Advancing Short‐Term Forecasts of Ice Conditions in the Beaufort Sea. Issue 2 (1st February 2019) - Yaremchuk, M.; Townsend, T.; Panteleev, G.; Hebert, D.; Allard, R.
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. Plain Language Summary: Four‐month‐long observations of ice concentration and thickness in the Beaufort Sea are processed using an updated version of the Navy Coastal Ocean Data Assimilation system run at 2‐km resolution. The updates include introduction of the ice thickness assimilation capability, flow‐dependent correlations, and improvement of the error statistics for ice concentration. We show that daily forecasts of ice conditions improve considerably during the freezing period in September–November. Causes of the improvement are discussed in the context of ice information content of the satellite data. Key Points: Ice concentration and thickness data collected in the Beaufort Sea in September–December 2015 were assimilated into a numerical model The 24‐hr forecast skill of the model was improved Improvement is achieved by introducing ice thickness assimilation capabilty, flow‐dependent correlations, and Gaussianization