Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation

High-resolution regional hindcasting of ocean and sea ice plays an important role in the assessment of shipping and operational risks in the Arctic Ocean. The ice-ocean model NEMO-LIM3 was modified to improve its simulation quality for appropriate spatio-temporal resolutions. A multigrid model setup with connected coarse- (14 km) and fine-resolution (5 km) model configurations was devised. These two configurations were implemented and run separately. The resulting computational cost was lower when compared to that of the built-in AGRIF nesting system. Ice and tracer boundary-condition schemes were modified to achieve the correct interaction between coarse- and fine grids through a long ice-covered open boundary. An ice-restoring scheme was implemented to reduce spin-up time. The NEMO-LIM3 configuration described in this article provides more flexible and customisable tools for high-resolution regional Arctic simulations

NEMO-Bohai 1.0 : a high-resolution ocean and sea ice modelling system for the Bohai Sea, China

Severe ice conditions in the Bohai Sea could cause serious harm to maritime traffic, offshore oil exploitation, aquaculture, and other economic activities in the surrounding regions. In addition to providing sea ice forecasts for disaster prevention and risk mitigation, sea ice numerical models could help explain the sea ice variability within the context of climate change in marine ecosystems, such as spotted seals, which are the only ice-dependent animal that breeds in Chinese waters. Here, we developed NEMO-Bohai, an ocean-ice coupled model based on the Nucleus for European Modelling of the Ocean (NEMO) model version 4.0 and Sea Ice Modelling Integrated Initiative (SI3) (NEMO4.0-SI3) for the Bohai Sea. This study will present the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of the ocean and sea ice. The model simulations agree with the observations with respect to sea surface height (SSH), temperature (SST), salinity (SSS), currents, and temperature and salinity stratification. The seasonal variation of the sea ice area is well simulated by the model compared to the satellite remote sensing data for the period of 1996-2017. Overall agreement is found for the occurrence dates of the annual maximum sea ice area. The simulated sea ice thickness and volume are in general agreement with the observations with slight overestimations. NEMO-Bohai can simulate seasonal sea ice evolution and long-term interannual variations. Hence, NEMO-Bohai is a valuable tool for long-term ocean and ice simulations and climate change studies.Peer reviewe

Explicit representation and parametrised impacts of under ice shelf seas in the z∗ coordinate ocean model NEMO 3.6

Ice-shelf-ocean interactions are a major source of freshwater on the Antarctic continental shelf and have a strong impact on ocean properties, ocean circulation and sea ice. However, climate models based on the ocean-sea ice model NEMO (Nucleus for European Modelling of the Ocean) currently do not include these interactions in any detail. The capability of explicitly simulating the circulation beneath ice shelves is introduced in the non-linear free surface model NEMO. Its implementation into the NEMO framework and its assessment in an idealised and realistic circum-Antarctic configuration is described in this study. Compared with the current prescription of ice shelf melting (i.e. at the surface), inclusion of open sub-ice-shelf cavities leads to a decrease in sea ice thickness along the coast, a weakening of the ocean stratification on the shelf, a decrease in salinity of high-salinity shelf water on the Ross and Weddell sea shelves and an increase in the strength of the gyres that circulate within the over-deepened basins on the West Antarctic continental shelf. Mimicking the overturning circulation under the ice shelves by introducing a prescribed meltwater flux over the depth range of the ice shelf base, rather than at the surface, is also assessed. It yields similar improvements in the simulated ocean properties and circulation over the Antarctic continental shelf to those from the explicit ice shelf cavity representation. With the ice shelf cavities opened, the widely used "three equation" ice shelf melting formulation, which enables an interactive computation of melting, is tested. Comparison with observational estimates of ice shelf melting indicates realistic results for most ice shelves. However, melting rates for the Amery, Getz and George VI ice shelves are considerably overestimated

Oceanic stochastic parametrizations in a seasonal forecast system

We study the impact of three stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the Stochastically Perturbed Parametrization Tendency (SPPT) scheme - which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely the western boundary currents and the Southern Ocean. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error. Whilst there are good grounds for implementing stochastic schemes in ocean models, our results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.Comment: 24 pages, 3 figure

Oceanic stochastic parametrizations in a seasonal forecast system

We study the impact of three stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the Stochastically Perturbed Parametrization Tendency (SPPT) scheme - which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely the western boundary currents and the Southern Ocean. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error. Whilst there are good grounds for implementing stochastic schemes in ocean models, our results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.Comment: 24 pages, 3 figure

Toward a data assimilation system for NEMO

International audienceIn this note, we discuss the project that has been conceived and the first achievement steps that have been carried out to set up a data assimila-­ tion system associated to NEMO. Of specific interest here are applications to operational oceanography. This data assimilation system is sche-­ matically made of three subcomponents: Interface Components, Built-in Components and External Components. Several elements of this NEMO data assimilation system have already been developed by various groups in France and in Europe and several of them could be introduced in the system (the linear Tangent and Adjoint Model, TAM, is one of the most important of them as far as variational assimilation is concerned), some others will require specific developments. Finally, we introduce the SEABASS reference configuration that is proposed to be the NEMO data as-­ similation demonstrator and the experimentation and training platform for data assimilation activities with NEMO. These various thoughts take advantage of the advances and discussions that have been carried out by the NEMOASSIM working group

NEMO-ICB (v1.0): interactive icebergs in the NEMO ocean model globally configured at eddy-permitting resolution

An established iceberg module, ICB, is used interactively with the Nucleus for European Modelling of the Ocean (NEMO) ocean model in a new implementation, NEMO–ICB (v1.0). A 30-year hindcast (1976–2005) simulation with an eddy-permitting (0.25°) global configuration of NEMO–ICB is undertaken to evaluate the influence of icebergs on sea ice, hydrography, mixed layer depths (MLDs), and ocean currents, through comparison with a control simulation in which the equivalent iceberg mass flux is applied as coastal runoff, a common forcing in ocean models. In the Southern Hemisphere (SH), drift and melting of icebergs are in balance after around 5 years, whereas the equilibration timescale for the Northern Hemisphere (NH) is 15–20 years. Iceberg drift patterns, and Southern Ocean iceberg mass, compare favourably with available observations. Freshwater forcing due to iceberg melting is most pronounced very locally, in the coastal zone around much of Antarctica, where it often exceeds in magnitude and opposes the negative freshwater fluxes associated with sea ice freezing. However, at most locations in the polar Southern Ocean, the annual-mean freshwater flux due to icebergs, if present, is typically an order of magnitude smaller than the contribution of sea ice melting and precipitation. A notable exception is the southwest Atlantic sector of the Southern Ocean, where iceberg melting reaches around 50% of net precipitation over a large area. Including icebergs in place of coastal runoff, sea ice concentration and thickness are notably decreased at most locations around Antarctica, by up to ~ 20% in the eastern Weddell Sea, with more limited increases, of up to ~ 10% in the Bellingshausen Sea. Antarctic sea ice mass decreases by 2.9%, overall. As a consequence of changes in net freshwater forcing and sea ice, salinity and temperature distributions are also substantially altered. Surface salinity increases by ~ 0.1 psu around much of Antarctica, due to suppressed coastal runoff, with extensive freshening at depth, extending to the greatest depths in the polar Southern Ocean where discernible effects on both salinity and temperature reach 2500 m in the Weddell Sea by the last pentad of the simulation. Substantial physical and dynamical responses to icebergs, throughout the global ocean, are explained by rapid propagation of density anomalies from high-to-low latitudes. Complementary to the baseline model used here, three prototype modifications to NEMO–ICB are also introduced and discussed

NeMO-Net The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%