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Since the last decades, previous long-term Wadden Sea studies revealed significant changes in the abundance, biomass and spatial distribution of characteristic macrofauna communities in response to environmental changes and anthropogenic stressors. In this study, we performed statistical community analysis for the East-Frisian Wadden Sea (EFWS, southern North Sea) on two reference datasets across a period with severe climatic and environmental changes (1980s-2018). Therefore, historical macrofauna data from the Quantitative Sensitivity Mapping (1980s, SENSI 1) were reanalyzed and compared with data from the Synoptic Intertidal Benthic Survey (SIBES/SENSI 3) collected in 2018. Our results revealed significant quantitative and spatial changes in the characteristic macrofauna communities between the 1980s and 2018, most likely in response to de-eutrophication and sea level rise mediated habitat changes. Since the 1980s, the total number of taxa remained relatively stable (1980s: 90, 2018: 81), but the total abundance decreased by ca. -31% and the total biomass decreased by ca. -45%, particularly in the eastern regions of the study site probably due to de-eutrophication processes. Thereby, the mean abundances/m 2 of ≥ -80% (1980s-2018) in the EFWS of several dominant species decreased: e.g. the gastropod Peringia ulvae , the polychaete Lanice conchilega and the bivalve Mya arenaria . In contrast, the mean abundance/m 2 of one dominant species increased by ≥ +80% (1980s-2018): the invasive bivalve Ensis leei . The mean biomass [g/m 2 ] of three dominant species decreased by ≥ -80% (1980s-2018): P. ulvae , L. conchilega and the amphipod Corophium volutator . In contrast, the mean biomass [g/m 2 ] of one dominant species increased by ≥ +75% (1980s-2018): the polychaete Arenicola marina . In the western part of the EFWS, not only higher abundances of A. marina, but also of L. conchilega and P. ulvae were found in 2018, caused i.a. by sea level rise mediated decreasing mud contents and sand accretion on tidal flats. At the community level, the Oligochaeta/Heteromastus community increased in spatial distribution in the western EFWS in 2018 in the vicinity of increasing mussel/oyster beds.

The issue of marine plastic pollution has been extensively studied by various scientific disciplines in recent decades due to its global threat. However, owing to its complexity, it requires an interdisciplinary approach to develop effective management strategies. The multidisciplinary scientific approach presented here focuses on understanding the sources and pathways of macroplastic litter and developing abatement strategies in the southern North Sea region. Over 2.5 years, more than 63,400 biodegradable wooden drifters were deployed with the help of citizen science to study the sources, pathways, and accumulation areas of floating marine litter. Rivers act as sinks of most of the floating marine litter released within their waterways. Short-term field experiments were also conducted to analyse the hydrodynamic and atmospheric processes that govern the transport of floating litter particles at the sea surface. Numerical models were used to examine the transport of virtual litter particles in the entire North Sea and in coastal regions. It was found that there are no permanent accumulation areas in the North Sea, and the Skagerrak and fronts can increase the residence times of floating marine litter and favour sinking. Field surveys revealed that the majority of litter objects originate from fisheries and consumer waste. To develop effective abatement strategies, the key stakeholder landscape was analysed on a regional level. The interdisciplinary approach developed in this study highlights the importance of synergizing scientific resources from multiple disciplines for a better understanding of marine plastic pollution and the development of effective management strategies.

The ROMS modeling system was applied to the California Upwelling System (CalUS) to understand the key hydrodynamic conditions and dynamics of the nitrogen-based ecosystem using the NPZD model proposed by Powell et al. (2006). A new type of sponge layer has been successfully implemented in the ROMS modelling system in order to stabilize the hydrodynamic part of the modeling system when using so-called reduced boundary conditions. The hydrodynamic performance of the model was examined using a tidal analysis based on tidal measurement data, a comparison of the modeled sea surface temperature (SST) with buoy and satellite data, and vertical sections of the currents along the coast and the water temperature. This validation process shows that the hydrodynamic module used in this study can reproduce the basic hydrodynamic and circulation characteristics within the CalUS. The results of the ecosystem model show the characteristic features of upwelling regions as well as the well-known spotty horizontal structures of the zooplankton community. The model thus provides a solid basis for the hydrodynamic and ecological characteristics of the CalUS and enables the ecological model to be expanded into a complex ecological model for investigating the effects of climate change on the ecological balance in the area investigated.

