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.

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

Advances on apple production under semiarid climate: N fertigation

The fertilizing management for apple tree is essential, especially for nitrogen, one of the most important nutrients affecting fruit yield. Thus, an experiment was conducted in 2012 and 2013 to evaluate the fruit production, yield and leaf chlorophyll of ?Princesa? and ?Eva? apples as a function of nitrogen fertigation under Brazilian semiarid conditions. The experimental design consisted of randomised blocks, with treatments distributed in a factorial arrangement 2 x 4, corresponding to apple cultivars (Eva and Princesa); and nitrogen doses (160, 120, 80 and 40 kg of N ha-1), with four replications and three plants. Calcium nitrate was used as nitrogen source (15.5% of N) with applications twice a week during 40 days, reaching 12 fertilizing performances through irrigation water. The following variables were evaluated: i) fruit production per plant (kg plant-1); ii) fruit yield (t ha-1); iii) number of fruits per plant; iv) leaf chlorophyll meter readings (index); and v) leaf nitrogen concentration (g kg-1). Princesa apple cultivar if compared to ?Eva? presents a better fruit production performance under Brazilian semiarid. Furthermore, nitrogen doses fertilized through irrigation water have no effect on fruit production of Eva and Princesa apple cultivars during the first production cycle.