'''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. 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_MULTIYEAR_PHY_ENS_001_031): GLORYS, C-GLORS, ORAS5, FOAM • Two observation based products: CORA (INSITU_GLO_PHY_TS_OA_MY_013_052) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). 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 1993-2022 at rate of 1.38±0.08 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

'''Short description:''' For the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability. Acces through CMEMS Catalogue after registration: http://marine.copernicus.eu/ '''Detailed description:''' The operational analysis system set up by the in-situ TAC Global component operated by Coriolis data centre. It produces temperature and salinity gridded fields. The system is based on a statistical estimation method (objective analysis). This system allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.

'''Short description:''' You can find here the biogeochemistry non assimilative hindcast simulation GLOBAL_REANALYSIS_BIO_001_018 at 1/4° over period 1998 - 2016. Outputs are delivered as monthly mean files with Netcdf format (CF/COARDS 1.5 convention) on the native tripolar grid (ORCA025) at ¼° resolution with 75 vertical levels. This simulation is based on the PISCES biogeochemical model. It is forced offline at a daily frequency by the equivalent of the GLOBAL-REANALYSIS-PHYS-001-009 physics product but without data assimilation. '''Detailed description: ''' There are 8 different datasets: * dataset-global-nahindcast-bio-001-018-no3 containing : nitrate concentration * dataset-global-nahindcast-bio-001-018-po4 containing : phosphate concentration * dataset-global-nahindcast-bio-001-018-si containing : silicate concentration * dataset-global-nahindcast-bio-001-018-o2 containing : dissolved oxygen concentration * dataset-global-nahindcast-bio-001-018-fe containing : iron concentration * dataset-global-nahindcast-bio-001-018-chl containing : chlorophyll concentration * dataset-global-nahindcast-bio-001-018-phyc containing : carbon phytoplankton biomass * dataset-global-nahindcast-bio-001-018-pp containing : primary production The horizontal grid is the standard ORCA025 tri-polar grid (1440 x 1021 grid points). The three poles are located over Antarctic, Central Asia and North Canada. The ¼ degree resolution corresponds to the equator. The vertical grid has 75 levels, with a resolution of 1 meter near the surface and 200 meters in the deep ocean.Biogeochemical and physical simulations start at rest (cold start) in December 1991. The spin-up period consists of 5 years of interannual simulation between 1992 and 1997. The simulation period covers the ocean color era (1998 – 2016).The biogeochemical model used is PISCES (Aumont, in prep). It is a model of intermediate complexity designed for global ocean applications (Aumont and Bopp, 2006) and is part of NEMO modeling platform. It has 24 prognostic variables and simulates biogeochemical cycles of oxygen, carbon and the main nutrients controlling phytoplankton growth (nitrate, ammonium, phosphate, silicic acid and iron). The model distinguishes four plankton functional types based on size: two phytoplankton groups <nowiki>(small = nanophytoplankton and large = diatoms)</nowiki> and two zooplankton groups <nowiki>(small = microzooplankton and large = mesozooplankton).</nowiki>Prognostic variables of phytoplankton are total biomass in C, Fe, Si (for diatoms) and chlorophyll and hence the Fe/C, Si/C, Chl/C ratios are variable. For zooplankton, all these ratios are constant and total biomass in C is the only prognostic variable. The bacterial pool is not modeled explicitly. PISCES distinguishes three non-living pools for organic carbon: small particulate organic carbon, big particulate organic carbon and semi-labile dissolved organic carbon. While the C/N/P composition of dissolved and particulate matter is tied to Redfield stoichiometry, the iron, silicon and carbonate contents of the particles are computed prognostically. Next to the three organic detrital pools, carbonate and biogenic siliceous particles are modeled. Besides, the model simulates dissolved inorganic carbon and total alkalinity. In PISCES, phosphate and nitrate + ammonium are linked by constant Redfield ratio <nowiki>(C/N/P = 122/16/1)</nowiki>, but cycles of phosphorus and nitrogen are decoupled by nitrogen fixation and denitrification. Biogeochemical model PISCES (NEMO3.5) is forced offline by daily fields of the physical model NEMO (OPA module in the NEMO platform) without any assimilation of physical data. The main features of this dynamical ocean are: * NEMO 3.1 * Atmospheric forcings from 3-hourly ERA-Interim reanalysis products, CORE bulk formulation * Vertical diffusivity coefficient is computed by solving the TKE equation * Tidal mixing is parameterized according to the works of Bessières et al. (2008) and Koch-Larrouy et al, (2006). * Sea-Ice model: LIM2 with the Elastic-Viscous-Plastic rheology * Initial conditions: Levitus 98 climatology for temperature and salinity, patched with PHC2.1 for the Arctic regions, and Medatlas for the Mediterranean Sea. A special treatment is done on vertical diffusivity coefficient (Kz): the daily mean is done on Log10(Kz) after a filtering of enhanced convection (Kz increased artificially to 10 m2.s-1 when the water column is unstable). The purpose of this Log10 is to average the orders of magnitudes and to give more weight to small values of vertical diffusivity. The atmospheric forcing fields are daily averages from ERA-Interim reanalysis product (CORE bulk formulation). Boundary fluxes account for nutrient supply from three different sources: Atmospheric deposition (Aumont et al., 2008), rivers for nutrients, dissolved inorganic carbon and alkalinity (Ludwig et al., 1996; Mayorga et al., 2010) and inputs of Fe from marine sediments. Nutrient and freshwater inflows by rivers are colocalized. River and dust inputs are balanced with sediment trapping of NO3, Si and Carbon. An annual and global value of atmospheric carbon dioxide is imposed at sea surface.

