Publicações

The Cerrado biome in Brazil is a tropical savanna and an important global biodiversity hot spot. Today, only a fraction of its original area remains undisturbed, and this habitat is at risk of conversion to agriculture, especially to soybeans. Here, we present the first quantitative analysis of expanding the Soy Moratorium (SoyM) from the Brazilian Amazon to the Cerrado biome. The SoyM expansion to the Cerrado would prevent the direct conversion of 3.6 million ha of native vegetation to soybeans by 2050. Nationally, this would require a reduction in soybean area of approximately 2%. Relative risk of future native vegetation conversion for soybeans would be driven by the Brazilian domestic market, China, and the European Union. We conclude that, to preserve the Cerrados biodiversity and ecosystem services, urgent action is required, including a zero native vegetation conversion agreement such as the SoyM.

Reducing uncertainties in the response of tropical forests to global change requires understanding how intra- and interannual climatic variability selects for different species, community functional composition and ecosystem functioning, so that the response to climatic events of differing frequency and severity can be predicted. Here we present an extensive dataset of hydraulic traits of dominant species in two tropical Amazon forests with contrasting precipitation regimes – low seasonality forest (LSF) and high seasonality forest (HSF) – and relate them to community and ecosystem response to the El Nino-Southern Oscillation (ENSO) of 2015. Hydraulic traits indicated higher drought tolerance in the HSF than in the LSF. Despite more intense drought and lower plant water potentials in HSF during the 2015-ENSO, greater xylem embolism resistance maintained similar hydraulic safety margin as in LSF. This likely explains how ecosystem-scale whole-forest canopy conductance at HSF maintained a similar response to atmospheric drought as at LSF, despite their water transport systems operating at different water potentials. Our results indicate that contrasting precipitation regimes (at seasonal and interannual time scales) select for assemblies of hydraulic traits and taxa at the community level, which may have a significant role in modulating forest drought response at ecosystem scales.

This chapter summarizes selected results from the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) and briefly describes a vision for future Amazonian research. The need for research on people and environment interactions is emphasized in the context of regional and global change. LBA developed institutional and scientific capacity in Amazonia, but its performance to promote sustainable development was restricted because the program has predominantly emphasized the advancement ofbasic knowledge, with less emphasis on integrative studies explicitly designed to influence public policies with consequences on land use and land cover. The challenge of transforming the natural goods ofAmazonia into human and economic benefits in an environmentally sustainable manner requires a new level of consciousness and collaborative work through the ability to move from simple diagnosis in the direction of actions at local, regional, and national levels. From the results of LBA, we may evolve to new experiences in which society will add value to the interactions between the biosphere and the atmosphere.

The capability of a set of 7 coordinated regional climate model simulations performed in the framework of the CLARIS-LPB Project in reproducing the mean climate conditions over the South American continent has been evaluated. The model simulations were forced by the ERA-Interim reanalysis dataset for the period 1990-2008 on a grid resolution of 50 km, following the CORDEX protocol. The analysis was focused on evaluating the reliability of simulating mean precipitation and surface air temperature, which are the variables most commonly used for impact studies. Both the common features and the differences among individual models have been evaluated and compared against several observational datasets. In this study the ensemble bias and the degree of agreement among individual models have been quantified. The evaluation was focused on the seasonal means, the area-averaged annual cycles and the frequency distributions of monthly means over target sub-regions. Results show that the Regional Climate Model ensemble reproduces adequately well these features, with biases mostly within ±2 °C and ±20 % for temperature and precipitation, respectively. However, the multi-model ensemble depicts larger biases and larger uncertainty (as defined by the standard deviation of the models) over tropical regions compared with subtropical regions. Though some systematic biases were detected particularly over the La Plata Basin region, such as underestimation of rainfall during winter months and overestimation of temperature during summer months, every model shares a similar behavior and, consequently, the uncertainty in simulating current climate conditions is low. Every model is able to capture the variety in the shape of the frequency distribution for both temperature and precipitation along the South American continent. Differences among individual models and observations revealed the nature of individual model biases, showing either a shift in the distribution or an overestimation or underestimation of the range of variability.

Several studies have explored the linkages between phenology and ecosystem productivity across the Amazon basin. However, few studies have focused on flooded forests, which correspond to c.a. 14% of the basin. In this study, we assessed the seasonality of ecosystem productivity (gross primary productivity, GPP) from eddy covariance measurements, environmental drivers and phenological patterns obtained from the field (leaf litter mass) and satellite measurements (enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer/multi-angle implementation correction (MODIS/MAIAC)) in an Amazonian floodplain forest. We found that ecosystem productivity is limited by soil moisture in two different ways. During the flooded period, the excess of water limits GPP (Spearmans correlation; rho = −0.22), while during non-flooded months, GPP is positively associated with soil moisture (rho = 0.34). However, GPP is maximized when cumulative water deficit (CWD) increases (rho = 0.81), indicating that GPP is dependent on the amount of water available. EVI was positively associated with leaf litter mass (Pearsons correlation; r = 0.55) and with GPP (r = 0.50), suggesting a coupling between new leaf production and the phenology of photosynthetic capacity, decreasing both at the peak of the flooded period and at the end of the dry season. EVI was able to describe the inter-annual variations on forest responses to environmental drivers, which have changed during an observed El Niño-Southern Oscillation (ENSO) year (2015/2016).

