A new state-dependent parameterization for the free drift of sea ice

Abstract

Abstract. Free-drift estimates of sea ice motion are necessary to produce a seamless observational record combining buoy and satellite-derived sea ice motion vectors. We develop a new parameterization for the free drift of sea ice based on wind forcing, wind turning angle, sea ice state variables (thickness and concentration), and estimates of the ocean currents. Given the fact that the spatial distribution of the wind–ice–ocean transfer coefficient has a similar structure to that of the spatial distribution of sea ice thickness, we take the standard free-drift equation and introduce a wind–ice–ocean transfer coefficient that scales linearly with ice thickness. Results show a mean bias error of −0.5 cm s−1 (low-speed bias) and a root-mean-square error of 5.1 cm s−1, considering daily buoy drift data as truth. This represents a 35 % reduction of the error on drift speed compared to the free-drift estimates used in the Polar Pathfinder dataset (Tschudi et al., 2019b). The thickness-dependent transfer coefficient provides an improved seasonality and long-term trend of the sea ice drift speed, with a minimum (maximum) drift speed in May (October), compared to July (January) for the constant transfer coefficient parameterizations which simply follow the peak in mean surface wind stresses. Over the 1979–2019 period, the trend in sea ice drift in this new model is +0.45 cm s−1 per decade compared with +0.39 cm s−1 per decade from the buoy observations, whereas there is essentially no trend in a free-drift parameterization with a constant transfer coefficient (−0.09 cm s−1 per decade) or the Polar Pathfinder free-drift input data (−0.01 cm s−1 per decade). The optimal wind turning angle obtained from a least-squares fitting is 25∘, resulting in a mean error and a root-mean-square error of +3 and 42∘ on the direction of the drift, respectively. The ocean current estimates obtained from the minimization procedure resolve key large-scale features such as the Beaufort Gyre and Transpolar Drift Stream and are in good agreement with ocean state estimates from the ECCO, GLORYS, and PIOMAS ice–ocean reanalyses, as well as geostrophic currents from dynamical ocean topography, with a root-mean-square difference of 2.4, 2.9, 2.6, and 3.8 cm s−1, respectively. Finally, a repeat of the analysis on two sub-sections of the time series (pre- and post-2000) clearly shows the acceleration of the Beaufort Gyre (particularly along the Alaskan coastline) and an expansion of the gyre in the post-2000s, concurrent with a thinning of the sea ice cover and the observed acceleration of the ice drift speed and ocean currents. This new dataset is publicly available for complementing merged observation-based sea ice drift datasets that include satellite and buoy drift records.

A Climate Data Record of Year-Round Global Sea Ice Drift from the EUMETSAT OSI SAF

“…|A| takes smaller values North for Greenland and the Canadian Archipelago where older and thicker sea ice is. This is expected from the derivation of the free-drift model(Leppäranta, 2011) and similar to results obtained byBrunette et al (2022) and that prompted their parametrization of |A| on sea-ice thickness from reanalysis data (which we do not adopt in this CDR). The spatial gradient of |A| shown on Fig.2is less pronounced than that of(Brunette et al, 2022, Fig.…”

“…Recently, Brunette et al (2022) revisited the formulation and tuning of the free-drift model for Arctic sea-ice motion. One of their motivation was to improve summer sea-ice motion as provided in the NSIDC data record, but their study includes year-round sea-ice motion.…”

“…It is not yet clear how to capture the multi-decadal evolution. The novel strategy of Brunette et al (2022) is effective, but it also brings an extra dependency on sea-ice thickness data that might not be as easily applicable in the southern hemisphere.…”

“…In this first version, the wind-derived drift is more seen as a summer gap-filler enabling some specific applications, rather than a basis for trend analysis. In any case, our summer sea-ice motion should be less biased than that in the NSIDC timeseries that use a constant wind-ice-ocean transfer coefficient of |A| = 0.01% (compare to the values in Table 3), which captures neither the spatial, seasonal, nor multi-decadal evolution, as shown by Brunette et al (2022). Product filenames follow the following convention:…”

Bridging Gaps in the Climate Observation Network: A Physics‐Based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data‐Driven Stochastic Models

“…On the other hand, in situ field measurements using buoys on ice floe surfaces have provided invaluable information. However, trajectories are often sparse (Brunette et al, 2022;Gabrielski et al, 2015;Hutchings et al, 2012;Itkin et al, 2017;Lei et al, 2020), with coastal regions undersampled relative to the central Arctic.…”