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Sea ice at the North Pole is vital to global climate dynamics. However, accurately forecasting sea ice poses a significant challenge due to the intricate interaction among multiple variables. Leveraging the capability to integrate multiple inputs and powerful performances seamlessly, many studies have turned to neural networks for sea ice forecasting. This paper introduces a novel deep architecture named Unicorn, designed to forecast weekly sea ice. Our model integrates multiple time series images within its architecture to enhance its forecasting performance. Moreover, we incorporate a bottleneck layer within the U-Net architecture, serving as neural ordinary differential equations with convolutional operations, to capture the spatiotemporal dynamics of latent variables. Through real data analysis with datasets spanning from 1998 to 2021, our proposed model demonstrates significant improvements over state-of-the-art models in the sea ice concentration forecasting task. It achieves an average MAE improvement of 12% compared to benchmark models. Additionally, our method outperforms existing approaches in sea ice extent forecasting, achieving a classification performance improvement of approximately 18%. These experimental results show the superiority of our proposed model. A preprint version of this work is available at this url https://arxiv.org/abs/2405.03929 .

The Arctic Ocean has a uniquely complex system associated with tightly coupled ocean–ice–atmosphere–land interactions. The Arctic Ocean is considered to be highly susceptible to global climate change, with the potential for dramatic environmental impacts at both regional and global scales, and its spatial differences particularly have been exacerbated. A comprehensive understanding of Arctic Ocean environmental responses to climate change thus requires classifying the Arctic Ocean into subregions that describe spatial homogeneity of the clusters and heterogeneity between clusters based on ocean physical properties and implementing the regional-scale analysis. In this study, utilizing the long-term optimum interpolation sea surface temperature (SST) datasets for the period 1982–2023, which is one of the essential indicators of physical processes, we applied the K-means clustering algorithm to generate subregions of the Arctic Ocean, reflecting distinct physical characteristics. Using the variance ratio criterion, the optimal number of subregions for spatial clustering was 12. Employing methods such as information mapping and pairwise multi-comparison analysis, we found that the 12 subregions of the Arctic Ocean well represent spatial heterogeneity and homogeneity of physical properties, including sea ice concentration, surface ocean currents, SST, and sea surface salinity. Spatial patterns in SST changes also matched well with the boundaries of clustered subregions. The newly identified physical subregions of the Arctic Ocean will contribute to a more comprehensive understanding of the Arctic Ocean’s environmental response to accelerating climate change.

Polar regions are too harsh to be continuously observed using ocean color (OC) sensors because of various limitations due to low solar elevations, ice effects, peculiar phytoplankton photosynthetic parameters, optical complexity of seawater and persistence of clouds and fog. Therefore, the OC data undergo a quality-control process, eventually accompanied by considerable data loss. We attempted to reconstruct these missing values for chlorophyll-a concentration (CHL) data using a machine-learning technique based on multiple datasets (satellite and reanalysis datasets) in the Ross Sea, Antarctica. This technique—based on an ensemble tree called random forest (RF)—was used for the reconstruction. The performance of the RF model was robust, and the reconstructed CHL data were consistent with satellite measurements. The reconstructed CHL data allowed a high intrinsic resolution of OC to be used without specific techniques (e.g., spatial average). Therefore, we believe that it is possible to study multiple characteristics of phytoplankton dynamics more quantitatively, such as bloom initiation/termination timings and peaks, as well as the variability in time scales of phytoplankton growth. In addition, because the reconstructed CHL showed relatively higher accuracy than satellite observations compared with the in situ data, our product may enable more accurate planktonic research.

PurposeTo evaluate long-term outcomes of maintenance of lacrimal silicone stent for the management of functional epiphora after anatomically patent external dacryocystorhinostomy (DCR).MethodsWe retrospectively reviewed the medical records of 101 eyes of 75 patients who were diagnosed to have functional epiphora after external DCR from 2005 to 2014. Functional epiphora was defined as epiphora that persisted or recurred even after patent DCR confirmed by a lacrimal irrigation test. Secondary silicone intubation was indicated when the patients wanted a further intervention. The stent was intended to be kept in situ unless there was a stent-related complication or the patient wanted removal.ResultsIn total, 34 of 75 patients (45.3%, 52 eyes) who agreed to the intervention underwent secondary silicone intubation. The success rates at 1, 3, and 5 years after surgery were 96.2%, 75.5%, and 70.2%, respectively. At the final follow-up (mean 72.7 ± 26.4 months), 32 (61.5%) eyes chose to retain the silicone tube: silicone stent was well maintained without epiphora and complications once inserted in 18 eyes (34.6%), whereas tube replacement was needed in 14 eyes (26.9%) because of nasal crust or whitish plaque formation on the tube surface. In 13 cases (25.0%), silicone stent was removed because of tube-related complications, and the most common complication was canaliculitis (n = 8, 15.4%).ConclusionsSecondary intubation and maintenance of the stent is an effective and simple procedure for functional epiphora. The main obstacle to long-term maintenance is tube-associated canaliculitis.

