# northamerica_canada_cana074 - Buffalo Lake - 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/4357
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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_canada_cana074 - Buffalo Lake - 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
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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:
#--------------------
#	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: Buffalo Lake
#	Location:
#	Country: Canada
#	Northernmost_Latitude: 60.28
#	Southernmost_Latitude: 60.28
#	Easternmost_Longitude: -115.27
#	Westernmost_Longitude: -115.27
#	Elevation: 803 m
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# Data_Collection
#	Collection_Name: northamerica_canada_cana074B
#	Earliest_Year: 1859
#	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":"4.42442465081","T2":"16.1817223417","M1":"0.0235988881475","M2":"0.494913330583"}}
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# Species
#	Species_Name: white spruce
#	Species_Code: PCGL
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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
#
age	trsgi
1859	0.988
1860	0.948
1861	0.863
1862	0.868
1863	0.866
1864	0.704
1865	0.523
1866	0.646
1867	0.855
1868	0.926
1869	0.861
1870	0.806
1871	0.775
1872	0.841
1873	0.979
1874	0.847
1875	0.791
1876	0.718
1877	0.731
1878	0.638
1879	0.504
1880	0.528
1881	0.571
1882	0.75
1883	0.874
1884	1.02
1885	1.048
1886	0.879
1887	0.984
1888	0.976
1889	1.106
1890	0.962
1891	1.199
1892	0.93
1893	1.126
1894	1.135
1895	1.131
1896	1.052
1897	1.066
1898	1.128
1899	1.144
1900	0.936
1901	1.063
1902	0.887
1903	0.866
1904	0.903
1905	1.093
1906	1.199
1907	0.94
1908	1.172
1909	1.097
1910	1.384
1911	1.411
1912	1.025
1913	0.857
1914	1.002
1915	0.989
1916	0.812
1917	0.849
1918	0.927
1919	1.093
1920	1.077
1921	1.386
1922	1.323
1923	1.127
1924	0.925
1925	1.183
1926	1.024
1927	1.13
1928	0.966
1929	1.055
1930	1.139
1931	1.174
1932	1.271
1933	0.966
1934	1.065
1935	1.147
1936	0.943
1937	1.111
1938	1.087
1939	0.75
1940	0.856
1941	0.833
1942	0.649
1943	0.711
1944	0.632
1945	0.661
1946	0.704
1947	0.774
1948	0.857
1949	0.835
1950	0.876
1951	0.802
1952	0.817
1953	0.942
1954	0.867
1955	1.075
1956	1.361
1957	1.225
1958	1.214
1959	1.288
1960	1.394
1961	1.012
1962	1.283
1963	1.427
1964	1.398
1965	1.454
1966	1.221
1967	0.976
1968	1.054
1969	0.894
1970	0.947
1971	0.718
1972	0.748
1973	0.673
1974	0.864
1975	1.012
1976	0.87
1977	0.84
1978	0.715
1979	0.893
1980	0.432
1981	0.723
1982	0.671
1983	0.597
1984	0.779
1985	0.907
1986	1.029
1987	1.101
1988	1.139