'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer (here: 0-700m) is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology (here 1993-2014) to obtain the fluctuations from an average field. The regional thermosteric sea level values from 1993 to close to real time 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 (IPCC, 2019). 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 (WCRP, 2018). '''CMEMS KEY FINDINGS''' Since the year 1993 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.5±0.1 mm/year.

'''DEFINITION''' The subsurface temperature trends have been derived from regional reanalysis results for the Baltic Sea (product references BALTICSEA_REANALYSIS_PHY_003_011). Horizontal averaging has been conducted over the Baltic Sea domain (13 °E - 31 °E and 53 °N - 66 °N; excluding the Skagerrak strait). The temperature trend has been obtained through a linear fit for each time series of horizontally averaged annual temperature and at each depth level (Mulet et al., 2018). '''CONTEXT''' The Baltic Sea is a semi-enclosed sea in North-Eastern Europe. The temperature of the upper mixed layer of the Baltic Sea is characterized by a strong seasonal cycle driven by the annual course of solar radiation (Leppäranta and Myrberg, 2008). The maximum water temperatures in the upper layer are reached in July and August and the minimum during February, when the Baltic Sea becomes partially frozen (CMEMS OMI Baltic Sea Sea Ice Extent, CMEMS OMI Baltic Sea Sea Ice Volume). Seasonal thermocline, developing in the depth range of 10-30 m in spring, reaches its maximum strength in summer and is eroded in autumn. During autumn and winter the Baltic Sea is thermally mixed down to the permanent halocline in the depth range of 60-80 meters (Matthäus, 1984). The 20–50 m thick cold intermediate layer forms below the upper mixed layer in March and is observed until October within the 15-65 m depth range (Chubarenko and Stepanova, 2018; Liblik and Lips, 2011). The deep layers of the Baltic Sea are disconnected from the ventilated upper ocean layers, and temperature variations are predominantly driven by mixing processes and horizontal advection. A warming trend of the sea surface waters is positively correlated with the increasing trend of diffuse attenuation of light (Kd490) and satellite-detected chlorophyll concentration (Kahru et al., 2016). Temperature increase in the water column could accelerate oxygen consumption during organic matter oxidation (Savchuk, 2018). '''CMEMS KEY FINDINGS''' The subsurface temperature over the 1993-2020 period shows warming trends of about 0.05 °C/year at all depths. The largest warming trend of 0.06 °C/year is recorded at the 20 m depth, which corresponds to seasonal thermocline. Similar positive trend is at the depth of 60-70 meters, which corresponds to the depth of the upper part of the permanent halocline. A positive trend in the sea surface waters has been detected since the 1990s (BACCII Author Team, 2015) as well as a decreasing trend of the start day of the spring phytoplankton bloom (Raudsepp et al., 2019; Kahru et al., 2016). From the measurements Savchuk (2018) has calculated the temperature trend of 0.04◦oC/year since 1979 on average in the deep layers (>60m) of the Baltic Proper. '''DOI (product):''' https://doi.org/10.48670/moi-00208

'''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 for coastal ecosystems and humans. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics as expressed through he incidence of severe (90th percentile) wave events . 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 European 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 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 1992-2019 with 99% confidence. The maximum wave event reported in region WAV1 (B) occurred in February 2014, it produced an increment of 34% of strong wave conditions in the region. The maximum wave event found in WAV2 (C) implied an increment of 30% of high wave conditions in February 2014; additionally, the region show another significant storm in November 2009 that produced an increment of 28% of high wave conditions. As in regions WAV1 and WAV2, in the region WAV3 (D), a strong wave event took place in February 2014, this event is the maximum event reported in the region with an increment of strong wave conditions of 22%, two months before (December 2013) there was a storm of similar characteristics affecting this region. The region WAV4 (E) present its maximum wave event in December 2000, such event produced a 33% 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 November-December 2019 (WAV1 and WAV4). The analysis of the NRT period (2020 onwards) depicts a significant stormy event affecting the WAV3 region in February 2020 (increment of 20% of high wave conditions). '''DOI (product):''' https://doi.org/10.48670/moi-00251

'''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. '''DOI (product):''' https://doi.org/10.48670/moi-00245

'''DEFINITION''' Net sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79°N, 20°W - 10°E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80°N, 27°E - 60°E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60°E, 76°N - 80°N (positive westward). For further details, see Lien et al. (2021). '''CONTEXT''' The Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015). '''CMEMS KEY FINDINGS''' The sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. Moreover, there is a significant, negative trend in the volume transport over the last two and a half decades, explained partly by reduced area transport. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021). '''DOI (product):''' https://doi.org/10.48670/moi-00192

