# Central Europe 1000 Year Documentary Temperature Reconstruction #----------------------------------------------------------------------- # 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:10432 # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/24611 # # Original_Source_URL: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/historical/europe/glaser2009temperature/glaser2009temperature.txt # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Climate Reconstructions # -------------------- # Contribution_date # Date: 2010 # -------------------- # Title # Study_Name: Central Europe 1000 Year Documentary Temperature Reconstruction # -------------------- # Investigators # Investigators: Glaser, R.; Riemann, D. # -------------------- # Description_and_Notes # Description: Temperature indices and reconstruction for Germany and Central Europe over the past 1000 years, based on documentary data. Three time series are presented: seasonally resolved indices for 1000-1500 A.D., monthly resolved indices for 1500-2006 A.D., and 11-year smoothed temperature reconstruction for 1000-2000 A.D. Large parts of the underlying historical data on which the reconstruction is based can be accessed at HISKLID (Historische Klimadatenbank Deutschland - Historical Climate Database Germany), database of climate and environment related information from documentary sources for Central Europe: www.hisklid.de # -------------------- # Publication # Authors: R. Glaser and D. Riemann # Published_Date_or_Year: 2009 # Published_Title: A thousand-year record of temperature variations for Germany and Central Europe based on documentary data # Journal_Name: Journal of Quaternary Science # Volume: 24 # Edition: # Issue: # Pages: 437-449 # DOI: 10.1002/jqs.1302 # Online_Resource: # Full_Citation: # Abstract: This paper presents a temperature reconstruction of the past 1000 years for Central Europe, based on chronological records. The advantages and limitations of this hermeneutic, text-based approach are discussed and the statistic methodology is introduced. Historical documents represent direct observation of weather and atmospheric conditions with highest temporal resolution available and precise dating. A major advantage of these extensive data is that they allow the reconstruction of large numbers of variables such as winter temperature, precipitation, pressure patterns or climate extremes as well as floods or storms. Within this hermeneutic climatological research approach, even human impacts and social dimensions of climate development can be examined. In order to quantify the historical information, statistical methods are applied, based on an index approach. # -------------------- # 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: European Community research contract # Grant: 017008-2 # -------------------- # Funding_Agency_Name: National Science Foundation # Grant:AGS-1304263 # Funding_Agency_Name: National Oceanic and Atmospheric Administration # Grant:NA14OAR4310176 #------------------ # Site_Information # Site_Name: Central Europe # Location: Europe>Central Europe>Germany # Country: Germany # Northernmost_Latitude: 53 # Southernmost_Latitude: 45 # Easternmost_Longitude: 6 # Westernmost_Longitude: 20 # Elevation: # -------------------- # Data_Collection # Collection_Name: 09Germ01 # Earliest_Year: 1005 # Most_Recent_Year: 2001 # Time_Unit: y_ad # # -------------------- # Variables # # Data variables follow that are preceded by "##" in columns one and two. # Data line variables format: Variables 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