Identifying Global-Scale Patterns of Vegetation Change During the Last Deglaciation From Paleoclimate Networks

Abstract

During the last deglaciation (∼19–11 ka before present), the global mean temperature increased by 3–8 K. The concurrent hydroclimate and land cover changes are not well constrained. Here, we use a pollen database to quantify global-scale vegetation changes during this transitional period at orbital (∼104 years) and millennial timescales (∼103 years). We focus on the proportion of tree and shrub pollen, the arboreal pollen (AP) fraction. Temporal similarities over long distances are identified by a paleoclimate network approach. At the orbital scale, we find coherent AP variations in the low and mid-latitudes which we attribute to the global climate forcing. While AP fractions predominantly increased through the deglaciation, we identify regions where AP fractions decreased. For millennial timescales, we do not observe spatially coherent similarity structures. We compare our results with networks computed from three deglacial climate simulations with the CCSM3, HadCM3, and LOVECLIM models. Networks based on simulated precipitation patterns reproduce the characteristics of the AP network. Sensitivity experiments with statistical emulators indicate that, indeed, precipitation variations explain the diagnosed patterns of vegetation change better than temperature and CO2 variations. Our findings support previous interpretations of deglacial forest evolution in the mid-latitudes being the result of atmospheric circulation changes. The network analysis identifies differences in the vegetation-climate-CO2 relationship simulated by CCSM3 and HadCM3. We conclude that network analyses are a promising tool to benchmark transient climate simulations with dynamical vegetation changes. This may result in stronger constraints of future hydroclimate and land cover changes.