Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions

Arctic sea ice drift motion affects the global material balance, energy exchange and climate change and seriously affects the navigational safety of ships along certain channels. Due to the Arctic's special geographical location and harsh natural conditions, observations and broad understanding of the Arctic sea ice motion are very limited. In this study, sea ice motion data released by the National Snow and Ice Data Center (NSIDC) were used to analyze the climatological, spatial and temporal characteristics of the Arctic sea ice drift from 1979 to 2018 and to understand the multiscale variation characteristics of the three major Arctic sea ice drift patterns. The empirical orthogonal function (EOF) analysis method was used to extract the three main sea ice drift patterns, which are the anticyclonic sea ice drift circulation pattern on the scale of the Arctic basin, the average sea ice transport pattern from the Arctic Ocean to the Fram Strait, and the transport pattern moving ice between the Kara Sea (KS) and the northern coast of Alaska. By using the ensemble empirical mode decomposition (EEMD) method, each temporal coefficient series extracted by the EOF method was decomposed into multiple timescale sequences. We found that the three major drift patterns have four significant interannual variation periods of approximately 1, 2, 4 and 8 years. Furthermore, the second pattern has a significant interdecadal variation characteristic with a period of approximately 19 years, while the other two patterns have no significant interdecadal variation characteristics. Combined with the atmospheric and oceanic geophysical variables, the results of the correlation analysis show that the first EOF sea ice drift pattern is mainly related to atmospheric environmental factors, the second pattern is related to the joint action of atmospheric and oceanic factors, and the third pattern is mainly related to oceanic factors. Our study suggests that the ocean environment also has a strong correlation with sea ice movement. Especially for some sea ice transport patterns, the correlation even exceeds atmospheric forcing.

Interdecadal variations of different types of summer heatwaves in Northeast China associated with AMO and PDO

The summer heatwaves (HWs) in Northeast China (NEC) during 1961-2016 can be classified into two types, namely wave-train HWs and blocking HWs based on the hierarchical clustering algorithm by using ERA-Interim daily reanalysis datasets. Wave-train HWs occurred accompanied by eastward-moving wave trains with a "-+-+" structure formed over Eurasia, while the blocking HWs occurred with blocking circulation anomalies over Eurasia. In general, the blocking HWs could cause the positive temperature anomalies in NEC to last longer than wave-train HWs. During the period from 1961 to 2016, the wave-train HWs experienced an interdecadal variation from less to more, while the blocking HWs experienced interdecadal variations of less-more-less. Regression analysis and information flow indicate that the interdecadal variation of the wave-train HWs is associated with Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), while the interdecadal variation of the blocking HWs is more likely associated with PDO. The positive phase of AMO (negative phase of PDO) could increase the wave-train (blocking) HWs by strengthening the zonal wave-train similar to the Silk Road pattern (the arched wave-train like the polar-Eurasian pattern). The observed results are in agreement with the numerical experiments with the NCAR Community Atmosphere Model version 5.3.

Multiscale Variations in Arctic Sea Ice Motion, Links to Atmospheric and Oceanic Conditions

Arctic sea ice drift motion affects the global material balance, energy exchange and climate change and seriously affects the navigation safety of ships along certain channels. Due to the Arctic's special geographical location and harsh natural conditions, observations and broad understanding of the Arctic sea ice motion of sea ice are very limited. In this study, sea ice motion data released by the National Snow and Ice Data Center (NSIDC) were used to analyze the climatological, spatial and temporal characteristics of the Arctic sea ice drift from 1979 to 2018 and to understand the multiscale variation characteristics of the three major Arctic sea ice drift patterns. The results show that the sea ice drift velocity is greater in winter than in summer. The empirical orthogonal function (EOF) analysis method was used to extract the three main sea ice drift patterns, which are the anticyclonic sea ice drift circulation pattern on the scale of the Arctic basin, the average sea ice transport pattern from the Arctic Ocean to the Fram Strait and the transport pattern moving ice between the Kara Sea (KS) and the northern coast of Alaska. By using the ensemble empirical mode decomposition (EEMD) method, each temporal coefficient series extracted by the EOF method was decomposed into multiple time-scale sequences. We found that the three major drift patterns have 4 significant interannual variation periods of approximately 1, 2, 4 and 8 years. Furthermore, the second pattern has a significant interdecadal variation characteristic with a period of approximately 19 years, while the other two patterns have no significant interdecadal variation characteristics. Combined with the atmospheric and oceanic physical environmental data, the results of the correlation analysis show that the first EOF sea ice drift pattern is mainly affected by atmospheric environmental factors, the second pattern is affected by the joint action of atmospheric and oceanic factors, and the third pattern is mainly affected by oceanic factors. Our study suggests that the ocean environment also has a significant influence on sea ice movement. Especially for some sea ice transport patterns, the influence even exceeds atmospheric forcing.