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Successive IPCC reports have assessed the level of human-induced warming above preindustrial, but much less emphasis has been placed on quantifying the rate of anthropogenic warming, despite the rate of warming being a key variable for ambitious policymaking. The decadal global temperature anomaly trend can be considered a combination of the forced responses from the full range of radiatively-active pollutants, plus the additional trend introduced by natural variability over the previous decade.The global temperature anomaly trend likely increased in the 2010s, following a temporary pause through the 2000s. Estimates of the globally averaged radiative forcing (RF) timeseries, which are used to attribute the anthropogenic contribution to this recent behaviour, suggest a 50% increase in the anthropogenic RF trend, which largely arises from aerosol RF trend changes since 2000. When these RF timeseries are used to complete a global temperature anomaly attribution (following the technique outlined in the IPCC’s Special Report on the Global Warming of 1.5°C), they suggest that the attributed anthropogenic warming rate has increased by between 50 and 100% since 2000, pushing the estimated rate of net anthropogenic warming up to around 0.3°C/decade since 2010.We study the global observational evidence supporting the aerosol trends presented in these RF datasets, and thus aim to determine the likely anthropogenic contribution to the perceived warming acceleration behaviour since 2000. We argue that while observations do support the claim that RF trends are partly responsible for the warming trend (and importantly do support the best-estimate RF trend estimates in this ensemble), observational evidence is circumstantial, with a counterhypothesis that aerosol RFs make only a small contribution to the warming trend since 2000 consistently failing to be disproven across the full ensemble of RFs.This occurs because observed trends in radiative fluxes and global temperatures are significantly influenced by internal variability, principally ENSO and PDO, precluding a clearer assessment of the externally forced behaviour over the short global observational records we have. In light of this uncertainty, considerable caution is required in predictions or policy judgments that depend on the precise current anthropogenic warming trend, such as the time remaining to, or the outstanding carbon budget consistent with, a warming of 1.5°C, since these may be influenced considerably by recent changes in aerosol forcing behaviour.

Precise knowledge of the location and height of the volcanic sulfur dioxide (SO2) plume is essential for accurate determination of SO2 emitted by volcanic eruptions, however so far not available in operational near-real time UV satellite retrievals. The FP_ILM algorithm (Full-Physics Inverse Learning Machine) enables for the first time to extract the SO2 layer height information in a matter of seconds for current UV satellites and is thus applicable in NRT environments.The FP_ILM combines a principal component analysis (PCA) and a neural network approach (NN) to extract the information about the volcanic SO2 layer height from high-resolution UV satellite backscatter measurements. So far, UV based SO2 layer height retrieval algorithms were very time-consuming and therefore not suitable for near-real-time applications like aviation control, although the SO2 LH is essential for accurate determination of SO2 emitted by volcanic eruptions.In this presentation, we will present the latest FP_ILM algorithm improvements and show results of recent volcanic eruptions.The SO2 layer height product for Sentinel-5p/TROPOMI is developed in the framework of the SO2 Layer Height (S5P+I: SO2 LH) project, which is part of ESA Sentinel-5p+ Innovation project (S5P+I). The S5P+I project aims to develop novel scientific and operational products to exploit the potential of the S5P/TROPOMI capabilities. The S5P+I: SO2 LH project is dedicated to the generation of an SO2 LH product and its extensive verification with collocated ground- and space-born measurements.

Precise knowledge of the location and height of the volcanic sulfur dioxide (SO2) plume is essential for accurate determination of SO2 emitted by volcanic eruptions. So far, UV based SO2 plume height retrieval algorithms are very time-consuming and therefore not suitable for near-real-time applications like aviation control. We have therefore developed the Full-Physics Inverse Learning Machine (FP_ILM) algorithm for extremely fast and accurate retrieval of volcanic SO2 layer heights based on the UV satellite instruments Sentinel-5 Precursor/TROPOMI and MetOp/GOME-2.In this presentation, we will present the FP-ILM algorithm and show results of the 2019 Raikoke eruption; a strong volcanic eruption which has emitted a huge ash cloud accompanied by more than 1300 DU of SO2, which could be detected even two months after the end of eruptive event. We will also present first results of the recent Taal volcanic eruption on 13 January 2020 in Indonesia, which has injected a huge ash and SO2 plume into the upper atmosphere, with plume heights of up to 20km. The algorithm is developed in the framework of ESA's "Sentinel-5p+ Innovation: SO2 Layer Height project" (S5P+I: SO2 LH), dedicated to the generation of an SO2 LH product and its extensive verification with collocated ground- and space-born measurements.The high-resolution UV spectrometer GOME-2 aboard the three EPS MetOp-A, -B, and –C satellites perform global daily atmospheric trace-gas measurements with a spatial resolution of 40x40km2 at an overpass time of 8:30h local time. The UV spectrometer TROPOMI aboard the ESA Sentinel-5P satellite provides a much higher spatial resolution of currently 5.6x3.6km2 per ground pixel, at an overpass time of 13:30h. In the future, also UV instruments aboard the Sentinel-4 (geostationary) and Sentinel-5 will complement the satellite-based global monitoring of atmospheric trace gases.

