OS4.5

Since 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only alternative to monitor such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and others phenomena. All together, they lead to false detections that cannot be discriminated with classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model based on spatial features to filter false detections. More specifically, Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum detections, five spatial indices were extracted for describing their shape and surrounding context and then used by a random forest binary classifier. Contextual features were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the classifier performs the filtering of daily false detections with an accuracy of 90%. This leads to a reduction of detected Sargassum pixels of 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution on 2016-2020 is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. In particular, it retrieves the two areas of consolidation in the western and eastern part of the Tropical Atlantic Ocean associated with distinct temporal dynamics. At full resolution, the dataset allowed us to semi-automatically extract Sargassum aggregations trajectories from successive filtered images. Using those trajectories will help to better quantify the drift of aggregations with respect to the currents, the wind and sea state. Overall, this new dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models.

How to cite: Podlejski, W., Descloitres, J., Chevalier, C., Minghelli, A., Lett, C., and Berline, L.: Sargassum observations from MODIS: using aggregations context to filter false detections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2900, https://doi.org/10.5194/egusphere-egu22-2900, 2022.

Coastal ocean areas are very dynamic regions subject to strong anthropogenic pressure (e.g. industry, tourism, renewable energies, population). Satellite data constitute a unique tool that allows one to study and monitor these areas at a unique spatial and temporal resolution. The spatial and temporal scales needed to assess changes at coastal regions are higher than what can be rendered by individual missions: for example, satellites like Sentinel-3 provide daily temporal resolution, but the sensors onboard these satellites do not measure at the necessary high spatial resolution to resolve complex coastal dynamics; on the other hand, high spatial resolution sensors, like MSI onboard Sentinel-2 (10 m resolution), are able to resolve these small scales, but with a low temporal revisit time (about 5 days). Both high spatial resolution datasets and traditional ones are hindered by the presence of clouds, resulting in a large amount of missing data. The complementarity of Sentinel-2 and Sentinel-3 datasets can be exploited to derive a super-resolution dataset of total suspended matter and chlorophyll concentration in the Belgian coast (North Sea), with the spatial resolution of Sentinel-2 and the temporal resolution of Sentinel-3. Moreover, as the approach used is based in DINEOF (Data Interpolating Empirical Orthogonal Functions), missing data can be interpolated as well, to provide a gap-free super-resolution dataset retaining the spatial scales of the highest resolution dataset (Sentinel-2 in this case). The influence of ocean dynamics in the region will be assess using these ocean colour variables.

How to cite: Alvera-Azcárate, A., Van der Zande, D., Barth, A., and Beckers, J.-M.: Super-resolution total suspended matter and chlorophyll concentration using Sentinel-2 and Sentinel-3 data., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7545, https://doi.org/10.5194/egusphere-egu22-7545, 2022.

Using global high-resolution elevation measurements from the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2), it is possible to distinguish individual surface ocean waves. As the majority of ocean surveying missions are radar satellites, ICESat-2 observations are an important addition to ocean surveys and can provide additional observations not possible with radar. By utilizing the coincident orbits between CryoSat-2 and ICESat-2 during the CRYO2ICE campaign, the observations from ICESat-2 are compared along long stretches of the ground tracks, rather than at the usual crossover points. Therefore, from August 2020 to August 2021, 136 orbit segments from ICESat-2 in the Pacific and Atlantic oceans are used in the comparison. To allow for comparison of ICESat-2 during the coincident orbits, CryoSat-2 is validated against in-situ stations as well as satellite altimetry measurements. Using the validated CryoSat-2 observations, the significant wave height (SWH) is determined from the individual photon heights observed by ICESat-2, by three different methods. First, by using the standard ocean data output (ATL12), the SWH determined from this can be further validated. Then, the two methods derived in this study contain a model of deriving the SWH directly from the observed surface waves, as well as a model using the same method as ATL12, to act as a baseline for the wave-based model. The validation of this wave-based model for extended stretches with CryoSat-2 would allow for the further use of this model for studies. The carried out comparisons result in correlations between ICESat-2 and CryoSat-2 of 0.97 for ATL12 and 0.95 for the wave-based model, with a small mean deviation between the altimeters. The observations from ICESat-2 experience a larger variance than other altimeter crossover-comparison studies, however being constrained by a larger time-lag (<3h) between the coincident orbits for ICESat-2 and CryoSat-2 this is expected. From the study, ICESat-2 is found to agree with observations from CryoSat-2, and utilizing the possibility of distinguishing the surface waves, would therefore provide beneficial for ocean observations.

