Search Results (36)

Arctic regions are warming rapidly, elevating extinction risks and accelerating ecosystem change, yet widely used bioclimatic datasets rarely represent polar-specific ecological constraints. Here we present BioClimPolar_2300 v1.0, a raster bioclimatic dataset designed for terrestrial Arctic biodiversity research under climate change. The dataset includes 33 gridded bioclimatic layers at a 10 km spatial resolution, covering seven discrete temporal intervals from 2010 to 2300 AD. In addition to conventional variables used globally, BioClimPolar_2300 incorporates three polar-relevant constraint domains: (1) polar day–night phenomena (PDNs), including degree-day metrics during polar night and polar day; (2) temperature-defined seasonal cycles (TSCs), including seasonal temperature, precipitation, aridity, and season length; (3) hot/cold stresses (HCSs), capturing indices of extreme summer heat and winter cold. Precipitation during snow-melting days (P_melting) is also included due to its relevance for species depending on subnivean habitats. Climate fields were extracted from CMIP6 models and statistically downscaled to 10 km using a change-factor approach under a polar projection. Monthly fields were linearly interpolated to derive daily grids, enabling the computation of variables that require daily inputs. Validation against observations from 30 Arctic weather stations indicates performance suitable for biodiversity applications, and two exemplar range shift case studies (one animal and one plant) illustrate biological relevance and provide practical guidance for data extraction and use. BioClimPolar_2300 fills a key gap in Arctic bioclimatic resources and supports more realistic biodiversity assessments and conservation planning through 2300. Full article

Iceberg detection in Arctic sea-ice environments is essential for navigation safety and climate monitoring, yet remains challenging due to observational and environmental constraints. The scarcity of labelled data, limited optical coverage caused by cloud and polar night conditions, and the small, irregular signatures of icebergs in synthetic aperture radar (SAR) imagery make automated detection difficult. This study evaluates the environmental feasibility of applying a modern deep learning model for iceberg detection within land-fast sea ice. We adapt a YOLOv8 convolutional neural network within the Dual Polarisation Intensity Ratio Anomaly Detector (iDPolRAD) framework using dual-polarised Sentinel-1 SAR imagery from the Franz Josef Land region, validated against Sentinel-2 optical data. A total of 2344 icebergs were manually labelled to generate the training dataset. Results demonstrate that the network is capable of detecting icebergs embedded in fast ice with promising precision under highly constrained data conditions (precision = 0.81; recall = 0.68; F1 = 0.74; mAP = 0.78). These findings indicate that deep learning can function effectively within the physical and observational limitations of current Arctic monitoring, establishing a foundation for future large-scale applications once broader datasets become available. Full article

Cloud feedback is a major source of uncertainty in climate projections. In particular, Arctic clouds, arguably one of the most poorly understood aspects of the climate system, strongly modulate radiative energy fluxes from the Earth’s surface to the top of the atmosphere. In situ and satellite observations reveal the existence of ubiquitous optically thin ice clouds (TICs) in the Arctic during polar nights, whose influence on atmospheric energy is still poorly understood. This study quantifies the effect of TICs on the atmospheric energy budget during polar winter. A reanalysis-driven simulation based on the Canadian Regional Climate Model version 6 (CRCM6) was used with the Predicted Particle Properties (P3) scheme (2016) to produce an ensemble of 3 km mesh simulations. This set is composed of three simulations: CRCM6 (reference, the original dynamically coupled cloud formation), CRCM6 (nocld) (clear-sky) and CRCM6 (100%cld) (overcast, 100% cloud cover as a forcing perturbation). Using the regional energetic equations (Nikiema and Laprise), we compare the three cases to assess TIC forcing. The results show that TICs cool the atmosphere, with the difference between two simulations (cloud/no clouds) reaching up to −2 K/day, leading to a decrease in temperature on the order of ~−4 KMonth⁻¹. The energetics cycle indicates that the time-mean enthalpy generation term G_M and baroclinic conversion dominate Arctic circulation. The G_M acting on the available enthalpy reservoir (A_M) increased by a maximum value of ~5 W·m⁻² (58% on average) due to the effects of TICs, increasing in energy conversion. TICs also lead to average changes of 9% in time-mean available enthalpy and −5.9% in time-mean kinetic energy. Our work offers valuable insights into the Arctic winter atmosphere and provides the means to characterize clouds for radiative transfer calculations, to design measurement instruments, and to understand their climate feedback. Full article

