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Abstract Iodine and bromine play central roles in polar atmospheric chemistry: iodine influences the atmospheric oxidative capacity and can generate cloud condensation nuclei, while bromine participates in ozone depletion reactions, known as bromine explosions. Here we present a very sensitive analytical method for Br and I speciation by coupling the ion chromatography system (IC) with an inductively coupled plasma sector field mass spectrometer (ICP-SFMS). We achieved sub-picogram-per gram (pg g−1) as limits of detection (LODs) ranging from 0.4 pg g−1 for I−, 0.8 pg g−1 for IO3 −, 4 pg g−1 for Br−, and 1 pg g−1 for BrO3 −, respectively. These values represent a decrease of up to 30 times compared to the LODs reported in other studies. The method was validated using deep snow samples from the Svalbard Islands, collected at the end of the polar night to quantify various oxidized compounds during their seasonal minimum. In the future, this method could prove useful in the paleoclimatic study of ice cores and snow, as well as in ice chemistry research.

Iodine and bromine play central roles in polar atmospheric chemistry: iodine influences the atmospheric oxidative capacity and can generate cloud condensation nuclei, while bromine participates in ozone depletion reactions, known as bromine explosions. Here we present a very sensitive analytical method for Br and I speciation by coupling the ion chromatography system (IC) with an inductively coupled plasma sector field mass spectrometer (ICP-SFMS). We achieved sub-picogram-per gram (pg g−1) as limits of detection (LODs) ranging from 0.4 pg g−1 for I−, 0.8 pg g−1 for IO3−, 4 pg g−1 for Br−, and 1 pg g−1 for BrO3−, respectively. These values represent a decrease of up to 30 times compared to the LODs reported in other studies. The method was validated using deep snow samples from the Svalbard Islands, collected at the end of the polar night to quantify various oxidized compounds during their seasonal minimum. In the future, this method could prove useful in the paleoclimatic study of ice cores and snow, as well as in ice chemistry research. [ABSTRACT FROM AUTHOR]

Increased surface temperatures (0.7°C per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties at resolutions (<100 m) that influence ecological habitats and permafrost thaw. A machine learning method using topographic parameters with the Random Forest (RF) algorithm previously developed in alpine environments was applied over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up‐wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Addition of an ecotype parameter (proxy for vegetation height) showed minimal predictive improvement. Using RF, snow depth distributions were predicted from topographic parameters with a root mean square error = 8 cm (23%) (R2 = 0.79) at 10 m resolution for an arctic watershed (1500 km2) in western Nunavut, Canada. [ABSTRACT FROM AUTHOR]

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Wrightia antidysenterica is a small compact and evergreen ornamental shrub. Little leaf and yellowing symptoms were recorded on W. antidysenterica plants in June 2018 at College of Agriculture campus, Lembucherra, Tripura. The expected amplicons of ~1.25 kb and ~480 bp were obtained in symptomatic leaf samples with the phytoplasma specific universal 16S rRNA and secA gene primers. Sequence comparison analysis of 16S rRNA and secA gene confirmed the association of 'Ca. P. asteris'-related strain. However, comparing the restriction profiles of 16S rRNA gene in virtual RFLP analysis revealed that the phytoplasma was a 16SrI group related strain. To our knowledge, this is the first report of phytoplasma association with W. antidysenterica little leaf and yellowing in world. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a sea ice prognostic model involving a one‐dimensional thermodynamic diffusion model, nudging satellite‐derived snow/ice temperatures, and two‐dimensional Lagrangian ice tracking. The aim of the model is to produce the evolvement of the physical properties of the snow and ice over the Arctic Ocean during the winter season. While the one‐dimensional column process solves the solution at a specific time and location, the evolvement of physical properties of the same ice target can be continuously simulated along the trajectory of ice movement determined by the Lagrangian tracking method. The main inputs were reanalysis‐based atmospheric forcings, thermal conditions constrained through nudging of snow skin temperature and snow‐ice interface temperature, and satellite‐derived ice motion vectors. The simulation results showed that the model can successfully reproduce well‐known regional features and geographical distributions of snow depth and ice thickness. The model‐simulated variables (i.e., snow depth, total freeboard, ice freeboard, ice thickness, and temperature) showed high correlations with the in situ or satellite measurements. In particular, the simulated temperatures were in excellent agreement with drifting buoy measurements. Since the nudging of the satellite‐derived temperature data into the model improved the thermal structure considerably, these data appear to be a key element for the successful simulation of other variables as well.

