chore: update external product types reference

[github-actions]

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

Changed file

eodag/resources/ext_product_types.json

commit a9c738b8aed00e1e4ff7f374e81163beda6dcb99

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Note: Detailed diffs are available in the job summary.

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Changes grouped by JSON paths:

abstract
10 product_type(s) affected (cop_marine)

Click to expand for detailed diffs

cop_marine - product_types_config - BALTICSEA_MULTIYEAR_WAV_003_015

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+++ new
@@ -1,5 +1,5 @@
 {
-    "abstract": "**This product has been archived** \n\n\n\nThis Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products.  The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00014",
+    "abstract": "This Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products.  The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00014",
     "doi": "10.48670/moi-00014",
     "instrument": null,
     "keywords": "baltic-sea,balticsea-multiyear-wav-003-015,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,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-spectral-peak,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,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting",

cop_marine - product_types_config - GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated

--- old
+++ new
@@ -1,5 +1,5 @@
 {
-    "abstract": "**DEFINITION**\n\nThe global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to year-1. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble.\n\n**CONTEXT**\n\nSince the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277\u00b13 ppm (Joos and Spahni, 2008) to 412.44\u00b10.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 \u00b1 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). \n\n**CMEMS KEY FINDINGS**\n\nThe rate of change of the integrated yearly surface downward flux has increased by 0.04\u00b10.03e-1 PgC/yr2 over the period 1985 to year-1. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06\u00b10.04e-1 PgC/yr2. In  2021 (resp. 2020), the global ocean CO2 sink was    2.41\u00b10.13 (resp.  2.50\u00b10.12) PgC/yr. The average over the full period is 1.61\u00b10.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr.  In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of  0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45\u00b10.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78\u00b10.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00223\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Ciais, P., Sabine, C., Govindasamy, B., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Que\u0301re\u0301, C., Myneni, R., Piao, S., and Thorn- ton, P.: Chapter 6: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013 The Physical Science Basis, edited by: Stocker, T., Qin, D., and Platner, G.-K., Cambridge University Press, Cambridge, 2013.\n* Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), http://www.esrl. noaa.gov/gmd/ccgg/trends/global.html, last access: 11 March 2022.\n* Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105, 1425\u20131430, https://doi.org/10.1073/pnas.0707386105, 2008.\n* Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Qu\u00e9r\u00e9, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., G\u00fcrses, \u00d6., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., K\u00f6rtzinger, A., Landsch\u00fctzer, P., Lauvset, S. K., Lef\u00e8vre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., R\u00f6denbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., S\u00e9f\u00e9rian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917\u20132005, https://doi.org/10.5194/essd-14-1917-2022, 2022.\n* Jacobson, A. R., Mikaloff Fletcher, S. E., Gruber, N., Sarmiento, J. L., and Gloor, M. (2007), A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global-scale fluxes, Global Biogeochem. Cycles, 21, GB1019, doi:10.1029/2005GB002556.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* Resplandy, L., Keeling, R. F., R\u00f6denbeck, C., Stephens, B. B., Khatiwala, S., Rodgers, K. B., Long, M. C., Bopp, L. and Tans, P. P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nature Geoscience, 11(7), p.504, 2018.\n",
+    "abstract": "**DEFINITION**\n\nThe global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to year-1. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble.\n\n**CONTEXT**\n\nSince the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277\u00b13 ppm (Joos and Spahni, 2008) to 412.44\u00b10.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 \u00b1 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). \n\n**CMEMS KEY FINDINGS**\n\nThe rate of change of the integrated yearly surface downward flux has increased by 0.04\u00b10.01e-1 PgC/yr2 over the period 1985 to year-1. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06\u00b10.04e-1 PgC/yr2. In  2021 (resp. 2020), the global ocean CO2 sink was    2.41\u00b10.13 (resp.  2.50\u00b10.12) PgC/yr. The average over the full period is 1.61\u00b10.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr.  In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of  0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45\u00b10.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78\u00b10.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00223\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Ciais, P., Sabine, C., Govindasamy, B., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Que\u0301re\u0301, C., Myneni, R., Piao, S., and Thorn- ton, P.: Chapter 6: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013 The Physical Science Basis, edited by: Stocker, T., Qin, D., and Platner, G.-K., Cambridge University Press, Cambridge, 2013.\n* Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), http://www.esrl. noaa.gov/gmd/ccgg/trends/global.html, last access: 11 March 2022.\n* Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105, 1425\u20131430, https://doi.org/10.1073/pnas.0707386105, 2008.\n* Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Qu\u00e9r\u00e9, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., G\u00fcrses, \u00d6., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., K\u00f6rtzinger, A., Landsch\u00fctzer, P., Lauvset, S. K., Lef\u00e8vre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., R\u00f6denbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., S\u00e9f\u00e9rian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917\u20132005, https://doi.org/10.5194/essd-14-1917-2022, 2022.\n* Jacobson, A. R., Mikaloff Fletcher, S. E., Gruber, N., Sarmiento, J. L., and Gloor, M. (2007), A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global-scale fluxes, Global Biogeochem. Cycles, 21, GB1019, doi:10.1029/2005GB002556.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* Resplandy, L., Keeling, R. F., R\u00f6denbeck, C., Stephens, B. B., Khatiwala, S., Rodgers, K. B., Long, M. C., Bopp, L. and Tans, P. P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nature Geoscience, 11(7), p.504, 2018.\n",
     "doi": "10.48670/moi-00223",
     "instrument": null,
     "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-co2-flux-integrated,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting",

