# northamerica_usa_ca095 - Sharp Mountain - 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/3550
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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: northamerica_usa_ca095 - Sharp Mountain - 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 VaganovÃÂ¢ÃÂÃÂShashkin 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 4ÃÂ¢ÃÂÃÂ6 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 modelÃÂ¢ÃÂÃÂs 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: Sharp Mountain
#	Location:
#	Country: United States
#	Northernmost_Latitude: 41.72
#	Southernmost_Latitude: 41.72
#	Easternmost_Longitude: -121.8
#	Westernmost_Longitude: -121.8
#	Elevation: 1417 m
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# Data_Collection
#	Collection_Name: northamerica_usa_ca095B
#	Earliest_Year: 1730
#	Most_Recent_Year: 1982
#	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":"3.39221745626","T2":"13.779614419","M1":"0.0239477099572","M2":"0.567872591467"}}
#--------------------
# Species
#	Species_Name: western juniper
#	Species_Code: JUOC
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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
1730	0.686
1731	0.759
1732	0.838
1733	0.387
1734	1.22
1735	0.907
1736	0.957
1737	1.153
1738	1.18
1739	0.763
1740	0.92
1741	0.767
1742	0.964
1743	0.964
1744	0.923
1745	1.174
1746	0.938
1747	1.001
1748	0.725
1749	1.181
1750	0.892
1751	1.074
1752	1.11
1753	0.834
1754	0.886
1755	0.843
1756	0.673
1757	0.642
1758	1.087
1759	1.052
1760	1.071
1761	1.092
1762	1.099
1763	1.087
1764	1.0
1765	1.232
1766	1.207
1767	1.077
1768	1.134
1769	1.226
1770	1.343
1771	0.817
1772	0.853
1773	1.014
1774	1.132
1775	1.212
1776	0.816
1777	1.045
1778	0.949
1779	1.126
1780	1.218
1781	1.337
1782	0.879
1783	0.648
1784	1.06
1785	1.217
1786	0.824
1787	0.669
1788	0.361
1789	0.812
1790	0.967
1791	1.157
1792	1.337
1793	0.894
1794	0.586
1795	0.704
1796	1.021
1797	1.042
1798	1.045
1799	1.535
1800	0.832
1801	1.263
1802	1.538
1803	1.687
1804	1.228
1805	1.451
1806	1.211
1807	0.982
1808	1.112
1809	1.183
1810	1.092
1811	0.984
1812	1.152
1813	0.806
1814	1.306
1815	0.882
1816	0.93
1817	0.989
1818	1.488
1819	1.077
1820	0.851
1821	0.906
1822	0.798
1823	1.267
1824	0.482
1825	1.391
1826	1.188
1827	1.042
1828	0.841
1829	0.363
1830	0.821
1831	0.786
1832	1.11
1833	0.922
1834	0.818
1835	1.181
1836	0.983
1837	0.7
1838	0.95
1839	1.29
1840	0.937
1841	0.444
1842	0.967
1843	0.285
1844	0.547
1845	1.05
1846	0.986
1847	0.833
1848	0.907
1849	1.037
1850	0.68
1851	1.166
1852	0.875
1853	1.161
1854	0.865
1855	0.961
1856	0.59
1857	1.058
1858	1.006
1859	0.737
1860	0.698
1861	0.921
1862	0.919
1863	0.891
1864	0.645
1865	0.656
1866	1.142
1867	0.732
1868	1.221
1869	0.917
1870	0.702
1871	0.569
1872	0.814
1873	0.593
1874	0.71
1875	0.25
1876	1.027
1877	1.271
1878	1.673
1879	1.222
1880	0.799
1881	1.18
1882	1.01
1883	1.061
1884	1.511
1885	1.511
1886	1.249
1887	1.352
1888	1.188
1889	0.778
1890	1.087
1891	1.397
1892	0.8
1893	0.987
1894	1.448
1895	0.865
1896	1.273
1897	0.752
1898	0.932
1899	0.654
1900	1.196
1901	1.14
1902	1.247
1903	1.018
1904	1.214
1905	1.294
1906	1.227
1907	1.144
1908	0.793
1909	0.724
1910	0.645
1911	0.91
1912	0.88
1913	1.193
1914	1.294
1915	1.131
1916	1.062
1917	0.603
1918	0.383
1919	0.512
1920	0.32
1921	0.986
1922	0.834
1923	0.781
1924	0.07
1925	0.783
1926	0.388
1927	0.803
1928	0.504
1929	0.387
1930	0.519
1931	0.202
1932	0.603
1933	0.138
1934	0.18
1935	0.411
1936	0.421
1937	0.1
1938	0.463
1939	0.201
1940	0.938
1941	1.256
1942	1.046
1943	1.348
1944	0.98
1945	1.186
1946	0.921
1947	0.993
1948	1.49
1949	0.864
1950	0.915
1951	0.766
1952	1.019
1953	1.478
1954	1.36
1955	0.989
1956	1.626
1957	1.303
1958	1.893
1959	0.986
1960	1.061
1961	1.236
1962	1.232
1963	1.64
1964	1.76
1965	1.672
1966	1.201
1967	1.587
1968	1.206
1969	1.331
1970	1.3
1971	1.887
1972	1.179
1973	0.72
1974	1.191
1975	0.978
1976	0.643
1977	1.158
1978	1.36
1979	0.793
1980	1.394
1981	1.359
1982	1.097