# asia_indi009 - Pahalgam - 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/4085
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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_indi009 - Pahalgam - 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: Pahalgam
#	Location:
#	Country: India
#	Northernmost_Latitude: 34.03
#	Southernmost_Latitude: 34.03
#	Easternmost_Longitude: 75.7
#	Westernmost_Longitude: 75.7
#	Elevation: 2900 m
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# Data_Collection
#	Collection_Name: asia_indi009B
#	Earliest_Year: 1848
#	Most_Recent_Year: 1982
#	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":"4.81494713604","T2":"13.2336385743","M1":"0.0234204409412","M2":"0.563791354122"}}
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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
1848	0.978
1849	1.054
1850	1.085
1851	1.281
1852	0.841
1853	1.051
1854	0.969
1855	1.291
1856	0.977
1857	1.004
1858	1.088
1859	1.103
1860	1.0
1861	0.825
1862	0.994
1863	0.887
1864	0.828
1865	0.732
1866	0.868
1867	0.875
1868	0.855
1869	0.93
1870	0.889
1871	0.955
1872	0.793
1873	1.353
1874	1.226
1875	0.776
1876	1.19
1877	1.22
1878	1.077
1879	0.638
1880	0.647
1881	0.797
1882	0.836
1883	0.691
1884	0.863
1885	0.775
1886	0.869
1887	0.961
1888	1.137
1889	1.163
1890	1.164
1891	1.214
1892	1.237
1893	1.377
1894	1.46
1895	1.193
1896	1.108
1897	0.899
1898	0.746
1899	0.926
1900	1.084
1901	1.21
1902	1.188
1903	1.25
1904	1.212
1905	0.912
1906	0.822
1907	0.873
1908	0.803
1909	0.946
1910	0.992
1911	0.63
1912	0.856
1913	0.987
1914	1.049
1915	0.704
1916	0.905
1917	0.954
1918	0.769
1919	1.058
1920	0.805
1921	0.61
1922	0.953
1923	0.988
1924	0.962
1925	0.767
1926	0.977
1927	0.816
1928	1.07
1929	1.228
1930	1.348
1931	1.407
1932	1.336
1933	0.888
1934	0.991
1935	0.893
1936	0.91
1937	0.454
1938	0.556
1939	0.615
1940	0.735
1941	0.92
1942	1.234
1943	1.467
1944	1.126
1945	1.029
1946	0.661
1947	0.618
1948	1.128
1949	1.186
1950	0.99
1951	1.074
1952	1.184
1953	0.916
1954	1.049
1955	1.194
1956	0.91
1957	0.945
1958	1.194
1959	1.209
1960	1.175
1961	0.806
1962	0.816
1963	0.757
1964	0.979
1965	1.082
1966	1.128
1967	0.934
1968	0.838
1969	0.931
1970	0.862
1971	0.701
1972	0.922
1973	0.902
1974	1.018
1975	1.092
1976	1.041
1977	1.14
1978	1.122
1979	0.883
1980	1.125
1981	1.177
1982	0.537