The Effect of Wind Stress on Seasonal Sea-Level Change on the Northwestern European Shelf

Projections of relative sea-level change (RSLC) are commonly reported at an annual mean basis. The seasonality of RSLC is often not considered, even though it may modulate the impacts of annual mean RSLC. Here, we study seasonal differences in 21st-century ocean dynamic sea-level change (DSLC, 2081-2100 minus 1995-2014) on the Northwestern European Shelf (NWES) and their drivers, using an ensemble of 33 CMIP6 models complemented with experiments performed with a regional ocean model. For the high-end emissions scenario SSP5-8.5, we find substantial seasonal differences in ensemble mean DSLC, especially in the southeastern North Sea. For example, at Esbjerg (Denmark), winter mean DSLC is on average 8.4 cm higher than summer mean DSLC. Along all coasts on the NWES, DSLC is higher in winter and spring than in summer and autumn. For the low-end emissions scenario SSP1-2.6, these seasonal differences are smaller. Our experiments indicate that the changes in winter and summer sea-level anomalies are mainly driven by regional changes in wind-stress anomalies, which are generally southwesterly and east-northeasterly over the NWES, respectively. In spring and autumn, regional wind-stress changes play a smaller role. We also show that CMIP6 models not resolving currents through the English Channel cannot accurately simulate the effect of seasonal wind-stress changes on he NWES. Our results imply that using projections of annual mean RSLC may underestimate the projected changes in extreme coastal sea levels in spring and winter. Additionally, changes in the seasonal sea-level cycle may affect groundwater dynamics and the inundation characteristics of intertidal ecosystems.

Formulation of a new explicit tidal scheme in ocean general circulation model

Tides play an important role in ocean energy transfer and mixing, and provide major energy for maintaining thermohaline circulation. This study proposes a new explicit tidal scheme and assesses its performance in a global ocean model. Instead of using empirical specifications of tidal amplitudes and frequencies, the new scheme directly uses the positions of the Moon and Sun in a global ocean model to incorporate tides. Compared with the traditional method that has specified tidal constituents, the new scheme can better simulate the diurnal and spatial characteristics of the tidal potential of spring and neap tides as well as the spatial patterns and magnitudes of major tidal constituents (K1 and M2). It significantly reduces the total errors of eight tidal constituents (with the exception of N2 and Q1) in the traditional explicit tidal scheme. Relative to the control simulation without tides, both the new and traditional tidal schemes can lead to better dynamic sea level (DSL) simulation in the North Atlantic, reducing significant negative biases in this region. The new tidal scheme also shows smaller positive bias than the traditional scheme in the Southern Ocean. The new scheme is suited to calculate regional distributions of sea level height in addition to tidal mixing.

Predicting ocean-induced ice-shelf melt rates using a machine learning image segmentation approach

Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused by increased ocean-induced melt along their undersides is now understood to be one of the key drivers of ice loss from the Antarctic Ice Sheet. However, despite the importance of this forcing mechanism most ice-sheet simulations currently rely on simple melt-parametrisations of this ocean-driven process, since a fully coupled ice-ocean modelling framework is prohibitively computationally expensive. Here, we provide an alternative approach that is able to capture the greatly improved physical description of this process provided by large-scale ocean-circulation models over currently employed melt-parameterisations but with trivial computational expense. We introduce a new approach that brings together deep learning and physical modelling to develop a deep neural network framework, MELTNET, that can emulate ocean model predictions of sub-ice shelf melt rates. We train MELTNET on synthetic geometries, using the NEMO ocean model as a ground-truth in lieu of observations to provide melt rates both for training and to evaluate the performance of the trained network. We show that MELTNET can accurately predict melt rates for a wide range of complex synthetic geometries and outperforms more traditional parameterisations for > 95 % of geometries tested. Furthermore, we find MELTNET's melt rate estimates show sensitivity to established physical relationships such as a changes in thermal forcing and ice shelf slope. This study demonstrates the potential for a deep learning framework to calculate melt rates with almost no computational expense, that could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models.

