# southamerica_arge062 - Valle de Andorra - 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.
#
#
# 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/2787
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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_arge062 - Valle de Andorra - 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: Valle de Andorra
#	Location:
#	Country: Argentina
#	Northernmost_Latitude: -54.78
#	Southernmost_Latitude: -54.78
#	Easternmost_Longitude: -68.18
#	Westernmost_Longitude: -68.18
#	Elevation: 250 m
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# Data_Collection
#	Collection_Name: southamerica_arge062B
#	Earliest_Year: 1699
#	Most_Recent_Year: 1984
#	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":"5.10041898522","T2":"17.6747145433","M1":"0.0224834782561","M2":"0.373653744606"}}
#--------------------
# Species
#	Species_Name: lenga nothofagus
#	Species_Code: NOPU
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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
#
#--------------------
# Data:
# Data lines follow (have no #)
# Data line format - tab-delimited text, variable short name as header
# Missing Values: nan
#
age	trsgi
1699	0.955
1700	1.084
1701	1.131
1702	1.12
1703	1.325
1704	0.951
1705	0.969
1706	1.39
1707	1.225
1708	0.961
1709	0.877
1710	0.976
1711	0.737
1712	0.893
1713	0.733
1714	0.958
1715	0.97
1716	0.87
1717	0.742
1718	0.514
1719	0.815
1720	0.888
1721	1.043
1722	0.511
1723	0.56
1724	0.905
1725	0.846
1726	1.054
1727	1.081
1728	1.268
1729	1.3
1730	1.051
1731	1.273
1732	1.079
1733	0.944
1734	1.121
1735	1.255
1736	0.973
1737	0.788
1738	0.912
1739	0.951
1740	0.882
1741	0.917
1742	0.934
1743	0.912
1744	0.683
1745	0.488
1746	0.855
1747	0.804
1748	1.046
1749	0.925
1750	0.994
1751	0.773
1752	0.734
1753	0.745
1754	0.954
1755	0.972
1756	0.906
1757	0.962
1758	0.823
1759	1.198
1760	1.171
1761	0.951
1762	0.937
1763	0.992
1764	1.033
1765	1.069
1766	0.981
1767	1.034
1768	0.803
1769	0.779
1770	0.961
1771	1.232
1772	1.18
1773	0.905
1774	1.143
1775	0.829
1776	0.83
1777	0.738
1778	0.801
1779	0.592
1780	0.846
1781	0.958
1782	0.656
1783	0.828
1784	0.982
1785	0.746
1786	0.903
1787	1.269
1788	1.102
1789	1.028
1790	1.071
1791	1.124
1792	1.221
1793	1.176
1794	1.013
1795	1.128
1796	1.316
1797	1.302
1798	1.146
1799	1.179
1800	1.195
1801	1.155
1802	1.089
1803	0.881
1804	1.06
1805	1.433
1806	1.175
1807	1.27
1808	1.208
1809	0.961
1810	0.851
1811	1.433
1812	1.039
1813	1.217
1814	1.115
1815	1.023
1816	1.401
1817	0.925
1818	1.054
1819	0.65
1820	0.935
1821	0.841
1822	1.13
1823	1.204
1824	1.338
1825	1.128
1826	1.379
1827	1.239
1828	1.138
1829	1.283
1830	1.231
1831	1.426
1832	1.282
1833	1.052
1834	1.092
1835	1.179
1836	1.034
1837	1.258
1838	1.076
1839	1.235
1840	1.045
1841	0.961
1842	0.892
1843	0.829
1844	0.946
1845	0.851
1846	0.954
1847	1.118
1848	0.885
1849	1.308
1850	0.806
1851	0.85
1852	0.963
1853	0.924
1854	0.673
1855	0.443
1856	0.64
1857	0.773
1858	0.51
1859	0.445
1860	0.543
1861	0.816
1862	0.98
1863	0.886
1864	0.827
1865	0.825
1866	1.026
1867	1.337
1868	1.126
1869	1.085
1870	1.097
1871	0.95
1872	1.305
1873	1.149
1874	0.985
1875	1.087
1876	0.851
1877	0.947
1878	0.776
1879	0.999
1880	1.167
1881	1.103
1882	0.96
1883	0.865
1884	0.852
1885	1.035
1886	0.762
1887	0.77
1888	0.536
1889	0.693
1890	0.648
1891	0.593
1892	0.597
1893	0.815
1894	1.057
1895	0.905
1896	1.102
1897	1.515
1898	1.274
1899	1.015
1900	1.01
1901	1.268
1902	1.197
1903	1.016
1904	1.301
1905	0.698
1906	0.738
1907	0.847
1908	0.755
1909	0.868
1910	1.016
1911	0.973
1912	0.967
1913	0.852
1914	0.819
1915	0.874
1916	0.919
1917	1.277
1918	1.16
1919	1.039
1920	0.914
1921	0.819
1922	1.04
1923	1.025
1924	0.976
1925	0.976
1926	0.846
1927	0.975
1928	0.876
1929	0.984
1930	0.612
1931	0.701
1932	0.558
1933	0.528
1934	0.618
1935	0.723
1936	0.875
1937	1.174
1938	1.11
1939	0.851
1940	1.079
1941	1.194
1942	0.815
1943	1.165
1944	0.794
1945	0.946
1946	0.882
1947	0.907
1948	0.929
1949	1.047
1950	1.212
1951	0.742
1952	0.734
1953	0.895
1954	0.99
1955	0.64
1956	0.937
1957	0.888
1958	0.789
1959	0.944
1960	1.058
1961	0.623
1962	0.936
1963	1.111
1964	0.888
1965	0.873
1966	1.086
1967	0.983
1968	1.203
1969	0.79
1970	0.964
1971	1.303
1972	0.87
1973	1.013
1974	1.005
1975	1.082
1976	0.983
1977	0.813
1978	1.112
1979	1.106
1980	0.975
1981	1.225
1982	0.996
1983	1.553
1984	1.0