Phaeocystis aggregates in the ice-free region. Using microbial source tracking, we found that ice-covered regions were also associated with more surface-born microbial clades exported to the deep sea. Taken together, our results showed that ice-covered regions are responsible for high export efficiency and provide strong vertical microbial connectivity. Therefore, continuous sea-ice loss may decrease the vertical export efficiency, and thus the pelagic-benthic coupling, with potential repercussions for Arctic deep-sea ecosystems.Arctic Ocean sea ice cover is shrinking due to warming. Long-term sediment trap data shows higher export efficiency of particulate organic carbon in regions with seasonal sea ice compared to regions without sea ice. To investigate this sea-ice enhanced export, we compared how different early summer phytoplankton communities in seasonally ice-free and ice-covered regions of the Fram Strait affect carbon export and vertical dispersal of microbes. In situ collected aggregates revealed two-fold higher carbon export of diatom-rich aggregates in ice-covered regions, compared to  Fadeev et al. explore carbon export dynamics along the water column using microscopic analysis, 16S rRNA gene amplicon sequencing, and physical modeling of data from long-term sediment traps in the Fram Strait. Their results indicate that larger aggregates from sea-ice and under-ice diatom blooms are responsible for higher export efficiency and vertical microbial connectivity, suggesting that continuous sea-ice loss may result in decreased pelagic-benthic coupling, with resultant impacts on marine food webs. Increasing temperature, combined with declining sea ice extent, ice thickness and multiyear ice3 are impacting the composition of primary producers in the Arctic Ocean4. For example, Atlantic phytoplankton species, such as Phaeocystis spp., are already seasonally reoccurring in the Fram Strait5, and were also recently observed during a phytoplankton bloom in the central Arctic Ocean6. It has been suggested that temperate phytoplankton species will become resident in the Eurasian Basin of the Arctic Ocean if the intrusion of warming Atlantic waters continues7. As primary producers form the base of the food web, such shifts are likely to have drastic consequences, not just in the pelagic realm, but also for pelagic-benthic coupling and biogeochemical cycling in the Arctic Ocean8. However, the complexity of factors driving Arctic productivity regionally makes it difficult to generalize to the future carbon flux in the entire Arctic Ocean9.The Arctic Ocean is currently undergoing unprecedented changes due to advancing climate warming. Ice coverage is declining by ~13% per decade compared to the mean September extent for 1981–201010, which likely enhances vertical carbon and nutrient fluxes11. Furthermore, the transformation from thick multiyear to thin first-year sea ice is increasing light transmission through it12. Accordingly, field observations show an increased spatial and temporal extent of sea-ice algae and under-ice pelagic phytoplankton in the Arctic basins, for which ocean color based primary production assessments are not available15. When sea ice melts, such ice-algae and under-ice phytoplankton blooms that are mostly composed of diatoms can also deliver substantial pulses of carbon and nutrients to benthic ecosystems17. For example in 2012, the release of fast-sinking ice-algae, the Melosiraarctica, from melting sea ice delivered up to 9 g of carbon per square meter of seafloor, which was more than 85% of the total carbon export that year16. Primary production models suggest that with the northward propagation of ice-edge blooms along leads and polynyas, the impact of ice-algae and under-ice phytoplankton blooms on productivity is likely to increase18, with ecological consequences for pelagic and benthic ecosystems19. However, it remains unclear what ecological effects an earlier and a stronger retreat of the ice-edge will have for carbon export on the Arctic shelf seas and margins. Key factors could be the timing of stratification, e.g. by meltwater, the phytoplankton composition during the bloom, mismatches to grazers, and other effects on carbon export efficiency, including the microbial loop in the euphotic zone and remineralization of aggregates during sinking to the deep ocean.Remote sensing of ocean color in the Arctic shelf seas suggests an increase in net primary production by 57% since 199820. The microbial loop retains most of the carbon and the nutrients in the surface ocean, and can influence carbon export efficiency substantially, next to other factors such as particle size and activity of grazers21. Recent studies have also revealed that sinking particles function as connecting vectors that disperse microbes between the surface and the deep ocean, some of which may carry specific heterotrophic functions in the remineralization of sinking matter25. Hence, beyond the supply of energy and nutrients, sinking particles can play a key role in determining the structure and functioning of microbial communities in the deep sea by importing microbes from surface waters25. To date, such so-called vertical microbial connectivity has mainly been demonstrated in temperate and tropical oceanic settings27. However, Rapp et al. 28 found that in Central Arctic sea-ice algae which reached the seafloor influenced its microbial community composition. Considering the changes that the Arctic Ocean is currently undergoing, it is critical to understand how carbon export will be impacted by changing sea-ice regimes and associated alterations in the composition and rates of primary producers. Potential changes in carbon export may also affect vertical microbial connectivity through the water column. With increasing evidence of functional microbial groups exported from surface waters into the deep sea, alterations in vertical microbial connectivity may impact biogeochemical processes of the deep ocean and the seafloor.It is well established that sinking particles are an essential conduit of carbon and nutrients to heterotrophic organisms in the deep ocean29. If Atlantic warming of the Arctic Ocean continues, it is projected that stronger ice-melt will occur in the western Eurasian Basin, eventually reducing ice export through Fram Strait30. Here we studied the effect of sea ice distribution on phytoplankton community composition, carbon export efficiency and vertical microbial connectivity in the Fram Strait. We test the hypotheses (1) that settling aggregates formed in ice-covered regions sink faster than those formed in ice-free regions, which may result in lower export flux for ice-free regions. With regard to the effect on microbial community structure we postulated that (2) particles are colonized by free-living microbes in surface waters and have little colonization at depth, (3) that there is stronger vertical microbial connectivity in ice-covered compared to ice-free regions, and (4) that the stronger microbial connectivity leads to higher representation of surface-born microbes in the deep sea and the sediment. We found large and fast-settling diatom aggregates in ice-covered regions that led to higher carbon export efficiency near the sea ice, as well as stronger vertical microbial connectivity, compared to the seasonally ice-free region of the Fram Strait, which was dominated by smaller, slow-sinking Phaeocystis spp. aggregates. Our results suggest that with the ice-edge seasonally retreating from the Eurasian Basin, carbon export efficiency and vertical connectivity may decline in large regions of the Arctic Ocean.Here, we assess the efficiency of particle export and vertical connectivity in the Fram Strait under seasonally ice-covered and ice-free conditions, which were defined based on temporal duration of sea ice presence during the productive season. The Fram Strait represents the major deep-water gateway to the Arctic Ocean basins. Warm Atlantic-water flows northwards via the West Spitzbergen Current through the eastern part of Fram Strait, whereas cold Arctic water and sea ice flows southwards into the Atlantic via the East Greenland Current in its western part Fig. . The annWe studied microbial communities associated with settling aggregates in contrasting sea ice conditions between June 24th and July 16th 2016 at the Long-Term Ecological Research (LTER) observatory HAUSGARTEN in the Fram Strait (expedition PS99.2 with RV Polarstern). Based on remote sensing sea ice concentrations during June-July 2016, we classified ice concentrations >15% as ice-covered conditions and ice concentrations <15% as ice-free conditions. According to this classification, the stations in the West Spitsbergen Current  were seasonally ice-free, as the majority of the productive season in 2016 (March–July) they had no sea ice Fig. ; Table 1−2 d−1) early in the productive season , during the period when sea ice is present in the region and hence defined as ice-associated carbon export, and again later in the season  during the ice-free period due to POC export of pelagic production31. During the ice-associated flux peak , the POC export between 2001 and 2013 showed an inverse relationship to the distance from the ice edge that was present at up to 86 km from the trap  fluxes at 200 m depth in the seasonally ice-free HG4 station  were collected using a long-term moored sediment trap between 2001 and 2013. The POC fluxes showed peaks ; as well as hypothetical sinking velocities of 2) faster-sinking (60 m d−1) and 3) slower sinking particles (20 m d−1), which were chosen based on previous observations in the region32. In all three scenarios, the modeled sinking trajectories showed that the majority of particles in the ice-free region (‘HG’ stations) originated from ice-free surface waters . This suggested that both regions had similar productivity prior and during our study (Supplementary Data a maximum (13–28 m depth) of both ice-free and ice-covered regions were dominated by Phaeocystis spp. and planktonic diatoms . This was further observed in the composition of in situ formed aggregates collected using a marine snow catcher (MSC) directly below the chlorophyll a maximum (60 m depth), where Phaeocystis spp. dominated aggregates of the ice-free region  and planktonic diatoms dominated the aggregates collected in the ice-covered regions  and planktonic diatom-dominated