fix: update external product types reference

[github-actions]

Update external product types reference from daily fetch. See Python API User Guide / Product types discovery

Changed files

May be truncated.
commit 2276c3a60c1322fcb9f4253f96755f45b9c16d26

eodag/resources/ext_product_types.json
1153c1153
<         "abstract": "The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system  at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year’s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency.                                                                           \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related [QuID](http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-028.pdf)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00015",
---
>         "abstract": "The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system  at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year’s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency.                                                                           \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related [QuID](https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-028.pdf)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00015",
3393c3393
<         "abstract": "**Short  Description:**\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butensch&ouml;n et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009). Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [CMEMS-NWS-QUID-004-011](http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-011.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually,  providing a six-month extension of the time series. See [CMEMS-NWS-PUM-004-009_011](http://resources.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009_011.pdf) for details.\n\n**Associated products:**\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009).\nAn analysis-forecast product is available from: [NWSHELF_MULTIYEAR_BGC_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00058\n\n**References:**\n\n* Ciavatta, S., Brewin, R. J. W., Skákala, J., Polimene, L., de Mora, L., Artioli, Y., & Allen, J. I. (2018). [https://doi.org/10.1002/2017JC013490 Assimilation of ocean‐color plankton functional types to improve marine ecosystem simulations]. Journal of Geophysical Research: Oceans, 123, 834–854. https://doi.org/10.1002/2017JC013490\n",
---
>         "abstract": "**Short  Description:**\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butensch&ouml;n et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009). Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [CMEMS-NWS-QUID-004-011](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually,  providing a six-month extension of the time series. See [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for details.\n\n**Associated products:**\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009).\nAn analysis-forecast product is available from: [NWSHELF_MULTIYEAR_BGC_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00058\n\n**References:**\n\n* Ciavatta, S., Brewin, R. J. W., Skákala, J., Polimene, L., de Mora, L., Artioli, Y., & Allen, J. I. (2018). [https://doi.org/10.1002/2017JC013490 Assimilation of ocean‐color plankton functional types to improve marine ecosystem simulations]. Journal of Geophysical Research: Oceans, 123, 834–854. https://doi.org/10.1002/2017JC013490\n",
3421c3421
<         "abstract": "**Short  Description:**\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution.  \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [GLOBAL_REANALYSIS_PHY_001_026](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026) and at the Baltic boundary by the [BALTICSEA_REANALYSIS_PHY_003_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011). The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [CMEMS-NWS-QUID-004-009](http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-009.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [CMEMS-NWS-PUM-004-009_011](http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009_011.pdf) for further details.\n\n**Associated products:**\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011). An analysis-forecast product is available from [NWSHELF_ANALYSISFORECAST_PHY_LR_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00059",
---
>         "abstract": "**Short  Description:**\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution.  \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [GLOBAL_REANALYSIS_PHY_001_026](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026) and at the Baltic boundary by the [BALTICSEA_REANALYSIS_PHY_003_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011). The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [CMEMS-NWS-QUID-004-009](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for further details.\n\n**Associated products:**\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011). An analysis-forecast product is available from [NWSHELF_ANALYSISFORECAST_PHY_LR_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00059",
3449c3449
