'''This product has been archived''' For operational and online products, please visit https://marine.copernicus.eu '''Short description:''' For the '''North Atlantic''' Ocean '''Satellite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

'''This product has been archived''' For operational and online products, please visit https://marine.copernicus.eu '''Short description:''' For the '''North Atlantic''' Ocean '''Satellite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-00285

'''DEFINITION:''' The regional annual chlorophyll anomaly is computed by subtracting a reference climatology (1997-2014) from the annual chlorophyll mean, on a pixel-by-pixel basis and in log10 space. Both the annual mean and the climatology are computed employing the regional products as distributed by CMEMS, derived by application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al., 2018a). '''CONTEXT:''' Phytoplankton – and chlorophyll concentration as their proxy – respond rapidly to changes in their physical environment. In the North Atlantic region these changes present a distinct seasonality and are mostly determined by light and nutrient availability (González Taboada et al., 2014). By comparing annual mean values to a climatology, we effectively remove the seasonal signal at each grid point, while retaining information on potential events during the year (Gregg and Rousseaux, 2014). In particular, North Atlantic anomalies can then be correlated with oscillations in the Northern Hemisphere Temperature (Raitsos et al., 2014). Chlorophyll anomalies also provide information on the status of the North Atlantic oligotrophic gyre, where evidence of rapid gyre expansion has been found for the 1997-2012 period (Polovina et al. 2008, Aiken et al., 2017, Sathyendranath et al., 2018b). '''CMEMS KEY FINDINGS:''' The average chlorophyll anomaly in the North Atlantic is -0.02 log10(mg m-3), with a maximum value of 1.0 log10(mg m-3) and a minimum value of -1.0 log10(mg m-3). That is to say that, in average, the annual 2019 mean value is slightly lower (96%) than the 1997-2014 climatological value. A moderate increase in chlorophyll concentration was observed in 2019 over the Bay of Biscay and regions close to Iceland and Greenland, such as the Irminger Basin and the Denmark Strait. In particular, the annual average values for those areas are around 160% of the 1997-2014 average (anomalies > 0.2 log10(mg m-3)). While the significant negative anomalies reported for 2016-2017 (Sathyendranath et al., 2018c) in the area west of the Ireland and Scotland coasts continued to manifest, the Irish and North Seas returned to their normative regime during 2019, with anomalies close to zero. A change in the anomaly sign (positive to negative) was also detected for the West European Basin, with annual values as low as 60% of the 1997-2014 average. This reduction in chlorophyll might be matched with negative anomalies in sea level during the period, indicating a dominance of upwelling factors over stratification.

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the North Atlantic and Arctic oceans, the ESA Ocean Colour CCI Remote Sensing Reflectance (merged, bias-corrected Rrs) data are used to compute surface Chlorophyll (mg m-3, 1 km resolution) using the regional OC5CCI chlorophyll algorithm. The Rrs are generated by merging the data from SeaWiFS, MODIS-Aqua, MERIS, VIIRS and OLCI-3A sensors and realigning the spectra to that of the MERIS sensor. The algorithm used is OC5CCI - a variation of OC5 (Gohin et al., 2002) developed by IFREMER in collaboration with PML. As part of this development, an OC5CCI look up table was generated specifically for application over OC-CCI merged daily remote sensing reflectances. The resulting OC5CCI algorithm was tested and selected through an extensive calibration exercise that analysed the quantitative performance against in situ data for several algorithms in these specific regions. Processing information: PML's Remote Sensing Group has the capability to automatically receive, archive, process and map global data from multiple polar-orbiting sensors in both near-real time and delayed time. OLCI products are downloaded at level-2 from CODA, the Copernicus Hub and/or via EUMETCAST. These products are remapped at nominal 300m and 1 Km spatial resolution using cylindrical equirectangular projection. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in situ measurements, an estimate of chlorophyll content can be derived. '''Processing information:''' ESA OC-CCI Rrs raw data are provided by Plymouth Marine Laboratory, currently at 4km resolution globally. These are processed to produce chlorophyll concentration using the same in-house software as in the operational processing. The entire CCI data set is consistent and processing is done in one go. Both OC CCI and the REP product are versioned. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (70deg), large spacecraft zenith angle (56deg), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 560 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). For the regional products, a variant of the OC-CCI chain is run to produce high resolution data at the 1km resolution necessary. A detailed description of the ESA OC-CCI processing system can be found in OC-CCI (2014e). '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality documentation. '''DOI (product) :''' https://doi.org/10.48670/moi-00070

'''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) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deasonalised time series is derived by subtracting the mean seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. '''CONTEXT''' Phytoplankton – and chlorophyll concentration as a proxy for phytoplankton – respond rapidly to changes in environmental conditions, such as temperature, light and nutrients availability, and mixing. The response in the North Atlantic ranges from cyclical to decadal oscillations (Henson et al., 2009); it is therefore 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 North Atlantic are known to respond to climate variability associated with the North Atlantic Oscillation (NAO), with the initiation of the spring bloom showing a nominal correlation with sea surface temperature and the NAO index (Zhai et al., 2013). '''CMEMS KEY FINDINGS''' While the overall trend average for the 1997-2020 period in the North Atlantic Ocean is slightly positive (0.92 ± 0.13 % per year), an underlying low frequency harmonic signal can be seen in the deseasonalised data. The annual average for the region in 2020 is 0.31 mg m-3. Though no appreciable changes in the timing of the spring and autumn blooms have been observed during 2020, these reached higher chlorophyll values than the average for the time series. In particular, the spring bloom maximum in 2020, circa 0.80 mg m-3, showed an increase in chlorophyll concentration from the observations during the 2016-2019 spring blooms. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00194

