'''Short description:''' The iceberg product contains 9 (6+3) datasets: Six gridded datasets in netCDF format: IW, EW and RCMNL modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors. Three datasets – individual iceberg positions – in shapefile format: The shapefile format allows the best representation of the icebergs. Each shapefile-dataset also includes a shapefile holding the polygonized satellite coverage Despite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned. '''DOI (product) :''' https://doi.org/10.48670/moi-00129

'''DEFINITION''' The OMI_CLIMATE_sst_ist_ARCTIC_sst_ist_trend product includes the cumulative/net trend in combined sea and ice surface temperature anomalies for the Arctic Ocean from 1982-2024. The cumulative trend is the rate of change (°C/year) scaled by the number of years (43 years). The SST/IST Level 4 analysis that provides the input to the trend calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2024. The product has a spatial resolution of 0.05 degrees in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the reference climatology defined over the period 1991-2020, according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate), using daily level 4 SST/IST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen’s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018). '''CONTEXT''' SST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al., 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). The Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). Previously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region. '''CMEMS KEY FINDINGS''' SST/IST trends were calculated for the Arctic Ocean over the period January 1982 to December 2024. The cumulative trends are upwards of 2°C for the greatest part of the Arctic Ocean, with the largest trends occur in the Beaufort Sea, Chukchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. Zero to slightly negative trends are found at the North Atlantic part of the Arctic Ocean. The combined sea and sea ice surface temperature trend is 0.104+/-0.005°C/yr, i.e. an increase by around 4.47°C between 1982 and 2024. The 2d map of Arctic anomalies reveals regional peak warming exceeding 5.5°C. '''DOI (product):''' https://doi.org/10.48670/mds-00324

'''DEFINITION''' The OMI_CLIMATE_SST_IST_ARCTIC_sst_ist_anomaly product includes the 2D annual mean surface temperature anomaly for the Arctic Ocean for 2024. The annual mean surface temperature anomaly is calculated from the climatological mean estimated from 1991 to 2020 (30 years), defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). The SST/IST Level 4 analysis that provides the input to the climatology and mean anomaly calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2024. The product has a spatial resolution of 0.05 degrees in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen’s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018). '''CONTEXT''' SST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). The Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). Previously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region. '''CMEMS KEY FINDINGS''' The area average anomaly of 2024 is 1.94±1.09°C (± means 1 standard deviation in this case). The majority of anomalies are positive and exceed 2°C for most areas of the Arctic Ocean, while the largest regional anomalies exceeded 5.5°C. Near zero and slightly negative anomalies are observed in some areas of the Norwegian and Greenland Sea and around the Bering Strait. '''DOI (product):''' https://doi.org/10.48670/mds-00353

'''Short description:''' Arctic Sea and Ice surface temperature '''Detailed description:''' Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone. '''DOI (product) :''' https://doi.org/10.48670/moi-00123

'''Short description:''' Arctic Sea and Ice surface temperature '''Detailed description:'' 'Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone. '''DOI (product) :''' https://doi.org/10.48670/moi-00315

'''DEFINITION ''' The OMI_CLIMATE_SST_IST_ARCTIC_sst_ist_area_averaged_anomalies product includes time series of monthly mean SST/IST anomalies over the period 1982-2024, relative to the 1991-2020 climatology (30 years), averaged for the Arctic Ocean. The SST/IST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2024. The product has a spatial resolution of 0.05 degrees in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. The climatological period used is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen’s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018). '''CONTEXT''' SST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). The Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). Previously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region. '''KEY FINDINGS''' The basin-average trend of SST/IST anomalies for the Arctic Ocean region amounts to 0.104±0.005 °C/year over the period 1982-2024 (43 years) which corresponds to an average warming of 4.47°C. The 2-d map of warming trends indicates these are highest for the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea, Barents Sea and parts of Baffin Bay. The 2d map of Arctic anomalies for 2024 reveals regional peak warming exceeding 6°C. '''DOI (product):''' https://doi.org/10.48670/mds-00323

'''Short description:''' Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 and RCM 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 Antarctic Sea - A sea ice concentration product based on satellite SAR imagery and microwave radiometer data: The algorithm uses SENTINEL-1 SAR EW and IW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. '''DOI (product) :''' https://doi.org/10.48670/mds-00320

'''Short description:''' Arctic L3 sea ice product providing concentration, stage-of-development and floe size information retrieved from Sentinel-1 and RCM SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm and delivered on a 0.5 km grid. '''DOI (product) :''' https://doi.org/10.48670/mds-00343

'''Short description:''' Arctic Sea and Sea-Ice surface temperature product based on observations from AVHRR on METOP, VIIRS on NPP and NOAA20 and SLSTR on Sentinel 3A/B. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone. '''DOI (product) :''' https://doi.org/10.48670/mds-00365