'''Short description:''' Arctic sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and Aprilt to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the European Ocean - Sea Surface Temperature Mono-Sensor L3 Observations. One SST file per 24h per area and per sensor (bias corrected) closest to the original resolution: SLSTR-A, AMSR2, SEVIRI, AVHRR_METOP_B, AVHRR18_G, AVHRR_19L, MODIS_A, MODIS_T, VIIRS_NPP. One SST file per file window per area and per sensor (bias corrected) closest to the original resolution , while still manageable in terms volume over the processed area. '''Description of observation methods/instruments:''' The METOP_B derived SSTs are not bias corrected because METOP_B is used as the reference sensor for the correction method. '''DOI (product) :''' https://doi.org/10.48670/moi-00162

'''DEFINITION''' Heat transport across lines are obtained by integrating the heat 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 PetaWatt (PW). '''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. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00245

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the North Atlantic and Arctic oceans, the ESA Ocean Colour CCI Remote Sensing Reflectance (merged, bias-corrected Rrs) data are used to compute surface Chlorophyll (mg m-3, 1 km resolution) using the regional OC5CCI chlorophyll algorithm. The Rrs are generated by merging the data from SeaWiFS, MODIS-Aqua, MERIS, VIIRS and OLCI-3A sensors and realigning the spectra to that of the MERIS sensor. The algorithm used is OC5CCI - a variation of OC5 (Gohin et al., 2002) developed by IFREMER in collaboration with PML. As part of this development, an OC5CCI look up table was generated specifically for application over OC-CCI merged daily remote sensing reflectances. The resulting OC5CCI algorithm was tested and selected through an extensive calibration exercise that analysed the quantitative performance against in situ data for several algorithms in these specific regions. Processing information: PML's Remote Sensing Group has the capability to automatically receive, archive, process and map global data from multiple polar-orbiting sensors in both near-real time and delayed time. OLCI products are downloaded at level-2 from CODA, the Copernicus Hub and/or via EUMETCAST. These products are remapped at nominal 300m and 1 Km spatial resolution using cylindrical equirectangular projection. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in situ measurements, an estimate of chlorophyll content can be derived. '''Processing information:''' ESA OC-CCI Rrs raw data are provided by Plymouth Marine Laboratory, currently at 4km resolution globally. These are processed to produce chlorophyll concentration using the same in-house software as in the operational processing. The entire CCI data set is consistent and processing is done in one go. Both OC CCI and the REP product are versioned. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (70deg), large spacecraft zenith angle (56deg), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 560 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). For the regional products, a variant of the OC-CCI chain is run to produce high resolution data at the 1km resolution necessary. A detailed description of the ESA OC-CCI processing system can be found in OC-CCI (2014e). '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality documentation. '''DOI (product) :''' https://doi.org/10.48670/moi-00070

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''Description:''' This product is a NRT L4 global total velocity field at 0m and 15m. It consists of the zonal and meridional velocity at a 6h frequency and at 1/4 degree regular grid produced on a daily basis. These total velocity fields are obtained by combining CMEMS NRT satellite Geostrophic Surface Currents and modelled Ekman current at the surface and 15m depth (using ECMWF NRT wind). 6 hourly product, daily and monthly mean are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program). '''DOI (product) :''' https://doi.org/10.48670/moi-00049

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description :''' Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface currents and current vertical profiles. It concerns three delayed time datasets dedicated to near-surface currents measurements coming from two platforms (Lagrangian surface drifters and High Frequency radars) and velocity profiles within the water column coming from the Acoustic Doppler Current Profiler (ADCP, vessel mounted only) platform '''DOI (product) :''' https://doi.org/10.17882/86236

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ibi_omi_tempsal_sst_area_averaged_anomalies product for 2021 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the Iberia-Biscay-Irish Seas. The IBI SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish Seas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Anomalies are computed against the 1993-2014 reference period. '''CONTEXT''' Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). '''CMEMS KEY FINDINGS''' The overall trend in the SST anomalies in this region is 0.011 ±0.001 °C/year over the period 1993-2021. '''DOI (product):''' https://doi.org/10.48670/moi-00256

'''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

'''This product has been archived''' '''Short description:''' Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. On the other hand, CS2 uses radar altimetry to measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Experimental altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 5Hz (~1.3km) sampling. All the missions are homogenized with respect to a reference mission (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 Atmosphic Correction, Ocean Tides, Long Wavelength Errors, Internal tide, …) that can be used to change the physical content for specific needs This product was generated as experimental products in a CNES R&D context. It was processed by the DUACS multimission altimeter data processing system. '''DOI (product) :''' https://doi.org/10.48670/moi-00137