# asia_nepa008 - Bhratang - 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/3764
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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_nepa008 - Bhratang - Breitenmoser Tree Ring Chronology Data
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# 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
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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:
#--------------------
#	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: Bhratang
#	Location:
#	Country: Nepal
#	Northernmost_Latitude: 28.48
#	Southernmost_Latitude: 28.48
#	Easternmost_Longitude: 84.1
#	Westernmost_Longitude: 84.1
#	Elevation: 3095 m
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# Data_Collection
#	Collection_Name: asia_nepa008B
#	Earliest_Year: 1852
#	Most_Recent_Year: 1994
#	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":"6.95151599468","T2":"15.5560128318","M1":"0.0225335805686","M2":"0.495409653097"}}
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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
#
age	trsgi
1852	0.96
1853	0.842
1854	0.887
1855	0.862
1856	0.939
1857	0.827
1858	0.823
1859	1.292
1860	1.285
1861	1.344
1862	0.936
1863	0.987
1864	0.968
1865	1.098
1866	0.973
1867	0.962
1868	0.806
1869	0.873
1870	0.849
1871	0.869
1872	0.759
1873	0.948
1874	0.861
1875	0.835
1876	0.864
1877	0.839
1878	1.046
1879	1.039
1880	0.892
1881	1.246
1882	1.461
1883	1.133
1884	1.062
1885	1.154
1886	1.174
1887	1.021
1888	1.051
1889	1.144
1890	1.197
1891	0.867
1892	0.621
1893	0.94
1894	0.821
1895	0.923
1896	0.87
1897	1.204
1898	0.959
1899	1.087
1900	0.939
1901	0.892
1902	0.984
1903	0.392
1904	0.618
1905	0.483
1906	0.72
1907	0.896
1908	0.778
1909	0.892
1910	1.001
1911	0.802
1912	0.605
1913	0.858
1914	0.858
1915	1.137
1916	1.176
1917	1.088
1918	1.044
1919	1.159
1920	1.367
1921	0.936
1922	1.148
1923	0.798
1924	1.113
1925	1.033
1926	1.045
1927	0.994
1928	1.099
1929	1.277
1930	1.185
1931	1.388
1932	1.255
1933	1.321
1934	1.186
1935	1.019
1936	1.35
1937	1.325
1938	1.093
1939	0.898
1940	0.811
1941	0.864
1942	0.932
1943	1.113
1944	1.117
1945	1.236
1946	1.127
1947	1.323
1948	0.996
1949	1.045
1950	0.756
1951	1.181
1952	0.94
1953	1.044
1954	1.001
1955	1.07
1956	1.026
1957	1.09
1958	0.974
1959	1.036
1960	1.097
1961	0.862
1962	0.725
1963	0.565
1964	0.667
1965	0.687
1966	0.601
1967	0.707
1968	0.837
1969	0.979
1970	0.875
1971	0.963
1972	0.87
1973	0.966
1974	1.003
1975	0.969
1976	1.024
1977	1.146
1978	0.884
1979	1.205
1980	1.133
1981	1.032
1982	0.999
1983	0.959
1984	0.947
1985	0.894
1986	0.935
1987	1.111
1988	1.251
1989	1.123
1990	0.75
1991	1.071
1992	0.806
1993	0.926
1994	1.069