Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.

How Much Do Clouds Mask the Impacts of Arctic Sea Ice and Snow Cover Variations? Different Perspectives from Observations and Reanalyses

Decreasing sea ice and snow cover are reducing the surface albedo and changing the Arctic surface energy balance. How these surface albedo changes influence the planetary albedo is a more complex question, though, that depends critically on the modulating effects of the intervening atmosphere. To answer this question, we partition the observed top of atmosphere (TOA) albedo into contributions from the surface and atmosphere, the latter being heavily dependent on clouds. While the surface albedo predictably declines with lower sea ice and snow cover, the TOA albedo decreases approximately half as much. This weaker response can be directly attributed to the fact that the atmosphere contributes more than 70% of the TOA albedo in the annual mean and is less dependent on surface cover. The surface accounts for a maximum of 30% of the TOA albedo in spring and less than 10% by the end of summer. Reanalyses (ASR versions 1 and 2, ERA-Interim, MERRA-2, and NCEP R2) represent the annual means of surface albedo fairly well, but biases are found in magnitudes of the TOA albedo and its contributions, likely due to their representations of clouds. Reanalyses show a wide range of TOA albedo sensitivity to changing sea ice concentration, 0.04–0.18 in September, compared to 0.11 in observations.

The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean

In July 2011, the observation of a massive phytoplankton bloom underneath a sea ice-covered region of the Chukchi Sea shifted the scientific consensus that regions of the Arctic Ocean covered by sea ice were inhospitable to photosynthetic life. Although the impact of widespread phytoplankton blooms under sea ice on Arctic Ocean ecology and carbon fixation is potentially marked, the prevalence of these events in the modern Arctic and in the recent past is, to date, unknown. We investigate the timing, frequency, and evolution of these events over the past 30 years. Although sea ice strongly attenuates solar radiation, it has thinned significantly over the past 30 years. The thinner summertime Arctic sea ice is increasingly covered in melt ponds, which permit more light penetration than bare or snow-covered ice. Our model results indicate that the recent thinning of Arctic sea ice is the main cause of a marked increase in the prevalence of light conditions conducive to sub-ice blooms. We find that as little as 20 years ago, the conditions required for sub-ice blooms may have been uncommon, but their frequency has increased to the point that nearly 30% of the ice-covered Arctic Ocean in July permits sub-ice blooms. Recent climate change may have markedly altered the ecology of the Arctic Ocean.

Parameter identification for volumetric heat capacity and thermal conductivity in a quasi-linear thermodynamic system of sea ice

This paper is intended to determine physical parameters describing volumetric heat capacity and thermal conductivity of sea ice in a quasi-linear thermodynamic system using field observations. The quasi-linear thermodynamic system of sea ice with unknown physical parameters is described, and the existence and uniqueness of its solution is proved. Then the physical parameters are taken as control variable, temperature deviations as objective function, and a parameter identification model is established. The existence of its optimal solution is discussed. To solve the identification model, a new algorithm containing genetic algorithm, Hooke–Jeeves algorithm and semi-implicit finite difference scheme is constructed. The physical parameters are calculated using the observations measured at Nella Fjord around Zhongshan Station, Antarctic in CHINARE 2006. For comparability and consistency with other works, a new international standard named TEOS-10 is used. To examine the validity of the identified results, another simulation for temperature profiles in different measurement period is operated. Numerical results show that better simulations of temperature distribution are possible with the identified parameters than EC1993. Therefore not only the identified parameters can be applied in sea ice modeling, but also this study can enrich and supplement observations of sea ice.

Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model

We present a modelling study of processes controlling the summer melt of the Arctic sea ice cover. We perform a sensitivity study and focus our interest on the thermodynamics at the ice-atmosphere and ice-ocean interfaces. We use the Los Alamos community sea ice model CICE, and additionally implement and test three new parametrization schemes: (i) a prognostic mixed layer; (ii) a three equation boundary condition for the salt and heat flux at the ice-ocean interface; and (iii) a new lateral melt parametrization. Recent additions to the CICE model are also tested, including explicit melt ponds, a form drag parametrization and a halodynamic brine drainage scheme. The various sea ice parametrizations tested in this sensitivity study introduce a wide spread in the simulated sea ice characteristics. For each simulation, the total melt is decomposed into its surface, bottom and lateral melt components to assess the processes driving melt and how this varies regionally and temporally. Because this study quantifies the relative importance of several processes in driving the summer melt of sea ice, this work can serve as a guide for future research priorities.

Suppression of the water ice and snow albedo feedback on planets orbiting red dwarf stars and the subsequent widening of the habitable zone

M stars comprise 80% of main sequence stars, so their planetary systems provide the best chance for finding habitable planets, that is, those with surface liquid water. We have modeled the broadband albedo or reflectivity of water ice and snow for simulated planetary surfaces orbiting two observed red dwarf stars (or M stars), using spectrally resolved data of Earth's cryosphere. The gradual reduction of the albedos of snow and ice at wavelengths greater than 1 μm, combined with M stars emitting a significant fraction of their radiation at these same longer wavelengths, means that the albedos of ice and snow on planets orbiting M stars are much lower than their values on Earth. Our results imply that the ice/snow albedo climate feedback is significantly weaker for planets orbiting M stars than for planets orbiting G-type stars such as the Sun. In addition, planets with significant ice and snow cover will have significantly higher surface temperatures for a given stellar flux if the spectral variation of cryospheric albedo is considered, which in turn implies that the outer edge of the habitable zone around M stars may be 10-30% farther away from the parent star than previously thought.

On the numerical resolution in a thermodynamic sea-ice model

The numerical integration of the heat-conduction equation is one of the main components in a thermodynamic sea-ice model. The spatial resolution in the ice normally varies from a minimum of three layers up to a few tens of layers. The temporal resolution varies from a few minutes up to hours. In this paper the impact of numerical resolution on the prediction of a one-dimensional thermodynamic ice model is studied. Analytical solutions for idealized cases were derived and compared with the numerical results. For the full ice model, groups of simulations were made, applying average climatic weather-forcing data corresponding to the ice-freezing, ice-thermal equilibrium and ice warm-up seasons. Special attention was paid to the effect of model spatial resolution. Early in the freezing season, the influence of resolution on model predictions is not significant. When the shortwave radiation becomes large, its absorption within the ice or snow cover was found to modulate the effect of numerical resolution on predictions of ice temperature and surface heat fluxes (e.g. the model run with a coarse spatial resolution yielded large daily variations in surface temperature). Resolution also affects the in-ice temperature profile. For process studies, a two-layer scheme for the solar radiation penetrating into the ice is suitable for a fine-spatial-resolution ice model.

Improved sea ice parcel trajectories in the Arctic via data assimilation

An assimilated sea ice motion product is used to track ice parcels in several regions of the Arctic over time periods of one day to several weeks during 1992-1993. Motions simulated using a two-dimensional, dynamic-thermodynamic sea ice model are combined with motions derived from daily 85 GHz special sensor microwave/imager (SSM/I) imagery using an optimal interpolation method that minimizes error covariance. Assimilation attenuates the tracking error over the stand-alone model in comparison to buoy trajectories with the same starting location and time. The average 14-day assimilated trajectory's displacement error is as much as 34% lower than the model trajectory, while the RMS direction error is decreased by up to 10 degrees (24%). Assimilation can also yield an estimate of dispersion, which is not retrievable by point buoy observations. An assimilation approach improves estimates of ice drift and has the potential to further the understanding of ice mass flux, freshwater flux, and pollutant transport in the polar regions.