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 2af561fd5d69d4a412619fd03748c5f686fddb55

eodag/resources/ext_product_types.json
933c933
<         "abstract": "The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon and is continuously updated every six months, covering the period since January 1979. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40° x 1/40°) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data.\n\n**Product Citation**: \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4\n\n**References:**\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_WAV_007_006_EAS4\n",
---
>         "abstract": "The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40° x 1/40°) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data.\n\n**Product Citation**: \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4\n\n**References:**\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_WAV_007_006_EAS4\n",
1160c1160
<         "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,forecast,global-analysisforecast-wav-001-027,global-ocean,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,surface-snow-thickness,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting",
---
>         "keywords": "coastal-marine-environment,forecast,global-analysisforecast-wav-001-027,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting",
1185c1185
<         "abstract": "The biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields for the time period 1993-2019 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the [NEMO](https://www.nemo-ocean.eu/) modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: GLORYS2V4-FREE [](https://www.mercator-ocean.eu/solutions-expertises/acceder-aux-donnees-numeriques/produits-de-loffre/?offer=4217979b-2662-329a-907c-602fdc69c3a3) ocean physics produced at Mercator-Ocean and [ERA-Interim](https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim) atmosphere produced at ECMWF at a daily frequency\n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\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 Quality Information Document\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00019",
---
>         "abstract": "The biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the [NEMO](https://www.nemo-ocean.eu/) modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: [FREEGLORYS2V4](https://www.mercator-ocean.fr/en/solutions-expertise/how-to-access-the-mercator-ocean-services/let-s-define-your-needs/) ocean physics produced at Mercator-Ocean and [ERA-Interim](https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim) atmosphere produced at ECMWF at a daily frequency                                                                           \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\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-001-029.pdf)\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00019",
1188c1188
<         "keywords": "cell-thickness,coastal-marine-environment,global-multiyear-bgc-001-029,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting",
---
>         "keywords": "coastal-marine-environment,global-multiyear-bgc-001-029,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting",
2198c2198
<         "missionStartDate": "2024-01-28T00:00:00Z",
---
>         "missionStartDate": "1841-03-21T00:00:00Z",
2310c2310
<         "missionStartDate": "1970-01-01T00:00:00.000000Z",
---
>         "missionStartDate": "1901-01-01T08:00:00Z",
2534c2534
<         "missionStartDate": "1970-01-01T00:00:00.000000Z",
---
>         "missionStartDate": "1970-04-27T18:00:00Z",
3089c3089
<         "abstract": "The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called “SMOS-OI”. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses (Copernicus Marine Service products INSITU_GLO_PHY_TS_OA_NRT_013_002 and INSITU_GLO_PHY_TS_OA_MY_013_052) to reduce large scale and temporal variable bias and Soil Moisture Ocean Salinity (SMOS) satellite image with satellite SST information.\n\n**DOI (product):** \nhttps://doi.org/10.1175/JTECH-D-20-0093.1\n\n**References:**\n\n* Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421.https://doi.org/10.1175/JTECH-D-20-0093.1\n",
---
>         "abstract": "The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called “SMOS-OI”. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version: https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version: https://archimer.ifremer.fr/doc/00665/77702/\n\n**DOI (product):** \nhttps://doi.org/10.1175/JTECH-D-20-0093.1\n\n**References:**\n\n* Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n",
3114c3114
<         "title": "SSS SMOS L4 OI - LOPS-v2021"
---
>         "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"
3257c3257
