'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia–Biscay–Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue. The IBI wave model system is based on the MFWAM model and runs on a grid of 5 km of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by Météo-France), and it is forced by currents provided by the IBI ocean circulation system. The product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum and fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run. '''Product Citation''': Please refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169] '''DOI (Product)''': https://doi.org/10.48670/moi-00025

'''DEFINITION''' The CMEMS IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSIS_FORECAST_WAV_005_005). Two parameters have been considered for this OMI: • Map of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2021). • Anomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2022. This indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (Pérez Gómez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Álvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (Álvarez- Fanjul et al., 2019). '''CONTEXT''' The sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (González-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions. The Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world’s greatest wave generating regions (Mørk et al., 2010; Folley, 2017), the southern half, located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes. The North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Martínez-Asensio et al. 2016; Bacon and Carter, 1991; Bouws et al., 1996; Bauer, 2001; Wolf et al., 2002; Gleeson et al., 2017). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes (Marshall et al., 2001; Wolf et al., 2002; Wolf and Woolf, 2006). Additionally a variety of previous studies have uniquevocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Martínez-Asensio et al., 2016). In this context, long‐term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences. '''CMEMS KEY FINDINGS''' The climatic mean of 99th percentile (1993-2021) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region and previously described. The 99th percentile anomalies in 2023 show that during this period, the central latitudes of the domain (between 37 ºN and 50 ºN) were affected by extreme wave events that exceeded up to twice the standard deviation of the anomalies. These events impacted not only the open waters of the Northeastern Atlantic but also European coastal areas such as the west coast of Portugal, the Spanish Atlantic coast, and the French coast, including the English Channel. Additionally, the impact of significant wave extremes exceeding twice the standard deviation of anomalies was detected in the Mediterranean region of the Balearic Sea and the Algerian Basin. This pattern is commonly associated with the impact of intense Tramontana winds originating from storms that cross the Iberian Peninsula from the Gulf of Biscay. '''Figure caption''' Iberia-Biscay-Ireland Significant Wave Height extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey). '''DOI (product):''' https://doi.org/10.48670/moi-00249

'''DEFINITION''' Ocean heat content (OHC) is defined here as the deviation from a reference period (1993-20210) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 2000 m depth: With a reference density of ρ0 = 1030 kgm-3 and a specific heat capacity of cp = 3980 J/kg°C (e.g. von Schuckmann et al., 2009) Averaged time series for ocean heat content and their error bars are calculated for the Iberia-Biscay-Ireland region (26°N, 56°N; 19°W, 5°E). This OMI is computed using IBI-MYP, GLO-MYP reanalysis and CORA, ARMOR data from observations which provide temperatures. Where the CMEMS product for each acronym is: • IBI-MYP: IBI_MULTIYEAR_PHY_005_002 (Reanalysis) • GLO-MYP: GLOBAL_REANALYSIS_PHY_001_031 (Reanalysis) • CORA: INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b (Observations) • ARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations) The figure comprises ensemble mean (blue line) and the ensemble spread (grey shaded). Details on the product are given in the corresponding PUM for this OMI as well as the CMEMS Ocean State Report: von Schuckmann et al., 2016; von Schuckmann et al., 2018. '''CONTEXT''' Change in OHC is a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and can impact marine ecosystems and human livelihoods (IPCC, 2019). Additionally, OHC is one of the six Global Climate Indicators recommended by the World Meterological Organisation (WMO, 2017). In the last decades, the upper North Atlantic Ocean experienced a reversal of climatic trends for temperature and salinity. While the period 1990-2004 is characterized by decadal-scale ocean warming, the period 2005-2014 shows a substantial cooling and freshening. Such variations are discussed to be linked to ocean internal dynamics, and air-sea interactions (Fox-Kemper et al., 2021; Collins et al., 2019; Robson et al 2016). Together with changes linked to the connectivity between the North Atlantic Ocean and the Mediterranean Sea (Masina et al., 2022), these variations affect the temporal evolution of regional ocean heat content in the IBI region. Recent studies (de Pascual-Collar et al., 2023) highlight the key role that subsurface water masses play in the OHC trends in the IBI region. These studies conclude that the vertically integrated trend is the result of different trends (both positive and negative) contributing at different layers. Therefore, the lack of representativeness of the OHC trends in the surface-intermediate waters (from 0 to 1000 m) causes the trends in intermediate and deep waters (from 1000 m to 2000 m) to be masked when they are calculated by integrating the upper layers of the ocean (from surface down to 2000 m). '''CMEMS KEY FINDINGS''' The ensemble mean OHC anomaly time series over the Iberia-Biscay-Ireland region are dominated by strong year-to-year variations, and an ocean warming trend of 0.41±0.4 W/m2 is barely significant. '''Figure caption''' Time series of annual mean area averaged ocean heat content in the Iberia-Biscay-Ireland region (basin wide) and integrated over the 0-2000m depth layer during 1993-2022: ensemble mean (blue line) and ensemble spread (shaded area). The ensemble mean is based on different data products i.e., the IBI Reanalysis, global ocean reanalysis, and the global observational based products CORA, and ARMOR3D. Trend of ensemble mean (dashed line and bottom-right box) with 95% confidence interval computed in the period 1993-2022. Details on the products are given in the corresponding PUM and QUID for this OMI. '''DOI (product):''' https://doi.org/10.48670/mds-00316

'''DEFINITION''' The Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas. The anomaly of exceeding a threshold of Significant Wave Height is used to characterize the wave behavior. A sensitivity test of the threshold has been performed evaluating the differences using several ones (percentiles 75, 80, 85, 90, and 95). From this indicator, it has been chosen the 90th percentile as the most representative, coinciding with the state-of-the-art. Two CMEMS products are used to compute the Strong Wave Incidence index: • IBI-WAV-MYP: IBI_REANALYSIS_WAV_005_006 • IBI-WAV-NRT: IBI_ANALYSIS_FORECAST_WAV_005_005 The Strong Wave Incidence index (SWI) is defined as the difference between the climatic frequency of exceedance (Fclim) and the observational frequency of exceedance (Fobs) of the threshold defined by the 90th percentile (ThP90) of Significant Wave Height (SWH) computed on a monthly basis from hourly data of IBI-WAV-MYP product: SWI = Fobs(SWH > ThP90) – Fclim(SWH > ThP90) Since the Strong Wave Incidence index is defined as a difference of a climatic mean and an observed value, it can be considered an anomaly. Such index represents the percentage that the stormy conditions have occurred above/below the climatic average. Thus, positive/negative values indicate the percentage of hourly data that exceed the threshold above/below the climatic average, respectively. '''CONTEXT''' Ocean waves have a high relevance over the coastal ecosystems and human activities. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics. However, the incidence of severe (90th percentile) wave events also have valuable relevance affecting the development of human activities and coastal environments. The Strong Wave Incidence index based on the CMEMS regional analysis and reanalysis product provides information on the frequency of severe wave events. The IBI-MFC covers the Europe’s Atlantic coast in a region bounded by the 26ºN and 56ºN parallels, and the 19ºW and 5ºE meridians. The western European coast is located at the end of the long fetch of the subpolar North Atlantic (Mørk et al., 2010), one of the world’s greatest wave generating regions (Folley, 2017). Several studies have analyzed changes of the ocean wave variability in the North Atlantic Ocean (Bacon and Carter, 1991; Kursnir et al., 1997; WASA Group, 1998; Bauer, 2001; Wang and Swail, 2004; Dupuis et al., 2006; Wolf and Woolf, 2006; Dodet et al., 2010; Young et al., 2011; Young and Ribal, 2019). The observed variability is composed of fluctuations ranging from the weather scale to the seasonal scale, together with long-term fluctuations on interannual to decadal scales associated with large-scale climate oscillations. Since the ocean surface state is mainly driven by wind stresses, part of this variability in Iberia-Biscay-Ireland region is connected to the North Atlantic Oscillation (NAO) index (Bacon and Carter, 1991; Hurrell, 1995; Bouws et al., 1996, Bauer, 2001; Woolf et al., 2002; Tsimplis et al., 2005; Gleeson et al., 2017). However, later studies have quantified the