A new regional climate model for POLAR-CORDEX : evaluation of a 30-year hindcast with COSMO-CLM2 over Antarctica

Continent-wide climate information over the Antarctic Ice Sheet (AIS) is important to obtain accurate information of present climate and reduce uncertainties of the ice sheet mass balance response and resulting global sea level rise to future climate change. In this study, the COSMO-CLM2 Regional Climate Model is applied over the AIS and adapted for the specific meteorological and climatological conditions of the region. A 30-year hindcast was performed and evaluated against observational records consisting of long-term ground-based meteorological observations, automatic weather stations, radiosoundings, satellite records, stake measurements and ice cores. Reasonable agreement regarding the surface and upper-air climate is achieved by the COSMO-CLM2 model, comparable to the performance of other state-of-the-art climate models over the AIS. Meteorological variability of the surface climate is adequately simulated, and biases in the radiation and surface mass balance are small. The presented model therefore contributes as a new member to the COordinated Regional Downscaling EXperiment project over the AIS (POLAR-CORDEX) and the CORDEX-CORE initiative

An improved algorithm for polar cloud-base detection by ceilometer over the ice sheets

Optically thin ice and mixed-phase clouds play an important role in polar regions due to their effect on cloud radiative impact and precipitation. Cloud-base heights can be detected by ceilometers, low-power backscatter lidars that run continuously and therefore have the potential to provide basic cloud statistics including cloud frequency, base height and vertical structure. The standard cloud-base detection algorithms of ceilometers are designed to detect optically thick liquid-containing clouds, while the detection of thin ice clouds requires an alternative approach. This paper presents the polar threshold (PT) algorithm that was developed to be sensitive to optically thin hydrometeor layers (minimum optical depth τ ≥ 0.01). The PT algorithm detects the first hydrometeor layer in a vertical attenuated backscatter profile exceeding a predefined threshold in combination with noise reduction and averaging procedures. The optimal backscatter threshold of 3 × 10^−4 km⁻¹ sr⁻¹ for cloud-base detection near the surface was derived based on a sensitivity analysis using data from Princess Elisabeth, Antarctica and Summit, Greenland. At higher altitudes where the average noise level is higher than the backscatter threshold, the PT algorithm becomes signal-to-noise ratio driven. The algorithm defines cloudy conditions as any atmospheric profile containing a hydrometeor layer at least 90 m thick. A comparison with relative humidity measurements from radiosondes at Summit illustrates the algorithm's ability to significantly discriminate between clear-sky and cloudy conditions. Analysis of the cloud statistics derived from the PT algorithm indicates a year-round monthly mean cloud cover fraction of 72% (±10%) at Summit without a seasonal cycle. The occurrence of optically thick layers, indicating the presence of supercooled liquid water droplets, shows a seasonal cycle at Summit with a monthly mean summer peak of 40 % (±4%). The monthly mean cloud occurrence frequency in summer at Princess Elisabeth is 46% (±5%), which reduces to 12% (±2.5%) for supercooled liquid cloud layers. Our analyses furthermore illustrate the importance of optically thin hydrometeor layers located near the surface for both sites, with 87% of all detections below 500 m for Summit and 80% below 2 km for Princess Elisabeth. These results have implications for using satellite-based remotely sensed cloud observations, like CloudSat that may be insensitive for hydrometeors near the surface. The decrease of sensitivity with height, which is an inherent limitation of the ceilometer, does not have a significant impact on our results. This study highlights the potential of the PT algorithm to extract information in polar regions from various hydrometeor layers using measurements by the robust and relatively low-cost ceilometer instrument

Substantiation of approaches to the correction of lipid metabolism disorders and non-alcoholic fatty liver disease in children with exogenous obesity