The most problematic issue in the ocean color application is the presence of heavy clouds, especially in polar regions. For that reason, the demand for the ocean color application in polar regions is increased. As a way to overcome such issues, we conducted the reconstruction of the chlorophyll-a concentration (CHL) data using the machine learning-based models to raise the usability of CHL data. This analysis was first conducted on a regional scale and focused on the biologically-valued Cape Hallett, Ross Sea, Antarctica. Environmental factors and geographical information associated with phytoplankton dynamics were considered as predictors for the CHL reconstruction, which were obtained from cloud-free microwave and reanalysis data. As the machine learning models used in the present study, the ensemble-based models such as Random forest (RF) and Extremely randomized tree (ET) were selected with 10-fold cross-validation. As a result, both CHL reconstructions from the two models showed significant agreement with the standard satellite-derived CHL data. In addition, the reconstructed CHLs were close to the actual CHL value even where it was not observed by the satellites. However, there is a slight difference between the CHL reconstruction results from the RF and the ET, which is likely caused by the difference in the contribution of each predictor. In addition, we examined the variable importance for the CHL reconstruction quantitatively. As such, the sea surface and atmospheric temperature, and the photosynthetically available radiation have high contributions to the model developments. Mostly, geographic information appears to have a lower contribution relative to environmental predictors. Lastly, we estimated the partial dependences for the predictors for further study on the variable contribution and investigated the contributions to the CHL reconstruction with changes in the predictors.

The northern Bering Sea and the southern Chukchi Sea are undergoing rapid regional biophysical changes in connection with the recent extreme climate change in the Arctic. The ice concentration in 2018 was the lowest since observations began in the 1970s, due to the unusually warm southerly wind in winter, which continued in 2019. We analyzed the characteristics of spring phytoplankton biomass distribution under the extreme environmental conditions in 2018 and 2019. Our results show that higher phytoplankton biomass during late spring compared to the 18-year average was observed in the Bering Sea in both years. Their spatial distribution is closely related to the open water extent following winter-onset sea ice retreat in association with dramatic atmospheric conditions. However, despite a similar level of shortwave heat flux, the 2019 springtime biomass in the Chukchi Sea was lower than that in 2018, and was delayed to summer. We confirmed that this difference in bloom timing in the Chukchi Sea was due to changes in seawater properties, determined by a combination of northward oceanic heat flux modulation by the disturbance from more extensive sea ice in winter and higher surface net shortwave heat flux than usual.

The multi-temporal scales of two physical characteristics (areas and occurrence time) of the Ross Sea Polynya (RSP) in Antarctica were analysed using a sea-ice concentration data set (1979-2014) derived from the Scanning Multichannel Microwave Radiometer, the Special Sensor Microwave Imager and Sensor Microwave Imager Sounder. Then, the Ensemble Empirical Mode Decomposition (EEMD) was applied to the data sets to decompose signals into finite numbers of intrinsic mode functions and a residual mode: long time trend. This approach allowed us to understand the long-term variability of the RSP area and occurrence in response to atmospheric forcing through teleconnections between low and high latitudes by comparing the Nino3.4 and Southern Annular Mode (SAM) indices. The nonlinear trend of the RSP areas derived from the EEMD residual had an upward trending shift in the early 1990s and was fairly consistent with the nonlinear trend of Nino3.4. However, the trend of RSP occurrence time progressively increased and had a significant effect on the long time scale. The trend of the RSP area is significantly correlated (+0.98) with the ratio of the trend of the meridional to zonal wind components related with the nonlinearity of Nino3.4, suggesting that meridional wind stress dominated the changes of the polynya area in the Ross Sea. In addition, the nonlinear trends between the SAM and RSP occurrence time show a strong positive correlation, contributing to the earlier onset of polynya expansion and delayed connection with the open ocean owing to enhanced southerly winds.

