# asia_nepa020 - GhurchiLehk - 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/3777
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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_nepa020 - GhurchiLehk - 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:
#--------------------
#	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: GhurchiLehk
#	Location:
#	Country: Nepal
#	Northernmost_Latitude: 29.3
#	Southernmost_Latitude: 29.3
#	Easternmost_Longitude: 82.05
#	Westernmost_Longitude: 82.05
#	Elevation: 3450 m
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# Data_Collection
#	Collection_Name: asia_nepa020B
#	Earliest_Year: 1755
#	Most_Recent_Year: 1979
#	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":"4.83729422414","T2":"17.5697224744","M1":"0.0226170115485","M2":"0.380637151671"}}
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# Species
#	Species_Name: silver fir
#	Species_Code: ABSB
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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
1755	0.837
1756	0.793
1757	0.738
1758	0.837
1759	1.081
1760	0.963
1761	0.925
1762	0.855
1763	1.027
1764	0.914
1765	0.946
1766	1.066
1767	1.049
1768	0.927
1769	0.945
1770	0.87
1771	1.032
1772	0.837
1773	0.865
1774	0.85
1775	0.779
1776	0.801
1777	0.838
1778	0.937
1779	0.901
1780	0.811
1781	0.848
1782	0.972
1783	1.07
1784	1.203
1785	1.156
1786	1.167
1787	1.031
1788	1.041
1789	1.002
1790	0.848
1791	0.885
1792	1.118
1793	0.922
1794	1.028
1795	0.765
1796	0.774
1797	1.044
1798	1.125
1799	0.924
1800	0.747
1801	0.755
1802	0.877
1803	1.094
1804	0.947
1805	0.888
1806	0.932
1807	0.878
1808	0.879
1809	1.117
1810	1.009
1811	0.842
1812	0.886
1813	0.83
1814	0.915
1815	0.764
1816	0.738
1817	0.581
1818	0.646
1819	0.682
1820	0.758
1821	0.749
1822	0.662
1823	0.676
1824	0.825
1825	0.909
1826	1.161
1827	1.146
1828	1.023
1829	1.106
1830	1.002
1831	0.992
1832	1.049
1833	1.024
1834	0.875
1835	0.864
1836	0.844
1837	0.872
1838	0.883
1839	0.898
1840	1.136
1841	1.12
1842	1.073
1843	1.055
1844	1.071
1845	1.277
1846	1.383
1847	1.537
1848	1.564
1849	1.425
1850	1.376
1851	1.35
1852	1.211
1853	1.212
1854	1.239
1855	1.274
1856	1.451
1857	1.474
1858	1.386
1859	1.078
1860	1.157
1861	1.179
1862	1.055
1863	1.234
1864	1.114
1865	1.113
1866	1.037
1867	1.019
1868	0.965
1869	0.921
1870	0.865
1871	0.899
1872	0.911
1873	1.06
1874	0.986
1875	1.037
1876	1.151
1877	0.965
1878	0.997
1879	1.013
1880	1.109
1881	1.353
1882	1.265
1883	1.269
1884	1.097
1885	1.113
1886	1.294
1887	1.049
1888	1.031
1889	1.065
1890	1.018
1891	0.978
1892	0.856
1893	0.895
1894	0.951
1895	0.831
1896	1.277
1897	1.212
1898	1.084
1899	1.012
1900	1.118
1901	0.97
1902	1.009
1903	1.039
1904	0.829
1905	0.805
1906	0.946
1907	1.003
1908	1.065
1909	0.958
1910	0.935
1911	1.105
1912	1.064
1913	1.018
1914	1.039
1915	0.965
1916	1.076
1917	1.145
1918	0.944
1919	1.04
1920	1.118
1921	0.933
1922	0.848
1923	0.837
1924	0.944
1925	0.803
1926	0.788
1927	0.711
1928	0.678
1929	0.683
1930	0.749
1931	0.804
1932	0.709
1933	0.803
1934	0.932
1935	0.755
1936	0.769
1937	0.765
1938	0.882
1939	0.874
1940	0.904
1941	0.857
1942	0.988
1943	0.917
1944	0.843
1945	0.888
1946	0.87
1947	1.118
1948	0.905
1949	0.743
1950	0.726
1951	0.798
1952	0.835
1953	0.818
1954	0.756
1955	0.759
1956	0.926
1957	1.044
1958	1.126
1959	0.944
1960	0.94
1961	0.951
1962	0.792
1963	0.851
1964	1.036
1965	0.851
1966	0.998
1967	0.811
1968	0.725
1969	0.911
1970	0.892
1971	0.972
1972	1.128
1973	1.1
1974	0.933
1975	0.973
1976	1.137
1977	1.319
1978	1.17
1979	1.217