<         "abstract": "**Short  description:**\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [ECMWF ERA-5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [CMEMS-NWS-PUM-004-015](http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-015.pdf) for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [CMEMS-NWS-QUID-004-015](http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-015.pdf).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**Associated products:**\n\n[NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00060",
---
>         "abstract": "**Short  description:**\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [ECMWF ERA-5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [CMEMS-NWS-PUM-004-015](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-015.pdf) for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [CMEMS-NWS-QUID-004-015](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-015.pdf).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**Associated products:**\n\n[NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00060",
4793c4793
<         "abstract": "**DEFINITION**\n\nOcean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth:\n¯S=1/V ∫V S dV\nTime series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3.\n\n**CONTEXT**\n\nThe freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link 
to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity.\nThe OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the “multi-product” approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year.\nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24°horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4°horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, \nGLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022)\n\tTwo observation-based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI:  https://doi.org/10.17882/46219, Szekely et al., 2022) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\n**CMEMS KEY FINDINGS**\n\nThe Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2019 at rate of 5.6*10-3 ±3.5*10-4 psu yr-1. \nThe overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993–2019 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s.\n\n**Figure caption**\n\nTime series of annual mean volume ocean salt content in the Mediterranean Sea (basin wide), integrated over the 0-300m depth layer during 1993-2019 (or longer according to data availability) including ensemble mean and ensemble spread (shaded area). The ensemble mean and associated ensemble spread are based on different data products, i.e., Mediterranean Sea Reanalysis (MED-REA), global ocean reanalysis (GLORYS, C-GLORS, ORAS5, and FOAM) and global observational based products (CORA and ARMOR3D). Details on the products are given in the corresponding PUM and QUID for this OMI.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00325\n\n**References:**\n\n* Aydogdu, A., Miraglio, P., Escudier, R., Clementi, E., Masina, S.: The dynamical role of upper layer salinity in the Mediterranean Sea, State of the Planet, accepted, 2023.\n* Desportes, C., Garric, G., Régnier, C., Drévillon, M., Parent, L., Drillet, Y., Masina, S., Storto, A., Mirouze, I., Cipollone, A., Zuo, H., Balmaseda, M., Peterson, D., Wood, R., Jackson, L., Mulet, S., Grenier, E., and Gounou, A.: EU Copernicus Marine Service Quality Information Document for the Global Ocean Ensemble Physics Reanalysis, GLOBAL_REANALYSIS_PHY_001_031, Issue 1.1, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-031.pdf (last access: 3 May 2023), 2022.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cretí, S., Masina, S., Coppini, G., & Pinardi, N. (2020).\n* Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Grenier, E., Verbrugge, N., Mulet, S., and Guinehut, S.: EU Copernicus Marine Service Quality Information Document for the Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD, MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, Issue 1.1, Mercator Ocean International, https: //catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MOB-QUID-015-012.pdf (last access: 3 May 2023), 2021.\n* Szekely, T.: EU Copernicus Marine Service Quality Information Document for the Global Ocean-Delayed Mode gridded CORA – In-situ Observations objective analysis in Delayed Mode, INSITU_GLO_PHY_TS_OA_MY_013_052, issue 1.2, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-052.pdf (last access: 4 April 2023), 2022.\n",
---
>         "abstract": "**DEFINITION**\n\nOcean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth:\n¯S=1/V ∫V S dV\nTime series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3.\n\n**CONTEXT**\n\nThe freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link 
