The mean and time-varying position of the main Southern Ocean polar fronts has been calculated from a combination of gridded altimetric sea level anomalies and a new climatology of mean sea level constructed from historical data and ARGO T / S profiles.

'''Short description:''' You can find here the biogeochemistry non assimilative hindcast simulation GLOBAL_REANALYSIS_BIO_001_018 at 1/4° over period 1998 - 2016. Outputs are delivered as monthly mean files with Netcdf format (CF/COARDS 1.5 convention) on the native tripolar grid (ORCA025) at ¼° resolution with 75 vertical levels. This simulation is based on the PISCES biogeochemical model. It is forced offline at a daily frequency by the equivalent of the GLOBAL-REANALYSIS-PHYS-001-009 physics product but without data assimilation. '''Detailed description: ''' There are 8 different datasets: * dataset-global-nahindcast-bio-001-018-no3 containing : nitrate concentration * dataset-global-nahindcast-bio-001-018-po4 containing : phosphate concentration * dataset-global-nahindcast-bio-001-018-si containing : silicate concentration * dataset-global-nahindcast-bio-001-018-o2 containing : dissolved oxygen concentration * dataset-global-nahindcast-bio-001-018-fe containing : iron concentration * dataset-global-nahindcast-bio-001-018-chl containing : chlorophyll concentration * dataset-global-nahindcast-bio-001-018-phyc containing : carbon phytoplankton biomass * dataset-global-nahindcast-bio-001-018-pp containing : primary production The horizontal grid is the standard ORCA025 tri-polar grid (1440 x 1021 grid points). The three poles are located over Antarctic, Central Asia and North Canada. The ¼ degree resolution corresponds to the equator. The vertical grid has 75 levels, with a resolution of 1 meter near the surface and 200 meters in the deep ocean.Biogeochemical and physical simulations start at rest (cold start) in December 1991. The spin-up period consists of 5 years of interannual simulation between 1992 and 1997. The simulation period covers the ocean color era (1998 – 2016).The biogeochemical model used is PISCES (Aumont, in prep). It is a model of intermediate complexity designed for global ocean applications (Aumont and Bopp, 2006) and is part of NEMO modeling platform. It has 24 prognostic variables and simulates biogeochemical cycles of oxygen, carbon and the main nutrients controlling phytoplankton growth (nitrate, ammonium, phosphate, silicic acid and iron). The model distinguishes four plankton functional types based on size: two phytoplankton groups <nowiki>(small = nanophytoplankton and large = diatoms)</nowiki> and two zooplankton groups <nowiki>(small = microzooplankton and large = mesozooplankton).</nowiki>Prognostic variables of phytoplankton are total biomass in C, Fe, Si (for diatoms) and chlorophyll and hence the Fe/C, Si/C, Chl/C ratios are variable. For zooplankton, all these ratios are constant and total biomass in C is the only prognostic variable. The bacterial pool is not modeled explicitly. PISCES distinguishes three non-living pools for organic carbon: small particulate organic carbon, big particulate organic carbon and semi-labile dissolved organic carbon. While the C/N/P composition of dissolved and particulate matter is tied to Redfield stoichiometry, the iron, silicon and carbonate contents of the particles are computed prognostically. Next to the three organic detrital pools, carbonate and biogenic siliceous particles are modeled. Besides, the model simulates dissolved inorganic carbon and total alkalinity. In PISCES, phosphate and nitrate + ammonium are linked by constant Redfield ratio <nowiki>(C/N/P = 122/16/1)</nowiki>, but cycles of phosphorus and nitrogen are decoupled by nitrogen fixation and denitrification. Biogeochemical model PISCES (NEMO3.5) is forced offline by daily fields of the physical model NEMO (OPA module in the NEMO platform) without any assimilation of physical data. The main features of this dynamical ocean are: * NEMO 3.1 * Atmospheric forcings from 3-hourly ERA-Interim reanalysis products, CORE bulk formulation * Vertical diffusivity coefficient is computed by solving the TKE equation * Tidal mixing is parameterized according to the works of Bessières et al. (2008) and Koch-Larrouy et al, (2006). * Sea-Ice model: LIM2 with the Elastic-Viscous-Plastic rheology * Initial conditions: Levitus 98 climatology for temperature and salinity, patched with PHC2.1 for the Arctic regions, and Medatlas for the Mediterranean Sea. A special treatment is done on vertical diffusivity coefficient (Kz): the daily mean is done on Log10(Kz) after a filtering of enhanced convection (Kz increased artificially to 10 m2.s-1 when the water column is unstable). The purpose of this Log10 is to average the orders of magnitudes and to give more weight to small values of vertical diffusivity. The atmospheric forcing fields are daily averages from ERA-Interim reanalysis product (CORE bulk formulation). Boundary fluxes account for nutrient supply from three different sources: Atmospheric deposition (Aumont et al., 2008), rivers for nutrients, dissolved inorganic carbon and alkalinity (Ludwig et al., 1996; Mayorga et al., 2010) and inputs of Fe from marine sediments. Nutrient and freshwater inflows by rivers are colocalized. River and dust inputs are balanced with sediment trapping of NO3, Si and Carbon. An annual and global value of atmospheric carbon dioxide is imposed at sea surface.