'''DEFINITION''' Ocean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth: OHC=∫_(z_1)^(z_2)▒ρ_0 c_p (T_yr-T_clim )dz [1] with a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 °C-1 (e.g. von Schuckmann et al., 2009). Time series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and is evaluated for topography deeper than 300m. '''CONTEXT''' Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future (Faizal and Rafiuddin, 2011). The quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the “multi-product” approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean’s experience (Masina et al., 2017). Six global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are: The Mediterranean Sea Reanalysis at 1/24 degree horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020) Four global reanalyses at 1/4 degree horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031): GLORYS, C-GLORS, ORAS5, FOAM Two observation based products: CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) and ARMOR3D (GLOBAL_REP_PHY_001_021). Details on the products are delivered in the PUM and QUID of this OMI. '''CMEMS KEY FINDINGS''' The ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2018 at rate of 1.5±0.2 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018). '''DOI (product):''' https://doi.org/10.48670/moi-00261

'''DEFINITION:''' Estimates of Arctic liquid Freshwater Content (FWC in meters) are obtained from integrated differences of the measured salinity and a reference salinity (set to 34.8) from the surface to the bottom per unit area in the Arctic region with a water depth greater than 500m as function of salinity (S), the vertical cell thickness of the dataset (dz) and the salinity reference (Sref). Waters saltier than the 34.8 reference are not included in the estimation. The regional FWC values from 1993 up to real time are then averaged aiming to: # obtain the mean FWC as expressed in cubic km (km3) # monitor the large-scale variability and change of liquid freshwater stored in the Arctic Ocean (i.e. the change of FWC in time). '''CONTEXT:''' The Arctic region is warming twice as fast as the global mean and its climate is undergoing unprecedented and drastic changes, affecting all the components of the Arctic system. Many of these changes affect the hydrological cycle. Monitoring the storage of freshwater in the Arctic region is essential for understanding the contemporary Earth system state and variability. Variations in Arctic freshwater can induce changes in ocean stratification. Exported southward downstream, these waters have potential future implications for global circulation and heat transport. '''CMEMS KEY FINDINGS:''' Since 1993, the Arctic Ocean freshwater has experienced a significant increase of 423 ± 39 km3/year. The year 2016 witnessed the highest freshwater content in the Artic since the last 24 years. Second half of 2016 and first half of 2017 show a substantial decrease of the FW storage. '''DOI (product):''' https://doi.org/10.48670/moi-00193

'''DEFINITION''' The subsurface salinity trends have been derived from regional reanalysis and forecast modelling results of the CMEMS BAL MFC group for the Baltic Sea (product references BALTICSEA_REANALYSIS_PHY_003_011). The salinity trend has been obtained through a linear fit for each time series of horizontally averaged (13 °E - 31 °E and 53 °N - 66 °N; excluding the Skagerrak strait) annual salinity and at each depth level (Mulet et al., 2018). '''CONTEXT''' The Baltic Sea is a brackish semi-enclosed sea in North-Eastern Europe. The surface salinity varies horizontally from ~10 near the Danish Straits down to ~2 at the northernmost and easternmost sub-basins of the Baltic Sea. The halocline, a vertical layer with rapid changes of salinity with depth that separates the well-mixed surface layer from the weakly stratified layer below, is located at the depth range of 60-80 meters (Matthäus, 1984). The bottom layer salinity below the halocline depth varies from 15 in the south down to 3 in the northern Baltic Sea (Väli et al., 2013). The long-term salinity is determined by net precipitation and river discharge as well as saline water inflows from the North Sea (BACCII Author Team, 2015). Long-term salinity decrease may reduce the occurrence and biomass of the Fucus vesiculosus - Idotea balthica association/symbiotic aggregations (Kotta et al., 2019). Changes in salinity and oxygen content affect the survival of the Baltic cod eggs (Raudsepp et al, 2019; von Dewitz et al., 2018). '''CMEMS KEY FINDINGS''' The subsurface salinity over the 1993-2020 period shows no trend in the surface layer of 40-m. Then, the trend increases monotonically until the depth of 80-m, where it reaches the value of about 0.04 per year. Below that depth the salinity trend is uniform within the uncertainty limits. '''DOI (product):''' https://doi.org/10.48670/moi-00207

'''Short Description:''' The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago, the Baffin and Hudson Bays. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ4, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating OSI-SAF sea ice concentrations (both SSMI and AMSR2) from the SI TAC and providing 7-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity and snow depths, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products. '''DOI (product) :''' https://doi.org/10.48670/moi-00004

'''Short description:''' The biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium). '''DOI (product)''': https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI