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The atmosphere is made up of several components, among them are present solid and liquid particles in suspension called aerosols. Aerosols make an important role by absorbing and reflecting radiation from space. The work aims to evaluate the relationship between aerosols and Photosynthetically Active Radiation (PAR) reaching the earth’s surface on cloudless-sky days with development of statistical models. PAR radiation data were acquired in the SONDA Project network sites located in Campo Grande – MS, Cuiabá – MT and Petrolina PE. In addition, data acquired in Alta Floresta – MT were provided by AERONET. Aerosol Optical Thickness (AOT) daily average data for all stations were obtained from the AERONET. The time periods vary according to PAR and AOT data availability in cloudless-sky days. For Campo Grande, 43 clear sky days in 2008 were analyzed; 116 days from 2006 to 2008 for Cuiabá; 31 days for the years 2005 and 2007 in Petrolina; and for Alta Floresta 30 days from 1999 to 2003. Most of the available data occurs in June, July and August due to dry season in Brazilian Mid-West and Central regions. Due to the spectral range of the PAR radiation, 400 nm to 700 nm, AOT data at wavelengths 675 nm, 500 nm and 440 nm were used in this work. Linear, polynomial of second degree and exponential empirical models were developed with 70% of the available data relating the KPAR parameter and AOT at the specified wavelengths. The KPAR index is defined as the ratio between the daily PAR irradiation at the surface and the solar radiation incident on the top of the atmosphere. The 30% of the remaining data were used to evaluate model performance. The statistical deviations MBE (Mean Bias Error) and RMSE (Root Mean Square Error) were used for the models validation. In general, all models showed satisfactory performance, as indicated by low statistical deviations. The polynomial models have presented higher deviations from that of the others regressions models used. The polynomial models achieve RMSE values of 11.54% for Alta Floresta in the wavelength of 500 nm, while the exponential and linear models showed, respectively, 2.94 % and 5.78%. The statistical models showed lower deviations at Campo Grande and Petrolina with an RMSE values around 1.5%. For the future, it is intend to perform further analysis with larger data numbers aiming better performance for empirical models, using also, data from the Reference Station SONDA SMS, installed at the Southern Space Observatory – SSO/CRS/CCR/INPE – MCT, (29º26′ S 53º48′ W), for a Brazilian territory extensive investigation.

Changes in land surface properties, often associated with changing vegetation and soil characteristics, have dramatic effects on the climate simulated by general circulation models (GCMs). Thus, during the last several years efforts are aimed to evaluating land surface parameterizations. Currently, there is a diversity of soil-vegetation-atmosphere schemes. The objectives of these models are mainly in the improvement of bottom boundary condition of the atmospheric models by accurately evaluating the ground-surface impact on the atmosphere. The surface schemes ranges from those that simulate only the exchange of momentum, energy and water vapor between vegetated surface and atmosphere, as the “Simplified Simple Biosphere Model” SSiB, even more sophisticated models, as, for example, the “Integrated Biosphere Simulator Model” IBIS, which includes, in addition to processes listed above, the terrestrial carbon and nutrient cycling, and vegetation dynamics. The SSiB is coupled to the global and regional meteorological models of the Brazilian Center for Weather Forecasting and Climate Studies/Brazilian National Institute for Space Research (CPTEC/INPE). It includes a complex treatment of the albedo, the energy balance of the surface and the soil moisture. Recently, the IBIS-2.6 model of dynamical vegetation was also coupled to the GCM/CPTEC to make it able to diagnose the loss of biomass and its impact on storage of energy and water. To test the surface modelxs performance, different simulations are been proposed. The goal of this work is evaluating the energy balance simulated by SSiB and IBIS models against two sets of measurements. The data were collected at a tropical rainforest in the Brazilian Amazon and in the caatinga in Northeast Brazil. The dataset for Amazonian forest used in this work was collected at the Reserva Biológica do Jaru (62º22’W; 10º45’S) forest site, located in the state of Rondônia, Brazil. Micrometeorological and hydrological data were collected during 2001, as part of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia LBA. The caatinga is a type of vegetation adapted to the semi-arid conditions, with varied physiognomy, and covers the northeast portion of Brazil. It occupies an extension of about 800,000 km2 that corresponds to 70% of the region. Micrometeorological and hydrological data were collected from July 2004 to June 2005, in a site located in the Agricultural Research Center of the Semi-Arid Tropic CPATSA (9°03′ S; 40°19′ W; 350 m, Petrolina, Pernambuco, Brazil. In this study, the surface schemes were used in the so-called off-line mode. In this version, the models were not coupled with the atmospheric model and were driven using prescribed atmospheric forcing. The results of the energy balance simulated by both models and for each vegetation type were compared. The results showed that the net radiation was well simulated by SSiB model for both caatinga and forest sites, while the latent heat fluxes were overestimated for both sites. In contrast, the sensible heat flux was underestimated for forest and overestimated for caatinga, mainly during the night period; notwithstanding, the values for forest were nearer to the observed ones. With the IBIS model, the simulations for the radiation balance were close to the observed data for forest site, in contrast to the obtained for caatinga site. The latent heat flux and the sensible heat flux were underestimated by the model for caatinga. For forest site, the energy partition was not well simulated; thus, the SSiB model provides better results. It is highlighted that the simulations with the IBIS model were made considering the parameters previously calibrated for both forest and caatinga. Thus, these results are preliminary and other analyses are still necessary to better understand the biophysical processes involved. ion balance were close to the observed data for forest site, in contrast to the obtained for caatinga site. The latent heat flux and the sensible heat flux were underestimated by the model for caatinga. For forest site, the energy partition was not well simulated; thus, the SSiB model provides better results. It is highlighted that the simulations with the IBIS model were made considering the parameters previously calibrated for both forest and caatinga. Thus, these results are preliminary and other analyses are still necessary to better understand the biophysical processes involved.