relationships between the wave climate and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Matínez-Asensio et al., 2016). The Strong Wave Incidence index provides information on incidence of stormy events in four monitoring regions in the IBI domain. The selected monitoring regions (Figure 1.A) are aimed to provide a summarized view of the diverse climatic conditions in the IBI regional domain: Wav1 region monitors the influence of stormy conditions in the West coast of Iberian Peninsula, Wav2 region is devoted to monitor the variability of stormy conditions in the Bay of Biscay, Wav3 region is focused in the northern half of IBI domain, this region is strongly affected by the storms transported by the subpolar front, and Wav4 is focused in the influence of marine storms in the North-East African Coast, the Gulf of Cadiz and Canary Islands. More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Pascual et al., 2020). '''CMEMS KEY FINDINGS''' The analysis of the index in the last decades do not show significant trends of the strong wave conditions over the period 1992-2021 with 99% confidence. The maximum wave event reported in region WAV1 (B) occurred in February 2014, producing an increment of 25% of strong wave conditions in the region. Two maximum wave events are found in WAV2 (C) with an increment of 15% of high wave conditions in November 2009 and February 2014. As in regions WAV1 and WAV2, in the region WAV3 (D), a strong wave event took place in February 2014, this event is one of the maximum events reported in the region with an increment of strong wave conditions of 20%, two months before (December 2013) there was a storm of similar characteristics affecting this region, other events of similar magnitude are detected on October 2000 and November 2009. The region WAV4 (E) present its maximum wave event in January 1996, such event produced a 25% of increment of strong wave conditions in the region. Despite of each monitoring region is affected by independent wave events; the analysis shows several past higher-than-average wave events that were propagated though several monitoring regions: November-December 2010 (WAV3 and WAV2); February 2014 (WAV1, WAV2, and WAV3); and February-March 2018 (WAV1 and WAV4). The analysis of the NRT period (January 2022 onwards) depicts a significant event that occurred in November 2022, which affected the WAV2 and WAV3 regions, resulting in a 15% and 25% increase in maximum wave conditions, respectively. In the case of the WAV3 region, this event was the strongest event recorded in this region. In the WAV4 region, an event that occurred in February 2024 was the second most intense on record, showing an 18% increase in strong wave conditions in the region. In the WAV1 region, the NRT period includes two high-intensity events that occurred in February 2024 (21% increase in strong wave conditions) and April 2022 (18% increase in maximum wave conditions). '''Figure caption''' (A) Mean 90th percentile of Sea Wave Height computed from IBI_REANALYSIS_WAV_005_006 product at an hourly basis. Gray dotted lines denote the four monitoring areas where the Strong Wave Incidence index is computed. (B, C, D, and E) Strong Wave Incidence index averaged in monitoring regions WAV1 (A), WAV2 (B), WAV3 (C), and WAV4 (D). Panels show merged results of two CMEMS products: IBI_REANALYSIS_WAV_005_006 (blue), IBI_ANALYSIS_FORECAST_WAV_005_005 (orange). The trend and 99% confidence interval of IBI-MYP product is included (bottom right). '''DOI (product):''' https://doi.org/10.48670/moi-00251

'''DEFINITION''' The CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001). Two parameters have been considered for this OMI: • Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2021). • Anomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). '''CONTEXT''' The Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018). The IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003). Also relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980). '''CMEMS KEY FINDINGS''' In the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in Álvarez Fanjul (2019) for the period 1993-2016. The analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43ºN parallel. Extreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer. It is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis. '''Figure caption''' Iberia-Biscay-Ireland Surface Temperature extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey). '''DOI (product):''' https://doi.org/10.48670/moi-00254

