'''Short description:''' This product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp), Chlorophyll-a concentration (Chla), Downwelling Photosynthetic Available Radiation (PAR) and downwelling irradiance, at 0.25°x0.25° resolution from the surface to 1000 m. A neural network estimates the vertical distribution of Chla and bbp from surface ocean color measurements with hydrological properties and additional drivers. The SOCA-light models is used to integrate light. '''DOI (product):''' https://doi.org/10.48670/moi-00046

'''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±0.01e-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''' Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). 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 OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). '''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 ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). '''CMEMS KEY FINDINGS''' Since the year 2005, the upper (0-700m) near-global (60°S-60°N) ocean warms at a rate of 0.6 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00234

Bonne Condititions Agricoles Environnementales (BCAE): - Périmètres des ADAR (Association pour le Développement Agricole Rural) - Réseau hydrographique de référence PAC (Politique Agricole Commune) (métadonnée en cours)

'''DEFINITION''' The Iberia Biscay Ireland (IBI) Sea Surface Temperature extreme from Reanalysis ocean monitoring indicator (OMI) (OMI_CLIMATE_TEMPSAL_IBI_extreme_var_temp_mean_and_anomaly) is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different Copernicus Marine products are used to compute the indicator: The IBI Reanalysis (IBI_MULTIYEAR_PHY_005_002) and the IBI 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 reanalysis product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2023). * '''Anomaly of the 99th percentile in 2024''': The 99th percentile of the year 2024 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2024 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 (SST) 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 SST 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 IBI 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). Upwelling processes, taking place in the coastal margins, are also relevant in the IBI region. 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 1993-2023 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 SST anomaly for the year 2024 reveals that the northeastern Atlantic region, between latitudes 36° N and 48° N, experienced thermal anomalies exceeding twice the standard deviation. Similar anomalies are also observed near the northeastern Iberian Peninsula, suggesting that inshore and coastal areas may have been affected as well. In contrast, the upwelling region west of the Iberian Peninsula shows negative anomalies in maximum SST, indicating an intensification of upwelling processes in this area. '''DOI (product):''' https://doi.org/10.48670/moi-00254

'''This product has been archived''' '''DEFINITION''' The linear change of zonal mean subsurface temperature over the period 1993-2019 at each grid point (in depth and latitude) is evaluated to obtain a global mean depth-latitude plot of subsurface temperature trend, expressed in °C. The linear change is computed using the slope of the linear regression at each grid point scaled by the number of time steps (27 years, 1993-2019). A multi-product approach is used, meaning that the linear change is first computed for 5 different zonal mean temperature estimates. The average linear change is then computed, as well as the standard deviation between the five linear change computations. The evaluation method relies in the study of the consistency in between the 5 different estimates, which provides a qualitative estimate of the robustness of the indicator. See Mulet et al. (2018) for more details. '''CONTEXT''' Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions, while the deeper ocean temperature in the main thermocline and below varies due to many dynamical forcing mechanisms (Bindoff et al., 2019). Together with ocean acidification and deoxygenation (IPCC, 2019), ocean warming can lead to dramatic changes in ecosystem assemblages, biodiversity, population extinctions, coral bleaching and infectious disease, change in behavior (including reproduction), as well as redistribution of habitat (e.g. Gattuso et al., 2015, Molinos et al., 2016, Ramirez et al., 2017). Ocean warming also intensifies tropical cyclones (Hoegh-Guldberg et al., 2018; Trenberth et al., 2018; Sun et al., 2017). '''CMEMS KEY FINDINGS''' The results show an overall ocean warming of the upper global ocean over the period 1993-2019, particularly in the upper 300m depth. In some areas, this warming signal reaches down to about 800m depth such as for example in the Southern Ocean south of 40°S. In other areas, the signal-to-noise ratio in the deeper ocean layers is less than two, i.e. the different products used for the ensemble mean show weak agreement. However, interannual-to-decadal fluctuations are superposed on the warming signal, and can interfere with the warming trend. For example, in the subpolar North Atlantic decadal variations such as the so called ‘cold event’ prevail (Dubois et al., 2018; Gourrion et al., 2018), and the cumulative trend over a quarter of a decade does not exceed twice the noise level below about 100m depth. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00244

