# southamerica_arge042 - 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/5189
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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_arge042 - 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:
#--------------------
#	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_arge042B
#	Earliest_Year: 1846
#	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":"5.03087222923","T2":"19.8363705978","M1":"0.0225192259905","M2":"0.153438150541"}}
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# Species
#	Species_Name: cedrela
#	Species_Code: CESP
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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
1846	0.584
1847	1.166
1848	0.454
1849	1.54
1850	0.784
1851	1.304
1852	1.757
1853	1.238
1854	1.041
1855	1.664
1856	1.59
1857	1.581
1858	1.015
1859	1.036
1860	1.451
1861	0.87
1862	1.002
1863	0.667
1864	0.646
1865	0.675
1866	0.901
1867	1.146
1868	1.577
1869	0.798
1870	1.196
1871	0.631
1872	0.598
1873	0.354
1874	0.074
1875	0.095
1876	0.093
1877	0.051
1878	0.224
1879	0.221
1880	0.39
1881	0.661
1882	0.698
1883	0.716
1884	1.309
1885	1.53
1886	0.967
1887	1.047
1888	1.085
1889	1.219
1890	0.813
1891	0.714
1892	0.955
1893	1.113
1894	0.682
1895	0.425
1896	1.07
1897	0.884
1898	1.102
1899	0.2
1900	0.229
1901	0.011
1902	-0.094
1903	-0.055
1904	0.11
1905	0.45
1906	0.487
1907	0.697
1908	0.7
1909	0.858
1910	1.322
1911	1.433
1912	1.585
1913	1.342
1914	1.46
1915	1.631
1916	1.971
1917	1.408
1918	0.993
1919	0.543
1920	0.438
1921	0.281
1922	0.445
1923	0.894
1924	0.854
1925	1.655
1926	1.724
1927	2.501
1928	2.463
1929	1.545
1930	1.495
1931	1.622
1932	0.968
1933	0.866
1934	0.798
1935	0.059
1936	0.519
1937	1.15
1938	1.5
1939	1.293
1940	1.359
1941	1.276
1942	0.763
1943	0.804
1944	1.024
1945	1.82
1946	1.317
1947	0.834
1948	0.621
1949	0.912
1950	0.893
1951	1.126
1952	1.334
1953	1.148
1954	1.038
1955	0.455
1956	0.521
1957	0.534
1958	0.864
1959	0.966
1960	0.826
1961	0.404
1962	0.855
1963	0.447
1964	0.426
1965	0.932
1966	1.039
1967	0.824
1968	0.973
1969	0.595
1970	1.161
1971	1.355
1972	1.413
1973	1.009
1974	1.149
1975	1.187
1976	1.401
1977	0.887
1978	0.709
1979	0.47
1980	0.983
1981	0.898
1982	0.702