1 edition of Data analysis and modeling of Arctic sea ice subsurface roughness found in the catalog.
Data analysis and modeling of Arctic sea ice subsurface roughness
Donald P. Gaver
Statistical data analysis and models are used to characterize and summarize the roughness of the underside of sea ice in the Arctic. Keel spacings and depths are modeled by sculptured exponentials, and by gamma distributions. The data studied was obtained by upward looking sonar on the submarine GURNARD during April, 1976, in the Beaufort Sea. The models and methods should be more widely applicable.
|Statement||by D.P. Gaver, P. A. Jacobs|
|Contributions||Jacobs, P. A., Naval Postgraduate School (U.S.)|
|The Physical Object|
|Pagination||ii, 66 p.:|
|Number of Pages||66|
1 Technical Note 2 Arctic sea ice surface roughness derived from multi- 3 angular reflectance satellite imagery 4 Anne W. Nolin 1 5 1 Department of Geography, University of Nevada, Reno, NV, , USA; @ 6 * Correspondence: [email protected] 7 8 Abstract: Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice. Discussions about the amount of sea ice in the Arctic often confuse two very different measures of how much ice there is. One measure is sea-ice extent which, as the name implies, is a measure of coverage of the ocean where ice covers 15% or more of the surface. It is a two-dimensional measurement; extent does not tell us how thick the ice is. The other measure of Arctic ice, using all three.
II. DETERMINISTIC SEA ICE MODEL DEVELOPMENT. Robert S. Pritchard A Simulation of Nearshore Winter Ice Dynamics in the Beaufort Sea. Miles G. McPhee An Analysis of Pack Ice Drift in Summer. W. W. Denner and L. D. Ashim Operational Determination of Wind Stress on the Arctic Ice Pack. R. T. Hall A Test of the AIDJEX Ice Model Using Landsat Images. ARCTIC SEA ICE LOSS: A NEED FOR MULTI‐SECTORIAL COLLABORATION Wilkinson, J. British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET Lead author email address: [email protected] One of the most visible aspects of climate change is the dramatic loss of Arctic sea ice; both sea ice extent and.
The prediction of future changes in Arctic sea ice, and consequent effects on the ocean 12 and atmosphere 2, relies on global climate models properly reproducing changes in ice . Why would griff use actual observations when he can use model output instead/sarc. Sea Ice Volume is calculated using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock, ) developed at APL/PSC.
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Statistical data analysis and models are used to characterize and summarize the roughness of the underside of sea ice in the Arctic. Keel spacings and depths are modeled by sculptured exponentials, and by gamma distributions. The data studied was obtained by upward looking sonar on the submarine GURNARD during April,in the Beaufort : Roughness, age and drift trajectories of sea ice in large-scale simulations and their use in model verifications, Annals of Glaciol in press Google Scholar Hibler, W.D.
A dynamic-thermodynamic sea ice model, J. Phys. Oceanogr., 9, – CrossRef Google ScholarAuthor: Nadja Steiner, Markus Harder, Peter Lemke.
DATA ANALYSIS AND MODELING OF ARCTIC SEA ICE SUBSURFACE ROUGHNESS D. Gaver P. Jacobs Pepartment of Operations Research Naval Postgraduate School Monterey, California 1. Introduction The spatial pattern of the sea ice cover in the Arctic has been of considerable scientific interest to geophysicists and oceanographers for some : Donald P.
Gaver, P.A. Jacobs. The Sea Ice Analysis Tool allows users to analyze monthly-averaged or daily sea ice extent and concentration via interactive maps. In addition, users can plot monthly ice extent anomalies, map sea ice concentration anomalies, and display images of trends in sea ice concentration, all for a variety of date, climatology, and trend ranges.
The Arctic sea ice cover is thinning and retreating, causing changes in surface roughness that in turn modify the momentum flux from the atmosphere through the ice into the ocean.
New model simulations comprising variable sea ice drag coefficients for both the air and water interface demonstrate that the heterogeneity in sea ice surface. Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer meltponds.
Field Techniques for Snow Observations on Sea Ice; Ice Thickness and Roughness Measurements ; Ice Sampling and Basic Sea-Ice Core Analysis ; Thermal, Electrical, and Hydraulic Properties of Sea Ice ; Ice Strength – In Situ Measurement; Sea Ice Optics Measurements; Measurements and Modeling of the Ice – Ocean.
