Regional storm tidal levels of the South African coastline are investigated by means of a calibrated and validated numerical model. The model was developed utilizing the shallow water hydrodynamic model, Delft3D. This model was coupled (online) with a non-stationary spectral wave model (developed in the Simulating WAves in the Nearshore (SWAN) numerical code). A local, 4.4 km version of the Unified Model was applied as atmospheric forcing for the coupled system. The models presented in this study form part of the operational marine forecasts of the South African Weather Service, Wave and Storm Surge model. The operational protocol and model calibration and validation are presented via statistical correlations with measured water levels at six South African coastal locations. The main calibration parameters and thus physical drivers were winds, atmospheric pressure and waves. The validated numerical model is used to provide an experimental physical description of South African storm surge characteristics, per coastline. The dominant driver of South African storm surge is winds associated with mid-latitude cyclones. Further novelty in the present study is the quantification of the relative contribution of extreme storm wave set-up to the South African storm surge signal. This wave set-up contributes approximately 20% of the total surge signal in the southwest, with winds contributing approximately 55%. The importance of the continental shelves is also elucidated concerning the frictional shoaling effects of the long surge wave propagation.

Antarctic sea ice is prevalently seen as a major player in the climate system, but it is also an important factor in polar maritime safety. Remote sensing and forecasting of Southern Ocean sea ice at time scales suitable for navigation and research planning remain challenging. In this study, numerical sea ice drift forecasts are assessed from the perspective of informing shipping operations. A series of tests is performed to ascertain whether an operational global ocean and sea ice model and a simple free-drift model can provide accurate drift estimates over short lead times. Both approaches are evaluated against ice drift measurements from buoys deployed during two research cruises in the Southern Ocean marginal ice zone during winter and spring. The numerical forecast model was able to forecast sea ice trajectories over 24 h with an average position error of 16.6 km during winter and 9.2 km during spring. The simpler free-drift model, using empirically optimised wind scaling, returned an average position error of 15.9 and 9.3 km during winter and spring respectively. Model skill for both the dynamical and free-drift models is lower in winter than in spring. Free-drift model skill appears linked with sea ice consolidation, which may assist in determining when and where this approach is fit for purpose. Lingering uncertainties regarding the rheological representation of sea ice in the dynamical model and the quality of the wind and ocean forcing remain, potentially affecting model skill over tactical navigation time frames.

Two ensembles of buoys, deployed in the marginal ice zone (MIZ) of the north-eastern Weddell Sea region of the Southern Ocean, are analysed to characterise the dynamics driving sea ice drift and deformation during the winter-growth and the spring-retreat seasons of 2019. The results show that although the two buoy arrays were deployed within the same region of ice-covered ocean, their trajectory patterns were vastly different. This indicates a varied response of sea ice in each season to the local winds and currents. Analyses of the winter data showed that the Antarctic Circumpolar Current modulated the drift near the sea ice edge. This led to a highly energetic and mobile ice cover, characterised by free-drift conditions. The resulting drift and deformation were primarily driven by large-scale atmospheric forcing, with negligible contributions due to the wind-forced inertial response. For this highly advective coupled ice–ocean system, ice drift and deformation linearly depended on atmospheric forcing. We also highlight the limits of commercial floating ice velocity profilers in this regime since they may bias the estimates of sea ice drift and the ice type detection. On the other hand, the spring drift was governed by the inertial response as increased air temperatures caused the ice cover to melt and break up, promoting a counterintuitively less wind-driven ice–ocean system that was more dominated by inertial oscillations. In fact, the deformation spectra indicate a strong decoupling to large-scale atmospheric forcing. Further analyses, extended to include the deformation datasets from different regions around Antarctica, indicate that, for similar spatial scales, the magnitude of deformation varies between seasons, regions, and the proximity to the sea ice edge and the coastline. This implies the need to develop rheology descriptions that are aware of the ice types in the different regions and seasons to better represent sea ice dynamics in the MIZ.

A coupled numerical hydrodynamic model is presented for the Cape Peninsula region of South Africa. The model is intended to support a range of interdisciplinary coastal management and research applications, given the multifaceted socio-economic and ecological value of the study area. Calibration and validation are presented, with the model reproducing the mean circulation well. Maximum differences between modelled and measured mean surface current speeds and directions of 3.9 × 10⁻² m s⁻¹ and 20.7°, respectively, were produced near Cape Town, where current velocities are moderate. At other measurement sites, the model closely reproduces mean surface and near-bed current speeds and directions and outperforms a global model. In simulating sub-daily velocity variability, the model’s skill is moderate, and similar to that of a global model, where comparison is possible. It offers the distinct advantage of producing information where the global model cannot, however. Validation for temperature and salinity is provided, indicating promising performance. The model produces a range of expected dynamical features for the domain including upwelling and vertical current shear. Nuances in circulation patterns are revealed; specifically, the development of rotational flow patterns within False Bay is qualified and an eddy in Table Bay is identified.