Dynamic vegetation highlights first-order climate feedbacks and their dependence on the climate mean state

<p>We investigate how first-order albedo and water vapor radiative feedbacks are triggered by climate-vegetation interactions using mid-Holocene and pre-industrial climate simulations. The mid Holocene greening of the Sahara and northward shift of the northern tree line in the Northern Hemisphere illustrate these climate-vegetation interactions and challenge the development of Earth System models. We consider four different configurations for the IPSL Earth System model with dynamical vegetation to quantify vegetation and radiative feedbacks. They combine different parameterizations of key factors controlling vegetation functioning: bare soil evaporation, photosynthesis and associated parameters, and tree mortality. Whatever the model setup, the major differences between the mid-Holocene and pre-industrial climates are consistent with climate and vegetation reconstructions from pollen records. However, model setup differences modulate the way in which vegetation-climate interactions trigger first-order radiative surface albedo and water vapour feedbacks. Cascading effects involve both local snow-vegetation interactions and remote water vapour and long-wave radiative feedbacks. We show that the parameterization of bare soil evaporation is a key factor that controls tree growth in mid and high latitudes. Photosynthesis parameterization appears to be critical in controlling the seasonal evolution of the vegetation and leaf area index, as well as their effect on radiative feedbacks and the sensitivity of the vegetation feedback to the climate mean state. It even affects the sign of the global annual mean changes in temperature and precipitation between the mid-Holocene and pre-industrial periods. Dynamical vegetation highlights behaviours that can only be fully studied in a fully coupled Earth system model. The sensitivity of these vegetation-induced feedbacks to the mean climate state needs to be better considered when developing and tuning climate models.</p>