Search Results (9)

F_TIDE has been proven to be effective in obtaining the time-varying harmonic parameters of nonstationary tidal signals, and the results near the two endpoints of the analyzed time series are more accurate than those obtained by S_TIDE, which provides good conditions for the prediction of future sea levels. In this paper, F_TIDE is used for the short-term prediction of nonstationary tides in Nome (Alaska) and South Beach (Oregon). The significance of each standard parameter of F_TIDE is quantified by calculating its signal-to-noise ratio to determine the appropriate parameters that can be used for prediction. F_TIDE performs well in forecasting the sea level for three weeks at the Nome gauge and one week at the South Beach gauge. F_TIDE causes 30.1% and 42.0% decreases in the mean absolute errors between the forecasts and the observations compared to T_TIDE. F_TIDE is applied to the original signal at the Nome gauge, and the results show a strong correlation between the variation in M₂ amplitude and the variation in the mean sea level. A potential mechanism is speculated in that changes in tides are affected by the changes in water depth on different time scales, which the sea level pressure, wind, sea ice, and other marine motions may contribute to. Full article

The well-documented decrease in the annual minimum Arctic sea ice extent over the past few decades is an alarming indicator of current climate change. However, much less is known about the thickness of the Arctic sea ice. Developing accurate forecasting models is critical to better predict its changes and monitor the impacts of global warming on the total Arctic sea ice volume (SIV). Significant improvements in forecasting performance are possible with the advances in signal processing and deep learning. Accordingly, here, we set out to utilize the recent advances in machine learning to develop non-physics-based techniques for forecasting the sea ice volume with low computational costs. In particular, this paper aims to provide a step-wise decision process required to develop a more accurate forecasting model over short- and mid-term horizons. This work integrates variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) for multi-input multi-output pan-Arctic SIV forecasting. Different experiments are conducted to identify the impact of several aspects, including multivariate inputs, signal decomposition, and deep learning, on forecasting performance. The empirical results indicate that (i) the proposed hybrid model is consistently effective in time-series processing and forecasting, with average improvements of up to 60% compared with the case of no decomposition and over 40% compared with other deep learning models in both forecasting horizons and seasons; (ii) the optimization of the VMD level is essential for optimal performance; and (iii) the use of the proposed technique with a divide-and-conquer strategy demonstrates superior forecasting performance. Full article

The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. Monthly data on potential climate change causal factors, including greenhouse gas concentrations, sunspot numbers, humidity, ice sheets mass, and sea ice coverage, from January 2003 to December 2021, have been utilized in the analysis. We first applied the vector autoregressive model (VAR) and Granger causality test to gauge the linear Granger causal relationships among climate factors. We then adopted the vector error correction model (VECM) as well as the autoregressive distributed lag model (ARDL) to quantify the linear long-run equilibrium and the linear short-term dynamics. Cointegration analysis has also been adopted to examine the dual directional Granger causalities. Furthermore, in this work, we have presented a novel pipeline based on the artificial neural network (ANN) and the VAR and ARDL models to detect nonlinear causal relationships embedded in the data. The results in this study indicate that the global sea level rise is affected by changes in ice sheet mass (both linearly and nonlinearly), global mean temperature (nonlinearly), and the extent of sea ice coverage (nonlinearly and weakly); whereas the global mean temperature is affected by the global surface mean specific humidity (both linearly and nonlinearly), greenhouse gas concentration as measured by the global warming potential (both linearly and nonlinearly) and the sunspot number (only nonlinearly and weakly). Furthermore, the nonlinear neural network models tend to fit the data closer than the linear models as expected due to the increased parameter dimension of the neural network models. Given that the information criteria are not generally applicable to the comparison of neural network models and statistical time series models, our next step is to examine the robustness and compare the forecast accuracy of these two models using the soon-available 2022 monthly data. Full article

The safe and efficient navigation of ships traversing the Northern Sea Route demands accurate information regarding sea ice concentration. However, the sea ice concentration forecasts employed to support such navigation are often flawed. To address this challenge, this study advances a statistical interpolation method aimed at reducing errors arising from traditional interpolation approaches. Additionally, this study introduces an autoregressive integrated moving average model, derived from ERA5 reanalysis data, for short-term sea ice concentration forecasts along the Northern Sea Route. The validity of the model has been confirmed through comparison with ensemble experiments from the Coupling Model Intercomparison Project Phase 5, yielding reliable outcomes. The route availability is assessed on the basis of the sea ice concentration forecasts, indicating that the route will be available in the upcoming years. The proposed statistical models are also shown the capacity to facilitate effective management of Arctic shipping along the Northern Sea Route. Full article

Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model’s quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions. Full article

Mid- and long-term predictions of Arctic sea ice concentration (SIC) are important for the safety and security of the Arctic waterways. To date, SIC predictions mainly rely on numerical models, which have the disadvantages of a short prediction time and high computational complexity. Another common forecasting approach is based on a data-driven model, which is generally based on traditional statistical analysis or simple machine learning models, and achieves prediction by learning the relationships between data. Although the prediction performance of such methods has been improved in recent years, it is still difficult to find a balance between unstable model structures and complex spatio-temporal data. In this study, a classical statistical method and a deep learning model are combined to construct a data-driven rolling forecast model of SIC in the Arctic, named the EOF–LSTM–DNN (abbreviated as ELD) model. This model uses the empirical orthogonal function (EOF) method to extract the temporal and spatial features of the Arctic SIC, then the long short-term memory (LSTM) network is served as a feature extraction tool to effectively encode the time series, and, finally, the feature decoding is realized by the deep neural network (DNN). Comparisons of the model with climatology results, persistence predictions, other data-driven model results, and the hybrid coordinate ocean model (HYCOM) forecasts show that the ELD model has good prediction performance for the Arctic SIC on mid- and long-term time scales. When the forecast time is 100 days, the forecast root-mean-square error (RMSE), Pearson correlation coefficient (PCC), and anomaly correlation coefficient (ACC) of the ELD model are 0.2, 0.77, and 0.74, respectively. Full article

In response to declining sea ice cover, human activity in the Arctic is increasing, with access to the Arctic Ocean becoming more important for socio-economic reasons. Accurate knowledge of sea ice conditions is therefore becoming increasingly important for reducing the risk and operational cost of human activities in the Arctic. Satellite-based sea ice charting is routinely used for tactical ice management, but the marine sector does not yet make optimal use of sea ice thickness (SIT) or sea ice concentration (SIC) forecasts on weekly timescales. This is because forecasts have not achieved sufficient accuracy, verification and resolution to be used in situations where maritime safety is paramount, and assessing the suitability of forecasts can be difficult because they are often not available in the appropriate format. In this paper, existing SIT forecasts currently available on the Copernicus Marine Service (CMS) or elsewhere in the public domain are evaluated for the first time. These include the seven-day forecasts from the UK Met Office, MET Norway, the Nansen Environmental and Remote Sensing Center (NERSC) and the European Centre for Medium-Range Weather Forecasts (ECMWF). Their forecast skills were assessed against unique in situ data from five moorings deployed between 2016 and 2019 by the Barents Sea Metocean and Ice Network (BASMIN) and Barents Sea Exploration Collaboration (BaSEC) Joint Industry Projects. Assessing these models highlights the importance of data assimilation in short-term forecasting of SIT and suggests that improved assimilation of sea ice data could increase the utility of forecasts for navigational purposes. This study also demonstrates that forecasts can achieve similar or improved correlation with observations when compared to a persistence model at a lead time of seven days, providing evidence that, when used in conjunction with sea ice charts, SIT forecasts could provide valuable information on future sea ice conditions. Full article

With the advancement of global warming, the Arctic sea routes (ASRs) may open for the entire year. The ASRs will be far more competitive than they are now, and they will be the major international sea routes in the future. To date, most studies have researched the economic feasibility in the short term from a company’s perspective. To help to plan the shipping market in the future, we developed a three-stage model to simulate the trade demand of the ASRs for the long term. This model firstly considers the seasonal sea ice dynamics in the future and plans new paths for vessels shipping through the Arctic. Additionally, an improved trade prediction model was developed to adapt to the long-term forecasts. After verification, the accuracy of the model was found to be very high (R² = 0.937). In comparison with another transportation cost model and a trade prediction model, our model was more reasonable. This study simulated the trade volumes of China, Europe (EU), and North America (NA) in 2100 with the ASRs open. The results show that the percentage of port trade can be up to 26% in representative concentration pathway (RCP)2.6, and the percentage of port trade can be up to 52% in RCP8.5. Full article

We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty (i.e., the absolute spread of the ensemble), while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread—i.e., trajectories drift divergently in different directions. We suggest that in order to obtain enough uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where at least wind forcing and sea ice cohesion are perturbed. Full article