Impact of Sea Ice Drift Retrieval Errors, Discretization and Grid Type on Calculations of Sea Ice Deformation

We studied two issues to be considered in the calculation of parameters characterizing sea ice deformation: the effect of uncertainties in an automatically retrieved sea ice drift field, and the influence of the type of drift vector grid. Sea ice deformation changes the local ice mass balance and the interaction between atmosphere, ice, and ocean, and constitutes a hazard to marine traffic and operations. Due to numerical effects, the results of deformation retrievals may predict, e.g., openings and closings of the ice cover that do not exist in reality. We focus specifically on fields of ice drift obtained from synthetic aperture radar (SAR) imagery and analyze the Propagated Drift Retrieval Error (PDRE) and the Boundary Definition Error (BDE). From the theory of error propagation, the PDRE for the calculated deformation parameters can be estimated. To quantify the BDE, we devise five different grid types and compare theoretical expectation and numerical results for different deformation parameters assuming three scenarios: pure divergence, pure shear, and a mixture of both. Our findings for both sources of error help to set up optimal deformation retrieval schemes and are also useful for other applications working with vector fields and scalar parameters derived therefrom

Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations

An unusual, large, latent-heat polynya opened and then closed by freezing and convergence north of Greenland's coast in late winter 2018. The closing presented a natural but well-constrained full-scale ice deformation experiment. We observed the closing of and deformation within the polynya with satellite synthetic-aperture radar (SAR) imagery and measured the accumulated effects of dynamic and thermodynamic ice growth with an airborne electromagnetic (AEM) ice thickness survey 1 month after the closing began. During that time, strong ice convergence decreased the area of the refrozen polynya by a factor of 2.5. The AEM survey showed mean and modal thicknesses of the 1-month-old ice of 1.96 ± 1.5 m and 1.1 m, respectively. We show that this is in close agreement with modeled thermodynamic growth and with the dynamic thickening expected from the polynya area decrease during that time. We found significant differences in the shapes of ice thickness distributions (ITDs) in different regions of the refrozen polynya. These closely corresponded to different deformation histories of the surveyed ice that we reconstructed from Lagrangian ice drift trajectories in reverse chronological order. We constructed the ice drift trajectories from regularly gridded, high-resolution drift fields calculated from SAR imagery and extracted deformation derived from the drift fields along the trajectories. Results show a linear proportionality between convergence and thickness change that agrees well with the ice thickness redistribution theory. We found a proportionality between the e folding of the ITDs' tails and the total deformation experienced by the ice. Lastly, we developed a simple, volume-conserving model to derive dynamic ice thickness change from the combination of Lagrangian trajectories and high-resolution SAR drift and deformation fields. The model has a spatial resolution of 1.4 km and reconstructs thickness profiles in reasonable agreement with the AEM observations. The modeled ITD resembles the main characteristics of the observed ITD, including mode, e folding, and full width at half maximum. Thus, we demonstrate that high-resolution SAR deformation observations are capable of producing realistic ice thickness distributions

Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data

This thesis discusses the retrieval of sea ice parameters using the combination of remote sensing data and a sea ice model for the region of the Baffin Bay, Hudson Bay, Labrador Sea and the Gulf of St. Lawrence. The Los Alamos sea ice model, CICE, which is used as a module for coupled global ice-ocean models, was used for this work. The model was implemented with a 7-category thickness distribution, open boundaries and a variable coefficient for ice-ocean heat flux. A slab ocean mixed-layer model based on density criteria was used for the standalone regional implementation of the model. The model estimates of ice concentration were validated using seasonal means, and anomalies. A combined optimal interpolation and nudging scheme was implemented to assimilate Sea Surface Temperature (SST) and ice concentration from Advanced very-high-resolution radiometer (AVHRR) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) respectively. The inclusion of the variable drag coefficient required updates of ice volume and dependent tracers corresponding to the updates in the ice concentration estimates. The sea ice variables of thickness, freeboard, level ice draft and keel depth were compared with the estimates derived from Soil Moisture and Ocean Salinity (SMOS), CryoSat2, and a ULS instrument respectively. The assimilated model provided better estimates of ice concentration, thickness, freeboard and level ice draft. The model estimated ice thickness compared well with the thin ice thickness estimated from the SMOS data, except during March, when there is significant ice extent. The reason for this discrepancy could be attributed to the absence of mixed layer heat flux forcing in the model and also the effect of snow and the onset of melt that alters the observation. Field measurements were also used for the comparison of model estimates. The measurements from the Upward Looking Sonar (ULS) instrument located at Makkovick Bank were used to estimate the level ice draft and keel depth. The observations from ULS along with model estimates were used to determine the coefficient that relates the sail and keel measurements. The level ice draft showed a good match with the values extracted from the ULS data, while the sail to keel relationship coefficient seems to vary between a value of 3 during January and February and a value of 7 from March to May. Further studies have to be conducted to understand these variations. The ice concentration estimates from the assimilated model were compared with the ice concentration estimates derived from the images that were obtained during a field survey along the Labrador coast. The results of the ice concentration derived from the images showed a good match with the model values. The results were also compared with the estimates from Canadian Ice Service (CIS) ice charts and Advanced Microwave Scanning Radiometer-Earth observation (AMSR-E)

