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Sea level rise represents one of the most compelling aspects of anthropogenic climate change. The potential social and economic impacts are enormous, with little that can be done to mitigate them. It is therefore of critical importance that we are able to correctly anticipate these impacts in advance. This study presents a new, open-source platform that integrates numerical modelling with socioeconomic and physical datasets, whilst also allowing for the uncertainty in climate change projections. This tool therefore allows for new and improved estimates of the global costs of future sea level rise and is likely to be of widespread interest.

This paper provides key insight into the methodology of Working Group III of the IPCC Sixth Assessment Report on climate mitigation. The paper will be essential reading for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement.

Ship tracks are enhanced regions of cloud brightness that trail behind ships that are known to be created by their effluent. They are widely used as observational test beds to deepen understanding of how pollution might affect cloud microphysical processes and climate at larger regional and global scales.  This study uses satellite observations of cloud properties and records of ship position and their effluent to assess perturbations from ship emissions on cloud droplet number and liquid water path. The study found that, as expected, the droplet number perturbation in shiptracks scales with ship emission rates of aerosol particles. Surprisingly, however, the liquid water path in drizzling clouds increased by an amount that was nearly fixed. The observation points to novel non-linear threshold behaviours of relevance to representations of aerosol indirect effects in climate models.

Achieving both performance and portability in a whole dynamical core implemented in a high-productivity language such as Python is an eye-opening result which rebuts some widely held assumptions in the geoscientific modelling community. This is a paper which everyone who writes geoscientific models should read.

Machine Learning is a rapidly expanding technique in the field of weather and climate modelling. This paper takes stock of the state of the field at the present time, and will be invaluable to participants across the field and beyond who wish to understand the impact of Machine Learning on the field, its limitations, and current scope.

This paper proposes a novel method for translating past observed extreme weather events into current or future climates. It illustrates this with the analysis of an extreme windstorm that occurred in 1903. The latter storm would likely be more damaging if it occurred today rather than 120 years ago.

Understanding how sediment moves in rivers is fundamental to the shape of our landscapes and how they evolve. A key part of this understanding is measuring the size and shape of cobbles, pebbles and grains in the bed of a river. Often this measuring task is laborious and carried out by hand. However, this paper presents code and describes a method for measuring this using 3d point cloud data (from a laser scan for example) enabling the automation and rapid measurement.

This paper examines the warning deficits in conjunction with the major flooding event in Germany of summer 2021. The event caused many fatalities and enormous damage. The warning procedures proved to be insufficient, and after investigations it was decided to install a cell broadcast system in Germany, which is already in place today. The deeper investigations on the warning perceptions and the influencing factors is of major interest to readers beyond the scientific community.

The manuscript presents a reconstruction of global temperature spanning the Holocene, which extends the scope of previous exercises in palaeoclimate data assimilation. The resulting reconstruction presents new insights into changes in global temperature over this period. Most notably, it confirms the results of previous studies that have shown a global cooling trend over the past 6,000 years. It also shows that a cooling trend is found even after allowing for potential biases in the proxies. These results are likely to be of considerable interest to the broader geoscience community.

This paper clearly demonstrates the value of the rescue of historical meteorological data, as they help to come to a better assessment of the characteristics of events in the past. Using such data, the study is able to give evidence for physical processes of particular relevance for the intensity of an historical hazardous event. The approach makes an assessment of such events in the context of climate change and variability possible.

This article describes the effect of the recent (January 2022) Hunga Tonga-Hunga Ha’apai volcanic eruption on the stratosphere. The eruption was highly energetic and as a result erupted material reached altitudes of around 30km. Such eruptions, with the 1991 Pinatubo eruption being a noteworthy example, often have a significant effect on tropospheric weather and climate, through the radiative effects of the volcanic aerosol, which may remain in the stratosphere for 2 or 3 years or more. In the 30 years since the Pinatubo eruption observations of the stratosphere, primarily from satellites, have improved enormously and in this Letter the authors provide a detailed description of the evolution of volcanic aerosol and of other chemical species injected by the eruption over a 6-month period following the eruption. The authors show that one important effect of the eruption was to inject a large quantity of water vapour into the stratosphere and suggest that the largest impact of the eruption on tropospheric weather and climate will be via the radiative effect of this water vapour, rather than of the injected aerosol. The initial detailed picture of the impact of the Hunga Tonga-Hunga Ha’apai eruption on the stratosphere provided in this Letter will stimulate further study of this remarkable natural event, which provides a rare opportunity to test our scientific understanding.

I agree with the handling editor and suggest considering it as a highlight paper.

The role of chemical reactions within the atmospheric multiphase system, i.e. aerosol particles and cloud droplets surrounded by a gas phase, has been recognized for several decades to affect the atmospheric composition. In this article, significant advancements in measurement techniques during the past 20 years are briefly described that led to the identification of thousands of organic compounds and molecular-scale processes within the atmospheric multiphase system. Similarly, the complexity of multiphase chemistry models has been increasing to include in detail the underlying chemical processes and phase transfers between gas, aqueous, and organic phases. The authors propose how complementary techniques, such as machine learning and molecular dynamics, can be used to constrain the resulting model complexity. They also identify chemical parameters to be further constrained in lab and field studies. Overall, the article highlights the need and future directions of increasingly interdisciplinary efforts to characterize the atmospheric multiphase chemistry system and its impacts on climate-chemistry interactions, atmospheric oxidation capacity and human health.

The health of the amazon ecosystem is a key indicator of the health of our planet. Hence, the editor feels that this paper will attract the attention of a broad audience and the media.

This study assesses climate impacts on the economy and society of the USA, using a model that can downscale impacts to regional scale. The findings are thus both scientifically and policy-relevant.

The study covers a relevant contemporary topic (wildfire) and focusses on the recent major European summer of wildfires. The authors demonstrate that the assessment of burned area alone is not enough to fully characterize the impact of fires. Novel methods to assess burned area and above ground biomass losses are described to provide a more informative impact-based characterization of fires.