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  • 1
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    Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High‐Resolution Sea Ice Simulations (2022)
    Details
    Publication Date: 2022-09-22
    Description: Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small‐scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state‐of‐the‐art sea ice models and compared to deformation features derived from the RADARSAT Geophysical Processor System. All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric boundary condition affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2 km) the concentration and thickness anomalies along LKFs are large enough to affect air‐ice‐ocean interaction processes.
    Description: Plain Language Summary: Winds and ocean currents continuously move and deform the sea ice cover of the Arctic Ocean. The deformation eventually breaks an initially closed ice cover into many individual floes, piles up floes, and creates open water. The distribution of ice floes and open water between them is important for climate research, because ice reflects more light and energy back to the atmosphere than open water, so that less ice and more open water leads to warmer oceans. Current climate models cannot simulate sea ice as individual floes. Instead, a variety of methods is used to represent the movement and deformation of the sea ice cover. The Sea Ice Rheology Experiment (SIREx) compares these different methods and assesses the deformation of sea ice in 36 numerical simulations. We identify and track deformation features in the ice cover, which are distinct narrow areas where the ice is breaking or piling up. Comparing specific spatial and temporal properties of these features, for example, the different amounts of fractured ice in specific regions, or the duration of individual deformation events, to satellite observations provides information about the realism of the simulations. From this comparison, we can learn how to improve sea ice models for more realistic simulations of sea ice deformation.
    Description: Key Points: All models simulate linear kinematic features (LKFs), but none accurately reproduces all LKF statistics. Resolved LKFs are affected strongest by spatial and temporal resolution of model grid and atmospheric forcing and rheology. Accurate scaling of deformation rates is a proxy only for realistic LKF numbers but not for any other LKF static.
    Description: DOE
    Description: HYCOM NOPP
    Description: Innovation Fund Denmark and the Horizon 2020 Framework Programme of the European Union
    Description: National centre for Climate Research, SALIENSEAS, ERA4CS
    Description: German Helmholtz Climate Initiative REKLIM (Regional Climate Change)
    Description: Gouvernement du Canada, Natural Sciences and Engineering Research Council of Canada (NSERC) http://dx.doi.org/10.13039/501100000038
    Description: Environment and Climate Change Canada Grants & Contributions program
    Description: Office of Naval Research Arctic and Global Prediction program
    Description: U.S. Department of Energy Regional and Global Model Analysis program
    Description: National Science Foundation Arctic System Science program
    Description: Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659
    Description: https://zenodo.org/communities/sirex
    Keywords: ddc:550.285
    Language: English
    Type: doc-type:article
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  • 2
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    A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System (2019)
    In:  EPIC3Frontiers in Marine Science, 6, pp. 451
    Details
    Publication Date: 2020-07-07
    Description: Rapid Arctic warming drives profound change in the marine environment that have significant socio-economic impacts within the Arctic and beyond, including climate and weather hazards, food security, transportation, infrastructure planning and resource extraction. These concerns drive efforts to understand and predict Arctic environmental change and motivate development of an Arctic Region Component of the Global Ocean Observing System (ARCGOOS) capable of collecting the broad, sustained observations needed to support these endeavors. This paper provides a roadmap for establishing the ARCGOOS. ARCGOOS development must be underpinned by a broadly endorsed framework grounded in high-level policy drivers and the scientific and operational objectives that stem from them. This should be guided by a transparent, internationally accepted governance structure with recognized authority and organizational relationships with the national agencies that ultimately execute network plans. A governance model for ARCGOOS must guide selection of objectives, assess performance and fitness-to-purpose, and advocate for resources. A requirements-based framework for an ARCGOOS begins with the Societal Benefit Areas (SBAs) that underpin the system. SBAs motivate investments and define the system�s science and operational objectives. Objectives can then be used to identify key observables and their scope. The domains of planning/policy, strategy, and tactics define scope ranging from decades and basins to focused observing with near real time data delivery. Patterns emerge when this analysis is integrated across an appropriate set of SBAs and science/operational objectives, identifying impactful variables and the scope of the measurements. When weighted for technological readiness and logistical feasibility, this can be used to select Essential ARCGOOS Variables, analogous to Essential Ocean Variables of the Global Ocean Observing System. The Arctic presents distinct needs and challenges, demanding novel observing strategies. Cost, traceability and ability to integrate region-specific knowledge have to be balanced, in an approach that builds on existing and new observing infrastructure. ARCGOOS should benefit from established data infrastructures following the Findable, Accessible, Interoperable, Reuseable Principles to ensure preservation and sharing of data and derived products. Linking to the Sustaining Arctic Observing Networks (SAON) process and involving Arctic stakeholders, for example through liaison with the International Arctic Science Committee (IASC), can help ensure success.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 3
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    Over what area did the oil and gas spread during the 2010 Deepwater Horizon oil spill? (2016)
    The Oceanography Society
    Details
    Publication Date: 2022-05-25
    Description: Author Posting. © The Oceanography Society, 2016. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 29, no. 3 (2016): 96–107, doi:10.5670/oceanog.2016.74.