<         "abstract": "The NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 5-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided, and monthly frequency.\n\n**Product Citation**: \nPlease refer to our Technical FAQ for citing [products.](http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00054\n\n**References:**\n\n* The impact of a new high-resolution ocean model on the Met Office North-West European Shelf forecasting system (Tonani, M., Sykes, P., King, R.R., McConnell, N., Péquignet A-C., O’Dea, E., Graham, J.A., Polton, J., Siddorn, J) in Ocean Science., '''15''', 1133–1158, 2019. https://doi.org/10.5194/os-15-1133-2019\n* The impact of ocean-wave coupling on the upper ocean circulation during storm events (Bruciaferri, D., Tonani, M., Lewis, H., Siddorn, J., Saulter, A., Castillo, J.M., Garcia Valiente, N., Conley, D., Sykes, P., Ascione, I., McConnell, N.) in Journal of Geophysical Research, Oceans, 2021, 126, 6. https://doi.org/10.1029/2021JC017343\n* Can wave coupling improve operational regional ocean forecasts for the North-West European Shelf (Lewis, H., Castillo Sanchez, J. M., Siddorn, J., King, R., Tonani, M., Saulter, A., Sykes, P., Péquignet, A.-C., Weedon, G., Palmer, T., Staneva, J., and Bricheno, L.) in Ocean Science, '''15''', 669–690. https://doi.org/10.5194/os-15-669-2019\n* An approach to the verification of high-resolution ocean models using spatial methods (Crocker, R., Maksymczuk, J., Mittermaier, M., Tonani, M., and Péquignet A-C.) in Ocean Science, '''16''', 831–845, 2020. https://doi.org/10.5194/os-16-831-2020\n",
---
>         "abstract": "The NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 5-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided, and monthly frequency.\n\n**Product Citation**: \nPlease refer to our Technical FAQ for citing [products.](http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00054",
3268c3268
<             "name": "Met Office (UK)",
---
>             "name": "NOLOGIN",
3296c3296
<             "name": "Met Office (UK)",
---
>             "name": "NOLOGIN",
4657c4657
<         "abstract": "**DEFINITION**\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry base  d on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe mean sea level evolution estimated in the Baltic Sea is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004) to consider the ongoing movement of land due to post-glacial rebound.\nDuring 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020).   Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimates for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g., in situ) - necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b).   \nThe Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gräwe et al., 2019).\n\n**CMEMS KEY FINDINGS**\n\nOver the [1993/01/01, 2022/08/04] period, the basin-wide RMSL in the Baltic Sea rises at a rate of 4.8  0.84 mm/year. \n\n**Figure caption**\n\nRegional mean sea level daily evolution (in cm) over the [1993/01/01, 2022/08/04] period, from the satellite altimeter observations estimated in the Baltic Sea, derived from the basin-wide average of the gridded sea level maps weighted by the cosine of the latitude. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after)   altimeter sea level gridded product distributed by the Copernicus Climate Change Service (C3S), and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The annual and semi-annual periodic signals are removed, the timeseries is low-pass filtered (175 days cut-off) and the time series is corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00202\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358–361, https://doi.org/10.1038/nclimate2159, 2014.\n* Gräwe, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J. Clim., 32, 3089–3108, https://doi.org/10.1175/JCLI-D-18-0174.1, 2019.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., Döll, P., Cáceres, D., Müller Schmied, H., Johannessen, J. A., Nilsen, J. E. Ø., Raj, R. P., Forsberg, R., Sandberg Sørensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers [H.-O. Pörtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77–s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111–149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993–2019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993–present, Earth Syst. Sci. Data, 10, 1551–1590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n",
---
>         "abstract": "**DEFINITION**\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Baltic Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nThe Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gräwe et al., 2019).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Baltic Sea rises at a rate of 4.1  0.8 mm/year with an acceleration of 0.10 0.07 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00202\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358–361, https://doi.org/10.1038/nclimate2159, 2014.\n* Gräwe, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J. Clim., 32, 3089–3108, https://doi.org/10.1175/JCLI-D-18-0174.1, 2019.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., Döll, P., Cáceres, D., Müller Schmied, H., Johannessen, J. A., Nilsen, J. E. Ø., Raj, R. P., Forsberg, R., Sandberg Sørensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers [H.-O. Pörtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77–s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111–149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993–2019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993–present, Earth Syst. Sci. Data, 10, 1551–1590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n",
4685c4685
