Search Results (682)

Atmospheric warming results in increase in temperatures for the mean, the coldest, and the hottest day of the year, season, or month. Global warming leads to a large increase in the atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. Using the GISS-E2.1 climate model, we present the future changes in the coldest and hottest daily temperatures as well as in extreme precipitation indices (under four main Shared Socioeconomic Pathways (SSPs)). The increase in the wet-day precipitation ranges between 6% and 15% per 1 °C global surface temperature warming. Scaling of the 95th percentile versus the total precipitation showed that the sensitivity for the extreme precipitation to the warming is about 10 times stronger than that for the mean total precipitation. For six precipitation extreme indices (Total Precipitation, R95p, RX5day, R10mm, SDII, and CDD), the histograms of probability density functions become flatter, with reduced peaks and increased spread for the global mean compared to the historical period of 1850–2014. The mean values shift to the right end (toward larger precipitation and intensity). The higher the GHG emission of the SSP scenario, the more significant the increase in the index change. We found an intensification of precipitation over the globe but large uncertainties remained regionally and at different scales, especially for extremes. Over land, there is a strong increase in precipitation for the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere. There is an enlargement of the drying patterns in the subtropics including over large regions around Mediterranean, southern Africa, and western Eurasia. For the continental averages, the reduction in total precipitation was found for South America, Europe, Africa, and Australia, and there is an increase in total precipitation over North America, Asia, and the continental Russian Arctic. Over the continental Russian Arctic, there is an increase in all precipitation extremes and a consistent decrease in CDD for all SSP scenarios, with the maximum increase of more than 90% for R95p and R10 mm observed under SSP5–8.5. Full article

Arctic shelf waters exhibit high optical variability due to terrestrial inputs and elevated colored dissolved organic matter (CDOM) concentrations, posing significant challenges for the accurate retrieval of chlorophyll-a (Chl-a) and downwelling diffuse attenuation coefficients (${{\rm K}}_{\mathrm{d}}\left(\lambda \right)$). These retrieval biases contribute to substantial uncertainties in estimates of primary productivity and upper-ocean heat flux in the Arctic Ocean. However, the performance and constraints of existing ocean color algorithms in Arctic shelf environments remain insufficiently characterized, particularly under seasonally variable and optically complex conditions. In this study, we present a systematic multi-year evaluation of commonly used empirical and semi-analytical ocean color algorithms across the western Arctic shelf, based on seven expeditions and 240 in situ observation stations. Building on these evaluations, regionally optimized retrieval schemes were developed to enhance algorithm performance under Arctic-specific bio-optical conditions. The proposed OCx-AS series for Chl-a and ${{\rm K}}_{\mathrm{d}}$-DAS models for ${{\rm K}}_{\mathrm{d}}\left(\lambda \right)$ significantly reduce retrieval errors, achieving RMSE improvements of over 50% relative to global standard algorithms. Additionally, we introduce QAA-LS, a modified semi-analytical model specifically adapted for the Laptev Sea, which addresses the strong absorption effects of CDOM and corrects the significant overestimation observed in previous QAA versions. Full article

The paper presents the results of a study on littoral algae communities along the Murmansk coast from 2021–2024. The emphasis is on fucus algae and green algae communities as the most abundant ones. For the first time, an annual monitoring of littoral algae distribution in the bays of the Barents Sea was performed using a set of methods, allowing a better understanding of the dynamics of their biomass. Unlike most classical studies, which only focus on biomass and population structure, this work shows the results of using UAV-based remote sensing in combination with traditional coastal sampling techniques. The features and limitations of this approach in Arctic latitudes are discussed. According to the monitoring results, an increase in fucus algae biomass is observed in the study area, which may be associated with an increase in summer temperatures and water salinity. Fucus serratus and Pelvetia canaliculata populations remain stable. Ulvophycean algae show seasonal peaks of development with abnormally high biomass in areas of anthropogenic impact, which may indicate local eutrophication. The map of algae spatial distribution is presented. The results are important for understanding the structure and functioning of the Arctic ecosystem and for assessing the environmental impact in the region. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

