Seasonal and interannual variability of the Arctic sea ice: A comparison between AO-FVCOM and observations

Abstract

A high‐resolution (up to 2 km), unstructured‐grid, fully ice‐sea coupled Arctic Ocean Finite‐Volume Community Ocean Model (AO‐FVCOM) was used to simulate the sea ice in the Arctic over the period 1978–2014. The spatial‐varying horizontal model resolution was designed to better resolve both topographic and baroclinic dynamics scales over the Arctic slope and narrow straits. The model‐simulated sea ice was in good agreement with available observed sea ice extent, concentration, drift velocity and thickness, not only in seasonal and interannual variability but also in spatial distribution. Compared with six other Arctic Ocean models (ECCO2, GSFC, INMOM, ORCA, NAME, and UW), the AO‐FVCOM‐simulated ice thickness showed a higher mean correlation coefficient of ∼0.63 and a smaller residual with observations. Model‐produced ice drift speed and direction errors varied with wind speed: the speed and direction errors increased and decreased as the wind speed increased, respectively. Efforts were made to examine the influences of parameterizations of air‐ice external and ice‐water interfacial stresses on the model‐produced bias. The ice drift direction was more sensitive to air‐ice drag coefficients and turning angles than the ice drift speed. Increasing or decreasing either 10% in water‐ice drag coefficient or 10° in water‐ice turning angle did not show a significant influence on the ice drift velocity simulation results although the sea ice drift speed was more sensitive to these two parameters than the sea ice drift direction. Using the COARE 4.0‐derived parameterization of air‐water drag coefficient for wind stress did not significantly influence the ice drift velocity simulation.

Supplementary material to "Long-term variation of sea ice and its response to thermodynamic factors in the Northwest Passage of the Canadian Arctic Archipelago"

“…The driving forces included tidal forcing with eight major constituents (M2, S2, N2, K2, K1, P1, O1, and Q1), surface wind stress, net heat flux at the surface plus shortwave irradiance in the water column, surface air pressure gradients, precipitation (P) minus evaporation (E), and river discharges. We conducted a detailed validation and comparison of the AO-FVCOM sea ice thickness data with the Arctic multisource sea ice thickness data, including ICESat-2 satellite data, field drill-hole observations, airborne electromagnetic observations, and sea ice station data (Zhang et al, 2016b). The results revealed that the sea ice thickness data of the AO-FVCOM well captured the spatial distribution, as well as seasonal and interannual variation characteristics, of Arctic sea ice thickness data.…”

“…Sea ice conditions exert a dominant impact on ship navigation in the NWP. As a result of global warming, coverage by Arctic sea ice is significantly decreasing (Parkinson and Comiso, 2013;Zhang et al, 2016b) and the sea ice thickness is also diminishing (Kwok et al, 2009;Lindsay and Schweiger, 2015). In particular, the Arctic sea ice extent and area dropped to historic lows of 3.4 × 10 6 km 2 and 3.0 × 10 6 km 2 on 13 September 2012 (Parkinson and Comiso, 2013).…”

Impacts of Oceanic Mixed Layer on Hurricanes: A Simulation Experiment With Hurricane Sandy

“…The Global‐FVCOM was driven by astronomical tidal forcing with eight constituents (M 2 , S 2 , N 2 , K 2 , K 1 , P 1 , O 1 , and Q 1 ), surface wind stress, net heat flux at the surface plus shortwave irradiance in the water column, surface air pressure gradients, precipitation minus evaporation, and river discharge at 766 river mouth locations. Supported by the International Center for Marine Studies (ICMS) at Shanghai Ocean University (SHOU), the Global‐FVCOM was validated for 50‐year spin‐up validation experiments and a 40‐year hindcast with assimilation from 1978 to 2017 (C. Chen et al, 2009, 2016; Gao et al, 2011; Zhang, Chen, Beardsley, Gao, Lai, et al, 2016; Zhang, Chen, Beardsley, Gao, Qi, et al, 2016). We also repeated the simulation experiment by using the fields of h m and calculated from the Hybrid Coordinate Ocean Model (HYCOM) hindcast data set (HYCOM.org , 2013) (see Figures 2b and 2d).…”

Long-term variation of sea ice and its response to thermodynamic factors in the Northwest Passage of the Canadian Arctic Archipelago

“…The driving forces included tidal forcing with eight major constituents (M2, S2, N2, K2, K1, P1, O1, and Q1), surface wind stress, net heat flux at the surface plus shortwave irradiance in the water column, surface air pressure gradients, precipitation (P) minus evaporation (E), and river discharges. We conducted a detailed validation and comparison of the AO-FVCOM sea ice thickness data with the Arctic multisource sea ice thickness data, including ICESat-2 satellite data, field drill-hole observations, airborne electromagnetic 95 observations, and sea ice station data (Zhang et al, 2016b). The results revealed that the sea ice thickness data of the AO-FVCOM well captured the spatial distribution, as well as seasonal and interannual variation characteristics, of Arctic sea ice thickness data.…”

“…The simulated sea ice thickness data were from the Arctic Ocean-Finite Volume Community Ocean Model (AO-FVCOM) (Chen et al, 2009;Chen et al, 2016;Gao et al, 2011;Zhang et al, 2016a;Zhang et al, 2016b) for the period 1979-2017, with a horizontal resolution as high as 1 km in the CAA. These high-resolution data could be used to study the spatial distribution, as well as the seasonal and long-term variation characteristics, of sea ice thickness in the NWP.…”