The Arctic, characterized by extensive ice and snow cover with persistent low solar elevation angles and prolonged polar nights, poses significant challenges for conventional spectral threshold methods in cloud detection and cloud-top thermodynamic phase classification. The study addressed these limitations by combining active and passive remote sensing and developing a machine learning framework for cloud detection and cloud-top thermodynamic phase classification. Utilizing the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud product from 2021 as the truth reference, the model was trained with spatiotemporally collocated datasets from FY3D/MERSI-II (Medium Resolution Spectral Imager-II) and CALIOP. The AdaBoost (Adaptive Boosting) machine learning algorithm was employed to construct the model, with considerations for six distinct Arctic surface types to enhance its performance. The accuracy test results showed that the cloud detection model achieved an accuracy of 0.92, and the cloud recognition model achieved an accuracy of 0.93. The inversion performance of the final model was then rigorously evaluated using a completely independent dataset collected in July 2022. Our findings demonstrated that our model results align well with results from CALIOP, and the detection and identification outcomes across various surface scenarios show high consistency with the actual situations displayed in false-color images. Full article

Against the backdrop of intensified global climate change, the frequency and intensity of extreme weather events in mainland China continue to rise due to its unique topography and complex climate types. In-depth research on the trends and impacts of climate extremes can help develop effective adaptation and mitigation strategies to protect the environment and enhance social resilience. In this research, temperature data from 2029 meteorological stations for the period 1961–2021 were used to study 15 extreme temperature indices and 3 extreme composite temperature indices. Linear propensity estimation and the Mann–Kendall test were applied to analyze the spatial and temporal variations in extreme temperatures in China, and Pearson’s correlation analysis was used to reveal the relationship between these indices and atmospheric circulation. The results show that in the past 60 years, the extreme temperature index in China has shown a trend of decreasing low-temperature events and increasing high-temperature events; in particular, the increase in warm nights is significantly higher than that of warm days. In terms of spatial distribution, daily maximum temperature less than the 10th percentile (TX10P) and daily minimum temperature greater than the 90th percentile (TN90P) increased significantly in the warm temperate sub-humid (WTSH) region, north subtropical humid (NSH) region, and marginal tropical humid (MTH) region, whereas frost days (FD0) and diurnal temperature range (DTR) decreased significantly. In the extreme composite temperature index, extreme temperature range (ETR) showed a downward trend, while compound heatwave (CHW) and compound heatwave and relative humidity (CHW-RH20) increased, with the latter mainly concentrated in the WTSH and NSH regions. Correlation analysis with climate oscillation shows that Arctic Oscillation (AO), Atlantic Multiannual Oscillation (AMO), and El Niño–Southern Oscillation (ENSO) are positively correlated with extremely high temperatures, whereas North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) are negatively correlated. Full article

The climate impact of Arctic aerosols, like the Arctic Haze, and their origin are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study, we present a homogenised data set from a sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, of the 20 years from 2004–2023. Due to polar day and polar night, it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that autumn and winter are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn. A clear signal of large particles during the Arctic Haze can not be distinguished from large aerosols in winter. With autocorrelation analysis, we found that AOD events usually occur with a duration of several hours. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions, or wildfires in the Northern Hemisphere but did not find one single cause that clearly determines the Arctic AOD. Therefore the observed optical depth is a superposition of different aerosol sources. Full article

This study explores the relationship between the light features of the Arctic spring equinox and circadian rhythms, sleep and metabolic health. Residents (N = 62) provided week-long actigraphy measures, including light exposure, which were related to body mass index (BMI), leptin and cortisol. Lower wrist temperature (wT) and higher evening blue light exposure (BLE), expressed as a novel index, the nocturnal excess index (NEIbl), were the most sensitive actigraphy measures associated with BMI. A higher BMI was linked to nocturnal BLE within distinct time windows. These associations were present specifically in carriers of the MTNR1B rs10830963 G-allele. A larger wake-after-sleep onset (WASO), smaller 24 h amplitude and earlier phase of the activity rhythm were associated with higher leptin. Higher cortisol was associated with an earlier M10 onset of BLE and with our other novel index, the Daylight Deficit Index of blue light, DDIbl. We also found sex-, age- and population-dependent differences in the parametric and non-parametric indices of BLE, wT and physical activity, while there were no differences in any sleep characteristics. Overall, this study determined sensitive actigraphy markers of light exposure and wT predictive of metabolic health and showed that these markers are linked to melatonin receptor polymorphism. Full article

