# europe_ital017 - Monte Cantiere - 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/4079
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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_ital017 - Monte Cantiere - 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
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# 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: Monte Cantiere
#	Location:
#	Country: Italy
#	Northernmost_Latitude: 44.28
#	Southernmost_Latitude: 44.28
#	Easternmost_Longitude: 10.8
#	Westernmost_Longitude: 10.8
#	Elevation: 800 m
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# Data_Collection
#	Collection_Name: europe_ital017B
#	Earliest_Year: 1863
#	Most_Recent_Year: 1989
#	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":"4.33211700702","T2":"14.1282229134","M1":"0.0224217828745","M2":"0.537016205963"}}
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# Species
#	Species_Name: pine
#	Species_Code: PISP
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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
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age	trsgi
1863	1.263
1864	1.229
1865	1.157
1866	1.094
1867	1.175
1868	1.182
1869	1.176
1870	1.149
1871	1.042
1872	1.025
1873	0.977
1874	1.005
1875	0.983
1876	0.919
1877	0.751
1878	0.776
1879	0.815
1880	0.981
1881	0.975
1882	1.113
1883	1.038
1884	1.057
1885	0.876
1886	0.784
1887	0.83
1888	0.811
1889	0.894
1890	0.883
1891	0.912
1892	0.884
1893	0.928
1894	1.047
1895	1.119
1896	1.203
1897	1.137
1898	1.163
1899	1.218
1900	1.145
1901	1.168
1902	1.068
1903	0.861
1904	0.841
1905	0.935
1906	0.911
1907	0.92
1908	0.959
1909	0.845
1910	0.921
1911	1.163
1912	1.153
1913	1.077
1914	1.194
1915	1.078
1916	1.09
1917	1.109
1918	0.938
1919	0.775
1920	0.673
1921	0.854
1922	0.833
1923	0.781
1924	0.769
1925	0.83
1926	0.845
1927	0.948
1928	0.957
1929	0.71
1930	1.066
1931	1.021
1932	0.933
1933	0.94
1934	0.948
1935	0.944
1936	0.865
1937	0.959
1938	1.094
1939	0.95
1940	0.87
1941	0.881
1942	0.882
1943	0.676
1944	0.789
1945	0.884
1946	0.991
1947	1.013
1948	0.941
1949	0.985
1950	0.999
1951	1.163
1952	1.084
1953	1.183
1954	1.029
1955	1.041
1956	0.881
1957	1.087
1958	1.059
1959	1.101
1960	0.923
1961	1.025
1962	1.198
1963	1.092
1964	1.276
1965	1.108
1966	1.164
1967	1.288
1968	1.117
1969	1.094
1970	0.931
1971	0.848
1972	0.776
1973	0.785
1974	1.018
1975	1.093
1976	1.005
1977	0.941
1978	0.938
1979	0.992
1980	0.823
1981	0.85
1982	0.885
1983	1.111
1984	0.949
1985	0.893
1986	0.819
1987	1.036
1988	1.073
1989	1.047