# asia_nepa040 - TilaNala - 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/5409
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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
#--------------------
# Title
#	Study_Name: asia_nepa040 - TilaNala - Breitenmoser Tree Ring Chronology Data
#--------------------
# 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
#--------------------
# 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: TilaNala
#	Location:
#	Country: Nepal
#	Northernmost_Latitude: 29.05
#	Southernmost_Latitude: 29.05
#	Easternmost_Longitude: 81.5
#	Westernmost_Longitude: 81.5
#	Elevation: 2080 m
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# Data_Collection
#	Collection_Name: asia_nepa040B
#	Earliest_Year: 1739
#	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":"3.92468787608","T2":"16.8581822381","M1":"0.0229647039756","M2":"0.549280152642"}}
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# Species
#	Species_Name: chir pine
#	Species_Code: PIRO
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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
1739	1.467
1740	1.259
1741	1.03
1742	1.243
1743	1.192
1744	0.947
1745	0.977
1746	0.941
1747	1.003
1748	1.001
1749	1.335
1750	1.256
1751	1.229
1752	1.026
1753	0.895
1754	1.045
1755	0.577
1756	0.639
1757	0.295
1758	0.313
1759	0.57
1760	0.709
1761	0.677
1762	0.748
1763	0.837
1764	1.268
1765	1.253
1766	1.128
1767	0.97
1768	1.08
1769	1.162
1770	1.129
1771	1.22
1772	1.355
1773	1.274
1774	0.935
1775	0.819
1776	0.743
1777	0.745
1778	0.83
1779	0.772
1780	1.145
1781	1.14
1782	1.174
1783	0.934
1784	1.172
1785	1.576
1786	1.383
1787	1.522
1788	0.754
1789	0.933
1790	0.666
1791	0.575
1792	0.673
1793	0.861
1794	1.177
1795	1.03
1796	1.399
1797	1.012
1798	1.222
1799	0.818
1800	1.107
1801	0.941
1802	0.769
1803	0.828
1804	0.917
1805	1.118
1806	1.198
1807	1.258
1808	1.369
1809	1.532
1810	1.024
1811	1.007
1812	1.011
1813	1.005
1814	1.102
1815	0.861
1816	0.355
1817	0.565
1818	0.316
1819	0.411
1820	0.7
1821	0.518
1822	1.192
1823	1.217
1824	1.12
1825	1.349
1826	1.035
1827	1.102
1828	1.033
1829	1.028
1830	1.41
1831	1.127
1832	0.979
1833	1.084
1834	1.091
1835	1.142
1836	0.77
1837	0.831
1838	1.159
1839	1.208
1840	1.203
1841	1.261
1842	1.186
1843	1.334
1844	1.039
1845	1.152
1846	1.131
1847	0.195
1848	0.537
1849	0.748
1850	1.414
1851	1.683
1852	1.688
1853	1.299
1854	1.211
1855	0.842
1856	0.748
1857	0.953
1858	0.993
1859	1.279
1860	1.02
1861	0.95
1862	0.977
1863	0.886
1864	0.875
1865	1.153
1866	1.249
1867	0.843
1868	0.974
1869	1.238
1870	1.121
1871	0.982
1872	0.897
1873	0.477
1874	0.659
1875	0.707
1876	0.77
1877	1.05
1878	1.796
1879	1.176
1880	1.002
1881	0.982
1882	1.13
1883	0.434
1884	0.524
1885	0.79
1886	1.051
1887	0.822
1888	0.776
1889	0.859
1890	0.582
1891	0.755
1892	0.522
1893	0.391
1894	0.536
1895	0.424
1896	0.367
1897	0.816
1898	0.833
1899	0.773
1900	0.939
1901	1.025
1902	1.297
1903	0.937
1904	0.993
1905	0.677
1906	0.728
1907	0.997
1908	0.806
1909	0.746
1910	0.776
1911	1.087
1912	1.195
1913	1.349
1914	1.672
1915	1.838
1916	0.941
1917	1.074
1918	0.9
1919	0.939
1920	1.038
1921	0.808
1922	0.8
1923	0.951
1924	0.721
1925	0.726
1926	0.703
1927	0.853
1928	1.163
1929	1.159
1930	1.197
1931	1.218
1932	0.979
1933	1.332
1934	0.989
1935	0.563
1936	0.773
1937	0.836
1938	0.916
1939	0.768
1940	0.758
1941	0.973
1942	1.114
1943	0.956
1944	0.82
1945	0.935
1946	0.965
1947	1.055
1948	0.997
1949	1.04
1950	0.95
1951	1.024
1952	1.1
1953	0.704
1954	0.935
1955	0.906
1956	1.019
1957	0.974
1958	0.964
1959	1.011
1960	0.83
1961	1.056
1962	0.884
1963	0.892
1964	0.894
1965	0.904
1966	1.054
1967	0.935
1968	0.883
1969	1.188
1970	1.077
1971	1.333
1972	1.052
1973	1.779
1974	1.363
1975	1.049
1976	1.102
1977	1.201
1978	1.361
1979	1.486