Seasonal and inter-annual variations of the primary types of the Arctic sea-ice drifting patterns

Abstract Monthly mean sea ice motion vectors and monthly mean sea level pressure （SLP） for the period of 1979-2006 are investigated to understand the spatial and temporal changes of Arctic sea-ice drift. According to the distinct differences in monthly mean ice velocity field as well as in the distribution of SLP, there are four primary types in the Arctic Ocean： Beaufort Gyre＋Transpolar Drift, Anticyclonic Drift, Cyclonic Drift and Double Gyre Drift. These four types account for 81% of the total, and reveal distinct seasonal variations. The Cyclonic Drift with a large-scale anticlockwise ice motion pattern trends to prevail in summer while the Anticyclonic Drift with an opposite pattern trends to prevail in winter and spring. The prevailing seasons for the Beaufort Gyre＋Transpolar Drift are spring and autumn, while the Double Gyre Drift trends to prevail in winter, especially in Feb- ruary. The annual occurring times of the Anticyclonic Drift and the Cyclonic Drift are closely correlated with the yearly mean Arc- tic Oscillation （AO） index, with a correlation coefficient of -0.54 and 0.54 （both significant with the confident level of 99%）, re- spectively. When the AO index stays in a high positive （negative） condition, the sea-ice motion in the Arctic Ocean demonstrates a more anticlockwise （clockwise） drifting pattern as a whole. When the AO index stays in a neutral condition, the sea-ice motion becomes much more complicated and more transitional types trend to take place.

Evaluation of Arctic Sea Ice Drift and its Relationship with Near-surface Wind and Ocean Current in Nine CMIP6 Models from China

The simulated Arctic sea ice drift and its relationship with the near-surface wind and surface ocean current during 1979-2014 in nine models from China that participated in the sixth phase of the Coupled Model Intercomparison Project(CMIP6)are examined by comparison with observational and reanalysis datasets.Most of the models reasonably represent the Beaufort Gyre(BG)and Transpolar Drift Stream(TDS)in the spatial patterns of their long-term mean sea ice drift,while the detailed location,extent,and strength of the BG and TDS vary among the models.About two-thirds of the models agree with the observation/reanalysis in the sense that the sea ice drift pattern is consistent with the near-surface wind pattern.About the same proportion of models shows that the sea ice drift pattern is consistent with the surface ocean current pattern.In the observation/reanalysis,however,the sea ice drift pattern does not match well with the surface ocean current pattern.All nine models missed the observational widespread sea ice drift speed acceleration across the Arctic.For the Arctic basin-wide spatial average,five of the nine models overestimate the Arctic long-term(1979-2014)mean sea ice drift speed in all months.Only FGOALS-g3 captures a significant sea ice drift speed increase from 1979 to 2014 both in spring and autumn.The increases are weaker than those in the observation.This evaluation helps assess the performance of the Arctic sea ice drift simulations in these CMIP6 models from China.

Extreme Value Estimation of Beaufort Sea Ice Dynamics Driven by Global Wind Effects

The purpose of the present study is to investigate the extreme values of the ice drift speed,which are also considered in the light of the magnitude of the simultaneous wind speed.The relationship between wind speed and ice drift speed is studied.The long-term ice drift data is collected by using local subsurface measurements based on acoustic Doppler current profilers(ADCP)in the Beaufort Sea during the period of 2006-2017.Upward-looking sonars(ULS)are deployed in order to observe the ice thickness as well as to identify events that correspond to open water conditions.The relationship between the ice drift speed and the wind speed is also investigated.It is found that the magnitude of the average ice drift speed is approximately 2.5%of the wind speed during the winter season.Estimation of the extreme values of the ice drift speed is studied by application of the average conditional exceedance rate(ACER)method.It is found that the extreme ice drift speed during the ice melt season(i.e.the summer season)is approximately20%-30%higher than that during the ice growth season(i.e.the winter season).The extreme ice drift speed can be effectively estimated based on the 2.5%wind speed.Moreover,the extreme ice drift speed can be obtained based on the extreme values of 2.5%of the wind speed based on multiplying with an amplification factor which varies in the range from 1.7 to 2.0 during the growth season,corresponding to increasing return periods of 10,25,50 and 100years.

Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018

In August 2018,a remarkable polynya was observed off the north coast of Greenland,a perennial ice zone where thick sea ice cover persists.In order to investigate the formation process of this polynya,satellite observations,a coupled iceocean model,ocean profiling data,and atmosphere reanalysis data were applied.We found that the thinnest sea ice cover in August since 1978(mean value of 1.1 m,compared to the average value of 2.8 m during 1978-2017) and the modest southerly wind caused by a positive North Atlantic Oscillation(mean value of 0.82,compared to the climatological value of-0.02) were responsible for the formation and maintenance of this polynya.The opening mechanism of this polynya differs from the one formed in February 2018 in the same area caused by persistent anomalously high wind.Sea ice drift patterns have become more responsive to the atmospheric forcing due to thinning of sea ice cover in this region.