cop_marine - product_types_config - GLOBAL_OMI_HEALTH_carbon_ph_area_averaged

--- old
+++ new
@@ -1,5 +1,5 @@
 {
-    "abstract": "**DEFINITION**\n\nOcean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, \n* Obtain values for alkalinity based on the so called \u201clocally interpolated alkalinity regression (LIAR)\u201d method after Carter et al., 2016; 2018. \n* Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/)\n* Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity.\nThe global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1\u03c3  ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017\u00b10.0004e-1 pH units per year.\nThe indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time.  Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements.  \nA full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020).\n\n**CONTEXT**\n\nThe decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as \u201ca reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean\u201d. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14).  \n\n**CMEMS KEY FINDINGS**\n\nSince the year 1985, global ocean surface pH is decreasing at a rate of  -0.0017\u00b10.0004e-1 per year. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00224\n\n**References:**\n\n* Bakker, D. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383-413, https://doi.org/10.5194/essd-8-383-2016, 2016.\n* Bopp, L. et al.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225\u20136245, doi: 10.5194/bg-10-6225-2013, 2013.\n* Carter, B.R., et al.: Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnol. Oceanogr.: Methods 16, 119\u2013131, 2018.\n* Carter, B. R., et al.: Locally interpolated alkalinity regression for global alkalinity estimation. Limnol. Oceanogr.: Methods 14: 268\u2013277. doi:10.1002/lom3.10087, 2016.\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022. Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, K.J. Mach, G.-K. Plattner, M.D. Mastrandrea, M. Tignor and K.L. Ebi (eds.)]. IPCC Working Group II Technical Support Unit, Carnegie Institution, Stanford, California, United States of America, pp.164.\n* Kroeker, K. J. et al.: Meta- analysis reveals negative yet variable effects of ocean acidifica- tion on marine organisms, Ecol. Lett., 13, 1419\u20131434, 2010.\n* Orr, J. C. et al.: Anthropogenic ocean acidification over the twenty-first century and its impact on cal- cifying organisms, Nature, 437, 681\u2013686, 2005.\n* van Heuven, S., et al.: MATLAB program developed for CO2 system calculations, ORNL/CDIAC-105b, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., US DOE, Oak Ridge, Tenn., 2011.\n",
+    "abstract": "**DEFINITION**\n\nOcean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, \n* Obtain values for alkalinity based on the so called \u201clocally interpolated alkalinity regression (LIAR)\u201d method after Carter et al., 2016; 2018. \n* Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/)\n* Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity.\nThe global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1\u03c3  ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017\u00b10.0004e-1 pH units per year.\nThe indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time.  Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements.  \nA full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020).\n\n**CONTEXT**\n\nThe decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as \u201ca reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean\u201d. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14).  \n\n**CMEMS KEY FINDINGS**\n\nSince the year 1985, global ocean surface pH is decreasing at a rate of  -0.0017\u00b10.019 decade-1\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00224\n\n**References:**\n\n* Bakker, D. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383-413, https://doi.org/10.5194/essd-8-383-2016, 2016.\n* Bopp, L. et al.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225\u20136245, doi: 10.5194/bg-10-6225-2013, 2013.\n* Carter, B.R., et al.: Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnol. Oceanogr.: Methods 16, 119\u2013131, 2018.\n* Carter, B. R., et al.: Locally interpolated alkalinity regression for global alkalinity estimation. Limnol. Oceanogr.: Methods 14: 268\u2013277. doi:10.1002/lom3.10087, 2016.\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022. Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, K.J. Mach, G.-K. Plattner, M.D. Mastrandrea, M. Tignor and K.L. Ebi (eds.)]. IPCC Working Group II Technical Support Unit, Carnegie Institution, Stanford, California, United States of America, pp.164.\n* Kroeker, K. J. et al.: Meta- analysis reveals negative yet variable effects of ocean acidifica- tion on marine organisms, Ecol. Lett., 13, 1419\u20131434, 2010.\n* Orr, J. C. et al.: Anthropogenic ocean acidification over the twenty-first century and its impact on cal- cifying organisms, Nature, 437, 681\u2013686, 2005.\n* van Heuven, S., et al.: MATLAB program developed for CO2 system calculations, ORNL/CDIAC-105b, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., US DOE, Oak Ridge, Tenn., 2011.\n",
     "doi": "10.48670/moi-00224",
     "instrument": null,
     "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-area-averaged,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting",

