Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions

We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice surface fraction (ISF) - SIC minus the per-grid-cell melt pond fraction (MPF) on sea ice - as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20 %-25 % for groups I and III and up to 30 %-35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from group I products agrees with MODIS within 2 %-5 % accuracy during the entire melt period from May through September. Group II and IV products overestimate MODIS Arctic average SIC by 5 %-10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates MODIS Arctic average SIC by 5 %-10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for (i) the observed differences between PMW SIC and MODIS ISF and for (ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role - particularly for groups I and II - and recommend conducting further research in this field

Application of gel microsphere processes to preparation of Sphere-Pac nuclear fuel

Sphere-Pac fabrication of nuclear fuels using two or more sizes of oxide or carbide spheres is ideally suited to nonproliferation-fuel cycles and remote refabrication. The sizes and compositions of spheres necessary for such fuel cycles have not been commonly prepared; therefore, modifications of sol-gel processes to meet these requirements are being developed and demonstrated

A model for the consolidation of rafted sea ice

Rafting is one of the important deformation mechanisms of sea ice. This process is widespread in the north Caspian Sea, where multiple rafting produces thick sea ice features, which are a hazard to offshore operations. Here we present a one-dimensional, thermal consolidation model for rafted sea ice. We consider the consolidation between the layers of both a two-layer and a three-layer section of rafted sea ice. The rafted ice is assumed to be composed of layers of sea ice of equal thickness, separated by thin layers of ocean water. Results show that the thickness of the liquid layer reduced asymptotically with time, such that there always remained a thin saline liquid layer. We propose that when the liquid layer is equal to the surface roughness the adjacent layers can be considered consolidated. Using parameters representative of the north Caspian, the Arctic, and the Antarctic, our results show that for a choice of standard parameters it took under 15 h for two layers of rafted sea ice to consolidate. Sensitivity studies showed that the consolidation model is highly sensitive to the initial thickness of the liquid layer, the fraction of salt release during freezing, and the height of the surface asperities. We believe that further investigation of these parameters is needed before any concrete conclusions can be drawn about rate of consolidation of rafted sea ice features

Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

We report on results of a systematic intercomparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of - 0.4 % to -1.0 % (Arctic) and -0.3 % to -1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from -0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and -3.7 % (ASI), but similar for the Antarctic, -5.4 % and -5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends

Use of Aria to simulate laser weld pool dynamics for neutron generator production.

This report documents the results for the FY07 ASC Integrated Codes Level 2 Milestone number 2354. The description for this milestone is, 'Demonstrate level set free surface tracking capabilities in ARIA to simulate the dynamics of the formation and time evolution of a weld pool in laser welding applications for neutron generator production'. The specialized boundary conditions and material properties for the laser welding application were implemented and verified by comparison with existing, two-dimensional applications. Analyses of stationary spot welds and traveling line welds were performed and the accuracy of the three-dimensional (3D) level set algorithm is assessed by comparison with 3D moving mesh calculations

Temporal dynamics of ikaite in experimental sea ice

Ikaite (CaCO3 · 6H2O) is a metastable phase of calcium carbonate that normally forms in a cold environment and/or under high pressure. Recently, ikaite crystals have been found in sea ice, and it has been suggested that their precipitation may play an important role in air-sea CO 2 exchange in ice-covered seas. Little is known, however, of the spatial and temporal dynamics of ikaite in sea ice. Here we present evidence for highly dynamic ikaite precipitation and dissolution in sea ice grown at an outdoor pool of the Sea-ice Environmental Research Facility (SERF) in Manitoba, Canada. During the experiment, ikaite precipitated in sea ice when temperatures were below -4 °C, creating three distinct zones of ikaite concentrations: (1) a millimeter-to-centimeter-thin surface layer containing frost flowers and brine skim with bulk ikaite concentrations of >2000 μmol kg-1, (2) an internal layer with ikaite concentrations of 200-400 μmol kg -1, and (3) a bottom layer with ikaite concentrations of <100 μmol kg-1. Snowfall events caused the sea ice to warm and ikaite crystals to dissolve. Manual removal of the snow cover allowed the sea ice to cool and brine salinities to increase, resulting in rapid ikaite precipitation. The observed ikaite concentrations were on the same order of magnitude as modeled by FREZCHEM, which further supports the notion that ikaite concentration in sea ice increases with decreasing temperature. Thus, varying snow conditions may play a key role in ikaite precipitation and dissolution in sea ice. This could have a major implication for CO2 exchange with the atmosphere and ocean that has not been accounted for previously

Antarctic Sea Ice Area in CMIP6

Fully coupled climate models have long shown a wide range of Antarctic sea ice states and evolution over the satellite era. Here, we present a high‐level evaluation of Antarctic sea ice in 40 models from the most recent phase of the Coupled Model Intercomparison Project (CMIP6). Many models capture key characteristics of the mean seasonal cycle of sea ice area (SIA), but some simulate implausible historical mean states compared to satellite observations, leading to large intermodel spread. Summer SIA is consistently biased low across the ensemble. Compared to the previous model generation (CMIP5), the intermodel spread in winter and summer SIA has reduced, and the regional distribution of sea ice concentration has improved. Over 1979–2018, many models simulate strong negative trends in SIA concurrently with stronger‐than‐observed trends in global mean surface temperature (GMST). By the end of the 21st century, models project clear differences in sea ice between forcing scenarios

Aria 1.5 : user manual.

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes the incompressible Navier-Stokes equations, energy transport equation, species transport equations, nonlinear elastic solid mechanics, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking. Coupled physics problems are solved in several ways including fully-coupled Newton's method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods, fully-coupled Picard's method, and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic h-adaptivity and dynamic load balancing are some of Aria's more advanced capabilities. Aria is based on the Sierra Framework

Advances in understanding and parameterization of small-scalephysical processes in the marine Arctic climate system: a review

The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007–2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice–ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave–turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice–ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.publishedVersio

A higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR)

The MPI‐ESM1.2 is the latest version of the Max Planck Institute Earth System Model and is the baseline for the Coupled Model Intercomparison Project Phase 6 and current seasonal and decadal climate predictions. This paper evaluates a coupled higher‐resolution version (MPI‐ESM1.2‐HR) in comparison with its lower‐resolved version (MPI‐ESM1.2‐LR). We focus on basic oceanic and atmospheric mean states and selected modes of variability, the El Niño/Southern Oscillation and the North Atlantic Oscillation. The increase in atmospheric resolution in MPI‐ESM1.2‐HR reduces the biases of upper‐level zonal wind and atmospheric jet stream position in the northern extratropics. This results in a decrease of the storm track bias over the northern North Atlantic, for both winter and summer season. The blocking frequency over the European region is improved in summer, and North Atlantic Oscillation and related storm track variations improve in winter. Stable Atlantic meridional overturning circulations are found with magnitudes of ~16 Sv for MPI‐ESM1.2‐HR and ~20 Sv for MPI‐ESM1.2‐LR at 26°N. A strong sea surface temperature bias of ~5°C along with a too zonal North Atlantic current is present in both versions. The sea surface temperature bias in the eastern tropical Atlantic is reduced by ~1°C due to higher‐resolved orography in MPI‐ESM‐HR, and the region of the cold‐tongue bias is reduced in the tropical Pacific. MPI‐ESM1.2‐HR has a well‐balanced radiation budget and its climate sensitivity is explicitly tuned to 3 K. Although the obtained reductions in long‐standing biases are modest, the improvements in atmospheric dynamics make this model well suited for prediction and impact studies