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Poster presented at the 11th International Workshop on Sea Ice Modelling, Assimilation, Observations, Predictions and Verification of the International Ice Charting Working Group (IICWG) at the Norwegian Meteorological Institute in Oslo, 21-23 March 2023. The effects of global climate change are amplified in the arctic, making arctic sea ice an important climate indicator. Current sea ice climate data records (CDRs), based on satellite data, are beginning in the late 1970’s. However, there are also satellite missions from the early and mid 1970s which can be used for mapping sea ice and for extending the current CDRs. One example is the data of the Electrically Scanning Microwave Radiometer (ESMR) on board the NIMBUS 5 satellite, which was operating between 1972 and 1977. As part of the ESA CCI+ sea ice project a new sea ice concentration (SIC) data set was created from the ESMR L1 data and is presented here.

Available observations and a theoretical simulation are used to explore the consistency and relationship between sea surface temperature (SST) and sea ice concentration (SIC) within open-ocean-sea ice mixed satellite pixels as a function of grid resolution. The maximum limiting SST value for a specified SIC and spatial resolution is first examined within collocated satellite-derived products contained within existing Level 4 SST analyses distributed using the data specification from the Group for High Resolution Sea Surface Temperature. The shape of the interdependence is further validated with manually quality-controlled buoy SST and SIC collocations. A parametric equation for the limiting SST value is derived from simulations of a mixed ocean/ice pixel with specified ice fraction and a linear SST gradient extending away from the ice edge. The exponential curve matching the observed interdependence suggests a maximum 5 km pixel-averaged SST at SIC values approaching zero between 6 and 8 °C. This maximum value is significantly greater than the previously assumed limiting values of ~3 °C and the corresponding SST gradient is larger than those typically observed with satellite SST products, but agrees well with recent Saildrone SST observations near ice. The curve provides a conservative limit with which inconsistent SST/SIC pairings can be identified, not only near the ice edge but at intermediate ice concentrations. Application of the filter improves the agreement between the SST/SIC relationship in satellite products and available Saildrone observations as well as the internal consistency of the different satellite products.

Manual ice charting from multi-sensor satellite data analysis has for many years been the primary method at the National Ice Services for producing sea ice information for marine safety. Ice analysts primarily use satellite synthetic aperture radar (SAR) imagery due to the high spatial resolution and the capability to image the surface through clouds and in polar darkness, but also optical imagery in clear sky and daylight conditions, thermal-infrared and microwave radiometer data from e.g. AMSR2. Ice analysts mention the spatial resolution of microwave radiometers as the primarily limitation to use the data. The traditional manual ice charting method is time-consuming and limited in spatial and temporal coverage. Further, it is challenged by an increasing amount of available satellite imagery, along with a growing number of users accessing wider parts of the Arctic due to the thinning of the Arctic sea ice. The automation of the time-consuming and labour-intensive sea ice charting process has potential to provide users with near-real time sea ice products of higher spatial resolution, larger spatial and temporal coverage, and increased consistency.To automate the generation of sea ice information from satellite imagery we use a Convolutional Neural Network (CNN) designed for prediction of sea ice in Greenland waters. Automating the process on SAR data alone is challenging. SAR images show patterns related to ice formations, but backscatter intensities can be ambiguous, which complicates the discrimination between ice and open water, e.g. at high wind speeds. Our CNN model tackles the challenges by fusing Sentinel-1 active microwave (SAR) data with Microwave Radiometer (MWR) data from AMSR2 to exploit the advantages of both instruments. While SAR data has ambiguities, it has a very high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. The CNN model has been trained with a large dataset of 461 ice charts manually produced by the ice analysts in the DMI Greenland Ice Service based on Sentinel-1 imagery. The dataset also contains thecorresponding AMSR2 swath co-located with the ice charts and Sentinel-1 images. The sea ice training dataset (https://doi.org/10.11583/DTU.13011134.v2) has been co-produced in the ASIP and AI4Arctic (ESA) projects. We will present the results of merging active and passive microwave data from Sentinel-1 and AMSR2 as input to a CNN and show how the input from the passive microwave data has a positive effect on the CNN performance. Furthermore, we will discuss the benefits and improvements that can be expected with the higher resolution CIMR observations and give inputs to the requirements for simulated CIMR test data in the context of CNN development. The CNN model is in production at DMI and its ice products have been tested with users in the Fall of 2020.