<         "abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after)   sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe mean sea level evolution estimated in the Black Sea is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004) to consider the ongoing movement of land due to post-glacial rebound.\nDuring 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period).   A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimated for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g. in situ)- necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c).   \nIn the Black Sea, major drivers of change have been attributed to  anthropogenic climate change (steric expansion), and mass changes induced by various water exchanges with the Mediterranean Sea, river discharge, and precipitation/evaporation changes (e.g. Volkov and Landerer, 2015). The sea level variation in the basin also shows an important interannual variability, with an increase observed before 1999 predominantly linked to steric effects, and comparable lower values afterward (Vigo et al., 2005).\n\n\n**CMEMS KEY FINDINGS**\n\nOver the [1993/01/01, 2022/08/04] period, the basin-wide RMSL in the Black Sea rises at a rate of 1.4  0.83 mm/year.  \n\n**Figure caption**\n\nRegional mean sea level daily evolution (in cm) over the [1993/01/01, 2022/08/04] period, from the satellite altimeter observations estimated in the Black Sea, derived from the basin-wide average of the gridded sea level maps weighted by the cosine of the latitude. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after)   altimeter sea level gridded product distributed by the Copernicus Climate Change Service (C3S), and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The annual and semi-annual periodic signals are removed, the timeseries is low-pass filtered (175 days cut-off) and the time series is corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00215\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358–361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., Döll, P., Cáceres, D., Müller Schmied, H., Johannessen, J. A., Nilsen, J. E. Ø., Raj, R. P., Forsberg, R., Sandberg Sørensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. Pörtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77–s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111–149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993–2019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085–1100, https://doi.org/10.1357/002224005775247607, 2005.\n* Volkov, D. L. and Landerer, F. W.: Internal and external forcing of sea level variability in the Black Sea, Clim. Dyn., 45, 2633–2646, https://doi.org/10.1007/s00382-015-2498-0, 2015.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993–present, Earth Syst. Sci. Data, 10, 1551–1590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n",
---
>         "abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Black Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \nIn the Black Sea, major drivers of change have been attributed to  anthropogenic climate change (steric expansion), and mass changes induced by various water exchanges with the Mediterranean Sea, river discharge, and precipitation/evaporation changes (e.g. Volkov and Landerer, 2015). The sea level variation in the basin also shows an important interannual variability, with an increase observed before 1999 predominantly linked to steric effects, and comparable lower values afterward (Vigo et al., 2005).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Black Sea rises at a rate of 1.00 ± 0.80 mm/year with an acceleration of -0.47 ± 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00215\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358–361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., Döll, P., Cáceres, D., Müller Schmied, H., Johannessen, J. A., Nilsen, J. E. Ø., Raj, R. P., Forsberg, R., Sandberg Sørensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. Pörtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77–s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111–149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993–2019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085–1100, https://doi.org/10.1357/002224005775247607, 2005.\n* Volkov, D. L. and Landerer, F. W.: Internal and external forcing of sea level variability in the Black Sea, Clim. Dyn., 45, 2633–2646, https://doi.org/10.1007/s00382-015-2498-0, 2015.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993–present, Earth Syst. Sci. Data, 10, 1551–1590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n",
4713,4714c4713,4714
<         "abstract": null,
<         "doi": null,
---
>         "abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Northeast Atlantic Ocean and adjacent  seas Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nUncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, sea level variations are influenced by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016) and the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. This region encompasses the Mediterranean, IBI, North-West shelf and Baltic regions with different sea level dynamics detailed in the regional indicators.\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Northeast Atlantic Ocean and adjacent  seas area rises at a rate of 3.2 ± 0.80 mm/year with an acceleration of 0.10 ± 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00335\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358–361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. Ø., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941–962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: Summary for Policymakers [H.-O. Pörtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77–s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993–2019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055–5075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993–present, Earth Syst. Sci. Data, 10, 1551–1590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n",