The observations of atmospheric, oceanic, and sea ice data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition were used to analyze the influence of snow redistribution and melt-pond processes on the evolution of sea ice thickness (SIT) in 2019 and 2020. To mitigate the effect of missing atmospheric observations from the time of the expedition, we used ERA5 atmospheric reanalysis along the MOSAiC drift trajectory to force the single-column sea ice model Icepack. SIT simulations from six combinations of two melt-pond schemes and three snow-redistribution configurations of Icepack were compared with observations and analyzed to investigate the sources of model–observation discrepancies. The three snow-redistribution configurations are the bulk scheme, the snwITDrdg scheme, and one simulation conducted without snow redistribution. The bulk scheme describes snow loss from level ice to leads and open water, and snwITDrdg describes wind-driven snow redistribution and compaction. The two melt-pond schemes are the TOPO scheme and the LVL scheme, which differ in the distribution of melt water. The results show that Icepack without snow redistribution simulates excessive snow–ice formation, resulting in an SIT thicker than that observed in spring. Applying snow-redistribution schemes in Icepack reduces snow–ice formation while enhancing the congelation rate. The bulk snow-redistribution scheme improves the SIT simulation for winter and spring, while the bias is large in simulations using the snwITDrdg scheme. During the summer, Icepack underestimates the sea ice surface albedo, resulting in an underestimation of SIT at the end of simulation. The simulations using the TOPO scheme are characterized by a more realistic melt-pond evolution compared to those using the LVL scheme, resulting in a smaller bias in SIT simulation. Full article

The global shipping industry is evolving towards deep integration of digital transformation, intelligent upgrading, and green development. Meanwhile, recent geopolitical shifts have introduced heightened uncertainties into international shipping, compounding the challenges and escalating the demands for weather routing services for ocean-going ships. This paper provides a systematic review and expert perspective on China’s current status and key challenges in ocean-going weather routing services. Based on operational insights from China’s national meteorological service synthesized with a review of current trends and the literature, it further explores the future development of China’s ocean-going weather routing services and technologies from multiple dimensions: enhancing maritime weather observation capabilities, developing advanced weather routing service models, upgrading autonomous and controllable global satellite communication systems, promoting intelligent navigation technologies to facilitate shipping’s low-carbon transition, and expanding meteorological support capabilities for Arctic shipping routes. The analysis identifies critical gaps and proposes strategic directions, offering a unique contribution to understanding the trajectory of weather routing services within China’s specific national context from the perspective of its primary national service provider. Full article

In Arctic regions, ship structures face low temperatures, overloads, thickness effects, and fluctuating stress ratios, which significantly influence the fatigue crack growth (FCG) behavior of marine steels. This study investigates the FCG behaviors of DH36 steel by a series of experiments under the combined effects of low temperatures, overload ratios R_ol, specimen thickness B, and stress ratios R. Experiment results show that the yield strength, ultimate tensile strength, and elastic modulus of DH36 steel exhibit negative correlations with temperature varying within the Arctic temperature range. A reduction in fatigue crack growth rate (FCGR) is observed under the combined effects of low temperature and overload, and the magnitude of decrease shows a positive correlation with R_ol. Notably, low temperatures weaken the FCG retardation effect induced by overload, and this attenuation becomes more pronounced as temperature decreases. Under low temperatures, while maintaining constant peak load, increasing R significantly reduces both initial and terminal stress intensity factor ranges ΔK₀ and ΔK_e, resulting in diminished effective crack driving force and thereby substantially extending FCG life. Although increased B enhances FCGR at low temperatures, thinner plates demonstrate shorter FCG life due to their higher ΔK₀ values. Full article

In this paper, the time-series model is used to predict whether an ocean buoy is about to be inside a vortex. Marine buoys are an important tool for collecting ocean data and studying ocean dynamics, climate change, and ecosystem health. A vortex is an important ocean dynamic process. If we can predict that a buoy is about to enter a vortex, we can automatically adjust the buoy’s sampling frequency to better observe the vortex’s structure and development. To address this requirement, based on the profile data, including latitude and longitude, temperature, and salinity, collected by 56 buoys in the Arctic Ocean from 2014 to 2023, this paper uses the TSMixer time-series model to predict whether an ocean buoy is about to be inside a vortex. The TSMixer model effectively captures the spatio-temporal characteristics of multivariate time series through time-mixing and feature-mixing mechanisms, and the accuracy of the model reaches 84.6%. The proposed model is computationally efficient and has a low memory footprint, which is suitable for real-time applications and provides accurate prediction support for marine monitoring. Full article

