Impacts of Ice-Ocean Stress on the Subpolar Southern Ocean:Role of the Ocean Surface Current

The mechanical influences involved in the interaction between the Antarctic sea ice and ocean surface current(OSC)on the subpolar Southern Ocean have been systematically investigated for the first time by conducting two simulations that include and exclude the OSC in the calculation of the ice-ocean stress(IOS), using an eddy-permitting coupled ocean-sea ice global model. By comparing the results of these two experiments, significant increases of 5%, 27%, and 24%, were found in the subpolar Southern Ocean when excluding the OSC in the IOS calculation for the ocean surface stress,upwelling, and downwelling, respectively. Excluding the OSC in the IOS calculation also visibly strengthens the total mechanical energy input to the OSC by about 16%, and increases the eddy kinetic energy and mean kinetic energy by about38% and 12%, respectively. Moreover, the response of the meridional overturning circulation in the Southern Ocean yields respective increases of about 16% and 15% for the upper and lower branches;and the subpolar gyres are also found to considerably intensify, by about 12%, 11%, and 11% in the Weddell Gyre, the Ross Gyre, and the Australian-Antarctic Gyre, respectively. The strengthened ocean circulations and Ekman pumping result in a warmer sea surface temperature(SST), and hence an incremental surface heat loss. The increased sea ice drift and warm SST lead to an expansion of the sea ice area and a reduction of sea ice volume. These results emphasize the importance of OSCs in the air-sea-ice interactions on the global ocean circulations and the mass balance of Antarctic ice shelves, and this component may become more significant as the rapid change of Antarctic sea ice.

STCANet:Spatiotemporal Coupled Attention Network for Ocean Surface Current Prediction

Currently,numerical models based on idealized assumptions,complex algorithms and high computational costs are unsatisfactory for ocean surface current prediction.Moreover,the complex temporal and spatial variability of ocean currents also makes the prediction methods based on time series data challenging.The deep network model can automatically learn and extract complex features hidden in large amount of complex data,so it is a promising method for high quality prediction of ocean currents.In this paper,we propose a spatiotemporal coupled attention deep network model STCANet that can extract abundant temporal and spatial coupling information on the behavior characteristics of ocean currents for improving the prediction accuracy.Firstly,Spatial Module is designed and implemented to extract the spatiotemporal coupling characteristics of ocean currents,and meanwhile the spatial correlations and dependencies among adjacent sea areas are obtained through Spatial Channel Attention Module(SCAM).Secondly,we use the GatedRecurrent-Unit(GRU)to extract temporal relationships of ocean currents,and design and implement the nearest neighbor time attention module to extract the interdependences of ocean currents between adjacent times,which can further improve the accuracy of ocean current prediction.Finally,a series of comparative experiments on the MediSea_Dataset and EastSea_Dataset showed that the prediction quality of our model greatly outperforms those of other benchmark models such as History Average(HA),Autoregressive Integrated Moving Average Model(ARIMA),Long Short-term Memory(LSTM),Gate Recurrent Unit(GRU)and CNN_GRU.

Validation and error analysis of wave-modifi ed ocean surface currents in the northwestern Pacifi c Ocean

By incorporating the wave-induced Coriolis-Stokes forcing into the classical Ekman layer,the wave-modifi ed ocean surface currents in the northwestern Pacifi c Ocean were estimated.Thus,the ocean surface currents are the combination of classical Ekman current from the cross-calibrated multi-platform(CCMP)wind speed,geostrophic current from the mean absolute dynamic topography(MADT),and wave-induced current based on the European Centre for Medium-Range Weather Forecasts(ECMWF)Interim Re-Analysis(ERA-Interim)surface wave datasets.Weight functions are introduced in the Ekman current formulation as well.Comparisons with in-situ data from Lagrangian drifters in the study area and Kuroshio Extension Observatory(KEO)observations at 32.3°N,144.6°E,and 15-m depth indicate that wave-modifi ed ocean surface currents provide accurate time means of zonal and meridional currents in the northwestern Pacifi c Ocean.Result shows that the wave-modifi ed currents are quite consistent with the Lagrangian drifter observations for the period 1993-2017 in the deep ocean.The correlation(root mean square error,RMSE)is 0.96(1.45 cm/s)for the zonal component and 0.90(1.07 cm/s)for the meridional component.However,wave-modifi ed currents underestimate the Lagrangian drifter velocity in strong current and some off shore regions,especially in the regions along the Japan coast and the southeastern Mindanao.What’s more,the wave-modifi ed currents overestimate the pure Eulerian KEO current which does not consider the impact of waves,and the zonal(meridional)correlation and RMSE are 0.95(0.90)and 11.25 cm/s(12.05 cm/s)respectively.These comparisons demonstrate that our wave-modifi ed ocean surface currents have high precision and can describe the real-world ocean in the northwestern Pacifi c Ocean accurately and intuitively,which can provide important routes to calculate ocean surface currents on large spatial scales.