cop_marine - product_types_config - GLOBAL_OMI_HEALTH_carbon_ph_trend

--- old
+++ new
@@ -1,5 +1,5 @@
 {
-    "abstract": "**DEFINITION**\n\nThis ocean monitoring indicator (OMI) consists of annual mean rates of changes in surface ocean pH (yr-1) computed at 0.25\u00b0\u00d70.25\u00b0 resolution from 1985 until the last year. This indicator is derived from monthly pH time series distributed with the Copernicus Marine product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (Chau et al., 2022a). For each grid cell, a linear least-squares regression was used to fit a linear function of pH versus time, where the slope (\u03bc) and residual standard deviation (\u03c3) are defined as estimates of the long-term trend and associated uncertainty. Finally, the estimates of pH associated with the highest uncertainty, i.e.,  \u03c3-to-\u00b5 ratio over a threshold of 1  0%, are excluded from the global trend map (see QUID document for detailed description and method illustrations). This threshold is chosen at the 90th confidence level of all ratio values computed across the global ocean.\n\n**CONTEXT**\n\nA decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions (Bates et al, 2014; Gattuso et al, 2015; P\u00e9rez et al, 2021).      As projected in Gattuso et al (2015), \u201cunder our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050\u201d. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; P\u00f6rtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2022b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables (e.g. dissolved inorganic carbon, total alkalinity, and CO2 fugacity) since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and P\u00e9rez et al (2021) put attention on analyzing secular trends of pH and their drivers from time-series stations to ocean basins. This OMI consists of the global maps of long-term pH trends and associated 1\u03c3-uncertainty derived from the Copernicus Marine data-based product of monthly surface water pH (Chau et al., 2022a) at 0.25\u00b0\u00d70.25\u00b0 grid cells over the global ocean.\n\n**CMEMS KEY FINDINGS**\n\nSince 1985, pH has been decreasing at a rate between -0.0008 yr-1 and -0.0022 yr-1 over most of the global ocean basins. Tropical and subtropical regions, the eastern equatorial Pacific excepted, show pH trends falling in the interquartile range of all the trend estimates (between -0.0012 yr-1 and -0.0018 yr-1). pH over the eastern equatorial Pacific decreases much faster, reaching a growth rate larger than -0.0024 yr-1. Such a high rate of change in pH is also observed over a sector south of the Indian Ocean. Part of the polar and subpolar North Atlantic and the Southern Ocean has no significant trend.  \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00277\n\n**References:**\n\n* Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S.-I. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383\u2013413, DOI:10.5194/essd-8-383- 2016, 2016.\n* Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E., Gonzalez-Davila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J., and Magdalena Santana-Casiano, J.: A Time-Series View of Changing Surface Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean Acidification, Oceanography, 27, 126\u2013141, 2014.\n* Chau, T. T. T., Gehlen, M., Chevallier, F. : Global Ocean Surface Carbon: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, E.U. Copernicus Marine Service Information, DOI:10.48670/moi-00047, 2022a.\n* Chau, T. T. T., Gehlen, M., Chevallier, F.: Global mean seawater pH (GLOBAL_OMI_HEALTH_carbon_ph_area_averaged), E.U. Copernicus Marine Service Information, DOI: 10.48670/moi-00224, 2022b.\n* Doney, S. C., Balch, W. M., Fabry, V. J., and Feely, R. A.: Ocean Acidification: A critical emerging problem for the ocean sciences, Oceanography, 22, 16\u201325, 2009.\n* Gattuso, J-P., Alexandre Magnan, Rapha\u00ebl Bill\u00e9, William WL Cheung, Ella L. Howes, Fortunat Joos, Denis Allemand et al. \"\"Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios.\"\" Science 349, no. 6243 (2015).\n* Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Chau T T T., Conchon A., Denvil-Sommer A., Chevallier F., Vrac M., Mejia C. : Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI:10.1080/1755876X.2020.1785097, 2020.\n* Lauvset, S. K., Gruber, N., Landsch\u00fctzer, P., Olsen, A., and Tjiputra, J.: Trends and drivers in global surface ocean pH over the past 3 decades, Biogeosciences, 12, 1285\u20131298, DOI:10.5194/bg-12-1285-2015, 2015.\n* Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., \u00c1lvarez, M., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterstr\u00f6m, S., Jones, S. D., Karlsen, M. K., Lo Monaco, C., Michaelis, P., Murata, A., P\u00e9rez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., Woosley, R. J., and Key, R. M.: An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021, Earth Syst. Sci. Data, 13, 5565\u20135589, DOI:10.5194/essd-13-5565-2021, 2021.\n* P\u00f6rtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Wiley IPCC Intergovernmental Panel on Climate Change, Geneva, 2019).\n* P\u00e9rez FF, Olafsson J, \u00d3lafsd\u00f3ttir SR, Fontela M, Takahashi T. Contrasting drivers and trends of ocean acidification in the subarctic Atlantic. Sci Rep 11, 13991, DOI:10.1038/s41598-021-93324-3, 2021.\n",
