The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil.

Previous studies demonstrate statistically significant associations between disease and climate variations, highlighting the potential for developing climate-based epidemic early warning systems. However, limitations include failure to allow for non-climatic confounding factors, limited geographical/temporal resolution, or lack of evaluation of predictive validity. Here, we consider such issues for dengue in Southeast Brazil using a spatio-temporal generalised linear mixed model with parameters estimated in a Bayesian framework, allowing posterior predictive distributions to be derived in time and space.

On the visualization, verification and recalibration of ternary probabilistic forecasts.

We develop a graphical interpretation of ternary probabilistic forecasts in which forecasts and observations are regarded as points inside a triangle. Within the triangle, we define a continuous colour palette in which hue and colour saturation are defined with reference to the observed climatology. In contrast to current methods, forecast maps created with this colour scheme convey all of the information present in each ternary forecast.

Model complexity versus ensemble size: allocating resources for climate prediction.

A perennial question in modern weather forecasting and climate prediction is whether to invest resources in more complex numerical models or in larger ensembles of simulations. If this question is to be addressed quantitatively, then information is needed about how changes in model complexity and ensemble size will affect predictive performance. Information about the effects of ensemble size is often available, but information about the effects of model complexity is much rarer.

Development of probability density functions for future South American rainfall.

*We estimate probability density functions (PDFs) for future rainfall in five regions of South America, by weighting the predictions of the 24 Coupled Model Intercomparison Archive Project 3 (CMIP3) General Circulation Models (GCMs). The models are rated according to their relative abilities to reproduce the inter-annual variability in seasonal rainfall. *The relative weighting of the climate models is updated sequentially according to Bayes' theorem, based on the biases in the mean of the predicted time-series and the distributional fit of the bias-corrected time-series.

MEP and planetary climates: insights from a two-box climate model containing atmospheric dynamics.

A two-box model for equator-to-pole planetary heat transport is extended to include simple atmospheric dynamics. The surface drag coefficient CD is treated as a free parameter and solutions are calculated analytically in terms of the dimensionless planetary parameters eta (atmospheric thickness), omega (rotation rate) and xi (advective capability). Solutions corresponding to maximum entropy production (MEP) are compared with solutions previously obtained from dynamically unconstrained two-box models.

Increasing risk of Amazonian drought due to decreasing aerosol pollution.

The Amazon rainforest plays a crucial role in the climate system, helping to drive atmospheric circulations in the tropics by absorbing energy and recycling about half of the rainfall that falls on it. This region (Amazonia) is also estimated to contain about one-tenth of the total carbon stored in land ecosystems, and to account for one-tenth of global, net primary productivity. The resilience of the forest to the combined pressures of deforestation and global warming is therefore of great concern, especially as some general circulation models (GCMs) predict a severe drying of Amazonia in the twenty-first century.