ALBERT

Description: Spaceborne sea surface salinity (SSS) measurements provided by the European Space Agency's (ESA) “Soil Moisture and Ocean Salinity” (SMOS) and the National Aeronautical Space Agency's (NASA) “Aquarius/SAC-D” missions, covering the period from May 2012 to April 2013, are compared against in situ salinity measurements obtained in the northern North Atlantic between 20°N and 80°N. In cold water, SMOS SSS fields show a temperature-dependent negative SSS bias of up to −2 g/kg for temperatures 〈5°C. Removing this bias significantly reduces the differences to independent ship-based thermosalinograph data but potentially corrects simultaneously also other effects not related to temperature, such as land contamination or radio frequency interference (RFI). The resulting time-mean bias, averaged over the study area, amounts to 0.1 g/kg. A respective correction applied previously by the Jet Propulsion Laboratory to the Aquarius data is shown here to have successfully removed an SST-related bias in our study area. For both missions, resulting spatial structures of SSS variability agree very well with those available from an eddy-resolving numerical simulation and from Argo data and, additionally they also show substantial salinity changes on monthly and seasonal time scales. Some fraction of the root-mean-square difference between in situ, and SMOS and Aquarius data (approximately 0.9 g/kg) can be attributed to short time scale ocean processes, notably at the Greenland shelf, and could represent associated sampling errors there.

Description: Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter (EnKF), the filtered anomaly initialization (FAI) and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter (EDF) corrects each ensemble member with the ensemble mean during model integration. And the bred vectors (BV) perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the EnKF and FAI show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the BV, the EDF and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability, and with respect to the temporal evolution at the 26° N latitude.