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Southeastern South America is subject to considerable precipitation variability on seasonal to decadal timescales and has undergone very heavy land‐cover changes (LCCs) since the middle of the past century. The influence of local LCC and precipitation as drivers of regional evapotranspiration (ET) long‐term trends and variability remains largely unknown in the region. Here, ensembles of stand‐alone dynamic global vegetation models (DGVMs) with different atmospheric forcings are used to disentangle the influence of those two drivers on austral summer ET from 1950 to 2010. This paper examines the influence of both the El Niño‐Southern Oscillation (ENSO) and the dipole‐like first‐mode of southeastern South American precipitation variability (EOF1) on regional ET. We found that in the lower La Plata Basin, ET was driven by precipitation variability and showed a positive summer trend. Moreover, the region showed marked seasonal anomalies during El Niño and La Niña summers but mainly during EOF1 phases. On the contrary, in the upper La Plata Basin, LCCs forced the negative summer ET trend and particularly reduced the summer anomalies of the late 1990s, a period of ENSO and EOF1‐positive phases. In the South Atlantic Convergence Zone region, the high ET uncertainty across ensemble members impeded finding robust results, which highlights the importance of using multiple DGVMs and atmospheric forcings instead of relying on single model/forcing results. [ABSTRACT FROM AUTHOR]

A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration rates, productivity and the geographical distribution of evergreen forests in tropical South America. However, none of those studies have acknowledged resource investment, timing and physical constraints of tree rooting depth within a competitive environment, undermining the ecological realism of their results. Here, we present an approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 (Lund-Potsdam-Jena managed Land) dynamic global vegetation model (DGVM) and apply it to tropical and sub-tropical South America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulation of intra-annual productivity and evapotranspiration and consequently of forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and below-ground biomass across the study region. Our findings support the hypothesis that the ability of evergreen trees to adjust their rooting systems to seasonally dry climates is crucial to explaining the current dominance, productivity and evapotranspiration of evergreen forests in tropical South America. [ABSTRACT FROM AUTHOR]

Vegetation fires influence global vegetation distribution, ecosystem functioning, and global carbon cycling. Specifically in South America, changes in fire occurrence together with land-use change accelerate ecosystem fragmentation and increase the vulnerability of tropical forests and savannas to climate change. Dynamic global vegetation models (DGVMs) are valuable tools to estimate the effects of fire on ecosystem functioning and carbon cycling under future climate changes. However, most fire-enabled DGVMs have problems in capturing the magnitude, spatial patterns, and temporal dynamics of burned area as observed by satellites. As fire is controlled by the interplay of weather conditions, vegetation properties, and human activities, fire modules in DGVMs can be improved in various aspects. In this study we focus on improving the controls of climate and hence fuel moisture content on fire danger in the LPJmL4-SPITFIRE DGVM in South America, especially for the Brazilian fire-prone biomes of Caatinga and Cerrado. We therefore test two alternative model formulations (standard Nesterov Index and a newly implemented water vapor pressure deficit) for climate effects on fire danger within a formal model–data integration setup where we estimate model parameters against satellite datasets of burned area (GFED4) and aboveground biomass of trees. Our results show that the optimized model improves the representation of spatial patterns and the seasonal to interannual dynamics of burned area especially in the Cerrado and Caatinga regions. In addition, the model improves the simulation of aboveground biomass and the spatial distribution of plant functional types (PFTs). We obtained the best results by using the water vapor pressure deficit (VPD) for the calculation of fire danger. The VPD includes, in comparison to the Nesterov Index, a representation of the air humidity and the vegetation density. This work shows the successful application of a systematic model–data integration setup, as well as the integration of a new fire danger formulation, in order to optimize a process-based fire-enabled DGVM. It further highlights the potential of this approach to achieve a new level of accuracy in comprehensive global fire modeling and prediction. [ABSTRACT FROM AUTHOR]

Tropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture. Inspired by social-ecological systems theory, our approach helps to explain: (i) how drivers of natural vegetation decline affect the functioning of ecosystems, and thus ecological integrity, (ii) under which conditions, where, and at which scales the loss of ecological integrity may lead to biodiversity tipping points, and (iii) how these biodiversity tipping points may impact human well-being. Implementing such an approach with the greater aim of furthering more sustainable land use in the Dry Diagonal requires a transdisciplinary collaborative network, which in a first step integrates extensive observational data from the field and remote sensing with advanced ecosystem and biodiversity models. Secondly, it integrates knowledge obtained from dialogue processes with local and regional actors as well as meta-models describing the actor network. The co-designed methodological framework can be applied not only to define, detect, and map biodiversity tipping points across spatial and temporal scales, but also to evaluate the effects of tipping points on ES and livelihoods. This framework could be used to inform policy making, enrich planning processes at various levels of governance, and potentially contribute to prevent biodiversity tipping points in the Dry Diagonal and beyond. [ABSTRACT FROM AUTHOR]

•We estimate probability density functions (PDFs) for future rainfall in five regions of South America, by weighting the predictions of the 24 Coupled Model Intercomparison Archive Project 3 (CMIP3) General Circulation Models (GCMs). The models are rated according to their relative abilities to reproduce the inter-annual variability in seasonal rainfall. •The relative weighting of the climate models is updated sequentially according to Bayes’ theorem, based on the biases in the mean of the predicted time-series and the distributional fit of the bias-corrected time-series. •Depending on the season and the region, we find very different rankings of the GCMs, with no single model doing well in all cases. However, in some regions and seasons, differential weighting of the models leads to significant shifts in the derived rainfall PDFs. •Using a combination of the relative model weightings for each season we have also derived a set of overall model weightings for each region that can be used to produce PDFs of forest biomass from the simulations of the Lund–Potsdam–Jena Dynamic Global Vegetation Model for managed land (LPJmL). [ABSTRACT FROM AUTHOR]

Tree water access via roots is crucial for forest functioning and therefore a vast variety of rooting strategies have evolved in the global forest systems. Especially, the distribution of roots within the soil column is important for buffering temporal shortages of precipitation. However, dynamic global vegetation models (DGVMs) often condense this variety into biome scale averages, potentially overestimating the dependence of forest functioning on short term precipitation. Here we present first results of implementing variable root systems into a DGVM (LPJmL 4.0) applied to Central- and South-America. We show how variable root systems constrained by soil & sediment thickness enable a better reproduction of state variables like forest cover or biomass pattern as well as intra-annual variability of e.g. evapotranspiration. We find that trade-offs between water accessibility and below ground carbon investment explain local diversity and co-existence of rooting strategies. We present mean rooting depth maps and below ground carbon investment maps based on our modelling results. Conclusively, we propose a stabilizing effect of realized rooting depth on ecosystem productivity. [ABSTRACT FROM AUTHOR]