Modelos espacialmente explícitos são simulações computacionais que descrevem algum fenômeno que tem uma ocorrência bem definida no espaço e no tempo. Desse modo, o desflorestamento é um fenômeno espaço temporal. Ele ocorre em uma determinada localização no espaço e no tempo. Então, é possível fazer modelos que buscam entender este fenômeno com relação a estas duas variáveis. Por exemplo, aproximação a infra-estruturas afeta o desflorestamento? Ou um incentivo de algum programa político que ocorre em um determinado período do tempo? Este trabalho descreve alguns elementos básicos para construir modelos espacialmente explícitos.

The Amazon forest plays an important and complex role in the global water and carbon cycle, and important advances have been made in understanding the underlying processes in recent years. However, reconciling modeled mechanisms with observations across scales remains a challenge. To better address this challenge, we initiated a Model Intercomparison Project for the Large-Scale Biosphere Atmosphere Experiment in Amazonia (LBA-MIP) to integrate modeling and observational studies for improved understanding of Amazon basin water and carbon cycling. Here, we report on further analysis from this project, which uses a network of meteorological observational sites (the BrasilFlux network) in forest and converted lands to drive a suite of land surface ecosystem models that simulate energy, water and CO2 fluxes. We focus here on controls on gross primary photosynthesis (GPP) and evapotranspiration (ET), and in particular on how well models capture the observed diurnal and seasonal cycles across sites. Light, moisture, and phenological controls on GPP and ET were intercompared across models and observations. Available energy as the dominant control on ET across the Amazon basin is best reproduced by those models with high moisture storage capacity, but models were able to accomplish this via different ecohydrological mechanisms. Further, some models which capture observed seasonal cycles in ET predict unrealistic patterns of leaf area index (LAI) and/or overestimate photosynthesis and light use efficiency (LUE). Modeled photosynthesis by many models also remains oversensitive to short-term drought in evergreen seasonally dry forests. This analysis highlights the need to incorporate more realistic soil-root-leaf continuum hydraulics in models and invest in ancillary measurements at flux tower sites (e.g., deep soil moisture profiles, LAI, and litterfall) designed to aid empirical discrimination among different model mechanisms used to match observed seasonal patterns.

Based on studies of the United Nations Population Fund (UNPA), 82% of South America populations live in urban areas as of 2007, with a rate of growth about 1,7% a year. In Brazil, 70% of the population lives in the area originally covered by the Atlantic Forest (Mata Atlântica). The urban sprawl has been degrading the original ecosystem, and the current impact on the environment will not be easily reversed. Therefore, the goal of this paper is to analysize the changes in precipitation patterns over the past 5 decades and their possible associations to the gradient of urbanization in Eastern of São Paulo State, Brazil. We used precipitation time series of from Agência Nacional das Águas (ANA) and NOAA Analysis (National Oceanic and Atmospheric Administration), and satellite images from LANDSAT. The low frequency effects are filtered in the rainfall time series. The data was grouped in 10-year subsets and then, the annual distribution was analyzed comparing each decade in order to evaluate the changes in precipitation over the last 50 years. The results show a change in the annual distribution along the time.