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Autonomous and unmanned vessels are already in use in the defence industry for mine clearance and targets; in the oil and gas industry for subsea positioning, surveying and environmental monitoring; and in oceanographic data collection. While these vessels are still on a small scale, the technology can be easily scaled up to SOLAS size. The paper will look at the issues involved with increasing autonomy in shipping with an introduction to the concept; an overview of current developments; the technology present and future; safety and assurance of vessels; what changes mean for shipowners; legal and insurance implications; and the human element – monitoring and navigating autonomous ships

The spatial and temporal characteristics of the seasonal sea level cycle in the South China Sea (SCS) and its forcing mechanisms are investigated using tide gauge records and satellite altimetry observations along with steric and meteorological data. The coastal mean annual amplitude of the seasonal cycle varies between zero and 24 cm, reaching a maximum between July and January. The maximum mean semiannual amplitude is 7 cm, peaking between March and June. Along the coast, the seasonal cycle accounts for up to 92% of the mean monthly sea level variability. Atmospheric pressure explains a significant portion of the seasonal cycle with dominant annual signals in the northern SCS, the Gulf of Thailand and the north-western Philippines Sea. The wind forcing is dominant on the shelf areas of the SCS and the Gulf of Thailand where a simple barotropic model forced by the local wind shows annual amplitudes of up to 27 cm. In the deep basin of the SCS, the Philippines Sea and the shallow Malacca Strait, the steric component is the major contributor with the maximum annual amplitudes reaching 15 cm. Significant variability in the seasonal cycle is found on a year-to-year basis. The annual and semiannual amplitudes vary by up to 63% and 45% of the maximum values, 15 cm and 11 cm, respectively. On average, stepwise regression analysis of contribution of different forcing factors accounts for 66% of the temporal variability of the annual cycle. The zonal wind was found to exert considerable influence in the Malacca Strait.

Storm surges are responsible for great damage to coastal property and loss of life every year. Coastal management and adaptation practices are essential to reduce such damage. Numerical models provide a useful tool for informing these practices as they simulate sea level with high spatial resolution. Here we investigate the ability of a barotropic version of the HAMSOM model to simulate sea level extremes of meteorological origin in the Mediterranean Sea, including those caused by explosive cyclones. For this purpose, the output of the model is compared to hourly sea level observations from six tide gauge records (Valencia, Barcelona, Marseille, Civitavecchia, Trieste, and Antalya). It is found that the model underestimates the positive extremes significantly at all stations, in some cases by up to 65%. At Trieste, the model can also sometimes overestimate the extremes significantly. The differences between the model and the residuals are not constant for extremes of a given height, which limits the applicability of the numerical model for storm surge forecasting because calibration is difficult. The 50 and 10 year return levels are reasonably well captured by the model at all stations except Barcelona and Marseille, where they are underestimated by over 30%. The number of exceedances of the 99.9th and 99.95% percentiles over a period of 25 years is severely underestimated by the model at all stations. The skill of the model for predicting the timing and value of the storm surges seems to be higher for the events associated with explosive cyclones at all stations. © 2014. American Geophysical Union. All Rights Reserved.

Inter-annual to decadal sea-level variations from tide gauge records in the coastal zones of the Norwegian and Siberian Seas are examined for the period 1950–2010 using a combination of hydrographic observations, wind data, and theory. We identify two large areas of highly coherent sea-level variability: one that includes the Norwegian, Barents, and Kara Seas, and another one that includes the Laptev, East Siberian, and Chukchi Seas. We provide evidence of a new contribution to the sea-level variability along the Norwegian coast associated with the poleward propagation of sea-level fluctuations along the eastern boundary of the North Atlantic. When this propagating signal is combined with the local wind, we are able to explain over 70% of the variance along the Norwegian coast. The steric component explains ~61% of the sea-level (corrected for the inverse barometer) variability along the Norwegian coast. The high coherency between the sea level along the Norwegian coast and that in the Barents and Kara Seas suggests that part of the Norwegian signal propagates further north into these regions. We introduce an atmospheric vorticity index that explains much of the sea-level variability in the Laptev, East Siberian, and Chukchi Seas with correlations ranging from 0.73 to 0.81. In the East Siberian Sea, we identify a sea-level increase of ~22 cm between 2000 and 2003, which is partly explained by the vorticity index, and a decline of ~15 cm after 2003, which we relate to the strengthening of the Beaufort Gyre.

Large-scale patterns of precipitation are important for the changes they may effect upon the circulation of the ocean. However, marine precipitation is very hard to quantify accurately. Four independent climatologies are examined to compare their estimates of the annual mean precipitation, and the seasonal and interannual variations. One data set, Global Precipitation Climatology Project (GPCP), is based upon satellite data, the other three on output of weather forecast reanalyses from the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-range Weather Forecasts (ECMWF). Although all datasets have their errors, there is general agreement on the geographical patterns of precipitation. All the models had higher rain rates in the tropics than shown by the satellite data, and also greater seasonal ranges. However, GPCP has 10-25% more precipitation than NCEP and ECMWF in most of the southern regions, because of their weak representation of convergence zones; NCEP2, a more recent version of the NCEP reanalysis, shows a marked improvement in this area. However, in most regions NCEP2 exhibits a larger seasonal range than shown by other datasets, particularly for the tropical Pacific. Both NCEP and NCEP2 often show a seasonal cycle lagging two months or more behind GPCP. Of the three reanalysis climatologies, ECMWF appears best at realising the position and migration of rain features. The interannual variations are correlated between all four datasets, however the correlation coefficient is only large for regions that have a strong response to El Niño and La Niña event, or for comparisons of the two NCEP reanalyses. Of the datasets evaluated, GPCP has the most internal consistency, with no long-term trend in its regional averages, and it alone shows the deficit in Mediterranean precipitation coincident with the Eastern Mediterranean Transient.