Assessing Regional Climate Model Sensitivity to Vegetation Dynamics Informed by Remote Sensing

Abstract. Climate change significantly impacts vegetation ecosystems, and their modification may create feedback loops exacerbating regional effects of global warming. Accurately simulating vegetation dynamics and their interactions with the atmosphere is crucial for understanding and mitigating these impacts. Regional earth system models offer the possibility to study the retroaction between the atmosphere and the vegetation at regional to continental scale by incorporating vegetation dynamics in climate models. In this study, we quantify the sensitivity of the Modèle Atmosphérique Régional (MAR) to vegetation representation at daily to annual scales over a temperate region of Europe, by means of both synthetic experiments and realistic studies.

Our sensitivity study on the Leaf Area Index (LAI) dynamics reveals non-linear responses on meteorological variables, with asymmetric effects relative to the direction of the change. For example, a 92 % reduction in LAI led to an 83.4 % decrease in evapotranspiration and an 88.9 % drop in evaporation. Conversely, a 178.4 % increase in LAI resulted in smaller, yet significant, increases of 29.8 %, 27.4 % respectively. At the seasonal-time scale, evapotranspiration and albedo have the strongest shifts in summer and winter, while relative humidity and rainfall responded more prominently in spring.

Furthermore, we assessed the model performance in simulating daily evapotranspiration and daily maximum temperature during extreme events by comparing simulations incorporating 8-day MODIS LAI data with those based on climatological LAI. Although the improvements were more subtle than those resulting from a change in LAI source, the 8-day observation-based LAI enhanced the model’s capacity to capture shorter events compared to the static LAI. This refinement also helped understanding how various vegetation types respond to extreme events.

The findings highlight the need to integrate dynamic vegetation into regional climate models to enhance their representation of biosphere- atmosphere interactions and provide more accurate tools to assess the impacts of climate change on natural ecosystems.