Aim: Although much tropical ecology generally focuses on trees, grasses are fundamental for characterizing the extensive tropical grassy biomes (TGBs) and, together with the tree functional types, for determining the contrasting functional patterns of TGBs and tropical forests (TFs). To study the factors that determine African biome distribution and the transitions between them, we performed the first continental analysis to include grass and tree functional types.Location: Sub-Saharan Africa.Time period: 2000-2010.Major taxa studied: Savanna and forest trees and C 4 grasses. Methods:We combined remote-sensing data with a land cover map, using tree functional types to identify TGBs and TFs. We analysed the relationships of grass and tree cover with fire interval, rainfall annual average and seasonality.Results: In TGBs experiencing < 630 mm annual rainfall, grass growth was water limited. Grass cover and fire recurrence were strongly and directly related over the entire subcontinent. Some TGBs and TFs with annual rainfall > 1,200 mm had the same rainfall seasonality but displayed strongly different fire regimes.Main conclusions: Water limitation to grass growth was fundamental in the driest TGBs, acting alongside the well-known limitation to tree growth. Marked differences in fire regimes across all biomes indicated that fire was especially relevant for maintaining mesic and humid TGBs. At high rainfall, our results support the hypothesis of TGBs and TFs being alternative stable states maintained by a vegetation-fire feedback for similar climatic conditions.
Arid and semiarid savannas are characterized by the coexistence of trees and grasses in water limited conditions. As in all dry lands, also in these savannas rainfall is highly intermittent. In this work, we develop and use a simple implicit-space model to conceptually explore how precipitation intermittency influences tree-grass competition and savanna occurrence. The model explicitly includes soil moisture dynamics, and life-stage structure of the trees. Assuming that water availability affects the ability of both plant functional types to colonize new space and that grasses outcompete tree seedlings, the model is able to predict the expected sequence of grassland, savanna, and forest along a range of mean annual rainfall. In addition, rainfall intermittency allows for tree-grass coexistence at lower mean annual rainfall values than for constant precipitation. Comparison with observations indicates that the model, albeit very simple, is able to capture some of the essential dynamical processes of natural savannas. The results suggest that precipitation intermittency affects savanna occurrence and structure, indicating a new point of view for reanalyzing observational data from the literature.
Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model.
Abstract. The interactions between climate, vegetation and fire can strongly influence the future trajectories of vegetation in Earth system models. We evaluate the relationships between tropical climate, vegetation and fire in the global vegetation model JSBACH, using a simple fire scheme and the complex fire model SPITFIRE with the aim to identify potential for model improvement. We use two remote-sensing products (based on MODIS and Landsat) in different resolutions to assess the robustness of the obtained observed relationships. We evaluate the model using a multivariate comparison that allows us to focus on the interactions between climate, vegetation and fire and test the influence of land use change on the modelled patterns. Climate–vegetation–fire relationships are known to differ between continents; we therefore perform the analysis for each continent separately. The observed relationships are similar in the two satellite data sets, but maximum tree cover is reached at higher precipitation values for coarser resolution. This shows that the spatial scale of models and data needs to be consistent for meaningful comparisons. The model captures the broad spatial patterns with regional differences, which are partly due to the climate forcing derived from an Earth system model. Compared to the simple fire scheme, SPITFIRE strongly improves the spatial pattern of burned area and the distribution of burned area along increasing precipitation. The correlation between precipitation and tree cover is higher in the observations than in the largely climate-driven vegetation model, with both fire models. The multivariate comparison identifies excessive tree cover in low-precipitation areas and a too-strong relationship between high fire occurrence and low tree cover for the complex fire model. We therefore suggest that drought effects on tree cover and the impact of burned area on tree cover or the adaptation of trees to fire can be improved. The observed variation in the relationship between precipitation and maximum tree cover between continents is higher than the simulated one. Land use contributes to the intercontinental differences in fire regimes with SPITFIRE and strongly overprints the modelled multimodality of tree cover with SPITFIRE. The multivariate model–data comparison used here has several advantages: it improves the attribution of model–data mismatches to model processes, it reduces the impact of biases in the meteorological forcing on the evaluation and it allows us to evaluate not only a specific target variable but also the interactions.
Although it is well known that mean annual rainfall (MAR) and rainfall seasonality have a key role in influencing the distribution of tree and grass cover in African tropical grassy biomes (TGBs), the impact of intra-seasonal rainfall variability on these distributions is less agreed upon. Since the prevalent mechanisms determining biome occurrence and distribution change with MAR, this research investigates the role of intra-seasonal rainfall variability for three different MAR ranges, assessing satellite data on grass and tree cover, rainfall and fire intervals at a sub-continental scale in sub-Saharan Africa. For MAR below 630 mm y−1, rainfall frequency had a positive relationship with grass cover; this relationship however became mostly negative at intermediate MAR (630–1200 mm y−1), where tree cover correspondingly mostly increased with rainfall frequency. In humid TGBs, tree cover decreased with rainfall intensity. Overall, intra-seasonal rainfall variability plays a role in determining vegetation cover, especially in mesic TGBs, where the relative dominance of trees and grasses has previously been largely unexplained. Importantly, the direction of the effect of intra-seasonal variability changes with MAR. Given the predicted increases in rainfall intensity in Africa as a consequence of climate change, the effects on TGBs are thus likely to vary depending on the MAR levels.
Oceanic frontal zones have been shown to deeply influence the distribution of primary producers and, at the other extreme of the trophic web, top predators. However, the relationship between these structures and intermediate trophic levels is much more obscure. In this paper we address this knowledge gap by comparing acoustic measurements of mesopelagic fish concentrations to satellite-derived fine-scale Lagrangian Coherent Structures in the Indian sector of the Southern Ocean. First, we demonstrate that higher fish concentrations occur more frequently in correspondence with strong Lagrangian Coherent Structures. Secondly, we illustrate that, while increased fish densities are more likely to be observed over these structures, the presence of a fine-scale feature does not imply a concomitant fish accumulation, as other factors affect fish distribution. Thirdly, we show that, when only chlorophyll-rich waters are considered, front intensity modulates significantly more the local fish concentration. Finally, we discuss a model representing fish movement along Lagrangian features, specifically built for mid-trophic levels. Its results, obtained with realistic parameters, are qualitatively consistent with the observations and the spatio-temporal scales analysed. Overall, these findings may help to integrate intermediate trophic levels in trophic models, which can ultimately support management and conservation policies.
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