[1] We study gravity drainage using a new 1-D, multiphase sea ice model. A parametrization of gravity drainage based on the convective nature of gravity drainage is introduced, whose free parameters are determined by optimizing model output against laboratory measurements of sea ice salinity evolution. Optimal estimates of the free parameters as well as the parametrization performance remain stable for vertical grid resolutions from 1 to 30 mm. We find a strong link between sea ice growth rate and bulk salinity for constant boundary conditions but only a weak link for more realistic boundary conditions. We also demonstrate that surface warming can trigger brine convection over the whole ice layer. Over a growth season, replacing the convective parametrization with constant initial salinities leads to an overall 3% discrepancy of stored energy, thermal resistance, and salt release. We also derive from our convective parametrization a simplified, numerically cheap and stable gravity-drainage parametrization. This parametrization results in an approximately 1% discrepancy of stored energy, thermal resistance, and salt release compared to the convective parametrization. A similarly low discrepancy to our complex parametrization can be reached by simply prescribing a depthdependent salinity profile.Citation: Griewank, P. J., and D. Notz (2013), Insights into brine dynamics and sea ice desalination from a 1-D model study of gravity drainage,

In this study, the spatial structure of cumulus cloud populations is investigated using three-dimensional snapshots from large-domain LES experiments. The aim is to understand and quantify the internal variability in cloud size distributions due to subsampling effects and spatial organization. A set of idealized shallow cumulus cases is selected with varying degrees of spatial organization, including a slowly organizing marine precipitating case and five more quickly organizing diurnal cases over land. A subdomain analysis is applied, yielding cloud number distributions at sample sizes ranging from severely undersampled to nearly complete. A strong power-law scaling is found in the relation between cloud number variability and subdomain size, reflecting an inverse linear relation. Scaling subdomain size by cloud size yields a data collapse across time points and cases, highlighting the role played by cloud spacing in controlling the stochastic variability. Spatial organization acts on top of this baseline model by increasing the maximum cloud size and by enhancing the variability in the number of smallest clouds. This reflects that the smaller clouds start to live on top of larger-scale thermodynamic structures, such as cold pools, which favor or inhibit their formation. Compositing all continental cumulus cases suggests the existence of a prototype diurnal time dependence in the spatial organization. A simple stochastic expression for cloud number variability is proposed that is formulated in terms of two dimensionless groups, which allows objective estimation of the degree of spatial organization in simulated and observed cumulus cloud populations.

Abstract. 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 in order to address this. neXtSIM is a continuous and fully Lagrangian model, and the equations are discretised with the finite-element method. In this model, sea ice physics are driven by a synergic combination of two core components: a model for sea ice dynamics built on a new mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The results of a thorough evaluation of the model performance for the Arctic are presented for the period September 2007 to October 2008. They show 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 a very promising tool for simulating the sea ice over a wide range of spatial and temporal scales.

Abstract. We use a 1-D model to study how salinity evolves in Arctic sea ice. To do so, we first explore how sea-ice surface melt and flooding can be incorporated into the 1-D thermodynamic Semi-Adaptive Multi-phase Sea-Ice Model (SAMSIM) presented by Griewank and Notz (2013). We introduce flooding and a flushing parametrization which treats sea ice as a hydraulic network of horizontal and vertical fluxes. Forcing SAMSIM with 36 years of ERA-interim atmospheric reanalysis data, we obtain a modelled Arctic seaice salinity that agrees well with ice-core measurements. The simulations thus allow us to identify the main drivers of the observed mean salinity profile in Arctic sea ice. Our results show a 1.5-4 g kg −1 decrease of bulk salinity via gravity drainage after ice growth has ceased and before flushing sets in, which hinders approximating bulk salinity from ice thickness beyond the first growth season. In our simulations, salinity interannual variability of first-year ice is mostly restricted to the top 20 cm. We find that ice thickness, thermal resistivity, freshwater column, and stored energy change by less than 5 % on average when the full salinity parametrization is replaced with a prescribed salinity profile.