This paper analyses secular changes and interannual variability in the wind wave, swell, and significant wave height (SWH) characteristics over the North Atlantic and North Pacific on the basis of wind wave climatology derived from the visual wave observations of voluntary observing ship (VOS) officers. These data are available from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) collection of surface meteorological observations for 1958–2002, but require much more complicated preprocessing than standard meteorological variables such as sea level pressure, temperature, and wind. Visual VOS data allow for separate analysis of changes in wind sea and swell, as well as in significant wave height, which has been derived from wind sea and swell estimates. In both North Atlantic and North Pacific midlatitudes winter significant wave height shows a secular increase from 10 to 40 cm decade−1 during the last 45 yr. However, in the North Atlantic the patterns of trend changes for wind sea and swell are quite different from each other, showing opposite signs of changes in the northeast Atlantic. Trend patterns of wind sea, swell, and SWH in the North Pacific are more consistent with each other. Qualitatively the same conclusions hold for the analysis of interannual variability whose leading modes demonstrate noticeable differences for wind sea and swell. Statistical analysis shows that variability in wind sea is closely associated with the local wind speed, while swell changes can be driven by the variations in the cyclone counts, implying the importance of forcing frequency for the resulting changes in significant wave height. This mechanism of differences in variability patterns of wind sea and swell is likely more realistic than the northeastward propagation of swells from the regions from which the wind sea signal originates.

Climatology of the atmospheric cyclone sizes and their change over the cyclone life cycle is analyzed on the basis of tracking 57 yr of NCEP–NCAR reanalysis sea level pressure data over the Northern Hemisphere. To quantify the atmospheric cyclone sizes a coordinate transform was used, which allows for the collocation of the cyclone center with the virtual pole and for the establishment of a unique coordinate system for the further determination of cyclone geometry. This procedure was incorporated into a numerical cyclone tracking scheme and provided quantitative estimation of cyclone geometry at every stage of the cyclone development. Climatological features of the distribution of the cyclone size characteristics (effective radius, asymmetry) are considered for the cyclones with different central pressure, deepening rate, and lifetime. Mean effective cyclone radius may experience significant changes, ranging from 300–400 km over the continents to more than 900 km over the oceans. There is found to be a strong dependence of the cyclone effective radius on the cyclone lifetime and intensity, implying the largest cyclone sizes for the most intense and long-living transients. Analysis of size changes during the cyclone life cycle implies that the cyclone radius increases during the development stage from 50% to 150%. Size evolution during the cyclone life cycle implies a universal dependence of the normalized cyclone effective radius and the normalized cyclone age. The actual maximum cyclone radius can be determined from these two nondimensional parameters and cyclone central pressure. Further application of the analysis of cyclone size and shape are discussed.

Decadal-scale climate variability in the North Atlantic thermohaline circulation is simulated using a sigma-coordinate primitive equation model, forced by NCEP–NCAR reanalysis surface forcing fields for the period from 1958 to 1997. Surface heat and freshwater flux are expressed in terms of surface thermal and haline density inputs, diagnosed by the model. Variability in surface density fluxes is closely correlated with the North Atlantic Oscillation and demonstrates differences with the original surface heat and freshwater fluxes. Leading modes of surface water mass transformation are considered in the T–S plane. They identify decadal-scale variability associated with the transformation of the Labrador Sea Waters and Subtropical Mode Waters. Analysis of the model responses to the surface forcing shows an immediate reaction of meridional heat transport to the wind stress curl, resulting in a decrease of meridional heat transport at 48°N and an increase in the subtropics. Delayed baroclinic respon... Abstract Decadal-scale climate variability in the North Atlantic thermohaline circulation is simulated using a sigma-coordinate primitive equation model, forced by NCEP–NCAR reanalysis surface forcing fields for the period from 1958 to 1997. Surface heat and freshwater flux are expressed in terms of surface thermal and haline density inputs, diagnosed by the model. Variability in surface density fluxes is closely correlated with the North Atlantic Oscillation and demonstrates differences with the original surface heat and freshwater fluxes. Leading modes of surface water mass transformation are considered in the T–S plane. They identify decadal-scale variability associated with the transformation of the Labrador Sea Waters and Subtropical Mode Waters. Analysis of the model responses to the surface forcing shows an immediate reaction of meridional heat transport to the wind stress curl, resulting in a decrease of meridional heat transport at 48°N and an increase in the subtropics. Delayed baroclinic respon...

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.