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Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR. 

Ocean surface rain layers (RLs) form when relatively colder, fresher, less dense rain water stably stratifies the upper ocean. RLs cool sea surface temperature (SST) by confining surface evaporative cooling to a thin near‐surface layer, and generate sharp SST gradients between the cool RL and the surrounding ocean. In this study, ocean‐atmosphere coupled simulations of the November 2011 Madden‐Julian Oscillation (MJO) event are conducted with and without RLs to evaluate two pathways for RLs to influence the atmosphere. The first, termed the “SST gradient effect,” arises from the hydrostatic adjustment of the boundary layer to RL‐enhanced SST gradients. The second, termed the “SST effect,” arises from RL‐induced SST reductions impeding the development of deep atmospheric convection. RLs are found to sharpen SST gradients throughout the MJO suppressed and suppressed‐to‐enhanced convection transition phases, but their effect on convection is only detected during the MJO suppressed phase when RL‐induced SST gradients enhance low‐level convergence/divergence and broaden the atmospheric vertical velocity probability distribution below 5 km. The SST effect is more evident than the SST gradient effect during the MJO transition phase, as RLs reduce domain average SST by 0.03 K and narrow vertical velocity distribution, thus delaying onset of deep convection. A delayed SST effect is also identified, wherein frequent RLs during the MJO transition phase isolate accumulated subsurface ocean heat from the atmosphere. The arrival of strong winds at the onset of the MJO active phase erodes RLs and releases subsurface ocean heat to the atmosphere, supporting the development of deep convection.

 

Abstract. In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS (Scripps–KAUST Regional Integrated Prediction System). The WAVEWATCH III model is implemented with flexibility, meaning the coupled system can run with or without the wave component. In our implementations, we considered the effect of Stokes drift, Langmuir turbulence, sea surface roughness, and wave-induced momentum fluxes. To demonstrate the impact of coupling we performed a case study using a series of coupled and uncoupled simulations of Tropical Cyclone Mekunu, which occurred in the Arabian Sea in May 2018. We examined the model skill in these simulations and further investigated the impact of Langmuir turbulence in the coupled system. Because of the chaotic nature of the atmosphere, we ran an ensemble of 20 members for each coupled and uncoupled experiment. We found that the characteristics of the tropical cyclone are not significantly different due to the effect of surface waves when using different parameterizations, but the coupled models better capture the minimum pressure and maximum wind speed compared with the benchmark stand-alone Weather Research and Forecasting (WRF) model. Moreover, in the region of the cold wake, when Langmuir turbulence is considered in the coupled system, the sea surface temperature is about 0.5 ∘C colder, and the mixed layer is about 20 m deeper. This indicates the ocean model is sensitive to the parameterization of Langmuir turbulence in the coupled simulations. 

Abstract Atmospheric rivers (ARs) result in precipitation over land and ocean. Rainfall on the ocean can generate a buoyant layer of freshwater that impacts exchanges between the surface and the mixed layer. These “fresh lenses” are important for weather and climate because they may impact the ocean stratification at all time scales. Here we use in situ ocean data, collocated with AR events, and a one-dimensional configuration of a general circulation model, to investigate the impact of AR precipitation on surface ocean salinity in the California Current System (CCS) on seasonal and event-based time scales. We find that at coastal and onshore locations the CCS freshens through the rainy season due to AR events, and years with higher AR activity are associated with a stronger freshening signal. On shorter time scales, model simulations suggest that events characteristic of CCS ARs can produce salinity changes that are detectable by ocean instruments (≥0.01 psu). Here, the surface salinity change depends linearly on rain rate and inversely on wind speed. Higher wind speeds ( U > 8 m s −1 ) induce mixing, distributing freshwater inputs to depths greater than 20 m. Lower wind speeds ( U ≤ 8 m s −1 ) allow freshwater lenses to remain at the surface. Results suggest that local precipitation is important in setting the freshwater seasonal cycle of the CCS and that the formation of freshwater lenses should be considered for identifying impacts of atmospheric variability on the upper ocean in the CCS on weather event time scales. Significance Statement Atmospheric rivers produce large amounts of rainfall. The purpose of this study is to understand how this rain impacts the surface ocean in the California Current System on seasonal and event time scales. Our results show that a greater precipitation over the rainy season leads to a larger decrease in salinity over time. On shorter time scales, these atmospheric river precipitation events commonly produce a surface salinity response that is detectable by ocean instruments. This salinity response depends on the amount of rainfall and the wind speed. In general, higher wind speeds will cause the freshwater input from rain to mix deeper, while lower wind speeds will have reduced mixing, allowing a layer of freshwater to persist at the surface. 

