Probabilistic Forecasts of Arctic Sea Ice Thickness

In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.

Arctic Sea Ice Thickness Estimation Based on CryoSat-2 Radar Altimeter and Sentinel-1 Dual-Polarized SAR

We present a method to combine CryoSat-2 (CS-2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara Seas. Our approach yields larger spatial coverage and better accuracy compared to estimates based on either CS-2 or SAR alone. The SIT estimation method developed here is based on interpolation and extrapolation of CS-2 sea ice thickness (SIT) utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts, to ORAS5. PIOMAS and TOPAZ4 ocean-sea ice data assimilation system reanalyses, and to daily MODIS based ice thickness charts. Our results are directly applicable to the future CRISTAL mission and Copenicus programme SAR missions.

Sea Ice Thickness Retrieval based on Predictive Regression Neural Networks using L-band Microwave Radiometry Data from the FSSCat mission

<p>Several approaches have been developed to yield Arctic sea ice thickness based on satellite observations. Microwave radiometry operating at L-band is sensitive to sea ice properties and allows to retrieve thin sea ice up to ~ 0.5 m. Sea ice thickness retrievals above 1 m can be successfully derived using sea ice freeboard data from satellite altimeters. Current inference models are build upon empirically determined assumptions on the physical composition of sea ice and are validated against regionally available data. However, sea ice can exhibit time-dependent non-linear relations between sea ice properties during the process of formation and melting, which cannot be fully addressed by simple inversion models. Until now, an accurate estimation of sea ice thickness requires specific conditions and is only viable during Arctic freeze up from mid-October to mid-April. Neural networks are an efficient model-based learning technique capable of resolving complex systems while recognizing hidden links among large amounts of data. Models have the advantage to be adaptive to new data and can therefore reflect seasonally changing sea ice conditions to make predictions based on the relationship between a set of input features. FSSCat is a two 6-unit CubeSat mission launched on September 3, 2020, which carries the FMPL-2 payload on board the 3Cat-5/A, one out of two spacecrafts. FMPL-2 encompasses the first L-band radiometer and GNSS-Reflectometer on a CubeSat, designed to provide global brightness temperature data suitable for soil moisture retrieval on land and sea ice applications.</p><p>In this work a predictive regression neural network was built to predict thin sea ice thickness up to 0.6 m at Arctic scale based on FMPL-2 observations and ancillary data including sea ice concentration and surface temperature. The network was trained based on CubeSat acquisitions during early Arctic freeze up from October 15 to December 4, 2020, using existing maps of daily estimated sea ice thickness derived from the Soil Moisture and Ocean Salinity (SMOS) mission as ground truth data. Hyperparameters were optimized to prevent the model from overfitting and overgeneralization with the best fit resulting in an overall mean absolute error of 6.5 cm. Preliminary results reveal good performance up to 0.5 m, whereas predicted values are slightly underestimated for higher thickness. The thin ice model allows to produce weekly composites of Arctic sea ice thickness maps. Future work involves the complementation of the input features with sea ice freeboard observations from the Cryosat-2 mission to extend the sensitivity range of the current network and to validate the findings with on-site data. Aim is to apply the model trained on Arctic data to retrieve full-range Arctic and Antarctic sea ice thickness maps. The presented approach demonstrated the potential of neural networks for sea ice parameter retrieval and indicated that data acquired by moderate-cost CubeSat missions can offer scientifically valuable contributions to applications in Earth observation.</p>

A data assimilation application for improving estimates of Arctic sea ice thickness variability and change since the turn of the 21st century

<p>Current sea ice prediction systems exhibit significant room for improvement compared to idealized estimates of sea ice predictability, a gap that could be closed by improving the initial conditions provided to prognostic models. Sea ice volume, the area-weighted integral of sea ice thickness (SIT), in particular, demonstrates long initial value predictability; in other words, accurate forecasting of Arctic sea ice requires highly accurate SIT initial conditions. Continuous records of SIT are, unfortunately, few and far between. To address this conundrum, we have explored applications of the Data Assimilation Research Testbed (DART) to constrain the Los Alamos Sea Ice Model (CICE5) within the Community Earth System Model using satellite-derived SIT observations from 2003 to present day. Our data assimilation system has been fine-tuned using new and highly accurate freeboard measurements from NASA’s ICESat-2 mission. Using SIT information alone, we generate two assimilation products: the first using DART with CICE5 and the second with an offline assimilation method. We compare these products to one another and to the community standard SIT record, PIOMAS. Future work will introduce multivariate assimilation of SIT with other sea ice variables, including sea ice concentration, sea ice skin temperature, and sea surface temperature.</p>

On the 2011 record low Arctic sea ice thickness: a combination of dynamic and thermodynamic anomalies

The sea ice thickness is recognized as an early indicator of climate changes. The mean Arctic sea ice thickness has been declining for the past four decades, and a sea ice thickness record minimum is confirmed occurring in autumn 2011. We used a daily sea ice thickness reanalysis data covering the melting season to investigate the dynamic and thermodynamic processes leading to the minimum thickness. Ice thickness budget analysis demonstrates that the ice thickness loss is associated with an extraordinarily large amount of multiyear ice volume export through the Fram Strait during the season of sea ice advance. Due to the loss of multiyear ice, the Arctic ice thickness becomes more sensitive to atmospheric anomalies. The positive net surface energy flux anomalies melt roughly 0.22 m of ice more than usual from June to August. An analysis of clouds and radiative fluxes from ERA5 reanalysis data reveals that the increased net surface energy absorption supports the enhanced sea ice melt. The enhanced cloudiness led to positive anomalies of net long-wave radiation. Furthermore, the enhanced sea ice melt reduces the surface albedo, triggering an ice–albedo amplifying feedback and contributing to the accelerating loss of multiyear ice. The results demonstrate that the dynamic transport of multiyear ice and the subsequent surface energy budget response is a critical mechanism actively contributing to the evolution of Arctic sea ice thickness.