Global variability in LGM cooling amongst paleoclimate datasets affects biome reconstructions in mountains

Downscaled paleoclimate datasets are widely used in biogeographical research, aiding our understanding of past environmental shifts and species’ responses to climate change. Numerous datasets exist, varying in spatiotemporal resolution and underlying methodologies, resulting in variation in estimated temperature. Understanding this variability is important for accurately reconstructing past biogeographical dynamics, especially in complex regions like mountains. We compare the Mean Annual Temperature (MAT) at the Last Glacial Maximum (LGM) from five different downscaled paleoclimate datasets — Beyer, CHELSA-TraCE21k, EcoClimate, PALEO-PGEM-series, WorldClim — against MAT estimates from paleoenvironmental proxy records (fossil pollen and plant macrofossils) within and outside mountains. Additionally, we test the performance of a ‘global grid cooling’ method (i.e. lowering local temperatures by a global LGM estimate) against proxy records. Then, we evaluate the implications of inter-dataset variability for reconstructing temperature-delimited biomes in mountains by reconstructing LGM treeline elevations. We find that LGM temperature cooling and treeline reconstructions strongly vary amongst paleoclimate datasets and between datasets and proxy records. The temperature gradient with elevation is poorly captured by datasets with a coarser spatial resolution. Paleoclimate datasets generally suggest a warmer LGM than proxy records, especially in mountains, while the global grid cooling method more closely aligns with proxy records. Inter-dataset variability can strongly affect the outcome of temperature-delimited reconstructions of biomes and their boundaries, such as treelines. We call for greater awareness and more transparency about the limitations of downscaled paleoclimate datasets in mountainous areas and suggest further research to be aimed at capturing the small-scale heterogeneity of mountains in paleotemperature datasets. LGM cooling is globally both over- and underestimated by downscaled paleoclimate datasets, resulting in overestimation (i.e. too high) and underestimation (i.e. too low) of LGM treeline elevations. Differences in LGM treeline elevation reconstructions can range from 288 to 2779 metres, depending on the paleoclimate dataset. The resolution of several downscaled paleoclimate datasets is unsuitable to capture LGM temperatures in mountainous regions. The median temperature difference between paleoclimate datasets and proxy records is larger within than outside mountain ranges, with substantial differences amongst datasets. Paleoclimate dataset choice strongly impacts biogeographical hypotheses, reconstructions and conclusions and should be carefully evaluated.