Résumé
Pollen records are among the highest-resolution spatial and temporal proxies for reconstructing past vegetation dynamics, environmental changes and climate variability. Over the past decade, a large variety of methods based on different ecological or mathematical concepts has been used to reconstruct paleoclimatic conditions from pollen assemblages. However, the accuracy of these climate reconstructions strongly depends on the choice of the modern calibration dataset, the taxonomic resolution, and/or the modelling assumptions. The lack of a univocal response still limits the application of pollen-based climate reconstructions to assess key climate changes over multiple time periods especially during the Last Glacial Maximum (LGM, ∼ 23-19 ka BP).
Here, we present a multi-method approach, including the Modern Analogue Technique (MAT), the Weighted Averaging Partial Least Squares regression (WA-PLS) and the probability density function-based Climate REconstruction SofTware (CREST), to reconstruct European climates during the LGM. The quality and performance of our climate reconstructions show strong heterogeneity when based on large calibration datasets encompassing wide climatic and vegetation gradients, making local sampling for climate reconstructions difficult. Instead of sampling the global calibration dataset, we test the effect of the latest biomization and megabiomization methods (local calibrations based on megabiomization approaches) on climate reconstructions by introducing a new biome-based approach. Unlike previous studies, we use the weighted mean of climate variables from all megabiome scores rather than only considering the dominant (i.e., highest score) megabiome. This significantly reduces some of the statistical noise of climate reconstructions, drastically minimizing threshold and non-linear effects associated with megabiome classification changes. With these methodological advancements and our multi-method comparison, we evaluate the uncertainties (RMSEP) of the paleoclimate reconstructions for the LGM in Europe. Across climate reconstruction methods (MAT, WA-PLS and CREST methods), European LGM annual temperatures from the biomization method were on average 6.4±2.0 °C (mean SD) colder than today, consistent with megabiomization results (6.8 ± 2.2 °C colder). Winter temperature (mean temperature of the coldest month, MTCO) results exhibit substantial spatial variability across Europe. Local calibration techniques significantly reduce uncertainties in LGM MTCO reconstructions, but they remain highly sensitive to the choice of calibration datasets.