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SummaryBackground: With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played.

Summary• Climate change will very likely affect most forests in Amazonia during the course of the 21st century, but the direction and intensity of the change are uncertain, in part because of differences in rainfall projections. In order to constrain this uncertainty, we estimate the probability for biomass change in Amazonia on the basis of rainfall projections that are weighted by climate model performance for current conditions.• We estimate the risk of forest dieback by using weighted rainfall projections from 24 general circulation models (GCMs) to create probability density functions (PDFs) for future forest biomass changes simulated by a dynamic vegetation model (LPJmL).• Our probabilistic assessment of biomass change suggests a likely shift towards increasing biomass compared with nonweighted results. Biomass estimates range between a gain of 6.2 and a loss of 2.7 kg carbon m )2 for the Amazon region, depending on the strength of CO 2 fertilization.• The uncertainty associated with the long-term effect of CO 2 is much larger than that associated with precipitation change. This underlines the importance of reducing uncertainties in the direct effects of CO 2 on tropical ecosystems.

Previous studies have demonstrated statistically significant associations between disease and climate variations, highlighting the potential for developing climate-based epidemic early warning systems. However, limitations to such studies 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 in the context of dengue fever in South East Brazil, where dengue epidemics impact heavily on Brazilian public health services. A spatio-temporal generalised linear mixed model (GLMM) is developed, including both climate and non-climate covariates. Overdispersion and unobserved confounding factors are accounted for via a Negative Binomial formulation and inclusion of both spatial and temporal random effects. Model parameters are estimated in a Bayesian framework to allow full posterior predictive distributions for disease risk to be derived in time and space. Detailed probabilistic forecasts can then be issued for any pre-defined 'alert' thresholds, allowing probabilistic early warnings for dengue epidemics to be made. Using this approach with the criterion 'greater than a 50% chance of exceeding 300 cases per 100,000 inhabitants', successful epidemic alerts would have been issued for 81% of the 54 regions that experienced epidemic dengue incidence rates in South East Brazil, during the major 2008 epidemic. Use of seasonal climate forecasts in this model allows predictions to be made several months ahead of an impending epidemic. We argue that the general modelling framework, described here in the context of dengue in Brazil, is potentially valuable in similar applications, both outside of Brazil and for other climate-sensitive diseases.

There is a remarkable difference between the maximum temperature of black smoker effluent (350 degrees C-400 degrees C) and the temperature of the solidifying magma which heats it (approximately 1,200 degrees C). It has been suspected for some time that the nonlinear thermodynamic properties of water might be responsible for this discrepancy. Here, we translate this hypothesis into a physical model, by examining the internal temperature structure of convection cells in a porous medium. We demonstrate that, at pressures appropriate to seafloor crust, plumes of pure water form naturally at approximately 400 degrees C for any heat source with temperature greater than approximately 500 degrees C. Higher temperatures are confined to a boundary layer at the base of the convection cell, where the flow is horizontal. The phenomenon is explained analytically using the thermodynamic properties of water, and is illustrated by numerical simulations. Our model predicts the existence of the high-temperature 'reaction zone' found in ophiolites and suggests that vent temperatures will remain steady as magma chambers solidify and cool.

[1] Time series measurements of the temperature and exit velocity of hydrothermal effluent suggest that seafloor hydrothermal systems are modulated by tidal processes. Here we apply the theory of poroelasticity to predict the magnitude and phase of tidally induced changes in the temperature and flow rate of hydrothermal effluent at the seafloor. We construct a model in which the steady state upwelling of buoyant fluid in the crust is modulated by tidal loading of a one-dimensional seafloor by the overlying water column. The nature of the solution is controlled by the relative magnitudes of three length scales. These are (1) the depth H of the heat source below the seafloor, (2) the skin depth D over which pore pressure signals can diffuse during one tidal cycle, and (3) the advective length scale A over which the upwelling flow advects thermal signals during one tidal cycle. We consider the likely magnitude of the parameters in a real system as well as the limitations of a one-dimensional representation of that system. We then discuss how observational data on the magnitude and phase lag of temperature and flow rate could be used to constrain the subseafloor parameters that govern hydrothermal circulation within the seafloor.

[1] We use a simplified model of convection in a porous medium to investigate the balances of mass and energy within a subseafloor hydrothermal convection cell. These balances control the steady state structure of the system and allow scalings for the height, permeability, and residence time of the ''reaction zone'' at the base of the cell to be calculated. The scalings are presented as functions of (1) the temperature T D of the heat source driving the convection and (2) the total power output F U . The model is then used to illustrate how the nonlinear thermodynamic properties of water may impose the observed upper limit of $400°C on vent temperatures. The properties of water at hydrothermal conditions are contrasted with those of a hypothetical ''Boussinesq fluid'' for which temperature variations in fluid properties are either linearized or ignored. At hydrothermal pressures, water transports a maximum amount of energy by buoyancy-driven advection at $400°C. This maximum is a consequence of the nonlinear thermodynamic properties of water and does not arise for a simple Boussinesq fluid. Inspired by the ''Malkus hypothesis'' and by recent work on dissipative systems, we speculate that convection cells in porous media attain a steady state in which the upwelling temperature T U maximizes the total power output of the cell. If true, this principle would explain our observation (in previous numerical simulations) that water in hydrothermal convection cells upwells at T U $ 400°C when driven by a heat source above $500°C. INDEX TERMS: 3015 Marine

Russia's forests play an important role in the global carbon cycle. Because of their scale and interannual variability, forest fires can change the direction of the net carbon flux over Eurasia. 2002 and 2003 were the first two consecutive years in the atmospheric record in which the carbon content rose by more than 2 ppm per year. Northern Hemisphere fires could be the reason. We show that 2002 and 2003 were the two years with the largest fire extent in Central Siberia since 1996 using new measurements of burned forest area in Central Siberia derived from remote sensing. To quantify the relationship between Siberian forest fires and climate variability, we compare these measurements with time‐series of large‐scale climatic indices for the period 1992–2003. This paper is amongst the first studies that analyse statistical relationships between interannual variability of forest fires in Russia and climate indices. Significant relationships of annual burned forest area with the Arctic Oscillation, summer temperatures, precipitation, and the El Niño index NINO4 were found (p < 0.1). In contrast, we find no significant relation with the El Niño indices NINO1, NINO3 or SOI (p > 0.1). Interannual forest fire variability in Central Siberia could best be explained by a combination of the Arctic Oscillation index and regional summer temperatures (r2 = 0.80).