DOI: 10.25921/9bt9-3d58
title: NOAA/WDS Paleoclimatology - PAGES2k 2,000 Year Arctic Gridded Temperature Reconstructions

description: 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.
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