Geodesic length measurement in medical images: Effect of the discretization by the camera chip and quantitative assessment of error reduction methods

After interventions such as bypass surgeries the vascular function is checked qualitatively and remotely by observing the blood dynamics inside the vessel via Fluorescence Angiography. This state-of-the-art method has to be improved by introducing a quantitatively measured blood flow. Previous approaches show that the measured blood flow cannot be easily calibrated against a gold standard reference. In order to systematically address the possible sources of error, we investigated the error in geodesic length measurement caused by spatial discretization on the camera chip. We used an in-silico vessel segmentation model based on mathematical functions as a ground truth for the length of vessel-like anatomical structures in the continuous space. Discretization errors for the chosen models were determined in a typical magnitude of 6%. Since this length error would propagate to an unacceptable error in blood flow measurement, counteractions need to be developed. Therefore, different methods for the centerline extraction and spatial interpolation have been tested and compared against their performance in reducing the discretization error in length measurement by re-continualization. In conclusion, the discretization error is reduced by the re-continualization of the centerline to an acceptable range. The discretization error is dependent on the complexity of the centerline and this dependency is also reduced. Thereby the centerline extraction by erosion in combination with the piecewise Bézier curve fitting performs best by reducing the error to 2.7% with an acceptable computational time

Configuration and Assessment of the GISS ModelE2 Contributions to the CMIP5 Archive

We present a description of the ModelE2 version of the Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) and the configurations used in the simulations performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). We use six variations related to the treatment of the atmospheric composition, the calculation of aerosol indirect effects, and ocean model component. Specifically, we test the difference between atmospheric models that have noninteractive composition, where radiatively important aerosols and ozone are prescribed from precomputed decadal averages, and interactive versions where atmospheric chemistry and aerosols are calculated given decadally varying emissions. The impact of the first aerosol indirect effect on clouds is either specified using a simple tuning, or parameterized using a cloud microphysics scheme. We also use two dynamic ocean components: the Russell and HYbrid Coordinate Ocean Model (HYCOM) which differ significantly in their basic formulations and grid. Results are presented for the climatological means over the satellite era (1980-2004) taken from transient simulations starting from the preindustrial (1850) driven by estimates of appropriate forcings over the 20th Century. Differences in base climate and variability related to the choice of ocean model are large, indicating an important structural uncertainty. The impact of interactive atmospheric composition on the climatology is relatively small except in regions such as the lower stratosphere, where ozone plays an important role, and the tropics, where aerosol changes affect the hydrological cycle and cloud cover. While key improvements over previous versions of the model are evident, these are not uniform across all metrics

Resolving Leads in Sea-Ice Models : New Analysis Methods for Frontier Resolution Arctic Simulations