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global ocean, the ESA Ocean Colour CCI surface Chlorophyll (mg m-3, 4 km resolution) using the OC-CCI recommended chlorophyll algorithm is made available in CMEMS format. L3 products are daily files, while the L4 are monthly composites. ESA-CCI data are provided by Plymouth Marine Laboratory at 4km resolution. These are processed using the same in-house software as in the operational processing. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (actually a high air mass cutoff, but approximating to 70deg zenith), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 555 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. A detailed description of calibration & validation is given in the relevant QUID, associated validation reports and quality documentation. '''Processing information:''' ESA-CCI data are provided by Plymouth Marine Laboratory at 4km resolution. These are processed using the same in-house software as in the operational processing. The entire CCI data set is consistent and processing is done in one go. Both OC CCI and the REP product are versioned. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (actually a high air mass cutoff, but approximating to 70deg zenith), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 555 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality documentation. '''Suitability, Expected type of users / uses:''' This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. '''DOI (product) :''' https://doi.org/10.48670/moi-00101

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the North Atlantic and Arctic oceans, the ESA Ocean Colour CCI Remote Sensing Reflectance (merged, bias-corrected Rrs) data are used to compute surface Chlorophyll (mg m-3, 1 km resolution) using the regional OC5CCI chlorophyll algorithm. The Rrs are generated by merging the data from SeaWiFS, MODIS-Aqua, MERIS, VIIRS and OLCI-3A sensors and realigning the spectra to that of the MERIS sensor. The algorithm used is OC5CCI - a variation of OC5 (Gohin et al., 2002) developed by IFREMER in collaboration with PML. As part of this development, an OC5CCI look up table was generated specifically for application over OC- CCI merged daily remote sensing reflectances. The resulting OC5CCI algorithm was tested and selected through an extensive calibration exercise that analysed the quantitative performance against in situ data for several algorithms in these specific regions. L3 products are daily files, while the L4 are monthly composites. ESA-CCI Rrs raw data are provided by PML. These are processed to produce chlorophyll concentration using the same in-house software as in the operational processing. Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. '''Processing information:''' ESA OC-CCI Rrs raw data are provided by Plymouth Marine Laboratory, currently at 4km resolution globally. These are processed to produce chlorophyll concentration using the same in-house software as in the operational processing. The entire CCI data set is consistent and processing is done in one go. Both OC CCI and the REP product are versioned. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (70deg), large spacecraft zenith angle (56deg), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 560 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). For the regional products, a variant of the OC-CCI chain is run to produce high resolution data at the 1km resolution necessary. A detailed description of the ESA OC-CCI processing system can be found in OC-CCI (2014e). '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality documentation. '''Suitability, Expected type of users / uses:''' This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. '''DOI (product) :''' https://doi.org/10.48670/moi-00074

'''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

'''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 MY 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 2021 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''' The coastal and shelf waters, especially between 30 and 400N that showed active oligotrophication flags for 2020 have reduced in 2021 and a reversal to eutrophic flags can be seen in places. Again, the eutrophication index is positive only for a small number of coastal locations just north of 40oN in 2021, however south of 40oN there has been a significant increase in eutrophic flags, particularly around the Azores. In general, the 2021 indicator map showed an increase in oligotrophic areas in the Northern Atlantic and an increase in eutrophic areas in the Southern Atlantic. 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. '''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 '''Short description:''' For the North Atlantic and Arctic oceans, the ESA Ocean Colour CCI Remote Sensing Reflectance (merged, bias-corrected Rrs) data are used to compute surface Chlorophyll (mg m-3, 1 km resolution) using the regional OC5CCI chlorophyll algorithm. The Rrs are generated by merging the data from SeaWiFS, MODIS-Aqua, MERIS, VIIRS and OLCI-3A sensors and realigning the spectra to that of the MERIS sensor. The algorithm used is OC5CCI - a variation of OC5 (Gohin et al., 2002) developed by IFREMER in collaboration with PML. As part of this development, an OC5CCI look up table was generated specifically for application over OC-CCI merged daily remote sensing reflectances. The resulting OC5CCI algorithm was tested and selected through an extensive calibration exercise that analysed the quantitative performance against in situ data for several algorithms in these specific regions. Processing information: PML's Remote Sensing Group has the capability to automatically receive, archive, process and map global data from multiple polar-orbiting sensors in both near-real time and delayed time. OLCI products are downloaded at level-1 from CODA, the Copernicus Hub and/or via EUMETCAST. These products are remapped at nominal 300m and 1 Km spatial resolution using cylindrical equirectangular projection. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in situ measurements, an estimate of chlorophyll content can be derived. '''Processing information:''' ESA OC-CCI Rrs raw data are provided by Plymouth Marine Laboratory, currently at 4km resolution globally. These are processed to produce chlorophyll concentration using the same in-house software as in the operational processing. The entire CCI data set is consistent and processing is done in one go. Both OC CCI and the REP product are versioned. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (70deg), large spacecraft zenith angle (56deg), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 560 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). For the regional products, a variant of the OC-CCI chain is run to produce high resolution data at the 1km resolution necessary. A detailed description of the ESA OC-CCI processing system can be found in OC-CCI (2014e). '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality documentation. '''DOI (product) :''' https://doi.org/10.48670/moi-00073