Mesh generation using a correspondence distance field

The central tool of this work is a correspondence distance field to discrete surface points embedded within a quadtree data structure. The theory, development, and implementation of the distance field tool are described, and two main applications to two-dimensional mesh generation are presented with extension to three-dimensional capabilities in mind. First is a method for surface-oriented mesh generation from a sufficiently dense set of discrete surface points without connectivity information. Contour levels of distance from the body are specified and correspondences oriented normally to the contours are created. Regions of merging fronts inside and between objects are detected in the correspondence distance field and incorporated automatically. Second, the boundaries in a Voronoi diagram between specified coordinates are detected adaptively and used to make Delaunay tessellation. Tessellation of regions with holes is performed using ghost nodes. Images of meshed for each method are given for a sample set of test cases. Possible extensions, future work, and CFD applications are also discussed

Impacts of tropopause polar vortices on Arctic sea ice loss

The Arctic and sea ice cover play fundamental roles in the environment of the Earth system. Improved causal understanding of their changes is needed for meaningful predictions and planning. Among the linkages composing our understanding and predictions, hypothesized mechanisms and historical cases suggest that potentially long-lived, (sub-)synoptic coherent circulation features termed tropopause polar vortices (TPVs) can impact the evolution of Arctic sea ice on daily to seasonal time scales. Diagnostics and causal dynamical experiments are developed to evaluate whether there are mechanisms sufficient for TPVs to significantly impact Arctic summer sea ice loss. A TPV's place, structure, and history are intimately related to its dynamics and associated impacts. A restricted regional watershed segmentation and major correspondence overlap TPV tracking method more robustly defines tracked TPVs' spatial structure (through restricted regional watershed basins with variable shapes and intensities) and time evolution (through similarity overlap with mergers and splits) relative to previous methods. Tracking with a more restrictive definition of lifetime and more robust, variable size, individual cyclonic TPVs can exceed radii of 1000~km, amplitudes of 40~K, and lifetimes of 2 months, coincide with multi-day extreme sea ice loss, and contribute seasonal-scale geographic anomalies. To represent the potential, integrated impacts of TPVs on Arctic sea ice, it is argued that a comprehensive model should resolve TPVs and feedbacks with the larger polar circulation, consistently couple and realistically evolve sea ice, and be computationally tractable. Motivated by limitations in limited-area and coarser general circulation models to satisfy these requirements, the Model for Predictions Across Scales non-hydrostatic atmospheric dynamical core is embedded within the Community Atmospheric Model of the Community Earth System Model (CESM-CAM-MPAS). A global, Arctic-refined atmospheric configuration efficiently provides needed local resolution to TPVs with two-way feedbacks to the polar circulation. Coupling with an Earth system model evolves sea ice through process-based exchanges. With mixed historical and analog initial conditions intended to balance considerations of realism and consistency, summer simulations capture mean polar circulation anomalies and yield competitive September sea ice extent forecasts with skill for the sea ice edge. An effective, localized tendency-based modification strategy permits sensitivity experiments to quantify causal responses throughout the Earth system to input TPV perturbations. Sensitivity experiments are conducted with directly modified TPV intensity within the coupled Earth system to causally explore and evaluate whether TPVs can have extreme sea ice impacts. Strong intensification of cyclonic TPVs in the Arctic can cause less summer sea ice loss. Multi-scale, thermodynamic, mechanical, and multi-component mechanisms contribute to differences in sea ice mass, momentum, and energy, with reduced upward surface ocean heat fluxes the largest of the factors associated with maintaining ice cover. The artificial intensification sufficient to realize sensitivity beyond expectations from historical and internal variability in individual simulations of July through September 2006 is near $-5$~K~d$^{-1}$. Larger than composite mean diabatic heating rates in TPVs, operational forecasts can exhibit initial forecast errors in TPV intensification of the same order. Since large errors in individual vortices can change the trajectory of fundamental components of the simulated Earth system, characteristics and roles of TPV shape and place and integrated atmosphere-sea ice-ocean coupling emerge as important factors for linkages and predictions. Multi-scale, coupled system models offer one class of approaches that can provide qualitative depictions, quantitative sensitivities, and dynamical insights into relationships throughout the Earth system. The aggregated work motivates directions for future process and prediction studies

Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system

The Los Alamos Sea Ice Model (CICE) is used by several Earth system models where sea ice boundary conditions are not necessary, given their global scope. However, regional and local implementations of sea ice models require boundary conditions describing the time changes of the sea ice and snow being exchanged across the boundaries of the model domain. The physical detail of these boundary conditions regarding, for example, the usage of different sea ice thickness categories or the vertical resolution of thermodynamic properties, must be considered when matching them with the requirements of the sea ice model. Available satellite products do not include all required data. Therefore, the most straightforward way of getting sea ice boundary conditions is from a larger-scale model. The main goal of our study is to describe and evaluate the implementation of time-varying sea ice boundaries in the CICE model using two regional coupled ocean–sea ice models, both covering a large part of the Barents Sea and areas around Svalbard: the Barents-2.5 km, implemented at the Norwegian Meteorological Institute (MET), and the Svalbard 4 km (S4K) model, implemented at the Norwegian Polar Institute (NPI). We use the TOPAZ4 model and a Pan-Arctic 4 km resolution model (A4) to generate the boundary conditions for the sea ice and the ocean. The Barents-2.5 km model is MET’s main forecasting model for ocean state and sea ice in the Barents Sea. The S4K model covers a similar domain but it is used mainly for research purposes. Obtained results show significant improvements in the performance of the Barents-2.5 km model after the implementation of the time-varying boundary conditions. The performance of the S4K model in terms of sea ice and snow thickness is comparable to that of the TOPAZ4 system but with more accurate results regarding the oceanic component because of using ocean boundary conditions from the A4 model. The implementation of time-varying boundary conditions described in this study is similar regardless of the CICE versions used in different models. The main challenge remains the handling of data from larger models before its usage as boundary conditions for regional/local sea ice models, since mismatches between available model products from the former and specific requirements of the latter are expected, implying case-specific approaches and different assumptions. Ideally, model setups should be as similar as possible to allow a smoother transition from larger to smaller domains.Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling systempublishedVersio

Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system

The Los Alamos Sea Ice Model (CICE) is used by several Earth system models where sea ice boundary conditions are not necessary, given their global scope. However, regional and local implementations of sea ice models require boundary conditions describing the time changes of the sea ice and snow being exchanged across the boundaries of the model domain. The physical detail of these boundary conditions regarding, for example, the usage of different sea ice thickness categories or the vertical resolution of thermodynamic properties, must be considered when matching them with the requirements of the sea ice model. Available satellite products do not include all required data. Therefore, the most straightforward way of getting sea ice boundary conditions is from a larger-scale model. The main goal of our study is to describe and evaluate the implementation of time-varying sea ice boundaries in the CICE model using two regional coupled ocean–sea ice models, both covering a large part of the Barents Sea and areas around Svalbard: the Barents-2.5 km, implemented at the Norwegian Meteorological Institute (MET), and the Svalbard 4 km (S4K) model, implemented at the Norwegian Polar Institute (NPI). We use the TOPAZ4 model and a Pan-Arctic 4 km resolution model (A4) to generate the boundary conditions for the sea ice and the ocean. The Barents-2.5 km model is MET’s main forecasting model for ocean state and sea ice in the Barents Sea. The S4K model covers a similar domain but it is used mainly for research purposes. Obtained results show significant improvements in the performance of the Barents-2.5 km model after the implementation of the time-varying boundary conditions. The performance of the S4K model in terms of sea ice and snow thickness is comparable to that of the TOPAZ4 system but with more accurate results regarding the oceanic component because of using ocean boundary conditions from the A4 model. The implementation of time-varying boundary conditions described in this study is similar regardless of the CICE versions used in different models. The main challenge remains the handling of data from larger models before its usage as boundary conditions for regional/local sea ice models, since mismatches between available model products from the former and specific requirements of the latter are expected, implying case-specific approaches and different assumptions. Ideally, model setups should be as similar as possible to allow a smoother transition from larger to smaller domains.Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling systempublishedVersio

A Kilometer-Scale Coupled Atmosphere-Wave Forecasting System for the European Arctic

Abstract Accurately simulating the interactions between the components of a coupled Earth modelling system (atmosphere, sea-ice, and wave) on a kilometer-scale resolution is a new challenge in operational numerical weather prediction. It is difficult due to the complexity of interactive mechanisms, the limited accuracy of model components and scarcity of observations available for assessing relevant coupled processes. This study presents a newly developed convective-scale atmosphere-wave coupled forecasting system for the European Arctic. The HARMONIE-AROME configuration of the ALADIN-HIRLAM numerical weather prediction system is coupled to the spectral wave model WAVEWATCH III using the OASIS3 model coupling toolkit. We analyze the impact of representing the kilometer-scale atmosphere-wave interactions through coupled and uncoupled forecasts on a model domain with 2.5 km spatial resolution. In order to assess the coupled model’s accuracy and uncertainties we compare 48-hour model forecasts against satellite observational products such as Advanced Scatterometer 10 m wind speed, and altimeter based significant wave height. The fully coupled atmosphere-wave model results closely match both satellite-based wind speed and significant wave height observations as well as surface pressure and wind speed measurements from selected coastal station observation sites. Furthermore, the coupled model contains smaller standard deviation of errors in both 10m wind speed and significant wave height parameters when compared to the uncoupled model forecasts. Atmosphere and wave coupling reduces the short term forecast error variability of 10 m wind speed and significant wave height with the greatest benefit occurring for high wind and wave conditions