How to cite: Nilsson, B., Andersen, O. B., Ranndal, H., and Rasmussen, M. L.: Consolidating ICESat-2 ocean wave characteristics with CryoSat-2 during the CRYO2ICE campaign, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10395, https://doi.org/10.5194/egusphere-egu22-10395, 2022.

How to cite: Loveday, B., Träger-Chatterjee, C., Wannop, S., Evers-King, H., Brando, V., Rosmorduc, V., Ruescas, A., and Troupin, C.: Expanding the use of Copernicus marine satellite data: EUMETSAT’s user support and training activities., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11436, https://doi.org/10.5194/egusphere-egu22-11436, 2022.

Sea Surface Salinity (SSS) is an increasingly-used Essential Ocean and Climate Variable. The SMOS, Aquarius, and SMAP satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data [Boutin et al., 2021]. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well-suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale.

In this presentation, we review recent advances and performances of the last (version 3) CCI+SSS product.

The CCI v3 processing has been updated to improve the long-term stability of the SMOS SSS [Perrot et al., 2021] and to improve the level 4 SSS uncertainty estimates. A correction for the instantaneous rainfall impact [Supply et al., 2020] is applied, so that, in rainy regions the CCI v3 fields are close to bulk salinities. In the level 4 optimal interpolation, a full least square propagation of the errors is implemented, instead of a simplified propagation.

When compared with Argo upper salinities, the robust standard deviation of the pairwise difference is 0.16 pss. However, this number includes a sampling mismatch between the in-situ near-surface salinity done at a single space and time and the two-dimensional satellite SSS. We use a small-scale resolution simulation (1/12° GLORYS) to quantitatively estimate the sampling uncertainty. A quantitative validation of CCI v3 SSS and its associated uncertainties is performed by considering the satellite minus Argo salinity normalized by the sampling and retrieval uncertainties [Merchant et al., 2017]. We find that, at global scale, the sampling mismatch contributes to ~20% of the observed differences between Argo and satellite data; in highly variable regions (river plumes, fronts), the sampling mismatch is the dominant term explaining satellite minus Argo salinity differences.

References

Boutin, J., et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, JGR-Oceans, 126(11), doi:10.1029/2021JC017676.

Merchant, C. J., et al. (2017), Uncertainty information in climate data records from Earth observation, Earth Syst. Sci. Data, doi:10.5194/essd-9-511-2017.

Perrot, X., et al. (2021), CCI+SSS: A New SMOS L2 Reprocessing Reduces Errors on Sea Surface Salinity Time Series, IGARSS proceedings, doi: 10.1109/IGARSS47720.2021.9554451.

Supply, A.et al. (2020), Variability of Satellite Sea Surface Salinity Under Rainfall, in Satellite Precipitation Measurement: Volume 2, doi:10.1007/978-3-030-35798-6_34.

How to cite: Boutin, J., Martin, A., Thouvenin-Masson, C., Reul, N., Catany, R., and Consortium, C. C. I. S. S. S.: Sea Surface Salinity and its uncertainty in 2010-2020 CCI version 3 fields, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3071, https://doi.org/10.5194/egusphere-egu22-3071, 2022.

Sea surface salinity (SSS) is an important indicator of hydrological cycle, oceanic processes and climate variability, and has been obtained from various methods including remote sensing, in-situ observations and numerical modelings. Due to the differences of instruments used, error correction algorithm and gridding strategy, each dataset has unique strengths and weaknesses. In this study, we conducted a multi-scale comparison of SSS among eight datasets, including satellite-based, in-situ-based and ocean reanalysis products from 2012 to 2020. Compared with WOA18 climatology, all products show good consistency in describing the dominant mode of global SSS distribution. Among eight datasets, the ISAS20 product is of the best quality, and observation-based products are generally more accurate than reanalysis products. Analysis on zonal average shows that positive bias appears in subtropic regions while negative bias distributes in subpolar areas. It was found that reanalysis products have significantly large negative biases at the polar region compared with satellite products and in-situ observations. On both the seasonal and interannual scales, high correlation coefficients (0.65-0.95) are found in the global mean SSSs between individual satellite products, in-situ analysis and ocean reanalysis products, with the differences relatively smaller among the same types of datasets. This analysis provides information on the consistency and discrepancy of different SSS products to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

