Modelling seasonal vegetation feedback with Earth system models
Title: Modelling seasonal vegetation feedback with Earth system models
DNr: NAISS 2023/5-485
Project Type: NAISS Medium Compute
Principal Investigator: Minchao Wu <>
Affiliation: Lunds universitet
Duration: 2023-12-01 – 2024-12-01
Classification: 10501 10503 10599


This application is to support the implementation of my new research project titled “Improving African sub-seasonal vegetation feedback in Earth system models using multiple satellite observations and field measurements” granted by the Swedish National Space Agency (dnr 2021-00111). The applicant is the PI of this project, in which the state-of-the-art Earth system models EC-EARTH and RC-GUESS will be calibrated using high-resolution remote-sensed observation, and a couple of numerical experiments will be conducted to understand the role of vegetation in African climate. This project probes for closing the knowledge gaps of sub-seasonal vegetation-climate interaction in Africa and improving the Earth system model’s ability in simulating sub-seasonal vegetation dynamics and hydrological cycle over Africa. We will also use FLUXNET field measurements from semi-arid sites to validate satellite observations and evaluate model uncertainties in the coupled vegetation regional Earth system model. The improved Earth system model calibrated with satellite observations will be applied under high- to low-emission scenarios (RCP8.5, RCP4.5, RCP2.6) over Africa following the World Climate Research Programme (WCRP) framework. We will further assess changes in simulated precipitation, future climate extremes and the impacts on carbon cycle, which would provide valuable implications for climate change impact studies related to agriculture, forestry and land use management.