Simona Masina

Marine heatwaves (MHWs) pose significant threats to Mediterranean marine ecosystems and coastal economies, with frequency and severity projected to increase under future climate change. While Med-CORDEX fully-coupled Regional Climate System Models (RCSMs) offer enhanced resolution and improved representation of local processes relative to their parent Global Climate Models (GCMs), a systematic assessment of their added value for sea surface temperature (SST) and MHW properties has been lacking. This study quantifies the added value of Med-CORDEX RCSMs over the Mediterranean basin, evaluating their capacity to correct GCM biases and improve the representation of SST and MHW probability distributions. Results show that added value is scale-dependent and metric-specific. RCSMs generally improve the SST spatial pattern and the shape and upper tail of its temporal distribution, but mostly fail to correct Mediterranean basin-averaged errors in the mean, standard deviation, 90th percentile and linear trend. SST improvements are most consistent along coastlines and in semi-enclosed areas, where fine-scale ocean-atmosphere interactions and topographic constraints are best resolved by RCSMs. For MHW duration, downscaling provides consistent and spatially widespread improvements across nearly all models, driven by a better representation of short-lived events. For MHW intensity, added value is model-dependent and not systematic: while the majority of RCSMs improve this metric, some models exhibit deterioration linked to model-specific features. These results demonstrate that higher horizontal resolution is a necessary but not sufficient condition for improved MHW representation, and that simultaneous advances in other model components are required to fully exploit the potential of regional downscaling. In particular, within the Med-CORDEX ensemble, inter-model differences in MHW added value appear to be correlated with improvements in the thickness of the first layer.

Data Assimilation (DA) is a type of data fusion approach that integrates observational data with the simulated state produced by a numerical model. One of the primary objectives of DA is to enhance the accuracy of numerical simulations by achieving a more accurate and realistic representation of the system’s state. This method is extensively applied in fields such as weather forecasting and oceanography, where it plays a crucial role in improving model accuracy. In these disciplines, two of the most commonly employed DA approaches are the variational methods and sequential methods such as the Kalman Filter and the Kalman Smoother. Although effective, all such methods are computationally expensive. Recently, deep learning algorithms have been developed to reduce the time required by classical assimilation methods while maintaining or improving assimilation accuracy. We propose a novel architectural framework based on a Conditional Denoising Diffusion Probabilistic Model (CDDPM), incorporating a land-masking strategy and a novel latent-space interpolation mechanism. While the approach is designed to be domain-independent for geophysical data assimilation, we demonstrate its effectiveness in performing simultaneous assimilation and bias correction over the complex Mediterranean Sea domain. In addition, to improve CDDPM assimilation skills, we also fine-tuned our model by adding a tail convolutional network that conforms CDDPM output with satellite observations. Furthermore, by training on ocean simulation-reanalysis pairs, the CDDPM learns how to generate corrected ocean state variables that are statistically closer to the reference fields, even without observational input at inference time. This dual capability allows the model to operate flexibly under different data availability scenarios. Extensive experiments show that our data-driven model achieves good performance similar to, and in some cases better than the reference system, demonstrating the ability to generate a state closer to satellite observations while maintaining consistency with numerical solutions.

The Global Ocean Forecast System GOFS16 is an operational ocean analysis and forecast system that has been running daily at the Euro-Mediterranean Center on Climate Change since early 2017. GOFS16 produces 6-day forecasts of the state of the global ocean and sea ice (three-dimensional ocean temperature, salinity, and currents, as well as sea level, sea ice thickness, concentration and drift) with a system based on the NEMO model configured in a global eddying ocean at 1/16° horizontal resolution and 98 vertical levels. To compute the initial conditions for the forecasting, in situ observations of temperature and salinity, altimeter data of sea level anomaly and satellite sea surface temperature fields are jointly assimilated each day over a 1-day observation window with a 3DVar scheme, OceanVar, adapted to handle the global high-resolution grid. This paper introduces the first version of the GOFS16 system and describes its components and the validation procedure, which routinely produces statistics of the system skill. The scientific assessment is presented for the period January 2022–December 2023, at global and regional scales. Results indicate that GOFS16 performs within the expected range of skill for current global systems. However, the impact of the eddy-resolving resolution is hidden either by system weaknesses or traditional verification metrics not adequate at such resolution.