to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity.\nThe OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the “multi-product” approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year.\nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24°horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4°horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, \nGLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022)\n\tTwo observation-based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI:  https://doi.org/10.17882/46219, Szekely et al., 2022) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\n**CMEMS KEY FINDINGS**\n\nThe Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2019 at rate of 5.6*10-3 ±3.5*10-4 psu yr-1. \nThe overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993–2019 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s.\n\n**Figure caption**\n\nTime series of annual mean volume ocean salt content in the Mediterranean Sea (basin wide), integrated over the 0-300m depth layer during 1993-2019 (or longer according to data availability) including ensemble mean and ensemble spread (shaded area). The ensemble mean and associated ensemble spread are based on different data products, i.e., Mediterranean Sea Reanalysis (MED-REA), global ocean reanalysis (GLORYS, C-GLORS, ORAS5, and FOAM) and global observational based products (CORA and ARMOR3D). Details on the products are given in the corresponding PUM and QUID for this OMI.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00325\n\n**References:**\n\n* Aydogdu, A., Miraglio, P., Escudier, R., Clementi, E., Masina, S.: The dynamical role of upper layer salinity in the Mediterranean Sea, State of the Planet, accepted, 2023.\n* Desportes, C., Garric, G., Régnier, C., Drévillon, M., Parent, L., Drillet, Y., Masina, S., Storto, A., Mirouze, I., Cipollone, A., Zuo, H., Balmaseda, M., Peterson, D., Wood, R., Jackson, L., Mulet, S., Grenier, E., and Gounou, A.: EU Copernicus Marine Service Quality Information Document for the Global Ocean Ensemble Physics Reanalysis, GLOBAL_REANALYSIS_PHY_001_031, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-031.pdf (last access: 3 May 2023), 2022.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cretí, S., Masina, S., Coppini, G., & Pinardi, N. (2020).\n* Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Grenier, E., Verbrugge, N., Mulet, S., and Guinehut, S.: EU Copernicus Marine Service Quality Information Document for the Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD, MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-MOB-QUID-015-012.pdf (last access: 3 May 2023), 2021.\n* Szekely, T.: EU Copernicus Marine Service Quality Information Document for the Global Ocean-Delayed Mode gridded CORA – In-situ Observations objective analysis in Delayed Mode, INSITU_GLO_PHY_TS_OA_MY_013_052, issue 1.2, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-INS-QUID-013-052.pdf (last access: 4 April 2023), 2022.\n",
5106c5106
<         "missionStartDate": "1993-01-01T00:00:00Z",
---
>         "missionStartDate": "1982-01-01T00:00:00Z",
5246c5246
<         "missionStartDate": "1993-01-01T00:00:00Z",
---
>         "missionStartDate": "1982-01-01T00:00:00Z",
5353c5353
<         "abstract": "**DEFINITION**\n\nSignificant wave height (SWH), expressed in metres, is the average height of the highest one-third of waves. This OMI provides time series of seasonal mean and extreme SWH values in three oceanic regions as well as their trends from 2002 to 2020, computed from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum of SWH over the chosen period and region. The 95th percentile represents the value below which 95% of the data points fall, indicating higher wave heights than usual. The mean and the 95th percentile of SWH are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February, and March, and July, August, and September) and are in m while the trends are in cm/yr.\n\n**CONTEXT**\n\nGrasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends.\n\n**KEY FINDINGS**\n\nOver the period from 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere. The 95th percentile of wave heights (q95), increases more rapidly than the average values, indicating that extreme waves are growing faster than the average wave height. In the North Atlantic, SWH has increased in summertime (July August September) and decreased during the wintertime: the trend for the 95th percentile SWH is decreasing by 2.1 ± 3.3 cm/year, while the mean SWH shows a decreasing trend of 2.2 ± 1.76 cm/year. In the south of Australia, in boreal winter, the 95th percentile SWH is increasing at a rate of 2.6 ± 1.5 cm/year (a), with the mean SWH increasing by 0.7 ± 0.64 cm/year (b). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 ± 2.15 cm/year (a) and the mean SWH trend is 1.4 ± 0.82 cm/year (b). This variation highlights that waves evolve differently across different basins and seasons, illustrating the complex and region-specific nature of wave height trends. A full discussion regarding this OMI can be found in A. Laloue et al. (2024).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00352\n\n**References:**\n\n* Barnard, P. L., Hoover, D., Hubbard, D. M., Snyder, A., Ludka, B. C., Allan, J., Kaminsky, G. M., Ruggiero, P., Gallien, T. W., Gabel, L., McCandless, D., Weiner, H. M., Cohn, N., Anderson, D. L., and Serafin, K. A.: Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño, Nature Communications, 8, https://doi.org/10.1038/ncomms14365, 2017.\n* Donelan, M. A., Drennan, W. M., and Katsaros, K. B.: The air–sea momentum flux in conditions of wind sea and swell, Journal of Physical Oceanography, 27, 2087–2099, https://doi.org/10.1175/1520-0485(1997)0272.0.co;2, 1997.\n* Mentaschi, L., Vousdoukas, M. I., Voukouvalas, E., Dosio, A., and Feyen, L.: Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns, Geophysical Research Letters, 44, 2416–2426, https://doi.org/10.1002/2016gl072488, 2017\n* Thomas, S., Babanin, A. V., Walsh, K. J. E., Stoney, L., and Heil, P.: Effect of wave-induced mixing on Antarctic sea ice in a high-resolution ocean model, Ocean Dynamics, 69, 737–746, https://doi.org/10.1007/s10236-019-01268-0, 2019.