aggregates in the ice-covered regions . The gel traps showed similar numbers of particles exported in the ice-free and ice-covered regions, but confirmed that the aggregates in the ice-covered regions had on average two-fold larger diameters than those collected in the ice-free regions  and ice-covered regions (station ‘N’). The drifting sediment traps collected both particulate organic carbon and nitrogen flux , and were equipped with a viscous gel to capture and preserve the size and structure of intact settling aggregates. The gel traps confirmed the MSC observations of ing Fig. , as well33. The final dataset consisted of 3,709,676 sequences from 66 samples that were assigned to 6253 ASVs associated with bacterial and archaeal lineages , SAR324 (Marine group B), SAR406 (Marinimicrobia), and the archaeal class Nitrososphaeria, each comprising 1–6% of the sequences and 3–6% of the ASVs in the entire dataset  and particle-associated (PA) microbial communities of four distinct water layers: surface 10–30 m), epipelagic (100 m), mesopelagic (1000 m) and bathypelagic (~50 m above the seafloor), were characterized using Illumina 16S rRNA gene sequencing. For the analyses of the microbial community composition we chose amplicon sequence variants (ASVs) as the highest possible taxonomic resolution that the method provides0 m, epipp < 0.001), and diversity , with no significant differences from meso- to bathypelagic layers . In contrast, in the PA communities, the richness did not show significant changes from surface to epipelagic, and from epi- to mesopelagic layers, but significantly increased between meso- and bathypelagic layers . The diversity of the PA communities did not change significantly in the upper 100 m of the water column , but increased at meso- and bathypelagic layers . In both fractions the communities clustered according to the four distinct water layers . In surface and epipelagic water layers both FL and PA communities differed between the ice-covered and ice-free regions , while at higher depths (meso- and bathypelagic) there was no apparent difference in community composition between the two regions . In ice-covered regions the dissimilarity between FL and PA communities strongly increased along the entire water column , in contrast to ice-free region where the dissimilarity between the fractions increased mainly between the epi- and mesopelagic depths . Thus, this suggests a higher exchange between communities of the FL and PA fraction  in the ice-free region.The composition of microbial communities showed a separation between the FL and PA communities along the entire water column  microbes are adapted to colonize particles in the water column. Thus, the observed vertical dissimilarity pattern of the PA communities could be associated with colonization by different FL microbes, and due to the diversity changes in the FL communities as a function of water depth we can test aggregate colonization by FL microbes through the water column. In order to test where and which FL communities colonized the settling aggregates, we applied a microbial source tracking (MST) Bayesian algorithm ‘SourceTracker’. This MST approach assumes that ASVs diversity in various ‘source’  and corresponding ‘sink’  communities allows identification of statistically probable links between them (for detailed explanation see Methods section). The MST analysis showed a strong link between the surface and epipelagic FL microbes and the composition of PA communities throughout the entire water column Fig. . Within 2 fold-change of absolute value higher than 1  and an adjusted p value lower than 0.1 , Planctomycetes , Bacteroidia , and the poorly characterized class OM190 . Overall, we observed larger changes with increasing depth in the PA communities from the ice-free region (where sinking velocity of particles was lower), resulting in more than double the amount of PA-enriched ASVs, in comparison to the ice-covered regions .By statistical tests of comparative sequence enrichment, we identified the microbial taxonomic groups that became significantly more abundant on sinking particles as a function increasing depth. The ASVs within the PA communities were defined as enriched when they had a log0.1 Fig. . This teNitrosopumilacea, which comprised 3–4% and 6–19% of sequences in FL communities of the ice-covered and ice-free regions, respectively.Some of the vertically PA-enriched ASVs were also present in the FL communities along the water column Fig. . In the Alphaproteobacteria, Gammaproteobacteria, and Nitrososphaeria  that were sampled at the same stations as the water column across the Fram Strait. These communities were characterized using a similar approach to the FL and PA communities in the water column, and the final dataset consisted of 1,209,785 sequences that were assigned to 11,145 ASVs associated with bacterial and archaeal lineages (Supplementary Data Nitrosopumilaceae (17 ASVs) and the bacterial family Woeseiaceae , each family comprised ca. 2–4% of the sequences in the sediment communities. Interestingly, in contrast to the PA-enriched ASVs of the family Nitrosopumilaceae that were also abundant in the FL communities of the bathypelagic, the shared ASVs of the family Woeseiaceae were absent from the FL communities . Overall, in ice-free region, 109 of the PA-enriched ASVs were present in the sediment comprised and comprised ca. 17% of the sequences in them. In contrast, in ice-covered regions, 61 of the PA-enriched ASVs were present in the sediment and comprised ca. 11% of the sequences in them  and the ice-free (‘HG’) regions, suggesting that it was the differences in aggregate settling and size, rather than differences in production that caused the increased POC export in the ice-free region.Here, we studied the role of sea ice on settling particle characteristics and vertical microbial connectivity, and postulate links to carbon export efficiency in the Fram Strait. Our long-term assessment of the role of ice-coverage on particle export during periods with sea ice near the HG4 station suggested an important function of sea ice distance on export fluxes early in spring during the ice-influenced phytoplankton bloom period. On the other hand, during the summer (June–August), when the sea ice was much further from the trap, there was no clear relationship between the export flux and the distance to the ice-edge. This encouraged us to assess the underlying principles of this connection between ice-associated export and the fate of microbial communities attached to particles close to the sea ice edge. To test how ice-coverage impacts vertical connectivity and export of organic matter, we compared characteristics of sinking particles from ice-covered and ice-free regions of the Fram Strait during the productive season. At the time of sampling in June–July 2016, the phytoplankton bloom was dominated by diatoms in the ice-covered region while in the adjacent ice-free region it was dominated by 44. However, the POC export from the short-term drifting traps deployed at 100 m during June–July in 2016 matched well with the historical POC flux collected at 200 m between 2001 and 2013, when using the common method to estimate POC flux attenuation between 100 and 200 m via a power law function45 . Yet, in the ocean, during the sinking particles may change their velocities, due to microbial degradation and fragmentation by zooplankton50. To investigate the impact that this might have on our results, we expanded the trajectory analysis with two scenarios to represent the realistic extremities of fast- and slow-sinking phytoplankton aggregates in the upper 1000 m of the water column in the region . The trajectories of the slow- and fast-sinking aggregates observed at 1000 m depth were then determined for both the ice-free and ice-covered regions. In all cases, the simulated trajectories revealed that during sinking to the deep ocean only a small fraction of the particles were horizontally moved from ice-covered to ice-free regions, and vice versa. This suggested that the observed differences in vertical microbial connectivity from the surface to the deep ocean were not caused by horizontal movement of the aggregates between ice-free and ice-covered regions.In order to estimate the “fate” of sinking particles once they sink out of the ice-covered and ice-free surface waters towards the deep ocean, we modeled their sinking trajectories by combining a physical oceanographic model25, due to the shorter transit time through the water column. In both ice-covered and ice-free regions of the Fram Strait, free-living (FL) pelagic microbial communities from different depths had greater dissimilarities to each other than the particle-associated (PA) communities from the same depths. This suggests a stratified water column with distinct microbial communities in the different water layers, as well as a vertical dispersal of microbial communities between surface and deep ocean via sinking particles. In this context, settling particles are not only important for the export of organic matter to the deep ocean, but they also promote microbial heterotrophic activity and seeding28, and thereby shape microbial biogeography and biogeochemical functioning in meso- and bathypelagic realms.In this study, we tested the previously established hypothesis that vertical microbial connectivity is stronger in ecosystems dominated by fast-settling aggregatesBacteroidia, are associated with phytoplankton blooms in the region51, and are known to be highly active organic matter degraders52. Furthermore, it has recently been shown that there is a dominance of enzymatic activity phylogenetically linked to these taxonomic groups in the bathypelagic53 and that this enzymatic activity is predominantly linked to a particle-associated lifestyles54. This indicates that active microbes originating from surface waters and associated with sinking particles continue to process organic matter while they sink to the deep ocean, and thus may remain key players in the biogeochemical cycling in the deep ocean. The cold water-adapted  microbes of polar waters may potentially thrive at depth of the Arctic Ocean, which is characterized by a relatively uniform temperature. Our results show that almost half of the bathypelagic FL communities consisted of vertically PA-enriched ASVs. This suggests that surface water-originating