# northamerica_usa_co552 - Red Mountain Pass Silverton - 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/2847
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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_co552 - Red Mountain Pass Silverton - 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
#------------------
# Site_Information
#	Site_Name: Red Mountain Pass Silverton
#	Location:
#	Country: United States
#	Northernmost_Latitude: 37.9
#	Southernmost_Latitude: 37.9
#	Easternmost_Longitude: -107.72
#	Westernmost_Longitude: -107.72
#	Elevation: 3400 m
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# Data_Collection
#	Collection_Name: northamerica_usa_co552B
#	Earliest_Year: 1741
#	Most_Recent_Year: 1983
#	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":"5.43059591254","T2":"17.7509684869","M1":"0.0221651053292","M2":"0.389835931988"}}
#--------------------
# Species
#	Species_Name: Engelmann spruce
#	Species_Code: PCEN
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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
#
#--------------------
# Data:
# Data lines follow (have no #)
# Data line format - tab-delimited text, variable short name as header
# Missing Values: nan
#
age	trsgi
1741	0.855
1742	0.818
1743	0.967
1744	1.101
1745	1.067
1746	0.888
1747	1.099
1748	0.782
1749	1.04
1750	0.909
1751	0.986
1752	1.062
1753	0.978
1754	1.077
1755	1.087
1756	0.891
1757	1.104
1758	1.152
1759	1.211
1760	1.109
1761	0.975
1762	1.256
1763	1.128
1764	1.068
1765	1.124
1766	1.006
1767	1.204
1768	1.157
1769	1.127
1770	1.021
1771	1.096
1772	1.052
1773	0.853
1774	1.114
1775	0.93
1776	0.813
1777	0.718
1778	0.714
1779	0.809
1780	1.033
1781	0.837
1782	0.929
1783	1.156
1784	1.001
1785	1.014
1786	1.107
1787	1.158
1788	1.293
1789	1.259
1790	1.177
1791	1.13
1792	1.292
1793	1.134
1794	1.14
1795	1.334
1796	1.283
1797	1.219
1798	1.425
1799	1.135
1800	1.143
1801	1.379
1802	1.465
1803	1.068
1804	0.983
1805	1.163
1806	1.13
1807	1.435
1808	1.194
1809	1.035
1810	0.975
1811	1.083
1812	1.139
1813	1.047
1814	0.975
1815	1.157
1816	1.11
1817	1.2
1818	0.948
1819	0.992
1820	1.021
1821	1.066
1822	1.009
1823	0.981
1824	0.966
1825	0.851
1826	0.877
1827	1.16
1828	1.031
1829	1.049
1830	1.226
1831	1.285
1832	1.02
1833	1.628
1834	1.391
1835	1.242
1836	0.956
1837	1.315
1838	0.923
1839	1.05
1840	0.892
1841	0.916
1842	0.892
1843	1.053
1844	1.181
1845	1.024
1846	0.977
1847	0.899
1848	1.0
1849	0.942
1850	1.281
1851	0.854
1852	1.066
1853	1.046
1854	1.215
1855	1.066
1856	1.186
1857	1.067
1858	1.06
1859	1.292
1860	1.112
1861	1.309
1862	1.324
1863	1.362
1864	1.182
1865	1.196
1866	1.114
1867	0.883
1868	0.997
1869	1.069
1870	0.981
1871	1.01
1872	0.784
1873	0.952
1874	1.043
1875	0.792
1876	0.93
1877	0.925
1878	0.888
1879	0.802
1880	0.7
1881	1.101
1882	0.711
1883	0.709
1884	0.82
1885	1.002
1886	1.083
1887	0.732
1888	0.646
1889	0.575
1890	0.597
1891	0.709
1892	0.723
1893	0.523
1894	0.824
1895	0.747
1896	0.808
1897	0.736
1898	0.721
1899	0.576
1900	0.688
1901	0.588
1902	0.566
1903	0.92
1904	0.677
1905	0.685
1906	0.559
1907	0.707
1908	0.664
1909	0.9
1910	0.893
1911	0.847
1912	0.823
1913	0.835
1914	0.818
1915	0.865
1916	0.893
1917	1.015
1918	0.928
1919	0.874
1920	0.81
1921	0.908
1922	0.984
1923	0.911
1924	0.872
1925	0.867
1926	0.875
1927	0.889
1928	1.044
1929	1.08
1930	0.962
1931	1.013
1932	1.086
1933	0.967
1934	0.826
1935	1.039
1936	0.904
1937	1.046
1938	1.332
1939	1.165
1940	1.06
1941	1.093
1942	0.996
1943	0.995
1944	1.225
1945	1.198
1946	1.149
1947	1.242
1948	1.227
1949	1.205
1950	1.019
1951	1.242
1952	1.022
1953	1.174
1954	1.079
1955	1.085
1956	0.824
1957	0.792
1958	0.965
1959	0.77
1960	0.87
1961	0.952
1962	0.864
1963	1.083
1964	0.97
1965	1.201
1966	1.191
1967	0.81
1968	1.017
1969	1.069
1970	1.044
1971	0.967
1972	0.852
1973	0.963
1974	1.039
1975	1.044
1976	1.252
1977	0.839
1978	0.953
1979	0.9
1980	0.732
1981	0.684
1982	0.742
1983	0.866