# asia_indi019 - Dhanolti - 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/2788
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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_indi019 - Dhanolti - 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: Dhanolti
#	Location:
#	Country: India
#	Northernmost_Latitude: 30.75
#	Southernmost_Latitude: 30.75
#	Easternmost_Longitude: 78.42
#	Westernmost_Longitude: 78.42
#	Elevation: 2400 m
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# Data_Collection
#	Collection_Name: asia_indi019B
#	Earliest_Year: 1850
#	Most_Recent_Year: 1990
#	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":"2.29331867791","T2":"14.0699984408","M1":"0.022818262798","M2":"0.502792808333"}}
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# Species
#	Species_Name: Himalayan spruce
#	Species_Code: PCSM
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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
1850	1.053
1851	1.062
1852	0.817
1853	0.92
1854	0.875
1855	0.784
1856	1.055
1857	0.819
1858	0.773
1859	1.029
1860	1.095
1861	0.871
1862	0.866
1863	1.077
1864	0.804
1865	1.251
1866	1.274
1867	1.059
1868	1.089
1869	0.85
1870	0.54
1871	0.992
1872	0.842
1873	0.511
1874	0.657
1875	1.141
1876	1.062
1877	0.879
1878	1.085
1879	0.787
1880	1.351
1881	1.122
1882	1.372
1883	1.493
1884	0.969
1885	1.216
1886	1.18
1887	0.684
1888	0.985
1889	1.184
1890	0.702
1891	0.87
1892	0.473
1893	1.183
1894	1.077
1895	0.98
1896	0.822
1897	1.142
1898	1.196
1899	1.284
1900	1.426
1901	1.33
1902	1.226
1903	1.552
1904	1.603
1905	1.846
1906	1.249
1907	1.017
1908	1.062
1909	0.933
1910	1.23
1911	1.091
1912	1.646
1913	1.898
1914	1.734
1915	1.317
1916	1.183
1917	1.434
1918	1.444
1919	1.363
1920	1.046
1921	0.419
1922	0.803
1923	0.848
1924	0.75
1925	1.312
1926	1.632
1927	1.286
1928	1.391
1929	1.441
1930	1.08
1931	0.675
1932	0.336
1933	0.987
1934	0.663
1935	0.936
1936	0.826
1937	0.786
1938	0.746
1939	0.584
1940	0.973
1941	0.595
1942	0.825
1943	0.701
1944	0.73
1945	0.874
1946	0.984
1947	0.799
1948	0.502
1949	0.717
1950	0.982
1951	1.078
1952	0.997
1953	0.622
1954	1.031
1955	1.084
1956	1.177
1957	1.267
1958	0.823
1959	0.792
1960	0.646
1961	0.919
1962	0.924
1963	1.057
1964	0.807
1965	1.01
1966	0.564
1967	0.347
1968	0.634
1969	0.595
1970	0.511
1971	0.997
1972	0.845
1973	0.571
1974	0.311
1975	0.548
1976	0.684
1977	0.79
1978	0.74
1979	0.79
1980	0.753
1981	1.427
1982	1.053
1983	0.97
1984	0.715
1985	0.776
1986	0.864
1987	1.106
1988	1.086
1989	1.253
1990	1.8