Climate of the Past (Jul 2019)

Mid-Holocene climate change over China: model–data discrepancy

  • Y. Lin,
  • Y. Lin,
  • Y. Lin,
  • G. Ramstein,
  • H. Wu,
  • H. Wu,
  • H. Wu,
  • R. Rani,
  • P. Braconnot,
  • M. Kageyama,
  • Q. Li,
  • Q. Li,
  • Y. Luo,
  • R. Zhang,
  • Z. Guo,
  • Z. Guo,
  • Z. Guo

DOI
https://doi.org/10.5194/cp-15-1223-2019
Journal volume & issue
Vol. 15
pp. 1223 – 1249

Abstract

Read online

The mid-Holocene period (MH) has long been an ideal target for the validation of general circulation model (GCM) results against reconstructions gathered in global datasets. These studies aim to test GCM sensitivity, mainly to seasonal changes induced by the orbital parameters (longitude of the perihelion). Despite widespread agreement between model results and data on the MH climate, some important differences still exist. There is no consensus on the continental size (the area of the temperature anomaly) of the MH thermal climate response, which makes regional quantitative reconstruction critical to obtain a comprehensive understanding of the MH climate patterns. Here, we compare the annual and seasonal outputs from the most recent Paleoclimate Modelling Intercomparison Project Phase 3 (PMIP3) models with an updated synthesis of climate reconstruction over China, including, for the first time, a seasonal cycle of temperature and precipitation. Our results indicate that the main discrepancies between model and data for the MH climate are the annual and winter mean temperature. A warmer-than-present climate condition is derived from pollen data for both annual mean temperature (∼0.7 K on average) and winter mean temperature (∼1 K on average), while most of the models provide both colder-than-present annual and winter mean temperature and a relatively warmer summer, showing a linear response driven by the seasonal forcing. By conducting simulations in BIOME4 and CESM, we show that surface processes are the key factors creating the uncertainties between models and data. These results pinpoint the crucial importance of including the non-linear responses of the surface water and energy balance to vegetation changes.