3.2.2. WISHE Region

The salinity at the shelf-break of WISHE is validated by comparison against the mooring stations M8, M9 and M12, all situated in the Northern part of the area. Here, salinity is steadier than in the BISCA area and it is mainly controlled by the slope current. However, occasional episodes marked by the advection of lower salinity water masses coming from the shelf (and highly modified by river and coastal freshwater influence) are clearly identified in the mooring sites. As previously commented in the description of the CMEMS salinity product validation, the IBI operational system can capture to some extent such noticeable surface salinity drops occurred in the peripheries of ROFI areas (see the forecast contingency table shown in Figure 5 in Section 3.1). Likewise, the IBI model scenarios generally accurately reproduce these observed freshwater intrusions, with small variations in timing and/or intensity.

As an example, Figure 9 shows the salinity from March to May 2018 at the western shelf-break of Galicia (station M9). A first well-marked event occurred on 20 March 2018 and has been documented by Campuzano [68] and Lorente et al. [17], and a second one less clearly defined occurred around 22 April 2018. This event of low-salinity intrusion at the shelf-break took place all around the Galician shelf (indeed, the same salinity drop event was also observed at the M12 station for the same period, see Figure 5b). All the IBI model simulations capture the events, at both locations, but show different performance characteristics. Thus, IBI\_LAM is clearly too fresh during the first SSS drop event, but it is the only model run to replicate the low-salinity minimum observed on 30 April 2018. On the other hand, IBI\_REF seems to be the only simulation to accurately capture the salinity drop on 14 April 2018. Finally, IBI\_CLM is systematically too salty and IBI\_NOR features a false salinity drop on 22 April 2018 at M12, further south (figure not shown).

Snapshots of model salinity fields at the dates when salinity dropped are observed in-situ (figures not shown) and indicate that these recorded events of low salinity are caused by filaments of freshwater, extending from the shelf towards the open ocean. The model simulations, actually all of them, feature these freshwater filaments, but with small spatiotemporal variations (of the order of a day or a few kilometers), meaning that at a given location (such as the mooring station), missing an event can be a matter of a few model grid points. Essentially, the resolution of this dynamical scale is stochastic, and a systematic resolution cannot be expected from a regional configuration such as the IBI one. However, this assessment shows that the IBI model configuration can catch the level of dynamical activity, with all model scenarios reproducing main salinity drop events. Once

again, we emphasize that the use of a climatology slightly degrades the variability (timing and intensity) of the salinity.

**Figure 9.** Timeseries of salinity at mooring station M9, at the shelf-break of Northwestern Iberia, from 15 March to 15 May 2018. Observed salinity represented by black dots and the different IBI model scenarios by solid lines (IBI\_REF in blue, IBI\_CLM in light blue, IBI\_LAM in red and IBI\_NOR in orange color).

In order to complete the analysis in this area, the weekly salinity profiles obtained from the INTECMAR CTD station records are used, located at same latitude as moorings M10 and M11. These observations are very coastal and taken at the mouth of the Rias Baixas in Galicia. This area is affected by freshwater plumes originating from discharges of several rivers [49], which are not parameterized in the IBI model set-up as proper river sources, but somehow it should be taken into account by means of the extra coastal runoff climatological forcing.

For validating the IBI model scenarios, simulated salinity profiles at the closest model grid point are vertically interpolated into the depth levels of the observations. All the simulations accurately reproduce the variability of salinity with observed data: steady in summer, but with salinity drops in winter and spring, affecting the whole water column in March and April. Table 6 shows statistics for the model validation at the 5 CTD INTECMAR stations. Figure 10 shows temporal evolution of observed and modelled salinities at the INTECMAR station C3 (8.96◦ W/42.48◦ N), showing a negative bias for all simulations in winter and the beginning of spring (stronger in November and December for IBI\_LAM simulation), and a positive bias in the month of April (stronger for IBI\_CLM simulation). Statistically, the simulation without extra coastal runoff has a better variability than the other simulations, with a correlation most of the time better than the other test simulations at both the INTECMAR stations and at the mooring buoy station, and also a smaller bias. It seems that by removing this extra local freshwater input, the simulated variability of salinity is improved. This fact can sugges<sup>t</sup> that the salinity budget in this coastal point is rather controlled by the extension and variability of the western Iberian Buoyant Plume (mainly supplied and controlled by discharges of the Minho, Duero and Mondego rivers) than by the local runoff from the nearby Rias.

There are no timeseries of observed salinity from mooring buoys on the southern part of the WISHE area. However, the IPMA campaign recorded surface salinity transects across the Portuguese shelf, going South from 42◦ N. These thermo-salinometer measurements (taken from 28 April to 20 May 2018) have been used to validate the different IBI model scenarios in the area. Figure 11 shows how all the simulations have a negative surface salinity bias from 42◦ N to 39.5◦ N and at the Tejo river mouth, but inversely are too salty on the southern part of the measured area. As all the model scenarios feature the same pattern of bias in the region, it seems that the river discharge forcing data source is not the main cause of salinity errors on the shelf freshwater budget, with there being other dynamical factors that may explain this model behavior.

**Figure 10.** Salinity profiles measured by CTD at the INTECMAR station C3 (8.96◦ W/42.48◦ N) for 2018 in WISHE area (**left** panel) in comparison with salinity profiles simulated, at the closest model grid point, by the different model scenarios— IBI\_REF, IBI\_LAM, IBI\_NOR and IBI\_CLM (**middle** panel). Differences between modelled and observed salinity profiles (**right** panels). Note that IBI\_CLM run is shorter than other simulations.

**Table 6.** Mean difference (Bias) and Root Mean Squared Error (RMSE) between observed salinity (from mooring buoys, an IPMA campaign with thermo-salinograph and INTECMAR CTD stations) and simulated one (from the IBI\_REF, IBI\_LAM, IBI\_CLM, IBI\_NOR model scenarios), over the respective length of the simulations, in the WISHE area. The smallest model bias and RMSE for each dataset are in bold. The mooring data have hourly frequency, the IPMA data was measured every 10 min and INTECMAR measurements at each CTD station have a weekly frequency. N is the number of measurements.


\* IBI\_CLM timeseries are shorter than the others.

**Figure 11.** Surface salinity during the IPMA campaign in May 2018 in WISHE area. The **right** panel shows observed (OBS) salinity (measured by means of a thermo-salinometer), whereas panels on the **left** show the respective differences between the simulated salinity (from each model scenario: i.e., IBI\_REF, IBI\_LAM, IBI\_CLM, IBI\_NOR) and the IPMA observations.
