Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However, applications are hampered by the incompleteness of imagery and by some quality problems. The Data Interpolating Empirical Orthogonal Functions methodology (DINEOF) allows calculation of missing data in geophysical datasets without requiring a priori knowledge about statistics of the full data set and has previously been applied to SST reconstructions. This study demonstrates the reconstruction of complete space-time information for 4 years of surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) over the Southern North Sea (SNS) and English Channel (EC). Optimal reconstructions were obtained when synthesising the original signal into 8 modes for MERIS CHL and into 18 modes for MERIS TSM. Despite the very high proportion of missing data (70%), the variability of original signals explained by the EOF synthesis reached 93.5 % for CHL and 97.2 % for TSM. For the MODIS TSM dataset, 97.5 % of the original variability of the signal was synthesised into 14 modes. The MODIS SST dataset could be synthesised into 13 modes explaining 98 % of the input signal variability. Validation of the method is achieved for 3 dates below 2 artificial clouds, by comparing reconstructed data with excluded input information. Complete weekly and monthly averaged climatologies, suitable for use with ecosystem models, were derived from regular daily reconstructions. Error maps associated with every reconstruction were produced according to Beckers et al. (2006) [6]. Embedded in this error calculation scheme, a methodology was implemented to produce maps of outliers, allowing identification of unusual or suspicious data points compared to the global dynamics of the dataset. Various algorithms artefacts were associated with high values in the outlier maps (undetected cloud edges, haze areas, contrails, cloud shadows). With the production of outlier maps, the data reconstruction technique becomes also a very efficient tool for quality control of optical remote sensing data and for change detection within large databases.
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CO2 enhanced oil recovery can play a significant role in stimulating carbon capture and storage because of additional oil revenues generated. However, it also leads to additional greenhouse gas emissions. We estimate the global warming potential of different CO2 capture scenarios with and without enhanced oil recovery. During a 10 year period in which oil and electricity are produced without CO2 being captured, the global warming potential is 11 MtCO2 equivalents. We show that if CO2 is captured and used for 15 years of enhanced oil recovery, the global warming potential decreases to 3.4 MtCO2 equivalents. This level is 100% higher compared to the scenario in which the captured CO2 would be stored in an offshore aquifer instead. If the capture of CO2 is stopped when the oil reservoir is depleted, the global warming potential resulting from 10 years electricity production is 6 MtCO2 equivalents. However, if CO2 is stored in the depleted reservoir, the global warming potential is six times lower during that period. Electricity production and oil refining are the main contributors to the global warming potential. The net present value analysis indicates that for CO2 prices lower than or equal to 15 €/t and oil prices greater than or equal to 115 €/t, it is most profitable to capture CO2 for enhanced oil recovery only. Because of the low CO2 price considered, large incomes from oil production are required to stimulate CO2 capture. The environmental economic trade-off analysis shows that if CO2-enhanced oil recovery is followed by CO2 capture and storage, costs increase, but the net present value remains positive and the global warming potential is reduced. Authorities could use these outcomes to support the development of economic mechanisms for shared investments in CO2 capture installations and to mandate both oil producer and large CO2 emitting firms to store CO2 in depleted oil fields.
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RBINS Staff Publications 2019
