Vegetation is a relevant and highly dynamic component of the Earth System controlling, amongst others, surface roughness, albedo and evapotranspiration; its variability shows changes in seasons, interannual, decadal and longer timescales. In this study, we investigate the effects of improved representation of vegetation dynamics on climate predictions at different timescales: seasonal and decadal. To this aim, the latest generation satellite datasets of vegetation characteristics have been exploited, and a novel and improved parameterization of the effective vegetation cover has been developed. The new parameterization is implemented in the land surface scheme HTESSEL shared by two state-of-the-art Earth system models: ECMWF SEAS5 and EC-Earth3. The former model is used for sensitivity at seasonal timescale, while the latter is used for sensitivity at decadal timescale. Both seasonal and decadal experiments show considerable sensitivity of models' surface climate bias with large effects on December-January-February (DJF) T2M, mean sea level pressure and zonal wind over middle-to-high latitudes. Consistently, a significant improvement in the skill for DJF T2M is found, especially over Euro-Asian Boreal forests. In seasonal experiments, this improvement displays a strong interannual coupling with the local surface albedo. From the region with the most considerable T2M improvement, over Siberia, originates a large-scale effect on circulation encompassing Northern Hemisphere middle-to-high latitudes from Siberia to the North Atlantic. As a result, in seasonal experiments, the correlation between the model NAO index against the ERA5 NAO index improves significantly. These results show a non-negligible effect of the vegetation cover on the general circulation, especially for the northern hemisphere and on the prediction skill.

Effects of the realistic vegetation cover on predictions at seasonal and decadal time scales

Emanuele Di Carlo
;
Andrea Alessandri;Fransje van Oorschot;Annalisa Cherchi;Susanna Corti;
2023

Abstract

Vegetation is a relevant and highly dynamic component of the Earth System controlling, amongst others, surface roughness, albedo and evapotranspiration; its variability shows changes in seasons, interannual, decadal and longer timescales. In this study, we investigate the effects of improved representation of vegetation dynamics on climate predictions at different timescales: seasonal and decadal. To this aim, the latest generation satellite datasets of vegetation characteristics have been exploited, and a novel and improved parameterization of the effective vegetation cover has been developed. The new parameterization is implemented in the land surface scheme HTESSEL shared by two state-of-the-art Earth system models: ECMWF SEAS5 and EC-Earth3. The former model is used for sensitivity at seasonal timescale, while the latter is used for sensitivity at decadal timescale. Both seasonal and decadal experiments show considerable sensitivity of models' surface climate bias with large effects on December-January-February (DJF) T2M, mean sea level pressure and zonal wind over middle-to-high latitudes. Consistently, a significant improvement in the skill for DJF T2M is found, especially over Euro-Asian Boreal forests. In seasonal experiments, this improvement displays a strong interannual coupling with the local surface albedo. From the region with the most considerable T2M improvement, over Siberia, originates a large-scale effect on circulation encompassing Northern Hemisphere middle-to-high latitudes from Siberia to the North Atlantic. As a result, in seasonal experiments, the correlation between the model NAO index against the ERA5 NAO index improves significantly. These results show a non-negligible effect of the vegetation cover on the general circulation, especially for the northern hemisphere and on the prediction skill.
2023
Istituto di Scienze dell'Atmosfera e del Clima - ISAC
vegetation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/539563
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