+    "abstract": "**DEFINITION**\n\nThis ocean monitoring indicator (OMI) consists of annual mean rates of changes in surface ocean pH (yr-1) computed at 0.25\u00b0\u00d70.25\u00b0 resolution from 1985 until the last year. This indicator is derived from monthly pH time series distributed with the Copernicus Marine product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (Chau et al., 2022a). For each grid cell, a linear least-squares regression was used to fit a linear function of pH versus time, where the slope (\u03bc) and residual standard deviation (\u03c3) are defined as estimates of the long-term trend and associated uncertainty. Finally, the estimates of pH associated with the highest uncertainty, i.e.,  \u03c3-to-\u00b5 ratio over a threshold of 1  0%, are excluded from the global trend map (see QUID document for detailed description and method illustrations). This threshold is chosen at the 90th confidence level of all ratio values computed across the global ocean.\n\n**CONTEXT**\n\nA decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions (Bates et al, 2014; Gattuso et al, 2015; P\u00e9rez et al, 2021).      As projected in Gattuso et al (2015), \u201cunder our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050\u201d. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; P\u00f6rtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2022b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables (e.g. dissolved inorganic carbon, total alkalinity, and CO2 fugacity) since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and P\u00e9rez et al (2021) put attention on analyzing secular trends of pH and their drivers from time-series stations to ocean basins. This OMI consists of the global maps of long-term pH trends and associated 1\u03c3-uncertainty derived from the Copernicus Marine data-based product of monthly surface water pH (Chau et al., 2022a) at 0.25\u00b0\u00d70.25\u00b0 grid cells over the global ocean.\n\n**CMEMS KEY FINDINGS**\n\nSince 1985, pH has been decreasing at a rate between -0.001 yr-1 and -0.0023 yr-1 over most of the global ocean basins. Tropical and subtropical regions show pH trends falling within the interquartile range of all the trend estimates (between -0.0016 yr-1 and -0.0018 yr-1). In sectors south of the Indian Ocean and Pacific Ocean, pH decreases much faster, reaching a growth rate of up to -0.0022 yr-1. An even more significant rate of change in pH is observed in the Greenland area and on the northeast coastal side of Canada, where values reach up to -0.0028 yr-1. Some polar or coastal areas show no significant trends.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00277\n\n**References:**\n\n* Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S.-I. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383\u2013413, DOI:10.5194/essd-8-383- 2016, 2016.\n* Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E., Gonzalez-Davila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J., and Magdalena Santana-Casiano, J.: A Time-Series View of Changing Surface Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean Acidification, Oceanography, 27, 126\u2013141, 2014.\n* Chau, T. T. T., Gehlen, M., Chevallier, F. : Global Ocean Surface Carbon: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, E.U. Copernicus Marine Service Information, DOI:10.48670/moi-00047, 2022a.\n* Chau, T. T. T., Gehlen, M., Chevallier, F.: Global mean seawater pH (GLOBAL_OMI_HEALTH_carbon_ph_area_averaged), E.U. Copernicus Marine Service Information, DOI: 10.48670/moi-00224, 2022b.\n* Doney, S. C., Balch, W. M., Fabry, V. J., and Feely, R. A.: Ocean Acidification: A critical emerging problem for the ocean sciences, Oceanography, 22, 16\u201325, 2009.\n* Gattuso, J-P., Alexandre Magnan, Rapha\u00ebl Bill\u00e9, William WL Cheung, Ella L. Howes, Fortunat Joos, Denis Allemand et al. \"\"Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios.\"\" Science 349, no. 6243 (2015).\n* Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Chau T T T., Conchon A., Denvil-Sommer A., Chevallier F., Vrac M., Mejia C. : Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI:10.1080/1755876X.2020.1785097, 2020.\n* Lauvset, S. K., Gruber, N., Landsch\u00fctzer, P., Olsen, A., and Tjiputra, J.: Trends and drivers in global surface ocean pH over the past 3 decades, Biogeosciences, 12, 1285\u20131298, DOI:10.5194/bg-12-1285-2015, 2015.\n* Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., \u00c1lvarez, M., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterstr\u00f6m, S., Jones, S. D., Karlsen, M. K., Lo Monaco, C., Michaelis, P., Murata, A., P\u00e9rez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., Woosley, R. J., and Key, R. M.: An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021, Earth Syst. Sci. Data, 13, 5565\u20135589, DOI:10.5194/essd-13-5565-2021, 2021.\n* P\u00f6rtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Wiley IPCC Intergovernmental Panel on Climate Change, Geneva, 2019).\n* P\u00e9rez FF, Olafsson J, \u00d3lafsd\u00f3ttir SR, Fontela M, Takahashi T. Contrasting drivers and trends of ocean acidification in the subarctic Atlantic. Sci Rep 11, 13991, DOI:10.1038/s41598-021-93324-3, 2021.\n",
     "doi": "10.48670/moi-00277",
     "instrument": null,
     "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-trend,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,trend-of-surface-ocean-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting",

