The near-surface temperature in Mediterranean climate-type regions has increased overall similarly or more rapidly than the global mean rates. Although these regions have comparable climate characteristics and are located at similar latitudes, recent warming acceleration is most pronounced in the Mediterranean Basin. Here, we investigate the contributions of several climate drivers to regional warming anomalies. We consider greenhouse gases, aerosols, solar irradiance, land–atmosphere interactions, and natural climate variability modes. Our results highlight the dominant role of anthropogenic greenhouse gas radiative forcing in all Mediterranean climate-type regions, particularly those in the northern hemisphere. In the Mediterranean Basin, the recent warming acceleration is largely due to the combined effect of declining aerosols and a negative trend in near-surface soil moisture. While land-atmosphere feedbacks are also important in other locations (e.g., California and Southern Africa), this synergy is unique in the Mediterranean Basin. These two regional climate drivers have natural and anthropogenic components of equivalent importance. Such feedbacks are not fully resolved in the current regional climate projections.

The ecological impact of night-time lighting is of concern because of its well-demonstrated effects on animal behaviour. However, the potential of light pollution to change plant phenology and its corresponding knock-on effects on associated herbivores are less clear. Here, we test if artificial lighting can advance the timing of budburst in trees. We took a UK-wide 13 year dataset of spatially referenced budburst data from four deciduous tree species and matched it with both satellite imagery of night-time lighting and average spring temperature. We find that budburst occurs up to 7.5 days earlier in brighter areas, with the relationship being more pronounced for later-budding species. Excluding large urban areas from the analysis showed an even more pronounced advance of budburst, confirming that the urban ‘heat-island’ effect is not the sole cause of earlier urban budburst. Similarly, the advance in budburst across all sites is too large to be explained by increases in temperature alone. This dramatic advance of budburst illustrates the need for further experimental investigation into the impact of artificial night-time lighting on plant phenology and subsequent species interactions. As light pollution is a growing global phenomenon, the findings of this study are likely to be applicable to a wide range of species interactions across the world.

Dengue is hyperendemic in Brazil, with outbreaks affecting all regions. Previous studies identified geographical barriers to dengue transmission in Brazil, beyond which certain areas, such as South Brazil and the Amazon rainforest, were relatively protected from outbreaks. Recent data shows these barriers are being eroded. In this study, we explore the drivers of this expansion and identify the current limits to the dengue transmission zone. We used a spatio-temporal additive model to explore the associations between dengue outbreaks and temperature suitability, urbanisation, and connectivity to the Brazilian urban network. The model was applied to a binary outbreak indicator, assuming the official threshold value of 300 cases per 100,000 residents, for Brazil’s municipalities between 2001 and 2020. We found a nonlinear relationship between higher levels of connectivity to the Brazilian urban network and the odds of an outbreak, with lower odds in metropoles compared to regional capitals. The number of months per year with suitable temperature conditions forAedesmosquitoes was positively associated with the dengue outbreak occurrence. Temperature suitability explained most interannual and spatial variation in South Brazil, confirming this geographical barrier is influenced by lower seasonal temperatures. Municipalities that had experienced an outbreak previously had double the odds of subsequent outbreaks. We identified geographical barriers to dengue transmission in South Brazil, western Amazon, and along the northern coast of Brazil. Although a southern barrier still exists, it has shifted south, and the Amazon no longer has a clear boundary. Few areas of Brazil remain protected from dengue outbreaks. Communities living on the edge of previous barriers are particularly susceptible to future outbreaks as they lack immunity. Control strategies should target regions at risk of future outbreaks as well as those currently within the dengue transmission zone.

This study has investigated serial (temporal) clustering of extratropical cyclones simulated by 17 climate models participating in CMIP5. Clustering was estimated by calculating the dispersion (ratio of variance to mean) of 30 December–February counts of Atlantic storm tracks passing near each grid point. Results from single historical simulations of 1975–2005 were compared to those from historical ERA40 reanalyses from 1958 to 2001 and single future model projections of 2069–2099 under the RCP4.5 climate change scenario.Models were generally able to capture the broad features in reanalyses reported previously: underdispersion/regularity (i.e. variance less than mean) in the western core of the Atlantic storm track surrounded by overdispersion/clustering (i.e. variance greater than mean) to the north and south and over Western Europe. Regression of counts onto North Atlantic Oscillation (NAO) indices revealed that much of the overdispersion in the historical reanalyses and model simulations can be accounted for by NAO variability.Future changes in dispersion were generally found to be small and not consistent across models. The overdispersion statistic, for any 30‐year sample, is prone to large amounts of sampling uncertainty which obscures the climate change signal. For example, the projected increase in dispersion for storm counts near London in the CNRMCM5 model is 0.1 compared to a standard deviation of 0.25. Projected changes in the mean and variance of NAO are insufficient to create changes in overdispersion that are discernible above natural sampling variations.

Losses from natural hazards, including geophysical and hydrometeorological hazards, have been increasing worldwide. This review focuses on the process by which scientific evidence about natural hazards is applied to support decision making. Decision analysis typically involves estimating the probability of extreme events; assessing the potential impacts of those events from a variety of perspectives; and evaluating options to plan for, mitigate, or react to events. We consider issues that affect decisions made across a range of natural hazards, summarize decision methodologies, and provide examples of applications of decision analysis to the management of natural hazards. We conclude that there is potential for further exchange of ideas and experience between natural hazard research communities on decision analysis approaches. Broader application of decision methodologies to natural hazard management and evaluation of existing decision approaches can potentially lead to more efficient allocation of scarce resources and more efficient risk management.

Forecasting the weather is an increasingly data-intensive exercise. Numerical weather prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the ‘ideal’ forecast for decision support: probabilities of future weather outcomes. First, we use quantile regression forests to learn the error profile of each numerical model, and use these to apply empirically derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution, which we demonstrate has properties suitable for decision support. Our results suggest that this approach provides an effective and operationally viable framework for the cohesive post-processing of weather forecasts across multiple models and lead times to produce a well-calibrated probabilistic output.This article is part of the theme issue ‘Machine learning for weather and climate modelling’.