Quantitative climatic reconstruction of the Last Glacial Maximum in China

Quantitative paleoclimatic reconstruction is crucial for understanding the operation and evolution of the global climate system. For example, a quantitative paleoclimatic reconstruction for the Last Glacial Maximum (18 +/- 2 ka C-14, LGM) is fundamental to understanding the evolution of Earth's climate during the last glacial-interglacial cycle. Previous quantitative palaeoclimate reconstructions in China are generally based on statistical comparison of modern pollen assemblages and modern climate data. These methods are based on the premise that vegetation-climate interactions remain the same through time, and implicitly assume that the interactions are independent of changes in seasonality and atmospheric CO2 concentration. However, these assumptions may not always be valid, which may affect the reconstructions. Here, we present the results of a quantitative study of the LGM climate of China based on an improved inverse vegetation model which incorporates physiological processes combined with a new China Quaternary Pollen Database. The results indicate that during the LGM, mean annual temperature (ANNT), mean temperature of the coldest month (MTCO) and mean temperature of the warmest month in China were lower by similar to 5.6 +/- 0.8, similar to 11.0 +/- 1.6 and similar to 2.6 +/- 0.9 degrees C, respectively, compared to today, and that the changes in ANNT were mainly due to the decrease of MTCO. The ANNT decrease in southern China was similar to 5.5 +/- 0.5 degrees C. Mean annual precipitation was lower by similar to 46.3 +/- 17.8 mm compared to today and was especially low in northern China (similar to 51.2 +/- 21.4 mm) due to the decrease in summer rainfall. Comparison of our results with recent outputs from paleoclimatic modelling reveals that while the latter are broadly consistent with our estimated changes in mean annual climatic parameters, there are substantial differences in the seasonal climatic parameters. Our results highlight the crucial importance of developing seasonal simulation on paleoclimatic models, as well as the need to improve the quality of paleoclimatic reconstructions based on proxy records from geological archives.