The Weddell Gyre mediates carbon exchange between the abyssal ocean and atmosphere, which is critical to global climate. This region also features large and highly variable freshwater fluxes due to seasonal sea ice, net precipitation, and glacial melt; however, the impact of these freshwater fluxes on the regional carbon cycle has not been fully appreciated. Using a novel budget analysis of dissolved inorganic carbon (DIC) mass in the Biogeochemical Southern Ocean State Estimate, we highlight two freshwater‐driven transports. Where freshwater with minimal DIC enters the ocean, it displaces DIC‐rich seawater outwards, driving a lateral transport of 75 ± 5 Tg DIC/year. Additionally, sea ice export requires a compensating import of seawater, which carries 48 ± 11 Tg DIC/year into the gyre. Though often overlooked, these freshwater displacement effects are of leading order in the Weddell Gyre carbon budget in the state estimate and in regrouped box‐inversion estimates, with implications for evaluating basin‐scale carbon transport.

 

Forecasting Antarctic atmospheric, oceanic, and sea ice conditions on subseasonal to seasonal scales remains a major challenge. During both the freezing and melting seasons current operational ensemble forecasting systems show a systematic overestimation of the Antarctic sea-ice edge location. The skill of sea ice cover prediction is closely related to the accuracy of cloud representation in models, as the two are strongly coupled by cloud radiative forcing. In particular, surface downward longwave radiation (DLW) deficits appear to be a common shortcoming in atmospheric models over the Southern Ocean. For example, a recent comparison of ECMWF reanalysis 5th generation (ERA5) global reanalysis with the observations from McMurdo Station revealed a year-round deficit in DLW of approximately 50 Wm⁻²in marine air masses due to model shortages in supercooled cloud liquid water. A comparison with the surface DLW radiation observations from the Ocean Observatories Initiative mooring in the South Pacific at 54.08° S, 89.67° W, for the time period January 2016–November 2018, confirms approximately 20 Wm⁻²deficit in DLW in ERA5 well north of the sea-ice edge. Using a regional ocean model, we show that when DLW is artificially increased by 50 Wm⁻²in the simulation driven by ERA5 atmospheric forcing, the predicted sea ice growth agrees much better with the observations. A wide variety of sensitivity tests show that the anomalously large, predicted sea-ice extent is not due to limitations in the ocean model and that by implication the cause resides with the atmospheric forcing.

 

The origins of the upper limb of the Atlantic meridional overturning circulation and the partition among different routes has been quantified with models at eddy-permitting and one eddy-resolving model or with low-resolution models assimilating observations. Here, a step toward bridging this gap is taken by using the Southern Ocean State Estimate (SOSE) at the eddy-permitting 1/6° horizontal resolution to compute Lagrangian diagnostics from virtual particle trajectories advected between 6.7°S and two meridional sections: one at Drake Passage (cold route) and the other from South Africa to Antarctica (warm route). Our results agree with the prevailing concept attributing the largest transport contribution to the warm route with 12.3 Sv (88%) (1 Sv ≡ 10⁶m³s⁻¹) compared with 1.7 Sv (12%) for the cold route. These results are compared with a similar Lagrangian experiment performed with the lower-resolution state estimate from Estimating the Circulation and Climate of the Ocean. Eulerian and Lagrangian means highlight an overall increase in the transport of the major South Atlantic currents with finer resolution, resulting in a relatively larger contribution from the cold route. In particular, the Malvinas Current to Antarctic Circumpolar Current (MC/ACC) ratio plays a more important role on the routes partition than the increased Agulhas Leakage. The relative influence of the mean flow versus the eddy flow on the routes partition is investigated by computing the mean and eddy kinetic energies and the Lagrangian-based eddy diffusivity. Lagrangian diffusivity estimates are largest in the Agulhas and Malvinas regions but advection by the mean flow dominates everywhere.