Barcelona Supercomputing Center

RumC1 is a structurally unique bacteriocin with broad-spectrum efficacy, including against multidrug-resistant pathogens, yet acting by an undefined mechanism. By integrating genetics, biochemistry, computational modeling and single-cell fluorescence microscopy, we demonstrate that RumC1 is a distinct cell-wall-targeting toxin. First, all RumC1-resistant mutants isolated through a high-rate, genome-wide mutagenic screening exhibited specific impairments in peptidoglycan homeostasis regulation, pinpointing this pathway as critical for RumC1 activity. Second, RumC1 selectively accumulates within neosynthesized peptidoglycan, leading to cell growth arrest and death in a dose-dependent manner. Third, we characterize the RumIc1 immunity protein of the RumC1 biosynthetic cluster as a peptidase acting at the cell surface to protect the cells by trimming the stem peptide crucial for cell-wall assembly. As such, RumIc1 provides cross-protection against vancomycin, while RumC1 is demonstrated to act differently from this glycopeptide antibiotic. Collectively, these findings establish RumC1 as a toxin targeting a key peptidoglycan intermediate of cell wall maturation.

The wine industry is among the agri-food sectors most strongly influenced by climate variability and climate change across multiple time scales. In particular, the integration of reliable and timely sub-seasonal, seasonal, and decadal climate information into decision-making processes can support the wine sector in better managing climate-related risks, such as spring frost events or water-use restrictions. This has led to growing interest in climate information at these different temporal scales.Despite this interest, several challenges continue to limit the uptake of climate information by users, including the coarse spatial resolution of climate model outputs and the lack of coherence between climate predictions from different forecast systems operating at different time scales. To address these limitations and produce coherent regional climate information tailored to the wine sector, the suitability of various statistical downscaling methods has been assessed to enhance the spatial resolution of user-relevant climate variables and indicators at specific locations in Catalonia.In addition, a novel methodology for the temporal merging of seasonal and decadal predictions has been developed to improve the accuracy and consistency of key climate variables. This approach has also been explored for sub-seasonal and seasonal predictions, contributing to a more seamless climate information framework.This new scientific knowledge has been developed within the EU-funded ASPECT project (Adaptation-oriented Seamless Predictions of European ClimaTe) and the SINFONIA Marie Skłodowska-Curie Postdoctoral Fellowship (Towards Seamless climate INFOrmation: merging sub-seasonal and seasonal predictioNs to better manage climate-related rIsks Affecting the wine sector). In both initiatives, the scientific methods are co-developed in close collaboration with representative stakeholders. Building on these interactions, a climate bulletin has been designed to support key vineyard management decisions by integrating seasonal and decadal predictions, with future versions incorporating seamless and higher-resolution climate information as results become available.

This work presents the sPanisH EmissioN mOnitoring systeM for grEeNhouse gAses (PHENOMENA), a python-based, open-source, multiscale emission model that computes high resolution (up to 1km2 and daily) and low latency greenhouse gas (GHG) emissions for Spain. The system uses a bottom-up approach, based on emission factors and activity data, and consists of four different modules: First, the downloading module retrieves low latency activity data from multiple sources, including APIs, open data repositories, websites, and private providers, with error handling and automatic retrials to minimize manual intervention. Next, the preprocessing module standardizes the data and applies quality-control checks. The activity data is then combined with emission factors in the calculation module, which covers 11 emission sectors. Finally, the resulting emissions are post-processed to meet the requirements of an open web platform where the results are displayed.PHENOMENA is based on the OOP paradigm and designed to run on High Performance Computing (HPC) infrastructures. While each one of the emission sectors can run in parallel using MPI strategies, it is still not feasible to run all of them at the same time or download all the activity data at once, as different data providers have different temporal availability. Thanks to the modularity of the system, it can be split into different HPC jobs to handle the heterogeneous data frequencies, increase robustness through automatic retrials, run different instances at the same time and automatize monthly uploads to the web portal, using the Autosubmit workflow manager.The resulting product is a web app which provides daily 1 km x 1 km gridded emission maps and emission totals aggregated per region and sector. The system's latency is determined by the availability of the activity data from external providers, ranging from daily updates to delays of up to four months.PHENOMENA allows monitoring low-latency GHG emissions for Spain at high temporal and spatial resolution, providing information in an accessible way to support national to local policymakers. The system is scalable, robust against failures, and easily adaptable to new data providers, regions and emission sectors.