BACKGROUND: According to the involvement of oxidative stress in the pathogenesis of obesity, the plasma level of coenzyme q10 in the correlation relationship with lipid metabolism disorders and functional liver state is of interest to study.AIM: Substantiation of approaches to the correction of lipid metabolism disorders and non-alcoholic fatty liver disease in children with exogenous obesity based on the content of coenzyme Q10 and its relationship with lipid profile and liver enzymes.MATERIALS AND METHODS: The single-center cross-sectional study enlisted the control (n=32, -1.0≤BMI SD score ≤+2.0) and obese (n=40, BMI SD score>+2.0) groups of children with the mean age of 12 yr. In all children BMI, lipidogram, liver enzymes (ALT and AST), plasma coenzyme Q10 and liver ultrasound examination were assessed.RESULTS: Patients of both groups were comparable (p> 0.05) in age and gender. The level of coenzyme Q10 in the compared groups was comparable (p> 0.05) and did not differ in patients with different degrees of obesity. According to the results of the study of the lipid profile in the obese children, the level of HDL was lower, and the level of LDL was higher than that in control group. The highest value of HDL was obtained in the patients with the 1st degree of obesity and the highest level of triglycerides — in the patients with the 4th degree of obesity. The control group demonstrated moderate correlations between endogenous coenzyme Q10 and total cholesterol (r=0.474, p=0.009) which persists in patients with the first degree of obesity (r = 0.548, p = 0.035). There was no difference in AST in the study groups, however, the main group demonstrated elevated ALT and ALT/AST ratio (p <0.001). The highest ALT and ALT / AST ratio were observed in patients with greatest degree of obesity. Eighteen obese children (45%) had ALT / AST ratio ≥1 (in the control group –one patient (3%) (p <0.001), while fourteen patients showed liver enlargement and structure change according to ultrasound (80%). The control group demonstrated moderate correlations between endogenous coenzyme Q10 and total cholesterol (r=0.474, p=0.009) and between coenzyme Q10 and ALT / AST ratio (r=0.412, p=0.023) . In the obese group there was correlation between AI and ALT / AST (r = 0.436, p = 0.006) and in patients with the 1st degree of obesity — between also coenzyme Q10 and ALT (r = 0.875, p <0.001).CONCLUSION: The disturbances in adequate control of cholesterol by coenzyme Q10 in obese children possibly confirming the involvement of oxidative stress in the pathogenesis of dyslipidemia and non-alcoholic fatty liver disease can serve as indication to use coenzyme Q10 in order to correct these complications

Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars

In situ observations of snowfall over the Antarctic Ice Sheet are scarce. Currently, continent-wide assessments of snowfall are limited to information from the Cloud Profiling Radar on board the CloudSat satellite, which has not been evaluated up to now. In this study, snowfall derived from CloudSat is evaluated using three ground-based vertically profiling 24&thinsp;GHz precipitation radars (Micro Rain Radars: MRRs). Firstly, using the MRR long-term measurement records, an assessment of the uncertainty caused by the low temporal sampling rate of CloudSat (one revisit per 2.1 to 4.5 days) is performed. The 10–90th-percentile temporal sampling uncertainty in the snowfall climatology varies between 30&thinsp;% and 40&thinsp;% depending on the latitudinal location and revisit time of CloudSat. Secondly, an evaluation of the snowfall climatology indicates that the CloudSat product, derived at a resolution of 1∘ latitude by 2∘ longitude, is able to accurately represent the snowfall climatology at the three MRR sites (biases&thinsp;&lt;&thinsp;15&thinsp;%), outperforming ERA-Interim. For coarser and finer resolutions, the performance drops as a result of higher omission errors by CloudSat. Moreover, the CloudSat product does not perform well in simulating individual snowfall events. Since the difference between the MRRs and the CloudSat climatology are limited and the temporal uncertainty is lower than current Climate Model Intercomparison Project Phase 5 (CMIP5) snowfall variability, our results imply that the CloudSat product is valuable for climate model evaluation purposes.</p

FORMATION OF NANOCRYSTALLINE COMPOSITIONS TiC–Co AND TiN–Co IN THE PRESENCE OF MOLYBDENUM AND ITS REFRACTORY COMPOUNDS DURING PLASMA RECONDENSATION

Методом плазменной переконденсации в низкотемпературной азотной плазме были переработаны механические смеси микрокристаллических порошков TiC–Co и TiN–Co. В ходе рентгенографических и электронно-микроскопических исследований, в том числе с использованием методик EDX-анализа, было установлено, что нанокристаллические композиции имеют радиально-слоевую структуру, состоящую из тугоплавкого ядра и металлической оболочки, содержащей кобальт, молибден или их взаимные твердые растворы.Mechanical mixtures of microcrystalline TiC–Co and TiN–Co powders were processed by plasma recondensation in a low-temperature nitrogen plasma. It was found that during radiographic and electron microscopic studies, including using EDX analysis techniques, nanocrystalline compositions have a radial layer structure consisting of a refractory core and a metallic shell containing cobalt, molybdenum or their mutual solid solutions.Авторы выражают благодарность канд. техн. наук Э. К. Добринскому (ФГУП ГНИИХТЭОС) за помощь в проведении экспериментов по плазменной переконденсации механической смеси TiN–Co и TiC–Co

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

The Southern Ocean is a critical component of Earth’s climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedi�tion (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To re�duce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between envi�ronmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shap�ing the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question

Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof-of-concept trial run designed to illustrate the utility and feasibility of the ARTMIP project

Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC)3 Project

Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC)3 project has been established in 2016. It comprises modeling and data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, ship-borne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data