microbes may realize an ecological niche in bathypelagic waters. Evidence for this is provided by the archaeal family Nitrosopumilaceae, which was the most abundant among taxonomic groups with PA-enriched ASVs in bathypelagic FL communities. Previous analyses showed that epipelagic and bathypelagic members of this family are phylogenetically closer to each other than those found in the intermediate mesopelagic waters55. Based on our results, this pattern may be explained by a niche realization in the bathypelagic of Nitrosopumilaceae family members exported from the surface ocean on sinking particles.The surface water-originating microbial families that were significantly enriched on particles collected at depth, such as various members of the class 27. Even in the bathypelagic, a substantial proportion of the PA community was still comprised of microbes recruited in the surface ocean. Notably, we observed an increasing dissimilarity between FL and PA communities with depth, and particles in the meso- and bathypelagic contained a high proportion of sequences that were not linked to the FL community at any depth, indicating a shift in population densities in the particle, e.g., by growth of otherwise rare microbial clades in the particles. Alternatively, the relatively long sinking time of days to weeks could have led to PA microbial communities at depth containing an imprint of surface water’s microbial communities that were no longer present or horizontally offset during our sampling. One of the known weaknesses of the applied here MST method is its limited ability to differentiate between sources with very similar communities56. Thus, a fraction of the unidentified sources of PA communities at depth could be a result of internal constrains of the predictive algorithm. We further observed higher dissimilarity between FL and PA communities in surface and epipelagic waters of the ice-covered region where fast-sinking particles were more abundant, compared to the ice-free region. Overall, we conclude from our data that the observed vertical changes in the PA communities are substantially affected by sinking velocity, causing different encounter rates and colonization in the surface ocean57, and differences in time for ecological succession within the particles58. The succession can result from transformations in the aggregate composition during aging and turnover59, grazing by protozoans60, viral infection61, or changing environmental conditions, such as increasing hydrostatic pressure63. Common to all these processes is that on slower sinking particles there will be more time for a stronger selection of some taxonomic groups and the demise of others, potentially allowing rare taxa to become abundant at depth while those that were abundant at shallower depths become rarer. Furthermore, a large fraction of sinking particles remains suspended in the bathypelagic64. In this way, settling aggregates should be viewed as constantly changing microcosms that have some exchange with their surroundings in the deep ocean27, but where particle sinking velocity is an important driver of succession. In Arctic waters, it seems that fast aggregate sinking velocity is strongly related to ecological impact from sea ice cover.In both ice-free and adjacent ice-covered regions, the high similarity between surface PA and FL communities suggested that particles were colonized in surface waters, similar to other oceanic regions28, and are higher than the global average of <10%65. Interestingly, the family Woeseiaceae (class Gammaproteobacteria) showed the strongest benthic-pelagic connectivity via sinking particles, indicating its export from surface to bathypelagic waters via sinking particles22. Recent genomic characterization of this largely unknown taxonomic group (which was conducted using sediment samples collected in the Fram Strait) suggests their involvement in the cycling of detrital proteins in marine benthic environments66. Using targeted fluorescence microscopy of the total pelagic microbial communities , Hoffmann et al.66 also showed, that cells of this taxonomic group were present throughout the entire water column, comprising < 1% of the community. We found that pelagic members of the Woeseiaceae were associated with sinking particles, but not free-living, suggesting that this important benthic heterotroph is one of the few types of bacteria that cover all water depths by a particle-associated life style.Since the seafloor is the final destination for those particles that make the journey through the water column, we tested whether the vertical microbial connectivity extends to deep-sea sediment. We found that ca. 10–20% of the sequences in the deep-sea sediment were related to PA-enriched microbes originating from the surface waters and deposited via sinking particles, in both ice-covered and ice-free regions. These results are comparable to observations in the Central Arctic Ocean after deposition of ice algae on the deep seafloorPhaeocystis spp. aggregates associated with seasonally ice-free regions may lead to more pelagic recycling and less vertical connectivity. These changes may substantially alter deep water and seafloor communities in the Arctic Ocean.In conclusion, our study supports the notion that sea-ice retreat can have an important ecological impact on carbon flux characteristics, and on long-term potentially affect the deep-ocean microbial diversity. Fast-settling ice-associated diatom aggregates drive higher export efficiency and cause stronger pelagic-benthic coupling including the transport of functionally important microbial groups, whereas slow settling a fluorometer, and transmissometer. At all stations water samples were collected from surface at 10–30 m, 100 m, 1000 m and ~50 m above the seafloor . Water samples were collected using 12 L Niskin bottles mounted on a CTD rosette (Sea-Bird Electronics Inc. SBE 911 plus probe) equipped with double temperature and conductivity sensors, a pressure sensor, chlorophyll 51.Hydrographic data of the seawater including temperature and salinity (doi:10.1594/PANGAEA.871952), as well as the inorganic nutrient concentrations (doi:10.1594/PANGAEA.906132) were retrieved from PANGAEA. Inorganic nutrient consumption (Δ) at each station was calculated by subtracting the mean value of all collected measurements above 50 m from the mean value of all collected measurements between 50 and 100 m www.esri.com) in a WGS 1984 Arctic Polar Stereographic map projection. The mean monthly sea ice concentrations for Fig. http://data.seaiceportal.de67, and sea surface temperature was obtained from NOAA NCEP real-time analysis (http://polar.ncep.noaa.gov/sst/rtg_high_res/).The map in Fig. 2 and 20 collection cups) was deployed and recovered yearly from 2001 to 2013 at the central LTER observatory HAUSGARTEN station  were manually removed with forceps and rinsed under a dissecting microscope. Triplicate subsamples were measured for particulate organic carbon (POC) by filtering the material onto pre-combusted Whatman GF/F filters, soaking them in 0.1 N HCl, and drying at 60 °C before analyzing with a CHN elemental analyzer38. The data was retrieved from PANGAEA (doi: 10.1594/PANGAEA.855473).The long-term moored KIEL sediment trap (sampling area 0.5 mhttp://nsidc.org/data/nsidc-0051). The images were generated using the NASA Team algorithm68 and mapped to a 25 × 25 km grid. This satellite dataset was derived from brightness and temperature data generated from Scanning Multichannel Microwave Radiometer and Sensor Microwave Imager and Sounder equipped on the Nimbus-7 satellite and the Defence Meteorological Satellite Program, respectively. The distance to the ice edge was defined at the position with 15% sea ice concentration. The ice-edge nearest the HG4 position was used to calculate the daily ice distance and averaged for each opening time of the collection cups (~14 days) on the long-term moored sediment traps.To evaluate the impact of the sea ice concentration on the POC flux during periods when peak POC flux was associated with sea ice (March-May), we used POC flux collected by the long-term sediment trap of the central station HG4. The daily distance between the sea-ice edge and HG4 was estimated using daily sea ice concentration satellite images from NSIDC/NOAA 47. The particle sinking velocity was computed by adding a constant sinking velocity to the modeled vertical velocity. In this study, we used a FESOM configuration optimized for the Fram Strait, applying a mesh resolution of 1 km48. The performance of the model was validated for the sampling time period by oceanographic observations .Particles were “released” around 300 m above the seafloor once per day during the year 2016, however we restricted the analysis to particles that reached the ocean surface between March and July 2016 . A time step of 30 min was used for the trajectory calculation, and bi-hourly positions and corresponding temperature and salinity values were stored. To quantify the vertical distribution of particles, their positions were binned into a grid with bin sizes of 25 m depth × 0.05° Longitude/Latitude and then divided by the total number of particles to determine the fraction of particles originating from each grid box. The daily concentrations of sea ice were retrieved from Centre d’Exploitation et de Recherche SATellitaire  and stored in brown glass bottles. For microscopic analyses an aliquot of 50 mL was transferred to Utermöhl settling chambers where the cells were allowed to settle for 48 h. At least 500 cells of the dominant phytoplankton species or groups were counted with an inverted microscope at three different magnifications using phase contrast according to Edler et al.69.The plankton community composition at the chlorophyll 70 filled with Whatman GF/F filtered seawater collected from the same MSC and kept at in situ temperature. The x-, y-, and z-axes of each aggregate were measured in the vertical flow system using a horizontal dissection microscope and an ocular with a scale. The aggregate volume was thereafter calculated assuming an ellipsoidal shape and the equivalent spherical diameter (ESD) was calculated from the aggregate’s volume. The sinking velocity was measured by increasing the upward flow in the flow-chamber until the aggregate was floating one diameter above the net. The sinking velocity was thereafter calculated by