# Svalbard Annual d18O Data (d18O Austfonna) #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite original reference when using these data, # If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed # # # # Online_Resource: # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/24611 # # Original_Source_URL: # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Ice Cores # -------------------- # Contribution_date # Date: 2012 # -------------------- # Title # Study_Name: Svalbard Annual d18O Data (d18O Austfonna) # -------------------- # Investigators # Investigators: Isaksson, E.; Divine, D.; Kohler, J.; Martma, T.; Pohjola, V.; Motoyama, H.; Watanabe, O. # -------------------- # Description_and_Notes # Description: # # -------------------- # Publication # Authors: Elisabeth Isaksson, Dmitry Divine, Jack Kohler, Tonu Martma, Veijo Pohjola, Hideaki Motoyama, Okitsugu Watanabe # Published_Date_or_Year: 2005-07-22 # Published_Title: Climate oscillations as recorded in Svalbard ice core d18O records between 1200-1997 AD # Journal_Name: Geografiska Annaler # Volume: 87 # Edition: # Issue: 1 # Pages: # DOI: # Online_Resource: # Full_Citation: # Abstract: We apply two different time series analytical tools to d18O records from two Svalbard icecores. One ice core is from Lomonosovfonna at 1250 m a.s.l. and the other from Austfonna at 750 m a.s.l. These cores are estimated to cover at least the past 800 years and have been dated using a combination of known reference horizons and glacial modelling. Wavelet analysis reveals low frequency oscillations on the 60–120-year scale on the lower elevation site Austfonna while the higher altitude site on Lomonosovfonna does not reveal such variability throughout the record. The second method, Significant Zero Crossing of Derivates (SiZer) does not resolve the low-frequency periodicity seen in the wavelet analysis. The low-frequency variability resolved by the wavelet analysis is similar to what has been found in various climate records including instrumental temperatures and tree-rings, and hasbeen proposed as the most important oscillation for the observed trends in Arctic air temperatures. # -------------------- # 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. #------------------ # Funding_Agency # Funding_Agency_Name: # Grant: # -------------------- # Funding_Agency_Name: National Science Foundation # Grant:AGS-1304263 # Funding_Agency_Name: National Oceanic and Atmospheric Administration # Grant:NA14OAR4310176 #------------------ # Site_Information # Site_Name: Austfonna # Location: Ocean>Atlantic Ocean>North Atlantic Ocean>Svalbard And Jan Mayen # Country: # Northernmost_Latitude: 79.830 # Southernmost_Latitude: 79.830 # Easternmost_Longitude: 24.270 # Westernmost_Longitude: 24.270 # Elevation: 750 m # -------------------- # Data_Collection # Collection_Name: 05Sval02 # Earliest_Year: 1400 # Most_Recent_Year: 1997 # Time_Unit: y_ad # Notes: {"database":"LMR"} # # -------------------- # 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 ##d18O delta 18 oxygen,,,permil SMOW,,Ice Cores,,,N # # -------------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing values: NAN # age d18O 1997 -18.0652 1996 -15.1718 1995 -15.0128 1994 -17.2023 1993 -16.0754 1992 -16.144 1991 -15.808 1990 -16.5345 1989 -15.6436 1988 -15.8198 1987 -15.1793 1986 -15.2112 1985 -16.0376 1984 -16.1097 1983 -15.7731 1982 -16.3866 1981 -15.7999 1980 -14.5506 1979 -15.1729 1978 -15.9216 1977 -16.2051 1976 -15.5317 1975 -15.9686 1974 -16.0747 1973 -15.4876 1972 -15.5942 1971 -14.6975 1970 -15.9259 1969 -15.3114 1968 -16.1278 1967 -16.2707 1966 -17.728 1965 -17.0006 1964 -17.8423 1963 -15.9632 1962 -16.7671 1961 -16.7647 1960 -15.8767 1959 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