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.

Canopy management effects on Syrah grapevines under tropical semi-arid conditions.

The aim of this study was to evaluate the effects of shoot topping and leaf removal practices on vegetative vigor, yield and physicochemical characteristics of the 'Syrah' grape in the semi-arid tropical climate conditions of the São Francisco River Valley in the northeast of Brazil. The experiment was conducted over five growing cycles from 2010 to 2012 in a 'Syrah' commercial vineyard in the municipality of Casa Nova, Bahia, Brazil. Treatments consisted of leaf removal once or twice during fruit set or at the beginning of bunch closure, and one or two shoot toppings of the berry when pea-sized or 10 days after berry growth had commenced. Leaf removal and shoot topping did not affect yield, plant vigor components nor the main attributes of fruit quality. In contrast, there was a seasonal effect with higher yield and better balance between production and vigor in grapes produced in the first half of 2012. The Syrah grape quality was favored in cycles in the first half of the year when its ripening phase coincided with the period of mild temperatures.