# Arctic 2,000 Year Gridded Summer Temperature Reconstructions #----------------------------------------------------------------------- # World Data Service for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program # National Centers for Environmental Information (NCEI) #----------------------------------------------------------------------- # Template Version 3.0 # Encoding: UTF-8 # 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. # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/23031 # Description: NOAA Landing Page # Online_Resource: https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/werner2018arctic/readme-werner2018.txt # Description: NOAA location of the template # # Original_Source_URL: # Description: # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Climate Reconstructions # # Dataset DOI: # # Parameter_Keywords: air temperature #-------------------- # Contribution_Date # Date: 2018-04-30 #-------------------- # File_Last_Modified_Date # Date: 2018-04-30 #-------------------- # Title # Study_Name: Arctic 2,000 Year Gridded Summer Temperature Reconstructions #-------------------- # Investigators # Investigators: Werner, J.P.; Divine, D.V.; Ljungqvist, F.C.: Nilsen, T.; Francus, P. #-------------------- # Description_Notes_and_Keywords # Description: PAGES Arctice2k data, Werner et al. 2018 Clim. Past: # # Data Data used (input data file) # InputData Input data (instrumental data, proxies, grid cell description) # Results Results as presented in the article # Scripts The scripts to construct the input data and evaluate the reconstruction # # Barcast # |-> source source code for Barcast (see also Werner and Tingley, Clim.Past 2015) # | | Readme.md installation and use description # # # InputData: # Arc2Kout_chrons_conserv_GreenlandTheta.mat time uncertain proxies # CRU_SummerData_G5000.RData instrumental (gridded) data # FullGridCells.RData relevant grid cells # McCarroll.2013.AllData.csv data from McCarroll, as taken from NOAA # PAGES2k_ArcticRegionData_corrected.RData Input proxy data # PAGES2k_ArcticRegionData.RData Input proxy data as taken from the PAGES2k database # Tornetrask.MXD.dat Tornetrask # # ./Results: # ADMs.pdf pdf showing the age model selection, not cleaned up # Arc2Ktrend_1_1850_REV1.txt resulting trends over the common era, starting at 1CE # Arc2Ktrend_750_1850_REV1.txt trends staring at 750 CE # Arctic2k_ensembles.nc File containing the reconstruction ensemble (750CE on) # Arctic2k_ensembles_early.nc File containing the reconstruction ensemble (1-749CE) # ArcticRecon_2k0_Summer_PAGES2kv2.0_longseries_CRU_G5000.RData input data # AreaMeanRecon.RData mean arctic recon # AreaMeanRecon.txt - " - # AR_Table.mat scaling behaviour of the reconstruction # ExtremeCenturies.RData most extreme centuries, as plotted # ExtremeDecades.RData most extreme decades, as plotted # ggmcmc.pdf traces of the drawn parameters # mean_Arc2K_spatial_variances_REV1.txt spatial variability, intra-ensemble # Parameters.Final.RData final parameters, thinned, only for unheated chain # PriorPars.RScript priors # ProxSNR_table.csv SNR of the used proxies # ProxySNR_Data.RData -"- # Recon_corrected.RData reconstruction, thinned, only unheated chain # SortChains.RData which chain is active when # transtimes.txt scaling information # # ./Scripts: # 00_Correct_PAGES2k.R # 01_convert_table_tex.sh # 01_Get_bibtex_fromDOI.sh # 01_ReadPAGES.Arctic_CRU_G5000-grid.R # 01_ReadPAGES.Arctic_CRU_G5000_InstData-grid.R # 02_ConstructGridEdges.R # 02_plotDataOverview.R # 09_Correct_Reconstruction.R # 10_ExportRecon-netcdf.R # 10_MakeMeanRecon.R # 11_plotMeanRecon.R # 11_plotScore.R # 12_plotCompareData.R # 13_plotExtremePeriods.R # 14_plotProxResponse.R # 15_plotProxyTrends.R # 15_plotScore.R # 16_plotTrendAnalysis.R # 17_plotVarMaps.R # 18_plotProxyScaling.R # 19_plotLTM_Analysis.R # 99_Common_helper.R # 99_ScoringRules.R # #-------------------- # Publication # Authors: Johannes P. Werner, Dmitry V. Divine, Fredrik Charpentier Ljungqvist, Tine Nilsen, and Pierre Francus # Published_Date_or_Year: 2017-04-24 # Published_Title: Spatio-temporal variability of Arctic summer temperatures