'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer (here: 0-700m) is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology (here 1993-2014) to obtain the fluctuations from an average field. The regional thermosteric sea level values from 1993 to close to real time are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins (IPCC, 2019). Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (WCRP, 2018). '''CMEMS KEY FINDINGS''' Since the year 1993 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.5±0.1 mm/year.

'''This product has been archived''' '''DEFINITION''' The CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001). Two parameters have been considered for this OMI: • Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2021). • Anomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). '''CONTEXT''' The Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018). The IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003). Also relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980). '''CMEMS KEY FINDINGS''' In the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in Álvarez Fanjul (2019) for the period 1993-2016. The analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43ºN parallel. Extreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer. It is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis. '''DOI (product):''' https://doi.org/10.48670/moi-00254

'''DEFINITION''' Meridional Heat Transport is computed by integrating the heat fluxes along the zonal direction and from top to bottom of the ocean. They are given over 3 basins (Global Ocean, Atlantic Ocean and Indian+Pacific Ocean) and for all the grid points in the meridional grid of each basin. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW). '''CONTEXT''' The ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth’s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth’s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. '''CMEMS KEY FINDINGS''' After an anusual 2016 year (Bricaud 2016), with a higher global meridional heat transport in the tropical band explained by, the increase of northward heat transport at 5-10 ° N in the Pacific Ocean during the El Niño event, 2017 northward heat transport is lower than the 1993-2014 reference value in the tropical band, for both Atlantic and Indian + Pacific Oceans. At the higher latitudes, 2017 northward heat transport is closed to 1993-2014 values. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00246

Ocean tide and loading altimetric corrections computed - with SIROCCO Tools : https://sirocco.obs-mip.fr/other-tools/prepost-processing/comodo-tools - from FES2014 atlases : https://doi.org/10.6096/3001 - for all altimetric missions. Validation available at ftp://ftp.legos.obs-mip.fr/pub/ecola/FES2014/CTOH/2021-02-diff-libfes-tools.pdf

'''DEFINITION''' The OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). '''CONTEXT''' Sea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial. The Iberia Biscay Ireland area is characterized by a great complexity in terms of processes that take place in it. The sea surface temperature varies depending on the latitude with higher values to the South. In this area, the clear warming trend observed in other European Seas is not so evident. The northwest part is influenced by the refreshing trend in the North Atlantic, and a mild warming trend has been observed in the last decade (Pisano et al. 2020). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The mean 99th percentiles showed in the area present a range from 16-21ºC in the Southwest of the British Isles and the English Channel, 21-22ºC in the Galician Coast, 22-24ºC in the South of Bay of Biscay and the Gulf of Cadiz to 24.5ºC in the Canary Island. The standard deviations are between 0.6ºC and 1.8ºC in the Southwest of the British Isles and the English Channel and between 0.6ºC and 1.3ºC in the south of Bay of Biscay with a narrow margin in the rest of the areas (0.5-0.8ºC). Results for this year show a general positive anomaly in all the areas reaching +0.5ºC in the Gulf of Cadiz, and +1.3ºC in the Southwest of the British Isles and the English Channel inside the std margin in these areas. Only one coastal stations in the Irish Sea present a low negative anomaly (-0.4ºC). More significant are the positve anomalies found in the other areas: +1.2ºC in the South of Bay of Biscay +1.5º in the Canary Island, 2.2ºC in the Galician Coast, all of them out of the std margin of the respective areas. '''DOI (product):''' https://doi.org/10.48670/moi-00255