Advancing Short‐Term Forecasts of Ice Conditions in the Beaufort Sea

Abstract

Numerical experiments with a regional configuration of the CICE model (Los‐Alamos Community sea Ice CodE) in the Beaufort Sea assimilating Special Sensor Microwave Imager/Sounder ice concentration (IC) and CryoSat ice thickness (IT) data acquired for September–December of 2015 are presented. We explore sensitivity of the 24‐hr IT/IC forecast skill to the system updates, which include introduction of the IT assimilation capability, flow‐dependent correlations, and Gaussianization of IC innovations. Experiments with IC data assimilation have shown that the flow‐dependent correlations provide 5–7% improvement of the forecast skill during the freezing period (10 September to 10 November) while Gaussianization contributes an additional improvement of 3–4% in most of the cases. In winter (11 November to 31 December) IC assimilation did not produce any statistically significant improvement of the skill due to the loss of dynamical information in the IC fields associated saturation of the ice cover. In contrast, IT assimilation provides larger improvement in December compared to October–November due to the better coverage of the Beaufort Sea by observations and their higher relative accuracy in winter. Comparison of the IT forecast fields with independent in situ observations by two upward looking sonars demonstrates statistically insignificant improvements. Much better improvement (15–25%) is observed when comparing monthly mean IT assimilation runs against independent Advanced Microwave Scanning Radiometer for Earth observing system (AMSR‐E) data. Introduction of the heuristic in situ IC/IT correlations into the background covariance model did not produce any improvements of the forecast skill.

Analysis of the Beaufort Gyre Freshwater Content in 2003–2018

“…Solid freshwater fluxes at the boundaries were calculated using monthly satellite-based estimates of ice motion, concentration, and thickness (section 2.5). Results of sea ice draft measured at the four BGOS moorings are sufficiently representative to characterize changes of sea ice thickness in the Beaufort Gyre region (e.g., Krishfield et al, 2014;Proshutinsky, Krishfield, & Barber, 2009;Proshutinsky, Krishfield, Timmermans, et al, 2009;Yaremchuk et al, 2019, in this special issue). Sea ice plays a significant role in…”

Brief Communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

“…Thickness products featuring a constant-density assumption are near-ubiquitous and have also fed forecast and reanalysis models (e.g. Yaremchuk et al, 2019;Blockley and Peterson, 2018). While these biases may be small compared to the effects of partial radar penetration into the snow cover as a function of snow pack variables (e.g.…”

Parameter optimization in sea ice models with elastic–viscoplastic rheology

“…Many of these models (e.g., Heimbach et al, 2010;Zhang and Rothrock, 2003;Vancoppenolle et al, 2009;Massonnet et al, 2015) are based on the viscoplastic (VP) rheology proposed by Hibler (1979). In the last decades, several numerical approaches have been proposed for solving the VP problem (Hibler, 1979;Zhang and Hibler, 1997;Lemieux et al, 2008). These approaches are based on implicit solvers and require a significant number of iterations to achieve full convergence.…”