Michael Geoffrey Hart-Davis

Coastal cities are increasingly exposed to flooding as sea level rises and as compound extremes involving tides, storm surge, waves, and river discharge act together. Reliable estimation of extreme coastal water levels is therefore fundamental to flood risk assessment, forecasting, and coastal management, yet it remains constrained by sparse in-situ networks and by the partial, scale-dependent representations that satellite altimetry and hydrodynamic models each provide. Over the past decade, advances in coastal altimetry and in regional-to-global hydrodynamic modelling have expanded the information available for coastal water-level estimation, but their combined use remains dominated by region-specific applications, and globally consistent methodologies are still emerging. Here we review approaches for combining satellite altimetry and hydrodynamic models across four interconnected domains: the detection of coastal sea-level and sea-state extremes from along-track altimetry; strategies for integrating altimetry with hydrodynamic models; vertical referencing and datum consistency; and validation and uncertainty characterisation, particularly in data-sparse settings. We argue that the central challenge is not how to merge two sources of information, but how to ensure that they represent commensurable quantities. No single integration strategy is universally optimal, and differences in process representation, sampling, and vertical reference must be treated explicitly rather than absorbed into residual error. While satellite altimetry does not resolve wave-driven processes at the shoreline and hydrodynamic models often approximate baroclinic and compound contributions, consistent vertical referencing and coherent propagation of uncertainty can render altimetry- and model-derived estimates comparable. Progress therefore depends less on combining datasets more widely than on defining when such combinations are physically meaningful, vertically consistent, uncertainty-aware, and transferable across coastal regimes, laying the foundation towards globally consistent estimation of coastal water levels.

This paper presents GESLA-4, the latest major update to the quasi-global, higher-frequency sea-level dataset known as GESLA (Global Extreme Sea Level Analysis). Since its first release in 2009, GESLA has become a widely used observational resource for investigating tides, storm surges, extreme sea levels, and other coastal and oceanographic processes, supporting numerous published studies and major national and international assessments, including assessment reports of the Intergovernmental Panel on Climate Change. GESLA-4 represents a substantial expansion of the dataset, containing 136,069 station-years of observations across 6,682 records obtained from 43 international, national, and regional data sources. This represents an increase of 45,048 station-years and 1,563 records relative to GESLA-3. In addition to extending existing records and incorporating new data sources, GESLA-4 includes improvements to data processing and consistency, including a systematic review of record time zones. Data accessibility has also been enhanced through programmatic access via an Environmental Research Division's Data Access Program (ERDDAP) server and direct download links for individual stations via Amazon Web Services. This paper describes the assembly, processing, format and accessibility of GESLA-4, and also examines how the dataset has evolved across its four releases since 2009, and reviews how its scientific use has grown and diversified. GESLA-4 provides an extensive and readily accessible observational resource for investigating coastal sea levels, their variability and extremes, and their response to a changing climate.

We present a composite front detection approach that uses horizontal gradients in Absolute Dynamic Topography (ADT) and Sea Surface Temperature (SST) to identify oceanic frontal structures on the Southwestern Continental Atlantic Shelf, a region characterized by strong mesoscale variability and multiple front types. SST fields are derived from the OSTIA satellite product, while four ADT datasets are compared: SWOT MIOST, CMEMS, OK-STv2, and GLORYS12v1. A joint front probability metric is introduced to quantify the co-occurrence of SST- and ADT-derived frontal signatures and to evaluate consistency and differences across products. The method is applied to assess the spatial and seasonal variability of major regional fronts, including the Shelf-Break Front, the San Matías Front, and the Magellan Plume Front. The ADT datasets showed significant differences in their ability to co-detect continental shelf fronts. The Shelf-Break Front was consistently represented across all datasets, with maximum joint front probabilities occurring in austral summer (DJF) and exceeding 96 % between 35 and 45° S. In contrast, the seasonal and coastal San Matías Front exhibited stronger dataset dependence, with the highest joint probabilities obtained using SWOT MIOST ADT (73.63 %), followed by CMEMS (54.95 %), OK-STv2 (41.86 %), and GLORYS12v1 (35.16 %). In the mid-shelf region (38–41° S), elevated joint frontal probabilities indicate that ADT and SST products, particularly altimetry-based datasets, capture a persistent Mid-Shelf Front. The Magellan Plume Front showed strong joint signatures, most pronounced in SWOT MIOST (97.85 %), followed by CMEMS (80.65 %), OK-STv2 (72.83 %), and GLORYS12v1 (51.09 %), with a distinct coastal plume structure during austral winter (JJA). The approach could be further evaluated using ADT from SWOT KaRIn L3 data during SWOT's Cal/val phase, potentially enabling improved detectability through the sharper ADT gradients provided. Overall, the results show that combining SST- and ADT-based gradient detection enhances the characterization of frontal dynamics. The intercomparison further demonstrates that SWOT KaRIn–enhanced gridded altimetry (SWOT MIOST) substantially improves the detection of coastal and seasonal fronts compared with conventional ADT products.