Sea ice deforms constantly under the forcing of winds and ocean currents. Eventually the ice cover of the Arctic Ocean breaks into a multitude of ice floes. Strips of open ocean, so-called leads, and pressure ridges, where the collision of floes piled up the ice, are found along the floe boundaries. These features have a strong impact on the interaction of sea ice with the atmosphere and the ocean, as they affect heat loss and surface drag. Currently, climate models do not resolve leads and pressure ridges in simulated sea ice fields due to their coarse resolution. They parameterize the effects of leads on the Arctic climate, if at all. The goal of this thesis is to develop Arctic simulations that reproduce leads sufficiently to be used in climate simulations. By decreasing the horizontal grid-spacing, a numerical ocean sea-ice model is shown to resolve leads explicitly. To test how realistic these lead-resolving sea-ice simulations are, the following research questions are addressed: (1) what are good metrics to evaluate the simulated leads with observational data? (2) Which observed characteristics of sea ice deformation and deformation features are reproduced by the model? In a first step, the sea ice deformation in a 1-km lead-resolving sea-ice simulation is analyzed with a spatio-temporal scaling analysis. The simulated sea ice deformation is strongly localized in failure zones and dominated by spontaneous fracture. This heterogeneity and intermittency of sea ice deformation shows that the simulation captures the fracture processes that form leads. In a second step, two new algorithms are described that detect and track leads and pressure ridges, combined into Linear Kinematic Features (LKFs). Both algorithms are applied to deformation data observed from satellite to establish a data set of deformation features that can be used as a reference in model evaluation. LKFs in two lead-resolving sea-ice simulations are extracted with the same algorithms, and found to agree with the LKF data set with respect to their spatial characteristics and temporal evolutions. In conclusion, high resolution sea-ice simulations can explicitly resolve leads. These simulations reproduce the characteristics of sea ice deformation and the representation of LKFs that are both observed from satellite. In future work, these simulations could be used as prototypes for the configuration of the sea-ice component in a climate model to directly simulate air-ice-ocean interaction processes in the Arctic

100 Years of Earth System Model Development

This is the final version. Available from American Meteorological Society via the DOI in this recordToday’s global Earth System Models began as simple regional models of tropospheric weather systems. Over the past century, the physical realism of the models has steadily increased, while the scope of the models has broadened to include the global troposphere and stratosphere, the ocean, the vegetated land surface, and terrestrial ice sheets. This chapter gives an approximately chronological account of the many and profound conceptual and technological advances that made today’s models possible. For brevity, we omit any discussion of the roles of chemistry and biogeochemistry, and terrestrial ice sheets

Thickness retrieval and emissivity modeling of thin sea ice at L-band for SMOS satellite observations

In this study we have developed an empirical retrieval for thickness of young and first-year ice during the freeze up period for the L-band passive microwave radiometer Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) on the Soil Moisture and Ocean Salinity (SMOS) satellite. The retrieval is based on intensity and polarization difference using the incidence angle range of 40° to 50° and is validated using data from airborne EM-Bird, Moderate-resolution Imaging Spectroradiometer (MODIS) thermal imagery, and self consistency checks for ice thicknesses up to 50 cm with an error of 30 % on average. In addition, we modeled the microwave emission for Arctic first-year ice using the sea ice version of the Microwave Emission Model of Layered Snowpacks (MEMLS). The sea ice conditions used as input for MEMLS were generated using a thermodynamic energy balance model (based on the Crocus model) driven by reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF). From unexpected features in the modeled microwave emission and disagreements with the empirically trained SMOS retrieval several shortcomings of the energy balance model and MEMLS were identified and corrected. The corrections include a treatment of mismatch of layer definition between the energy balance model and MEMLS, an adaptation of the reflection coefficient for lossy media in MEMLS, and several smaller corrections. For comparison, two simple models ignoring volume scattering, one incoherent and one coherent, were set up and were found to be able to reproduce the results of the more complex MEMLS model on average. With the simple models, the effects of thin coherent layers, the snow cover, the interface roughness and three different dielectric mixture models for sea ice were explored. It was found that the choice of the mixture model is essential for the relation of sea ice thickness to brightness temperatures in L-band, suggesting sea ice thickness sensitivities from few centimeters to several meters for salinity conditions of the global oceans. The interface properties, especially at the sea ice bottom, were found to be a major uncertainty source when modeling the microwave emission of thin sea ice. In addition, the variability in snow depth, the interface roughness, and the ice surface salinity and temperature were found to have a similar influence on the resulting brightness temperatures, with a strong effect on horizontally (up to 30 K) and weak effect on vertically polarized radiation (up to 10 K) for temperatures below 260 K. A model for simulating coherent microwave emission for thickness distributions of ice and snow was prepared to overcome weaknesses from the single thickness coherent and incoherent models. Comparison to the incoherent model showed that for realistic snow depth distributions obtained from Operation IceBridge (OIB) coherence effects can change the brightness temperatures on the scale of a SMOS footprint up to 10 K in horizontal polarization. These findings suggest that the retrieval for the thickness of thin sea ice with satellite based L-band sensors yield higher uncertainties than expected from earlier studies

Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications