Abstract
AbstractTwin 5-month seasonal forecast experiments are performed to predict the September 2018 mean and minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M km2 (5.27 M km2) vs the ensemble forecasts initialized with CS2 ice thickness that had an error of 0.40 M km2 (4.99 M km2), a 43% reduction in error. The September mean integrated ice edge error shows a 18% improvement for the Pan-Arctic with the CS2 data vs the control forecasts. Comparison against upward looking sonar ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of forecasts show ice free conditions as observed at moorings A and D.
Highlights
Seasonal sea ice prediction (Merryfield and others, 2013; Stroeve and others, 2014; Blockley and Peterson, 2018) is gaining in importance as operational ice production centers (e.g., National Ice Center (NIC), Environment and Climate Change Canada) are fielding requests to provide extended-range forecasts of sea ice conditions to support navigation, in some cases months in advance
Global Ocean Forecasting System (GOFS) 3.1 ice thickness was over-predicted by 0.64 M km2 vs the ensemble set of forecasts initialized with CS2 ice thickness which had an error of 0.36 M km2, a 43% reduction in error
We attribute this higher volume to the thicker initial ice shown in the Central Arctic between the Canadian Archipelago and the North Pole shown in Figure 1 where CS2 ice thickness is 2–2.5 m thicker than the control run
Summary
Seasonal sea ice prediction (Merryfield and others, 2013; Stroeve and others, 2014; Blockley and Peterson, 2018) is gaining in importance as operational ice production centers (e.g., National Ice Center (NIC), Environment and Climate Change Canada) are fielding requests to provide extended-range forecasts of sea ice conditions to support navigation, in some cases months in advance. Dirkson and others (2017) utilized the Canadian Climate Model version 3 (CanCM3) and three statistical models used to derive initial sea ice thickness estimate to initialize a realtime forecasting system for the period of 1981–2012 They found that the combination of sea ice thickness fields that represented the thinning of the ice pack over many years and interannual variability led to the best predictive skill for pan-Arctic ice area and regional sea ice concentration. Seasonal predictions were initialized on three different spring start dates for an eight-member ensemble prediction system for the years 2011–2015 They found a significant improvement vs a control set of ensembles for the same time periods (without using CS2 data) for sea ice extent and ice edge location for September forecasts as well as improvements to near-surface air temperature and pressure fields. An evaluation of the ice thickness prediction at the Beaufort Gyre Exploration Project (BGEP) Upward Looking Sonar (ULS) moorings in the Beaufort Sea showed a significant reduction in the RMSE at all three moorings compared to the operational Climate Forecast System Version 2 (CFSv2) ensembles which were not initialized with satellite-derived ice thickness data
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