In high-nutrient-low-chlorophyll regions, phytoplankton growth is limited by the availability of water-soluble iron. The eruption of Kasatochi volcano in August 2008 led to ash deposition into the iron-limited NE Pacific Ocean. Volcanic ash released iron upon contact with seawater and generated a massive phytoplankton bloom. Here we investigate this event with a one-dimensional ocean biogeochemical column model to illuminate the ocean response to iron fertilisation by volcanic ash. The results indicate that the added iron triggered a phytoplankton bloom in the summer of 2008. Associated with this bloom, macronutrient concentrations such as nitrate and silicate decline and zooplankton biomass is enhanced in the ocean mixed layer. The simulated development of the drawdown of carbon dioxide and increase of pH in surface seawater is in good agreement with available observations. Sensitivity studies with different supply dates of iron to the ocean emphasise the favourable oceanic conditions in the NE Pacific to generate massive phytoplankton blooms in particular during July and August in comparison to other months. By varying the amount of volcanic ash and associated bio-available iron supplied to the ocean, model results demonstrate that the NE Pacific Ocean has higher, but limited capabilities to consume CO2 after iron fertilisation than those observed after the volcanic eruption of Kasatochi
Satellite data of both physical properties as well as ocean colour can be assimilated into coupled ocean-biogeochemical models with the aim to improve the model state. The physical observations like sea surface temperature usually have smaller errors than ocean colour, but it is unclear how far they can also constrain the biogeochemical model variables. Here, the effect of assimilating satellite sea surface temperature into the coastal ocean-biogeochemical model HBM-ERGOM with nested model grids in the North and Baltic Seas is investigated. Weakly and strongly-coupled assimilation is performed with an ensemble Kalman filter. For weaklycoupled assimilation, the assimilation only directly influences the physical variables, while the biogeochemical variables react only dynamically during the 12-hour forecast phases in between the assimilation times. For strongly-coupled assimilation, both the physical and biogeochemical variables are directly updated by the assimilation. The strongly-coupled assimilation is assessed in two variants using the actual concentrations and the common approach to use the logarithm of the concentrations of the biogeochemical fields. In this coastal domain, both the weakly and strongly-coupled assimilation are stable, but only if the actual concentrations are used for the strongly-coupled case. Compared to the weakly-coupled assimilation, the strongly-coupled assimilation leads to stronger changes of the biogeochemical model fields. Validating the resulting field estimates with independent in situ data shows only a clear improvement for the temperature and for oxygen concentrations, while no clear improvement of other biogeochemical fields was found. The oxygen concentrations were more strongly improved with strongly-coupled than weakly-coupled assimilation. The experiments further indicate that for the strongly-coupled assimilation of physical observations the biogeochemical fields should be used with their actual concentrations rather than the logarithmic concentrations.
Abstract. This paper describes Nemo-Nordic 2.0, an operational marine model for the Baltic Sea. The model is used for both near-real-time forecasts and hindcast purposes. It provides estimates of sea surface height, water temperature, salinity, and velocity, as well as sea ice concentration and thickness. The model is based on the NEMO (Nucleus for European Modelling of the Ocean) circulation model and the previous Nemo-Nordic 1.0 configuration by Hordoir et al. (2019). The most notable updates include the switch from NEMO version 3.6 to 4.0, updated model bathymetry, and revised bottom friction formulation. The model domain covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. Vertical grid resolution has been increased from 3 to 1 m in the surface layer. In addition, the numerical solver configuration has been revised to reduce artificial mixing to improve the representation of inflow events. Sea ice is modeled with the SI3 model instead of LIM3. The model is validated against sea level, water temperature, and salinity observations, as well as Baltic Sea ice chart data for a 2-year hindcast simulation (October 2014 to September 2016). Sea level root mean square deviation (RMSD) is typically within 10 cm throughout the Baltic basin. Seasonal sea surface temperature variation is well captured, although the model exhibits a negative bias of approximately −0.5 ∘C. Salinity RMSD is typically below 1.5 g kg−1. The model captures the 2014 major Baltic inflow event and its propagation to the Gotland Deep. The model assessment demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
<p>We present Nemo-Nordic 2.0, the latest version of the operational marine forecasting model for the Baltic Sea used and developed in the Baltic Monitoring Forecasting Centre (BAL MFC) under the Copernicus Marine Environment Monitoring Service (CMEMS). The most notable differences between Nemo-Nordic 2.0 and its predecessor Nemo-Nordic 1.0 are the switch from NEMO 3.6 to NEMO 4.0 and an increase in horizontal resolution from 2 to 1 nautical mile. In addition, the model's bathymetry and bottom friction formulation have been updated. The model configuration was specially tuned to represent Major Baltic Inflow events. Focusing on a 2-year validation period from October 1, 2014, covering one Major Baltic Inflow event, Nemo-Nordic 2.0 simulates Sea Surface Height (SSH) well: centralized Root-Mean-Square Deviation (CRMSD) is within 10 cm for most stations outside the Inner Danish Waters. CRMSD is higher at some stations where small-scale topographical features cannot be correctly resolved. SSH variability tends to be overestimated in the Baltic Sea and underestimated in the Inner Danish Waters. Nemo-Nordic 2.0 represents Sea Surface Temperature (SST) and Salinity (SSS) well, although there is a negative bias around -0.5&#176;C in SST. The 2014 Major Baltic Inflow event is well reproduced. The simulated salt pulse agrees well with observations in the Arkona basin and progresses into the Gotland basin in 3 to 4 months.</p>
ABSTRACT:Remote sensing Synthetic Aperture Radar (SAR) data from TerraSAR-X and Tandem-X (TS-X and TD-X) satellites have been used for validation and verification of newly developed coastal forecast models in the German Bight of the North Sea. The empirical XWAVE algorithm for estimation of significant wave height has been adopted for coastal application and implemented for NRT services. All available TS-X images in the German Bight collocated with buoy measurements (6 buoys) since 2013 were processed and analysed (total of 46 scenes/passages with 184 StripMap images). Sea state estimated from series of TS-X images cover strips with length of ~200km and width of 30km over the German Bight from East-Frisian Islands to the Danish coast. The comparisons with results of wave prediction model show a number of local variations due to variety in bathymetry and wind fronts.
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