# asia_indi008 - 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/4084
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# Description/Documentation lines begin with #
# Data lines have no #
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# Archive: Tree Rings
#--------------------
# Contribution_Date
#	Date: 2016-01-07
#--------------------
# Title
#	Study_Name: asia_indi008 - Pahalgam - 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.
#--------------------
#	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: 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_indi008B
#	Earliest_Year: 1676
#	Most_Recent_Year: 1980
#	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":"7.14858239886","T2":"18.066749234","M1":"0.0223429206655","M2":"0.380039177141"}}
#--------------------
# Species
#	Species_Name: Himalayan silver fir
#	Species_Code: ABPI
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# Chronology:
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# Variables
#
# 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)
#
##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
1676	1.11
1677	1.045
1678	0.996
1679	1.177
1680	1.069
1681	1.109
1682	1.083
1683	1.034
1684	1.083
1685	1.111
1686	1.3
1687	1.118
1688	1.05
1689	1.102
1690	1.113
1691	1.126
1692	1.111
1693	0.957
1694	0.755
1695	0.959
1696	1.023
1697	0.909
1698	0.863
1699	0.875
1700	1.007
1701	1.013
1702	0.846
1703	1.005
1704	0.962
1705	0.812
1706	1.027
1707	0.873
1708	1.118
1709	1.061
1710	1.267
1711	1.091
1712	1.208
1713	1.283
1714	1.204
1715	1.02
1716	1.064
1717	0.762
1718	0.816
1719	0.844
1720	0.833
1721	0.738
1722	0.754
1723	0.642
1724	0.58
1725	0.626
1726	0.575
1727	0.505
1728	0.676
1729	0.635
1730	0.727
1731	0.732
1732	0.937
1733	0.982
1734	0.988
1735	1.08
1736	0.92
1737	0.822
1738	0.967
1739	1.083
1740	1.055
1741	0.991
1742	1.182
1743	0.911
1744	1.052
1745	1.064
1746	0.874
1747	1.041
1748	1.173
1749	0.724
1750	0.643
1751	0.814
1752	0.961
1753	0.961
1754	1.086
1755	0.959
1756	1.222
1757	1.117
1758	1.086
1759	1.294
1760	1.094
1761	1.172
1762	1.151
1763	1.208
1764	1.247
1765	1.179
1766	1.309
1767	1.17
1768	1.099
1769	1.202
1770	1.489
1771	1.291
1772	0.967
1773	1.128
1774	0.823
1775	0.99
1776	0.983
1777	0.913
1778	1.005
1779	0.931
1780	0.656
1781	0.79
1782	0.707
1783	0.809
1784	0.748
1785	0.473
1786	0.694
1787	0.768
1788	0.696
1789	0.857
1790	0.657
1791	0.713
1792	0.917
1793	0.719
1794	0.601
1795	0.522
1796	0.826
1797	0.823
1798	0.813
1799	0.898
1800	0.757
1801	0.883
1802	0.637
1803	0.655
1804	0.721
1805	0.878
1806	0.843
1807	0.813
1808	0.883
1809	0.859
1810	0.83
1811	0.721
1812	0.649
1813	0.677
1814	0.828
1815	0.792
1816	0.954
1817	0.873
1818	0.747
1819	0.706
1820	0.573
1821	0.602
1822	0.679
1823	0.648
1824	0.755
1825	0.912
1826	0.773
1827	0.703
1828	0.745
1829	0.629
1830	0.663
1831	0.664
1832	0.807
1833	0.731
1834	0.796
1835	0.992
1836	0.942
1837	0.964
1838	0.87
1839	0.81
1840	0.836
1841	0.932
1842	0.715
1843	0.681
1844	0.635
1845	0.715
1846	0.505
1847	0.628
1848	0.817
1849	0.715
1850	0.757
1851	0.772
1852	0.732
1853	0.718
1854	0.946
1855	0.909
1856	0.959
1857	0.929
1858	0.807
1859	1.023
1860	0.953
1861	0.911
1862	0.842
1863	0.817
1864	0.932
1865	0.837
1866	0.774
1867	0.879
1868	0.794
1869	0.726
1870	0.761
1871	0.769
1872	0.71
1873	0.807
1874	0.803
1875	0.783
1876	0.816
1877	0.926
1878	0.91
1879	0.931
1880	0.84
1881	0.925
1882	0.804
1883	0.717
1884	0.642
1885	0.814
1886	0.669
1887	0.63
1888	0.735
1889	0.796
1890	0.842
1891	0.871
1892	0.911
1893	0.894
1894	0.959
1895	0.738
1896	1.032
1897	1.062
1898	0.873
1899	0.826
1900	0.88
1901	0.906
1902	1.047
1903	1.254
1904	1.597
1905	1.414
1906	1.199
1907	1.085
1908	1.243
1909	1.254
1910	1.017
1911	0.949
1912	0.979
1913	1.008
1914	1.331
1915	1.031
1916	1.099
1917	1.246
1918	1.275
1919	1.318
1920	1.438
1921	1.302
1922	1.803
1923	1.738
1924	1.783
1925	1.373
1926	1.404
1927	1.764
1928	1.762
1929	1.47
1930	1.683
1931	1.487
1932	1.696
1933	1.125
1934	1.014
1935	0.84
1936	1.099
1937	1.025
1938	0.885
1939	0.898
1940	0.97
1941	1.187
1942	1.274
1943	1.6
1944	1.427
1945	1.425
1946	1.165
1947	0.968
1948	1.054
1949	1.068
1950	1.238
1951	1.438
1952	1.376
1953	1.157
1954	1.181
1955	1.145
1956	1.15
1957	1.114
1958	1.371
1959	1.266
1960	1.146
1961	1.085
1962	1.004
1963	1.314
1964	1.105
1965	1.153
1966	1.221
1967	1.275
1968	1.138
1969	1.024
1970	0.832
1971	0.955
1972	0.823
1973	1.087
1974	0.875
1975	0.842
1976	1.019
1977	1.133
1978	1.158
1979	1.096
1980	1.194