# europe_spai037 - Guadarrama Loma de Noruego - 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/4251
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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_spai037 - Guadarrama Loma de Noruego - 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: Guadarrama Loma de Noruego
#	Location:
#	Country: Spain
#	Northernmost_Latitude: 40.78
#	Southernmost_Latitude: 40.78
#	Easternmost_Longitude: -3.8
#	Westernmost_Longitude: -3.8
#	Elevation: 1950 m
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# Data_Collection
#	Collection_Name: europe_spai037B
#	Earliest_Year: 1727
#	Most_Recent_Year: 1985
#	Time_Unit: y_ad
#	Core_Length:
#	Notes: {"database":{"database1":"LMR","database2":"Breits"}} {"climateInterpretation":{"basis":"", "climateVariable":"T", "climateVariableDetail":"air", "interpDirection":"positive", "seasonality":"[6, 7, 8]"}}{"VSLite_parameters":{"T1":"6.1217303207","T2":"14.5731359437","M1":"0.0226755568428","M2":"0.264088575739"}}
#--------------------
# Species
#	Species_Name: Scots pine
#	Species_Code: PISY
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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
1727	1.173
1728	0.964
1729	1.033
1730	1.187
1731	0.916
1732	1.221
1733	1.141
1734	1.643
1735	1.241
1736	1.266
1737	1.712
1738	1.596
1739	1.197
1740	0.929
1741	1.126
1742	1.129
1743	0.835
1744	1.026
1745	1.093
1746	0.899
1747	0.921
1748	1.037
1749	0.816
1750	0.896
1751	0.924
1752	0.908
1753	0.893
1754	0.923
1755	0.868
1756	0.854
1757	1.009
1758	0.816
1759	0.915
1760	0.716
1761	0.927
1762	1.201
1763	0.856
1764	1.021
1765	1.031
1766	1.023
1767	0.959
1768	1.141
1769	1.062
1770	1.13
1771	0.785
1772	0.869
1773	0.954
1774	0.806
1775	0.825
1776	0.91
1777	0.934
1778	0.852
1779	0.857
1780	0.947
1781	1.024
1782	1.119
1783	1.202
1784	0.979
1785	0.884
1786	0.883
1787	0.816
1788	1.19
1789	1.162
1790	1.03
1791	1.019
1792	0.914
1793	1.231
1794	1.271
1795	1.208
1796	1.174
1797	0.923
1798	1.155
1799	1.056
1800	0.791
1801	0.915
1802	0.931
1803	0.829
1804	0.728
1805	0.802
1806	0.729
1807	1.197
1808	0.983
1809	0.989
1810	1.027
1811	1.113
1812	1.005
1813	1.029
1814	1.049
1815	0.938
1816	0.82
1817	0.872
1818	0.775
1819	0.864
1820	0.735
1821	0.948
1822	1.01
1823	1.067
1824	0.928
1825	1.145
1826	1.122
1827	1.08
1828	0.964
1829	0.686
1830	0.771
1831	0.718
1832	0.759
1833	0.805
1834	1.011
1835	1.046
1836	0.841
1837	1.328
1838	1.072
1839	0.911
1840	1.003
1841	1.064
1842	1.129
1843	0.87
1844	0.849
1845	0.726
1846	0.96
1847	0.788
1848	0.785
1849	1.017
1850	1.137
1851	1.027
1852	0.812
1853	0.83
1854	0.981
1855	0.815
1856	0.791
1857	0.926
1858	0.933
1859	1.117
1860	1.059
1861	1.004
1862	0.768
1863	0.806
1864	1.221
1865	0.964
1866	0.863
1867	1.295
1868	1.257
1869	1.259
1870	1.029
1871	1.026
1872	0.837
1873	1.001
1874	1.143
1875	1.112
1876	0.814
1877	0.704
1878	0.676
1879	0.716
1880	0.849
1881	0.968
1882	1.202
1883	1.11
1884	1.123
1885	1.013
1886	0.924
1887	1.125
1888	1.093
1889	0.843
1890	1.043
1891	1.067
1892	1.239
1893	1.366
1894	0.863
1895	0.952
1896	0.883
1897	0.911
1898	1.073
1899	1.022
1900	1.049
1901	1.159
1902	1.154
1903	1.382
1904	1.465
1905	1.377
1906	1.405
1907	1.236
1908	0.938
1909	0.93
1910	1.12
1911	1.108
1912	1.138
1913	1.113
1914	1.379
1915	1.153
1916	1.098
1917	1.059
1918	1.118
1919	1.08
1920	0.91
1921	0.617
1922	0.824
1923	1.297
1924	0.928
1925	1.01
1926	0.929
1927	0.957
1928	0.683
1929	1.0
1930	1.08
1931	1.095
1932	1.131
1933	1.195
1934	0.911
1935	0.891
1936	0.827
1937	1.057
1938	0.84
1939	0.769
1940	0.823
1941	0.415
1942	0.495
1943	0.678
1944	1.364
1945	1.293
1946	0.859
1947	1.114
1948	0.993
1949	0.753
1950	0.783
1951	1.009
1952	0.673
1953	0.921
1954	0.803
1955	1.21
1956	1.192
1957	1.339
1958	1.394
1959	1.372
1960	1.028
1961	0.881
1962	0.632
1963	0.376
1964	0.539
1965	0.606
1966	0.551
1967	0.478
1968	0.734
1969	0.863
1970	1.156
1971	0.949
1972	0.851
1973	1.084
1974	1.044
1975	0.808
1976	1.222
1977	1.052
1978	0.885
1979	0.882
1980	1.012
1981	1.307
1982	1.351
1983	1.373
1984	1.121
1985	1.089