cop_marine - product_types_config - MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012

--- old
+++ new
@@ -1,5 +1,5 @@
 {
-    "abstract": "You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year processing. This is 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. These are  NRT and MY  datasets of monthly and daily mean together with climatological uncertainties. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00052\n\n**References:**\n\n* Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711\u20131723. https://doi.org/10.5194/essd-12-1711-2020.\n",
+    "abstract": "You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year processing. This is 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. These are  NRT and MY  datasets of monthly and daily mean together with climatological uncertainties. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00052\n\n**References:**\n\n* Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845\u2013857.\n* Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77\u201380(0):70\u201381.\n",
     "doi": "10.48670/moi-00052",
     "instrument": null,
     "keywords": "coastal-marine-environment,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-tsuv-3d-mynrt-015-012,numerical-model,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting",

cop_marine - product_types_config - OCEANCOLOUR_ATL_BGC_L3_MY_009_113

--- old
+++ new
@@ -1,5 +1,5 @@
 {
-    "abstract": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and  Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00286\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Druon, JN., H\u00e9laou\u00ebt, P., Beaugrand, G. et al. Satellite-based indicator of zooplankton distribution for global monitoring. Sci Rep 9, 4732 (2019). https://doi.org/10.1038/s41598-019-41212-2.\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n",
+    "abstract": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the '''olci\"' products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword '''GlobColour\"'. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00286\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Druon, JN., H\u00e9laou\u00ebt, P., Beaugrand, G. et al. Satellite-based indicator of zooplankton distribution for global monitoring. Sci Rep 9, 4732 (2019). https://doi.org/10.1038/s41598-019-41212-2.\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n",
     "doi": "10.48670/moi-00286",
     "instrument": null,
     "keywords": "bbp,cdm,chl,coastal-marine-environment,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-atl-bgc-l3-my-009-113,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd",

cop_marine - product_types_config - OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged

--- old
+++ new
@@ -1,5 +1,5 @@
 {
-    "abstract": "**DEFINITION**\n\nNet (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1):  Faroe Shetland Channel (Water mass criteria, T > 5 \u00b0C); Barents Sea Opening (T > 3 \u00b0C) and the Fram Strait (T > 2 \u00b0C). Net volume transport of Overflow Waters (\u03c3\u03b8 >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018).\n\n**CONTEXT**\n\nThe poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sand\u00f8 et al., 2010; \u00c5rthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe flow of Atlantic Water through the F\u00e6r\u00f8y-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.5 Sv for the period 1993-2021. \nIn the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 1.8 Sv (Smedsrud et al., 2013).\nIn the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 \u00b0C).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00189\n\n**References:**\n\n* Berx B. Hansen B, \u00d8sterhus S, Larsen KM, Sherwin T, Jochumsen K. 2013. Combining in situ measurements and altimetry to estimate volume, heat and salt transport variability through the F\u00e6r\u00f8y-Shetland Channel. Ocean Sci. 9, 639-654\n* Dickson RR, Brown J. 1994. The production of North-Atlantic deep-water \u2013 sources, rates, and pathways. J Geophys Res Oceans. 99(C6), 12319-12341\n* Eldevik T, Nilsen JE\u00d8, Iovino D, Olsson KA, Sand\u00f8 AB, Drange H. 2009. Observed sources and variability of Nordic seas overflow. Nature Geosci. 2(6), 405-409\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan M.M, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Climate Change. 5, 673-678.\n* Mauritzen C. 1996. Production of dense overflow waters feeding the North Atlantic across the Greenland-Scotland Ridge. 1. Evidence for a revised circulation scheme. Deep-Sea Res Part I. 43(6), 769-806\n* Mauritzen C, Hansen E, Andersson M, Berx B, Beszczynzka-M\u00f6ller A, Burud I, Christensen KH, Debernard J, de Steur L, Dodd P, et al. 2011. Closing the loop \u2013 Approaches to monitoring the state of the Arctic Mediterranean during the International Polar Year 2007-2008. Prog Oceanogr. 90, 62-89\n* Onarheim IH, Eldevik T, \u00c5rthun M, Ingvaldsen RB, Smedsrud LH. 2015. Skillful prediction of Barents Sea ice cover. Geophys Res Lett. 42(13), 5364-5371\n* Raj RP, Johannessen JA, Eldevik T, Nilsen JE\u00d8, Halo I. 2016. Quantifying mesoscale eddies in the Lofoten basin. J Geophys Res Oceans. 121. doi:10.1002/2016JC011637\n* Rudels B. 2012. Arctic Ocean circulation and variability \u2013 advection and external forcing encounter constraints and local processes. Ocean Sci. 8(2), 261-286\n* Sand\u00f8, A.B., J.E.\u00d8. Nilsen, Y. Gao and K. Lohmann, 2010: Importance of heat transport and local air-sea heat fluxes for Barents Sea climate variability. J Geophys Res. 115, C07013\n* von Schuckmann K, et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. J Oper Oceanogr. 9, 235-320\n* von Schuckmann K. 2018. Copernicus Marine Service Ocean State Report, J Oper Oceanogr. 11, sup1, S1-S142. Smedsrud LH, Esau I, Ingvaldsen RB, Eldevik T, Haugan PM, Li C, Lien VS, Olsen A, Omar AM, Otter\u00e5 OH, Risebrobakken B, Sand\u00f8 AB, Semenov VA, Sorokina SA. 2013. The role of the Barents Sea in the climate system. Rev Geophys. 