Providentia is an evaluation software package designed for the in-depth analysis of in-situ surface observations and colocated model output, tailored specifically for the atmospheric modelling community.Reproducibility is a key concern when performing any type of model evaluation. A variety of factors can affect reproducibility, including how observations are processed and filtered, and how statistics are calculated. Even two scientists within the same institution may obtain markedly different results depending on their methodologies. Providentia addresses this challenge by leveraging harmonised observational datasets, such as GHOST and ACTRIS, which are widely used by the community, and by allowing precise customisation of fully documented statistics. Critically, by using the same configuration file, two users can be confident that their evaluations are exactly the same.Providentia offers a variety of use modes, these include an interactive dashboard for quick-look visualisations; a report mode designed for more exhaustive evaluations, generating PDF reports; a library mode that enables Providentia's backend functions to be used in scripts or Jupyter notebooks, for example for reading, filtering, or plotting data; a download mode that automatically retrieves and formats observational (e.g. GHOST and ACTRIS) and model datasets (e.g. CAMS model forecasts and reanalyses); and an interpolation mode that spatially colocates model output with observational stations. Providentia is publicly available on GitHub (https://github.com/BSC-ES/providentia), and is fully documented on a dedicated ReadTheDocs page (https://providentia.readthedocs.io/).

In late October 2024, the western Mediterranean experienced an extreme precipitation event centred over Valencia (southeastern Spain), producing record-breaking rainfall, flash floods, and severe societal impacts. The event was associated with a quasi-stationary cut-off low (COL; DANA in Spanish), which favoured sustained deep convection through strong instability, abundant moisture supply, and interaction with regional orography.The COL organised an atmospheric-river-like moisture transport from northwestern Africa, while additional moisture was supplied by the anomalously warm Mediterranean Sea. To assess the role of anthropogenic climate change in amplifying this event, we apply a storyline-based event attribution framework using high-resolution (∼9 km) simulations from the European Union’s Destination Earth initiative. An ensemble of simulations is performed with the coupled IFS-FESOM model, spectrally nudged to ERA5 to constrain large-scale circulation, and compares two climate states: a Counterfactual (~1950) and a Factual (present-day) climate. This approach isolates thermodynamic effects while preserving the observed synoptic evolution.Results show that the synoptic configuration alone was sufficient to generate extreme rainfall; however, human-induced warming substantially intensified the event. In the Factual scenario, atmospheric moisture content and horizontal moisture transport increased by 18–24%, convective instability (CAPE) increased by ~25%, and sea surface temperatures in the western Mediterranean were ~2°C warmer, enhancing evaporation. As a result, total precipitation over Valencia increased by ~20%, whereas peak precipitation rates on 29 October were ~36% higher, exceeding the Clausius-Clapeyron scaling implied by the mean warming across scenarios.These findings, which agree with those obtained by independent researchers using alternative methods, demonstrate that anthropogenic warming significantly amplified the intensity of this Mediterranean extreme precipitation event through thermodynamic mechanisms, even without changes in large-scale circulation. High-resolution, physically consistent storyline simulations provide a robust framework for quantifying the contributions of climate change to individual high-impact events, thereby supporting impact-relevant attribution in vulnerable coastal regions.

Clearly, communication, dissemination and outreach play an increasingly important role in the social impact of research. Beyond performing solid and high-quality scientific knowledge, research centres are expected and required to ensure that the results obtained are accessible, useful, meaningful, and relevant to a wide range of publics and audiences.This talk aims to showcase the communication, dissemination, and outreach activities implemented by the Earth Sciences Department at the Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BS-CNS). The actions carried out in the field of communication and dissemination of Earth Sciences will be presented, and the lessons learnt and the challenges ahead for fostering the exchange of knowledge among various stakeholders, including (multidisciplinary) research teams, communication and dissemination professionals, and stakeholders, will be discussed.The coordination of communication, dissemination and knowledge exchange activities within the framework of various research projects, which often pursue different objectives and have varying paces, will also be explained, as well as the role of teams dedicated to knowledge integration in building a bridge for dialogue with the user communities of the results obtained. The talk will explore how participatory approaches, co-creation processes, and different adaptive communication formats can contribute to reinforcing relevance, fostering mutual learning, and improving trust between researchers and stakeholders.While sharing transferable lessons and questions that are still open, this overview aims to encourage ongoing discussions and debates about how research institutions, in our particular case in the scientific field of Earth Sciences, should move from simple ad hoc dissemination activities to more strategic, integrated, and impact-oriented communication and engagement practices in society.

