# Global and Hemispheric 400 Year Glacier Length Temperature Reconstructions #----------------------------------------------------------------------- # 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: https://www.ncdc.noaa.gov/cdo/f?p=519:1:::::P1_STUDY_ID:13544 # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/24611 # # Original_Source_URL: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/contributions_by_author/leclercq2012/leclercq2012global.txt # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Climate Reconstructions # -------------------- # Contribution_date # Date: 2012-11-30 # -------------------- # Title # Study_Name: Global and Hemispheric 400 Year Glacier Length Temperature Reconstructions # -------------------- # Investigators # Investigators: Leclercq, P.W.; Oerlemans, J. # -------------------- # Description_and_Notes # Description: Global, Northern hemisphere, and Southern hemisphere temperature reconstructions for period 1600 to 2000, based on world-wide glacier length changes. Data are the reconstructed temperature anomaly w.r.t. the 1961-1990 average temperature, plus the uncertainty at 95% confidence interval. # -------------------- # Publication # Authors: Paul Willem Leclercq and Johannes Oerlemans # Published_Date_or_Year: 2012- 03- 01 # Published_Title: Global and hemispheric temperature reconstruction from glacier length fluctuations # Journal_Name: Climate Dynamics # Volume: 38 # Edition: 5-6 # Issue: 1065-1079 # Pages: 437-449 # DOI: 10.1007/s00382-011-1145-7 # Online_Resource: # Full_Citation: # Abstract: Temperature reconstructions for recent centuries provide a historical context for the warming over the twentieth century. We reconstruct annual averaged surface temperatures of the past 400 years on hemispherical and global scale from glacier length fluctuations. We use the glacier length records of 308 glaciers. The reconstruction is a temperature proxy with decadal resolution that is completely independent of other temperature records. Temperatures are derived from glacier length changes using a linear response equation and an analytical glacier model that is calibrated on numerical model results. The global and hemispherical temperatures reconstructed from glacier length fluctuations are in good agreement with the instrumental record of the last century. Furthermore our results agree with existing multi-proxy reconstructions of temperature in the pre-instrumental period. The temperature record obtained from glacier fluctuations confirms the pronounced warming of the twentieth century, giving a global cumulative warming of 0.94 ± 0.31 K over the period 1830-2000 and a cumulative warming of 0.84 ± 0.35 K over the period 1600-2000. # -------------------- # 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: Global # Location: Geographic Region> Global # Country: # Northernmost_Latitude: 90 # Southernmost_Latitude: -0 # Easternmost_Longitude: 180 # Westernmost_Longitude: -180 # Elevation: # -------------------- # Data_Collection # Collection_Name: 12Glob02 # Earliest_Year: 1600 # Most_Recent_Year: 2000 # Time_Unit: y_ad # # -------------------- # 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 ##temp_anom surface temperature anomaly, , , years AD, degrees C, Climate Reconstructions, , , anomaly w.r.t. the 1961-1990 average, N # # -------------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing values: NAN # age temp_anom 1601 -0.647861 1602 -0.639141 1603 -0.628673 1604 -0.619945 1605 -0.430137 1606 -0.423259 1607 -0.417669 1608 -0.411863 1609 -0.404188 1610 -0.395922 1611 -0.384674 1612 -0.370804 1613 -0.359246 1614 -0.350119 1615 -0.343476 1616 -0.339048 1617 -0.334994 1618 -0.329744 1619 -0.325231 1620 -0.323749 1621 -0.326009 1622 -0.330546 1623 -0.337138 1624 -0.343287 1625 -0.348764 1626 -0.354149 1627 -0.359266 1628 -0.364391 1629 -0.369662 1630 -0.374712 1631 -0.3793 1632 -0.383309 1633 -0.387018 1634 -0.390609 1635 -0.39338 1636 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