# northamerica_usa_ak019 - Mountain Lake Alaska - 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/5245
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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_ak019 - Mountain Lake Alaska - Breitenmoser Tree Ring Chronology Data
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# 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: Mountain Lake Alaska
#	Location:
#	Country: United States
#	Northernmost_Latitude: 59.17
#	Southernmost_Latitude: 59.17
#	Easternmost_Longitude: -139.33
#	Westernmost_Longitude: -139.33
#	Elevation: 350 m
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# Data_Collection
#	Collection_Name: northamerica_usa_ak019B
#	Earliest_Year: 1859
#	Most_Recent_Year: 1993
#	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":"4.45080438277","T2":"17.2685421175","M1":"0.0224380042912","M2":"0.397536595562"}}
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# Species
#	Species_Name: Sitka spruce
#	Species_Code: PCSI
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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
1859	1.02
1860	1.214
1861	1.183
1862	0.802
1863	0.721
1864	0.606
1865	0.881
1866	1.142
1867	0.968
1868	1.045
1869	1.107
1870	1.079
1871	0.995
1872	1.17
1873	0.983
1874	1.077
1875	0.77
1876	0.604
1877	0.513
1878	0.683
1879	0.847
1880	0.896
1881	1.06
1882	0.826
1883	1.049
1884	1.244
1885	0.93
1886	0.916
1887	0.726
1888	0.923
1889	0.789
1890	1.04
1891	1.216
1892	1.238
1893	1.076
1894	0.854
1895	0.835
1896	0.852
1897	1.017
1898	1.163
1899	1.173
1900	1.186
1901	1.114
1902	1.263
1903	1.065
1904	0.663
1905	0.915
1906	0.776
1907	0.902
1908	1.099
1909	1.022
1910	1.041
1911	0.911
1912	0.978
1913	1.215
1914	1.321
1915	1.428
1916	0.937
1917	1.043
1918	1.102
1919	0.876
1920	1.05
1921	1.168
1922	0.994
1923	1.252
1924	0.901
1925	1.106
1926	1.26
1927	1.029
1928	1.024
1929	1.195
1930	1.222
1931	1.46
1932	1.238
1933	1.221
1934	1.044
1935	0.945
1936	0.884
1937	0.606
1938	0.715
1939	0.925
1940	1.248
1941	1.093
1942	1.058
1943	1.15
1944	1.159
1945	1.119
1946	1.231
1947	1.012
1948	0.836
1949	0.722
1950	0.763
1951	0.697
1952	0.7
1953	1.0
1954	0.857
1955	0.898
1956	0.787
1957	0.983
1958	0.728
1959	0.88
1960	1.005
1961	1.002
1962	1.066
1963	1.019
1964	0.893
1965	0.941
1966	0.92
1967	0.821
1968	0.894
1969	0.784
1970	0.772
1971	0.837
1972	0.589
1973	0.633
1974	0.826
1975	0.787
1976	0.871
1977	1.096
1978	0.958
1979	1.04
1980	0.99
1981	1.012
1982	1.037
1983	1.278
1984	1.228
1985	1.06
1986	1.175
1987	1.033
1988	1.168
1989	1.069
1990	0.888
1991	0.901
1992	0.967
1993	0.956