'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' Marine primary production corresponds to the amount of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

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' We have derived an annual eutrophication and eutrophication indicator map for the North Atlantic Ocean using satellite-derived chlorophyll concentration. Using the satellite-derived chlorophyll products distributed in the regional North Atlantic CMEMS REP Ocean Colour dataset (OC- CCI), we derived P90 and P10 daily climatologies. The time period selected for the climatology was 1998-2017. For a given pixel, P90 and P10 were defined as dynamic thresholds such as 90% of the 1998-2017 chlorophyll values for that pixel were below the P90 value, and 10% of the chlorophyll values were below the P10 value. To minimise the effect of gaps in the data in the computation of these P90 and P10 climatological values, we imposed a threshold of 25% valid data for the daily climatology. For the 20-year 1998-2017 climatology this means that, for a given pixel and day of the year, at least 5 years must contain valid data for the resulting climatological value to be considered significant. Pixels where the minimum data requirements were met were not considered in further calculations. We compared every valid daily observation over 2020 with the corresponding daily climatology on a pixel-by-pixel basis, to determine if values were above the P90 threshold, below the P10 threshold or within the [P10, P90] range. Values above the P90 threshold or below the P10 were flagged as anomalous. The number of anomalous and total valid observations were stored during this process. We then calculated the percentage of valid anomalous observations (above/below the P90/P10 thresholds) for each pixel, to create percentile anomaly maps in terms of % days per year. Finally, we derived an annual indicator map for eutrophication levels: if 25% of the valid observations for a given pixel and year were above the P90 threshold, the pixel was flagged as eutrophic. Similarly, if 25% of the observations for a given pixel were below the P10 threshold, the pixel was flagged as oligotrophic. '''CONTEXT''' Eutrophication is the process by which an excess of nutrients – mainly phosphorus and nitrogen – in a water body leads to increased growth of plant material in an aquatic body. Anthropogenic activities, such as farming, agriculture, aquaculture and industry, are the main source of nutrient input in problem areas (Jickells, 1998; Schindler, 2006; Galloway et al., 2008). Eutrophication is an issue particularly in coastal regions and areas with restricted water flow, such as lakes and rivers (Howarth and Marino, 2006; Smith, 2003). The impact of eutrophication on aquatic ecosystems is well known: nutrient availability boosts plant growth – particularly algal blooms – resulting in a decrease in water quality (Anderson et al., 2002; Howarth et al.; 2000). This can, in turn, cause death by hypoxia of aquatic organisms (Breitburg et al., 2018), ultimately driving changes in community composition (Van Meerssche et al., 2019). Eutrophication has also been linked to changes in the pH (Cai et al., 2011, Wallace et al. 2014) and depletion of inorganic carbon in the aquatic environment (Balmer and Downing, 2011). Oligotrophication is the opposite of eutrophication, where reduction in some limiting resource leads to a decrease in photosynthesis by aquatic plants, reducing the capacity of the ecosystem to sustain the higher organisms in it. Eutrophication is one of the more long-lasting water quality problems in Europe (OSPAR ICG-EUT, 2017), and is on the forefront of most European Directives on water-protection. Efforts to reduce anthropogenically-induced pollution resulted in the implementation of the Water Framework Directive (WFD) in 2000. '''CMEMS KEY FINDINGS''' Some coastal and shelf waters, especially between 30 and 400N showed active oligotrophication flags for 2020, with some scattered offshore locations within the same latitudinal belt also showing oligotrophication. Eutrophication index is positive only for a small number of coastal locations just north of 40oN, and south of 30oN. In general, the indicator map showed very few areas with active eutrophication flags for 2019 and for 2020. The Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area (OSPAR ICG-EUT, 2017) reported an improvement from 2008 to 2017 in eutrophication status across offshore and outer coastal waters of the Greater North Sea, with a decrease in the size of coastal problem areas in Denmark, France, Germany, Ireland, Norway and the United Kingdom. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00195

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ibi_omi_tempsal_sst_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish Seas over the period 1993-2019, 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-ATL-SST.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. 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. '''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''' Over the period 1993-2021, the Iberia-Biscay-Irish Seas mean Sea Surface Temperature (SST) increased at a rate of 0.011 ± 0.001 °C/Year. '''DOI (product):''' https://doi.org/10.48670/moi-00257