\n* Gommenginger, C. P., Srokosz, M. A., Challenor, P. G., and Cotton, P. D.: Development and validation of altimeter wind speed algorithms using an extended collocated Buoy/Topex dataset, IEEE Transactions on Geoscience and Remote Sensing, 40, 251–260, https://doi.org/10.1109/36.992782, 2002.\n* Storlazzi, C. D., Gingerich, S. B., van Dongeren, A., Cheriton, O. M., Swarzenski, P. W., Quataert, E., Voss, C. I., Field, D. W., Annamalai, H., Piniak, G. A., and McCall, R.: Most atolls will be uninhabitable by the mid-21st century because of sea level rise exacerbating wave-driven flooding, Science Advances, 4, https://doi.org/10.1126/sciadv.aap9741, 2018.\n* Husson, R., Charles, E.: EU Copernicus Marine Service Product User Manual for the Global Ocean L 4 Significant Wave Height From Reprocessed Satellite Measurements Product, WAVE_GLO_PHY_SWH_L4_MY_014_007, Issue 2.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-005-006- 007.pdf, last access: 21 July 2023, 2021 Laloue, A., Ghantous, M., Faugère, Y., Dalphinet. A., Aouf, L.: Statistical analysis of global ocean significant wave heights from satellite altimetry over the past two decades. OSR-8 (under review)\n",
---
>         "abstract": "**DEFINITION**\n\nSignificant wave height (SWH), expressed in metres, is the average height of the highest one-third of waves. This OMI provides time series of seasonal mean and extreme SWH values in three oceanic regions as well as their trends from 2002 to 2020, computed from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum of SWH over the chosen period and region. The 95th percentile represents the value below which 95% of the data points fall, indicating higher wave heights than usual. The mean and the 95th percentile of SWH are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February, and March, and July, August, and September) and are in m while the trends are in cm/yr.\n\n**CONTEXT**\n\nGrasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends.\n\n**KEY FINDINGS**\n\nOver the period from 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere. The 95th percentile of wave heights (q95), increases more rapidly than the average values, indicating that extreme waves are growing faster than the average wave height. In the North Atlantic, SWH has increased in summertime (July August September) and decreased during the wintertime: the trend for the 95th percentile SWH is decreasing by 2.1 ± 3.3 cm/year, while the mean SWH shows a decreasing trend of 2.2 ± 1.76 cm/year. In the south of Australia, in boreal winter, the 95th percentile SWH is increasing at a rate of 2.6 ± 1.5 cm/year (a), with the mean SWH increasing by 0.7 ± 0.64 cm/year (b). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 ± 2.15 cm/year (a) and the mean SWH trend is 1.4 ± 0.82 cm/year (b). This variation highlights that waves evolve differently across different basins and seasons, illustrating the complex and region-specific nature of wave height trends. A full discussion regarding this OMI can be found in A. Laloue et al. (2024).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00352\n\n**References:**\n\n* Barnard, P. L., Hoover, D., Hubbard, D. M., Snyder, A., Ludka, B. C., Allan, J., Kaminsky, G. M., Ruggiero, P., Gallien, T. W., Gabel, L., McCandless, D., Weiner, H. M., Cohn, N., Anderson, D. L., and Serafin, K. A.: Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño, Nature Communications, 8, https://doi.org/10.1038/ncomms14365, 2017.\n* Donelan, M. A., Drennan, W. M., and Katsaros, K. B.: The air–sea momentum flux in conditions of wind sea and swell, Journal of Physical Oceanography, 27, 2087–2099, https://doi.org/10.1175/1520-0485(1997)0272.0.co;2, 1997.\n* Mentaschi, L., Vousdoukas, M. I., Voukouvalas, E., Dosio, A., and Feyen, L.: Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns, Geophysical Research Letters, 44, 2416–2426, https://doi.org/10.1002/2016gl072488, 2017\n* Thomas, S., Babanin, A. V., Walsh, K. J. E., Stoney, L., and Heil, P.: Effect of wave-induced mixing on Antarctic sea ice in a high-resolution ocean model, Ocean Dynamics, 69, 737–746, https://doi.org/10.1007/s10236-019-01268-0, 2019.\n* Gommenginger, C. P., Srokosz, M. A., Challenor, P. G., and Cotton, P. D.: Development and validation of altimeter wind speed algorithms using an extended collocated Buoy/Topex dataset, IEEE Transactions on Geoscience and Remote Sensing, 40, 251–260, https://doi.org/10.1109/36.992782, 2002.\n* Storlazzi, C. D., Gingerich, S. B., van Dongeren, A., Cheriton, O. M., Swarzenski, P. W., Quataert, E., Voss, C. I., Field, D. W., Annamalai, H., Piniak, G. A., and McCall, R.: Most atolls will be uninhabitable by the mid-21st century because of sea level rise exacerbating wave-driven flooding, Science Advances, 4, https://doi.org/10.1126/sciadv.aap9741, 2018.\n* Husson, R., Charles, E.: EU Copernicus Marine Service Product User Manual for the Global Ocean L 4 Significant Wave Height From Reprocessed Satellite Measurements Product, WAVE_GLO_PHY_SWH_L4_MY_014_007, Issue 2.0, Mercator Ocean International, https://documentation.marine.copernicus.eu/PUM/CMEMS-WAV-PUM-014-005-006-007.pdf, last access: 21 July 2023, 2021 Laloue, A., Ghantous, M., Faugère, Y., Dalphinet. A., Aouf, L.: Statistical analysis of global ocean significant wave heights from satellite altimetry over the past two decades. OSR-8 (under review)\n",