determining the volumetric flow rate three times, and dividing the average of these measurements by the area of the flow chamber. The composition of the aggregates was determined with an inverted light microscope using Utermöhl chambers  we sampled intact aggregates from 60 m at ice-free and ice-covered regions, and measured their size, composition, and sinking velocities. The aggregates were individually transferred to a vertical flow chamberers Fig. .27. The drifting traps consisted of a drifting array attached to a surface buoy equipped with a GPS satellite transmitter, two surface floats and 12 small buoyancy balls that served as wave breakers to reduce hydrodynamic mixing effects on the sediment traps. The 100 m collection depth was equipped with four gimbal-mounted cylinders, each 1 m tall and 10.4 cm in inner diameter. Three of the cylinders collected samples for biogeochemical measurements and the last collection cylinder contained 200 ml of a viscous gel, which intercepted and preserved settling particles without destroying their original sizes and structures. Upon recovery, the material collected for biogeochemical fluxes was fixed with HgCl2 and stored at 4 °C until further analyses in the home laboratory. The particles collected in the gels were photographed using a stereo microscope equipped with a 3.1 megapixel digital camera and a 105 mm macro lens, resulting in a pixel size of 12 µm. The image analyses were performed with a routine written in MATLAB (The MathWorks) using the image analysis toolbox. Each image was converted into gray scale and the background was removed by applying a threshold value. The calibrated pixel area (mm2) in each projected particle was converted into the equivalent spherical diameter (ESD).Aggregate and carbon export to 100 m depth was measured using the free-drifting surface-tethered sediment traps in the ice-free and ice-covered regions72. Sequences were obtained on the Illumina MiSeq platform in a 2 × 300 bp paired-end run , following the standard instructions of the 16S Metagenomic Sequencing Library Preparation protocol .Genomic bacterial and archaeal DNA was isolated from size-fractionated filtration through 5 µm and 0.22 µm filters membranes to analyze the particle-associated  and the free-living  communities. The isolations were conducted by a combined chemical and mechanical procedure using the PowerWater DNA Isolation Kit, and PowerSoil DNA Isolation Kit for the sediment samples . Prior to DNA isolation the Sterivex™ cartridges of the 0.22 μm membranes were opened in order to place the filters in the kit-supplied bead beating tubes. The isolation was continued according to the manufacturer’s instructions, and the DNA was stored at −20 °C. Library preparation was performed according to the standard instructions of the 16S Metagenomic Sequencing Library Preparation protocol . The hyper variable V4–V5 region of the 16S rRNA gene was amplified using bacterial primers 515F-Y (5‘-GTGYCAGCMGCCGCGGTAA-3‘) and 926 R (5‘-CCGYCAATTYMTTTRAGTTT-3‘)73. Further analyses were conducted using R  in RStudio . The trimmed libraries were processed using DADA2 (v1.14.1)33, following the suggested tutorial . Briefly, chimeras and singletons were filtered out. The produced amplicon sequence variants (ASVs) were taxonomically classified against the SILVA 16S rRNA gene reference database (release 138)74. The ASVs that were taxonomically unclassified on domain level, or not assigned to bacterial or archaeal lineages, were excluded from further analysis. Furthermore, all ASVs which were taxonomically assigned to mitochondria and chloroplast were removed from the dataset. Overall, ca. 25% of the total raw sequences were filtered out from the final dataset.The raw paired-end reads were primer-trimmed using cutadapt (v3.0)75 and plots were generated using R package ‘ggplot2’ (v3.3.0)76. The sample rarefaction analyses were conducted using R package ‘iNEXT’ (v2.0.20)34. Prior to beta-diversity analyses, a prevalence threshold  of 4% was applied on the ASV abundance table. Principal component analysis (PCA) and dissimilarity comparisons between FL and PA communities were conducted on a variance stabilized ASV abundance table based on the geometric mean77. The fold-change in abundance of each ASV between the water layers was calculated using the R package ‘DEseq2’ (v1.24.0)78. The method applies a generalized exact binomial test on variance stabilized ASV abundance.Sample data matrices were managed using the R package ‘phyloseq’ (v1.28.0)79 was applied on the ASV abundance table. The SourceTracker approach models the potential presence of a mixture of the source communities in the sink communities, where the mixing proportions of the different sources are unknown. The algorithm performance was validated using a ‘leave-one-out’ approach, in which each ‘source’  community was hidden, in turn, from the training dataset, and its origin was predicted based on the rest of the source samples in the dataset. All samples were randomly sub-sampled to 5,000 sequences prior to the SourceTracker analysis. The SourceTracker analysis was conducted according to default conditions, defined by the developers: burn-in period—100, restarts—10, dirichlet hyperparameters —0.001 .Based on the assumption that the particle-associated microbial communities  are the result of various events of colonization of marine aggregates by free-living microbes ; a Bayesian community-based microbial source tracking algorithm ‘SourceTracker‘ (v1.0)Further information on research design is available in the Supplemental MaterialDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Neisseria gonorrhoeae (NgCAα) and Vibrio cholerae (VchCAα) are inhibited by a panel of simple coumarins incorporating hydroxyl, amino, ketone or carboxylic acid ester moieties in various positions of the ring system. The nature and the position of the substituents in the coumarin ring were the factors which strongly influenced inhibitory efficacy. NgCAα was inhibited with KIs in the range of 28.6–469.5 µM, whereas VchCAα with KIs in the range of 39.8–438.7 µM. The two human (h)CA isoforms included for comparison reason in the study, hCA I and II, were less prone to inhibition by these compounds, with KIs of 137–948.9 µM for hCA I and of 296.5–961.2 µM for hCA II, respectively. These findings are relevant for discovering coumarin bacterial CA inhibitors with selectivity for the bacterial over human isoform, with potential applications as novel antibacterial agents.Coumarins are known to act as prodrug inhibitors of mammalian α-carbonic anhydrases  but they were not yet investigated for the inhibition of bacterial α-CAs. Here we demonstrate that such enzymes from the bacterial pathogens  It should be mentioned that the esterase activity of CAs is only documented and investigated in detail for the α-class of CAs. We have shown previously that β-, δ- and γ-CAs  do not possess esterase activity,Many coumarins proved to act as efficient and also isoform-selective CAIs2.2.1.2 hydration activity4 to maintain constant ionic strength, in order to follow the initial rates of the CA-catalysed CO2 hydration reaction for a period of 10–100 s. The CO2 concentrations ranged from 1.7 to 17 mM for the determination of the kinetic parameters and inhibition constants. For each inhibitor, at least six traces of the initial 5–10% of the reaction were used to determine the initial velocity. The uncatalyzed rates were determined in the same manner and subtracted from the total observed rates. Stock solutions of inhibitors (10–20 mM) were prepared in distilled-deionized water, and dilutions up to 10 nM were done thereafter with the assay buffer. Inhibitor and enzyme solutions were preincubated together for 1–6 h at 4 °C prior to the assay, in order to allow for the formation of the E-I complex. The inhibition constants were obtained by non-linear least-squares methods using Prism 3 and the Cheng-Prusoff equation, as reported previously,An Applied Photophysics stopped-flow instrument was used to assay the CA-catalysed CO2.2.1–14, buffers, acetazolamide AAZ and other reagents were of > 99% purity and were commercially available from Sigma-Aldrich .Coumarins 3.,Bacterial CAs were thoroughly investigated for their inhibition with the two main types of classical CAIs, the sulphonamides (and their isosteres) and the metal complexing anions1–14 . Thus, all coumarins were investigated as CAIs in the same conditions as for hCAs, and data of Is obtained after 6 h incubation time. The following SAR can be drawn from data of 1–14 inhibited the two bacterial enzymes with inhibition constants in the medium–high micromolar range. The modest potency is not unexpected considering the simple structures, but as our intention was to provide a proof-of-concept study that bacterial CAs are inhibited by non-sulphonamide compounds, the modest inhibition was acceptable. For NgCAα the KIs were in the range of 28.6–469.5 µM, whereas for VchCAα in the range of 39.8–438.7 µM. It should be observed that the two human isoforms included for comparison reason in the study, hCA I and II, were also weakly inhibited by these compounds, as the KIs were in the range of 137–948.9 µM for hCA I and of 296.5–961.2 µM for hCA II, respectively. It is in fact well-known that the cytosolic hCAs show a poor inhibitory effect with coumarins  whereas many trans-membrane, tumor-associated CAs, such as hCA IX and XII, lead in many cases to low nanomolar inhibitorsAll coumarins 1–14, similar to what was previously reported for hCAs13 and 14 against both bacterial enzymes; 5 against VchCAα), the same was also observed for 11, possessing a bulky group in position 4, which was the least effective coumarin against bacterial CAs in the investigated series. Smaller and more compact moieties in position 4, such as OH (compounds 4 and 12), methyl (compounds 6 and 8), CF3 (derivative 7) were tolerated and led to effective micromolar inhibitors  whereas 7-hydroxy-8-acetyl-coumarin 9 was the most effective VchCAα inhibitor (KI of 39.8 µM). With respect to placement of the substituents on the coumarin rings there are a couple factors to consider regarding potency. First, steric hinderance by substituents nearby the hydrolysable bond of the lactone may reduce the ability of the CA to cleave the ester. Second, there may be electronic considerations with the substituents they could make the