Abstract. The savanna ecosystem is one of the most dominant and complex terrestrial biomes, deriving from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root-water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of six TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root-water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil-water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe that this study is the first to assess how well TBMs simulate savanna ecosystems and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies.

Fires play an important role in ecosystem dynamics. Long-term controls on global burned area include fuel continuity and moisture, with ignitions and human activity becoming dominant in specific ecosystems. Changes in fuel continuity and moisture are the main drivers of changes of fire globally.

Abstract. Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley–Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.

A soil–plant–atmosphere model was used to estimate gross primary productivity (GPP) and evapotranspiration (ET) of a tropical savanna in Australia. This paper describes model modifications required to simulate the substantial C4 grass understory together with C3 trees. The model was further improved to include a seasonal distribution of leaf area and foliar nitrogen through 10 canopy layers. Model outputs were compared with a 5‐year eddy covariance dataset. Adding the C4 photosynthesis component improved the model efficiency and root‐mean‐squared error (RMSE) for total ecosystem GPP by better emulating annual peaks and troughs in GPP across wet and dry seasons. The C4 photosynthesis component had minimal impact on modelled values of ET. Outputs of GPP from the modified model agreed well with measured values, explaining between 79% and 90% of the variance and having a low RMSE (0.003–0.281 g C m−2 day−1). Approximately, 40% of total annual GPP was contributed by C4 grasses. Total (trees and grasses) wet season GPP was approximately 75–80% of total annual GPP. Light‐use efficiency (LUE) was largest for the wet season and smallest in the dry season and C4 LUE was larger than that of the trees. A sensitivity analysis of GPP revealed that daily GPP was most sensitive to changes in leaf area index (LAI) and foliar nitrogen (Nf) and relatively insensitive to changes in maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax) and minimum leaf water potential (ψmin). The modified model was also able to represent daily and seasonal patterns in ET, (explaining 68–81% of variance) with a low RMSE (0.038–0.19 mm day−1). Current values of Nf, LAI and other parameters appear to be colimiting for maximizing GPP. By manipulating LAI and soil moisture content inputs, we show that modelled GPP is limited by light interception rather than water availability at this site.