The Global Ocean Forecast System GOFS16 is an operational ocean analysis and forecast system that has been running daily at the Euro-Mediterranean Center on Climate Change since early 2017. GOFS16 produces 6-day forecasts of the state of the global ocean and sea ice (three-dimensional ocean temperature, salinity, and currents, as well as sea level, sea ice thickness, concentration and drift) with a system based on the NEMO model configured in a global eddying ocean at 1/16° horizontal resolution and 98 vertical levels. To compute the initial conditions for the forecasting, in situ observations of temperature and salinity, altimeter data of sea level anomaly and satellite sea surface temperature fields are jointly assimilated each day over a 1-day observation window with a 3DVar scheme, OceanVar, adapted to handle the global high-resolution grid. This paper introduces the first version of the GOFS16 system and describes its components and the validation procedure, which routinely produces statistics of the system skill. The scientific assessment is presented for the period January 2022–December 2023, at global and regional scales. Results indicate that GOFS16 performs within the expected range of skill for current global systems. However, the impact of the eddy-resolving resolution is hidden either by system weaknesses or traditional verification metrics not adequate at such resolution.

Marine heatwaves (MHWs) pose significant threats to Mediterranean marine ecosystems and coastal economies, and their frequency and severity are projected to increase under future climate change. In this context, coupled climate simulations are valuable tools to accurately characterize the properties of future MHWs in the Mediterranean. While Med-CORDEX fully-coupled Regional Climate System Models (RCSMs) offer enhanced resolution and improved representation of local processes relative to their parent Global Climate Models (GCMs), a systematic assessment of their added value for sea surface temperature (SST) and MHW properties has been lacking. This study quantifies the added value of Med-CORDEX RCSMs over the Mediterranean basin, evaluating their capacity to correct GCM biases and improve the spatiotemporal representation of SST and MHW probability distributions. Results show that added value is scale-dependent and metric-specific. RCSMs generally improve the SST spatial pattern and the shape and upper tail of its temporal distribution, but mostly fail to correct Mediterranean basin-averaged errors in the mean, standard deviation, 90th percentile and linear trend. For MHW duration, downscaling provides consistent and spatially widespread improvements across nearly all models, driven by a better representation of short-lived events. For MHW intensity, added value is model-dependent and not systematic: while the majority of RCSMs improve this metric, some models exhibit deterioration linked to model-specific features. These results demonstrate that higher horizontal resolution is a necessary but not sufficient condition for improved MHW representation, and that simultaneous advances in other model components are required to fully exploit the potential of regional downscaling.

Marine heatwaves (MHWs) pose significant threats to Mediterranean marine ecosystems and coastal economies, and their frequency and severity are projected to increase under future climate change. In this context, coupled climate simulations are valuable tools to accurately characterize the properties of future MHWs in the Mediterranean. While Med-CORDEX fully-coupled Regional Climate System Models (RCSMs) offer enhanced resolution and improved representation of local processes relative to their parent Global Climate Models (GCMs), a systematic assessment of their added value for sea surface temperature (SST) and MHW properties has been lacking. This study quantifies the added value of Med-CORDEX RCSMs over the Mediterranean basin, evaluating their capacity to correct GCM biases and improve the spatiotemporal representation of SST and MHW probability distributions. Results show that added value is scale-dependent and metric-specific. RCSMs generally improve the SST spatial pattern and the shape and upper tail of its temporal distribution, but mostly fail to correct Mediterranean basin-averaged errors in the mean, standard deviation, 90th percentile and linear trend. For MHW duration, downscaling provides consistent and spatially widespread improvements across nearly all models, driven by a better representation of short-lived events. For MHW intensity, added value is model-dependent and not systematic: while the majority of RCSMs improve this metric, some models exhibit deterioration linked to model-specific features. These results demonstrate that higher horizontal resolution is a necessary but not sufficient condition for improved MHW representation, and that simultaneous advances in other model components are required to fully exploit the potential of regional downscaling.

