Volcanic gas emissions are intimately linked to the dynamics of magma ascent and outgassing and, on geological time scales, constitute an important source of volatiles to the Earth's atmosphere. Measurements of gas composition and flux are therefore critical to both volcano monitoring and to determining the contribution of volcanoes to global geochemical cycles. However, significant gaps remain in our global inventories of volcanic emissions, (particularly for CO 2 , which requires proximal sampling of a concentrated plume) for those volcanoes where the near-vent region is hazardous or inaccessible. Unmanned Aerial Systems (UAS) provide a robust and effective solution to proximal sampling of dense volcanic plumes in extreme volcanic environments. Here we present gas compositional data acquired using a gas sensor payload aboard a UAS flown at Volcán Villarrica, Chile. We compare UAS-derived gas time series to simultaneous crater rim multi-GAS data and UV camera imagery to investigate early plume evolution. SO 2 concentrations measured in the young proximal plume exhibit periodic variations that are well correlated with the concentrations of other species. By combining molar gas ratios (CO 2 /SO 2 = 1.48-1.68, H 2 O/SO 2 = 67-75, and H 2 O/CO 2 = 45-51) with the SO 2 flux (142 ± 17 t/day) from UV camera images, we derive CO 2 and H 2 O fluxes of~150 t/day and~2,850 t/day, respectively. We observe good agreement between time-averaged molar gas ratios obtained from simultaneous UAS-and ground-based multi-GAS acquisitions. However, the UAS measurements made in the young, less diluted plume reveal additional short-term periodic structure that reflects active degassing through discrete, audible gas exhalations.

A 3 km long section of the Hooker Glacier near its terminus was studied in 1996 using GPS, tacheometric, and bathymetric surveys, as well as ground penetrating radar and gravity surveys. With reference to sparse surface levels and oblique photos dating back to 1889, the studies indicate that between c. 1915 and 1964 downwasting of an axial strip along the terminal section occurred at a rate of c. 0.7 m/yr. Between 1964 and 1986 the rate increased to 1.0 m/yr. Marginal segments of the glacier near the terminus experienced positive buoyancy from 1982 onwards, which promoted rapid melting. Apparent subaqueous melting rates of c. 9 m/yr occurred between 1986 and 1996 over large stretches of the downmelting terminal area. By 1996, a 1 4 km long sector of the glacier had melted down forming a melt lake (Hooker Lake) with a volume of c. 40 × 10 6 m 3 covering an area of 0.78 × 10 6 m 2. A maximum water depth of 135 m was measured near the retreating glacier front where the ice wall descends as a vertical cliff to the lake bottom and temperatures of 0.5°C prevail. Ice thickness measurements by radar surveys along profiles 1.7 and 3.0 km C97051

This Perspective looks back on recent experience of public engagement with biotechnologies and asks what can be learned for the governance of another controversial emerging area—geoengineering.

Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into “ash” and “ash free” classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes “ash” and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.Key PointsPresentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards

This work provides a sensitivity study of a twochannel passive infrared remote sensing retrieval of effective radius and optical depth using the Spinning Enhanced Visible and Infrared Imager with channels centred at 10.8 and 12.0 μm and a look-up table approach to calculate mass column loading. The retrieval is applied to images of two ash clouds from the 2010 Eyjafjallajökull eruption on 6 and 13 May 2010. The 2010 eruption of Eyjafjallajökull is well characterised, especially in terms of the airborne volcanic ash, which allows the relative uncertainties to be investigated within the realms of observation and reasonable approximation. The parameters investigated are as follows: refractive index, surface temperature, cloud top temperature, ash bulk density and, in particular, the uncertainties related to the spread of the ash particle size distribution-in terms of the geometric standard deviation of a lognormal particle size distribution. The lack of constraint on particle size distribution is shown to cause the largest uncertainty in retrieved mass column loading for the 6 May and 13 May ash cloud. A review of measured in situ size distributions of airborne particles is presented with justification for the choice of a lognormal size distribution for the 2010 Eyjafjallajökull eruption.