• Energy Research
• 13. Climate action close
• 15. Life on land close
• 6. Clean water close
• GB close
• AU close
• ES close

AbstractLightning is the main precursor of natural wildfires and Long-Continuing-Current (LCC) lightning flashes are proposed to be the main igniters of lightning-ignited wildfires (LIW). Previous studies predict a change of the global occurrence rate and spatial pattern of total lightning. Nevertheless, the sensitivity of lightning-ignited wildfire occurrence to climate change is uncertain. Here, we investigate space-based measurements of LCC lightning associated with lightning ignitions and present LCC lightning projections under the Representative Concentration Pathway RCP6.0 for the 2090s by applying a recent LCC lightning parameterization based on the updraft strength in thunderstorms. We find a 41% global increase of the LCC lightning flash rate. Increases are largest in South America, the western coast of North America, Central America, Australia, Southern and Eastern Asia, and Europe, while only regional variations are found in northern polar forests, where fire risk can affect permafrost soil carbon release. These results show that lightning schemes including LCC lightning are needed to project the occurrence of lightning-ignited wildfires under climate change.

AbstractObjectiveTo assess the epidemiological evidence on the joint effects of climate variability and socioecological factors on dengue transmission.MethodsFollowing PRISMA guidelines, a detailed literature search was conducted in PubMed, Web of Science and Scopus. Peer‐reviewed, freely available and full‐text articles, considering both climate and socioecological factors in relation to dengue, published in English from January 1993 to October 2015 were included in this review.ResultsTwenty studies have met the inclusion criteria and assessed the impact of both climatic and socioecological factors on dengue dynamics. Among those, four studies have further investigated the relative importance of climate variability and socioecological factors on dengue transmission. A few studies also developed predictive models including both climatic and socioecological factors.ConclusionsDue to insufficient data, methodological issues and contextual variability of the studies, it is hard to draw conclusion on the joint effects of climate variability and socioecological factors on dengue transmission. Future research should take into account socioecological factors in combination with climate variables for a better understanding of the complex nature of dengue transmission as well as for improving the predictive capability of dengue forecasting models, to develop effective and reliable early warning systems.

Abstract The impact of CO 2 leakage from underground storage formations on shallow water resources is a concerning aspect in CO 2 capture and storage (CCS) risk assessment. In Campo de Calatrava region (Spain), natural CO 2 fluxes from the Earth’s mantle interact with shallow aquifers, resulting in significant changes in their physical and chemical properties. The resultant water is slightly acidic (pH 5.9-6.4), oxidizing, and enriched in iron (up to 6.1×10 -4 mol·L -1 ) and other metals usually found at trace concentrations. Thermodynamic calculations reveal that aqueous Fe(III) carbonate complexes play an important role in the persistence of this high concentration of iron and trace metals in solution.

This paper presents a comparative study in predicting the monthly average solar radiation for Darwin, Australia (latitude 12.46 deg S longitude 130.84 deg E). The city of Darwin, Northern Territory (NT), has the highest and most consistent sunshine duration among all the other Australian states. This unique climate presents an opportunity for photovoltaic (PV) applications. Reliable and accurate predictions of solar radiation enable potential site locations, which exhibit high solar radiations and sunshine hours, to be identified for PV installation. Three predictive models were investigated in this study—the linear regression (LR), Angstrom–Prescott–Page (APP), and the artificial neural network (ANN) models. The mean global solar radiation coupled with the climate data (mean minimum and maximum temperatures, mean rainfall, mean evaporation, and sunshine fraction) obtained from the Australian Bureau of Meteorology (BoM) formed the basis of the dataset. Using simple and easily obtainable climate data presents an added advantage by reducing model complexity. Predictive results showed the root mean square errors (RMSEs) obtained were 6.72%, 13.29%, and 8.11% for the LR, APP, and ANN models, respectively. The predicted solar exposure from the LR model was then compared with the satellite-derived data to assess the accuracy of the LR method.

Most of the world’s mountain glaciers have been retreating for more than a century in response to climate change. Glacier retreat is evident on all continents, and the rate of retreat has accelerated during recent decades. Accurate, spatially explicit information on the position of glacier margins over time is useful for analyzing patterns of glacier retreat and measuring reductions in glacier surface area. This information is also essential for evaluating how mountain ecosystems are evolving due to climate warming and the attendant glacier retreat. Here, we present a non-comprehensive spatially explicit dataset showing multiple positions of glacier fronts since the Little Ice Age (LIA) maxima, including many data from the pre-satellite era. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings, and aerial or satellite images with a supplement of geochronology; and own field data. We provide ESRI shapefiles showing 728 past positions of 94 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years. From 2 to 46 past positions per glacier are depicted (on average: 7.8).