Variation of Sea Ice Temperature from CHINARE 2003 and Its Application on Sea Ice Model Evaluation

Variation of vertical profiles of sea ice temperature and adjacent atmosphere and ocean temperatures were measured by ice drifting buoys deployed in the northeast Chukchi Sea as part of the 2003 Chinese Arctic Research Expedition.The buoy observations (September 2003 to February 2005) show that the cooling of the ice began in late September,propagated down through the ice,reaching the bottom of the ice in December,and continued throughout the winter.In winter 2003/04,some obvious warmings were observed in the upper portion of the ice in response to major warmings in the overlying atmosphere associated with the periodicity of storms in the northeast Chukchi Sea.It is found that the melt season at the buoy site in 2004 was about 15% longer than normal.The buoy observed vertical ice temperature profiles were used as a diagnostic for sea ice model evaluation.The results show that the simulated ice temperature profiles have large discrepancies as compared with the observations.

Numerical Simulation of Turret Moored Ship Performance in Variable Ice Drift Based on Heading Control

The frequent change in ice drift direction poses a significant challenge for turret moored ship in ice. Variability in ice drift is mainly caused by the winds and currents. To solve this problem, a new method with numerical simulation based on heading control is applied to reduce the risk of operation of The Arctic Tandem Offloading Terminal(ATOT),which includes an offloading icebreaker(OIB) moored to a submerged turret and a shuttle tanker moored at the stern of the OIB in this paper. An icebreaking tanker, MT Uikku, was modeled in a simulation program. Then the level ice load on the tanker was calculated with different ice thicknesses and drift speeds, after which a heading controller assisted with mooring system is used to simulate the horizontal motion of the tanker under the ice action.

Validation of the Model Obtained Ice Drift Fields Based on Satellite Derived Data Using a Vector Correlation Indexes in an Invariant Form

The article considers results of validation of a sea ice modeling data using a novel methodology on the vector algebra theoretical bases. The vectorial-algebraic approach developed and was in use in Russia for an analysis of time series of vector values--wind, currents ice drift etc. The vectorial-algebraic approach allows significantly compressing the initial information and most adequately describes the vector time series of full-scale and model data restricted by a set of statistical characteristics in the invariant form. For an express analysis of correlation of the modeling data and in situ （or satellite derived） data a system of simplified correlation invariant indicators was used. This methodology was applied for validation of vector values fields at the first time. The method gives an opportunity to describe a field of a vector correlation and detect an areas with different levels of correlation between model and in situ （or other reference） vector data. The work was carried out in the frame of the My Ocean Project （FP7）.

Assessing CMIP6 simulations of Arctic sea ice drift:Role of near-surface wind and surface ocean current in model performance

Sea ice drift is a critical parameter for understanding the rapid changes in Arctic sea ice.Since the release of the Coupled Model Intercomparison Project Phase 6(CMIP6),there has been a lack of quantitative analysis regarding CMIP6's simulation of Arctic sea ice drift.This study aims to assess the simulated Arctic sea ice drift from 1979 to 2014 by fifteen CMIP6 models against recent satellite retrievals,utilizing various quantitative indices.Additionally,the influence of near-surface wind and surface ocean current on model performance is further analyzed.The CMIP6 models capture several aspects of the observed Arctic sea ice drift climatology and variability.The seasonal patterns of sea ice drift speed in all models exhibit similarities with the observed data,and the models agree with the evaluation datasets,indicating that the seasonal evolution of sea ice drift corresponds to near-surface wind patterns.However,notable discrepancies are identified.All models overestimate sea ice drift speed,exceeding the observational data by 36%e97%.Fourteen out of fifteen models display larger seasonal variability(ranging from 0.74 to 1.28 km d^(-1))compared to the observed data(0.54 km d^(-1)).Seven out of fifteen models exhibit a significant increasing trend in annual sea ice drift speed,similar to the observed trend of 0.58 km d^(-1) per decade,but with weaker trends(ranging from 0.11 to 0.33 km d^(-1) per decade).The remaining eight models reveal no statistically significant trend.The potential causes of such biases were further explored in this study.It suggests that the overestimation of sea ice drift speed in the models might be primarily attributed to the overestimation of near-surface wind speeds and their influence on sea ice drift speed.The models'overestimation of seasonal variability in near-surface wind speeds may account for the overestimation of seasonal variability in sea ice drift.The models'inability to represent the trend in sea ice drift speed may result from their failure to simulate an increasing trend in surface ocean current speed.

MEASURING SEA ICE DRIFT VIA CROSS-CORRELATION OF RADAR ICE IMAGES

The motion of sea ice has a great effect on winter navigation, and oil fieldexploration in the Bohai Sea. It is very important to measure the ice drift accurately andefficiently. As a practical technique, radar imagery has been used for sea ice monitoring andforecasting for a long time.Combining with the radar imagery and cross-correlation technique, a newmeasurement method based on the cross-correlation of radar ice images is specified in this paper toobtain full field measurement of sea ice drift. The theory and fast implementation ofcross-correlation are presented briefly in the paper, including the filtering method to modify theinvalid vectors. To show deeply the validity of the present method, the velocity maps of sea icedrift are provided in the paper, which are calculated from the radar images grabbed in the LiaodongGulf. The comparison with the traditional tracing method is also conducted.