# europe_brit004 - Enniscorthy - 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/4212
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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
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# Title
#	Study_Name: europe_brit004 - Enniscorthy - Breitenmoser Tree Ring Chronology Data
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# Investigators
#	Investigators:  Breitenmoser, P.; Bronnimann, S.; Frank, D.
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# 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:
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#	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: Enniscorthy
#	Location:
#	Country: Ireland
#	Northernmost_Latitude: 52.83
#	Southernmost_Latitude: 52.83
#	Easternmost_Longitude: -6.53
#	Westernmost_Longitude: -6.53
#	Elevation: 30 m
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# Data_Collection
#	Collection_Name: europe_brit004B
#	Earliest_Year: 1846
#	Most_Recent_Year: 1978
#	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":"5.08771310057","T2":"12.8680599134","M1":"0.023116873225","M2":"0.587733800739"}}
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# Species
#	Species_Name: durmast oak
#	Species_Code: QUPE
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# Chronology:
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# Variables
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# 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
1846	0.659
1847	0.642
1848	0.595
1849	0.613
1850	0.821
1851	0.762
1852	0.834
1853	0.559
1854	0.652
1855	0.613
1856	0.742
1857	0.82
1858	0.706
1859	0.696
1860	0.836
1861	0.734
1862	0.816
1863	0.993
1864	1.217
1865	1.366
1866	1.054
1867	1.255
1868	1.2
1869	1.161
1870	1.166
1871	1.629
1872	1.57
1873	1.351
1874	1.032
1875	1.76
1876	1.065
1877	1.278
1878	1.617
1879	1.405
1880	1.36
1881	1.276
1882	1.682
1883	1.551
1884	1.141
1885	0.985
1886	1.249
1887	0.929
1888	1.227
1889	0.82
1890	1.14
1891	1.157
1892	1.198
1893	0.711
1894	0.858
1895	1.108
1896	0.999
1897	1.312
1898	1.077
1899	1.012
1900	1.13
1901	0.983
1902	1.019
1903	1.118
1904	1.2
1905	0.94
1906	0.704
1907	0.841
1908	0.797
1909	0.829
1910	1.125
1911	0.855
1912	1.011
1913	0.835
1914	0.852
1915	0.79
1916	0.784
1917	1.231
1918	1.1
1919	0.799
1920	0.912
1921	0.646
1922	0.762
1923	0.624
1924	0.976
1925	0.931
1926	0.816
1927	1.04
1928	1.33
1929	0.986
1930	0.837
1931	0.828
1932	0.765
1933	0.74
1934	0.828
1935	1.007
1936	1.239
1937	0.928
1938	0.977
1939	1.131
1940	0.864
1941	0.944
1942	1.002
1943	1.035
1944	0.904
1945	1.053
1946	1.064
1947	1.459
1948	1.123
1949	0.704
1950	0.936
1951	0.878
1952	0.762
1953	0.721
1954	0.722
1955	0.799
1956	0.756
1957	0.884
1958	0.986
1959	0.992
1960	1.469
1961	1.096
1962	1.363
1963	1.231
1964	1.141
1965	1.19
1966	1.163
1967	0.797
1968	0.921
1969	0.747
1970	0.84
1971	1.137
1972	0.771
1973	0.773
1974	0.693
1975	0.882
1976	0.829
1977	0.651
1978	1.001