'''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 OMI_EXTREME_SST_NORTHWESTSHELF_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 North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. Høyer and Karagali, 2016). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The mean 99th percentiles showed in the area present a range from 14-15ºC in the North of the British Isles, 16-19ºC in the West of the North Sea to 19-20ºC in the Helgoland Bight. The standard deviation ranges from 0.7-0.8ºC in the North of the British Isles, 0.6-2ºC in the West of the North Sea to 0.8-3ºC in in the Helgoland Bight. Results for this year show positive moderate anomalies (+0.3/+1.0ºC) in all the positions except in one station in the West of the Noth Sea where the anomaly is negative (-0.3ºC), all of them inside the standard deviation margin. '''DOI (product):''' https://doi.org/10.48670/moi-00274