LGM and Mid-Holocene Pollen-Based Continental Climate Reconstructions ----------------------------------------------------------------------- World Data Center for Paleoclimatology, Boulder and NOAA Paleoclimatology Program ----------------------------------------------------------------------- NOTE: PLEASE CITE ORIGINAL REFERENCE WHEN USING THIS DATA!!!!! NAME OF DATA SET: LGM and Mid-Holocene Pollen-Based Continental Climate Reconstructions LAST UPDATE: 11/2010 (Original receipt by WDC Paleo) CONTRIBUTORS: Bartlein, P.J., et al. IGBP PAGES/WDCA CONTRIBUTION SERIES NUMBER: 2010-127 WDC PALEO CONTRIBUTION SERIES CITATION: Bartlein, P.J., et al. 2010. LGM and Mid-Holocene Pollen-Based Continental Climate Reconstructions. IGBP PAGES/World Data Center for Paleoclimatology Data Contribution Series # 2010-127. NOAA/NCDC Paleoclimatology Program, Boulder CO, USA. ORIGINAL REFERENCE: Bartlein, P.J., S.P. Harrison, S. Brewer, S. Connor, B.A.S. Davis, K. Gajewski, J. Guiot, T.I. Harrison-Prentice, A. Henderson, O. Peyron, I.C. Prentice, M. Scholze, H. Seppa, B. Shuman, S. Sugita, R.S. Thompson, A.E. Viau, J. Williams, and H. Wu. 2010. Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis. Climate Dynamics, DOI: 10.1007/s00382-010-0904-1 ABSTRACT: Subfossil pollen and plant macrofossil data derived from 14C-dated sediment profiles can provide quantitative information on glacial and interglacial climates. The data allow climate variables related to growing season warmth, winter cold, and plant-available moisture to be reconstructed. Continental-scale reconstructions have been made for the mid-Holocene (MH, around 6 ka) and Last Glacial Maximum (LGM, around 21 ka), allowing comparison with palaeoclimate simulations currently being carried out as part of the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. The synthesis of the available MH and LGM climate reconstructions and their uncertainties, obtained using modern analogue, regression and model-inversion techniques, is presented for four temperature variables and two moisture variables. Reconstructions of the same variables based on surface-pollen assemblages are shown to be accurate and unbiased. Reconstructed LGM and MH climate anomaly patterns are coherent, consistent between variables, and robust with respect to the choice of technique. They support a conceptual model of the controls of Late Quaternary climate change whereby the first-order effects of orbital variations and greenhouse forcing on the seasonal cycle of temperature are predictably modified by responses of the atmospheric circulation and surface energy balance. GEOGRAPHIC REGION: Global Continental PERIOD OF RECORD: 21 and 6 KYrBP FUNDING SOURCES: QUEST (Quantifying Uncertainties in the Earth System) programme of the UK Natural Environment Research Council, and Project 0801 (Evaluation of PMIP Palaeoclimate Model Simulations) of the International Quaternary Association (INQUA). Regional dataset compilations and the work of individual co-authors has been funded by the QUEST programme (SPH, ICP, TIH-P), the UK Natural Environment Research Council (SPH, ICP, MS), the US National Science Foundation (PJB, SB, BS, JWW), the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) (KEG, AV). DESCRIPTION: Continental-scale gridded reconstructions of four temperature and two moisture variables, for the mid-Holocene and Last Glacial Maximum, based on pollen and plant macrofossil data. Reconstructions are presented as 2x2 degree grid cell values, in netCDF or CSV (comma separated values) text files. File names and variables reconstructed are as follows: alpha_delta_06ka: alpha (AE/PE, Actual/Potential Evapotranspiration), 6kyrBP alpha_delta_21ka: alpha (AE/PE, Actual/Potential Evapotranspiration), 21kyrBP coldtemp_delta_06ka: Mean Temperature Anomaly of the coldest month, 6kyrBP coldtemp_delta_21ka: Mean Temperature Anomaly of the coldest month, 21kyrBP gdd5_delta_06ka: Growing Degree Days, above 5C base, Ann Mean, 6kyrBP gdd5_delta_21ka: Growing Degree Days, above 5C base, Ann Mean, 21kyrBP map_delta_06ka: Mean Annual Precipitation, 6kyrBP map_delta_21ka: Mean Annual Precipitation, 21kyrBP mat_delta_06ka: Mean Annual Temperature, 6kyrBP mat_delta_21ka: Mean Annual Temperature, 21kyrBP warmtemp_delta_06ka: Mean Temperature Anomaly of the warmest month, 6kyrBP warmtemp_delta_21ka: Mean Temperature Anomaly of the warmest month, 21kyrBP The CSV files are formatted as follows: Col 1: lat latitude of grid-cell center Col 2: lon longitude of grid-cell center Col 3: varname_anm_mean mean of the reconstructions that fall in the grid cell Col 4: varname_se_mean mean of the standard error of estimates for each reconstruction in the grid cell Col 5: varname_tstat t-statistic = mean anomaly/mean standard error of estimate (varname_anm_mean/varname_se_mean) Col 6: varname_sig significance of the t-statistic, i.e., varname_sig = +/- 1, i.e. = sign(tstat) if abs(tstat) > 2 Col 7: varname_sig_value value of the anm_mean if the t-statistic was significant, i.e. sig_val = anm_mean if sig.ne. Col 8: varname_npts number of reconstructions in the grid cell