'''Short description:''' For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean. '''DOI (product) :''' https://doi.org/10.48670/moi-00134

'''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 Copernicus Marine products are used to compute the Strong Wave Incidence index: * IBI-WAV-MYP: '''IBI_MULTIYEAR_WAV_005_006''' * IBI-WAV-NRT: '''IBI_ANALYSISFORECAST_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 Copernicus Marine 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; Kushnir 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, Martínez-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 trend analysis of the SWI index for the period 1980–2024 shows statistically significant trends (at the 99% confidence level) in wave incidence, with an increase of at least 0.05 percentage points per year in regions WAV1, WAV3, and WAV4. The analysis of the historical period, based on reanalysis data, highlights the major wave events recorded in each monitoring region. In region WAV1 (panel B), the maximum wave event occurred in February 2014, resulting in a 28% increase in strong wave conditions. In region WAV2 (panel C), two notable wave events were identified in November 2009 and February 2014, with increases of 16–18% in strong wave conditions. Similarly, in region WAV3 (panel D), a major event occurred in February 2014, marking one of the most intense events in the region with a 20% increase in storm wave conditions. Additionally, a comparable storm affected the region two months earlier, in December 2013. In region WAV4 (panel E), the most extreme event took place in January 1996, producing a 25% increase in strong wave conditions. Although each monitoring region is generally affected by independent wave events, the analysis reveals several historical events with above-average wave activity that propagated across multiple regions: November–December 2010 (WAV3 and WAV2), February 2014 (WAV1, WAV2, and WAV3), and February–March 2018 (WAV1 and WAV4). The analysis of the near-real-time (NRT) period (from January 2024 onward) identifies a significant event in February 2024 that impacted regions WAV1 and WAV4, resulting in increases of 20% and 15% in strong wave conditions, respectively. For region WAV4, this event represents the second most intense event recorded in the region. '''DOI (product):''' https://doi.org/10.48670/moi-00251

'''DEFINITION''' Volume transport across lines are obtained by integrating the volume fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models’ daily output. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in Sverdrup (Sv). '''CONTEXT''' The ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth’s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth’s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. '''CMEMS KEY FINDINGS''' The mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. At Drake Passage, the multi-product approach (product no. 2.4.1) is larger than the value (130 Sv) of Lumpkin and Speer (2007), but smaller than the new observational based results of Colin de Verdière and Ollitrault, (2016) (175 Sv) and Donohue (2017) (173.3 Sv). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00247

'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). '''CMEMS KEY FINDINGS''' Since the year 2005 the upper (0-2000m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.3±0.3 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00240

'''Short description:''' For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis provides daily gap-free sea surface temperature fields, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution. It is produced by the DMI Optimal Interpolation (DMIOI) system (Høyer and She, 2007) to provide a high resolution (1/50deg. - approx. 2km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth. It uses satellite data from infra-red radiometers, from the ESA SST_cci v3.0 (Embury et al., 2024) and Copernicus C3S projects, namely L2P data from (A)ATSRs, SLSTR and AVHRR for the period 1982-2021, L3U data from SLSTR and AVHRR for 2022-July 19 2024 and L2P data from SLSTR and AVHRR from July 20 2024 onward. For the Sea Ice Concentration it uses the Baltic high resolution sea ice concentration data from the Copernicus Marine Service SI TAC (SEAICE_BAL_PHY_L4_MY_011_019). '''DOI (product) :''' https://doi.org/10.48670/moi-00156

'''Short description:''' Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications. This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Seas. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) '''Associated products''' A time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033] describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document. '''DOI (product) :''' https://doi.org/10.48670/moi-00140

'''Short description: ''' For the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''""multi""''' products, and S3A & S3B only for the '''""olci""''' products. * Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS'''). * Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs. * Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY'''). To find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''""GlobColour""'''. '''DOI (product) :''' https://doi.org/10.48670/moi-00279

'''DEFINITION''' The OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). '''CONTEXT''' Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. The Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The completeness index criteria is fulfilled by 62 stations in 2023, five more than those available in 2022 (57), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol Channel (e.g.: 6.25 and 5.78 m above mean sea level in Newport and Hinkley, respectively), and in the English Channel (e.g.: 5.16 m above mean sea level in St. Helier). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-3 cm in the Canary Islands to 15 cm in Hinkley (Bristol Channel). Negative or close to zero anomalies of 2023 99th percentile prevail throughout the region this year, reaching < -20 cm in several stations of the UK western coast and the English Channel (e.g.: -22 cm in Newport; -21 cm in St.Helier). Significantly positive anomaly of 2023 99th percentile is only found in Arcklow Harbour, in the eastern Irish coast. '''DOI (product):''' https://doi.org/10.48670/moi-00253

'''Short description:''' These products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices. '''DOI (product) :''' https://doi.org/10.17882/70345

'''Short description:''' For the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''""multi""''' products. * Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT''') and Reflectance ('''RRS'''). * Temporal resolutions: '''daily''', '''monthly'''. * Spatial resolutions: '''4 km''' (multi). * Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY'''). To find these products in the catalogue, use the search keyword '''""ESA-CCI""'''. '''DOI (product) :''' https://doi.org/10.48670/moi-00282