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Melting of the Greenland ice sheet (GrIS) in response to anthropogenic global warming poses a severe threat in terms of global sea-level rise (SLR) 1 . Modelling and palaeoclimate evidence suggest that rapidly increasing temperatures in the Arctic can trigger positive feedback mechanisms for the GrIS, leading to self-sustained melting 2 – 4 , and the GrIS has been shown to permit several stable states 5 . Critical transitions are expected when the global mean temperature (GMT) crosses specific thresholds, with substantial hysteresis between the stable states 6 . Here we use two independent ice-sheet models to investigate the impact of different overshoot scenarios with varying peak and convergence temperatures for a broad range of warming and subsequent cooling rates. Our results show that the maximum GMT and the time span of overshooting given GMT targets are critical in determining GrIS stability. We find a threshold GMT between 1.7 °C and 2.3 °C above preindustrial levels for an abrupt ice-sheet loss. GrIS loss can be substantially mitigated, even for maximum GMTs of 6 °C or more above preindustrial levels, if the GMT is subsequently reduced to less than 1.5 °C above preindustrial levels within a few centuries. However, our results also show that even temporarily overshooting the temperature threshold, without a transition to a new ice-sheet state, still leads to a peak in SLR of up to several metres. Simulations using two state-of-the-art ice-sheet models show that abrupt melting of the Greenland ice sheet following overshooting of the global mean temperature critical threshold can be mitigated by subsequent cooling to below 1.5 °C.

The Arctic sea ice (ASI) is expected to decrease with further global warming. However, considerable uncertainty remains regarding the temperature range that would lead to a completely ice-free Arctic. Here, we combine satellite data and a large suite of models from the latest phase of the Coupled Model Intercomparison Project (CMIP6) to develop an empirical, observation-based projection of the September ASI area for increasing global mean surface temperature (GMST) values. This projection harnesses two simple linear relationships that are statistically supported by both observations and model data. First, we show that the September ASI area is linearly proportional to the area inside a specific northern hemisphere January–September mean temperature contour T c . Second, we use observational data to show how zonally averaged temperatures have followed a positive linear trend relative to the GMST, consistent with Arctic amplification. To ensure the reliability of these observations throughout the rest of the century, we validate this trend by employing the CMIP6 ensemble. Combining these two linear relationships, we show that the September ASI area decrease will accelerate with respect to the GMST increase. Our analysis of observations and CMIP6 model data suggests a complete loss of the September ASI (area below 10 6 km 2 ) for global warming between 1.5 ∘ C and 2.2 ∘ C above pre-industrial GMST levels.

The loss of Arctic sea ice (ASI) represents a major transformation in the Arctic region, impacting regional and global climate, ecosystems, and socio-economic structures. Observational and reanalysis data have consistently shown a notable shift in polar environmental conditions over recent decades, marked by a substantial reduction in the ASI area and a rise in the variability in its coverage and distribution. Utilizing data from the latest Coupled Model Intercomparison Project phase, our study reveals a consistent pattern highlighting a fundamental shift in ASI dynamics preceding total loss. We observe increasing fluctuations in the September ASI area as the threshold for an ice-free Arctic is approached across various scenarios and models. This pattern is particularly concentrated in the Central Arctic (CA) sub-region. Spatial analyses reveal increasing variance along the CA’s northern coastlines, accompanied by a substantial increase in open water coverage, underscoring the shift from stable to highly variable ice conditions in this region. Additionally, our findings suggest a potential link between increased ASI fluctuations and variability in surface wind speeds. These specific results underscore the urgency of multidisciplinary approaches in addressing the challenges posed by ASI variability, with implications for marine ecosystems, Indigenous communities, and navigational safety.

