Climate change can influence the transmission of vector-borne diseases (VBDs) through altering the habitat suitability of insect vectors. Here we present global climate model simulations and evaluate the associated uncertainties in view of the main meteorological factors that may affect the distribution of the Asian tiger mosquito ( Aedes albopictus ), which can transmit pathogens that cause chikungunya, dengue fever, yellow fever and various encephalitides. Using a general circulation model at 50 km horizontal resolution to simulate mosquito survival variables including temperature, precipitation and relative humidity, we present both global and regional projections of the habitat suitability up to the middle of the twenty-first century. The model resolution of 50 km allows evaluation against previous projections for Europe and provides a basis for comparative analyses with other regions. Model uncertainties and performance are addressed in light of the recent CMIP5 ensemble climate model simulations for the RCP8.5 concentration pathway and using meteorological re-analysis data (ERA-Interim/ECMWF) for the recent past. Uncertainty ranges associated with the thresholds of meteorological variables that may affect the distribution of Ae. albopictus are diagnosed using fuzzy-logic methodology, notably to assess the influence of selected meteorological criteria and combinations of criteria that influence mosquito habitat suitability. From the climate projections for 2050, and adopting a habitat suitability index larger than 70%, we estimate that approximately 2.4 billion individuals in a land area of nearly 20 million km ² will potentially be exposed to Ae. albopictus . The synthesis of fuzzy-logic based on mosquito biology and climate change analysis provides new insights into the regional and global spreading of VBDs to support disease control and policy making.

Anthropogenic emissions alter atmospheric composition and therefore the climate, with implications for air pollution- and climate-related human health. Mortality attributable to air pollution and non-optimal temperature is a major concern, expected to shift under future climate change and socioeconomic scenarios. In this work, results from numerical simulations are used to assess future changes in mortality attributable to long-term exposure to both non-optimal temperature and air pollution simultaneously. Here we show that under a realistic scenario, end-of-century mortality could quadruple from present-day values to around 30 (95% confidence level:12-53) million people/year. While pollution-related mortality is projected to increase five-fold, temperature-related mortality will experience a seven-fold rise, making it a more important health risk factor than air pollution for at least 20% of the world’s population. These findings highlight the urgent need to implement stronger climate policies to prevent future loss of life, outweighing the benefits of air quality improvements alone.

The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate and socio-economic change are expected to facilitate its range expansion and exacerbate the global vector-borne disease burden. To project shifts in the global habitat suitability of the vector, we developed an ensemble machine learning model, incorporating a combination of a Random Forest and XGBoost binary classifiers, trained with a global collection of vector surveillance data and an extensive set of climate and environmental constraints. We demonstrate the reliable performance and wide applicability of the ensemble model in comparison to the known global presence of the vector, and project that suitable habitats will expand globally, most significantly in the northern hemisphere, putting at least an additional billion people at risk of vector-borne diseases by the middle of the 21st century. We project several highly populated areas of the world will be suitable for Ae. albopictus populations, such as the northern parts of the USA, Europe, and India by the end of the century, which highlights the need for coordinated preventive surveillance efforts of potential entry points by local authorities and stakeholders.

This paper discusses the main achievements of DISARM (Drought and fIre ObServatory and eArly waRning system) project, which developed an early warning system for wildfires in the Eastern Mediterranean. The four pillars of this system include (i) forecasting wildfire danger, (ii) detecting wildfires with remote sensing techniques, (iii) forecasting wildfire spread with a coupled weather-fire modeling system, and (iv) assessing the wildfire risk in the frame of climate change. Special emphasis is given to the innovative and replicable parts of the system. It is shown that for the effective use of fire weather forecasting in different geographical areas and in order to account for the local climate conditions, a proper adjustment of the wildfire danger classification is necessary. Additionally, the consideration of vegetation dryness may provide better estimates of wildfire danger. Our study also highlights some deficiencies of both EUMETSAT (Exploitation of Meteorological Satellites) and LSA-SAF (Satellite Application Facility on Land Surface Analysis) algorithms in their skill to detect wildfires over islands and near the coastline. To tackle this issue, a relevant modification is proposed. Furthermore, it is shown that IRIS, the coupled atmosphere-fire modeling system developed in the frame of DISARM, has proven to be a valuable supporting tool in fire suppression actions. Finally, assessment of the wildfire danger in the future climate provides the necessary context for the development of regional adaptation strategies to climate change.

We consider $\mathcal{N}=(1,1)$ super Yang-Mills theory in $1+1$ dimensions with fundamentals at large $N_c$. A Chern-Simons term is included to give mass to the adjoint partons. Using the spectrum of the theory, we calculate thermodynamic properties of the system as a function of the temperature and the Yang-Mills coupling. In the large-$N_c$ limit there are two noncommunicating sectors, the glueball sector, which we presented previously, and the mesonlike sector that we present here. We find that the mesonlike sector dominates the thermodynamics. Like the glueball sector, the meson sector has a Hagedorn temperature $T_H$, and we show that the Hagedorn temperature grows with the coupling. We calculate the temperature and coupling dependence of the free energy for temperatures below $T_H$. As expected, the free energy for weak coupling and low temperature grows quadratically with the temperature. Also the ratio of the free energies at strong coupling compared to weak coupling, $r_{s-w}$, for low temperatures grows quadratically with $T$. In addition, our data suggest that $r_{s-w}$ tends to zero in the continuum limit at low temperatures.

We estimate the contamination risks from the atmospheric dispersion of radionuclides released by severe nuclear power plant accidents using the ECHAM/Modular Earth Submodel System (MESSy) atmospheric chemistry (EMAC) atmospheric chemistry-general circulation model at high resolution (50 km). We present an overview of global risks and also a case study of nuclear power plants that are currently under construction, planned and proposed in the Eastern Mediterranean and Middle East, a region prone to earthquakes. We implemented continuous emissions from each location, making the simplifying assumption that all potential accidents release the same amount of radioactivity. We simulated atmospheric transport and decay, focusing on ¹³⁷Cs and ¹³¹I as proxies for particulate and gaseous radionuclides, respectively. We present risk maps for potential surface layer concentrations, deposition and doses to humans from the inhalation exposure of ¹³¹I. The estimated risks exhibit seasonal variability, with the highest surface level concentrations of gaseous radionuclides in the Northern Hemisphere during winter.