The 1994 positive Indian Ocean Dipole event as investigated by the transfer routes of oceanic wave energy

The present study investigates the interannual variability of the tropical Indian Ocean (IO) based on the transfer routes of wave energy in a set of 61-year hindcast experiments using a linear ocean model. To understand the basic feature of the IO Dipole mode, this paper focuses on the 1994 pure positive event. Two sets of westward transfer episodes in the energy flux associated with Rossby waves (RWs) are identified along the equator during 1994. One set represents the same phase speed as the linear theory of equatorial RWs, while the other set is slightly slower than the theoretical phase speed. The first set originates from the reflection of equatorial Kelvin waves at the eastern boundary of the IO. On the other hand, the second set is found to be associated with off-equatorial RWs generated by southeasterly winds in the southeastern IO, which may account for the appearance of the slower group velocity. A combined empirical orthogonal function (EOF) analysis of energy-flux streamfunction and potential reveals the intense westward signals of energy flux are attributed to off-equatorial RWs associated with predominant wind input in the southeastern IO corresponding to the positive IO Dipole event.

The bulk parameterizations of turbulent air-sea fluxes in NEMO4: the origin of Sea Surface Temperature differences in a global model study

Wind stress and turbulent heat fluxes are the major driving forces which modify the ocean dynamics and thermodynamics. In the NEMO ocean general circulation model, these turbulent air-sea fluxes (TASFs), which are components of the ocean model boundary conditions, can critically impact the simulated ocean characteristics. This paper investigates how the different bulk parametrizations to calculated turbulent air-sea fluxes in the NEMO4 (revision 12957) drives substantial differences in sea surface temperature (SST). Specifically, we study the contribution of different aspects and assumptions of the bulk parametrizations in driving the SST differences in NEMO global model configuration at ¼ degree of horizontal resolution. These include the use of the skin temperature instead of the bulk SST in the computation of turbulent heat flux components, the estimation of wind stress and the estimation of turbulent heat flux components which vary in each parametrization due to the different computation of the bulk transfer coefficients. The analysis of a set of short-term sensitivity experiments, where the only experimental change is related to one of the aspects of the bulk parametrizations, shows that parametrization-related SST differences are primarily sensitive to the wind stress differences across parametrizations and to the implementation of skin temperature in the computation of turbulent heat flux components. Moreover, in order to highlight the role of SST-turbulent heat flux negative feedback at play in ocean simulations, we compare the TASFs differences obtained using NEMO ocean model with the estimations from Brodeau et al. (2017), who compared the different bulk parametrizations using prescribed SST. Our estimations of turbulent heat flux differences between bulk parametrizations is weaker with respect to Brodeau et al. (2017) differences estimations.

Improving Madden–Julian Oscillation Simulation in Atmospheric General Circulation Models by Coupling with Snow–Ice–Thermocline One-dimensional Ocean Model

A one-column turbulent kinetic energy–type ocean mixed-layer model Snow–Ice–Thermocline (SIT) when coupled with three atmospheric general circulation models (AGCMs) to yielded superior Madden–Julian Oscillation (MJO) simulation. SIT is designed to have fine layers similar to those observed near the ocean surface and therefore can realistically simulate the diurnal warm layer and cool skin. This refined discretization of the near ocean surface in SIT provides accurate sea surface temperature (SST) simulation, thus facilitating realistic air–sea interaction. Coupling SIT with European Centre Hamburg Model, Version 5 (ECHAM5); Community Atmosphere Model, Version 5 (CAM5); and High Resolution Atmospheric Model (HiRAM) significantly improved MJO simulation in three coupled AGCMs compared with the AGCM driven with prescribed SST. This study suggests two major improvements to the coupling process. First, during the preconditioning phase of MJO over Maritime Continent (MC), the over underestimated surface latent heat bias in AGCMs can be corrected. Second, during the phase of strongest convection over MC, the change of the intraseasonal circulation in the meridional circulation is the dominant factor in the coupled simulations relative to the uncoupled experiments. The study results indicate that a fine vertical resolution near the surface, which better captures temperature variations in the upper few meters of the ocean, considerably improves different models with different configurations and physical parameterization schemes; this could be an essential factor for accurate MJO simulation.