Marine heatwaves (MHWs) pose significant threats to Mediterranean marine ecosystems and coastal economies, and their frequency and severity are projected to increase under future climate change. In this context, coupled climate simulations are valuable tools to accurately characterize the properties of future MHWs in the Mediterranean. While Med-CORDEX fully-coupled Regional Climate System Models (RCSMs) offer enhanced resolution and improved representation of local processes relative to their parent Global Climate Models (GCMs), a systematic assessment of their added value for sea surface temperature (SST) and MHW properties has been lacking. This study quantifies the added value of Med-CORDEX RCSMs over the Mediterranean basin, evaluating their capacity to correct GCM biases and improve the spatiotemporal representation of SST and MHW probability distributions. Results show that added value is scale-dependent and metric-specific. RCSMs generally improve the SST spatial pattern and the shape and upper tail of its temporal distribution, but mostly fail to correct Mediterranean basin-averaged errors in the mean, standard deviation, 90th percentile and linear trend. For MHW duration, downscaling provides consistent and spatially widespread improvements across nearly all models, driven by a better representation of short-lived events. For MHW intensity, added value is model-dependent and not systematic: while the majority of RCSMs improve this metric, some models exhibit deterioration linked to model-specific features. These results demonstrate that higher horizontal resolution is a necessary but not sufficient condition for improved MHW representation, and that simultaneous advances in other model components are required to fully exploit the potential of regional downscaling.

Data Assimilation (DA) is a type of data fusion approach that integrates observational data with the simulated state produced by a numerical model. One of the primary objectives of DA is to enhance the accuracy of numerical simulations by achieving a more accurate and realistic representation of the system’s state. This method is extensively applied in fields such as weather forecasting and oceanography, where it plays a crucial role in improving model accuracy. In these disciplines, two of the most commonly employed DA approaches are the variational methods and sequential methods such as the Kalman Filter and the Kalman Smoother. Although effective, all such methods are computationally expensive. Recently, deep learning algorithms have been developed to reduce the time required by classical assimilation methods while maintaining or improving assimilation accuracy. We propose a novel architectural framework based on a Conditional Denoising Diffusion Probabilistic Model (CDDPM), incorporating a land-masking strategy and a novel latent-space interpolation mechanism. While the approach is designed to be domain-independent for geophysical data assimilation, we demonstrate its effectiveness in performing simultaneous assimilation and bias correction over the complex Mediterranean Sea domain. In addition, to improve CDDPM assimilation skills, we also fine-tuned our model by adding a tail convolutional network that conforms CDDPM output with satellite observations. Furthermore, by training on ocean simulation-reanalysis pairs, the CDDPM learns how to generate corrected ocean state variables that are statistically closer to the reference fields, even without observational input at inference time. This dual capability allows the model to operate flexibly under different data availability scenarios. Extensive experiments show that our data-driven model achieves good performance similar to, and in some cases better than the reference system, demonstrating the ability to generate a state closer to satellite observations while maintaining consistency with numerical solutions.

The Global Ocean Forecast System GOFS16 is an operational ocean analysis and forecast system that has been running daily at the Euro-Mediterranean Center on Climate Change since early 2017. GOFS16 produces 6-day forecasts of the state of the global ocean and sea ice (three-dimensional ocean temperature, salinity, and currents, as well as sea level, sea ice thickness, concentration and drift) with a system based on the NEMO model configured in a global eddying ocean at 1/16° horizontal resolution and 98 vertical levels. To compute the initial conditions for the forecasting, in situ observations of temperature and salinity, altimeter data of sea level anomaly and satellite sea surface temperature fields are jointly assimilated each day over a 1-day observation window with a 3DVar scheme, OceanVar, adapted to handle the global high-resolution grid. This paper introduces the first version of the GOFS16 system and describes its components and the validation procedure, which routinely produces statistics of the system skill. The scientific assessment is presented for the period January 2022–December 2023, at global and regional scales. Results indicate that GOFS16 performs within the expected range of skill for current global systems. However, the impact of the eddy-resolving resolution is hidden either by system weaknesses or traditional verification metrics not adequate at such resolution.