carbonyl more electrophilic for attack by water to provide the corresponding cinnamic acids. Alternatively, these substituents may also be involved in the binding interaction of the resulting cinnamic acids, and either improve inhibition of reduce it.Substituents in positions 6, 7 and/or 8 of the coumarin ring generally led to effective CAIs against both bacterial enzymes, a situation also observed for hCA IX/XII; as those groups do not interfere with the hydrolysis of the lactone ring, being further away from carbonyl site of hydrolysisI values < 100 µM, being thus amenable to be considered as viable hit compounds for developing tighter binding compounds. Indeed, some of these derivatives such as 2, 3, 4, 6, 7, 9, 10, and 12 incorporate free OH or NH2 moieties which are easy to derivatize in a multitude of ways, and in the case of hCAs led to much more effective CAIs compared to the leadA range of the tested coumarins had a behaviour of medium potency inhibitors against both bacterial isoforms, with K8 and 9), which may prove beneficial for obtaining potential antibacterials with selectivity over human isoforms, such as hCA I and II.The sulphonamide CAI acetazolamide (AAZ) used as standard compound was much more effective for the inhibition of the bacterial enzymes as expected. However, an interesting observation is that some of the coumarins investigated here do show a much better inhibitory profile for the bacterial over the human isoforms (e.g. We observed that as for the mammalian CAs for which coumarins were reported to act as inhibitors4.This is the first report demonstrating that bacterial α-class CAs are susceptible to inhibition by coumarins, a class of inhibitors investigated previously only for their interaction with human CA isoforms. Our data indicate that a panel of simple coumarin derivatives inhibit two enzymes from human bacterial pathogens with a medium efficacy in the low–medium micromolar range. This proof-of-concept study demonstrates that coumarins possess inhibitory potential and lays groundwork to further explore SAR modifications to probe steric and electronic contributions to bacterial CA hydrolysis of the lactone prodrug and/or contributions to binding and inhibition of the resulting cinnamic acids. Additionally, significant selectivity for inhibiting the bacterial over the human isoforms hCA I and II was observed suggesting promising data for obtaining more effective and bacterial CA–selective coumarin inhibitors. Bacterial CA active sites are slightly more voluminous compared to the mammalian CA isoforms active sites


Despite evidence for microstructural brain alterations in epilepsy patients, little is known about how these develop with age and the progress of the disease. The aim of this study was to investigate microstructural abnormalities of the white matter (WM) in children with new-onset, untreated idiopathic-generalized epilepsy (IGE) using the MRI technique of diffusion tensor imaging (DTI). The study was approved by the institutional review board, and all individuals or their parents gave signed informed consent. In total, 45 patients with IGE  and 32 healthy controls  were included. Voxel-based analysis (VBA) was used to compare patients and controls, and Pearson correlation analysis was used to investigate relationships between altered DTI metrics and clinical parameters. Compared with controls, patients with IGE showed increased mean diffusivity (MD) in the left splenium of the corpus callosum, increased fractional anisotropy (FA) in the right WM of the superior and middle frontal gyri, increased axial diffusivity (AD) in the WM of right corona radiata and left occipital lobe, and decreased AD in the WM of the left thalamus and the right middle cerebellar peduncle. There was no correlation between the altered diffusion parameters and clinical measures. Our study demonstrated several distinct microstructural impairments in children with new-onset, untreated IGE, of which altered AD might be the most sensitive marker of dysmyelination. The increased FA in the IGE group might suggest an initiating or compensatory mechanism that is activated prior to cognitive decline in these children. Epilepsy is one of the commonest non-communicable neurologic conditions and a significant cause of disability and mortality, affecting 70 million people worldwide . Idiopatin vivo structural and functional changes in neuropsychiatric diseases, such as epilepsy. The MRI technique of diffusion tensor imaging (DTI), which is sensitive to the directionally constrained water diffusion along myelinated axons  8,700 ms, optimized echo time (TE) 90–110 ms, the number of excitations (NEX) 1, field of view (FOV) 24 × 24 cm2, matrix size 128 × 128, in-plane resolution 1.875 mm, and slice thickness 3.5 mm. The average scanning time was 7 min. An average of 40 oblique axial slices was collected to cover the whole brain.All participants were scanned using a single-shot spin-echo echo-planar imaging (EPI) sequence on a 3.0T MR scanner . The EPI sequence was performed in 40 non-collinear gradient directions with a http://www.fmrib.ox.au.uk/fsl). First, the data were corrected for head motion and eddy currents using affine registration to the b0 image volume. Next, brain extraction was performed to remove non-brain tissue from the whole-head image. Finally, parameter maps for FA and MD were calculated using dtifit, which fits a diffusion tensor model to each voxel and estimates the principal directions of diffusion. Also calculated were AD, which represents the highest diffusion in the voxel and is assumed to run parallel to the main axon fiber, and RD, which represents diffusion perpendicular to the main diffusion direction of the axon fibers. A VBA was then performed with SPM8  running in MATLAB 2012b . Each b0 image was non-linearly normalized using the EPI template supplied with SPM8 to estimate the normalization parameter, which was applied to all parameter maps with each voxel sized 2 mm × 2 mm × 2 mm. Finally, the normalized parameter maps were smoothed using an isotropic Gaussian filter .Preprocessing was carried out using the FSL 4.1 software package  = 8 mm, and cluster connection radius = 5 mm] with an AlphaSim corrected (http://www.restfmri.net/forum/) P < 0.05 at the cluster level. The regions that showed group differences in FA MD, AD, and RD were extracted for each patient, and partial correlations using age and sex as covariates were computed to examine the relationships between the altered DTI parameters and clinical measures .Voxel-wise comparisons of FA, MD, AD, and RD between patients with IGE and controls were performed using two-sample P > 0.05) were generally well-balanced. There was no significant difference in MMSE scores between IGE and HC groups.Anatomical MRI data from 77 participants were included in the study: 45 patients with IGE and 32 HCs. The demographic and clinical characteristics of the subjects are given in After AlphaSim correction, the patients with IGE showed significant alterations in several diffusion parameters compared with the HCs . CompareThe current study investigated WM microstructural changes in children with new-onset, untreated IGE to help understand the dynamic microstructural alterations and the possible mechanisms leading to cognitive decline in the later, chronic stage of IGE. Consistent with previous studies , 19, we Our most striking observation is of increased FA in the deep WM of the right frontal lobe. The mesial frontal regions play a crucial role in the spreading and generalization of epileptic discharges in IGE , 24, andDecreased FA has been interpreted as reflecting poor fiber coherence and myelination, often considered a hallmark of neurodegenerative and similar disorders . IncreasAnother possible explanation of our finding of increased FA is a relationship to fiber crossings: several areas of the brain  with considerable fiber crossings have low FA , 30. It Taking together the absence of FA abnormalities reported using a similar whole-brain analytic method in children with new-onset IGE, but with greater illness duration than in our study , and thePrevious studies have reported a relationship between cognitive deficit and frontal dysfunction in IGE , 16, andAnother notable finding of the current study is the increased MD in the left splenium of the corpus callosum in patients with IGE. Increased diffusivity is usually explained by excitotoxic mechanisms caused by seizures, which lead to cell lysis and death  and resuChanges in AD were quite complicated , 4. AlthHowever, we did not find any alterations in RD, which is considered to reflect impaired myelin integrity or decreased axon fiber density in WM pathways . A studyWe found altered diffusion parameters in areas of the brain which have often been reported to be abnormal in epilepsy. In particular, the corona radiata, corpus callosum, thalamus, and frontal and occipital lobes frequently show abnormal diffusion indices in patients with IGE , 22, 36.First, the number of children studied was modest, and we were unable to conduct any subgroup analysis. Second, MMSE is not well-suited to evaluate subtle neuropsychological changes in new-onset epilepsy. Third, it is of course difficult to fully interpret the DTI findings in the absence of histopathological assessments: future studies using animal models of epilepsy may help with this. Finally, this cross-sectional study cannot address the dynamic evolution of these abnormalities: longitudinal follow-ups will help to elucidate this.In summary, our results indicate that microstructural abnormalities exist from the very beginning of IGE and that AD may be more affected than FA, MD, and RD in the initial stages in children with IGE. Calculating all the diffusion parameters allows the detection of more WM microstructural changes in children with IGE than simply analyzing FA and MD. The increased FA in children with new-onset, untreated IGE differs markedly from chronic patients with IGE, perhaps evidence of gradual progression of cognitive impairments and possible compensatory mechanisms in IGE prior to cognitive decline. Longitudinal studies are needed to determine how the FA and other diffusion parameters change over the course of IGE and the potential impact of therapeutic intervention on these parameters.