'''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 sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2018 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and are also available in the CMEMS catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057). To compute the regional mean sea level during the last year, the daily sea level maps of this year are first processed to obtain anomalies referenced to the 1993-2014 period. Then, the obtained individual maps are averaged during the last year. The altimeter data have not been corrected for the effect of the Glacial Isostatic Adjustment (GIA). '''CONTEXT''' Mean sea level evolution has a direct impact on coastal areas and is a crucial index of climate change since it reflects both the amount of heat added in the ocean and the mass loss due to land ice melt (e.g. IPCC, 2013; Dieng et al., 2017). Long-term and inter-annual variations of the sea level are observed at global and regional scales. They are related to the internal variability observed at basin scale and these variations can strongly affect population living in coastal areas. '''CMEMS KEY FINDINGS''' The sea level anomaly field for 2018 compared to the 1993-2014 climatology shows a large negative anomaly in the western subtropical Pacific Ocean and a positive anomaly along the equator, likely associated with ENSO (Schiermeier 2015). Note that an opposite pattern was observed with the 2017 anomaly. In 2019, a rather negative/positive dipole is observed in the West/East subtropical Pacific (the positive equatorial anomaly observed in 2018 is no more observed westward of 160°E. While in 2016, the northward extension of the positive anomaly reached the western US coast (Legeais et al. 2018), it is reduced during 2017 and a negative anomaly is observed in this area. In 2018, this anomaly has almost disappeared and in 2019, a positive anomaly is observed along all the western coast of North and South America. The slightly negative anomaly observed north of the Gulf Stream close to Greenland in 2017 is still observed in 2018 but has a reduced signature in 2019. And the negative anomaly found in 2017 in the North Indian ocean has disappeared in 2018 and a strong East/West dipole is observed in 2019. No major evolution has been observed in the South Atlantic Ocean between 2017, 2018 and 2019. In the Mediterranean Sea, a slightly higher sea level has been observed in 2018 compared to its climatological mean over the entire basin. Such a basin-wide pattern can be related to a response to changes in mass flux through the Strait of Gibraltar forced by the wind (Fukumori et al. 2007) but also to the interannual variability observed in this region (Pinardi & Masetti 2000). Reduced anomalies are observed in 2019 in the Mediterranean Sea. In the Baltic Sea, the positive anomaly observed in 2017 has been linked to a major inflow event (Mohrholz et al. 2015) that took place in 2015-2016 and the amplitude of the Baltic sea level anomaly has strongly reduced in 2018 and 2019.

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and are also available in the Copernicus Marine Service catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the global ocean (hereafter GMSL) is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The time series is corrected for the effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the GMSL has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record. Accounting for this correction changes the shape of the time series, which is no more linear but quadratic, indicating mean sea level acceleration during the altimetry era. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. '''CONTEXT''' The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability, and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). '''CMEMS KEY FINDINGS''' Over the [1993/01/01, 2021/08/02] period, global mean sea level rises at a rate of 3.3  0.4 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA (Peltier, 2004). The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). '''DOI (product):''' https://doi.org/10.48670/moi-00237

'''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

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The time series are derived from the regional chlorophyll reprocessed (REP) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3A-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2021). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. '''CONTEXT''' Phytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016). '''CMEMS KEY FINDINGS''' In the Mediterranean Sea, the trend average for the 1997-2020 period is slightly negative (-0.580.62% per year). Due to the change in processing techniques and chlorophyll retrieval, this trend estimate cannot be compared directly to those previously reported. The observations time series (in grey) shows minima values have been quite constant until 2015 and then there is a little decrease up to 2020, when an absolute minimum occurs with values lower than 0.04 mg m-3. Throughout the time series, maxima are variable year by year (with absolute maximum in 2015, >0.14 mg m-3), showing an evident reduction since 2016. In the last years of the series, the decrease of chlorophyll concentrations is also observed in the deseasonalized timeseries (in green) with a marked step in 2020. This attenuation of chlorophyll values in the last years results in an overall negative trend for the Mediterranean Sea. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00259

'''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 annual mean thermosteric sea level of the year 2017 is substracted from a reference climatology (1993-2014) at each grid point to obtain a global map of thermosteric sea level anomalies in the year 2017, expressed in millimeters per year (mm/yr). '''CONTEXT''' Most of the interannual variability and trends in regional sea level is caused by changes in steric sea level (Oppenheimer et al., 2019). At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60° latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. '''CMEMS KEY FINDINGS''' Higher-than-average thermosteric sea level is reported over most areas of the global ocean and the European regional seas in 2018. In some areas – e.g. the western boundary current regions of the Pacific and Atlantic Ocean in both hemispheres reach values of more than 0.2 m. There are two areas of lower-than-average thermosteric sea level, which stand out from the generally higher-than-average conditions: the western tropical Pacific, and the subpolar North Atlantic. The latter is linked to the so called “North Atlantic cold event” which persists since a couple of years (Dubois et al., 2018). However, its signature has significantly reduced compared to preceding years.

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. '''CONTEXT''' Phytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series. '''CMEMS KEY FINDINGS''' The average global trend for the 1997-2020 period was 0.59% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemisphehres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes continued to be observed after the 2020 extension, as well as the negative trends over the equatorial Pacific and the Indian Ocean Gyre. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00230