Polynyas in a dynamic-thermodynamic sea-ice model

The representation of polynyas in viscous-plastic dynamic-thermodynamic sea-ice models is studied in a simplified test domain, in order to give recommendations about parametrisation choices. Bjornsson et al. (2001) validated their dynamic-thermodynamic model against a polynya flux model in a similar setup and we expand on that work here, testing more sea-ice rheologies and new-ice thickness formulations. The two additional rheologies tested give nearly identical results whereas the two new-ice thickness parametrisations tested give widely different results. Based on our results we argue for using the new-ice thickness parametrisation of Hibler (1979). We also implement a new parametrisation for the parameter <i>h</i><sup>0</sup> from Hibler's scheme, based on ideas from a collection depth parametrisation for flux polynya models

neXtSIM: a new Lagrangian sea ice model

The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales

An approach to dynamic line rating state estimation at thermal steady state using direct and indirect measurements

Dynamic line rating has emerged as a solution for reducing congestion in overhead lines, allowing the optimization of power systems assets. This technique is based on direct and/or indirect monitoring of conductor temperature. Different devices and methods have been developed to sense conductor temperature in critical spans. In this work, an algorithm based on WLS is proposed to estimate temperature in all ruling spans of an overhead line. This algorithm uses indirect measurements - i.e. weather reports and/or downscaling nowcasting models as inputs as well as direct measurements of mechanical tension, sag and/or conductor temperature. The algorithm has been tested using typical atmospheric conditions in Iceland along with an overhead line´s real design, showing robustness, efficiency and the ability to minimize error in measurements.Fil: Alvarez, David L.. Universidad Nacional de Colombia; ColombiaFil: Faria da Silva, F.. Aalborg Universitet; DinamarcaFil: Mombello, Enrique Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Bak, Claus Leth. Aalborg Universitet; DinamarcaFil: Rosero, Javier A.. Universidad Nacional de Colombia; ColombiaFil: Ólason, Daníel Leó. Landsnet; Islandi

Towards improving short-term sea ice predictability using deformation observations

Short-term sea ice predictability is challenging despite recent advancements in sea ice modelling and new observations of sea ice deformation that capture small-scale features (open leads and ridges) at the kilometre scale. A new method for assimilation of satellite-derived sea ice deformation into numerical sea ice models is presented. Ice deformation provided by the Copernicus Marine Service is computed from sea ice drift derived from synthetic aperture radar at a high spatio-temporal resolution. We show that high values of ice deformation can be interpreted as reduced ice concentration or increased ice damage – i.e. scalar variables responsible for ice strength in brittle or visco-plastic sea ice dynamical models. This method is tested as a proof of concept with the neXt-generation Sea Ice Model (neXtSIM), where the assimilation scheme uses a data insertion approach and forecasting with one member. We obtain statistics of assimilation impact over a long test period with many realisations starting from different initial times. Assimilation and forecasting experiments are run on synthetic and real observations in January 2021 and show increased accuracy of deformation prediction for the first 3–4 d. Similar conclusions are obtained using both brittle and visco-plastic rheologies implemented in neXtSIM. Thus, the forecasts improve due to the update of sea ice mechanical properties rather than the exact rheological formulation. It is demonstrated that the assimilated information can be extrapolated in space – gaps in spatially discontinuous satellite observations of deformation are filled with a realistic pattern of ice cracks, confirmed by later satellite observations. The limitations and usefulness of the proposed assimilation approach are discussed in a context of ensemble forecasts. Pathways to estimate intrinsic predictability of sea ice deformation are proposed.</p

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases

"I am becoming more and more like my eldest brother!": the relationship between older siblings, adolescent gambling severity, and the attenuating role of parents in a large-scale nationally representative survey study

The present study examined the association between having older siblings who gamble and adolescent at-risk/problem gambling and how parents (i.e., parental knowledge of their whereabouts) and peers might moderate such effects. Data were drawn from the ESPAD®Italia2012 survey (European School Survey Project on Alcohol and Other Drugs) comprising a nationally representative Italian sample of adolescents. The analysis was carried out on a subsample of 10,063 Italian students aged 15–19 years (average age = 17.10; 55 % girls) who had at least one older sibling and who had gambled at some point in their lives. Respondents’ problem gambling severity, older gambler sibling, gambler peers, parental knowledge, and socio-demographic characteristics were individually assessed. Multinomial logistic regression analyses including two- and three-way interactions were conducted. The odds of being an at-risk/problem gambler were higher among high school students with older siblings that gambled and those with peers who gambled. Higher parental knowledge (of who the adolescent was with and where they were in their leisure time) was associated with lower rates of at-risk/problem gambling. There was also an interaction between gamblers with older siblings and parental knowledge. The combination of having siblings who gambled and a greater level of parental knowledge was associated with lower levels of problem gambling. The present study confirmed the occurrence of social risk processes (older siblings and peers who gambled) and demonstrated that gambling among older siblings and peers represents an important contextual factor for increased at-risk/problem gambling. However, parental knowledge appears to be sufficient to counterbalance the influence of older siblings

neXtSIM: a new Lagrangian sea ice model

The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales

Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories

We characterize sea-ice drift by applying a Lagrangian diffusion analysis to buoy trajectories from the International Arctic Buoy Programme (IABP) dataset and from two different models: the standalone Lagrangian sea-ice model neXtSIM and the Eulerian coupled ice–ocean model used for the TOPAZ reanalysis. By applying the diffusion analysis to the IABP buoy trajectories over the period 1979&ndash;2011, we confirm that sea-ice diffusion follows two distinct regimes (ballistic and Brownian) and we provide accurate values for the diffusivity and integral timescale that could be used in Eulerian or Lagrangian passive tracers models to simulate the transport and diffusion of particles moving with the ice. We discuss how these values are linked to the evolution of the fluctuating displacements variance and how this information could be used to define the size of the search area around the position predicted by the mean drift. By comparing observed and simulated sea-ice trajectories for three consecutive winter seasons (2007&ndash;2011), we show how the characteristics of the simulated motion may differ from or agree well with observations. This comparison illustrates the usefulness of first applying a diffusion analysis to evaluate the output of modeling systems that include a sea-ice model before using these in, e.g., oil spill trajectory models or, more generally, to simulate the transport of passive tracers in sea ice

NeXtSIM: A new Lagrangian sea ice model

The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales

NeXtSIM: A new Lagrangian sea ice model

The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales