Publicações

Observational data obtained at two wind towers located at the Alagoas State (Northeast of Brazil – NEB) were used to evaluate the influence of horizontal grid setup on the reliability of wind estimates provided by the numerical weather prediction model BRAMS. Statistical analysis were performed to evaluate the model skill in reproducing the temporal evolution, frequency distribution and the mean daily cycle of the wind observations in both locations. The study was performed for the two typical climate seasons: the rainy (May/2008) and the dry one (November/2008). The results demonstrated that the deviations of wind estimates gets lower if the horizontal grid resolution is increased from 8 km to 2 km. No results improvement occurred when using horizontal grid resolution higher than 2 km. The confiability on wind speed estimates were higher at Girau do Ponciano (GRP), located in the continental area where the sea breeze is less important to the atmospheric dynamic. The results demonstrate that wind data observations play an important role in defining the best configuration and parameterization model to better estimates the availability of wind energy on a specific region, particularly for NEB.

In this report monthly thunderstorm days (TD) in 14 large cities of Brazil from 1951 to 2010 are compared with data obtained in the first part of the 20th century in the same cities and correlated with sea surface temperature (SST) of the equatorial Pacific Ocean and Tropical Atlantic Ocean. On average over all cities, 77 TD are reported each year after 1951, against 43 TD in the first part of the 20th century, an increase of 79%. The increase seems to be related to increase in the urbanization in these cities, and not to global warming. A comparative analysis of TD for the whole country with El NiñoSouthern Oscillation (ENSO) events suggests that the tendency for El Niño to increase the thunderstorm activity is evident in the South (only in the Spring/Summer), Southeast, Northeast and North (only in the Fall/Winter) regions. Regarding the Tropical Atlantic Ocean SST, a similar analysis using the Tropical South Atlantic (TSA) and Tropical North Atlantic (TNA) indices suggests that no significant differences exist between the thunderstorm activity for the two extreme positive and negative TSA conditions in the South and Southeast regions, while in the Northeast and North regions there is a significant increase in the thunderstorm activity during extreme positive TSA. Differently, only in the Northeast region there is a significant influence of TNA on thunderstorm activity. The influence is such that the thunderstorm activity is larger for the extreme negative TNA than for extreme positive TNA. All significant changes were quantified and significant variations were observed. For all parameters investigated (ENSO, TSA and TNA) the Northeast region showed the most significant changes.

The performance of seven regional climate models in simulating the radiation and heat fluxes at the surface over South America (SA) is evaluated. Sources of uncertainty and errors are identified. All simulations have been performed in the context of the CLARIS-LPB Project for the period 1990-2008 and are compared with the GEWEX-SRB, CRU, and GLDAS2 dataset and NCEP-NOAA reanalysis. Results showed that most of the models overestimate the net surface short-wave radiation over tropical SA and La Plata Basin and underestimate it over oceanic regions. Errors in the short-wave radiation are mainly associated with uncertainties in the representation of surface albedo and cloud fraction. For the net surface long-wave radiation, model biases are diverse. However, the ensemble mean showed a good agreement with the GEWEX-SRB dataset due to the compensation of individual model biases. Errors in the net surface long-wave radiation can be explained, in a large proportion, by errors in cloud fraction. For some particular models, errors in temperature also contribute to errors in the net long-wave radiation. Analysis of the annual cycle of each component of the energy budget indicates that the RCMs reproduce generally well the main characteristics of the short- and long-wave radiations in terms of timing and amplitude. However, a large spread among models over tropical SA is apparent. The annual cycle of the sensible heat flux showed a strong overestimation in comparison with the reanalysis and GLDAS2 dataset. For the latent heat flux, strong differences between the reanalysis and GLDAS2 are calculated particularly over tropical SA.

Much research has been devoted to understanding the complexity of biogeochemical and physical processes responsible for the greenhouse gas (GHG) emissions from hydropower reservoirs. Spatial complexity and heterogeneity of GHG emission may be observed in these systems because it is dependent on flooded biomass, river inflow, primary production and dam operation. In this study, we investigate the relationships between waterair CO2 fluxes and phytoplanktonic biomass in Funil Reservoir, an old and stratified tropical reservoir, where intense phytoplankton blooms and low partial pressure of CO2 (pCO2) are observed. Our results showed that Funil Reservoir sea sonal and spatial variability of chlorophyll concentration (Chl) and pCO2 is more related to changes in river inflow over the year than environmental factor such as air temperature and solar radiation. Field data and hydrodynamic simulations reveal that the river inflow contributes to increased heterogeneity in dry season due to the variation of reservoir retention time and river temperature. Contradictory conclusion can be drawn if temporal data collected only near the dam is considered instead of spatial data to represent CO2 fluxes in whole reservoir. The average CO2 fluxes was −17.6 and 22.1 mmolm−2 d−2 considering data collected near the dam and spatial data, respectively, in periods of low retention time. In this case, the lack of spatial information can change completely the role of Funil Reservoir regarding GHG emissions. Our results support the idea that Funil Reservoir is a dynamic system where the hydrodynamics represented by changes in river inflow and retention time is potentially more important force driving both Chl and pCO2 spatial variability than in-system ecological factors.

