'''This product has been archived''' '''Short description:''' Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. On the other hand, CS2 uses radar altimetry to measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. On the other hand, CS2 uses radar altimetry to measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing. '''DOI (product) :''' https://doi.org/10.48670/mds-00321

'''Short description:''' DTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km. '''DOI (product) :''' https://doi.org/10.48670/moi-00135

'''DEFINITION''' Based on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). Analysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: 1. The daily analyses were averaged to create monthly means. 2. A climatology was calculated by averaging the monthly means over the period 1993 - 2014. 3. Monthly anomalies were calculated by differencing the monthly means and the climatology. 4. An area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area. 5. The time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. 6. The slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. '''CONTEXT''' Sea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016). '''CMEMS KEY FINDINGS''' Over the period 1993 to 2021, the global average linear trend was 0.015 ± 0.001°C / year (95% confidence interval). 2021 is nominally the sixth warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Niño and La Niña conditions. For example, peaks in the time series coincide with the strong El Niño events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018). '''DOI (product):''' https://doi.org/10.48670/moi-00242

'''Short description:''' For the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas. '''DOI (product) :''' https://doi.org/10.48670/moi-00152

'''Short description:''' For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02° resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain "per sensor type" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR) '''DOI (product) :''' https://doi.org/10.48670/moi-00310

'''Short description:''' Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm (SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023), gap-filled with OSI SAF EUMETSAT sea ice concentration products and delivered on a 1 km grid. '''DOI (product) :''' https://doi.org/10.48670/mds-00344

'''Short description:''' For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR. '''DOI (product) :''' https://doi.org/10.48670/moi-00156

'''Short description:''' The CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022 '''DOI (product) :''' https://doi.org/10.48670/moi-00136