# asia_nepa037 - Rara Goan - 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/3794
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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_nepa037 - Rara Goan - 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: Rara Goan
#	Location:
#	Country: Nepal
#	Northernmost_Latitude: 29.35
#	Southernmost_Latitude: 29.35
#	Easternmost_Longitude: 82.05
#	Westernmost_Longitude: 82.05
#	Elevation: 3000 m
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# Data_Collection
#	Collection_Name: asia_nepa037B
#	Earliest_Year: 1747
#	Most_Recent_Year: 1979
#	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":"2.80717536768","T2":"11.241846021","M1":"0.0228446925028","M2":"0.634163965262"}}
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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
1747	0.675
1748	0.78
1749	0.71
1750	0.71
1751	0.836
1752	0.982
1753	0.973
1754	1.205
1755	1.159
1756	1.032
1757	0.947
1758	0.785
1759	1.049
1760	0.976
1761	1.078
1762	1.105
1763	0.93
1764	0.989
1765	1.124
1766	0.922
1767	0.962
1768	0.968
1769	1.105
1770	0.925
1771	1.031
1772	0.845
1773	1.173
1774	0.875
1775	1.026
1776	1.211
1777	0.905
1778	0.929
1779	0.688
1780	0.804
1781	0.885
1782	0.646
1783	0.792
1784	0.515
1785	0.699
1786	0.725
1787	0.647
1788	0.733
1789	0.718
1790	1.101
1791	1.165
1792	0.989
1793	0.646
1794	0.812
1795	0.836
1796	1.294
1797	1.24
1798	0.919
1799	1.209
1800	0.882
1801	0.945
1802	1.085
1803	1.186
1804	0.718
1805	0.812
1806	0.971
1807	0.828
1808	1.036
1809	0.831
1810	0.797
1811	0.99
1812	1.035
1813	0.583
1814	1.144
1815	1.189
1816	0.923
1817	1.227
1818	1.119
1819	1.083
1820	0.925
1821	0.735
1822	1.039
1823	0.941
1824	1.056
1825	1.197
1826	0.884
1827	1.21
1828	1.277
1829	1.39
1830	1.482
1831	0.906
1832	1.059
1833	0.734
1834	0.755
1835	1.42
1836	1.026
1837	0.835
1838	1.214
1839	1.291
1840	1.006
1841	1.419
1842	0.8
1843	0.905
1844	1.043
1845	1.109
1846	1.071
1847	0.98
1848	0.969
1849	0.573
1850	0.758
1851	0.994
1852	1.295
1853	1.108
1854	1.163
1855	1.352
1856	1.001
1857	0.814
1858	0.696
1859	1.102
1860	0.78
1861	0.91
1862	1.19
1863	1.338
1864	1.389
1865	1.019
1866	1.142
1867	0.996
1868	1.024
1869	0.76
1870	0.96
1871	0.843
1872	1.114
1873	0.734
1874	0.765
1875	0.553
1876	0.718
1877	1.036
1878	1.305
1879	1.051
1880	1.191
1881	0.938
1882	0.979
1883	0.975
1884	0.892
1885	1.198
1886	1.419
1887	1.077
1888	1.379
1889	1.226
1890	0.985
1891	1.188
1892	0.395
1893	1.139
1894	0.982
1895	1.156
1896	1.031
1897	1.047
1898	0.658
1899	1.137
1900	1.277
1901	1.034
1902	1.079
1903	1.024
1904	0.904
1905	1.069
1906	0.829
1907	1.268
1908	0.588
1909	0.642
1910	0.785
1911	0.991
1912	1.02
1913	0.992
1914	0.992
1915	1.009
1916	0.859
1917	1.099
1918	0.774
1919	0.925
1920	1.085
1921	0.573
1922	0.887
1923	0.978
1924	1.12
1925	1.183
1926	1.025
1927	0.724
1928	1.066
1929	0.926
1930	0.954
1931	0.996
1932	0.776
1933	1.163
1934	0.979
1935	0.682
1936	1.004
1937	1.097
1938	1.197
1939	1.131
1940	0.907
1941	0.688
1942	0.807
1943	1.002
1944	0.906
1945	0.779
1946	0.965
1947	0.945
1948	0.789
1949	0.944
1950	1.118
1951	1.09
1952	1.048
1953	0.564
1954	0.458
1955	0.922
1956	0.887
1957	0.996
1958	0.512
1959	0.724
1960	0.793
1961	0.794
1962	0.972
1963	1.285
1964	1.11
1965	1.134
1966	0.805
1967	0.591
1968	0.767
1969	0.76
1970	0.9
1971	0.838
1972	0.936
1973	1.21
1974	0.824
1975	1.182
1976	1.327
1977	1.27
1978	1.465
1979	1.512