Gilvan Sampaio

Global Climatological Features in a Simulation Using the CPTEC–COLA AGCM

Abstract The Center for Weather Forecasting and Climate Studies–Center for Ocean–Land–Atmosphere Studies (CPTEC–COLA) atmospheric general circulation model (AGCM) is integrated with nine initial conditions for 10 yr to obtain the model climate in an ensemble mode. The global climatological characteristics simulated by the model are compared with observational data, and emphasis is given to the Southern Hemisphere and South America. Evaluation of the models performance is presented by showing systematic errors of several variables, and anomaly correlation and reproducibility are applied to precipitation. The model is able to simulate the main features of the global climate, and the results are consistent with analyses of other AGCMs. The seasonal cycle is reproduced well in all analyzed variables, and systematic errors occur at the same regions in different seasons. The Southern Hemisphere convergence zones are simulated reasonably well, although the model overestimates precipitation in the southern porti...

Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm

Significance The Amazonian tropical forests have been disappearing at a fast rate in the last 50 y due to deforestation to open areas for agriculture, posing high risks of irreversible changes to biodiversity and ecosystems. Climate change poses additional risks to the stability of the forests. Studies suggest “tipping points” not to be transgressed: 4° C of global warming or 40% of total deforested area. The regional development debate has focused on attempting to reconcile maximizing conservation with intensification of traditional agriculture. Large reductions of deforestation in the last decade open up opportunities for an alternative model based on seeing the Amazon as a global public good of biological assets for the creation of high-value products and ecosystem services. For half a century, the process of economic integration of the Amazon has been based on intensive use of renewable and nonrenewable natural resources, which has brought significant basin-wide environmental alterations. The rural development in the Amazonia pushed the agricultural frontier swiftly, resulting in widespread land-cover change, but agriculture in the Amazon has been of low productivity and unsustainable. The loss of biodiversity and continued deforestation will lead to high risks of irreversible change of its tropical forests. It has been established by modeling studies that the Amazon may have two “tipping points,” namely, temperature increase of 4 °C or deforestation exceeding 40% of the forest area. If transgressed, large-scale “savannization” of mostly southern and eastern Amazon may take place. The region has warmed about 1 °C over the last 60 y, and total deforestation is reaching 20% of the forested area. The recent significant reductions in deforestation—80% reduction in the Brazilian Amazon in the last decade—opens up opportunities for a novel sustainable development paradigm for the future of the Amazon. We argue for a new development paradigm—away from only attempting to reconcile maximizing conservation versus intensification of traditional agriculture and expansion of hydropower capacity—in which we research, develop, and scale a high-tech innovation approach that sees the Amazon as a global public good of biological assets that can enable the creation of innovative high-value products, services, and platforms through combining advanced digital, biological, and material technologies of the Fourth Industrial Revolution in progress.

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models

Abstract Understanding the processes that determine above‐ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin‐wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

A new world natural vegetation map for global change studies

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).

Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks

Reduced rainfall increases the risk of forest dieback, while in return forest loss might intensify regional droughts. The consequences of this vegetation–atmosphere feedback for the stability of the Amazon forest are still unclear. Here we show that the risk of self-amplified Amazon forest loss increases nonlinearly with dry-season intensification. We apply a novel complex-network approach, in which Amazon forest patches are linked by observation-based atmospheric water fluxes. Our results suggest that the risk of self-amplified forest loss is reduced with increasing heterogeneity in the response of forest patches to reduced rainfall. Under dry-season Amazonian rainfall reductions, comparable to Last Glacial Maximum conditions, additional forest loss due to self-amplified effects occurs in 10–13% of the Amazon basin. Although our findings do not indicate that the projected rainfall changes for the end of the twenty-first century will lead to complete Amazon dieback, they suggest that frequent extreme drought events have the potential to destabilize large parts of the Amazon forest.