Strategies for mitigating global climate change require accurate estimates of the emissions of greenhouse gases (GHGs). A strong consensus in the global scientific community states that efforts to control climate change require stabilization of the atmospheric concentration of GHGs (as per a recent compilation; (IPCC 2013)). Estimates of the amounts of carbon dioxide and other GHGs emitted to the atmosphere, as well as the amounts absorbed by terrestrial and aquatic systems, are crucial for planning, analyzing, validating and at global scale verifying mitigation efforts and for analyzing scenarios of future emissions. The magnitude and distribution of current emissions and the path of future emissions are both of considerable importance. It is critical that we have estimates of emissions and that we acknowledge and deal with the uncertainty in our best estimates. The range of issues that derive from uncertainty in emissions estimates was the subject of the 3rd International Uncertainty Workshop held in Lviv, Ukraine, 2010, and is the subject of this special issue.

We investigated spatial-temporal variability and long-term trends of rainfall over the Metropolitan Area of São Paulo (MASP). Due to a lack of continuous and consistent rainfall observations in the MASP, a database for the spatial analysis was created from a composite of 94 stations, with daily rainfall data for 1973-1997, plus data from the University of São Paulo’s Institute of Geophysics and Astronomy (IAG/USP) station for 1933-2011, as the base of the long-term variability analysis. It is shown that the seasonal and interannual rainfall variability result from underlying local influences and remote large-scale atmospheric dynamics. These effects appear to be more complex during the austral spring and fall seasons, and seem to determine the duration of the rainy period over the MASP. The intraseasonal (30-60 d) oscillation of rainfall does not exhibit any long-term modulation; interannual rainfall variability for 1975-1990 appears modulated by the El Niño-Southern Oscillation (ENSO) scale (2-8 yr). Nonetheless, there exists statistical evidence that the rainfall climate over the MASP had an abrupt change at the end of the 1950s, but the global climatic change that occurred in the middle of the 1970s is not evident. The progressive increase in the frequency of rainy days and total daily rainfall throughout the period of analysis are prominent factors in the trend of annual and seasonal rainfall. Furthermore, rainfall variability over the MASP may be modulated by the Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and ENSO at seasonal scales, altering the normal progression of the seasonal rainfall cycle, while at interannual scales, each one of these patterns drives the rainfall variability, mainly when each pattern reaches its extreme value.

The Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the integrity of the global climate: tropical forests and woodlands (e.g., savannas) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climates. Consider the importance of the Amazon biome to the global climate changes impacts and the role of the protected area in the conservation of biodiversity and state-of-art of downscaling model techniques based on ANN Calibrate and run a downscaling model technique based on the Artificial Neural Network (ANN) that is applied to the Amazon region in order to obtain regional and local climate predicted data (e.g., precipitation). Considering the importance of the Amazon biome to the global climate changes impacts and the state-of-art of downscaling techniques for climate models, the shower of this work is presented as follows: the use of ANNs good similarity with the observation in the cities of Bel´em and Manaus, with correlations of approximately 88.9% and 91.3%, respectively, and spatial distribution, especially in the correction process, representing a good fit.

We develop a systems framework for exploring adaptation pathways to climate change among people in remote and marginalized regions. The framework builds on two common and seemingly paradoxical narratives about people in remote regions. The first is recognition that people in remote regions demonstrate significant resilience to climate and resource variability, and may therefore be among the best equipped to adapt to climate change. The second narrative is that many people in remote regions are chronically disadvantaged and therefore are among the most vulnerable to climate change impacts. These narratives, taken in isolation and in extremis, can have significant maladaptive policy and practice implications. From a systems perspective, both narratives may be valid, because they form elements of latent and dominant feedback loops that require articulation for a nuanced understanding of vulnerability-reducing and resilience-building responses in a joint framework. Through literature review and community engagement across three remote regions on different continents, we test the potential of the framework to assist dialogue about adaptation pathways in remote marginalized communities. In an adaptation pathway view, short-term responses to vulnerability can risk locking in a pathway that increases specific resilience but creates greater vulnerability in the long-term. Equally, longer-term actions towards increasing desirable forms of resilience need to take account of short-term realities to respond to acute and multiple needs of marginalized remote communities. The framework was useful in uniting vulnerability and resilience narratives, and broadening the scope for adaptation policy and action on adaptation pathways for remote regions.