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by Ethics Committee of Yibin Second People's Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This study was supported by the funding of Changjiang Scholar Professorship Award (award no. T2014190), CMB Distinguished Professorship Award (award no. F510000/G16916411), Changjiang Scholars and Innovative Research Team in the University of China , the National Natural Science Foundation (grant no. 81621003), the Natural Science Youth fund of Southwest Medical University (grant no. 2017-ZRQN-086), Project of Sichuan Health Committee (grant no. 20PJ309), and the Key Project of Yibin Science and Technology Bureau of Sichuan Province (2013SF009).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss. Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions. Rising temperatures have played a key role in reducing Arctic sea ice, with September sea ice extent now around half that of 1979 when satellite measurements of the Arctic began4. This downward trend will continue, even in optimistic greenhouse gas emission reduction scenarios5. Climate simulations project the Arctic to be ice free in the summer by 20506. Other studies put this date as early as the 2030s7. Such unprecedented sea ice loss has profound local and regional consequences: it is the greatest threat to polar bear populations8; it has increased the intensity and frequency of algal blooms that propagate toxins throughout the food web9; and it poses significant challenges for Indigenous Peoples, with impacts ranging from food security9 to loss of culture10.Near-surface air temperatures in the Arctic have increased at two to three times the rate of the global average, a phenomenon known as ‘Arctic amplification’, caused by a number of positive feedbacks11, as well as extreme Northern Hemisphere winters by weakening the stratospheric polar vortex13. Although the existence of such teleconnections are still in debate14, improved forecasts of Arctic sea ice have the potential to improve predictions of mid-latitude weather15.Arctic sea ice is also a crucial component of the global climate system. Evidence is mounting that Arctic sea ice loss influences weather and climate beyond the Arctic region. For example, it may provoke wetter European summers through a southerly perturbation of the jet stream17. While there are inherent sea ice predictability limits, owing mostly to chaotic processes in the atmosphere20, studies have demonstrated that potential predictability is higher, suggesting that forecasts could be improved22.Producing accurate Arctic sea ice forecasting systems has been a major scientific effort with fundamental challenges at play. Current operational sea ice forecasting systems, based on deterministic coupled atmosphere-ice-ocean models, are often no better than simple statistical forecasts at seasonal lead times of two months and beyondIn this work, we introduce IceNet, a new sea ice prediction system based on deep learning. The system has been trained to forecast the next 6 months of monthly averaged sea ice concentration maps at 25 km resolution, learning from climate simulations covering 1850–2100 and observational data from 1979 to 2011. We show that IceNet outperforms a leading physics-based model in seasonal forecasts of Arctic sea ice, particularly for extreme summer sea ice events. IceNet directly predicts probabilities of sea ice occurring, expressing the level of confidence in its own predictions, unlike previous deterministic models. We find that IceNet’s predicted probabilities of sea ice display good calibration with observations. Leveraging this, we derive a simple framework for probabilistically bounding the ice edge within a region of lower predictive confidence, which has added utility over deterministic ice edge forecasts. Finally, a variable importance method is used to identify the climate variables most important for IceNet’s forecasts.23. There is great interest in the application of deep learning to the Earth sciences24, particularly with satellite data25. Satellite and climate model data are gridded; a specific time and altitude slice of a climate variable is arranged on a two-dimensional  grid, analogous to an image, and can be used as inputs to convolutional neural networks (CNNs)28. Satellite observations of sea ice are also presented as images: passive microwave measurements of microwave brightness temperature are converted to sea ice concentration (SIC) estimates of the fractional area covered by sea ice in a given grid cell, ranging between 0 and 100%.In contrast to physics-based dynamical models are data-driven artificial intelligence (AI) approaches like deep learning. Deep learning algorithms have been a game-changer in diverse areas where large volumes of data are available, using multiple nonlinear processing layers to extract increasingly high-level information from unprocessed input data29, is a CNN variant that takes images as input and produces images as output, and has proven effective in diverse applications at learning accurate, pixel-wise mappings31 (see Methods). IceNet’s monthly averaged inputs comprise SIC, 11 climate variables, statistical SIC forecasts, and metadata ; marginal ice (15% < SIC < 80%), and full ice (SIC ≥ 80%). At each grid cell and lead time, IceNet’s ensemble members produce a discrete probability distribution over each of the three SIC classes. IceNet’s ensemble-mean output is found by averaging the individual probability distributions of its 25 ensemble members (see Methods), which improves performance and probability calibration36.To reduce the effect of uncertainty in the SIC dataP(SIC > 15%), hereafter referred to as the sea ice probability (SIP), p, with binary classes open-water (SIC ≤ 15%) and ice (SIC > 15%). This aligns with previous work: 15% is the standard SIC threshold for defining the ice edge position38. By reducing the task to binary classification of SIC > 15%, the objective can be framed as that of predicting the ice edge. Examples of IceNet’s ice edge predictions for September forecasts at 4- to 1-month lead times are shown in Fig. The marginal ice class was included to increase the expressivity of IceNet’s forecasts. However, to simplify model evaluation, IceNet’s marginal ice and full ice class probabilities were summed to obtain 39, covering the period 1850–2100. Each climate simulation includes anthropogenic forcing effects from greenhouse gas emissions, following emission levels since 1850 and projecting a ‘middle of the road’ scenario for the twenty-first century40. This scenario involves moderate shifts from fossil fuel to renewable energy sources, resulting in a net global average radiative forcing effect of 4.5 Wm−2. After pre-training, systematic errors learned from the CMIP6 models are corrected by fine-tuning network weights on observational data from 1979 to 2011, followed by a probability calibration step known as temperature scaling41. The validation years from 2012–2017 were used for early stopping, hyperparameter search, and probability calibration, but were not used for training the models (see Methods). To further validate the predictive abilities of IceNet, test years spanning Jan 2018–Sept 2020 were left unused until IceNet was finalised, thus representing IceNet’s true ability to generalise to unseen future data.To account for the limited observational data record, which spans only 41 years, we use transfer learning by pre-training each IceNet ensemble member on 2220 years of climate simulation data from the Coupled Model Intercomparison Project phase 6 (CMIP6)42. IceNet’s 11 input climate variables  with state-of-the-art sea ice prediction skill38. For a fair comparison between the two, we use 25 ensemble members in both models (see Methods). As a statistical benchmark we use a SIC linear trend forecast, which extrapolates grid cell-wise lines of best fit—computed over the past 35 years of SIC values for a given calendar month—1 year ahead. Forecast performance relative to this benchmark represents an ability to forecast the interannual variations of sea ice beyond the linear decline component. SIC forecasts from the linear trend model are also passed into IceNet as inputs. This provides an additional layer of interpretability: the magnitude with which IceNet outperforms the linear trend model indicates how much IceNet can leverage its other input variables to forecast the nonlinear variations in sea ice under different forecasting regimes.We compare IceNet with SEAS5p > 0.5 and open-water if p ≤ 0.5. Deterministic forecasts of SIC in SEAS5 and the linear trend model were also converted to binary class predictions with ice if SIC > 15%. Predictive skill was quantified using a binary accuracy metric, measuring the percentage of predicted SIC classes that match the observed SIC class. The binary accuracy is computed over an active grid cell region for a given calendar month and can be seen as a normalised version of the integrated ice edge error47 (see Methods).IceNet’s probabilistic SIP outputs are mapped to binary class predictions of ice if IceNet’s mean binary accuracy across all lead times is only 0.13% higher on the validation than test years, suggesting its performance on validation data is also indicative of generalisation ability. Figure 48. Despite this, IceNet performs as well as or better than the other two models for this period  programme invites predictions for September SIE each year at 4-, 3-, and 2-month lead times. Comparing IceNet with the multi-model median September SIE predictions from the SIO shows that, on average, IceNet either matches or outperforms the SIO in terms of mean absolute SIE error over 2012–2020   by an average of 0.26%. This boost is small considering the increase in memory and computational load of the pre-training phase. The improvement is not uniform over each calendar month and lead time, even slightly hindering September forecasts for lead times longer than 3 months Fig. , potentiCalibrated probabilities are highly desirable in probabilistic forecasting systems, with perfect calibration indicating the predicted probability equals the true probability of an event occurring. IceNet’s SIP is almost perfectly calibrated over the test years Fig.  as a res37: we used a simple bias correction scheme for SEAS5 due to ease of implementation (see Methods), although a more sophisticated method could bring further improvements to its accuracy and calibration.Calibration issues in dynamical models can be improved with a-posteriori calibration methods51. A well-calibrated and sharp sea ice forecasting model also enables the ice edge to be bounded between two contours of SIP, p′ and 1 − p′, with p = p′ defining the maximum predicted ice edge location and p = 1 − p′ defining the minimum (see Methods). The choice of p′ involves a trade-off between reliability and precision  of the bounding region  the confident open-water region and p ∊ . The top-5 most important input variables arising from this procedure for forecasting September and March at 5-, 3-, and 1-month lead times are shown in Table One key question is: ‘How is IceNet using its input data to make predictions?’