Coastal cities are increasingly exposed to flooding as sea level rises and as compound extremes involving tides, storm surge, waves, and river discharge act together. Reliable estimation of extreme coastal water levels is therefore fundamental to flood risk assessment, forecasting, and coastal management, yet it remains constrained by sparse in-situ networks and by the partial, scale-dependent representations that satellite altimetry and hydrodynamic models each provide. Over the past decade, advances in coastal altimetry and in regional-to-global hydrodynamic modelling have expanded the information available for coastal water-level estimation, but their combined use remains dominated by region-specific applications, and globally consistent methodologies are still emerging. Here we review approaches for combining satellite altimetry and hydrodynamic models across four interconnected domains: the detection of coastal sea-level and sea-state extremes from along-track altimetry; strategies for integrating altimetry with hydrodynamic models; vertical referencing and datum consistency; and validation and uncertainty characterisation, particularly in data-sparse settings. We argue that the central challenge is not how to merge two sources of information, but how to ensure that they represent commensurable quantities. No single integration strategy is universally optimal, and differences in process representation, sampling, and vertical reference must be treated explicitly rather than absorbed into residual error. While satellite altimetry does not resolve wave-driven processes at the shoreline and hydrodynamic models often approximate baroclinic and compound contributions, consistent vertical referencing and coherent propagation of uncertainty can render altimetry- and model-derived estimates comparable. Progress therefore depends less on combining datasets more widely than on defining when such combinations are physically meaningful, vertically consistent, uncertainty-aware, and transferable across coastal regimes, laying the foundation towards globally consistent estimation of coastal water levels.

Tide gauges provide highly precise observations of coastal sea-level variability, yet their spatial sparsity remains a fundamental limitation in regions characterised by complex coastal geometry. This study utilises an extensive archive of 250 short-duration tide-gauge deployments by the Norwegian Mapping Authority (Kartverket) between 2009 and 2026 to characterise tides along the Norwegian coast. We characterise the major tidal constituents and several tidal datums along the entire coastline, including the maximum tidal range and high-tide durations. Further, these data provide a unique source for global model validation, as shown by comparisons against five state-of-the-art tide models, yielding median errors of 2.4 to 3.3 cm that scale with model spatial resolution. Collectively, these results provide a high-density in situ characterisation of Norwegian tides, reveal fine-scale tidal variability that is unresolved by sparse permanent networks, and establish an observational basis for future spaceborne investigations of tidal dynamics in Norway's fjords and coastal waters.

Tide gauges provide highly precise observations of coastal sea-level variability, yet their spatial sparsity remains a fundamental limitation in regions characterised by complex coastal geometry. This study utilises an extensive archive of 250 short-duration tide-gauge deployments by the Norwegian Mapping Authority (Kartverket) between 2009 and 2026 to characterise tides along the Norwegian coast. We characterise the major tidal constituents and several tidal datums along the entire coastline, including the maximum tidal range and high-tide durations. Further, these data provide a unique source for global model validation, as shown by comparisons against five state-of-the-art tide models, yielding median errors of 2.4 to 3.3 cm that scale with model spatial resolution. Collectively, these results provide a high-density in situ characterisation of Norwegian tides, reveal fine-scale tidal variability that is unresolved by sparse permanent networks, and establish an observational basis for future spaceborne investigations of tidal dynamics in Norway's fjords and coastal waters.

A new global coastal tide model for non-polar regions is presented: GOAT-1C. The GOAT-1C atlas makes use of an admittance-based empirical tide estimation approach to infer residual coastal tidal variability omitted by the FES2022 tidal atlas from a combination of OpenADB nadir altimetry and, for the first time, wide-swath observations from the Surface Water and Ocean Topography mission. This approach builds on the Global Operator-based Admittance Tidal process framework (GOAT) which treats the global oceanic tidal response as a continuous function that can be learned using a DeepONet neural operator. The model shows improved performance in coastal regions in terms of variance reduction, tide gauges, and shoreline imagery relative to FES2022. Independent SWOT Cal/Val validation indicates that approximately 1 % of coastal sea-level variance attributable to tidal-model error is associated with spatial structure resolved only at kilometre scales. Furthermore, using an ensemble approach, we provide per-constituent uncertainty estimates which allow users to understand where the product should be used and how much to trust it. Inclusion of wide-swath observations improves fine-scale corrections but must be handled with care. The final model and uncertainty estimates are released on a 4 km coastal grid.