5829c5829
<         "abstract": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithm over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi-Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b). \nMonthly 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.\n\n**CONTEXT**\n\nPhytoplankton   and chlorophyll concentration as a proxy for phytoplankton   respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014). 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, in the Baltic Sea phytoplankton is known to respond to the variations of SST in the basin associated with climate variability (Kabel et al. 2012).\n\n**KEY FINDINGS**\n\nThe Baltic Sea shows a slight positive trend over the 1997-2023 period, with a slope of 0.30±0.49% per year, indicating a decrease compared to the previous release. The maxima and minima values are relatively consistent year-to-year, with the absolute maximum occurring in 2008 and the minima observed in 2004 and 2014. A decrease in the chlorophyll signal has been evident over the past two years.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00197\n\n**References:**\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D’Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-080-097.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D’Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b (accepted). Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals. Remote Sens. 2021, 13, x.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kabel K, Moros M, Porsche C, Neumann T, Adolphi F, Andersen TJ, Siegel H, Gerth M, Leipe T, Jansen E, Sinninghe Damsté JS. 2012. Impact of climate change on the health of the Baltic Sea ecosystem over the last 1000 years. Nat Clim Change. doi:10.1038/nclimate1595.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., et al., 2018. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 3.1. Technical Report Centre for Environmental Data Analysis. doi:10.5285/9c334fbe6d424a708cf3c4cf0c6a53f5.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall’s tau. J Am Statist Assoc. 63:1379 1389.\n* Zibordi, G., Berthon, J.-F., Mélin, F., and D’Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104–2115, 2011.\n",
---
>         "abstract": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithm over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi-Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b). \nMonthly 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.\n\n**CONTEXT**\n\nPhytoplankton   and chlorophyll concentration as a proxy for phytoplankton   respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014). 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, in the Baltic Sea phytoplankton is known to respond to the variations of SST in the basin associated with climate variability (Kabel et al. 2012).\n\n**KEY FINDINGS**\n\nThe Baltic Sea shows a slight positive trend over the 1997-2023 period, with a slope of 0.30±0.49% per year, indicating a decrease compared to the previous release. The maxima and minima values are relatively consistent year-to-year, with the absolute maximum occurring in 2008 and the minima observed in 2004 and 2014. A decrease in the chlorophyll signal has been evident over the past two years.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00197\n\n**References:**\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D’Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D’Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b (accepted). Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals. Remote Sens. 2021, 13, x.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kabel K, Moros M, Porsche C, Neumann T, Adolphi F, Andersen TJ, Siegel H, Gerth M, Leipe T, Jansen E, Sinninghe Damsté JS. 2012. Impact of climate change on the health of the Baltic Sea ecosystem over the last 1000 years. Nat Clim Change. doi:10.1038/nclimate1595.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., et al., 2018. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 3.1. Technical Report Centre for Environmental Data Analysis. doi:10.5285/9c334fbe6d424a708cf3c4cf0c6a53f5.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall’s tau. J Am Statist Assoc. 63:1379 1389.\n* Zibordi, G., Berthon, J.-F., Mélin, F., and D’Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104–2115, 2011.\n",
5857c5857
