# southamerica_arge084 - San RamÃÂ³n - Breitenmoser Tree Ring Chronology Data
#-----------------------------------------------------------------------
#		World Data Center for Paleoclimatology, Boulder
#				and
#		NOAA Paleoclimatology Program
#-----------------------------------------------------------------------
# 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/5194
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# Description/Documentation lines begin with #
# Data lines have no #
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# Archive: Tree Rings
#--------------------
# Contribution_Date
#	Date: 2016-01-07
#--------------------
# Title
#	Study_Name: southamerica_arge084 - San RamÃÂ³n - Breitenmoser Tree Ring Chronology Data
#--------------------
# 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.
#--------------------
#	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: San RamÃÂ³n
#	Location:
#	Country: Argentina
#	Northernmost_Latitude: -41.05
#	Southernmost_Latitude: -41.05
#	Easternmost_Longitude: -70.98
#	Westernmost_Longitude: -70.98
#	Elevation: 1100 m
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# Data_Collection
#	Collection_Name: southamerica_arge084B
#	Earliest_Year: 1749
#	Most_Recent_Year: 1991
#	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":"3.43096355764","T2":"14.0166062043","M1":"0.0228662316719","M2":"0.553436753926"}}
#--------------------
# Species
#	Species_Name: Chilean cedar
#	Species_Code: AUCH
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# Chronology:
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# Variables
#
# 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)
#
##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
1749	0.785
1750	0.946
1751	0.931
1752	0.721
1753	0.736
1754	0.661
1755	0.446
1756	0.601
1757	0.63
1758	0.661
1759	0.747
1760	1.089
1761	0.98
1762	0.852
1763	1.011
1764	1.089
1765	0.945
1766	1.222
1767	0.908
1768	1.069
1769	1.348
1770	1.395
1771	1.373
1772	1.017
1773	0.998
1774	1.236
1775	1.323
1776	1.457
1777	1.386
1778	1.5
1779	1.493
1780	1.304
1781	1.403
1782	1.119
1783	0.985
1784	0.998
1785	0.9
1786	0.831
1787	0.799
1788	1.123
1789	0.981
1790	1.068
1791	1.157
1792	1.036
1793	0.752
1794	0.853
1795	0.627
1796	0.694
1797	1.218
1798	1.084
1799	0.716
1800	0.91
1801	0.888
1802	0.821
1803	0.872
1804	0.777
1805	1.007
1806	0.839
1807	0.802
1808	1.251
1809	1.232
1810	1.197
1811	1.245
1812	1.257
1813	0.716
1814	0.917
1815	0.918
1816	0.966
1817	0.787
1818	0.802
1819	0.697
1820	0.618
1821	0.523
1822	0.68
1823	0.725
1824	0.658
1825	0.83
1826	0.762
1827	0.485
1828	0.6
1829	1.001
1830	1.146
1831	1.139
1832	1.142
1833	0.981
1834	1.054
1835	0.887
1836	0.951
1837	1.092
1838	0.969
1839	1.017
1840	1.082
1841	0.746
1842	0.996
1843	1.308
1844	1.054
1845	0.681
1846	0.815
1847	0.744
1848	0.872
1849	0.851
1850	0.898
1851	0.673
1852	1.06
1853	0.894
1854	0.686
1855	1.069
1856	1.187
1857	1.334
1858	1.184
1859	1.022
1860	0.984
1861	1.015
1862	1.168
1863	1.733
1864	1.231
1865	0.842
1866	1.08
1867	1.073
1868	1.526
1869	1.735
1870	1.777
1871	1.284
1872	1.445
1873	1.42
1874	1.592
1875	1.315
1876	1.272
1877	0.658
1878	1.024
1879	1.599
1880	1.316
1881	1.042
1882	1.134
1883	1.251
1884	1.103
1885	0.931
1886	0.986
1887	0.959
1888	0.982
1889	1.092
1890	1.204
1891	1.263
1892	1.521
1893	0.874
1894	1.151
1895	0.938
1896	0.893
1897	0.828
1898	1.256
1899	0.962
1900	0.817
1901	0.678
1902	0.74
1903	0.71
1904	0.736
1905	0.714
1906	0.696
1907	0.736
1908	0.375
1909	0.633
1910	0.698
1911	0.447
1912	0.675
1913	0.58
1914	0.646
1915	0.951
1916	0.956
1917	0.584
1918	0.934
1919	0.745
1920	1.177
1921	0.969
1922	0.932
1923	0.629
1924	0.649
1925	0.774
1926	1.144
1927	0.865
1928	1.216
1929	1.063
1930	1.503
1931	1.289
1932	0.941
1933	1.23
1934	1.014
1935	1.111
1936	0.998
1937	0.848
1938	1.474
1939	1.228
1940	1.449
1941	1.607
1942	1.344
1943	0.772
1944	1.136
1945	1.731
1946	1.587
1947	1.055
1948	1.169
1949	1.258
1950	1.024
1951	1.256
1952	1.187
1953	1.205
1954	1.164
1955	1.58
1956	1.202
1957	0.719
1958	0.993
1959	0.932
1960	0.877
1961	0.684
1962	0.421
1963	0.726
1964	0.753
1965	0.944
1966	0.995
1967	0.687
1968	0.592
1969	0.695
1970	0.659
1971	0.824
1972	0.772
1973	0.906
1974	1.018
1975	0.914
1976	0.8
1977	0.964
1978	0.805
1979	0.771
1980	1.033
1981	0.995
1982	1.049
1983	0.908
1984	1.116
1985	0.846
1986	0.81
1987	0.792
1988	0.635
1989	0.508
1990	0.348
1991	0.744