DOI: 10.25921/zeyt-zq21
title: NOAA/WDS Paleoclimatology - LGM and Mid-Holocene Pollen-Based Continental Climate Reconstructions

description: 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.
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