. We go some way to answer this by using a permute-and-predict methodDeep learning systems like IceNet are adept at learning nonlinear statistical relationships between input and output data, but do not directly model causal relationships. Despite this, IceNet’s permute-and-predict variable importance results are consistent with known causal links between climate variables and sea ice, suggesting that physically plausible statistical relationships have been learned. Furthermore, the diminishing importance of IceNet’s initial conditions (relative to its linear trend forecast inputs) at greater lead times aligns with observed limits of sea ice predictability. This logic can be reversed: the predictability that IceNet can learn from training data provides evidence for the inherent timescales of memory in the Arctic climate system. For example, initial conditions are assigned negligible importance for IceNet’s March forecasts initialised in October (5 month lead time) Table , with IcThe full permute-and-predict results for each lead time, averaged over all calendar months, are reported in Supplementary Fig. 7. This reveals new patterns that are not apparent from the top-5 rankings in Table 52. In addition, as the mechanisms between sea ice extent and Northern Hemisphere weather are better understood, accurate seasonal predictions of sea ice could anticipate weather conditions in the mid-latitudes months in advance.We have introduced an Arctic sea ice forecasting AI system, IceNet, which outperforms the leading dynamical model, SEAS5, in seasonal predictions of Arctic sea ice. A further benefit of IceNet is speed: once trained, IceNet runs over 2000 times faster on a laptop than SEAS5 running on a supercomputer, taking less than ten seconds on a single graphics processing unit. A variable importance method provided insight into the input variables IceNet uses to achieve state-of-the-art performance, with sea ice and tropospheric initial conditions being key in short-range predictions for September. IceNet’s well-calibrated probabilistic forecasts enable the observed ice edge to be reliably bounded between two spatial contours of predicted sea ice probability, which could be used operationally to avoid shipping disasters, saving lives and preventing ocean contaminationA further significance of this work lies in posing a challenge to dynamical models. In regions and seasons where IceNet’s forecasts outperform dynamical models, deficiencies in model fidelity or forecast initialisation are likely to be substantial. This information, combined with insight into which observations are the most important for IceNet’s forecasts, provides valuable guidance for improving dynamical model parameterisations, data assimilation methods, and forecast calibration techniques.52, we argue that they could also play a pivotal role in adaptation and mitigation strategies for sea ice loss. Predictions for the timing and location of sea ice loss can provide early warnings for the possible sea ice conditions that lie ahead, which is critical for local communities, authorities, and Arctic ecosystem conservation groups. One example use case is with ‘mega haul-outs’ of Pacific walrus, occurring when tens of thousands of walrus are forced to congregate on land due to a lack of sea ice to rest on. Human disturbances can cause stampedes at haul-out sites and lead to high walrus mortality56. Information from IceNet’s forecasts, combined with known haul-out locations, could anticipate these events and help to prevent stampedes by managing human access. Furthermore, predictions for the migration of cetacean populations (which can coincide with sea ice advance and retreat57) could help to avoid fatal collisions between ships and endangered whale species. Such applications would help to fill an urgent gap in the integration of climate change in ecosystem management and planning tools58. The impacts of climate change on polar marine species and ecosystems, including the rapidly changing annual cycle of crucial sea ice habitat, means that dynamic approaches to conservation and management are imperative. For example, dynamic Arctic marine protected areas (MPAs) are likely to be more effective than those of static design58. Sea ice forecasts could inform the definition of such dynamic MPAs and provide advanced warning for stakeholders, allowing time to adapt activities to avoid areas critical for Arctic biodiversity. In such use cases, reliable quantification of uncertainty, as in IceNet forecasts, is likely to be crucial for the decision-making process.While the implications of accurate sea ice forecasts for shipping are well developedIceNet demonstrates the potential of AI methods as a powerful tool for seasonal sea ice forecasting and an enabler of conservation planning tools in the Arctic. Future work will explore whether including ice thickness in IceNet’s inputs improves its accuracy in summer. We will also implement a new online version of IceNet that operates on a daily temporal resolution, which is likely to improve performance at short lead times.The datasets used for training IceNet comprise observational sea ice concentration (SIC), observational reanalysis data, and climate simulation data.59, comprising retrieval algorithms OSI-450 (1979–2015)60 and OSI-430-b (2016 onwards)61, which use data from SMMR (1978–1987), SSM/I (1987–2009) and SSMIS (2003–today) sensors. The OSI-450/OSI-430-b algorithms have been shown to be more accurate than other retrieval algorithms when compared with direct optical satellite observations of summer SIC (while the Arctic Ocean is sunlit)34. Owing to limitations in passive microwave measurements, no retrieval algorithm matches the true SIC. In particular, notable issues arise on the coastline due to land-sea spillover effects caused by snow on the land surface, which has a similar passive microwave signature to sea ice62.Satellites have measured sea ice conditions since late 1978 using passive microwave sensors. A number of different sensors have been used during this observation period, including the Scanning Multichannel Microwave Radiometer (SMMR) on NASA’s Nimbus 7 satellite, the Special Sensor Microwave/Imager (SSM/I) on the Defence Meteorological Satellite Program’s (DMSP) satellites, and the Special Sensor Microwave Imager and Sounder (SSMIS) on the later DMSP satellites. SIC can be computed from passive microwave satellite measurements using several different retrieval algorithms, between which substantial differences in the estimated SIC can arise. We obtained SIC data from the European Organisation for the Exploitation of Meteorological Satellites’ (EUMETSAT) Ocean and Sea Ice Satellite Application Facilities (OSI-SAF) data record2. All other datasets considered in this study were re-gridded from a latitude-longitude grid to the EASE2 grid using bilinear interpolation.The OSI-450/OSI-430-b SIC dataset is provided on a Lambert Azimuthal Equal Area projection, with a grid spacing of 25 km. Also known as the Equal Area Scalable Earth 2 (EASE2) grid, this ensures areas on the Earth are preserved in the projection. The size of the SIC data is 432 × 432 on the EASE2 grid with each grid cell covering an area of 625 kmFor portions of the SIC data record, data surrounding the North Pole is missing due to satellite orbit and field of view restrictions. Known as the polar hole, the size of this data gap reduced over time as satellites were able to make observations closer to the North Pole. The missing area includes data north of 84° for SMMR data (1979 onwards), 87° for SSM/I data (1987 onwards), and 89° for SSMIS data (2003 onwards). We use bilinear interpolation to fill the SIC values in this region. These interpolated values form part of IceNet’s input data, but we chose to not include them as training samples at the output of the networks.Another source of gaps in the SIC dataset are missing daily observations due to satellite malfunctions, resulting in several months for which no monthly mean could be obtained: April–June 1986 and December 1987. Forecasts during the training years that depend on one of these missing months of SIC could not be made and thus were discarded from the training dataset. However, training forecasts that only included missing data at the output could be salvaged by masking out grid cells associated with the missing month from the training samples.63 at a 0.25° resolution. Reanalysis variables are based on data assimilation, combining observations with dynamical model data to form a consistent, gridded dataset using the laws of physics. We use the ERA5 monthly averaged data on single levels from 1979 to present dataset for surface variables64, and the ERA5 monthly averaged data on pressure levels from 1979 to present dataset for the upper air variables65.The non-SIC observational climate variables used as input to IceNet . Five simulations from the MRI-ESM2.0IceNet’s non-SIC input variables with strong seasonal cycles, such as temperature and solar radiation, are converted to anomalies in order to emphasise differences from typical values. The anomaly variables were found by subtracting the climatological mean for each calendar month, computed over the observational training data period (1979–2011).The speed and stability of neural network training can be improved by normalising the input data so that each variable takes values in similar ranges. We preprocessed the observational reanalysis variables by subtracting the mean and normalising by the standard deviation computed over the training years. To maintain direct correspondence between CMIP6 and ERA5 data, CMIP6 variables were normalised by the same mean and standard deviation values obtained from