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.

Growth and symbiosis of plants with arbuscular mycorrhizal fungi in soil submitted to biochar application.

Arbuscular mycorrhizal fungi (AMF), which is intrinsically present or may be introduced in soils by inoculation, is an example of natural and renewable resource to increase plant nutrient uptake. This kind of fungi produces structures (hyphae, arbuscles and sometimes vesicles) inside the plant root cortex. This mutualistic relationship promotes plant gains in terms of water and nutrient absorption (mainly phosphorus). Biochar can benefit plant interaction with AMF, however, it can contain potentially toxic compounds such as heavy metals and organic compounds (e.g. dioxins, furans and polycyclic aromatic hydrocarbons), depending on the feedstock and pyrolysis conditions, which may damage organisms. For these reasons, the present work will approach the impacts of biochar application on soil attributes, AMF-plant symbiosis and its responses in plant growth and phosphorus uptake. Eucalyptus biochar produced at high temperatures increases sorghum growth; symbiosis with AMF; and enhances spore germination. Enhanced plant growth in the presence of high temperature biochar and AMF is a response of root branching stimulated by an additive effect between biochar characteristics and root colonization. Biochar obtained at low temperature reduces AMF spore germination; however it does not affect plant growth and symbiosis in soil.