How to cite: Wang, H., Bao, S., Ni, W., Chen, W., Wang, W., and Ren, K.: Intercomparison of Global Sea Surface Salinity from Remote Sensing, Reanalysis and In-situ Products, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12714, https://doi.org/10.5194/egusphere-egu22-12714, 2022.

INTERANNUAL VARIABILITY OF SST THE NORWEGIAN SEA NEAR THE LOFOTEN ISLANDS IN 1998-2020 ACCORDING TO SATELLITE MONITORING

DURING THE COD SPAWNING PERIOD

G.P. Vanyushin and T.V. Bulatova

Russian Federal Research Institute of Fisheries and Oceanography (VNIRO), Moscow, Russia

e-mail: ladimon@mail.ru

Abstract

For fishing purposes, satellite monitoring of SST is used to study the influence of temperature conditions in water areas important for the development of aquatic organisms on the formation of their biological productivity. One of such water areas is the Lofoten Islands zone in the Norwegian Sea. This area (as a reference zone) is known as the main spawning region of one of the main fishing objects - the north-eastern Arctic cod. A digital massif of infrared data from NOAA satellites is used to monitor temperature conditions. Verification of satellite data was carried out by using quasi-synchronous measurements of water temperature from ships, buoys and shore stations. Matrices created on the basis of weekly SST maps for the period 1998-2020 were used to assess the temperature situation in this water area when analyzing the interannual variability of SST. Calculations of the average monthly and long-term average values of SST and SST anomalies were made, the dynamics of SST was evaluated.

The results of the analysis showed that in general, in 1998-2020, there was a positive trend of SST growth in the Lofoten Islands area. In the period from 1998 to 2001, the average annual indicators of SST, gradually decreasing, reached a minimum (6.91 °C) in 2001, but then up to 2006 (8.29 °C) showed high growth rates. In the period 2006-2020, the temperature situation in the area of the Lofoten Islands somewhat stabilized, the average annual SST values fluctuated from 7.85 °C (2008) to 8.55 ° C (2017). After the maximum reached in 2017, there was a slight decline in SST (to 8.05 °C). The comparative assessment of SST indicators with climatic data is especially relevant for the period of the main spawning of the north-eastern Arctic cod, which takes place on March-April. The obtained results showed that in 1998-2020, the SST in the period March-April was on average higher than the climatic one. Negative anomalies of SST were noted only in 2001 (-0.26°C). In general, the analysis of the dynamics of the long-term course of seasonal average values of SST in the period March-April 1998-2020 showed the presence of a trend for an increase in seasonal average anomalies of SST in area near the Lofoten Islands. A decrease in the values of SST anomalies (from 0.86 °C to -0.26 ° C) was noted only for the period 1998-2001. After 2001 began an increase of temperature anomalies which reached a maximum in 2015 (2.04 °C). Since this year, a certain decrease in the values of SST anomalies has been observed in the studied water area, which continues to the present.

Keywords: satellite monitoring, sea surface temperature (SST), the Lofoten Islands, the North-East Atlantic (NEA) cod, spawning area, comparative analysis.

How to cite: Vanyushin, G. and Bulatova, T.: Interannual variability of SST the Norwegian sea near the Lofoten Islands in 1998-2020 according to satellite monitoring during the cod spawning period, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2207, https://doi.org/10.5194/egusphere-egu22-2207, 2022.

High-resolution satellite images of sea surface temperature and ocean colour reveal an abundance of ocean fronts, swirls, vortices and filaments at horizontal scales below 10 km that permeate the global ocean, especially near mesoscale jets and eddies, in coastal seas and close to sea ice margins. These small-scale ocean features are the fingerprints of dynamic atmosphere-ocean interactions and intense ocean vertical processes that mediate exchanges across all the fundamental interfaces of the Earth System – between the atmosphere, the ocean surface, the ocean interior, the cryosphere and land – and impact major aspects of the global climate system.