We present version 1.1.0 of ECMWF's Artificial Intelligence Forecasting System (AIFS Single), operational since 25 February 2025. The revised system introduces a bounding-layer framework that enforces physical constraints, such as non-negativity and internal consistency within precipitation and cloud cover variables, alongside expanded training data, revised loss weighting, and an extended set of surface and atmospheric variables. Overall skill improves by 4 %–6 % in the upper air and near-surface variables without degradation of spatial variability. A controlled comparison shows that training data expansion is the dominant source of upper-air skill gains, highlighting the importance of frequent model updates. The bounding framework delivers the largest precipitation improvements, up to 12 % and an approximately 1 d advantage using a categorical measure of skill. We further show that enforcing precipitation non-negativity resolves a gradient ambiguity at the zero-precipitation boundary under MSE training, explaining the reduction in drizzle bias and the improvements in precipitation.

Ensemble weather forecasts provide a probabilistic description of the future state of the atmosphere and give users flow-dependent estimates of forecast uncertainty. Here, we introduce AIFS-CRPS, an ensemble variant of the machine-learned Artificial Intelligence Forecasting System (AIFS) developed at ECMWF. Its loss function is the almost fair Continuous Ranked Probability Score (afCRPS). It is based on a proper score, the CRPS, but approximately removes the bias in the score due to finite ensemble size yet avoids a degeneracy of the fair CRPS. The trained model is stochastic and can generate as many exchangeable members as desired. For medium-range forecasts AIFS-CRPS outperforms the physics-based Integrated Forecasting System (IFS) ensemble for the majority of variables and lead times. For subseasonal forecasts, AIFS-CRPS outperforms the IFS ensemble before calibration and is competitive with the IFS ensemble when forecasts are evaluated as anomalies to remove the influence of model biases.

Machine-learning (ML) models, such as the AIFS at the ECMWF, have revolutionised weather forecasting in recent years. We present an extension of the AIFS that jointly models the atmosphere and surface ocean, including ocean waves and sea ice. The primary objective of this extension is to enhance machine-learning medium-range forecasting and enable new use cases by expanding the weather state to better capture coupled surface processes. Our approach departs from traditional numerical models by not having two separate models for the atmosphere and marine components. The joint model instead learns correlations across the entire atmosphere-ocean interface in a component-agnostic way, and can exploit the expressive capacity of ML architectures to learn cross-component relationships directly from the data. We leverage tailored and targeted datasets and solve model design challenges such as missing values over land, multi-scale temporal dynamics, and physical realism of forecast fields and demonstrate the utility of loss scaling in guiding the learning process. We evaluate how representing the surface ocean affects medium-range weather forecasts. We also assess the model's ability to predict surface-ocean fields, including wave swell and tropical-cyclone cold wakes. For nearly all evaluated marine variables, we observe an improvement of approximately one day in forecast skill at medium-range lead times compared to physics-based models. Furthermore, we demonstrate that the model is robust to idealised initial conditions outside the training distribution and responds to them in a physically consistent way. Overall, our findings suggest that the joint AIFS modelling approach offers significant potential for combined atmosphere-ocean forecasting. Our work provides a solid foundation for future development of data-driven coupled Earth system models with greater flexibility and physical fidelity.

Over the last three decades, ensemble forecasts have become an integral part of forecasting the weather. They provide users with more complete information than single forecasts as they permit to estimate the probability of weather events by representing the sources of uncertainties and accounting for the day-to-day variability of error growth in the atmosphere. This paper presents a novel approach to obtain a weather forecast model for ensemble forecasting with machine-learning. AIFS-CRPS is a variant of the Artificial Intelligence Forecasting System (AIFS) developed at ECMWF. Its loss function is based on a proper score, the Continuous Ranked Probability Score (CRPS). For the loss, the almost fair CRPS is introduced because it approximately removes the bias in the score due to finite ensemble size yet avoids a degeneracy of the fair CRPS. The trained model is stochastic and can generate as many exchangeable members as desired and computationally feasible in inference. For medium-range forecasts AIFS-CRPS outperforms the physics-based Integrated Forecasting System (IFS) ensemble for the majority of variables and lead times. For subseasonal forecasts, AIFS-CRPS outperforms the IFS ensemble before calibration and is competitive with the IFS ensemble when forecasts are evaluated as anomalies to remove the influence of model biases.

We present an update to ECMWF's machine-learned weather forecasting model AIFS Single with several key improvements. The model now incorporates physical consistency constraints through bounding layers, an updated training schedule, and an expanded set of variables. The physical constraints substantially improve precipitation forecasts and the new variables show a high level of skill. Upper-air headline scores also show improvement over the previous AIFS version. The AIFS has been fully operational at ECMWF since the 25th of February 2025.