# europe_spai038 - Guadarrama Rascafria - 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/4252
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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: europe_spai038 - Guadarrama Rascafria - 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: Guadarrama Rascafria
#	Location:
#	Country: Spain
#	Northernmost_Latitude: 40.8
#	Southernmost_Latitude: 40.8
#	Easternmost_Longitude: -3.95
#	Westernmost_Longitude: -3.95
#	Elevation: 1850 m
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# Data_Collection
#	Collection_Name: europe_spai038B
#	Earliest_Year: 1697
#	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":"[6, 7, 8]"}}{"VSLite_parameters":{"T1":"5.19973076259","T2":"15.9375161234","M1":"0.0224387027748","M2":"0.313480680568"}}
#--------------------
# 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
1697	0.822
1698	0.658
1699	0.839
1700	0.879
1701	1.021
1702	1.044
1703	1.016
1704	1.015
1705	1.063
1706	1.332
1707	0.936
1708	0.915
1709	0.946
1710	1.264
1711	1.164
1712	0.989
1713	0.933
1714	0.993
1715	1.249
1716	0.92
1717	0.937
1718	1.074
1719	0.945
1720	0.864
1721	1.029
1722	0.914
1723	1.146
1724	0.917
1725	0.748
1726	0.925
1727	1.129
1728	0.927
1729	0.955
1730	1.195
1731	0.706
1732	1.12
1733	0.909
1734	1.352
1735	0.85
1736	0.974
1737	1.443
1738	1.144
1739	0.867
1740	0.812
1741	0.873
1742	0.888
1743	0.664
1744	0.818
1745	0.951
1746	0.696
1747	0.864
1748	1.016
1749	0.752
1750	1.119
1751	0.942
1752	0.956
1753	1.049
1754	0.888
1755	0.985
1756	0.952
1757	1.027
1758	0.519
1759	0.758
1760	1.039
1761	1.163
1762	1.411
1763	1.105
1764	0.888
1765	0.871
1766	0.886
1767	0.811
1768	0.806
1769	0.965
1770	0.946
1771	0.647
1772	0.841
1773	1.032
1774	0.867
1775	0.84
1776	0.958
1777	0.954
1778	0.777
1779	0.862
1780	1.007
1781	1.074
1782	0.976
1783	1.138
1784	0.746
1785	0.778
1786	0.974
1787	0.943
1788	1.437
1789	1.221
1790	0.879
1791	1.155
1792	1.048
1793	1.191
1794	1.393
1795	1.271
1796	1.049
1797	0.886
1798	1.136
1799	0.978
1800	0.827
1801	1.004
1802	0.813
1803	0.751
1804	0.603
1805	0.956
1806	0.862
1807	1.321
1808	1.158
1809	0.901
1810	0.929
1811	1.021
1812	0.951
1813	1.061
1814	1.151
1815	1.003
1816	0.883
1817	1.051
1818	0.928
1819	1.053
1820	1.053
1821	1.18
1822	1.174
1823	1.073
1824	1.003
1825	1.161
1826	0.982
1827	1.019
1828	1.446
1829	1.299
1830	1.23
1831	0.884
1832	0.821
1833	1.123
1834	1.28
1835	1.088
1836	0.985
1837	1.446
1838	1.245
1839	0.934
1840	0.958
1841	1.21
1842	0.982
1843	0.91
1844	0.804
1845	0.563
1846	0.867
1847	0.933
1848	0.976
1849	1.027
1850	1.082
1851	0.94
1852	0.786
1853	1.022
1854	1.233
1855	0.888
1856	0.772
1857	0.867
1858	0.984
1859	1.183
1860	1.109
1861	1.039
1862	0.938
1863	0.923
1864	1.482
1865	1.166
1866	1.108
1867	1.231
1868	1.213
1869	1.162
1870	1.205
1871	1.324
1872	1.121
1873	1.157
1874	1.149
1875	1.033
1876	0.915
1877	0.743
1878	0.698
1879	0.607
1880	0.673
1881	0.92
1882	1.129
1883	0.933
1884	0.919
1885	1.05
1886	0.926
1887	0.991
1888	1.09
1889	1.268
1890	1.117
1891	1.021
1892	1.183
1893	1.415
1894	0.889
1895	1.037
1896	0.773
1897	0.82
1898	0.893
1899	0.853
1900	0.807
1901	0.989
1902	1.156
1903	1.08
1904	1.239
1905	1.102
1906	1.166
1907	1.013
1908	0.716
1909	0.905
1910	0.974
1911	0.904
1912	0.959
1913	0.898
1914	1.222
1915	1.088
1916	0.913
1917	1.161
1918	1.074
1919	1.022
1920	0.73
1921	0.503
1922	0.752
1923	1.273
1924	0.899
1925	0.909
1926	0.853
1927	0.873
1928	0.614
1929	0.903
1930	0.899
1931	0.969
1932	1.098
1933	1.213
1934	0.989
1935	0.858
1936	0.988
1937	0.97
1938	0.788
1939	1.034
1940	1.262
1941	0.677
1942	0.66
1943	1.103
1944	1.291
1945	1.139
1946	0.763
1947	1.02
1948	0.715
1949	0.831
1950	0.963
1951	1.123
1952	1.066
1953	1.229
1954	0.867
1955	1.022
1956	1.182
1957	1.413
1958	1.215
1959	1.16
1960	1.166
1961	1.086
1962	0.49
1963	0.35
1964	0.896
1965	0.829
1966	0.663
1967	0.635
1968	0.732
1969	0.935
1970	1.027
1971	1.022
1972	0.877
1973	1.194
1974	1.102
1975	0.844
1976	1.318
1977	1.08
1978	0.906
1979	0.875
1980	1.192
1981	1.438
1982	1.078
1983	1.121
1984	0.869