'''DEFINITION''' The indicator of Volume Transport Anomaly in Selected Vertical Sections in the Iberia–Biscay–Ireland (IBI) region (OMI_CIRCULATION_VOLTRANS_IBI_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 selected to represent the temporal variability of key ocean currents within the IBI domain. The monitored ocean currents include the transport towards the North Sea through the Rockall Trough (RTE) (Holliday et al., 2008; Lozier and Stewart, 2008), the Canary Current (CC) (Knoll et al., 2002; Mason et al., 2011), the Azores Current (AC) (Mason et al., 2011), the Algerian Current (ALG) (Tintoré et al., 1988; Benzohra and Millot, 1995; Font et al., 1998), and the net transport along the 48° N latitude parallel (N48) (see OMI figure). To produce ensemble-based results, six datasets provided by the Copernicus Marine Service have been used: * '''IBI-REA''' & '''IBI-INT''': IBI_MULTIYEAR_PHY_005_002 (reanalysis and interim datasets) * '''GLO-REA''': GLOBAL_MULTIYEAR_PHY_001_030 (reanalysis) * '''ARMOR''': MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (reprocessed observations) * '''MED-REA''': MEDSEA_MULTIYEAR_PHY_006_004 (reanalysis) * '''NWS-REA''': NWSHELF_MULTIYEAR_PHY_004_009 (reanalysis) The time series displays the ensemble mean (blue line), the ensemble spread (grey shaded area), and the mean transport with reversed sign (red dashed line), which indicates the threshold of anomaly values corresponding to a reversal in the direction of the current transport. In addition, the trend analysis at the 95% confidence level is shown in the bottom-right corner of each diagram. Further details on the product are provided in the corresponding Product User Manual (de Pascual-Collar et al., 2026a) and Quality Information Document (de Pascual-Collar et al., 2026b), as well as in de Pascual-Collar et al., 2024. '''CONTEXT''' The IBI area is a highly complex region characterized by a remarkable variety of ocean currents. Among them, we can highlight those that originate as a result of the 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); the subsurface currents flowing northward along the continental slope (de Pascual-Collar et al., 2019; Pascual et al., 2018; Machín et al., 2010; Fricourt et al., 2007; Knoll et al., 2002; Mazé et al., 1997; White & Bowyer, 1997); and the exchange currents occurring in the Strait of Gibraltar and the Alboran Sea (Sotillo et al., 2016; Font et al., 1998; Benzohra & 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 processes. 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 only pathway for Atlantic Water to reach the Western Mediterranean. '''CMEMS KEY FINDINGS''' The volume transport time series reveal periods during which the monitored currents exhibited notably high or low variability. Specifically, the RTE current shows pronounced variability in 2010 and during 2014–2015; the N48 section between 2012 and 2014; the ALG current in 2006 and 2017; the AC current between 2005–2007 and in 2021; and the CC current between 2005–2007. Furthermore, certain periods display anomalies of sufficient magnitude (in absolute value) to indicate a reversal in the net transport direction of the current. This is the case for the ALG current in 2017 and 2024 (with net transport towards the west), and for the CC current in 2010 (with net transport towards the north). Trend analysis over the period 1993–2023 does not reveal any statistically significant trends for the monitored currents. However, the confidence interval for the trend in the ALG section is close to rejecting the null hypothesis of no trend. '''DOI (product):''' https://doi.org/10.48670/mds-00351

'''This product has been archived''' '''DEFINITION''' Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). '''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 ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). '''CMEMS KEY FINDINGS''' Since the year 2005, the upper (0-700m) near-global (60°S-60°N) ocean warms at a rate of 0.6 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00234

'''DEFINITION''' Oligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021). '''CONTEXT''' Oligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth’s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al. 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. '''CMEMS KEY FINDINGS''' The trend in the North Atlantic gyre area for the 1997 Sept – 2021 December period was positive, with a 0.14% year-1 increase in area relative to 2000-01-01 values. This trend has decreased compared with the 1997-2019 trend of 0.39%, and is no longer statistically significant (p>0.05). During the 1997 Sept – 2021 December period, the trend in chlorophyll concentration was negative (-0.21% year-1) inside the North Atlantic gyre relative to 2000-01-01 values. This is a slightly lower rate of change compared with the -0.24% trend for the 1997-2020 period but is still statistically significant (p<0.05). '''DOI (product):''' https://doi.org/10.48670/moi-00226