'''DEFINITION''' The product OMI_IBI_CURRENTS_VOLTRANS_section_integrated_anomalies is defined as the time series of annual mean volume transport calculated across a set of vertical ocean sections. These sections have been chosen to be representative of the temporal variability of various ocean currents within the IBI domain. The currents that are monitored include: transport towards the North Sea through Rockall Trough (RTE) (Holliday et al., 2008; Lozier and Stewart, 2008), Canary Current (CC) (Knoll et al. 2002, Mason et al. 2011), Azores Current (AC) (Mason et al., 2011), Algerian Current (ALG) (Tintoré et al, 1988; Benzohra and Millot, 1995; Font et al., 1998), and net transport along the 48ºN latitude parallel (N48) (see OMI Figure). To provide ensemble-based results, four Copernicus products have been used. Among these products are three reanalysis products (GLO-REA, IBI-REA and MED-REA) and one product obtained from reprocessed observations (GLO-ARM). • GLO-REA: GLOBAL_MULTIYEAR_PHY_001_030 (Reanalysis) • IBI-REA: IBI_MULTIYEAR_PHY_005_002 (Reanalysis) • MED-REA: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations) • MED-REA: MEDSEA_MULTIYEAR_PHY_006_004MEDSEA_MULTIYEAR_PHY_006_004 (Reanalysis) The time series comprises the ensemble mean (blue line), the ensemble spread (grey shaded area), and the mean transport with the sign reversed (red dashed line) to indicate the threshold of anomaly values that would entail a reversal of the current transport. Additionally, the analysis of trends in the time series at the 95% confidence interval is included in the bottom right corner of each diagram. Details on the product are given in the corresponding Product User Manual (de Pascual-Collar et al., 2024a) and QUality Information Document (de Pascual-Collar et al., 2024b) as well as the CMEMS Ocean State Report: de Pascual-Collar et al., 2024c. '''CONTEXT''' The IBI area is a very complex region characterized by a remarkable variety of ocean currents. Among them, Podemos destacar las que se originan como resultado del closure of the North Atlantic Drift (Mason et al., 2011; Holliday et al., 2008; Peliz et al., 2007; Bower et al., 2002; Knoll et al., 2002; Pérez et al., 2001; Jia, 2000), las corrientes subsuperficiales que fluyen hacia el norte a lo largo del talud continental (de Pascual-Collar et al., 2019; Pascual et al., 2018; Machin et al., 2010; Fricourt et al., 2007; Knoll et al., 2002; Mazé et al., 1997; White & Bowyer, 1997). Y las corrientes de intercambio que se producen en el Estrecho de Gibraltar y el Mar de Alboran (Sotillo et al., 2016; Font et al., 1998; Benzohra and Millot, 1995; Tintoré et al., 1988). The variability of ocean currents in the IBI domain is relevant to the global thermohaline circulation and other climatic and environmental issues. For example, as discussed by Fasullo and Trenberth (2008), subtropical gyres play a crucial role in the meridional energy balance. The poleward salt transport of Mediterranean water, driven by subsurface slope currents, has significant implications for salinity anomalies in the Rockall Trough and the Nordic Seas, as studied by Holliday (2003), Holliday et al. (2008), and Bozec et al. (2011). The Algerian current serves as the sole pathway for Atlantic Water to reach the Western Mediterranean. '''CMEMS KEY FINDINGS''' The volume transport time series show periods in which the different monitored currents exhibited significantly high or low variability. In this regard, we can mention the periods 1997-1998 and 2014-2015 for the RTE current, the period 2012-2014 in the N48 section, the years 2006 and 2017 for the ALG current, the year 2021 for the AC current, and the period 2009-2012 for the CC current. Additionally, periods are detected where the anomalies are large enough (in absolute value) to indicate a reversal of the net transport of the current. This is the case for the years 1999, 2003, and 2012-2014 in the N48 section (with a net transport towards the north), the year 2017 in the ALC current (with net transport towards the west), and the year 2010 in the CC current (with net transport towards the north). The trend analysis of the monitored currents does not detect any significant trends over the analyzed period (1993-2022). However, the confidence interval for the trend in the RTE section is on the verge of rejecting the hypothesis of no trend. '''Figure caption''' Annual anomalies of cross-section volume transport in monitoring sections RTE, N48, AC, ALC, and CC. Time series computed and averaged from different Copernicus Marine products for each window (see section Definition) providing a multi-product result. The blue line represents the ensemble mean, and shaded grey areas represent the standard deviation of the ensemble. Red dashed lines depict the velocity value at which the direction of the current reverses. This aligns with the average transport value (with sign reversed) and the point where absolute transport becomes zero. The analysis of trends (at 95% confidence interval) computed in the period 1993–2021 is included (bottom right box). Trend lines (gray dashed line) are only included in the figures when a significant trend is obtained. '''DOI (product):''' https://doi.org/10.48670/mds-00351