Simulations of the Greenland ice sheet (GrIS) at millennial timescales and beyond often assume spatially and temporally uniform temperature anomalies and precipitation sensitivities over these timescales or rely on simple parameterisation schemes for the precipitation rates. However, there is no a priori reason to expect spatially and temporally uniform sensitivities across the whole GrIS. Precipitation is frequently modelled to increase with the standard thermodynamic scaling of ∼7 % K−1 derived from the Clausius–Clapeyron relation and often based on older model generations. Here, we update the commonly used parameters for long-term modelling of the GrIS, based on the output of the latest generation of coupled Earth system models (CMIP6), using the historical time period and four different future emission scenarios. We show that the precipitation sensitivities in Greenland have a strong spatial dependence, with values ranging from −3 % K−1 in southern Greenland to 13 % K−1 in northeastern Greenland relative to the local annual mean near-surface temperature in the CMIP6 ensemble mean. Additionally, we show that the annual mean temperatures in Greenland increase between 1.29 and 1.53 times faster than the global mean temperature (GMT), with northern Greenland warming up to 2 times faster than southern Greenland in all emission scenarios. However, we also show that there is a considerable spread in the model responses that can, at least partially, be attributed to differences in the Atlantic meridional overturning circulation (AMOC) response across models. Finally, using the Parallel Ice Sheet Model (PISM), we show that assuming uniform temperature and precipitation anomalies and sensitivities leads to overestimation of near-surface temperatures and underestimation of precipitation in key regions of the GrIS, such as southwestern Greenland. This, in turn, can result in substantial overestimation of ice loss in the long-term evolution of the GrIS.

Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach we are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Niño and La Niña with very little given information. Our method outperforms the widely used statistical kriging method as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach we are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Niño and La Niña with very little given information. Our method outperforms the widely used statistical kriging method as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

This study is developed as part of the activities of the Italian Chapter of the European Association of Science Editors (EASE) with the aim to provide an overview of the Italian scientific publishing in biomedicine and highlight the need for professional training and certification. Even at European and international level, in fact, there is a general lack of training and accreditation programs for the certification of professional editors. After defining the editor's role (term that in the Italian language has a certain degree of ambiguity) and the different professionals involved in the publication process, the paper analyzes the biomedical production in Italy, reporting the results of a survey on professional associations, institutions, scientific societies, and others. Also reported are the results of a first hearing on views expressed by the publishers themselves. From this study the need of a constructive discussion with all stakeholders emerges not only to improve the quality of publications in the biomedical field, but also to enhance and recognize the editor's profession, both in Italy and in other countries.

This study is developed as part of the activities of the Italian Chapter of the European Association of Science Editors (EASE) with the aim to provide an overview of the Italian scientific publishing in biomedicine and highlight the need for professional training and certification. Even at European and international level, in fact, there is a general lack of training and accreditation programs for the certification of professional editors. After defining the editor’s role (term that in the Italian language has a certain degree of ambiguity) and the different professionals involved in the publication process, the paper analyzes the biomedical production in Italy, reporting the results of a survey on professional associations, institutions, scientific societies, and others. Also reported are the results of a first hearing on views expressed by the publishers themselves. From this study the need of a constructive discussion with all stakeholders emerges not only to improve the quality of publications in the biomedical field, but also to enhance and recognize the editor’s profession, both in Italy and in other countries.

Questo studio si sviluppa nell’ambito delle attività del Capitolo italiano della European Association of Science Editors (EASE) con l’obiettivo di offrire una panoramica dell’editoria scientifica italiana nel settore della biomedicina e di evidenziare la necessità di formazione e certificazione di una professione non adeguatamente riconosciuta anche a livello europeo e internazionale. Partendo dalla definizione del termine editor e del processo editoriale viene analizzata la produzione di editoria scientifica in ambito biomedico in Italia attraverso un’indagine dei siti web di enti e associazioni professionali, federazioni e società scientifiche. Vengono anche forniti i risultati di una prima indagine conoscitiva sulle opinioni espresse dagli stessi editori. Si ritiene necessario avviare un dibattito costruttivo con tutti gli stakeholder non solo per migliorare la qualità dei prodotti editoriali nel settore biomedico in Italia, ma anche per valorizzare una professionalità forse ancora poco conosciuta.