the ERA5 observational variables. The SIC data were converted from percentages in  to fractions in .The full set of input-output samples, including the CMIP6 pre-training data, take up multiple terabytes in memory, which is too large to fit into RAM. To circumvent this issue, we built a custom data loader in Python to load batches of data on the fly while IceNet is trained—a standard approach for large training datasets in computer vision.SEAS5’s ensemble is generated by running multiple forecasts, each with small perturbations to the initial state and the model’s internal parameters. Despite approximation of forecast uncertainty through an ensemble, fundamental limitations in a model’s representation of physics lead to systematic forecasts errors known as ‘bias’, which calibration methods attempt to alleviate. We bias correct its ensemble-mean forecasts for 2012 onwards by subtracting the mean error field for a given calendar month and lead time, computed retrospectively by averaging over the years 2002–11.26. The CNN architecture adopted for each IceNet ensemble member is a U-Net29. A U-Net is an encoder-decoder CNN where the feature-extracting encoding path of the network downsamples the input data, followed by a decoding path that upsamples the data. In IceNet, the output of the decoding path is fed into six different convolutional layers with linear activation functions and three feature maps each, corresponding to the 6 forecast months and three SIC classes. These feature maps are then divided by a temperature scaling parameter followed by a softmax activation function, mapping real values to probabilities that sum to 1 across the three SIC classes. In total, the IceNet architecture contains roughly 44 million trainable weights. IceNet’s architecture is detailed in Supplementary Table 70.IceNet is an ensemble of 25 CNNs71, which draws weights from a truncated Normal distribution with standard deviation dependent on the size of the previous layer. This helps in attaining a global minimum of the objective function faster and more efficiently. A different random seed was used for initialising each ensemble member, which results in different learned input-output mappings72. We used a focal loss73 as the objective function for training, which is an extension of the common cross-entropy function for imbalanced classification problems. During training, randomly selected batches of training data are fed as input to the network, with the network targets defined as the sea ice concentration classes over the future 6 months . IceNet’s weights were trained using backpropagation (gradient descent) of the focal loss with the Adam optimiser74. A batch size of 2 was used with an initial learning rate of 0.0005.Training IceNet begins with randomly initialising the network weights. We use He initialisationIn winter months, 18% of the 432 × 432 grid cells on the EASE2 grid have a non-zero chance of sea ice occurring due to many grid cells being over land or too far south. In September, this drops to just over 10%. To avoid the loss function being dominated by trivial 0% SIC grid cells, we define an active grid cell region which shrinks in the summer and expands in the winter based on the maximum sea ice extent observed in a given calendar month. Samples outside of the active grid cell region are weighted by zero in the loss function. The active grid cell region was also used when computing accuracy metrics.With no further modification to the loss function, the imbalance of samples towards the winter months would place more weight on forecasting winter during training. To ensure that each month contributes equally to the loss function, we use a month-wise weighting scheme based on the ratio of active grid cells relative to that of March. This results in September samples being weighted by a factor of 1.75 in the loss function, with the weighting decreasing to 1 for March.Before training on observational data, we first use transfer learning by pre-training each IceNet ensemble member on the CMIP6 data. The entire CMIP6 dataset is presented to IceNet in a shuffled manner; this avoids fitting to one specific model’s physics or to one time period at a given stage of training. After every 1000 pre-training batches, we compute the mean three-class accuracy of the network’s forecasts over the observational validation years 2012–2017 and all six lead times. When the mean validation accuracy exceeds its previous best, the model’s weights are checkpointed. Validating on the observational data during pre-training avoids overfitting to the CMIP6 models’ representations of physics, each with their own systematic biases and limitations. Pre-training is run for two full passes through the pre-training dataset .e–0.1 per epoch after the first 3 epochs. To avoid overfitting to the observational data, we used the aforementioned mean validation accuracy to perform model checkpointing and early stopping with a patience of 10 epochs. This ensures the network’s weights that give the best generalisation performance are used for the final model, before held-out performance starts to degrade due to overfitting.After pre-training, the IceNet ensemble members are fine-tuned on the observational data training years, 1979–2011. We reduced the learning rate by a factor of 2 before fine-tuning and used a learning rate schedule that further reduced the learning rate by a factor of IceNet was implemented in Python 3.7 using the deep learning library TensorFlow. All the computations were carried out using an Nvidia Quadro P4000 graphical processing unit (GPU). On our GPU, pre-training one ensemble model takes around one day, after which fine-tuning to the observational data can be done in 2 h or less.75 using the Python package wandb76 (Weights and Biases), optimising for the mean validation accuracy.Hyperparameters like the learning rate, number of convolutional filters, and batch size all influence the training process or model. These can have a substantial impact on the performance of the trained model but cannot be learned by the training algorithm. To determine appropriate values for the initial learning rate, number of filters in each convolutional layer and the batch size, we employed an automated Bayesian hyperparameter tuning process77. Specifically, we compute an ensemble-mean forecast by averaging the output probability distributions of each of the 25 ensemble members.IceNet is an ensemble of probabilistic predictors, as this has been shown to be a successful strategy for uncertainty quantification 41. To improve the calibration of IceNet’s probabilistic forecasts we use temperature scaling41, a simple post-hoc probability calibration method. Temperature scaling involves the inclusion of a single scalar parameter in the network, T, which divides the logits that are passed into the softmax activation function. Temperature scaling raises or lowers the entropy of the output probability distributions, making it systematically ‘more uncertain’ or ‘less uncertain’—it has no effect on a model’s most probable class and thus does not affect IceNet’s three-class forecast accuracy. While temperature scaling can shift the SIP above or below 0.5 and thus affect binary accuracy and predicted SIE, we found it to have a negligible overall effect on those metrics over the validation years.Modern neural networks are prone to miscalibration and are often systematically overconfident or underconfidentT is fixed to a value of 1 during the training and is optimised using the finished model. We find T in a scalar optimisation scheme using the Brent-Dekker method78, with the objective function being the categorical cross-entropy over the validation years. We employ a two-stage temperature scaling process: firstly, we calibrate each trained IceNet ensemble member individually by optimising T for each lead time); secondly, we calibrate the ensemble-mean model by optimising k in 1, 2, …, 6).47. Our binary accuracy metric can be seen as a normalised version of the IIEE based on the following relationship:The integrated ice edge error (IIEE), defined as the total area covered by grid cells where a binary error in predicting SIC > 15% was made, measures the closeness of the predicted and true ice edges2 in September and 210,000 km2 in March.Based on the areas covered by the active grid cell regions in September and March, a binary accuracy decrease of 1% corresponds to an IIEE increase of 120,000 km80.Here we introduce the processing pipeline and statistical framework developed to bound the ice edge and relate this to forecast calibration and sharpness. Our approach relates to prior works that bound the classification frontier and link it to prediction uncertaintyλ denote a binary variable indicating the ice edge position with λ = 1 at an ice edge contour grid cell and λ = 0 otherwise. The percentage of ice edge bounded by  can be computed from the integralp | λ = 1)dp, where p | λ = 1) denotes the empirical estimate of the true probability distribution P(p | λ = 1), which is the probability of obtaining a SIP output of p from IceNet at a random grid cell, forecast month, and lead time where λ = 1. In practice, this corresponds to finding the percentage of λ = 1 cells bounded by   and the observed ice edge dp. The distribution of SIP over error grid cells, P(p | e = 1), relates to the forecast calibration and sharpness through Bayes’ Rule: P(p | e = 1) ∝ P(e = 1 | p)⋅P(p), where P(e = 1 | p) measures calibration and P(p) measures sharpness. Supplementary Fig. 4 plots empirical estimates of these distributions for IceNet and SEAS5 over the validation years. Ability to bound the binary forecast errors (and thus ability to bound the observed ice edge) is therefore governed by forecast calibration and sharpness. Particularly crucial is the calibration of model probabilities close to p = 0 or p = 1 because the majority of forecasts are made there , and finding an appropriate threshold P(λ = 1) ≥ p′ for the ice edge region, as above. However, the distinction between SIC ≤ 15% and SIC > 15% classes in IceNet has added utility over predicting the boundary between those classes alone.An alternative scheme would be to train a binary classification model to predict the probability of a grid cell containing the ice edge, Supplementary Information for Seasonal Arctic sea ice forecasting with probabilistic deep learningPeer Review File