The Arctic is undergoing increased warming compared to the global mean, with major implications for the mass balance of glaciers. Direct observations of mass balance in the Russian Arctic are sparse and remotely sensed volume changes do not provide information about climatic drivers. Here, we present simulations of the climatic mass balance and meltwater runoff from glaciers in Franz Josef Land and Novaya Zemlya from 1991 to 2022. Based on simulations of glacier climatic mass balance over the period 1991–2022, we present a first detailed view of mass balance evolution in Franz Josef Land and Novaya Zemlya. The simulations are conducted at a 2.5 km resolution using the CryoGrid model forced by the Copernicus Arctic Regional ReAnalysis (CARRA) product. Over the 30 year simulation period, the climatic mass balance of both Franz Josef Land (0.21 m w.e. a−1) and Novaya Zemlya (0.07 m w.e. a−1) is positive on average without a significant trend in annual climatic mass balance. There is still a tendency towards more frequent high-melt years after 2010 and the associated glacier runoff has intensified with record melt years occurring during the model period.

In 2015, a continuous 15.4 m snow/firn core was recovered from central South Georgia Island at ∼850 m a.s.l. All firn core samples were analyzed for major (Al, Ca, Mg, Na, K, Ti and Fe) and trace element concentrations (Sr, Cd, Cs, Ba, La, Ce, Pr, Pb, Bi, U, As, Li, S, V, Cr, Mn, Co, Cu and Zn) and stable water isotopes. The chemical and isotopic signal is well preserved in the top 6.2 m of the core. Below this depth, down to the bottom of the core, signal dampening is observed in the majority of the elemental species making it difficult to distinguish a seasonal signal. Thirteen elements (As, Bi, Ca, Cd, Cu, K, Li, Mg, Na, Pb, S, Sr and Zn) have crustal enrichment factor values higher than 10 suggesting sources in addition to those found naturally in the crust. While this study shows that 850 m a.s.l. is not high enough to preserve a record including recent years, higher-elevation (>1250 m a.s.l.) glaciers may be likely candidates for ice core drilling to recover better-preserved, continuous, recent to past glaciochemical records.

As snowlines retreat, the bare ice of Central Asian glaciers is increasingly exposed to short-wave radiation and high temperatures. The importance of bare-ice albedo for glacier melt rates is thus rising. Little is known about the variability of bare-ice albedo, its drivers or its implications for glacier melt. We address this gap by presenting the sub-seasonal and interannual variability of bare-ice albedo of Abramov Glacier in Kyrgyzstan between 1999 and 2022. We derived albedo products from Landsat surface reflectance data, investigated the relationship between air temperature and bare-ice albedo variability and explored the implications of this variability for glacier melt. Our results indicate that bare-ice albedo undergoes a sub-seasonal cycle controlled by air temperature and elevation-dependent refreezing events. Bare-ice albedo decreased over the tongue in July and August between 1999 and 2017, while, in 2018, a lateral displacement of the ice resulted in a shift in the patterns of bare-ice albedo. We found significant correlations between bare-ice albedo variability and both temperature and glacier melt at various timescales. Rising temperatures are thus expected to lead to darker bare ice and amplified feedback melt cycles. Integrating albedo variability into glaciological models is thus crucial for accurate predictions of accelerated glacier response to intensifying climate change.

The two main large-scale features of Arctic sea-ice drift are the Beaufort Gyre and the Transpolar Drift Stream. They exhibit strong intraseasonal and interannual variability. Winter 2016/17 showed increased cyclone activity, leading to the collapse of the Beaufort Sea high and the reversal of the Beaufort Gyre. Winter 2020/21 displayed decreased cyclone activity and intense anticyclonic ice transport in the Beaufort Gyre. Here we show that the European Centre for Medium-Range Weather Forecasts’s (ECMWF) extended-range (46 days) retrospective forecasts were able to predict the ice motion during these cases. The initial contrasts in sea level pressure, surface winds and ice drift were well captured, and their temporal evolution—including the reversal of the usual drift direction—well reproduced by the forecasts initialized about a week before the event. Sea-ice thickness in the forecast exhibited initial errors even greater than 1 m that persisted throughout the forecast and negatively affected the ice speed forecast. Despite these shortcomings, the dynamic forecast outperformed the persistence and climatology forecast and represented the observed relation between surface winds and ice drift well. The benefit of dynamic forecasts is especially clear in cases that differ from climatology, like the one we focus on.

Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes; yet, existing approaches suffer from synchronization overhead in this setting. In this article, we exploit the formulation of data assimilation as a Bayesian inference problem and apply a message-passing algorithm to solve the spatial inference problem. Since message passing is inherently based on local computations, this approach lends itself to parallel and distributed computation. In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements.

Azooxanthellate corals of Rhizangiidae, with their distinctive morphological and ecological features, are widely distributed across global oceans but remain under-studied due to identification challenges. Comprehensive underwater surveys across diverse marine habitats such as rocky reefs and submerged shipwrecks were undertaken, and findings highlighted the exclusive presence of Culicia stellata in natural rocks, off the coast of Kaup for the first time in Indian EEZ. The study also records the presence of Cladangia exusta, nearly six decades after the first record from off Cochin, Laccadive Sea. This research presents a detailed study of the taxonomic description, distribution, and ecological preferences of Cladangia exusta and Culicia stellata in the Laccadive Sea. This study underscores the importance of accurate species identification for effective conservation strategies and enriching biodiversity records.

