Heterogeneity of impacts of high CO2 on the North Western European Shelf

The increase in atmospheric CO2 is a dual threat to the marine environment: from one side it drives climate change, leading to modifications in water temperature, circulation patterns and stratification intensity; on the other side it causes a decrease in marine pH (ocean acidification, or OA) due to the increase in dissolved CO2. Assessing the combined impact of climate change and OA on marine ecosystems is a challenging task. The response of the ecosystem to a single driver can be highly variable and remains still uncertain; additionally the interaction between these can be either synergistic or antagonistic. In this work we use the coupled oceanographic–ecosystem model POLCOMS-ERSEM driven by climate forcing to study the interaction between climate change and OA. We focus in particular on carbonate chemistry, primary and secondary production. The model has been run in three different configurations in order to assess separately the impacts of climate change on net primary production and of OA on the carbonate chemistry, which have been strongly supported by scientific literature, from the impact of biological feedbacks of OA on the ecosystem, whose uncertainty still has to be well constrained. The global mean of the projected decrease of pH at the end of the century is about 0.27 pH units, but the model shows significant interaction among the drivers and high variability in the temporal and spatial response. As a result of this high variability, critical tipping point can be locally and/or temporally reached: e.g. undersaturation with respect to aragonite is projected to occur in the deeper part of the central North Sea during summer. Impacts of climate change and of OA on primary and secondary production may have similar magnitude, compensating in some area and exacerbating in others.

Uncertainty estimates of remote sensing reflectance derived from comparison of ocean color satellite data sets

Assigning uncertainty to ocean-color satellite products is a requirement to allow informed use of these data. Here, uncertainty estimates are derived using the comparison on a 12th-degree grid of coincident daily records of the remote-sensing reflectance RRS obtained with the same processing chain from three satellite missions, MERIS, MODIS and SeaWiFS. The approach is spatially resolved and produces σ, the part of the RRS uncertainty budget associated with random effects. The global average of σ decreases with wavelength from approximately 0.7– 0.9 10−3 sr−1 at 412 nm to 0.05–0.1 10−3 sr−1 at the red band, with uncertainties on σ evaluated as 20–30% between 412 and 555 nm, and 30–40% at 670 nm. The distribution of σ shows a restricted spatial variability and small variations with season, which makes the multi-annual global distribution of σ an estimate applicable to all retrievals of the considered missions. The comparison of σ with other uncertainty estimates derived from field data or with the support of algorithms provides a consistent picture. When translated in relative terms, and assuming a relatively low bias, the distribution of σ suggests that the objective of a 5% uncertainty is fulfilled between 412 and 490 nm for oligotrophic waters (chlorophyll-a concentration below 0.1 mg m−3). This study also provides comparison statistics. Spectrally, the mean absolute relative difference between RRS from different missions shows a characteristic U-shape with both ends at blue and red wavelengths inversely related to the amplitude of RRS. On average and for the considered data sets, SeaWiFS RRS tend to be slightly higher than MODIS RRS, which in turn appear higher than MERIS RRS. Biases between mission-specific RRS may exhibit a seasonal dependence, particularly in the subtropical belt.