# asia_nepa005 - Alubari - 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/3759
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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_nepa005 - Alubari - 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: Alubari
#	Location:
#	Country: Nepal
#	Northernmost_Latitude: 28.45
#	Southernmost_Latitude: 28.45
#	Easternmost_Longitude: 83.4
#	Westernmost_Longitude: 83.4
#	Elevation: 3000 m
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# Data_Collection
#	Collection_Name: asia_nepa005B
#	Earliest_Year: 1859
#	Most_Recent_Year: 1993
#	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":"3.94788453013","T2":"13.0628482339","M1":"0.0225956332174","M2":"0.548345606145"}}
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# Species
#	Species_Name: Himalayan pine
#	Species_Code: PIWA
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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
1859	0.722
1860	0.378
1861	0.698
1862	0.962
1863	1.049
1864	1.236
1865	1.456
1866	1.263
1867	1.067
1868	1.165
1869	0.735
1870	1.074
1871	1.249
1872	1.088
1873	0.926
1874	0.391
1875	0.342
1876	0.411
1877	0.653
1878	0.979
1879	0.904
1880	1.183
1881	1.176
1882	1.157
1883	1.215
1884	0.521
1885	1.205
1886	1.417
1887	1.206
1888	1.583
1889	1.412
1890	1.797
1891	1.579
1892	1.033
1893	1.081
1894	1.28
1895	0.949
1896	0.673
1897	0.71
1898	1.018
1899	0.853
1900	0.939
1901	1.025
1902	1.366
1903	0.854
1904	1.486
1905	0.93
1906	0.846
1907	1.101
1908	0.26
1909	0.415
1910	0.358
1911	0.608
1912	0.738
1913	0.802
1914	0.816
1915	0.885
1916	0.547
1917	1.088
1918	0.741
1919	0.656
1920	0.884
1921	0.549
1922	1.025
1923	1.107
1924	1.255
1925	1.368
1926	1.26
1927	1.098
1928	1.176
1929	1.305
1930	0.887
1931	1.707
1932	1.383
1933	1.443
1934	1.596
1935	0.823
1936	1.061
1937	1.294
1938	1.468
1939	0.676
1940	0.938
1941	0.39
1942	0.903
1943	0.921
1944	1.13
1945	1.145
1946	0.973
1947	0.733
1948	0.428
1949	0.316
1950	0.603
1951	0.689
1952	1.202
1953	0.546
1954	0.818
1955	0.935
1956	1.138
1957	1.288
1958	0.651
1959	1.055
1960	0.874
1961	1.064
1962	1.321
1963	1.606
1964	1.065
1965	1.913
1966	0.341
1967	0.814
1968	0.217
1969	0.691
1970	0.976
1971	0.793
1972	1.156
1973	1.125
1974	1.424
1975	1.144
1976	1.052
1977	0.759
1978	1.002
1979	0.457
1980	0.777
1981	0.696
1982	1.181
1983	1.361
1984	1.551
1985	0.607
1986	1.58
1987	1.535
1988	1.875
1989	1.601
1990	1.191
1991	0.868
1992	0.424
1993	0.412