Morphological and molecular methods were used to describe a new species of Trapania Pruvot-Fol, 1931 from shallow water kelp forests on the north-central coast of Peru. The new species, Trapania huarmeyana sp. nov., is distinguished from other species along the Eastern Pacific by external morphological characters such as its translucent white body with brown stripes and small spots on the dorsum, blotches on the base of the extra-branchial processes, extra rhinophoral processes and gill branches. Internally, T. huarmeyana sp. nov. is distinguishable by several morphological characteristics of the radula, jaws and genital organs. Phylogenetic trees recovered using Bayesian Inference and Maximum Likelihood analysis of DNA sequences support its distinct status and clarify its relationship to other species from the Eastern Pacific. This new species constitutes the first record of Trapania from the Humboldt Current Ecosystem, contributing to our understanding of the distribution of the genus in the South-eastern Pacific.

Although nudibranchs are common and attractive animals, our understanding of these marine gastropods in Vietnam remains limited. Prior research has suggested that combining morphological examination with molecular analysis results in more accurate identification of nudibranchs. However, previous studies in Vietnam have typically relied solely on morphological methods for nudibranch identification. In this study, the nudibranch species Halgerda batangas was recorded in Vietnam for the first time based on both morphological and molecular approaches. Halgerda batangas was characterized by a network pattern consisting of orange lines, relatively low dorsal tubercles with red-orange caps and white basal rings, and an orange line along the foot margin. Molecular analysis corroborated the morphological findings. These results suggest that integrating morphological and molecular methods is an effective approach for identifying nudibranchs.

The yellownose skate (Dipturus chilensis) and roughskin skate (Dipturus trachyderma) are the only two elasmobranch species targeted by commercial fishing operations in Chile. Despite their importance, much of their biology and ecology remain poorly understood. This research aimed to evaluate the feasibility of tagging these species. In 2021, a pilot study was conducted at two locations, utilizing Petersen discs, acoustic transmitters, and pop-up satellite transmitters on both species. The results revealed a 6% recovery rate from the 50 skates tagged with Petersen discs, while 29.4% of those tagged with acoustic transmitters were successfully detected. Additionally, data from all ten satellite transmitters were successfully transmitted and recovered. The results revealed a maximum horizontal movement of 35.9 km, with the duration of liberty ranging from 8 to 275 days. Stocks of both species are currently depleted, and fishery management relies on closures and total allowable catches, where fishing effort is concentrated in short spatial and temporal windows. These particularities present significant challenges for implementing a national tagging programme, especially in terms of tag recovery. The main conclusion of this research is that the implementation of a tagging programme for both species is feasible. Satellite tagging provides the best results, but its higher implementation cost and limitations in use for relatively small skates could be mitigated by combining it with Petersen discs. Establishing a long-term tagging programme is essential for enhancing the understanding of distribution and migration patterns, which is crucial for enhancing conservation and management efforts for these skates in Chile.

While the giant anemone, Relicanthus daphneae, has been described as a characteristic inhabitant of the East Pacific Ocean since 1991, there are relatively few published occurrences worldwide. Here, we present the discovery and molecular verification of R. daphneae along the southern Central Indian Ridge, at the Rodriguez Triple Junction, and along the northern Southeast Indian Ridge within the BGR contract area for the exploration of marine massive sulphide deposits in the Indian Ocean. Individuals were solitary and attached exclusively to basalt hard substrates on the periphery of hydrothermal vent fields, at distances from active vents between 66 and 710 m. We report megafauna observed in close proximity to R. daphneae and, in one case, polychaetes on its tentacles and oral disc. For the first time, the giant anemone was observed capturing prey, a shrimp of the species Rimicaris kairei. Beyond this remark on the diet of these anemones, we also report other behavioural aspects for this species.

Wind speed at the sea surface is a key quantity for a variety of scientific applications and human activities. For its importance, many observation techniques exist, ranging from in situ to satellite observations. However, none of such techniques can capture the spatiotemporal variability of the phenomenon at the same time. Reanalysis products, obtained from data assimilation methods, represent the state-of-the-art for sea-surface wind speed monitoring but may be biased by model errors and their spatial resolution is not competitive with satellite products. In this work, we propose a scheme based on both data assimilation and deep learning concepts to process spatiotemporally heterogeneous input sources to reconstruct high-resolution time series of spatial wind speed fields. This method allows to us make the most of the complementary information conveyed by the different sea-surface information typically available in operational settings. We use synthetic wind speed data to emulate satellite images, in situ time series and reanalyzed wind fields. Starting from these pseudo-observations, we run extensive numerical simulations to assess the impact of each input source on the model reconstruction performance. We show that our proposed framework outperforms a deep learning–based inversion scheme and can successfully exploit the spatiotemporal complementary information of the different input sources. We also show that the model can learn the possible bias in reanalysis products and attenuate it in the output reconstructions.