Enhancing the representation of African vegetation in the JULES land surface model (version 7.1) through trait-informed parameterisation
Year: 2026
Type: preprint
Abstract: . African ecosystems remain underrepresented in the plant trait datasets used to parameterise land surface models (LSMs), limiting our confidence in simulations of African landscapes. We assessed whether African plant trait data can improve parameterisation of plant functional types (PFTs) in the Joint UK Land Environment Simulator (JULES vn 7.1) model by updating two key photosynthetic traits: leaf mass per area (LMA) and leaf nitrogen content (Nmass). First, we evaluated African-derived trait estimates relative to default JULES parameters. We then analysed impact of mean annual temperature, mean annual precipitation and an aridity index on variability in these traits using generalized additive mixed models (GAMM), and tested the effect of updated trait parameter values on simulated gross primary productivity (GPP) against data from 16 African eddy covariance sites spanning different biomes. Comparison with JULES default parameters showed that African trait values differed for several PFTs, highlighting the value of trait-informed regional parameterization for representing African vegetation more realistically. GAMM analysis showed that LMA and Nmass varied nonlinearly across climate space, indicating that African leaf trait variation is shaped by interacting environmental gradients rather than single climate drivers. Incorporating the revised trait values into JULES generally produced modest improvements in simulated GPP. Improvements were expressed primarily through reduced bias and root mean square error, with little effect on temporal correlation. Performance gains were most evident at grassland sites, highlighting the limitations of representing diverse vegetation using single PFT-level trait values. This study demonstrate that regionally derived plant trait data can refine LSM parameterisation and can improve aspects of model performance in African ecosystems. They also show that regionally derived trait data provide a practical basis for evaluating and updating default model parameters, while highlighting the need for approaches that better represent within-PFT trait variability across environmental gradients and biome types.
Authors: Enimhien Faith Akhabue, Andrew M. Cunliffe, Karina Bett-Williams, Anna B. Harper, Petra Holden, Tom Powell, Aleksander Wieckowski, Jonas Ardö, Carole Helfter, Mangaliso Gondwe, Olivier Roupsard, Ville Vakkari, Pieter G. van Zyl, Mika Aurela, Yann Nouvellon, Torbern Tagesson, Jerome Demarty, Matthew Saunders, Susan van Rensburg
Institutions: University of Exeter, Met Office, University of Georgia, University of Cape Town, Lund University, UK Centre for Ecology & Hydrology, University of Botswana, Okavango Research Institute, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Ecologie fonctionnelle & biogéochimie des sols & des agro-systèmes, Institut de Recherche pour le Développement, Institut Sénégalais de Recherches Agricoles, Institut de Recherche pour le Développement, Finnish Meteorological Institute, University of Helsinki, North-West University, Centre National de la Recherche Scientifique, Université de Montpellier, Laboratoire HydroSciences Montpellier, Trinity College Dublin, South African Environmental Observation Network
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Funders: Gordon and Betty Moore Foundation, University of Exeter, European Commission, Agence Nationale de la Recherche, Swedish National Space Agency, Svenska Forskningsrådet Formas, HORIZON EUROPE Framework Programme, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, Natural Environment Research Council
OpenAlex ID: W7223667246