The occurrence of coastal upwelling is influenced by the intensity and duration of sea surface wind stress and geophysical components such as vertical stratification, bottom topography, and the entrainment of water masses. In addition, strong alongshore currents can drive upwelling. Accordingly, this study analyzes how wind stress and ocean currents contribute to changing coastal upwelling along the southwest coast of the East Sea (Japan Sea), which has not yet been reported quantitatively. This study aims to estimate each geophysical factor affecting upwelling processes using the Upwelling Age index. The index assesses the major contributors to the upwelling process using the relationship between physical forcing and upwelling water fraction estimated from shipboard hydrographic data from January 1993 to October 2018. These findings reveal that wind-driven upwelling was dominant off the northern coast. In contrast, current-driven upwelling prevailed off the southern coast. These results suggest that persistent alongshore currents through the Korea Strait make the southern region a prolific upwelling area. Accordingly, it can shed light on the mechanisms of coastal upwelling in the study area, which is crucial for understanding the influence of physical forces on ocean ecosystems.

Antarctic continental margin sediments are eroded from the shelf and transported to the slope/rise in association with changing ice sheet configuration. Understanding the dynamics of this transport pathway is important for utilizing distal deep-sea sedimentary archives to determine past changes in the Antarctic ice sheet. However, these connections are poorly understood. Here we present multi-proxy records of two sediment cores (BS17-GC01 and BS17-GC02) from the Bellingshausen Sea continental rise, to explore relationships between depositional regime and ice sheet dynamics. Two cores show depositional/sedimentological variations on glacial-interglacial scales. Biogenic sediments were deposited during MIS 1, 5, and 7 under open ocean conditions. Glacial to deglacial sediments were laminated as a result of varying intensity of bottom currents. Terrestrially derived sediments are inferred to be transported from shelf both as grounded ice advanced during glacial expansion, and as ice retreated during deglacial periods. Sediment color shifted to brown after deglacial periods with high Mn/Ti and occurrence of bioturbation, indicating increasing bottom water oxygenation in the study area. Since surface water production started to increase from deglacial periods, we infer increased bottom water oxygenation in this setting is due to ventilation (i.e., Antarctic Bottom Water (AABW) formation), implying that AABW formation was increased during interglacial periods from deglacial period whereas was decreased during glacial periods. Thus, sedimentary/depositional changes in BS17-GC01 and BS17-GC02 are closely linked to ice sheet dynamics during the late Quaternary.

Reduced amounts of aerosols blowing into the Yellow Sea (YS), owing to the temporary lockdown of factories in China during COVID-19, resulted in a 15% decrease in spring chlorophyll- a concentration (CHL) in March 2020 compared to its mean March values from 2003 to 2021. Particularly, the effect of land-based AOD is insignificant compared with that of atmospheric aerosols flowing into the YS, as indicated by the currents and wind directions. Hence, the main objective of this study was to understand the relationship between atmospheric aerosols and CHL by quantitatively considering relevant environmental changes using a Random Forest (RF) algorithm. Various input physical forcing variables to RF were employed, including aerosol optical depth (AOD), sea surface temperature (SST), mixed layer depth (MLD), wind divergence (WD), and total precipitation (TP). From the RF-based analysis, we estimated the relative contribution of each physical forcing variable to the difference in CHL during and after the COVID-19 lockdown period. The sensitivity of the RF model to changes in aerosol levels indicated positive effects of increased amounts of aerosols during spring blooms. Additionally, we calculated the quantitative contribution of aerosols to CHL changes. When SST was warmer and TP was lower than their climatology in March 2020, CHL increased by 0.22 mg m -3 and 0.02 mg m -3 , respectively. Conversely, when MLD became shallower and AOD was lower than their climatology, CHL decreased as much as 0.01 mg m -3 and 0.20 mg m -3 . Variations in WD caused no significant change in CHL. Overall, the specific estimations for reduced spring blooms were caused by a reduction in aerosols during the COVID-19 lockdown period. Furthermore, the RF developed in this study can be used to examine CHL changes and the relative role of significant environmental changes in biological blooms in the ocean for any normal year.