# europe_spai010 - Tierra Muerta - 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/3291
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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
#--------------------
# Title
#	Study_Name: europe_spai010 - Tierra Muerta - 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.
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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: Tierra Muerta
#	Location:
#	Country: Spain
#	Northernmost_Latitude: 40.3
#	Southernmost_Latitude: 40.3
#	Easternmost_Longitude: -2.13
#	Westernmost_Longitude: -2.13
#	Elevation: 1350 m
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# Data_Collection
#	Collection_Name: europe_spai010B
#	Earliest_Year: 1786
#	Most_Recent_Year: 1988
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"database":{"database1":"LMR","database2":"Breits"}} {"climateInterpretation":{"basis":"", "climateVariable":"M", "climateVariableDetail":"air", "interpDirection":"positive", "seasonality":"[6, 7, 8]"}}{"VSLite_parameters":{"T1":"4.30266275471","T2":"15.8259029008","M1":"0.0229964844651","M2":"0.428592870524"}}
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# Species
#	Species_Name: Austrian pine
#	Species_Code: PINI
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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)
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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
1786	0.983
1787	1.024
1788	1.47
1789	0.708
1790	0.629
1791	0.955
1792	1.167
1793	1.21
1794	1.319
1795	1.344
1796	1.478
1797	0.952
1798	0.887
1799	1.006
1800	0.992
1801	1.185
1802	0.902
1803	0.528
1804	0.828
1805	0.763
1806	0.66
1807	0.867
1808	0.834
1809	0.932
1810	1.032
1811	1.142
1812	0.561
1813	1.039
1814	1.14
1815	1.113
1816	0.94
1817	1.238
1818	1.284
1819	1.039
1820	0.264
1821	0.545
1822	0.692
1823	0.816
1824	0.621
1825	0.852
1826	0.943
1827	0.847
1828	0.916
1829	1.1
1830	1.022
1831	1.27
1832	1.113
1833	1.12
1834	1.667
1835	1.111
1836	0.814
1837	1.001
1838	1.023
1839	0.995
1840	0.928
1841	0.522
1842	0.382
1843	0.967
1844	1.119
1845	1.375
1846	1.789
1847	1.044
1848	0.397
1849	0.665
1850	0.997
1851	0.991
1852	0.467
1853	0.299
1854	0.808
1855	0.976
1856	1.659
1857	1.669
1858	1.539
1859	1.429
1860	0.669
1861	0.905
1862	1.001
1863	1.224
1864	1.341
1865	1.263
1866	0.934
1867	1.04
1868	1.132
1869	1.262
1870	0.924
1871	0.866
1872	1.01
1873	0.911
1874	1.257
1875	1.231
1876	1.127
1877	1.302
1878	0.716
1879	0.42
1880	1.042
1881	1.33
1882	1.027
1883	1.378
1884	1.392
1885	1.717
1886	1.466
1887	0.948
1888	1.161
1889	1.177
1890	0.881
1891	0.824
1892	1.224
1893	0.97
1894	0.837
1895	0.848
1896	0.975
1897	0.915
1898	0.596
1899	0.399
1900	0.514
1901	0.634
1902	0.952
1903	1.274
1904	0.851
1905	1.042
1906	1.189
1907	1.028
1908	1.073
1909	0.696
1910	0.818
1911	1.107
1912	1.223
1913	1.071
1914	1.037
1915	0.597
1916	0.699
1917	0.85
1918	0.779
1919	1.044
1920	1.326
1921	1.004
1922	1.038
1923	1.471
1924	0.612
1925	0.713
1926	1.135
1927	0.945
1928	1.102
1929	1.124
1930	1.042
1931	0.88
1932	1.164
1933	1.397
1934	0.59
1935	0.568
1936	1.109
1937	1.282
1938	1.026
1939	1.128
1940	1.379
1941	1.133
1942	1.037
1943	1.022
1944	1.069
1945	1.045
1946	0.764
1947	0.556
1948	0.931
1949	0.705
1950	0.972
1951	1.096
1952	1.367
1953	1.183
1954	0.913
1955	0.962
1956	0.962
1957	0.956
1958	1.059
1959	1.113
1960	1.014
1961	1.173
1962	0.653
1963	0.408
1964	0.621
1965	0.519
1966	0.797
1967	0.664
1968	0.424
1969	0.612
1970	0.785
1971	0.883
1972	0.812
1973	1.151
1974	1.023
1975	1.192
1976	1.3
1977	1.366
1978	0.944
1979	0.987
1980	1.261
1981	0.85
1982	1.024
1983	0.869
1984	0.747
1985	0.937
1986	0.897
1987	0.969
1988	1.064