Polar-orbiting satellite observations are of fundamental importance to explore the main scientific challenges in the Arctic Ocean, as they provide information on bio-geo-physical variables with a denser spatial and temporal coverage than in-situ instruments in such a harsh and inaccessible environment. However, they are limited by the lack of coverage near the North Pole (Polar gap), the polar night, and frequent cloud cover or haze over the ocean and sea ice, which prevent the use of optical satellite instruments, as well as by the limited availability of external validation data. The satellite sensors’ coverage and repeat cycles may also have limitations in properly identifying and resolving the dominant spatial and temporal scales of atmospheric, ocean, cryosphere and land variability and their interactive processes and feedback mechanisms. In this paper, we provide a state of the art of contribution of satellite observations to the understanding of the polar environment and climate scientific challenges tackled within the Arktalas Hoavva project funded by the European Space Agency. We identify the current limitations to the wider use of polar orbiting remote sensing data, as well as the observational gaps of the existing satellite missions. A comprehensive overview of all satellite missions and applications is given provided with a primary focus on the European satellites. Finally, we assess the expected capability of the approved future satellite missions to answer today’s scientific challenges in the Arctic Ocean. Full article

Accurate monitoring of the spatiotemporal dynamics of snow and ice is essential for under-standing and predicting the impacts of climate change on Arctic ecosystems and their feedback on global climate. Traditional optical and Synthetic Aperture Radar (SAR) remote sensing still have limitations in the long-time series observation of polar regions. Although several studies have demonstrated the potential of moonlight remote sensing for mapping polar snow/ice covers, systematic evaluation on applying moonlight remote sensing to monitoring spatiotemporal dynamics of polar snow/ice covers, especially during polar night periods is highly demanded. Here we present a systematic assessment in Svalbard, Norway and using data taken from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) Day/Night Band (DNB) sensor to monitor the spatiotemporal dynamics of snow/ice covers during dark Arctic winters when no solar illumination available for months. We successfully revealed the spatiotemporal dynamics of snow/ice covers from 2012 to 2022 during polar night/winter periods, using the VIIRS/DNB time series data and the object-oriented Random Forests (RF) algorithm, achieving the average accuracy and kappa coefficient of 96.27% and 0.93, respectively. Our findings indicate that the polar snow/ice covers show seasonal and inter-seasonal dynamics, thus requiring more frequent observations. Our results confirm and realize the potential of moonlight remote sensing for continuous monitoring of snow/ice in the Arctic region and together with other types of remote sensing data, moonlight remote sensing will be a very useful tool for polar studies and climate change. Full article

This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June–September for the years 2017–2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments. Full article

Analysis of cetacean blow offers a unique potential for non-invasive assessments of their health. In recent years, the use of uncrewed aerial vehicles (UAVs) has revolutionized the way these samples are collected. However, the high cost and expertise associated with purpose-built waterproof UAVs, paired with the challenges of operating during difficult meteorological conditions, can be prohibitive for their standardized use worldwide. A pilot study was conducted in a Northern Norwegian fjord during winter, to assess the feasibility of using a minimally modified and affordable consumer drone to collect blow samples even during the polar nights’ challenging weather conditions. For each flight, six petri dishes were attached with velcro to a DJI Mavic 2 Pro. The flights were conducted under temperatures ranging from -1 to -18 degrees Celsius, wind speeds ranging from 9 to 31 km/h, and with the absence of the sun. During the 6-day-long boat survey, 16 blow samples were successfully collected from 11 distinct groups of humpback whales (Megaptera novaeangliae). With this study, we further validated the use of a consumer drone as a practical, affordable, and simplified tool for blow collection, functional under harsh meteorological conditions. Full article

A six-year global study of cloud distribution and cloud properties obtained from observations of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), the Atmospheric Infrared Sounder (AIRS), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data is presented in this study. From the CALIPSO observations, the highest clouds for both daytime and night-time were found in the Inter Tropical Convergence Zone (ITCZ) region. The lowest cloud heights were found towards the poles due to the decrease in the tropopause height. Seasonal studies also revealed a high dominance of clouds in the 70 °S–80 °S (Antarctic) region in the June–July–August (JJA) season and a high dominance of Arctic clouds in the December–January–February (DJF) and September–October–November (SON) seasons. The coldest cloud top temperatures (CTT) were mostly observed over land in the ITCZ and the polar regions, while the warmest CTTs were mostly observed in the mid-latitudes and over the oceans. Regions with CTTs greater than 0 °C experienced less precipitation than regions with CTTs less than 0 °C. Full article

