# northamerica_usa_ca545 - San Bernardino Mountains J - 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/4182
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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: northamerica_usa_ca545 - San Bernardino Mountains J - Breitenmoser Tree Ring Chronology Data
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# Investigators
#	Investigators:  Breitenmoser, P.; Bronnimann, S.; Frank, D.
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# 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:
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#	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: San Bernardino Mountains J
#	Location:
#	Country: United States
#	Northernmost_Latitude: 34.17
#	Southernmost_Latitude: 34.17
#	Easternmost_Longitude: -117.1
#	Westernmost_Longitude: -117.1
#	Elevation: 1500 m
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# Data_Collection
#	Collection_Name: northamerica_usa_ca545B
#	Earliest_Year: 1857
#	Most_Recent_Year: 1988
#	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":"3.27375168187","T2":"16.0422682268","M1":"0.02328158402","M2":"0.436934674998"}}
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# Species
#	Species_Name: bigcone Douglas fir
#	Species_Code: PSMA
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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
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age	trsgi
1857	0.539
1858	1.069
1859	1.285
1860	1.117
1861	0.869
1862	1.172
1863	0.602
1864	0.339
1865	0.73
1866	0.859
1867	0.71
1868	0.868
1869	0.963
1870	0.758
1871	0.898
1872	0.81
1873	0.591
1874	0.601
1875	0.561
1876	0.715
1877	0.592
1878	0.797
1879	0.492
1880	0.859
1881	0.974
1882	0.871
1883	1.133
1884	1.628
1885	1.325
1886	1.125
1887	1.096
1888	1.03
1889	1.14
1890	1.311
1891	1.568
1892	1.313
1893	1.1
1894	1.161
1895	0.996
1896	0.781
1897	0.741
1898	0.874
1899	0.522
1900	0.805
1901	1.013
1902	0.881
1903	1.025
1904	0.951
1905	1.026
1906	1.393
1907	1.172
1908	1.071
1909	1.002
1910	0.717
1911	0.792
1912	0.916
1913	1.114
1914	1.271
1915	1.245
1916	1.125
1917	1.326
1918	0.866
1919	1.071
1920	1.248
1921	1.416
1922	1.231
1923	1.179
1924	1.083
1925	1.177
1926	1.225
1927	1.166
1928	0.77
1929	0.838
1930	0.901
1931	1.279
1932	1.173
1933	0.96
1934	0.53
1935	1.09
1936	1.012
1937	1.07
1938	1.054
1939	0.993
1940	1.256
1941	1.431
1942	1.469
1943	1.29
1944	1.464
1945	1.586
1946	1.41
1947	1.182
1948	1.149
1949	0.8
1950	0.862
1951	0.666
1952	0.931
1953	1.094
1954	1.072
1955	1.222
1956	1.118
1957	0.971
1958	1.154
1959	0.669
1960	0.688
1961	-0.131
1962	0.686
1963	0.468
1964	0.896
1965	0.98
1966	0.856
1967	0.925
1968	0.667
1969	0.756
1970	0.727
1971	0.519
1972	0.222
1973	0.591
1974	0.492
1975	0.694
1976	0.704
1977	0.84
1978	0.934
1979	0.986
1980	1.056
1981	0.703
1982	1.034
1983	1.525
1984	0.664
1985	0.969
1986	0.961
1987	1.133
1988	1.002