Método de elaboração de mapas dos cenários climáticos futuros para o Brasil.

Human activities are altering greenhouse gas concentrations in the atmosphere and causing global climate change. The issue of impacts of human-induced climate change has become increasingly important in recent years. The objective of this work was to develop a database of climate information of the future scenarios using a Geographic Information System (GIS) tools. Future scenarios focused on the decades of the 2020?s, 2050?s, and 2080?s (scenarios A2 and B2) were obtained from the General Circulation Models (GCM) available on Data Distribution Centre from the Third Assessment Report (TAR) of Intergovernmental Panel on Climate Change (IPCC). The TAR is compounded by six GCM with different spatial resolutions (ECHAM4:2.8125×2.8125º, HadCM3: 3.75×2.5º, CGCM2: 3.75×3.75º, CSIROMk2b: 5.625×3.214º, and CCSR/NIES: 5.625×5.625º). The mean monthly of the climate variables was obtained by the average from the available models using the GIS spatial analysis tools (arithmetic operation). Maps of mean monthly variables of mean temperature, minimum temperature, maximum temperature, rainfall, relative humidity, and solar radiation were elaborated adopting the spatial resolution of 0.5° X 0.5° latitude and longitude. The method of elaborating maps using GIS tools allowed to evaluate the spatial and distribution of future climate assessments. Nowadays, this database is being used in studies of impacts of climate change on plant disease of Embrapa projects.

Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2.

Dynamic global vegetation models (DGVMs) simulate surface processes such as the transfer of energy, water, CO2, and momentum between the terrestrial surface and the atmosphere, biogeochemical cycles, carbon assimilation by vegetation, phenology, and land use change in scenarios of varying atmospheric CO2 concentrations. DGVMs increase the complexity and the Earth system representation when they are coupled with atmospheric global circulation models (AGCMs) or climate models. However, plant physiological processes are still a major source of uncertainty in DGVMs. The maximum velocity of carboxylation (Vcmax), for example, has a direct impact over productivity in the models. This parameter is often underestimated or imprecisely defined for the various plant functional types (PFTs) and ecosystems. Vcmax is directly related to photosynthesis acclimation (loss of response to elevated CO2), a widely known phenomenon that usually occurs when plants are subjected to elevated atmospheric CO2 and might affect productivity estimation in DGVMs. Despite this, current models have improved substantially, compared to earlier models which had a rudimentary and very simple representation of vegetation?atmosphere interactions. In this paper, we describe this evolution through generations of models and the main events that contributed to their improvements until the current state-of-the-art class of models. Also, we describe some main challenges for further improvements to DGVMs.