This paper presents a sea ice prognostic model involving a one‐dimensional thermodynamic diffusion model, nudging satellite‐derived snow/ice temperatures, and two‐dimensional Lagrangian ice tracking. The aim of the model is to produce the evolvement of the physical properties of the snow and ice over the Arctic Ocean during the winter season. While the one‐dimensional column process solves the solution at a specific time and location, the evolvement of physical properties of the same ice target can be continuously simulated along the trajectory of ice movement determined by the Lagrangian tracking method. The main inputs were reanalysis‐based atmospheric forcings, thermal conditions constrained through nudging of snow skin temperature and snow‐ice interface temperature, and satellite‐derived ice motion vectors. The simulation results showed that the model can successfully reproduce well‐known regional features and geographical distributions of snow depth and ice thickness. The model‐simulated variables (i.e., snow depth, total freeboard, ice freeboard, ice thickness, and temperature) showed high correlations with the in situ or satellite measurements. In particular, the simulated temperatures were in excellent agreement with drifting buoy measurements. Since the nudging of the satellite‐derived temperature data into the model improved the thermal structure considerably, these data appear to be a key element for the successful simulation of other variables as well. Plain Language Summary: The forecast skill in the polar region is still much lower than for other regions in the globe. One of the main reasons is the poor surface characterization including the surface emissivity, which determines the degree of radiation emission at the surface. Aiming at providing necessary emissivity data over the Arctic Ocean, we developed a one‐dimensional thermodynamic vertical heat transfer model for simulating physical states of snow and ice (e.g., their distributions and depths, thermal states, grain sizes) during the winter. The comparison against in situ observations and satellite measurements indicates that the model can simulate well‐known regional characteristics and geographic distributions of the snow and ice, which can be utilized for improving the surface characterization of the snow and sea ice over the Arctic during the winter. Key Points: A prognostic model to simulate physical states of multilayered snow/sea ice over the Arctic Ocean during winter was implementedSatellite‐derived temperatures were nudged into the model to constrain the thermal structure of the snow‐ice column layerSnow depth and ice thickness and their thermal structures were well simulated, compared to in situ observations and satellite estimates [ABSTRACT FROM AUTHOR]

Abstract: We present freeboard measurements from airborne laser scanner (ALS), the Airborne Synthetic Aperture and Interferometric Radar Altimeter System (ASIRAS), and CryoSat‐2 SIRAL radar altimeter; ice thickness measurements from both helicopter‐borne and ground‐based electromagnetic‐sounding; and point measurements of ice properties. This case study was carried out in April 2015 during the N‐ICE2015 expedition in the area of the Arctic Ocean north of Svalbard. The region is represented by deep snow up to 1.12 m and a widespread presence of negative freeboards. The main scattering surfaces from both CryoSat‐2 and ASIRAS are shown to be closer to the snow freeboard obtained by ALS than to the ice freeboard measured in situ. This case study documents the complexity of freeboard retrievals from radar altimetry. We show that even under cold (below −15°C) conditions the radar freeboard can be close to the snow freeboard on a regional scale of tens of kilometers. We derived a modal sea‐ice thickness for the study region from CryoSat‐2 of 3.9 m compared to measured total thickness 1.7 m, resulting in an overestimation of sea‐ice thickness on the order of a factor 2. Our results also highlight the importance of year‐to‐year regional scale information about the depth and density of the snowpack, as this influences the sea‐ice freeboard, the radar penetration, and is a key component of the hydrostatic balance equations used to convert radar freeboard to sea‐ice thickness. [ABSTRACT FROM AUTHOR]

There is significant spatial variability in snow cover characteristics across the Arctic. Current physical or empirical approaches to simulate or measure snow state variables suffer from poor spatial and/or temporal resolutions. Our current understanding of the spatio-temporal variability in Arctic snow cover leads to uncertainties in existing snow property retrievals from space or in models, thus leading to a poor representation of the snow cover in various climate models and reanalysis products. In this paper, we developed a method to derive total snow depth from a ground-based radar as well as distinguishing the two main layers generally observed in Arctic snowpacks: Depth Hoar and Wind Slab. This algorithm was developed for a 24 GHz Frequency Modulated Continuous Wave radar. The novelty of our approach resides in the fact that no validation snow pits are required if previous data on snow conditions (snow depth and density) are known. The impact of the underlying ecotypes on the radar-derived snow stratigraphy was also investigated. The RMSE of the snow depth ranges from 3.5 to 25 cm, but mostly varies between 10 and 15 cm (< 25%). The RMSE is based on snow pit data that has a corresponding radar measurement. • Depth Hoar and Wind Slab thicknesses in the Arctic are retrieved with an error of 15% using FMCW radar. • Retrieval algorithm works using an averaged empirical value for density and refractive index. • Snow water equivalent can be retrieved with an estimated error of <30%, with a dependence on ecotype. • Radar is portable, lightweight and can be mounted on remotely piloted aircraft systems. [ABSTRACT FROM AUTHOR]

Anthropogenic pollution of the Arctic atmosphere is of great interest due to the vulnerability of the Arctic ecosystems, as well as the processes of global transport and accumulation of atmospheric aerosols at high latitudes under conditions of cold climate. The present work throws light upon chemical composition of Arctic snow as a natural deposition matrix for atmospheric semi-volatile pollutants taken from the northernmost Arctic archipelago - Franz Josef Land, which is least affected by local sources of pollution and being a unique unstudied environmental object. The used methodology involved the liquid-liquid extraction of snow samples with dichloromethane and combination of targeted and non-targeted analyses of semi-volatile organic compounds with comprehensive two-dimensional gas chromatography – high-resolution mass spectrometry. While almost none of the known priority pollutants (except three dialkylphthalates) were identified in the studied samples, non-targeted screening revealed a specific class of biomass burning biomarkers – fatty amides with oleamide being the major component among them. Some peculiar organic pollutants (N,N-dimethylcyclohexylamine and N,N-dimethylbenzylamine) were identified in few samples. First results on the semi volatile pollutants in Franz Joseph Land snow were obtained using the most reliable GC × GC-HRMS non-target analysis. Image 1 • First results on the semivolatile pollutants in Franz Joseph Land snow. • GC × GC-HRMS significantly facilitates non-target analysis. • Fatty amides - new biomass burning markers in the Arctic. • Dialkyl phthlates were the only priority pollutants identified. [ABSTRACT FROM AUTHOR]