Seasonal changes in sea ice kinematics and deformation 1 in the Pacific Sector of the Arctic Ocean in 2018 / 19 2

Arctic sea ice kinematics and deformation play significant roles in heat and momentum 14 exchange between atmosphere and ocean. However, mechanisms regulating their changes at seasonal 15 scales remain poorly understood. Using position data of 32 buoys in the Pacific sector of the Arctic 16 Ocean (PAO), we characterized spatiotemporal variations in ice kinematics and deformation for 17 autumn–winter 2018/19. In autumn, sea ice drift response to wind forcing and inertia were stronger in the 18 southern and western than in the northern and eastern parts of the PAO. These spatial heterogeneities 19 decreased gradually from autumn to winter, in line with the seasonal evolution of ice concentration and 20 thickness. Areal localization index decreased by about 50 % from autumn to winter, suggesting the 21 enhanced localization of intense ice deformation as the increased ice mechanical strength. In winter 22 2018/19, a highly positive Arctic Dipole and a weakened high pressure system over the Beaufort Sea led 23 to a distinct change in ice drift direction and an temporary increase in ice deformation. During the 24 freezing season, ice deformation rate in the northern part of the PAO was about 2.5 times that in the 25 western part due to the higher spatial heterogeneity of oceanic and atmospheric forcing in the north. 26 North–south and east–west gradients in sea ice kinematics and deformation of the PAO observed in 27 autumn 2018 are likely to become more pronounced in the future as sea ice losses at higher rates in the 28 western and southern than in the northern and western parts. 29 https://doi.org/10.5194/tc-2020-211 Preprint. Discussion started: 25 August 2020 c © Author(s) 2020. CC BY 4.0 License.


141
Two parameters were used to characterize sea ice kinematic properties. First, IWSR was used to 142 investigate the response of sea ice motion to wind forcing. Impacts of resampling wind speed and ice 143 position data at various intervals between 1 and 48 h, meridional and zonal spatial variabilities, 144 https://doi.org/10.5194/tc-2020-211 Preprint. Discussion started: 25 August 2020 c Author(s) 2020. CC BY 4.0 License. intensity of wind forcing, near-surface air temperature, and ice concentration on IWSR were assessed.

145
The data used to characterize atmospheric forcing, including Sea Level air Pressure (SLP),  (1)

164
where f0 is inertial frequency, W is earth rotation rate, and θ is latitude. Inertial frequency ranges from 165 2.01 to 1.94 cycles day −1 between 90° N and 75° N. Rotary spectra calculated from sea ice velocity using complex Fourier analysis were used to identify signals of inertial and tidal origin, both of which where N and Dt are the number and temporal interval of velocity samples, t0 and tend are the start and end 171 times of the temporal window, ux and uy are zonal and meridional ice speeds at t+0.5Dt on an orthogonal 172 geographical grid, and w is angular frequency. The IMI was defined as the amplitude at the inertial 173 frequency after the complex Fourier transformation.

175
Ice positions were used to estimate differential kinematic properties (DKPs) of the sea ice deformation 176 field. The DKPs include divergence rate (div), shear rate (shr), and total deformation rate (D) of sea 177 ice within the area enclosed by any three buoys. Following Hutchings and Hibler (2008), DKPs were 178 calculated as follows:

181
and D= √ + ℎ , 188 where L is length scale, t is sampling interval, and b and a are spatial and temporal scaling exponents, 189 which indicate decay rates of the sea ice deformation in spatial or temporal domains. To estimate 190 spatial exponent b, length scale was divided into three bins of 5-10, 10-20, and 20-40 km for the 191 CHINARE buoy cluster because only few samples were outside these bins. To estimate temporal 192 exponent a, position data were resampled at intervals of 1, 2, 4, 8, 12, 24, and 48 h. Because the 193 T-ICE buoy cluster was mostly (> 70 %) in the bin of 40-80 km, data from this cluster were 194 unsuitable for the characterization of scale effect. Space-time coupling index, c, denoting temporal 195 (spatial) dependence of the spatial (temporal) scaling exponent, can be expressed as: where b0 is a constant. The areal localization index, d15%, was used to quantify localization of the 198 strongest sea ice deformation, which is defined as the fractional area accommodating the largest 15 %

278
Factors impacting IWSR are summarized in Table 1. Impact of geographical location was significant in 279 autumn, resulting in relatively high IWSR values in the southern or western parts of the study region.  2011), who identified that the west-east gradient of sea ice motion is larger than that in the north-south direction for the south of PAO during the freezing season.

285
In summer and early autumn, consolidation of the ice field is low, and interactions between ice floes

303
The inertial oscillation of ice motion is stimulated by sudden changes in external forces, majorly due to   was low for the CHINARE cluster (Fig. 10b) because of low wind speed and infrequent changes in 343 wind direction, and despite a weakly consolidated ice field (Fig. 2). For the T-ICE cluster, both ice 344 deformation rate and ratio between ice deformation rate and wind speed decreased rapidly between 20 and thickness increased and temperature decreased. However, ice deformation rate from the CHINARE trajectory of the T-ICE cluster was much straighter than that of the CHINARE cluster. As a result, ice 374 deformation rate and its ratio to wind speed were lower for the T-ICE cluster than for CHINARE 375 cluster.

376
Ice deformation rates obtained from the CHINARE buoy cluster at three representative lengths of 7.5, 377 15, and 30 km were estimated using Eq. (6). Influence of synoptic processes, e.g., cyclonic activities 378 https://doi.org/10.5194/tc-2020-211 Preprint. Discussion started: 25 August 2020 c Author(s) 2020. CC BY 4.0 License. and/or changes in wind direction, was filtered out by using a monthly window. Figure 11 shows that

404
The temporal scaling exponent a also exhibited a strong dependence on spatial scale (Fig. 13). The

414
The areal localization index denotes the area with the highest deformation. It had a strong dependence 415 on temporal scale, and increased linearly as logarithm of the temporal scale increased (Fig. 14), which 416 implies that the localization of ice deformation would be underestimated by the observations or models

436
Hutter and Losch, 2020). Dependence of the ratio of ice speed to wind speed on resampling frequency 437 implies that temporal resolution should be considered carefully when using wind forcing data to 438 parameterize or simulate sea ice drift (e.g., Shu et al., 2012).

439
The PAO is the region with the most significant summer sea ice loss across the entire Arctic Ocean

445
Pronounced loss of sea ice in the southern and western parts of the study region resulted in an inertial 446 signal and ice motion response to wind forcing that were stronger than those found to the north and the