Upward lightning studies took place in Rapid City, South Dakota, USA and S. Paulo, Brazil during the summer thunderstorm seasons from 2011 to 2016. One of the main objectives of these campaigns was to evaluate and characterize the triggering of upward positive leaders from tall objects due to preceding nearby flash activity. 110 upward flashes were observed with a combination of high-and standard-speed video and digital still cameras, electric field meters, fast electric-field antenna systems, and for two seasons, a Lightning Mapping Array. These data were analyzed, along with correlated lightning location system data, to determine the triggering flash type responsible for the initiation of upward leaders from towers. In this paper, we describe the various processes during flash activity that can trigger upward leaders from tall objects in the USA and in Brazil. We conclude that the most effective triggering component is the propagation of the in-cloud negative leader during the continuing current that follows a positive return stroke.

There is a momentum towards finding financing solutions for halting deforestation at the landscape level for the benefit of climate, biodiversity and delivery of ecosystem services. The Unlocking Forest Finance (UFF) project has, between 2013 and 2018, worked on the development of innovative financing mechanisms for sustainable landscapes in three sub-national Amazon regions of Brazil (Acre and Mato Grosso) and Peru (San Martín). This paper describes the approach of the UFF project as a case study of sustainable landscape financing, and portrays the key evolutions during the process. Relying on a reflection and consultation process among project partners, the paper then derives a set of lessons for sustainable landscape finance. It illustrates the current mismatch between the demand side of private impact investors (i.e., those who look for social and environmental impact of investments beyond financial return) and the supply side of sustainable land use investments on the ground. The paper discusses how blended finance models that combine funding from commercial, public, and philanthropic sources could contribute to financing sustainable landscapes.

This work uses the number concentration-effective diameter phase-space to test cloud sensitivity to variations in the aerosol population characteristics, such as the aerosol size distribution, number concentration and hygroscopicity. It is based on the information from the top of a cloud simulated by a bin-microphysics single-column model, for initial conditions typical of the Amazon, using different assumptions regarding the entrainment and the aerosol size distribution. It is shown that the cloud-top evolution can be very sensitive to aerosol properties, but the relative importance of each parameter is variable. The sensitivity to each aerosol characteristic varies as a function of the parameter tested and is conditioned by the base values of the other parameters, showing a specific dependence for each configuration of the model. When both the entrainment and the bin treatment of the aerosol are allowed, the largest influence on the droplet size distribution sensitivity was obtained for the median radius of the aerosols and not for the total number concentration of aerosols. Our results reinforce that the cloud condensation nuclei activity can not be predicted solely on the basis of the w/Na supersaturation-based regimes.

On the basis of measurements over different surfaces, an inertial sublayer (ISL), where Monin-Obukhov Similarity Theory applies, exists above z = 3h, where h is canopy height. The roughness sublayer is within h < z < 3h. Most studies of the surface layer above forests, however, are able to probe only a narrow region above h. Therefore, direct verification of an ISL above tall forests is difficult. In this study we conducted a systematic analysis of unstable turbulence characteristics at heights from 40 to 325 m, measured at an 80m, and the recently built 325-m Amazon Tall Tower Observatory towers over the Amazon forest. Our analyses have revealed no indication of the existence of an ISL; instead, the roughness sublayer directly merges with the convective mixed layer above. Implications for estimates of momentum and scalar fluxes in numerical models and observational studies can be significant. Plain Language Summary The Amazon forest interacts with the atmosphere by emitting and absorbing many substances, such as carbon dioxide, methane, ozone, and organic compounds, produced by the vegetation. These substances are very influential in both the regional and global climates, and until now, the estimates of their emission and absorption rates are based on classical theories developed originally over relatively short vegetation and valid for a region above the ground called the “inertial sublayer.” In this work we present evidence, obtained with the help of measurements from a very tall tower (325 m), that a classical inertial sublayer does not exist over the Amazon forest. New methods to quantify the emission and absorption rates, therefore, will be needed to improve their estimates.

The performance of the coupled ocean-atmosphere component of the Brazilian Earth System Model version 2.5 (BESM-OA2.5) was evaluated in simulating the historical period 1850-2005. After a climate model validation procedure in which the main atmospheric and oceanic variabilities were evaluated against observed and reanalysis datasets, the evaluation specifically focused on the mean climate state and the most important large-scale climate variability patterns simulated in the historical run, which was forced by the observed greenhouse gas concentration. The most significant upgrades in the model’s components are also briefly presented here. BESM-OA2.5 could reproduce the most important large-scale variabilities, particularly over the Atlantic Ocean (e.g., the North Atlantic Oscillation, the Atlantic Meridional Mode, and the Atlantic Meridional Overturning Circulation), and the extratropical modes that occur in both hemispheres. The model’s ability to simulate such large-scale variabilities supports its usefulness for seasonal climate prediction and in climate change studies.