The bottom boundary layer model approach of Beckmann and Doscher has been adopted for application in a coarse-resolution model of the North Atlantic Ocean. Both components of the approach (advective and conditional diffusive) are found to affect the deep water stratification and circulation. A significant deepening of the downward spreading North Atlantic Deep Water (NADW) is the major effect. This is associated with an enhanced spatial coverage of the NADW cell in the meridional circulation.

A novel dealiasing algorithm for Doppler radar velocity data has been developed at the Swedish Meteorological and Hydrological Institute (SMHI). Unlike most other methods, it does not need independent wind information from other instruments (e.g., nearby radiosonde or wind profiler) or numerical weather prediction (NWP) models. The innovation of the new technique is that it maps the measurements onto the surface of a torus. Dealiased volume radar data can be used in variational assimilation schemes for NWP models through the generation of so-called superobservations. Their use is expected to improve with the introduction of the proposed dealiasing method.

Long sea level records with high temporal resolution are of paramount importance for future coastal protection and adaptation plans. Here we discuss the application of machine learning techniques to some regression problems commonly encountered when analyzing such time series. The performance of artificial neural networks is compared with that of multiple linear regression models on sea level data from the Swedish coast. The neural networks are found to be superior when local sea level forcing is used together with remote sea level forcing and meteorological forcing, whereas the linear models and the neural networks show similar performance when local sea level forcing is excluded. The overall performance of the machine learning algorithms is good, often surpassing that of the much more computationally costly numerical ocean models used at our institute.

To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and river discharges in a coupled estuary-shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation method has been developed and implemented. The system assimilates the conductivity-temperature-depth (CTD) profilers, time series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary-shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary-shelf system. DA with a varied assimilation time window that is based on the intratidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model-observation mismatch at the analysis time and improve simulation greatly in both the estuary and coastal regions. Statistically, the overall root-mean-square error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity has been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher-resolution remote sensing SST data instead of lower-resolution satellite SST, the RMSE of SST is improved by similar to 18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary-shelf system under multiple forcings.

Within the Swedish Regional Climate Modelling Programme (SWECLIM) a 3D coupled ice-ocean model for the Baltic Sea has been developed to simulate physical processes on timescales of hours to decades. The model code is based on the global ocean GCM of the Ocean Circulation Climate Advanced Modelling (OCCAM) project and has been optimized for massively parallel computer architectures. The Hibler-type dynamic-thermodynamic sea ice model utilizes elastic-viscous-plastic rheology resulting in a fully explicit numerical scheme that improves computational efficiency. A detailed performance analysis shows that the ice model causes generic workload imbalance between involved processors. An improved domain partitioning technique minimizes load imbalance, but cannot solve the problem completely. However, it is shown that the total load imbalance is not more than 13% for a mild winter and about 8% for a severe winter. With respect to parallel processor performance, the code makes the best use of available computer resources.

In the most comprehensive pyrheliometer comparison known to date, 33 instruments were deployed to measure direct normal solar radiation over a 10-month period in Golden, Colorado. The goal was to determine their performance relative to four electrical-substitution cavity radiometers that were calibrated against the World Radiometric Reference (WRR) that is maintained at the World Radiation Center in Davos, Switzerland. Because of intermittent cabling problems with one of the cavity radiometers, the average of three windowed, electrical-substitution cavity radiometers served as the reference irradiance for 29 test instruments during the 10-month study. To keep the size of this work manageable, comparisons are limited to stable sunny conditions, passing clouds, calm and windy conditions, and hot and cold temperatures. Other variables could have been analyzed, or the conditions analyzed could have employed higher resolution. A more complete study should be possible now that the instruments are identified; note that this analysis was performed without any knowledge on the part of the analyst of the instruments' manufacturers or models. Apart from the windowed cavities that provided the best measurements, two categories of performance emerged during the comparison. All instruments exceeded expectations in that they measured with lower uncertainties than the manufacturers' own specifications. Operational 95% uncertainties for the three classes of instruments, which include the uncertainties of the open cavities used for calibration, were about 0.5%, 0.8%, and 1.4%. The open cavities that were used for calibration of all pyrheliometers have an estimated 95% uncertainty of 0.4%-0.45%, which includes the conservative estimate of 0.3% uncertainty for the WRR.