Abstract Atmospheric circulation associated with the Arctic dipole (AD) pattern plays a crucial role in modulating the variations of summertime sea ice concentration (SIC) within the Pacific Arctic sector (PAS). Based on reanalysis data and satellite observations, we found that the impacts of atmospheric circulation associated with a positive AD (AD+) on SIC change over different regions of the PAS [including the East Siberian Sea (ESS), Beaufort and Chukchi Seas (BCS), and Canadian Arctic Archipelago (CAA)] are dependent on the phase shifts of Pacific decadal oscillation (PDO). Satellite observations reveal that SIC anomalies, influenced by AD+ during PDO− relative to that during PDO+, varies significantly in summer by 4.9%, −7.3%, and −6.4% over ESS, BCS, and CAA, respectively. Overall, the atmospheric anomalies over CAA and BCS in terms of specific humidity, air temperature, and thereby downward longwave radiation (DLR), are enhanced (weakened) in the atmospheric conditions associated with AD+ during PDO− (PDO+). In these two regions, the larger (smaller) increases in specific humidity and air temperature, associated with AD+ during PDO− (PDO+), are connected to the increased (decreased) poleward moisture flux, strengthened (weakened) convergence of moisture and heat flux, and in part to adiabatic heating. As a consequence, the DLR and surface net energy flux anomalies over the two regions are reinforced in the atmospheric scenarios associated with AD+ during PDO− compared with that during PDO+. Therefore, smaller SIC anomalies are identified over CAA and BCS in the cases related to AD+ during PDO− than during PDO+. Essentially, the changes of the DLR anomaly in CAA and BCS are in alignment with geopotential height anomalies, which are modulated by the anticyclonic circulation pattern in association with AD+ during varying PDO phases. In contrast, the SIC changes over ESS is primarily attributed to the variations in mechanical wind forcing and sea surface temperature (SST) anomalies. The cloud fraction anomalies associated with AD+ during different PDO phases are found not to be a significant contributor to the variations of sea ice anomaly in the studied regions. Given the oscillatory nature of PDO, we speculate that the recent shift to the PDO+ phase may temporarily slow the observed significant decline trend of the summertime SIC within PAS of the Arctic.
Abstract A cyclone is an intensive synoptic activity that occurs frequently over Baffin Bay. By modifying the large‐scale distribution pattern of sea level pressure, a passing cyclone can serve as an important regulator of sea ice outflow via the Davis Strait. We obtain a nearly 40‐year‐long record (1979/1980–2017/2018) of the sea ice area flux (SIAF) through the Davis Strait and Arctic cyclone activities in winter. A case study and statistical results indicate that the sea ice concentration and motion fields can be greatly altered by the occurrence of cyclones, thereby contributing to changes in sea ice export. Moreover, the effects of cyclones on sea ice export in Baffin Bay are dependent on the spatial distribution pattern of the storms. In terms of the cyclone center count and intensity, the key regions with significant impacts on sea ice export out of Baffin Bay are identified, one around Baffin Island (80°W–60°W, 60°N–70°N) and the other over the southern Labrador Peninsula (70°W–50°W, 40°N–60°N). A robust correlation exists between the winter‐accumulated SIAF via the Davis Strait and the average winter cyclone intensity (center count) in the critical regions with R = −0.57 (+0.49), affirming the vital role of cyclone activity in modulating the interannual variability of sea ice export in Baffin Bay.
Abstract. The satellite observations unveiled that the July sea ice extent of the Arctic shrank to the lowest value in 2020 since 1979, with a major ice retreat in the Eurasian shelf seas including Kara, Laptev, and East Siberian Seas. Based on the ERA-5 reanalysis products, we explored the impacts of warm and moist air-mass transport on this extreme event. The results reveal that anomalously high energy and moisture converged into these regions in the spring months (April to June) of 2020, leading to a burst of high moisture content and warming within the atmospheric column. The convergence is accompanied by local enhanced downward longwave radiation and turbulent fluxes, which is favorable for initiating an early melt onset in the areas with severe ice loss. Once the melt begins, solar radiation played a decisive role in leading to further sea ice depletion due to ice-albedo positive feedback. The typical trajectories of the synoptic cyclones that occurred on the Eurasian side in spring 2020 agree well with the path of atmospheric flow. Assessments suggest that variations in characteristics of the spring cyclones are conducive to the severe melt of sea ice. We argue that large-scale atmospheric circulation and synoptic cyclones act in concert to trigger the exceptional poleward transport of total energy and moisture from April to June to cause this new record minimum of sea ice extent in the following July.
Abstract. In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Pacific-Arctic sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the sea ice concentration (SIC) anomaly correlation coefficient (ACC) between predictions and observations, increased by 32â% in the Bering Sea and 18â% in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. SIC trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. We find that subsurface ocean heat content (OHC) provides a crucial source of prediction skill in all seasons, especially in the cold season, and adding sea ice thickness (SIT) to the regional Markov model has a substantial contribution to the prediction skill in the warm season but a negative contribution in the cold season. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model.
Satellite remote sensing provides new insight into the large-scale changes within the Arctic sea ice cover. In this study, satellite-derived sea ice parameters (thickness and age) were explored to investigate age-dependent Arctic sea ice volume changes. Between 2003-2008 (ICESat) and 2011-2015 (CyroSat-2), Arctic Ocean sea ice experienced a net depletion of roughly ${\text{4.68}}\times {\text{10}}^{3}\,{\text{km}}^{3}$ during autumn (October-November) and about 87% (or ${\text{4.11}}\times {\text{10}}^{3}\,{\text{km}}^{3}$ ) is caused by the removal in multiyear ice (two years and older). In spring (February-March), the net ice depletion amounts to ${\text{1.46}}\times {\text{10}}^{3}\,{\text{km}}^{3}$, with the multiyear ice loss of ${\text{3.74}}\times {\text{10}}^{3}\,{\text{km}}^{3}$ and seasonal ice increment of ${\text{2.24}}\times {\text{10}}^{3}\,{\text{km}}^{3}$. Among multiyear ice loss, about 74% (autumn) and 93% (spring) of the loss were attributable to the depletion of the oldest ice type (5 years and older). Analyses also affirm that the marvelous volume loss of multiyear ice during cold months (October-May) in 2006/2007 and 2011/2012, along with the low replenishment of perennial ice as noted in the following autumns in 2007 and 2012, plays a major role in leading to a younger Arctic sea ice cover. Consequently, these processes together favors for the overall substantial volume loss observed in the Arctic sea ice cover.
Abstract. Sea ice export through Baffin Bay plays a vital role in modulating the meridional overturning process in the downstream Labrador Sea. In this study, satellite-derived sea ice products are explored to obtain the sea ice flux (SIF) through three passages (referred to as A, B, and C for the north, middle, and south passages, respectively) of Baffin Bay. Over the period 1988–2015, the average annual (October–September) sea ice area export is 555 × 103 km2, 642 × 103 km2, and 551 × 103 km2 through passages A, B, and C, respectively. These amounts are less than that observed through the Fram Strait (FS, 707 × 103 km2). Clear increasing trends in annual sea ice export on the order of 53.1 × 103 km2/de and 43.2 × 103 km2/de are identified at passages A and B, respectively. The trend at the south passage (C), however, is slightly negative (−13.3 × 103 km2/de). The positive trends in annual SIF at A and B are primarily attributable to the increase during winter months, which is triggered by the accelerated sea ice motion (SIM) and partly compensated by the reduced sea ice concentration (SIC). During the summer months, the sea ice export through each Baffin Bay passage usually presents a negative trend, primarily because of the decline in SIM and it is further enhanced by a dramatic decrease in SIC. A significant positive trend in the net SIF (i.e. net ice inflow) is found for between the passages A (or B) and C at 54.5 (or 64.2) × 103 km2/de. Therefore, Baffin Bay may have presented a greater convergence of ice. Overall, the connection between Baffin Bay sea ice export and the North Atlantic Oscillation (NAO) is tenuous, although the correlation is sensitive to variations in the selected time period. In contrast, the association with the cross-gate sea level pressure difference (SLPD) is robust in Baffin Bay (R = 0.69–0.71 depending on the passages), but relatively weaker compared with that in the FS (R = 0.74). Baffin Bay is bounded by Baffin Island to the west and Greenland to the east, thus, sea ice drift is not converted to the free state observed in the FS.
Abstract. The Arctic sea ice is suffering dramatic retreating in summer and fall, which has far-reaching consequences on the global climate and commercial activities. Accurate seasonal sea ice predictions are significant in inferring climate change and planning commercial activities. However, seasonally predicting the summer sea ice encounters a significant obstacle known as the spring predictability barrier (SPB): predictions made later than May demonstrate good skill in predicting summer sea ice, while predictions made on or earlier than May exhibit considerably lower skill. This study develops a transformer-based deep-learning model, SICNetseason (V1.0), to predict the Arctic sea ice concentration on a seasonal scale. Including spring sea ice thickness (SIT) data in the model significantly improves the prediction skill at the SPB point. A 20-year (2000–2019) testing demonstrates that the detrended anomaly correlation coefficient (ACC) of Sep. sea ice extent (sea ice concentration > 15 %) predicted by our model at May/Apr. is improved by 7.7 %/10.61 % over the ACC predicted by the state-of-the-art dynamic model from the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with the anomaly persistence benchmark, the mentioned improvement is 41.02 %/36.33 %. Our deep learning model significantly reduces prediction errors of Sep.'s sea ice concentration on seasonal scales compared to ECMWF and Persistence. The spring SIT data is key in optimizing the SPB, contributing to a more than 20 % ACC enhancement in Sep.'s SIE at four to five months lead predictions. Our model achieves good generalization in predicting the Sep. SIE of 2020–2023.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)