Constraints on long-term cliff retreat and intertidal weathering at weak rock coasts using cosmogenic 10Be, nearshore topography and numerical modelling Earth Surface Dynamics DOI 10.5194/esurf-11-429-2023 2 June 2023 This study uses a coastal evolution model to interpret cosmogenic beryllium-10 concentrations and topographic data and, in turn, quantify long-term cliff retreat rates for four chalk sites on the south coast of England. By using a process-based model, clear distinctions between intertidal weathering rates have been recognised between chalk and sandstone rock coast sites, advocating the use of process-based models to interpret the long-term behaviour of rock coasts. Read more
The extremely hot and dry 2018 summer in central and northern Europe from a multi-faceted weather and climate perspective Natural Hazards and Earth System Sciences DOI 10.5194/nhess-23-1699-2023 31 May 2023 The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favourable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming. Read more
Quantifying gender gaps in seismology authorship Solid Earth DOI 10.5194/se-14-485-2023 29 May 2023 We investigate women’s representation in seismology to raise awareness of existing gender disparities. By analysing the authorship of peer-reviewed articles, we identify lower representation of women among single authors, high-impact authors, and highly productive authors. Seismology continues to be a male-dominated field, and trends suggest that parity is decades away. These gaps are an obstacle to women’s career advancement and, if neglected, may perpetuate the leaky-pipeline problem. Read more
Opinion: The scientific and community-building roles of the Geoengineering Model Intercomparison Project (GeoMIP) – past, present, and future Atmospheric Chemistry and Physics DOI 10.5194/acp-23-5149-2023 26 May 2023 Geoengineering indicates methods aiming to reduce the temperature of the planet by means of reflecting back a part of the incoming radiation before it reaches the surface or allowing more of the planetary radiation to escape into space. It aims to produce modelling experiments that are easy to reproduce and compare with different climate models, in order to understand the potential impacts of these techniques. Here we assess its past successes and failures and talk about its future. Read more
Evaluation of liquefaction triggering potential in Italy: a seismic-hazard-based approach Natural Hazards and Earth System Sciences DOI 10.5194/nhess-23-1685-2023 24 May 2023 In the present study, we analyse ground-motion hazard maps and hazard disaggregation in order to define areas in Italy where liquefaction triggering due to seismic activity can not be excluded. The final result is a screening map for all of Italy that classifies sites in terms of liquefaction triggering potential according to their seismic hazard level. The map and the associated data are freely accessible at the following web address: www.distav.unige.it/rsni/milq.php. Read more
Reversible ice sheet thinning in the Amundsen Sea Embayment during the Late Holocene The Cryosphere DOI 10.5194/tc-17-1787-2023 22 May 2023 Samples of bedrock recovered from below the West Antarctic Ice Sheet show that part of the ice sheet was thinner several thousand years ago than it is now and subsequently thickened. This is important because of concern that present ice thinning in this region may lead to rapid, irreversible sea level rise. The past episode of thinning at this site that took place in a similar, although not identical, climate was not irreversible; however, reversal required at least 3000 years to complete. Read more
Reconstructing ocean carbon storage with CMIP6 Earth system models and synthetic Argo observations Biogeosciences DOI 10.5194/bg-20-1671-2023 19 May 2023 We present a new method for reconstructing ocean carbon using climate models and temperature and salinity observations. To test this method, we reconstruct modelled carbon using synthetic observations consistent with current sampling programmes. Sensitivity tests show skill in reconstructing carbon trends and variability within the upper 2000 m. Our results indicate that this method can be used for a new global estimate for ocean carbon content. Read more
Methane emissions are predominantly responsible for record-breaking atmospheric methane growth rates in 2020 and 2021 Atmospheric Chemistry and Physics DOI 10.5194/acp-23-4863-2023 17 May 2023 Our understanding of recent changes in atmospheric methane has defied explanation. Since 2007, the atmospheric growth of methane has accelerated to record-breaking values in 2020 and 2021. We use satellite observations of methane to show that (1) increasing emissions over the tropics are mostly responsible for these recent atmospheric changes, and (2) changes in the OH sink during the 2020 Covid-19 lockdown can explain up to 34% of changes in atmospheric methane for that year. Read more
Rescuing historical weather observations improves quantification of severe windstorm risks Natural Hazards and Earth System Sciences DOI 10.5194/nhess-23-1465-2023 15 May 2023 We examine a severe windstorm that occurred in February 1903 and caused significant damage in the UK and Ireland. Using newly digitized weather observations from the time of the storm, combined with a modern weather forecast model, allows us to determine why this storm caused so much damage. We demonstrate that the event is one of the most severe windstorms to affect this region since detailed records began. The approach establishes a new tool to improve assessments of risk from extreme weather. Read more
Causal deep learning models for studying the Earth system Geoscientific Model Development DOI 10.5194/gmd-16-2149-2023 12 May 2023 A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyse the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling. Read more