Substantial natural variability can obscure the detection of anthropogenic long-term trends in the marine carbonate system (e.g., ocean acidification). Yet the magnitude of the trends and variability remains poorly constrained due to limited marine carbonate system observations. Here, we use a Bayesian machine-learning approach based on Gaussian Process Regression (GPR) to decompose total variability of ocean acidification-related variables into seasonal, interannual and long-term components. The method is first applied to three decades of observations from the Line P carbon program, the longest marine carbonate system timeseries in the Northeast Pacific (1990-2019), typically taking samples three times per year. We found that over the period from 1990 to 2019, the local oceanic uptake of anthropogenic carbon dioxide from the atmosphere was the main driver of long-term changes in the marine carbonate system, including acidification. The seasonal cycle of dissolved inorganic carbon and the aragonite saturation state (both indicators of ocean acidification) was the dominant contributor to total variability in the top 60-70 m of the water column, with a mean surface seasonal amplitude of 35 ± 3 µmol kg−1 and 0.31 ± 0.04, respectively. In this depth range, the magnitude of the interannual variability was at least half of the seasonal variability for most variables. We then apply GPR to output from a global ocean biogeochemical model subsampled as per availability of observations, to assess the observational effort required to detect future ocean carbon trends, with a particular focus on detecting signals related to potential marine carbon dioxide removal interventions.

We present a co-creation methodology resulting from an interdisciplinary collaboration between artists and scientists in the production of the climate change play, "Crema, Groenlàndia" (Burn, Greenland). The play explains how the science of climate change is done, showing scientists in their real context and breaking stereotypes. It also aims to sensitise and disseminate knowledge on climate change and encourage audience’s critical thinking. Its uniqueness lies in combining climate scientists, social scientists, and performing artists in a co-creative process to construct the narrative and stage of the play. Using the Framework for Evaluating Impacts of Informal Science Education Projects (Friedman, 2008), we conducted an impact assessment based on a series of pre- and post-performance surveys targeting secondary school students and members of the general public, which were complemented with insights from a debate following the performances.A pre-performance survey of secondary school students revealed gaps in understanding and the need to strengthen climate literacy in educational settings. While most students recognised human activity as the main cause of climate change, many showed limited awareness of the scientific consensus, low interest in further learning about climate change and the scientists, and poor familiarity with governance mechanisms, such as the Paris Agreement and the Conference of the Parties. Participant feedback collected after a rehearsal performance was used to refine the play, leading to revisions of the script, simplification of technical content, and enhanced use of audiovisual elements to improve clarity and reduce cognitive overload. This illustrates how systematic assessment can directly inform and improve the effectiveness of the science communication practice.The impacts of the final production were evaluated through a post-performance survey completed by the general public. Audience satisfaction with the play was moderate to high. Participants reported positive learning outcomes, with 80.5% of respondents reporting feeling more informed about climate change after attending the performance. Attitudinal responses reflected high levels of trust in climate science, emotional engagement, and a strong interest in learning about personal and collective actions to address climate change (including different strategies for the reduction of major sources of greenhouse gas emissions, influence on others, and civic activities such as voting). Additional outcomes included the potential of the play to challenge stereotypes about scientists (particularly gendered perceptions) and to foster critical thinking about the lack of ambition of global climate policies and responsibilities.Overall, the findings demonstrate that, when coupled with iterative impact assessment, arts-based approaches can be an effective way to communicate complex scientific concepts while fostering audience’s engagement and reflection.

Reliable and timely information on greenhouse gas (GHG) emissions is essential for evaluating mitigation policies and supporting data assimilation and verification modelling frameworks. In this contribution, we present the sPanisH EmissioN mOnitoring systeM for grEeNhouse gAses (PHENOMENA), a low-latency GHG modelling framework developed within the RESPIRE-CLIMATE Spanish national project, which received formal endorsement from the WMO-IG3IS initiative.PHENOMENA provides harmonised daily and high spatial resolution (up to 1 km × 1 km) CO2 and CH4 emissions for the main combustion-related sectors, including electricity generation, manufacturing industry (cement and iron and steel), residential and commercial combustion, road transport, shipping and aviation. The system estimates CO2 and CH4 emissions by combining low latency activity data and fuel- and process-dependent emission factors through bottom-up and downscaling approaches. The data collected and pre-processed includes hourly near-real-time traffic counts from the national road network, hourly electricity production data reported by individual power plants, daily Copernicus ERA5-Land surface temperature, monthly industrial production statistics and AIS (Automatic Identification System) data, among others.PHENOMENA produces multiple GHG emission products, including high resolution maps of daily emissions per sector, as well as daily summaries of emissions aggregated at different regional levels and for the main Spanish metropolitan regions. The emissions computed with PHENOMENA allows representing the intra-weekly and seasonal variability of GHG emissions as well as changes in their spatial patterns, which can be linked to specific policy, socioeconomic, and weather impacts.The results produced with PHENOMENA are compared to official GHG emission inventories as well as to other state-of-the-art low latency GHG emission datasets, such as the ones produced by the CAMS Carbon Monitor initiative. Overall, these developments demonstrate the capability of PHENOMENA to deliver consistent, multisector and near-real-time GHG emission estimates, supporting national monitoring, policy evaluation and future verification and data-assimilation efforts.