51, 415-449\n* Sundby, S., 2000. Recruitment of Atlantic cod stocks in relation to temperature and advection of copepod populations. Sarsia. 85, 277-298.\n* Walczowski W, Piechura J, Goszczko I, Wieczorek P. 2012. Changes in Atlantic water properties: an important factor in the European Arctic marine climate. ICES J Mar Sys. 69(5), 864-869.\n* Piechura J, Walczowski W. 2009. Warming of the West Spitsbergen Current and sea ice north of Svalbard. Oceanol. 51(2), 147-164\n* \u00c5rthun, M., Eldevik, T., Smedsrud, L.H., Skagseth, \u00d8., Ingvaldsen, R.B., 2012. Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat. J. Climate. 25, 4736-4743.\n",
+    "abstract": "**DEFINITION**\n\nNet (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1):  Faroe Shetland Channel (Water mass criteria, T > 5 \u00b0C); Barents Sea Opening (T > 3 \u00b0C) and the Fram Strait (T > 2 \u00b0C). Net volume transport of Overflow Waters (\u03c3\u03b8 >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018).\n\n**CONTEXT**\n\nThe poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sand\u00f8 et al., 2010; \u00c5rthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe flow of Atlantic Water through the F\u00e6r\u00f8y-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.3 Sv for the full period 1991-2024. \nIn the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 2 Sv (Smedsrud et al., 2013).\nIn the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 \u00b0C).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00189\n\n**References:**\n\n* Berx B. Hansen B, \u00d8sterhus S, Larsen KM, Sherwin T, Jochumsen K. 2013. Combining in situ measurements and altimetry to estimate volume, heat and salt transport variability through the F\u00e6r\u00f8y-Shetland Channel. Ocean Sci. 9, 639-654\n* Dickson RR, Brown J. 1994. The production of North-Atlantic deep-water \u2013 sources, rates, and pathways. J Geophys Res Oceans. 99(C6), 12319-12341\n* Eldevik T, Nilsen JE\u00d8, Iovino D, Olsson KA, Sand\u00f8 AB, Drange H. 2009. Observed sources and variability of Nordic seas overflow. Nature Geosci. 2(6), 405-409\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan M.M, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Climate Change. 5, 673-678.\n* Mauritzen C. 1996. Production of dense overflow waters feeding the North Atlantic across the Greenland-Scotland Ridge. 1. Evidence for a revised circulation scheme. Deep-Sea Res Part I. 43(6), 769-806\n* Mauritzen C, Hansen E, Andersson M, Berx B, Beszczynzka-M\u00f6ller A, Burud I, Christensen KH, Debernard J, de Steur L, Dodd P, et al. 2011. Closing the loop \u2013 Approaches to monitoring the state of the Arctic Mediterranean during the International Polar Year 2007-2008. Prog Oceanogr. 90, 62-89\n* Onarheim IH, Eldevik T, \u00c5rthun M, Ingvaldsen RB, Smedsrud LH. 2015. Skillful prediction of Barents Sea ice cover. Geophys Res Lett. 42(13), 5364-5371\n* Raj RP, Johannessen JA, Eldevik T, Nilsen JE\u00d8, Halo I. 2016. Quantifying mesoscale eddies in the Lofoten basin. J Geophys Res Oceans. 121. doi:10.1002/2016JC011637\n* Rudels B. 2012. Arctic Ocean circulat