Radiometric dating re-evaluating the paleoenvironment and paleoclimate around the Plio–Pleistocene boundary in NE China (Changbai Mountains)

Abstract

Today, NE China is highly affected by cold and dry winter monsoon winds, causing long and severe winters with monthly mean temperatures below −10°C. Yet, the Neogene paleoclimatic history of this region is not well understood due to the lack of precisely dated paleontological localities. Herein, we present several Ar–Ar basalt ages round a plant-bearing diatomite sequence of Badaogou, Changbai Mountains (Jilin Province; NE China — border to N-Korea), which allow us to revise its age from Miocene to ~2.5Ma. A paleoenvironmental reconstruction based on fossil leaves, pollen and diatoms suggests deciduous forests rich in Acer, Quercus, Tilia, Ulmus and Zelkova mixed with conifers such as Tsuga, Picea and Abies within an already established mountain region close by. These forests were growing around a nutrient rich freshwater system with a pH >7 in a presumably deep basin indicated by the strong dominance of planktonic diatoms Stephanodiscus minutulus and Pliocaenicus changbaiensis. The presence of tree taxa known from southern China today, such as Sassafras, Nyssa, Liquidambar, Podocarpus and Cedrus, leads to significantly warmer temperature estimates by the Coexistence Approach (mean annual temperature 11.5–15.7°C, coldest month mean temperature −0.3–9.6°C, warmest month mean temperature 23.0–27.8°C). Rainfall values aren't as precise, but suggest a mean annual precipitation of 843–1577mm with monthly extremes of 109–220mm (wettest), 17–41mm (driest) and 73–175mm (warmest). Although records in Eastern and Northern Asia as well as Central China report a cooling and drying after the onset of the Northern Hemisphere glaciation, the paleovegetation in NE China was not yet or hardly affected by the intensification of the winter monsoon. This emphasizes the importance of accurately dated records to improve large-scale paleoclimatic reconstruction as well as our understanding of the complexity of climate change.