over the past 2 millennia # Journal_Name: Climate of the Past # Volume: 14 # Edition: # Issue: # Pages: 527-557 # Report_Number: # DOI: 10.5194/cp-14-527-2018 # Online_Resource: https://www.clim-past.net/14/527/2018/ # Full_Citation: # Abstract: In this article, the first spatially resolved and millennium-length summer (June-August) temperature reconstruction over the Arctic and sub-Arctic domain (north of 60N) is presented. It is based on a set of 44 annually dated temperature-sensitive proxy archives of various types from the revised PAGES2k database supplemented with six new recently updated proxy records. As a major advance, an extension of the Bayesian BARCAST climate field (CF) reconstruction technique provides a means to treat climate archives with dating uncertainties. This results not only in a more precise reconstruction but additionally enables joint probabilistic constraints to be imposed on the chronologies of the used archives. The new seasonal CF reconstruction for the Arctic region can be shown to be skilful for the majority of the terrestrial nodes. The decrease in the proxy data density back in time, however, limits the analyses in the spatial domain to the period after 750 CE, while the spatially averaged reconstruction covers the entire time interval of 1-2002 CE. The centennial to millennial evolution of the reconstructed temperature is in good agreement with a general pattern that was inferred in recent studies for the Arctic and its subregions. In particular, the reconstruction shows a pronounced Medieval Climate Anomaly (MCA; here ca. 920-1060 CE), which was characterised by a sequence of extremely warm decades over the whole domain. The medieval warming was followed by a gradual cooling into the Little Ice Age (LIA), with 1766-1865 CE as the longest centennial-scale cold period, culminating around 1811-1820 CE for most of the target region. In total over 600 independent realisations of the temperature CF were generated. As showcased for local and regional trends and temperature anomalies, operating in a probabilistic framework directly results in comprehensive uncertainty estimates, even for complex analyses. For the presented multi-scale trend analysis, for example, the spread in different paths across the reconstruction ensemble prevents a robust analysis of features at timescales shorter than ca. 30 years. For the spatial reconstruction, the benefit of using the spatially resolved reconstruction ensemble is demonstrated by focusing on the regional expression of the recent warming and the MCA. While our analysis shows that the peak MCA summer temperatures were as high as in the late 20th and early 21st centuries, the spatial coherence of extreme years over the last decades of the reconstruction (1980s onwards) seems unprecedented at least back until 750 CE. However, statistical testing could not provide conclusive support of the contemporary warming to exceed the peak of the MCA in terms of the pan-Arctic mean summer temperatures: the reconstruction cannot be extended reliably past 2002 CE due to lack of proxy data and thus the most recent warming is not captured. #------------------ # Funding_Agency # Funding_Agency_Name: # Grant: #------------------ # Site_Information # Site_Name: Arctic # Location: Geographic Region>Arctic # Country: # Northernmost_Latitude: 90 # Southernmost_Latitude: 60 # Easternmost_Longitude: 180 # Westernmost_Longitude: -180 # Elevation: #------------------ # Data_Collection # Collection_Name: Werner2018Arctic # Earliest_Year: 1 # Most_Recent_Year: 2002 # Time_Unit: Years CE # Core_Length: # Notes: #------------------ # Chronology_Information # Chronology: # #---------------- # Variables # # Data variables follow are preceded by "##" in columns one and two. # Data line variables format: one per line, shortname-tab-variable components (what, material, error, units, seasonality, data type,detail, method, C or N for Character or Numeric data, free text) # ## age_CE age, , , CE, , , , ,N, ## temperature temperature, , , degrees C, ,climate reconstructions,,,N, # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: # age_CE temperature