The influence of forest loss on rainfall remains poorly understood. Addressing this challenge, Spracklen et al. recently presented a pantropical study of rainfall and land cover that showed that satellite-derived rainfall measures were positively correlated withthe degree to whichmodel-derived air trajectories had been exposed to forest cover. This result confirms the influence of vegetation on regional rainfall patterns suggested in previous studies.However, the conclusion of Spracklen et al.-that differences in rainfall reflect air moisture content resulting from evapotranspiration while the circulation pattern remains unchanged-appears undermined by methodological inconsistencies. Here methodological problems are identified with the underlying analyses and the quantitative estimatesfor rainfall change predicted if forest cover is lost in theAmazon. Alternative explanations are presented that include the distinct role of forest evapotranspiration in creatinglow-pressure systems that draw moisture from the oceans to the continental hinterland. A wholly new analysis of meteorological data from three regions in Brazil, including thecentral Amazon forest, reveals a tendency for rainy days during the wet season with column water vapor (CWV) exceeding 50mm to have higher pressure than rainless days, while at lower CWV, rainy days tend to have lower pressure than rainless days. The coupling between atmospheric moisture content and circulation dynamics underlines that the danger posed by forest loss is greater than suggested by consideration of moisture recycling alone.

Rainfall over the northeast part of Brazil is highly influenced by several weather systems such as trade winds, the Intertropical Convergence Zone, Easterly Wave Disturbances, Bolivia High and Atlantic Sub-tropical Anticyclone. The objectives of this work were to identify, using cluster analysis, spatial and temporal rainfall patterns for the northeastern Brazilian state of Alagoas, and to relate those patterns with the weather systems that occur over that region, which is the most populated area of semiarid climate in the world. The dataset consists of 30-years long time series of rainfall from 36 stations located on the state. Hierarchical clustering using Ward’s algorithm was used to classify the sites into five groups. The stations were grouped according to the distance to the coast and orography. Rainfall is not uniformly distributed in space and time in all regions, such as in two groups within the arid zone, where more than 60% of annual precipitation occurs in a period of 5 months (March to July). Rainfall was highest in the coastal regions and over areas of the state with high altitude, due to orographic rainfall. Overall, rainfall over the state of Alagoas is characterized by strong gradients from the coast to the continent and from north to south due to the physiography of the region and the influence of weather systems with different time scales.

A coupled global atmosphere-ocean model is used to study the influence of the Antarctica ice sheet in a configuration that mimics that of the early Miocene on the atmospheric and oceanic circulations. Based on different climate simulations of the present day (CTR) and conducted with distinct Antarctic ice sheet topography (AIS-EXP), it is found that the reduction of the Antarctic ice sheet topography (AIS) induces warming of the Southern Hemisphere and reduces the meridional thermal gradient. Consequently, the atmospheric transient low level eddy heat flux [(v2T2)] and the eddy momentum flux [(u2v2)] are reduced causing the reduced transport of heat from the mid-latitudes to the pole. The stationary flow and transient wave anomalies generate changes in the SSTs which modify the rate of deep water formation, strengthening the formation of the Antarctic Bottom Water. Substantial changes are predicted to occur in the atmospheric and oceanic heat transport and a comparison between the total heat transport of the atmosphere-ocean system, as simulated by the AIS-EXP and the CTR runs, shows that the reduction of the AIS height leads to reduced Southern Hemisphere poleward and increased equatorward heat transport. These results are in agreement with reduced storm track activities and baroclinicity.

Resolving challenges related to the sustainability of natural capital and ecosystem services is an urgent issue. No roadmap on reaching sustainability exists; and the kind of sustainable land use required in a world that acknowledges both multiple environmental boundaries and local human well-being presents a quandary. In this commentary, we argue that a new globally consistent and expandable systems-analytical framework is needed to guide and facilitate decision making on sustainability from the planetary to the local level, and vice versa. This framework would strive to link a multitude of Earth system processes and targets; it would give preference to systemic insight over data complexity through being highly explicit in spatiotemporal terms. Its strength would lie in its ability to help scientists uncover and explore potential, and even unexpected, interactions between Earths subsystems with planetary environmental boundaries and socioeconomic constraints coming into play. Equally importantly, such a framework would allow countries such as Brazil, a case study in this commentary, to understand domestic or even local sustainability measures within a global perspective and to optimize them accordingly.