We present a composite front detection approach that uses horizontal gradients in Absolute Dynamic Topography (ADT) and Sea Surface Temperature (SST) to identify oceanic frontal structures on the Southwestern Continental Atlantic Shelf, a region characterized by strong mesoscale variability and multiple front types. SST fields are derived from the OSTIA satellite product, while four ADT datasets are compared: SWOT MIOST, CMEMS, OK-STv2, and GLORYS12v1. A joint front probability metric is introduced to quantify the co-occurrence of SST- and ADT-derived frontal signatures and to evaluate consistency and differences across products. The method is applied to assess the spatial and seasonal variability of major regional fronts, including the Shelf-Break Front, the San Matías Front, and the Magellan Plume Front. The ADT datasets showed significant differences in their ability to co-detect continental shelf fronts. The Shelf-Break Front was consistently represented across all datasets, with maximum joint front probabilities occurring in austral summer (DJF) and exceeding 96 % between 35 and 45° S. In contrast, the seasonal and coastal San Matías Front exhibited stronger dataset dependence, with the highest joint probabilities obtained using SWOT MIOST ADT (73.63 %), followed by CMEMS (54.95 %), OK-STv2 (41.86 %), and GLORYS12v1 (35.16 %). In the mid-shelf region (38–41° S), elevated joint frontal probabilities indicate that ADT and SST products, particularly altimetry-based datasets, capture a persistent Mid-Shelf Front. The Magellan Plume Front showed strong joint signatures, most pronounced in SWOT MIOST (97.85 %), followed by CMEMS (80.65 %), OK-STv2 (72.83 %), and GLORYS12v1 (51.09 %), with a distinct coastal plume structure during austral winter (JJA). The approach could be further evaluated using ADT from SWOT KaRIn L3 data during SWOT's Cal/val phase, potentially enabling improved detectability through the sharper ADT gradients provided. Overall, the results show that combining SST- and ADT-based gradient detection enhances the characterization of frontal dynamics. The intercomparison further demonstrates that SWOT KaRIn–enhanced gridded altimetry (SWOT MIOST) substantially improves the detection of coastal and seasonal fronts compared with conventional ADT products.

Coastal tidal dynamics play a crucial role in a variety of biogeophysical processes, ranging from compound flooding to sediment transport. Satellite altimetry has revolutionized our understanding of ocean tides, but coastal regions remain challenging for satellite altimetry-based observations. In recent years, important efforts have been made to produce shoreline measurements from satellite optical imagery. These datasets raise the question of whether shoreline observations can resolve tidal dynamics at fine coastal scales. Here, we show that shoreline measurements provide fine-scale insights into tidal characteristics, demonstrating variability at beach scales across the Pacific Rim. We explore how both harmonic analysis and response-based methods can be applied to these data to study a range of tidal constituents. Our results are contrasted with in situ measurements and state-of-the-art models as well as with wide-swath satellite observations. We demonstrate that satellite-derived shoreline measurements are a valuable resource for tidal research, particularly for model validation and the study of tidal constituent variability in the coastal zone.

We present a composite front detection approach that uses horizontal gradients in Absolute Dynamic Topography (ADT) and Sea Surface Temperature (SST) to identify oceanic frontal structures on the Southwestern Continental Atlantic Shelf, a region characterized by strong mesoscale variability and multiple front types. SST fields are derived from the OSTIA satellite product, while four ADT datasets are compared: SWOT MIOST, CMEMS, OK-STv2, and GLORYS12v1. A joint front probability metric is introduced to quantify the co-occurrence of SST- and ADT-derived frontal signatures and to evaluate consistency and differences across products. The method is applied to assess the spatial and seasonal variability of major regional fronts, including the Shelf-Break Front, the San Matías Front, and the Magellan Plume Front. The ADT datasets showed significant differences in their ability to co-detect continental shelf fronts. The Shelf-Break Front was consistently represented across all datasets, with maximum joint front probabilities occurring in austral summer (DJF) and exceeding 96 % between 35 and 45° S. In contrast, the seasonal and coastal San Matías Front exhibited stronger dataset dependence, with the highest joint probabilities obtained using SWOT MIOST ADT (73.63 %), followed by CMEMS (54.95 %), OK-STv2 (41.86 %), and GLORYS12v1 (35.16 %). In the mid-shelf region (38–41° S), elevated joint frontal probabilities indicate that ADT and SST products, particularly altimetry-based datasets, capture a persistent Mid-Shelf Front. The Magellan Plume Front showed strong joint signatures, most pronounced in SWOT MIOST (97.85 %), followed by CMEMS (80.65 %), OK-STv2 (72.83 %), and GLORYS12v1 (51.09 %), with a distinct coastal plume structure during austral winter (JJA). The approach could be further evaluated using ADT from SWOT KaRIn L3 data during SWOT's Cal/val phase, potentially enabling improved detectability through the sharper ADT gradients provided. Overall, the results show that combining SST- and ADT-based gradient detection enhances the characterization of frontal dynamics. The intercomparison further demonstrates that SWOT KaRIn–enhanced gridded altimetry (SWOT MIOST) substantially improves the detection of coastal and seasonal fronts compared with conventional ADT products.