Influence of solar variability, CO₂ and orbital forcing between 1000 and 1850 AD in the IPSLCM4 model

Abstract. Studying the climate of the last millennium gives the possibility to deal with a relatively well-documented climate essentially driven by natural forcings. We have performed two simulations with the IPSLCM4 climate model to evaluate the impact of Total Solar Irradiance (TSI), CO₂ and orbital forcing on secular temperature variability during the preindustrial part of the last millennium. The Northern Hemisphere (NH) temperature of the simulation reproduces the amplitude of the NH temperature reconstructions over the last millennium. Using a linear statistical decomposition we evaluated that TSI and CO₂ have similar contributions to secular temperature variability between 1425 and 1850 AD. They generate a temperature minimum comparable to the Little Ice Age shown by the temperature reconstructions. Solar forcing explains ~80% of the NH temperature variability during the first part of the millennium (1000–1425 AD) including the Medieval Climate Anomaly (MCA). It is responsible for a warm period which occurs two centuries later than in the reconstructions. This mismatch implies that the secular variability during the MCA is not fully explained by the response of the model to the TSI reconstruction.

With a signal-noise ratio (SNR) estimate we found that the temperature signal of the forced simulation is significantly different from internal variability over area wider than ~5.10⁶ km², i.e. approximately the extent of Europe. Orbital forcing plays a significant role in latitudes higher than 65° N in summer and supports the conclusions of a recent study on an Arctic temperature reconstruction over past two millennia. The forced variability represents at least half of the temperature signal on only ~30% of the surface of the globe. This study suggests that regional reconstructions of the temperature between 1000 and 1850 AD are likely to show weak signatures of solar, CO₂ and orbital forcings compared to internal variability.