Precipitation cyclicity plays a crucial role in regional water supply and climate predictions. In this study, we used observational data from 34 representative meteorological stations in the Xinjiang region, a major part of inland arid China, to characterize the interannual cyclicity of regional precipitation from 1951 to 2021 and analyze its contributing factors. The results indicated that the mean annual precipitation in Xinjiang (MAP_XJ) was dominated by a remarkably increasing trend over the past 70 years, which was superimposed by two bands of interannual cycles of approximately 3 years with explanatory variance of 56.57% (Band I) and 6–7 years with explanatory variance of 23.38% (Band II). This is generally consistent with previous studies on the cyclicity of precipitation in Xinjiang for both seasonal and annual precipitation. We analyzed the North Tropical Atlantic sea-surface temperature (NTA_SST), El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Indian Summer Monsoon (ISM) as potential forcing factors that show similar interannual cycles and may contribute to the identified precipitation variability. Two approaches, multivariate linear regression and the Random Forest model, were employed to ascertain the relative significance of each factor influencing Bands I and II, respectively. The multivariate linear regression analysis revealed that the AO index contributed the most to Band I, with a significance score of −0.656, whereas the ENSO index with a one-year lead (ENSO_−1yr) played a dominant role in Band II (significance score = 0.457). The Random Forest model also suggested that the AO index exhibited the highest significance score (0.859) for Band I, whereas the AO index with a one-year lead (AO_−1yr) had the highest significance score (0.876) for Band II. Overall, our findings highlight the necessity of employing different methods that consider both the linear and non-linear response of climate variability to driving factors crucial for future climate prediction. Full article

Seafloor communities along the eastern Svalbard coast remain poorly studied. To address this gap, we sampled benthic organisms on the soft sediments of Storfjord in 2017 and 2019, a large fjord predominantly influenced by cold Arctic waters, to study the local fauna and identify the key environmental drivers shaping community structure. In total, 314 taxa were recorded, with an increase in abundance (from 3923 to 8977 ind. m⁻², mean 6090 ind. m⁻²) and a decline in biomass (ranging from 265 to 104 g m⁻², mean 188 g m⁻²) toward the outer part of the fjord. However, no clear spatial trends were observed for alpha diversity (approximately 100 species per 0.3 m²) or the Shannon index (mean 3 per station). The primary factors influencing benthic abundance were the duration of the ice-free period (IFP) and the degree of siltation (DS), both of which are proxies for trophic conditions. The prevailing taxa displayed a high tolerance to temperature fluctuations and seasonal variability in nutrient inputs. Benthic biomass showed a negative relationship with IFP, DS, and water depth, but it was positively correlated with the proportion of fine-grained sediment. The Yoldia hyperborea community (mean abundance: 3700 ind. m⁻², mean biomass: 227 g m⁻²) was associated with Arctic waters characterized by higher inorganic suspension loads. In contrast, areas with reduced or weaker sedimentation were dominated by the communities of Maldane sarsi (6212 ind m⁻², 226 g m⁻²) and Maldane sarsi + Nemertini g.sp. (5568 ind m⁻², 165 g m⁻²). The Spiochaetopterus typicus community (7824 ind m⁻², 139 g m⁻²) was observed in areas under moderate influence of Atlantic waters, characterized by low sedimentation rates and increased fresh detritus flux. Full article

Sea surface temperature (SST) is considered a strong indicator of climate change, being an essential parameter for long-range weather and climate forecasting. Another important indicator of climate change is sea level (SL), which has a longer history of systematic instrumental observations. This paper aims to examine the relationships between low-latitude variations in ocean characteristics (SST and SL) and surface air temperature (SAT) anomalies in the Arctic and mid-latitudes, and discuss the possibility of using SST and SL as predictors to forecast seasonal SAT anomalies. Archives of meteorological observations, atmospheric and oceanic reanalyses, and long-term series of tide gauge data on SL were used in this study. An analysis of relationships between seasonal SAT in different mid-to-high latitude regions and SST made it possible to identify areas in the ocean that have the greatest influence on SAT patterns. The most commonly identified area is located in the tropical North Atlantic. Another area was found in the Indo-Pacific warm pool. The predictive potential of the relationships identified between ocean characteristics (SST and SL) and SAT will be used to build deep learning models aimed at predicting climate variability in mid-to-high latitudes. Full article

The thickness of the Arctic sea ice constitutes one of the crucial indicators of global climate change, and while deep learning has shown promise in predicting sea ice thickness (SIT), the field continues to grapple with the challenge of limited data availability. In this study, we introduce a Wasserstein Generative Adversarial Network–Long Short-Term Memory (WGAN-LSTM) model, which leverages the data generation capabilities of WGAN and the temporal prediction strengths of LSTM to perform single-step SIT prediction. During model training, the mean square error (MSE) and a novel comprehensive index, the Distance between Indices of Simulation and Observation (DISO), are used as two metrics of the loss function to compare. To thoroughly assess the model’s performance, we integrate the WGAN-LSTM model with the Monte Carlo (MC) dropout uncertainty estimation method, thereby validating the model’s enhanced generalization capabilities. Experimental results demonstrate that the WGAN-LSTM model, utilizing MSE and DISO as loss functions, improves comprehensive performance by 51.9% and 75.2%, respectively, compared to the traditional LSTM model. Furthermore, the MC estimates of the WGAN-LSTM model align with the distribution of actual observations. These findings indicate that the WGAN-LSTM model effectively captures nonlinear changes and surpasses the traditional LSTM model in prediction accuracy. The demonstrated effectiveness and reliability of the WGAN-LSTM model significantly advance short-term SIT prediction research in the Arctic region, particularly under conditions of data scarcity. Additionally, this model offers an innovative approach for identifying other physical features in the sea ice field based on sparse data. Full article