Numerous research studies and high-impact scientific publications confirm the key role of submesoscale processes in air-sea interactions, upper-ocean mixing, lateral transports and vertical exchanges with the ocean interior. Small-scale processes also visibly dominate in coastal, shelf seas and polar seas - regions of disproportionally high strategic and societal value as hosts to numerous human activities and natural resources. This paper will review some of the evidence about the fundamental role of small-scale ocean dynamics in the Earth System, making the case for new observations from space to characterise these important phenomena. The contribution ends by outlining the science drivers and objectives of the SEASTAR satellite candidate mission currently under study as one of four candidates to the European Space Agency Earth Explorer 11 programme.

How to cite: Gommenginger, C. and Martin, A.: Observing small-scale ocean surface dynamics and vertical ocean processes in coastal, shelf and polar seas with the Earth Explorer 11 SEASTAR mission candidate., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4502, https://doi.org/10.5194/egusphere-egu22-4502, 2022.

Accurate, high-resolution estimate of ocean surface currents is both a challenging issue and a growing end-user requirement. Yet, the global circulation is only indirectly monitored through satellite remote sensing; to benefit the end-user community (science, shipping, fishing, trading, insurance, offshore energy, defence), current information must be accurately constructed and validated from all relevant available resources. eOdyn develops since 2015 a transformative method to derive surface currents from ship motion and Automatic Identification System (AIS) data [1][2]. Currents, derived from AIS data, a complementary in- situ observing system so far under-exploited, have the potential to complete surface current picture with high- frequency part of ocean dynamics in areas with intensive marine traffic activities. The presentation will focus on recent results, using AIS data collected thanks to low earth orbit satellites and ship behaviour analysis to produce relayable high resolution ocean surface current measurements to monitor different currents of interest (off the south African coastline, the Indian ocean and the Mediterranean sea). Comparisons between AIS derived surface currents and independant data sets from altimetry satellites, HF radars and drifters will be presented. The use of this new technology to complement exisiting measurement systems will be demonstrated. [1] Clément Le Goff, Brahim Boussidi, Alexei Mironov, Yann Guichoux, Yicun Zhen, Pierre Tandeo, Simon Gueguen, and Bertrand Chapron. Monitoring the Greater Agulhas Current with AIS Data Information, Published in Journal of Geophysical Research: Oceans, 2021. [2] Guichoux, Y., Lennon, M. and Thomas, N., Sea surface currents calculation using vessel tracking data, Proceedings of theMaritime Knowledge Discovery and Anomaly Detection Workshop. Michele Vespe and Fabio Mazzarella. JRC Conference and Workshop Reports, pp.31-35, 2016.  

How to cite: guichoux, Y., le goff, C., boussidi, B., and mironov, A.: (encore abstract)  Ship drift and Automatic Identification System analysis used to monitor ocean surface currents, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8904, https://doi.org/10.5194/egusphere-egu22-8904, 2022.

High-resolution sea surface observations by spaceborne synthetic aperture radar (SAR) instruments are sorely neglected resources for meteorological applications in polar regions. Such radar observations provide information about wind speed and direction based on wind-induced roughness of the sea surface. The increasing coverage of SAR observations in polar regions calls for the development of SAR-specific applications that make use of the full information content of this valuable resource. Here we provide examples of the potential of SAR observations to provide details of the complex, mesoscale wind structure during polar low events, and examine the performance of two current wind retrieval methods. Furthermore, we suggest a new approach towards accurate wind vector retrieval of complex wind fields from SAR observations that does not require a priori wind direction input that the most common retrieval methods are dependent on. This approach has the potential to be particularly beneficial for numerical forecasting of weather systems with strong wind gradients, such as polar lows.

How to cite: Tollinger, M., Graversen, R. G., and Johnsen, H.: High-Resolution Polar-Low Winds Obtained from Unsupervised SAR-Wind Retrieval, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3844, https://doi.org/10.5194/egusphere-egu22-3844, 2022.