Abstract
Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn provide information on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporal resolution of radar freeboard is limited as 30 d are required in order to generate pan-Arctic coverage from CryoSat-2 and 27 d are required from Sentinel-3 satellites. This therefore hinders our ability to understand physical processes that drive sea ice thickness variability on sub-monthly timescales. In this study we exploit the consistency between CryoSat-2, Sentinel-3A, and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboard estimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian process regression to learn functional mappings between radar freeboard observations in space and time and to subsequently retrieve pan-Arctic freeboard as well as uncertainty estimates. We also employ an empirical Bayesian approach towards learning the free (hyper)parameters of the model, which allows us to derive daily estimates related to radar freeboard spatial and temporal correlation length scales. The estimated daily radar freeboard predictions are, on average across the 2018–2019 season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 1 mm (standard deviations <6 cm), and cross-validation experiments show that errors in predictions are, on average, ≤ 4 mm across the same period. We also demonstrate the improved temporal variability of a pan-Arctic daily product by comparing time series of the predicted freeboards, with 31 d running means from CryoSat-2 and Sentinel-3 freeboards, across nine sectors of the Arctic, as well as making comparisons with daily ERA5 snowfall data. Pearson correlations between daily radar freeboard anomalies and snowfall are as high as +0.52 over first-year ice and +0.41 over multi-year ice, suggesting that the estimated daily fields are able to capture real physical radar freeboard variability at sub-weekly timescales.
Highlights
Over the past 4 decades remote sensing has provided key insights into the changing state of the polar climate system
We showed an example of how our method uses 9 d of gridded freeboard observations from CryoSat-2 (CS2), Sentinel-3A (S3A), and Sentinel-3B (S3B) satellites in order to model spatio-temporal covariances between observation points, and make pan-Arctic predictions of radar freeboard, with uncertainty estimates, on any given day at 25 × 25 km resolution
We introduced an empirical Bayesian approach for estimating theparameters which define the covariance function, one which maximises the probability of the observations under the choice of model prior and allows us to derive estimates of the spatial and temporal correlation length scales of radar freeboard
Summary
Over the past 4 decades remote sensing has provided key insights into the changing state of the polar climate system. Estimates of sea ice thickness are crucial for monitoring the volumetric response of sea ice to long-term climatic change (Slater et al, 2021), as well as understanding the consequences for Arctic ecosystems, whose productivity rates are driven by the sea ice thickness distribution (Sakshaug et al, 1994; Stirling, 1997; Stroeve et al, 2021). Our ability to provide reliable forecasts is inherently dependent on the availability of observations which allow us to exploit sources of sea ice predictability (Guemas et al, 2016). Initialising climate models with observations of sea ice thickness for example has been shown to considerably improve seasonal sea ice forecasts compared
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