# asia_nepa024 - KatyaKhola-3 - 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/3782
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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_nepa024 - KatyaKhola-3 - 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: KatyaKhola-3
#	Location:
#	Country: Nepal
#	Northernmost_Latitude: 29.3
#	Southernmost_Latitude: 29.3
#	Easternmost_Longitude: 82.02
#	Westernmost_Longitude: 82.02
#	Elevation: 3480 m
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# Data_Collection
#	Collection_Name: asia_nepa024B
#	Earliest_Year: 1772
#	Most_Recent_Year: 1997
#	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":"5.12553373869","T2":"14.806867585","M1":"0.0224081073431","M2":"0.495970986492"}}
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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
#
age	trsgi
1772	0.865
1773	1.083
1774	0.747
1775	0.919
1776	1.06
1777	0.821
1778	0.91
1779	0.749
1780	0.809
1781	0.847
1782	0.652
1783	1.014
1784	0.911
1785	0.815
1786	0.954
1787	0.98
1788	1.133
1789	1.036
1790	1.07
1791	1.101
1792	1.121
1793	1.044
1794	0.995
1795	0.795
1796	1.247
1797	1.288
1798	1.216
1799	1.127
1800	1.221
1801	1.105
1802	1.099
1803	1.392
1804	1.016
1805	1.142
1806	1.103
1807	0.809
1808	1.006
1809	0.988
1810	0.885
1811	0.917
1812	1.008
1813	0.874
1814	1.062
1815	0.839
1816	0.814
1817	0.782
1818	0.791
1819	0.808
1820	0.779
1821	0.763
1822	0.844
1823	0.774
1824	0.783
1825	0.946
1826	0.991
1827	1.167
1828	0.977
1829	1.164
1830	1.147
1831	1.012
1832	1.149
1833	1.056
1834	0.957
1835	1.095
1836	0.825
1837	0.894
1838	0.842
1839	1.164
1840	1.021
1841	1.186
1842	0.926
1843	1.184
1844	1.41
1845	1.234
1846	1.109
1847	1.131
1848	0.909
1849	0.797
1850	0.848
1851	0.932
1852	0.874
1853	1.006
1854	1.093
1855	1.136
1856	0.956
1857	1.059
1858	1.036
1859	0.962
1860	0.983
1861	1.153
1862	1.024
1863	1.092
1864	1.103
1865	1.037
1866	0.978
1867	0.764
1868	0.871
1869	0.783
1870	0.804
1871	0.739
1872	0.867
1873	0.735
1874	0.984
1875	0.654
1876	0.746
1877	0.92
1878	0.941
1879	0.851
1880	1.08
1881	0.963
1882	0.904
1883	1.004
1884	0.865
1885	0.954
1886	1.146
1887	1.014
1888	1.136
1889	1.133
1890	0.941
1891	1.091
1892	0.57
1893	0.902
1894	0.747
1895	0.788
1896	0.807
1897	0.962
1898	0.704
1899	0.986
1900	0.88
1901	1.076
1902	0.96
1903	1.003
1904	0.787
1905	1.059
1906	0.859
1907	0.941
1908	0.67
1909	0.799
1910	0.943
1911	1.167
1912	1.016
1913	1.136
1914	1.461
1915	1.057
1916	0.878
1917	0.963
1918	1.019
1919	1.085
1920	0.904
1921	0.718
1922	0.878
1923	0.855
1924	0.975
1925	0.948
1926	0.916
1927	0.836
1928	1.084
1929	1.024
1930	1.187
1931	1.538
1932	0.936
1933	1.142
1934	1.174
1935	0.781
1936	0.935
1937	0.999
1938	1.165
1939	1.275
1940	1.229
1941	1.087
1942	1.215
1943	1.199
1944	1.207
1945	1.087
1946	0.854
1947	1.141
1948	0.989
1949	0.962
1950	0.95
1951	1.276
1952	1.047
1953	1.169
1954	0.844
1955	1.01
1956	0.945
1957	1.148
1958	0.92
1959	0.718
1960	1.023
1961	0.875
1962	0.938
1963	1.037
1964	0.963
1965	0.94
1966	1.016
1967	0.734
1968	0.876
1969	0.902
1970	0.806
1971	0.999
1972	0.968
1973	0.985
1974	0.985
1975	1.117
1976	1.148
1977	1.089
1978	1.093
1979	1.068
1980	1.276
1981	1.197
1982	1.125
1983	1.216
1984	0.896
1985	0.817
1986	0.887
1987	0.821
1988	0.792
1989	1.021
1990	0.994
1991	0.926
1992	0.831
1993	0.882
1994	0.782
1995	0.546
1996	0.689
1997	0.689