'''DEFINITION''' The OMI_EXTREME_SST_NORTHWESTSHELF_sst_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 surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, 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 surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial. The North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. Høyer and Karagali, 2016). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The mean 99th percentiles showed in the area present a range from 14-15ºC in the North of the British Isles, 16-19ºC in the West of the North Sea to 19-20ºC in the Helgoland Bight. The standard deviation ranges from 0.7-0.8ºC in the North of the British Isles, 0.6-2ºC in the West of the North Sea to 0.8-3ºC in in the Helgoland Bight. Results for this year show positive moderate anomalies (+0.3/+1.0ºC) in all the positions except in one station in the West of the Noth Sea where the anomaly is negative (-0.3ºC), all of them inside the standard deviation margin. '''DOI (product):''' https://doi.org/10.48670/moi-00274

'''DEFINITION''' The omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish areas over the period 1982-2024, i.e. the rate of change (°C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v3.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish areas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from re-processed ESA SST CCI, C3S (Embury et al., 2024). Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018). '''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 0.012 ±0.001 °C/year over the period 1982-2024. '''DOI (product):''' https://doi.org/10.48670/moi-00257

'''DEFINITION''' The OMI_EXTREME_WAVE_MEDSEA_swh_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 significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, 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''' Projections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023). For the Mediterranean Sea an interesting publication (De Leo et al., 2024) analyses recent studies in this basin showing the variability in the different results and the difficulties to reach a consensus, especially in the mean wave conditions. The only significant conclusion is the positive trend in extreme values for the western Mediterranean Sea and in particular in the Gulf of Lion and in the Tyrrhenian Sea. '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The mean 99th percentiles showed in the area present a range from 1.5-3.5 in the Gibraltar Strait and Alboran Sea with 0.25-0.6 of standard deviation (std), 2-5m in the East coast of the Iberian Peninsula and Balearic Islands with 0.2-0.4m of std, 3-4m in the Aegean Sea with 0.4-0.6m of std to 2-5m in the Gulf of Lyon with 0.3-0.5m of std. Results for this year show a slight negative anomaly in the Gibraltar Strait reaching -0.95m and the Gulf of Lyon (-0.3/-0.7m) slightly over the std in the respective areas, close to zero anomaly in the Aegean Sea (-0.1m) and slight positive or negative anomalies in the East coast of the Iberian Peninsula and Balearic Islands (-0.4/+0.3m) inside the margin of the std. '''DOI (product):''' https://doi.org/10.48670/moi-00263

'''DEFINITION''' The global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to year-1. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble. '''CONTEXT''' Since the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277±3 ppm (Joos and Spahni, 2008) to 412.44±0.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 ± 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). '''CMEMS KEY FINDINGS''' The rate of change of the integrated yearly surface downward flux has increased by 0.04±0.03e-1 PgC/yr2 over the period 1985 to year-1. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06±0.04e-1 PgC/yr2. In 2021 (resp. 2020), the global ocean CO2 sink was 2.41±0.13 (resp. 2.50±0.12) PgC/yr. The average over the full period is 1.61±0.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr. In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of 0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45±0.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78±0.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022). '''DOI (product):''' https://doi.org/10.48670/moi-00223

'''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