# southamerica_arge028 - Cerro Buenos Aires - 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/2771
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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_arge028 - Cerro Buenos Aires - Breitenmoser Tree Ring Chronology Data
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# Investigators
#	Investigators:  Breitenmoser, P.; Bronnimann, S.; Frank, D.
#--------------------
# 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: Cerro Buenos Aires
#	Location:
#	Country: Argentina
#	Northernmost_Latitude: -50.42
#	Southernmost_Latitude: -50.42
#	Easternmost_Longitude: -72.75
#	Westernmost_Longitude: -72.75
#	Elevation: 600 m
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# Data_Collection
#	Collection_Name: southamerica_arge028B
#	Earliest_Year: 1829
#	Most_Recent_Year: 1974
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"database":{"database1":"LMR","database2":"Breits"}} {"climateInterpretation":{"basis":"", "climateVariable":"M", "climateVariableDetail":"air", "interpDirection":"positive", "seasonality":"[-12, 1, 2]"}}{"VSLite_parameters":{"T1":"3.5037285214","T2":"15.4053401887","M1":"0.0230915974533","M2":"0.516973629612"}}
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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
1829	0.85
1830	0.99
1831	0.897
1832	1.074
1833	1.146
1834	0.856
1835	1.17
1836	0.815
1837	0.815
1838	1.037
1839	1.023
1840	0.992
1841	0.858
1842	0.432
1843	1.319
1844	1.368
1845	1.07
1846	0.773
1847	1.021
1848	1.338
1849	0.883
1850	0.748
1851	0.369
1852	0.867
1853	1.345
1854	1.128
1855	1.257
1856	1.291
1857	1.236
1858	1.269
1859	1.171
1860	1.547
1861	1.412
1862	0.933
1863	1.269
1864	1.271
1865	1.537
1866	1.36
1867	1.388
1868	1.228
1869	1.212
1870	1.294
1871	0.867
1872	0.78
1873	0.693
1874	0.934
1875	1.182
1876	1.071
1877	0.716
1878	0.89
1879	0.788
1880	1.196
1881	0.781
1882	0.728
1883	0.675
1884	0.601
1885	0.826
1886	0.988
1887	0.888
1888	0.794
1889	0.758
1890	1.193
1891	0.798
1892	0.696
1893	0.911
1894	1.173
1895	0.893
1896	0.613
1897	0.541
1898	0.685
1899	0.786
1900	0.77
1901	0.804
1902	1.08
1903	1.078
1904	1.122
1905	1.367
1906	0.813
1907	0.671
1908	0.697
1909	0.872
1910	1.02
1911	0.906
1912	1.126
1913	1.198
1914	1.079
1915	0.924
1916	1.274
1917	1.014
1918	0.806
1919	0.604
1920	1.172
1921	1.016
1922	0.895
1923	0.945
1924	1.1
1925	1.369
1926	1.121
1927	0.998
1928	0.884
1929	0.788
1930	0.992
1931	0.855
1932	0.981
1933	1.102
1934	1.358
1935	1.017
1936	1.288
1937	1.211
1938	1.122
1939	1.366
1940	0.922
1941	0.914
1942	1.365
1943	1.043
1944	0.78
1945	0.904
1946	1.0
1947	1.041
1948	1.125
1949	1.1
1950	1.125
1951	0.802
1952	0.76
1953	0.837
1954	1.198
1955	1.199
1956	0.97
1957	0.785
1958	0.673
1959	0.94
1960	0.769
1961	1.007
1962	0.924
1963	0.931
1964	0.788
1965	0.909
1966	0.856
1967	1.05
1968	0.946
1969	0.877
1970	1.007
1971	1.144
1972	1.184
1973	0.703
1974	0.947