Ocean surface current retrieval and imaging with a new shore-based X-band radar based on time-shifted up-and-down linear frequency modulated signal

This paper proposes a multifunction radar that can not only measure sea currents but also perform sea-surface imaging.The fundamental aspect of the proposed radar comprises transmitting time-shifted up-and-down continuous wave linear frequency modulated signals that allow for the offset of two one-dimensional range images of the sea surface that respectively correspond to the upward linear frequency modulated(LFM)signal and the downward LFM signal.Owing to the Doppler frequency shift from the sea surface,a range offset,which is proportional to the radial velocity of the sea surface,occurs between the upward and downward LFM signals.By using the least-squares linear fitting method in the transformed domain,the range offset can be measured and the current velocity can be retrieved.Finally,we verify the accuracy of current measurement with simulation results.

A new Doppler frequency anomaly algorithm for surface current measurement with SAR

Values for Doppler center frequency are calculated from the echo signal at the satellite using the Doppler centroid method and so include the predicted Doppler frequency caused by the relative motion of the satellite and the Earth,which is the main component of Doppler center frequency and must be removed to obtain the Doppler frequency anomaly for ocean current measurement.In this paper,a new Doppler frequency anomaly algorithm was proposed when measuring surface currents with synthetic aperture radar(SAR).The key of the proposed algorithm involved mean filtering method in the range direction and linear fitting in the azimuth direction to remove the radial and the azimuthal component of predicted Doppler frequency from the Doppler center frequency,respectively.The basis is that the theoretical Doppler center frequency model of SAR exhibits an approximately linear characteristic in both the range direction and in the azimuth direction.With the help of the new algorithm for predicted Doppler frequency removal,the estimation error of Doppler frequency anomaly can be reduced by avoiding employing the theoretical antenna pattern and imperfect satellite attitude parameters in the conventional Doppler frequency method.SAR measurement results demonstrated that,compared to the conventional Doppler frequency with/without error correction method,the proposed algorithm allows for a pronounced improvement in the current measuring accuracy in comparison with the global ocean multi-observation(MOB)products.In addition,the eff ectiveness and robustness of the proposed Doppler algorithm has been demonstrated by its application in the high velocity current in the Kuroshio region.

Assessing CMIP6 simulations of Arctic sea ice drift:Role of near-surface wind and surface ocean current in model performance

Sea ice drift is a critical parameter for understanding the rapid changes in Arctic sea ice.Since the release of the Coupled Model Intercomparison Project Phase 6(CMIP6),there has been a lack of quantitative analysis regarding CMIP6's simulation of Arctic sea ice drift.This study aims to assess the simulated Arctic sea ice drift from 1979 to 2014 by fifteen CMIP6 models against recent satellite retrievals,utilizing various quantitative indices.Additionally,the influence of near-surface wind and surface ocean current on model performance is further analyzed.The CMIP6 models capture several aspects of the observed Arctic sea ice drift climatology and variability.The seasonal patterns of sea ice drift speed in all models exhibit similarities with the observed data,and the models agree with the evaluation datasets,indicating that the seasonal evolution of sea ice drift corresponds to near-surface wind patterns.However,notable discrepancies are identified.All models overestimate sea ice drift speed,exceeding the observational data by 36%e97%.Fourteen out of fifteen models display larger seasonal variability(ranging from 0.74 to 1.28 km d^(-1))compared to the observed data(0.54 km d^(-1)).Seven out of fifteen models exhibit a significant increasing trend in annual sea ice drift speed,similar to the observed trend of 0.58 km d^(-1) per decade,but with weaker trends(ranging from 0.11 to 0.33 km d^(-1) per decade).The remaining eight models reveal no statistically significant trend.The potential causes of such biases were further explored in this study.It suggests that the overestimation of sea ice drift speed in the models might be primarily attributed to the overestimation of near-surface wind speeds and their influence on sea ice drift speed.The models'overestimation of seasonal variability in near-surface wind speeds may account for the overestimation of seasonal variability in sea ice drift.The models'inability to represent the trend in sea ice drift speed may result from their failure to simulate an increasing trend in surface ocean current speed.