The red king crab (RKC) is a large invasive species inhabiting bottom communities in the Barents Sea. Larval stages of RKC play an important role in determining the spread and recruitment of the population in the coastal waters. We present a review of studies concerned with the ecology of RKC larvae in the Barents Sea focusing on their dynamics and role in the trophic food webs as well as on the role of environmental factors in driving RKC zoeae. Zoeal stages are larger, and their development time is shorter in the Barents Sea compared to the North Pacific. RKC larvae appear in late January–February and can be found in the coastal plankton until mid-July. Mass hatching of RKC larvae in the Barents Sea starts in late March-early April. The highest densities of RKC larvae are located in small semi-enclosed bays and inlets with weak water exchange or local eddies as well as in inner parts of fjords. Size structures of the zoeal populations are similar in the inshore waters to the west of Kola Bay but slightly differ from those in more eastern regions. RKC larvae perform daily vertical migrations and move to deeper depths during bright daylight hours and tend to rise during night hours. RKC larvae are plankton feeders that ingest both phyto- and zooplankton. A set of environmental variables including food conditions, water temperature, and advective influence are the most important factors driving the spatial distribution, phenology, survival rates, development, growth, and interannual fluctuations of RKC larvae. Recent climatic changes in the Arctic may have both negative and positive consequences for RKC larvae. Full article

Analysis of variations in 12 extreme temperature indices at 68 meteorological stations on the Yunnan-Guizhou Plateau (YGP) in southwestern China during 1960–2019 revealed widespread significant changes in all temperature indices. The temperature of the hottest days and coldest nights show significantly increasing trends, and the frequencies of the warm days and nights also present similar trends. The temperature of the coldest night has a significant and strong warming trend (0.38 °C/decade), whereas the frequency of frost days shows the fastest decrease (1.5 days/decade). Increases in the summer days are statistically significant, while a decreasing trend for the diurnal temperature range is not significant. Furthermore, there were significant differences in the changes of temperature indices between 1960–1989 and 1990–2019. Most parts of the YGP underwent significant warning, manifesting that the mountainous regions are relatively sensitive and vulnerable to climate change. The correlation coefficients between the temperature indices and various geographical factors (latitude, longitude, and height) reflect the complexity of regional temperature variability and indicate enhanced sensitivity of extreme temperatures to geographical factors on the YGP. It was also found that extreme temperatures generally had weaker correlations with the El Nino-Southern Oscillation, North Pacific Index, Southern Oscillation Index, North Atlantic Oscillation, and East Asian Summer Monsoon Index than with the South Asian summer monsoon index, Nino4 indices and Arctic Oscillation, and there were more insignificant correlations. Regional trends of the extreme temperature indices reflect the non-uniform temperature change over the YGP, which is due to the complex interaction between atmospheric circulation patterns and local topography. The results of this study have important practical significance for mitigating the adverse effects of extreme climatic changes, in particular for the YGP with its typical karst geomorphology and fragile ecological environment. Full article

Observations from Raman backscatter-based Fiber-Optic Distributed Sensing (FODS) require reference sections of the fiber-optic cable sensor of known temperature to translate the primary measured intensities of Stokes and anti-Stokes photons to the secondary desired temperature signal, which also commonly forms the basis for other derived quantities. Here, we present the design and the results from laboratory and field evaluations of a novel Solid-Phase Bath (SoPhaB) using ultrafine copper instead of the traditional mechanically stirred liquid-phase water bath. This novel type is suitable for all FODS applications in geosciences and industry when high accuracy and precision are needed. The SoPhaB fully encloses the fiber-optic cable which is coiled around the inner core and surrounded by tightly interlocking parts with a total weight of 22 kg. The SoPhaB is thermoelectrically heated and/or cooled using Peltier elements to control the copper body temperature within ±0.04 K using commercially available electronic components. It features two built-in reference platinum wire thermometers which can be connected to the distributed temperature sensing instrument and/or external measurement and logging devices. The SoPhaB is enclosed in an insulated carrying case, which limits the heat loss to or gains from the outside environment and allows for mobile applications. For thermally stationary outside conditions the measured spatial temperature differences across SoPhaB parts touching the fiber-optic cable are <0.05 K even for stark contrasting temperatures of $\Delta T>$ 40 K between the SoPhaB’s setpoint and outside conditions. The uniform, stationary known temperature of the SoPhaB allows for substantially shorter sections of the fiber-optic cable sensors of less than <5 bins at spatial measurement resolution to achieve an even much reduced calibration bias and spatiotemporal uncertainty compared to traditional water baths. Field evaluations include deployments in contrasting environments including the Arctic polar night as well as peak summertime conditions to showcase the wide range of the SoPhaB’s applicability. Full article