The role of realistic vegetation variability in climate predictability and prediction

Vegetation is a relevant and highly dynamic component of the Earth system and its variability – at seasonal, interannual, decadal and longer timescales – modulates the coupling with the atmosphere by affecting surface variables such as roughness, albedo and evapotranspiration. In this study, we investigate the effects of improved representation of vegetation dynamics on climate predictability and prediction at the seasonal timescale. To this aim, the observational constraints from the latest generation satellite dataset of vegetation Leaf Area Index (LAI) have been integrated in the modeling, including a parameterization of the effective vegetation cover as a function of LAI. The improved vegetation representation is implemented in HTESSEL, which is the land surface model included in the seasonal forecasting (ECMWF SEAS5) systems used in this work. Our results show that the realistic representation of vegetation variability has significant effects on both potential predictability and actual prediction skill at the seasonal time scale. It is shown a significant improvement of the skill in predicting boreal winter (December-January-February; DJF) 2m Temperature (T2M) at 1-month lead time especially over Euro-Asian boreal forests; the improvement is at least in part due to the more realistic representation of the interannual albedo variability that is related to the changes in vegetation shading over snow. Remarkably, from the region with the most considerable T2M improvement originates a large-scale ameliorating effect on circulation encompassing Northern Hemisphere middle-to-high latitudes from Siberia to the North Atlantic. The results indicate that the coupling with the improved vegetation might operate by amplifying locally the signal originating from the North Atlantic sector, therefore improving both potential predictability and actual skill over the region. Concurrently, the improved predictability and skill over the Euro-Asian forests appears to feedback to the large-scale circulation enhancing the representation of the circulation pattern and associated interannual anomalies.