# asia_leba002 - Wadi Balat - 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/5560
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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: asia_leba002 - Wadi Balat - 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: Wadi Balat
#	Location:
#	Country: Lebanon
#	Northernmost_Latitude: 34.47
#	Southernmost_Latitude: 34.47
#	Easternmost_Longitude: 36.23
#	Westernmost_Longitude: 36.23
#	Elevation: 1175 m
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# Data_Collection
#	Collection_Name: asia_leba002B
#	Earliest_Year: 1860
#	Most_Recent_Year: 2001
#	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":"6.44605916919","T2":"15.0370983257","M1":"0.0223046049713","M2":"0.526152977542"}}
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# Species
#	Species_Name: Cilician fir
#	Species_Code: ABCI
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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
1860	0.622
1861	0.905
1862	0.905
1863	0.971
1864	1.074
1865	0.995
1866	0.767
1867	0.377
1868	0.81
1869	0.884
1870	0.927
1871	1.225
1872	1.256
1873	1.014
1874	1.375
1875	1.316
1876	1.112
1877	1.05
1878	1.503
1879	1.159
1880	1.404
1881	1.1
1882	1.025
1883	1.103
1884	1.009
1885	1.428
1886	0.52
1887	0.562
1888	0.613
1889	0.78
1890	0.779
1891	0.608
1892	0.659
1893	0.918
1894	0.935
1895	0.907
1896	0.824
1897	0.523
1898	0.524
1899	0.393
1900	0.707
1901	0.531
1902	0.611
1903	0.817
1904	1.147
1905	1.287
1906	1.141
1907	1.416
1908	1.267
1909	0.858
1910	0.718
1911	1.288
1912	0.66
1913	0.709
1914	0.826
1915	0.763
1916	0.443
1917	0.61
1918	0.747
1919	0.837
1920	1.114
1921	0.979
1922	0.808
1923	0.729
1924	0.9
1925	0.798
1926	0.742
1927	0.977
1928	0.593
1929	0.93
1930	0.763
1931	0.951
1932	0.771
1933	0.76
1934	1.152
1935	0.77
1936	1.189
1937	0.978
1938	0.949
1939	0.929
1940	0.889
1941	0.705
1942	0.904
1943	1.06
1944	1.137
1945	1.271
1946	1.233
1947	1.236
1948	1.2
1949	1.343
1950	1.759
1951	1.517
1952	1.394
1953	1.472
1954	1.292
1955	0.974
1956	1.266
1957	1.078
1958	0.717
1959	0.647
1960	0.731
1961	0.616
1962	0.685
1963	0.723
1964	0.886
1965	0.732
1966	0.882
1967	1.069
1968	1.193
1969	0.959
1970	1.036
1971	1.059
1972	1.098
1973	0.959
1974	1.054
1975	1.068
1976	1.227
1977	1.042
1978	1.015
1979	1.127
1980	1.048
1981	1.222
1982	1.375
1983	1.476
1984	1.1
1985	1.181
1986	0.939
1987	1.05
1988	0.855
1989	0.765
1990	0.762
1991	0.87
1992	1.253
1993	1.206
1994	1.255
1995	0.928
1996	0.763
1997	0.854
1998	0.909
1999	0.56
2000	0.876
2001	0.692