Eruptive column models are crucial for managing volcanic crises, forecasting future events, and reconstructing past eruptions. Given their central role in volcanology and the large uncertainties weakening their predictions, the evaluation and improvement of these models is critical. Such evaluation is challenging as it requires independent estimates of the main model inputs (e.g. mass eruption rate) and outputs (e.g. column height). Despite recent efforts to extend datasets of independently estimated eruption source parameters (ESP) (e.g. Mastin 2014, Aubry et al. 2017), there is no standardized, maintained, and community-based ESP database devoted to the evaluation of eruptive column models.Here we present a new ESP database designed to respond to the needs of the plume modelling community, and which will also be valuable to observatories, field volcanologists, and volcanic ash advisory centers. We compiled data for over 130 eruptive events with independent estimates of: i) the mass eruption rate; ii) the height reached by the column; and iii) atmospheric conditions during the eruption. In contrast with previous ESP datasets, we distinguish estimates of column height that relate to different phases (ash and SO2) and parts of the column (plume top or umbrella). We additionally provide the total grain size distribution, uncertainties in eruption parameters, and multiple sources for atmospheric profiles for events where these parameters are available. The database also includes a wealth of additional information which will enable modelers to distinguish between different eruptions when evaluating or calibrating models. This includes the type of eruption, the morphology of the plume (weak/transitional/strong), and the occurrence and mass entrained within pyroclastic density currents.We will apply the new database to revisit empirical scaling relationships between the mass eruption rate and “plume height”. In particular, we will show how such relationships depend on the type of height (e.g. SO2 height vs. ash top height) and eruption (e.g. magmatic vs. phreatomagmatic) considered. We will also discuss the difficulties and limitations of compiling ESP estimates from the literature as well as characterizing fundamentally unsteady volcanic events by a single value for each ESP.

A new optimal estimation algorithm for the retrieval of volcanic ash properties has been developed for use with the Infrared Atmospheric Sounding Interferometer (IASI). The retrieval method uses the wave number range 680–1200 cm−1, which contains window channels, the CO2 ν2 band (used for the height retrieval), and the O3 ν3 band.Assuming a single infinitely (geometrically) thin ash plume and combining this with the output from the radiative transfer model RTTOV, the retrieval algorithm produces the most probable values for the ash optical depth (AOD), particle effective radius, plume top height, and effective radiating temperature. A comprehensive uncertainty budget is obtained for each pixel. Improvements to the algorithm through the use of different measurement error covariance matrices are explored, comparing the results from a sensitivity study of the retrieval process using covariance matrices trained on either clear-sky or cloudy scenes. The result showed that, due to the smaller variance contained within it, the clear-sky covariance matrix is preferable. However, if the retrieval fails to pass the quality control tests, the cloudy covariance matrix is implemented.The retrieval algorithm is applied to scenes from the Eyjafjallajökull eruption in 2010, and the retrieved parameters are compared to ancillary data sources. The ash optical depth gives a root mean square error (RMSE) difference of 0.46 when compared to retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) instrument for all pixels and an improved RMSE of 0.2 for low optical depths (AOD

The European Space Agency project Satellite Monitoring of Ash and Sulphur Dioxide for the mitigation of Avi­ ation Hazards, was introduced after the eruption of the Icelandic volcano Eyjafjallajökull in the spring of 2010 to facilitate the development of an optimal End­to­End System for Volcanic Ash Plume Monitoring and Predic­ tion. The Eyjafjallajökull plume drifted towards Europe and caused major disruptions of European air traffic for several weeks affecting the everyday life of millions of people. The limitations in volcanic plume monitoring and prediction capabilities gave birth to this observational system which is based on comprehensive satellite­derived ash plume and sulphur dioxide [SO2] level estimates, as well as a widespread validation using supplementary satellite, aircraft and ground­based measurements. Inter­comparison of the volcanic total SO2 column and plume height observed by GOME­2/Metop­A and IASI/Metop­A are shown before, during and after the Eyjaf­ jallajökull 2010 eruptions as well as for the 2011 Grímsvötn eruption. Co­located ground­based Brewer Spectro­ photometer data extracted from the World Ozone and Ultraviolet Radiation Data Centre for de Bilt, the Nether­ lands, are also compared to the different satellite estimates. Promising agreement is found for the two different types of instrument for the SO2 columns with linear regression coefficients ranging around from 0.64 when comparing the different instruments and 0.85 when comparing the two different IASI algorithms. The agree­ ment for the plume height is lower, possibly due to the major differences between the height retrieval part of the GOME2 and IASI algorithms. The comparisons with the Brewer ground­based station in de Bilt, The Nether­ lands show good qualitative agreement for the peak of the event however stronger eruptive signals are required for a longer quantitative comparison.

Satellite measurements of volcano deformation have the potential to illuminate a wide range of volcanic processes and have provided us with the first opportunity to investigate volcano deformation as an arc-scale process. This thesis presents the results of an Interferometric Synthetic Aperture Radar (InSAR) survey of the Central American Volcanic Arc between 2007 and 2010. My measurements confirm a statistically significant absence of magmatic deformation in Central America relative to other well-studied volcanic arcs. I estimate a minimum detection threshold for deformation at 20 of the arc’s 26 active volcanoes using time series analysis of interferometric phase. I find that the majority (∼80%) of literature measurements of volcano deformation made at other arcs would have been possible with the average magnitude of noise in Central American volcanoes. The absence of measurable magmatic deformation in Central America may therefore be due to factors that limit the geodetic expression of magma movement, including the deep pooling of basalts and high parental melt volatile content. The quantification of measurement uncertainty also allows me to use the lack of deformation at specific erupting volcanoes to make order of magnitude estimations of the minimum depth for magma storage that would not result in measurable deformation. I present measurements and interpretation of non-magmatic deformation associated with edifice development at two Central American volcanoes: Arenal, Costa Rica and Santiaguito, Guatemala. At Arenal, I measure apparently steady slip (∼7 cm/yr) on the volcano’s western flanks, which I attribute to gravity-driven slip on the boundary between lavas emplaced over the past 50 years and older tephras and paleosols. At Santiaguito, I demonstrate the measurement of large-scale (∼10-200 m) topographic change from a small set of large baseline interferograms. Measurements of post-2000 lava fields allow me to estimate extrusion rate, map changes to flow morphology and make simultaneous measurements of lava flow thickness and subsidence rate.