The urban landscape of the Russian Arctic, shaped during the Soviet era of extensive urbanization, embeds narratives of colonial appropriation and serves as the foundation for ongoing urban development. In light of climatic, political, and social uncertainties, design disciplines must navigate the balance between environmental sustainability and the varied needs of residents, requiring a systemic approach to design. This study combines theoretical analysis with qualitative field research conducted in two Western Siberian cities (Novyy Urengoy and Tarko-Sale), including interviews, mental mapping, and systematic observation of urban life. Analysis of the collected data revealed significant challenges in current urban design practices, particularly regarding weather protection, seasonal adaptation, and social space creation. The proposed model constitutes a pioneering initiative in domestic Arctic urban research, aiming to conceptualize a context-sensitive approach to urban environmental formation, thereby challenging prevalent universal/mainstream methodologies and establishing a theoretical framework for future applications. Our theoretical model synthesizes representations, perceptions, and materiality, conceptualizing the architectural environment as a context-sensitive “life-support module”. This conceptualization emphasizes that successful Arctic urban design must emerge from specific local contexts rather than universal solutions, as demonstrated by our analysis of residents’ spatial practices and adaptations to extreme conditions. We reference media studies to analyze urban materiality as both an artificial construct that mediates perceptions of the immediate surroundings and as a generative force that actively shapes meanings, practices, and sensations. Our findings indicate that current standardized approaches to Arctic urban development often fail to address local needs and environmental conditions, suggesting the necessity for a fundamental shift in design methodology. Given that the urban realm is a fundamental component in shaping individual and collective perceptions, this conceptual shift has the potential to significantly influence prevailing societal views of the “empty” and “hostile” Arctic. Full article

Understanding lagoon behavior is crucial for both scientific research and engineering decisions, especially in delicate Arctic environments. Lagoons are vital to coastal areas, often bolstering infrastructure resilience. Since spring 2019, we have monitored the Longyearbyen lagoon (Spitsbergen), vital for coastal erosion defense and serving as a natural laboratory. The location’s well-developed infrastructure and accessible logistics make it an ideal testing site available at any time. It can be used for many natural scientific studies. The lagoon continually changes due to the primary action of waves and tides. This article focuses on gravel spit movement, accelerating in recent years to several meters monthly. Using methods of aerial and satellite images, laser scanning, and hydrodynamic measurements, we have delineated processes, rates, and mechanisms behind this movement. The measurements revealed an accelerating eastward movement of the lagoon spit, from 8 m in the first year to 86 m in the fourth year of observation. This can be explained by a combination of the reconstruction of the Longyearbyen riverbed and increased flow because of climate change. Notably, the expansion does not only occur in the summer months: from September 2022 to February 2023, the spit moved by 40 m, and then, by 19 m from February to June 2023. We found that the bed-load transport along the spit coupled with gravel slides are the primary drives of lagoon expansion and growth. We also investigated movements of groundwater in the spit and changes in gravel contents along the spit, influencing the water saturation of the gravel. Modelling these processes aids in forecasting lagoon system development, crucial for informed management and engineering decisions in Arctic coastal regions. Full article

Fine-scale maps of ground ice and related surface features are critical for permafrost-related modelling and management. However, such maps are lacking across almost the entire Arctic. Machine learning provides the potential to automate regional fine-scale ground ice mapping using remote sensing and topographic data. Here, we evaluate the predictive skill of XGBoost models for identifying (1) ice wedge and (2) top-5m visible ground ice in the Tuktoyaktuk Coastlands. We find high predictive skill for ice wedge occurrence (ROC AUC = 0.95, macro F1 = 0.80), with the most important predictors being slope, distance to the coast, and probability of depression. The model accurately predicted regional and local trends in ice wedge occurrence, with an increase in ice wedge polygon (IWP) probability towards the coast and in poorly drained depressions. The model also captured IWP in well-drained uplands of the study area, including locations with poorly visible troughs not contained in the training data. Spatial transferability analyses highlight the regional variability of ice wedge probability, reflecting contrasting climatic and surface conditions. Conversely, the low predictive skill for visible ground ice (ROC AUC = 0.67, macro F1 = 0.53) is attributed to limitations in training data and weak associations with the remotely sensed predictors. The varying predictive accuracy highlights the importance of high-quality reference data and site-specific conditions for improving ground ice studies with data-driven modelling from remote sensing observations. Full article