The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea-ice brightness temperatures at 6.9 GHz from climate model output

We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of 6.9 GHz. We investigate the influence of simplifying assumptions for the representation of sea-ice vertical properties on the simulation of microwave brightness temperatures. We do so in a one-dimensional setup, using a complex 1D thermodynamic sea-ice model and a 1D microwave emission model. We find that realistic brightness temperatures can be simulated in winter from a simplified linear temperature profile and a self-similar salinity profile in the ice. These realistic brightness temperatures can be obtained based on profiles interpolated to as few as five layers. Most of the uncertainty resulting from the simplifications is introduced by the simplification of the salinity profiles. In summer, the simplified salinity profile leads to too high liquid water fractions at the surface. To overcome this limitation, we suggest using a constant brightness temperature for the ice during summer and to treat melt ponds as water surfaces. Finally, in our setup, we cannot assess the effect of snow properties during melting. As periods of melting snow with intermediate moisture content typically last for less than a month, our approach allows one to estimate reasonable brightness temperatures at 6.9 GHz from climate model output for about 11 months throughout the year.

Novel Observations of Salinity Transport in Low-Salinity Waterflooding

Summary Low-salinity waterflooding (LSWF) is a promising process that could lead to increased oil recovery. To date, the greatest attention has been paid to the complex oil/water/rock chemical reactions that might explain the mechanisms of LSWF, and it is generally accepted that these result in behavior equivalent to changing oil and water mobility. This behavior is modeled using an effective salinity range and weighting function to gradually switch from high- to low-salinity relative permeability curves. There has been limited attention on physical transport of fluids during LSWF, particularly at large scale. We focus on how the salinity profile interacts with water fronts through the effective salinity range and dispersion to alter the transport behavior and change the flow velocities, particularly for the salinity profile. We examined a numerical simulation of LSWF at the reservoir scale. Various representations of the effective salinity range and weighting function were also examined. The dispersion of salinity was compared with a theoretical form of numerical dispersion based on input parameters. We also compared salinity movement with the analytical solution of the conventional dispersion/advection equation. From simulations we observed that salinity is dispersed as analytically predicted, although the advection velocity might be changed. In advection-dominated flow, the salinity profile moves at the speed of the injected water. However, as dispersion increases, the mixing zone falls under the influence of the faster-moving formation water and, thus, speeds up. To predict the salinity profile theoretically, we have modified the advection term of the analytical solution as a function of the formation- and injected-water velocities, Péclet number, and effective salinity range. This important result enables prediction of the salinity transport by this newly derived modification of the analytical solution for 1D flow. We can understand the correction to the flow behavior and quantify it from the model input parameters. At the reservoir scale, we typically simulate flow on coarse grids, which introduces numerical dispersion or must include physical dispersion from underlying heterogeneity. Corrections to the equations can contribute to improving the precision of the coarse-scale models, and, more generally, the suggested form of the correction can also be used to calculate the movement of any solute that transports across an interface between two mobile fluids. We can also better understand the relative behaviors of passive tracers and those that are adsorbed.

Salinity Profile Estimation in the Pacific Ocean from Satellite Surface Salinity Observations

A nonlinear empirical method, called the generalized regression neural network with the fruit fly optimization algorithm (FOAGRNN), is proposed to estimate subsurface salinity profiles from sea surface parameters in the Pacific Ocean. The purpose is to evaluate the ability of the FOAGRNN methodology and satellite salinity data to reconstruct salinity profiles. Compared with linear methodology, the estimated salinity profiles from the FOAGRNN method are in better agreement with the measured profiles at the halocline. Sensitivity studies of the FOAGRNN estimation model shows that, when applied to various types of sea surface parameters, latitude is the most significant variable in estimating salinity profiles in the tropical Pacific Ocean (correlation coefficient R greater than 0.9). In comparison, sea surface temperature (SST) and height (SSH) have minimal effects on the model. Based on FOAGRNN modeling, Pacific Ocean three-dimensional salinity fields are estimated for the year 2014 from remote sensing sea surface salinity (SSS) data. The performance of the satellite-based salinity field results and possible sources of error associated with the estimation methodology are briefly discussed. These results suggest a potential new approach for salinity profile estimation derived from sea surface data. In addition, the potential utilization of satellite SSS data is discussed.

Numerical Analysis of Microwave Scattering from Layered Sea Ice Based on the Finite Element Method

A two-dimensional scattering model based on the Finite Element Method (FEM) is built for simulating the microwave scattering of sea ice, which is a layered medium. The scattering problem solved by the FEM is formulated following a total- and scattered-field decomposition strategy. The model set-up is first validated with good agreements by comparing the results of the FEM with those of the small perturbation method and the method of moment. Subsequently, the model is applied to two cases of layered sea ice to study the effect of subsurface scattering. The first case is newly formed sea ice which has scattering from both air–ice and ice–water interfaces. It is found that the backscattering has a strong oscillation with the variation of sea ice thickness. The found oscillation effects can increase the difficulty of retrieving the thickness of newly formed sea ice from the backscattering data. The second case is first-year sea ice with C-shaped salinity profiles. The scattering model accounts for the variations in the salinity profile by approximating the profile as consisting of a number of homogeneous layers. It is found that the salinity profile variations have very little influence on the backscattering for both C- and L-bands. The results show that the sea ice can be considered to be homogeneous with a constant salinity value in modelling the backscattering and it is difficult to sense the salinity profile of sea ice from the backscattering data, because the backscattering is insensitive to the salinity profile.