_Name: southamerica_arge103B
#	Earliest_Year: 1862
#	Most_Recent_Year: 1991
#	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":"5.15071327006","T2":"12.2135491652","M1":"0.0225018511844","M2":"0.537079603538"}}
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# Species
#	Species_Name: lenga nothofagus
#	Species_Code: NOPU
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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
1862	0.361
1863	0.312
1864	0.498
1865	0.566
1866	0.335
1867	0.563
1868	0.537
1869	0.524
1870	0.665
1871	0.296
1872	0.216
1873	0.399
1874	0.947
1875	1.168
1876	1.074
1877	1.189
1878	1.217
1879	1.084
1880	0.939
1881	0.299
1882	0.59
1883	0.585
1884	0.884
1885	0.391
1886	0.687
1887	0.896
1888	0.967
1889	0.885
1890	1.625
1891	1.131
1892	0.671
1893	0.852
1894	0.819
1895	0.726
1896	1.08
1897	1.359
1898	0.832
1899	0.918
1900	1.061
1901	1.602
1902	1.291
1903	1.737
1904	1.337
1905	1.96
1906	1.518
1907	1.355
1908	1.594
1909	1.285
1910	2.059
1911	1.388
1912	1.254
1913	1.942
1914	0.963
1915	1.02
1916	1.231
1917	1.747
1918	1.458
1919	1.184
1920	1.149
1921	0.725
1922	0.854
1923	0.719
1924	1.772
1925	0.989
1926	0.759
1927	1.13
1928	0.997
1929	1.259
1930	0.946
1931	1.346
1932	1.266
1933	1.069
1934	1.417
1935	0.863
1936	0.881
1937	0.894
1938	0.781
1939	1.321
1940	1.057
1941	0.594
1942	1.416
1943	1.247
1944	1.126
1945	1.242
1946	1.274
1947	1.105
1948	0.638
1949	0.575
1950	0.056
1951	0.173
1952	0.153
1953	0.171
1954	0.527
1955	0.377
1956	0.577
1957	0.81
1958	0.572
1959	0.728
1960	0.842
1961	0.958
1962	1.328
1963	1.664
1964	1.784
1965	1.307
1966	1.128
1967	1.272
1968	1.324
1969	1.084
1970	0.5
1971	0.321
1972	0.557
1973	0.591
1974	0.659
1975	0.514
1976	0.624
1977	0.834
1978	0.751
1979	1.175
1980	0.587
1981	1.089
1982	1.149
1983	1.41
1984	0.95
1985	1.027
1986	0.838
1987	0.761
1988	1.166
1989	1.367
1990	1.137
1991	0.651