he Global ARMOR3D L4 Reprocessed dataset is obtained by combining satellite (Sea Level Anomalies, Geostrophic Surface Currents, Sea Surface Temperature) and in-situ (Temperature and Salinity profiles) observations through statistical methods. References : - ARMOR3D: Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845–857. - ARMOR3D: Guinehut S., P.-Y. Le Traon, G. Larnicol and S. Philipps, 2004: Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields - A first approach based on simulated observations. J. Mar. Sys., 46 (1-4), 85-98. - ARMOR3D: Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77–80(0):70–81.

'''Short description:''' For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05° resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. '''DOI (product) :''' https://doi.org/10.48670/moi-00311

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Global Ocean- Gridded objective analysis fields of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. '''DOI (product) :''' https://doi.org/10.48670/moi-00038

'''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

'''This product has been archived''' For operational and online products, please visit https://marine.copernicus.eu '''Short description:''' For the '''North Atlantic''' Ocean '''Satellite Observations''', Plymouth Marine Laboratory (PML) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI reflectance inputs. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''""multi""''' products, and S3A & S3B only for the '''""olci""''' products. * Variables: Chlorophyll-a ('''CHL''') and Diffuse Attenuation ('''KD490'''). * Temporal resolutions: '''monthly'''. * Spatial resolutions: '''1 km''' (multi) or '''300 meters''' (olci). * 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-00287

'''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

'''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''' Estimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to: i) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change. ii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The Arctic sea ice extent used here is based on the “multi-product” (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread. '''CONTEXT''' Sea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). The sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018). '''CMEMS KEY FINDINGS''' Since the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57*106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00190

'''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

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' Marine primary production corresponds to the amount of inorganic carbon which is converted into organic matter during the photosynthesis, and which feeds upper trophic layers. The daily primary production is estimated from satellite observations with the Antoine and Morel algorithm (1996). This algorithm modelized the potential growth in function of the light and temperature conditions, and with the chlorophyll concentration as a biomass index. The monthly area average is computed from monthly primary production weighted by the pixels size. The trend is computed from the deseasonalised time series (1998-2019), following the Vantrepotte and Mélin method. More details are provided in the Ocean State Reports 4 (Cossarini et al. ,2020). '''CONTEXT''' Marine primary production is at the basis of the marine food web and produce about 50% of the oxygen we breath every year (Behrenfeld et al., 2001). Study primary production is of paramount importance as ocean health and fisheries are directly linked to the primary production (Pauly and Christensen, 1995, Fee et al., 2019). Changes in primary production can have consequences on biogeochemical cycles, and specially on the carbon cycle, and impact the biological carbon pump intensity, and therefore climate (Chavez et al., 2011). Despite its importance for climate and socio-economics resources, primary production measurements are scarce and do not allow a deep investigation of the primary production evolution over decades. Satellites observations and modelling can fill this gap. However, depending of their parametrisation, models can predict an increase or a decrease in primary production by the end of the century (Laufkötter et al., 2015). Primary production from satellite observations present therefore the advantage to dispose an archive of more than two decades of global data. This archive can be assimilated in models, in addition to direct environmental analysis, to minimise models uncertainties (Gregg and Rousseaux, 2019). In the Ocean State Reports 4, primary production estimate from satellite and from modelling are compared at the scale of the Mediterranean Sea. This demonstrate the ability of such a comparison to deeply investigate physical and biogeochemical processes associated to the primary production evolution (Cossarini et al., 2020) '''CMEMS KEY FINDINGS''' The trend for the global ocean is negative over the period 1998-2019 with a decline in primary production of about 0.67 mgC.m-2.yr-1 or equivalently 0.2 %.yr-1. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00225