Variability of Carbon and Water Fluxes Following Climate Extremes over a Tropical Forest in Southwestern Amazonia

The carbon and water cycles for a southwestern Amazonian forest site were investigated using the longest time series of fluxes of CO2 and water vapor ever reported for this site. The period from 2004 to 2010 included two severe droughts (2005 and 2010) and a flooding year (2009). The effects of such climate extremes were detected in annual sums of fluxes as well as in other components of the carbon and water cycles, such as gross primary production and water use efficiency. Gap-filling and flux-partitioning were applied in order to fill gaps due to missing data, and errors analysis made it possible to infer the uncertainty on the carbon balance. Overall, the site was found to have a net carbon uptake of ≈5 t C ha−1 year−1, but the effects of the drought of 2005 were still noticed in 2006, when the climate disturbance caused the site to become a net source of carbon to the atmosphere. Different regions of the Amazon forest might respond differently to climate extremes due to differences in dry season length, annual precipitation, species compositions, albedo and soil type. Longer time series of fluxes measured over several locations are required to better characterize the effects of climate anomalies on the carbon and water balances for the whole Amazon region. Such valuable datasets can also be used to calibrate biogeochemical models and infer on future scenarios of the Amazon forest carbon balance under the influence of climate change.

Climate change in the Amazon Basin: Tipping points, changes in extremes, and impacts on natural and human systems

The Amazon River system is the single, largest source of freshwater on Earth and its flow regime is subject to interannual and long-term climate variability, which translate into large variations in downstream discharge (Richey et al., 1989; Marengo and Nobre, 2001; Marengo 2004, 2005, 2006, 2007; Milly et al., 2005, Marengo et al., 2008a, b; Cox et al., 2008; Zeng et al., 2008). To predict future climate (rainfall) change and consequent river variability an understanding of the physical mechanisms related to regional and large-scale atmospheric–oceanic–biospheric forcings is required. The temporal and spatial nature and impact of any variability in the hydrometeorology of the Amazon Basin must be considered in this context.

Long-term potential for tropical-forest degradation due to deforestation and fires in the Brazilian Amazon.

Biome models of the global climate-vegetation relationships indicate that most of the Brazilian Amazon has potential for being covered by tropical forests. From current land-use processes observed in the region, however, substantial deforestation and fire activity have been verified in large portions of the region, particularly along the Arc of Deforestation. In a first attempt to evaluate the long-term potential for tropical-forest degradation due to deforestation and fires in the Brazilian Amazon, we analysed large-scale data on fire activity and climate factors that drive the distribution of tropical forests in the region. The initial analyses and results from this study lead to important details on the relations between these quantities and have important implications for building future parameterizations of the vulnerability of tropical forests in the region.

Sensitivity of the Amazon biome to high resolution climate change projections

Despite the reduction in deforestation rate in recent years, the impact of global warming by itself can cause changes in vegetation cover. The objective of this work was to investigate the possible changes on the major Brazilian biome, the Amazon Rainforest, under different climate change scenarios. The dynamic vegetation models may simulate changes in vegetation distribution and the biogeochemical processes due to climate change. Initially, the Inland dynamic vegetation model was forced with initial and boundary conditions provided by CFSR and the Eta regional climate model driven by the historical simulation of HadGEM2-ES. These simulations were validated using the Santarem tower data. In the second part, we assess the impact of a future climate change on the Amazon biome by applying the Inland model forced with regional climate change projections. The projections show that some areas of rainforest in the Amazon region are replaced by deciduous forest type and grassland in RCP4.5 scenario and only by grassland in RCP8.5 scenario at the end of this century. The model indicates a reduction of approximately 9% in the area of tropical forest in RCP4.5 scenario and a further reduction in the RCP8.5 scenario of about 50% in the eastern region of Amazon. Although the increase of CO2 atmospheric concentration may favour the growth of trees, the projections of Eta-HadGEM2-ES show increase of temperature and reduction of rainfall in the Amazon region, which caused the forest degradation in these simulations.

Comparisons of the Noah-MP land surface model simulations with measurements of forest and crop sites in Amazonia

This study evaluates the simulations of the Noah-MP surface processes over a crop and a forest sites in Amazonia using tower observations. Soil moisture simulations agree with the observations in both land covers, mainly during the rainy season. However, simulations show cold biases in the soil temperature at both sites. The magnitude and seasonal cycle of the surface energy fluxes are better simulated at the crop site, although the model significantly underestimates the sensible heat flux at this site. The model reproduces the seasonal pattern of surface runoff at both sites. The Noah-MP model does not adequately simulate the base flow at the crop site, while the simulated total runoff at the forest site is closer to the observation than at the crop site. The results show that, in general, the Noah-MP model simulations for the two sites in Amazonia exhibit fairly realistic performance, particularly over the crop site. However, there are cold biases in soil temperature simulations, which could be related with the parameterization of the equilibrium relationship between soil moisture and soil temperature.