<         "abstract": "**DEFINITION**\n\nThis product includes the Baltic Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory (PML) using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b).\nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). 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 in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014) and anthropogenic climate change. Eutrophication is one of the most important issues for the Baltic Sea (HELCOM, 2018), therefore the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. 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.\n\n**KEY FINDINGS**\n\nOn average, the trend for the Baltic Sea over the 1997-2023 period is relatively flat (0.08%). The pattern of positive and negative trends is quite similar to the previous release, indicating a general decrease in absolute values. This result aligns with the findings of Sathyendranath et al. (2018), which show an increasing trend in chlorophyll concentration in most of the European Seas.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00198\n\n**References:**\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D’Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-080-097.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D’Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b. Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* HELCOM (2018): HELCOM Thematic assessment of eutrophication 2011-2016. Baltic Sea Environment Proceedings No. 156.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245–259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71–88. doi:10.1175/JCLI-3256.1.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., Mélin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall’s tau. J Am Statist Assoc. 63:1379–1389.\n* Zibordi, G., Berthon, J.-F., Mélin, F., and D’Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104–2115, 2011.\n",
---
>         "abstract": "**DEFINITION**\n\nThis product includes the Baltic Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory (PML) using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b).\nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). 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 in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014) and anthropogenic climate change. Eutrophication is one of the most important issues for the Baltic Sea (HELCOM, 2018), therefore the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. 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.\n\n**KEY FINDINGS**\n\nOn average, the trend for the Baltic Sea over the 1997-2023 period is relatively flat (0.08%). The pattern of positive and negative trends is quite similar to the previous release, indicating a general decrease in absolute values. This result aligns with the findings of Sathyendranath et al. (2018), which show an increasing trend in chlorophyll concentration in most of the European Seas.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00198\n\n**References:**\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D’Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D’Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b. Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* HELCOM (2018): HELCOM Thematic assessment of eutrophication 2011-2016. Baltic Sea Environment Proceedings No. 156.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245–259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71–88. doi:10.1175/JCLI-3256.1.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., Mélin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall’s tau. J Am Statist Assoc. 63:1379–1389.\n* Zibordi, G., Berthon, J.-F., Mélin, F., and D’Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104–2115, 2011.\n",
5885c5885
<         "abstract": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). 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.\n\n**CONTEXT**\n\nPhytoplankton   and chlorophyll concentration as a proxy for phytoplankton   respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014, 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 Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al .2003).\n\n**KEY FINDINGS**\n\nIn the Black Sea, the trend average for the 1997-2023 period is negative (-1.13±1.07% per year). Nevertheless, this negative trend is lower than the one estimated in the previous release (both 1997-2021 and 1997-2022). The negative trend is mainly due to the marked change on chlorophyll concentrations between 2002 and 2004. From 2004 onwards, minima and maxima are strongly variable year by year. However, on average, the minima/maxima variability can be considered quite constant with a continuous decrease of maxima from 2015 up to mid 2020 where signal seems to change again with relative high chlorophyll values in 2021, 2022 and especially in the last year (2023). The general negative trend in the Black Sea is also confirmed by the analysis of Sathyendranath et al. (2018), that reveals an increasing trend in chlorophyll concentration in all the European Seas, except for the Black Sea.\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00211\n\n**References:**\n\n* Basterretxea, G., Font-Muñoz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hernández-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D’Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf)\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kajiyama T., D. D’Alimonte, and G. Zibordi, “Algorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,” IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/¬10.1109/¬LGRS.2018.2883539\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Oguz, T., Cokacar, T., Malanotte‐Rizzoli, P., & Ducklow, H. W. (2003). Climatic warming and accompanying changes in the ecological regime of the Black Sea during 1990s. Global Biogeochemical Cycles, 17(3).\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., Mélin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\