# southamerica_arge024 - Bahia York - 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/3677
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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_arge024 - Bahia York - 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: Bahia York
#	Location:
#	Country: Argentina
#	Northernmost_Latitude: -54.83
#	Southernmost_Latitude: -54.83
#	Easternmost_Longitude: -64.33
#	Westernmost_Longitude: -64.33
#	Elevation: 60 m
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# Data_Collection
#	Collection_Name: southamerica_arge024B
#	Earliest_Year: 1748
#	Most_Recent_Year: 1986
#	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":"4.66150663668","T2":"17.1848606046","M1":"0.0224099096122","M2":"0.380387865832"}}
#--------------------
# Species
#	Species_Name: coihue de Magallanes
#	Species_Code: NOBE
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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
1748	0.598
1749	0.925
1750	1.276
1751	1.301
1752	0.875
1753	0.958
1754	0.979
1755	0.877
1756	0.995
1757	0.96
1758	0.913
1759	0.959
1760	1.246
1761	1.113
1762	0.999
1763	0.854
1764	0.992
1765	1.193
1766	0.719
1767	0.787
1768	0.652
1769	1.202
1770	0.948
1771	1.062
1772	1.081
1773	1.111
1774	0.783
1775	1.242
1776	1.358
1777	1.112
1778	1.021
1779	1.137
1780	1.026
1781	1.141
1782	1.268
1783	1.08
1784	0.966
1785	0.935
1786	0.902
1787	0.764
1788	1.086
1789	1.14
1790	0.693
1791	1.042
1792	0.804
1793	0.801
1794	0.701
1795	0.651
1796	0.678
1797	0.623
1798	0.661
1799	0.765
1800	0.853
1801	0.715
1802	0.464
1803	0.685
1804	0.619
1805	0.697
1806	0.673
1807	0.717
1808	0.418
1809	0.478
1810	0.366
1811	0.519
1812	0.622
1813	0.635
1814	0.59
1815	0.526
1816	0.729
1817	0.956
1818	0.966
1819	1.05
1820	0.875
1821	0.879
1822	0.921
1823	1.142
1824	0.935
1825	1.041
1826	1.115
1827	0.913
1828	0.939
1829	1.042
1830	1.09
1831	1.036
1832	1.111
1833	1.19
1834	0.951
1835	0.946
1836	0.622
1837	0.879
1838	0.649
1839	0.904
1840	0.73
1841	0.919
1842	0.922
1843	0.903
1844	1.174
1845	1.088
1846	1.106
1847	1.114
1848	0.944
1849	0.936
1850	1.045
1851	1.091
1852	0.785
1853	0.993
1854	0.628
1855	1.021
1856	0.637
1857	1.255
1858	1.213
1859	1.374
1860	1.772
1861	1.869
1862	1.561
1863	1.694
1864	1.144
1865	1.397
1866	1.163
1867	0.953
1868	1.28
1869	1.363
1870	1.581
1871	1.356
1872	0.839
1873	1.141
1874	0.821
1875	1.049
1876	1.199
1877	1.303
1878	1.052
1879	1.014
1880	1.013
1881	0.908
1882	0.935
1883	0.884
1884	0.881
1885	0.992
1886	0.832
1887	1.26
1888	0.867
1889	0.951
1890	0.909
1891	1.301
1892	0.942
1893	1.122
1894	0.965
1895	1.586
1896	1.285
1897	1.69
1898	1.23
1899	1.191
1900	1.402
1901	1.414
1902	1.104
1903	1.417
1904	1.275
1905	1.418
1906	0.993
1907	1.156
1908	0.997
1909	1.137
1910	0.962
1911	1.094
1912	0.73
1913	0.952
1914	0.708
1915	1.001
1916	0.857
1917	1.424
1918	1.03
1919	1.27
1920	1.221
1921	1.732
1922	1.599
1923	1.265
1924	1.364
1925	1.021
1926	1.145
1927	0.973
1928	1.031
1929	0.922
1930	1.211
1931	0.952
1932	0.939
1933	1.001
1934	0.711
1935	0.962
1936	0.702
1937	0.926
1938	0.89
1939	1.009
1940	1.04
1941	1.253
1942	1.008
1943	1.029
1944	1.193
1945	1.265
1946	1.118
1947	0.991
1948	0.475
1949	0.556
1950	0.519
1951	0.905
1952	0.328
1953	0.602
1954	0.786
1955	0.657
1956	0.576
1957	0.672
1958	0.312
1959	0.456
1960	0.23
1961	0.672
1962	0.476
1963	0.885
1964	0.615
1965	0.802
1966	0.828
1967	0.961
1968	0.722
1969	0.745
1970	0.69
1971	0.629
1972	0.68
1973	0.637
1974	0.707
1975	0.688
1976	0.371
1977	0.662
1978	0.53
1979	0.684
1980	0.488
1981	0.847
1982	0.631
1983	0.646
1984	0.298
1985	0.37
1986	0.268