>         "doi": "10.48670/mds-00335",
4716,4717c4716,4717
<         "keywords": ",omi-climate-sl-europe-area-averaged-anomalies",
<         "license": null,
---
>         "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,oceanographic-geographical-features,omi-climate-sl-europe-area-averaged-anomalies,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting",
>         "license": "proprietary",
4722,4723c4722,4732
<         "providers": [],
<         "title": null
---
>         "providers": [
>           {
>             "name": "Copernicus Marine Service",
>             "roles": [
>               "host",
>               "processor"
>             ],
>             "url": "https://marine.copernicus.eu"
>           }
>         ],
>         "title": "European Seas Mean Sea Level time series and trend from Observations Reprocessing"
4726c4735
<         "abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after)   sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and are also available in the Copernicus Marine Service catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe mean sea level evolution estimated in the global ocean (hereafter GMSL) is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The time series is corrected for the effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004) to consider the ongoing movement of land due to post-glacial rebound.\nDuring 1993-1998, the GMSL has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record. Accounting for this correction changes the shape of the time series, which is no more linear but quadratic, indicating mean sea level acceleration during the altimetry era. \nThe trend uncertainty of 0.3 mm/yr is provided at 90% confidence interval (Guérou et al., 2022). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c).\n\n**CMEMS KEY FINDINGS**\n\nOver the [1993/01/01, 2022/08/04] period, global mean sea level  rises at a rate of 3.3  0.3 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction to consider the ongoing movement of land (Peltier, 2004). The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). About 30% of this rise can be attributed to ocean thermal expansion (WCRP Global Sea Level Budget Group, 2018; von Schuckmann et al., 2018), 60% is due to land ice melt from glaciers and from the Antarctic and Greenland ice sheets. The remaining 10% is attributed to changes in land water storage, such as soil moisture, surface water and groundwater. From year to year, the global mean sea level record shows significant variations related mainly to the El Niño Southern Oscillation (Cazenave and Cozannet, 2014).\n\n**Figure caption**\n\nDaily global mean sea level evolution (in cm) from satellite altimetry from January 1993 to August 2022. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) se  a level gridded products distributed by the Copernicus Climate Change Service (C3S), also available in the CMEMS catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The timeseries corresponds to the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed and the timeseries is low-pass filtered (175 days cut-off). The timeseries is corrected for Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004) to consider the ongoing movement of land.\nDuring 1993-1998, the dashed line shows an estimate of the global mean sea level corrected for the TOPEX-A instrumental drift, based on comparisons between altimeter and tide gauges measurements (Ablain et al., 2017; Legeais et al., 2020). The GMSL trend given in the figure is deduced from the dashed curve, including the TOPEX-A drift correction, up to 1998 and the solid line up to August 2022.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00237\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358–361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., Döll, P., Cáceres, D., Müller Schmied, H., Johannessen, J. A., Nilsen, J. E. Ø., Raj, R. P., Forsberg, R., Sandberg Sørensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. Pörtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77–s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Oppenheimer, M., Glavovic, B. C., Hinkel, J., Van de Wal, R., Magnan, A. K., Abd-Elgaward, A., Cai, R., Cifuentes Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities — Special Report on the Ocean and Cryosphere in a Changing Climate: Chapter 4, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993–present, Earth Syst. Sci. Data, 10, 1551–1590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Guérou, A., Meyssignac, B., Prandi, P., Ablain, M., Ribes, A., and Bignalet-Cazalet, F.: Current observed global mean sea level rise and acceleration estimated from satellite altimetry and the associated uncertainty, EGUsphere, 1–43, https://doi.org/10.5194/egusphere-2022-330, 2022.\n* Cazenave, A. and Cozannet, G. L.: Sea level rise and its coastal impacts, Earths Future, 2, 15–34, https://doi.org/10.1002/2013EF000188, 2014.\n",
---
>         "abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Global Ocean weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and global GIA correction of -0.3mm/yr (com