Comparison of sea-ice draft data acquired on submarine cruises between and with similar data acquired between and indicates that the mean ice draft at the end of the melt.
A widely used sea ice reconstruction of this type is the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) (Schweiger et al. ; Zhang and Rothrock ).Estimated sea ice thickness and volume uncertainties from PIOMAS are of similar magnitude to those currently observable from satellite (Labe et al.
; Laxon et al. ; Schweiger et al. ; Stroeve et al. ; Tilling et. Mischa Ungermann, Martin Losch, An Observationally Based Evaluation of Subgrid Scale Ice Thickness Distributions Simulated in a Large‐Scale Sea Ice‐Ocean Model of the Arctic Ocean, Journal of Geophysical Research: Oceans, /JC,11, (), ().
As the neXtSIM model products were only provided for March, December, and May, i.e., months where ice coverage was >90%, we focused the analysis on these “winter” months.
There were 61 drifters that moved through the TOPAZ–EVP–WIM study domain in the Beaufort Sea where we have data from all four ice model data products. Here, we apply the recent data-adaptive harmonic (DAH) technique of Chekroun and Kondrashov, (), Chaos, 27 for the description, modeling and prediction of the Multisensor Analyzed Sea Ice Extent (MASIE, –) data set.
The DAH decomposition of MASIE identifies narrowband, spatio-temporal data-adaptive modes over four key Arctic regions. Monthly Sea Ice Outlook; from SEARCH/Arcus NASA IceBridge Sea Ice Freeboard, Snow Depth, and Thickness Quick Look data set Arctic Sea Ice images The Cryosphere Today at the University of Illinois; NASA Goddard Flickr page (search for Arctic Sea Ice) NASA Goddard Scientific Visualization Studio (search for Arctic Sea ice.
Highlights A SST analysis system called the Operational SST and Sea Ice Analysis is described. OSTIA uses a blend of satellite and in situ data products. OSTIA delivers daily fields at a grid spatial resolution of 1/20 degrees. Satellite data are adjusted for bias errors using AATSR satellite and in situ data.
OSTIA products have zero mean bias and an accuracy of ~ K. OSTIA has a positive. To evaluate the model skills and biases in simulating present Arctic sea ice cover, the simulated spatial distribution of sea ice concentration (fractional area of the ocean covered by ice) along with time series of the pan-Arctic sea ice extent (the area over which ice is present) and volume (product of ice thickness and concentration) are.
Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer melt ponds, and ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness.
This paper presents an analysis of snow depth and other Arctic sea ice data from the Sever expeditions and the North Pole drifting stations in the period – By merging snow depth data from the two extensive observing programmes, a new snow depth climate data set has been provided for the end of winter season (March, April and May).
Environmental Science Combining Data from a Small SAR on an Unmanned Aircraft with Satellite Observations: The microASAR on the NASA SIERRA UAS for the Characterization of Arctic Sea Ice Experiment (CASIE) Toward Model Free Atmospheric Sensing by. This manuscript presents an evaluation of global climate models to guide future projections of Arctic sea ice extent (SIE).
Thirty-five model simulations from Coupled Model Intercomparison Project, Phase 5 were examined to select model subsets using comparison to observational data (–).
The study extends previous work by highlighting the seasonality of sea ice trends, utilizing a. By 20th-century standards, the Central Pacific trade winds that drive the El Nino–Southern Oscillation feedback system to instability have been unusually strong in the 21st century.
The annual summer melts of Arctic sea ice are up to twice as large in area as in the 20th century. Arctic sea ice, upper atmospheric circulation, surface wind, and sea-surface temperature data provide evidence. In this study, six Arctic sea ice thickness products are compared: the AVHRR Polar Pathfinder-extended (APP-x), ICESat, CryoSat-2, SMOS, NASA IceBridge aircraft flights, and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS).
The satellite products are based on three different retrieval methods: [ ] Read more.Abstract. We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of.
The Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) is a coupled ice and ocean model with sea ice thickness data available over the satellite era (from ) (Zhang and Rothrock, ). PIOMAS has been widely validated against sea ice thickness data sets (such as ICESat), and its uncertainties are addressed in Schweiger et al.