On the uncertainty of sea-ice isostasy

During late winter 2007, coincident measurements of sea ice were collected using various sensors at an ice camp in the Beaufort Sea, Canadian Arctic. Analysis of the archived data provides new insight into sea-ice isostasy and its related R-factor through case studies at three scales using different combinations of snow and ice thickness components. At the smallest scale (\u3c1 m; point scale), isostasy is not expected, so we calculate a residual and define this as �� (‘zjey’) to describe vertical displacement due to deformation. From 1 to 10 m length scales, we explore traditional isostasy and identify a specific sequence of thickness calculations which minimize freeboard and elevation uncertainty. An effective solution exists when the R-factor is allowed to vary: ranging from 2 to 12, with mean of 5.17, mode of 5.88 and skewed distribution. At regional scales, underwater, airborne and spaceborne platforms are always missing thickness variables from either above or below sea level. For such situations, realistic agreement is found by applying small-scale skewed ranges for the R-factor. These findings encourage a broader isostasy solution as a function of potential energy and length scale. Overall, results add insight to data collection strategies and metadata characteristics of different thickness products

Impact of spatial aliasing on sea-ice thickness measurements

We explore spatial aliasing of non-Gaussian distributions of sea-ice thickness. Using a heuristic model and \u3e1000 measurements, we show how different instrument footprint sizes and shapes can cluster thickness distributions into artificial modes, thereby distorting frequency distribution, making it difficult to compare and communicate information across spatial scales. This problem has not been dealt with systematically in sea ice until now, largely because it appears to incur no significant change in integrated thickness which often serves as a volume proxy. Concomitantly, demands are increasing for thickness distribution as a resource for modeling, monitoring and forecasting air–sea fluxes and growing human infrastructure needs in a changing polar environment. New demands include the characterization of uncertainties both regionally and seasonally for spaceborne, airborne, in situ and underwater measurements. To serve these growing needs, we quantify the impact of spatial aliasing by computing resolution error (Er) over a range of horizontal scales (x) from 5 to 500 m. Results are summarized through a power law (Er = bxm) with distinct exponents (m) from 0.3 to 0.5 using example mathematical functions including Gaussian, inverse linear and running mean filters. Recommendations and visualizations are provided to encourage discussion, new data acquisitions, analysis methods and metadata formats

Impact of spatial aliasing on sea-ice thickness measurements

We explore spatial aliasing of non-Gaussian distributions of sea-ice thickness. Using a heuristic model and \u3e1000 measurements, we show how different instrument footprint sizes and shapes can cluster thickness distributions into artificial modes, thereby distorting frequency distribution, making it difficult to compare and communicate information across spatial scales. This problem has not been dealt with systematically in sea ice until now, largely because it appears to incur no significant change in integrated thickness which often serves as a volume proxy. Concomitantly, demands are increasing for thickness distribution as a resource for modeling, monitoring and forecasting air–sea fluxes and growing human infrastructure needs in a changing polar environment. New demands include the characterization of uncertainties both regionally and seasonally for spaceborne, airborne, in situ and underwater measurements. To serve these growing needs, we quantify the impact of spatial aliasing by computing resolution error (Er) over a range of horizontal scales (x) from 5 to 500 m. Results are summarized through a power law (Er = bxm) with distinct exponents (m) from 0.3 to 0.5 using example mathematical functions including Gaussian, inverse linear and running mean filters. Recommendations and visualizations are provided to encourage discussion, new data acquisitions, analysis methods and metadata formats

Promotoras as Mental Health Practitioners in Primary Care: A Multi-Method Study of an Intervention to Address Contextual Sources of Depression

We assessed the role of promotoras—briefly trained community health workers—in depression care at community health centers. The intervention focused on four contextual sources of depression in underserved, low-income communities: underemployment, inadequate housing, food insecurity, and violence. A multi-method design included quantitative and ethnographic techniques to study predictors of depression and the intervention’s impact. After a structured training program, primary care practitioners (PCPs) and promotoras collaboratively followed a clinical algorithm in which PCPs prescribed medications and/or arranged consultations by mental health professionals and promotoras addressed the contextual sources of depression. Based on an intake interview with 464 randomly recruited patients, 120 patients with depression were randomized to enhanced care plus the promotora contextual intervention, or to enhanced care alone. All four contextual problems emerged as strong predictors of depression (chi square, p < .05); logistic regression revealed housing and food insecurity as the most important predictors (odds ratios both 2.40, p < .05). Unexpected challenges arose in the intervention’s implementation, involving infrastructure at the health centers, boundaries of the promotoras’ roles, and “turf” issues with medical assistants. In the quantitative assessment, the intervention did not lead to statistically significant improvements in depression (odds ratio 4.33, confidence interval overlapping 1). Ethnographic research demonstrated a predominantly positive response to the intervention among stakeholders, including patients, promotoras, PCPs, non-professional staff workers, administrators, and community advisory board members. Due to continuing unmet mental health needs, we favor further assessment of innovative roles for community health workers

Large-Scale Comparison Between Buoy and SSM/I Drift and Deformation in the Eurasian Basin during Winter 1992-1993

A method for comparing sea ice velocity, divergence and shear at the large-scale between buoys and SSM/I is presented. For initial testing, the method is applied in the Eurasian Basin because of its relatively simple circulation dominated by the wind. Using 8 ARGOS buoys, 11 strain-rate arrays 100 to 600 km in size are constructed. Daily 100 km resolution sea ice motion derived from SSM/I 85 GHz brightness temperatures is sampled 100 to 1000 km from the center of the buoy arrays. Over this range of possible scales, a minimum RMS dierence for deformation is used to identify an optimal inclusion radius of 600 km corresponding to a length scale of 1000 km. This length scale is typical of local storms conrming a strong connection between wind and observed sea ice motion. Based on all 11 arrays, an average RMS dierence of 2.48 0.05 cm s 1 for velocity vector and 8.8 0.9 10 8 s 1 using all 4 deformation components (@u i =@x j ) is found at the optimal inclusion radius corresponding to average correlation coecients of 0.896 0.002 and 0.729 0.030, respectively. RMS dierences are found to scale with the temporal and spatial uncertainties of the SSM/I suggesting that even better results can be achieved with higher resolution instruments

Full-physics 3-D heterogeneous simulations of electromagnetic induction fields on level and deformed sea ice

Publisher's PDFIn this paper we explore simulated responses of electromagnetic (EM) signals relative to in situ field surveys and quantify the effects that different values of conductivity in sea ice have on the EM fields. We compute EM responses of ice types with a three-dimensional (3-D) finite-volume discretization of Maxwell’s equations and present 2-D sliced visualizations of their associated EM fields at discrete frequencies. Several interesting observations result: First, since the simulator computes the fields everywhere, each gridcell acts as a receiver within the model volume, and captures the complete, coupled interactions between air, snow, sea ice and sea water as a function of their conductivity; second, visualizations demonstrate how 1-D approximations near deformed ice features are violated. But the most important new finding is that changes in conductivity affect EM field response by modifying the magnitude and spatial patterns (i.e. footprint size and shape) of current density and magnetic fields. These effects are demonstrated through a visual feature we define as ‘null lines’. Null line shape is affected by changes in conductivity near material boundaries as well as transmitter location. Our results encourage the use of null lines as a planning tool for better ground-truth field measurements near deformed ice types.University of Delaware. Department of Geography.University of Delaware. Department of Electrical and Computer Engineering