    Description: The 2010 Deepwater Horizon (DWH) oil spill in the Gulf of Mexico resulted in the collection of a vast amount of situ and remotely sensed data that can be used to determine the spatiotemporal extent of the oil spill and test advances in oil spill models, verifying their utility for future operational use. This article summarizes observations of hydrocarbon dispersion collected at the surface and at depth and our current understanding of the factors that affect the dispersion, as well as our improved ability to model and predict oil and gas transport. As a direct result of studying the area where oil and gas spread during the DWH oil spill, our forecasting capabilities have been greatly enhanced. State-of-the-art oil spill models now include the ability to simulate the rise of a buoyant plume of oil from sources at the seabed to the surface. A number of efforts have focused on improving our understanding of the influences of the near-surface oceanic layer and the atmospheric boundary layer on oil spill dispersion, including the effects of waves. In the future, oil spill modeling routines will likely be included in Earth system modeling environments, which will link physical models (hydrodynamic, surface wave, and atmospheric) with marine sediment and biogeochemical components.
    Description: This research was made possible by a grant from BP/The Gulf of Mexico Research Initiative to the CARTHE and Deep-C Consortia, and by contract M12PC00003 from the Bureau of Ocean Energy Management (BOEM).
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 4
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    Arctic decadal variability : an auto-oscillatory system of heat and fresh water exchange (2010)
    American Geophysical Union
    Details
    Publication Date: 2022-05-25
    Description: Author Posting. © American Geophysical Union, 2004. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 31 (2004): L03302, doi:10.1029/2003GL019023.
    Description: This paper presents a mechanism of decadal variability in the Artic Ocean–GIN Sea (Greenland, Iceland and Norwegian Seas) atmosphere-ice-ocean system. We hypothesize that Arctic variability is regulated by heat and freshwater exchange between the Arctic Ocean and the GIN Sea. The interaction between basins is weak during anticyclonic circulation regimes (low AO/NAO) and strong during cyclonic circulation regimes (high AO/NAO). Regime shifts are controlled by the system itself through oceanic and atmospheric gradients (dynamic height and surface air temperature) that increase during the anticyclonic regime and decrease during the cyclonic regime. This conceptual mechanism for Arctic decadal variability has been reproduced in a model experiment. Both model results and observational data support the suggested mechanism.
    Description: This research has been supported by the National Science Foundation and by the International Arctic Research Center, University of Alaska Fairbanks, under auspices of the United States National Science Foundation and from the Alaska Sea Grant through the Center for Global Change, University of Alaska Fairbanks.
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
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  • 5
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    Arctic circulation regimes (2015)
    The Royal Society
    Details
    Publication Date: 2022-05-25
    Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Philosophical Transactions of the Royal Society A 373 (2015): 20140160, doi:10.1098/rsta.2014.0160.
    Description: Between 1948 and 1996, mean annual environmental parameters in the Arctic experienced a well-pronounced decadal variability with two basic circulation patterns: cyclonic and anticyclonic alternating at 5 to 7 year intervals. During cyclonic regimes, low sea-level atmospheric pressure (SLP) dominated over the Arctic Ocean driving sea ice and the upper ocean counterclockwise; the Arctic atmosphere was relatively warm and humid, and freshwater flux from the Arctic Ocean towards the subarctic seas was intensified. By contrast, during anticylonic circulation regimes, high SLP dominated driving sea ice and the upper ocean clockwise. Meanwhile, the atmosphere was cold and dry and the freshwater flux from the Arctic to the subarctic seas was reduced. Since 1997, however, the Arctic system has been under the influence of an anticyclonic circulation regime (17 years) with a set of environmental parameters that are atypical for this regime. We discuss a hypothesis explaining the causes and mechanisms regulating the intensity and duration of Arctic circulation regimes, and speculate how changes in freshwater fluxes from the Arctic Ocean and Greenland impact environmental conditions and interrupt their decadal variability.
    Description: Support was provided by US National Science Foundation PLR 1313614, 1203720, 1107277 and 0856531 to A.P., PLR-0804017 to D.D. and by the HYCOM consortium (no. N00014-09-1-0587) to D.D.
    Keywords: Arctic climate variability ; Circulation regimes ; Freshwater and heat content
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 6
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    Arctic decadal variability from an idealized atmosphere-ice-ocean model : 2. Simulation of decadal oscillations (2006)
    Details
    Publication Date: 2022-05-25
    Description: Author Posting. © American Geophysical Union, 2006. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 111 (2006): C06029, doi:10.1029/2004JC002820.
    Description: A simple model of the Arctic Ocean and Greenland Sea, coupled to a thermodynamic sea ice model and an atmospheric model, has been used to study decadal variability of the Arctic ice-ocean-atmosphere climate system. The motivating hypothesis is that the behavior of the modeled and ultimately the real climate system is auto-oscillatory with a quasi-decadal periodicity. This system oscillates between two circulation regimes: the Anticyclonic Circulation Regime (ACCR) and the Cyclonic Circulation Regime (CCR). The regimes are controlled by the atmospheric heat flux from the Greenland Sea and the freshwater flux from the Arctic Ocean. A switch regulating the intensity of the fluxes between the Arctic Ocean and Greenland Sea that depends on the inter-basin gradient of dynamic height is implemented as a delay mechanism in the model. This mechanism allows the model system to accumulate the “perturbation” over several years. After the perturbation has been released, the system returns to its initial state. Solutions obtained from numerical simulations with seasonally varying forcing, for scenarios with high and low interaction between the regions, reproduced the major anomalies in the ocean thermohaline structure, sea ice volume, and fresh water fluxes attributed to the ACCR and CCR.
    Description: This publication is the result of research sponsored by Alaska Sea Grant with funds from the National Oceanic and Atmospheric Administration Office of Sea Grant, Department of Commerce, under grant no. NA 86RG0050 (project no. GC/01-02), and from the University of Alaska with funds appropriated by the state. This research has also been supported by the National Science Foundation and by the International Arctic Research Center, University of Alaska Fairbanks, under auspices of the United States National Science Foundation.
    Keywords: Arctic decadal variability ; Arctic simple model ; Greenland Sea convection
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 7
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    Skill metrics for evaluation and comparison of sea ice models (2015)
    John Wiley & Sons
    Details
    Publication Date: 2022-05-25
    Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Oceans 120 (2015): 5910–5931, doi:10.1002/2015JC010989.
    Description: Five quantitative methodologies (metrics) that may be used to assess the skill of sea ice models against a control field are analyzed. The methodologies are Absolute Deviation, Root-Mean-Square Deviation, Mean Displacement, Hausdorff Distance, and Modified Hausdorff Distance. The methodologies are employed to quantify similarity between spatial distribution of the simulated and control scalar fields providing measures of model performance. To analyze their response to dissimilarities in two-dimensional fields (contours), the metrics undergo sensitivity tests (scale, rotation, translation, and noise). Furthermore, in order to assess their ability to quantify resemblance of three-dimensional fields, the metrics are subjected to sensitivity tests where tested fields have continuous random spatial patterns inside the contours. The Modified Hausdorff Distance approach demonstrates the best response to tested differences, with the other methods limited by weak responses to scale and translation. Both Hausdorff Distance and Modified Hausdorff Distance metrics are robust to noise, as opposed to the other methods. The metrics are then employed in realistic cases that validate sea ice concentration fields from numerical models and sea ice mean outlook against control data and observations. The Modified Hausdorff Distance method again exhibits high skill in quantifying similarity between both two-dimensional (ice contour) and three-dimensional (ice concentration) sea ice fields. The study demonstrates that the Modified Hausdorff Distance is a mathematically tractable and efficient method for model skill assessment and comparison providing effective and objective evaluation of both two-dimensional and three-dimensional sea ice characteristics across data sets.
    Description: U.S. National Science Foundation (NSF) Grant Number: PLR-0804017, NASA JPL OVWST, Bureau of Ocean Energy Management (BOEM), FSU Grant Number: M12PC00003, NSF Grant Numbers: projects PLR-0804010 , PLR-1313614 , PLR-1203720, BP/The Gulf of Mexico Research Initiative Grant Number: SA12-12, GoMRI-008, DoD High Performance Computing Modernization Program
    Keywords: Sea ice model ; Sea ice model validation ; Model skill assessment
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 8
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    Arctic decadal variability from an idealized atmosphere-ice-ocean model: 1. Model description, calibration, and validation (2006)
    American Geophysical Union
    Details
    Publication Date: 2022-05-26
    Description: Author Posting. © American Geophysical Union, 2006. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 111 (2006): C06028, doi:10.1029/2004JC002821.
    Description: This paper describes a simple “multibox” model of the Arctic atmosphere-ice-ocean system. The model consists of two major modules (an Arctic module and a Greenland Sea module) and several sub-modules. The Arctic module includes a shelf box model coupled with a thermodynamic sea ice model, and an Arctic Ocean model coupled with a sea ice model and an atmospheric box model. The Greenland Sea module includes an oceanic model coupled with a sea ice model and a statistical model of surface air temperature over the Greenland Sea. The full model is forced by daily solar radiation, wind stress, river runoff, and Pacific Water inflow through Bering Strait. For validation purposes, results from model experiments reproducing seasonal variability of the major system parameters are analyzed and compared with observations and other models. The model reproduces the seasonal variability of the Arctic system reasonably well and is used to investigate decadal Arctic climate variability in Part 2 of this publication.
    Description: This publication is the result of research sponsored by Alaska Sea Grant with funds from the National Oceanic and Atmospheric Administration Office of Sea Grant, Department of Commerce, under grant no. NA 86RG0050 (project no. GC/01-02), and from the University of Alaska with funds appropriated by the state. This research has also been supported by the National Science Foundation and by the International Arctic Research Center, University of Alaska Fairbanks, under auspices of the United States National Science Foundation.
    Keywords: Arctic decadal oscillations ; Idealized Arctic model ; Arctic–Greenland Sea interaction
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 9
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    Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High‐Resolution Sea Ice Simulations (2022)
    In:  EPIC3Journal of Geophysical Research: Oceans, 127(4), ISSN: 2169-9275
    Details
    Publication Date: 2022-07-05
    Description: Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small-scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state-of-the-art sea ice models and compared to deformation features derived from the RADARSAT Geophysical Processor System. All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric boundary condition affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2 km) the concentration and thickness anomalies along LKFs are large enough to affect air-ice-ocean interaction processes.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 10
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    Sea Ice Rheology Experiment (SIREx): 1. Scaling and Statistical Properties of Sea‐Ice Deformation Fields (2022)
    In:  EPIC3Journal of Geophysical Research: Oceans, 127(4), ISSN: 2169-9275
    Details
    Publication Date: 2022-07-05
    Description: As the sea-ice modeling community is shifting to advanced numerical frameworks, developing new sea-ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea-ice models. Initiated at the Forum for Arctic Modeling and Observational Synthesis, the Sea Ice Rheology Experiment (SIREx) aims at evaluating state-of-the-art sea-ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea-ice physics (rheology) and by model configuration. Part 1 of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea-ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the viscous-plastic (and the elastic-viscous-plastic variant), elastic-anisotropic-plastic, and Maxwell-elasto-brittle rheologies are compared in a single study. We find that both plastic and brittle sea-ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi-fractality. Model configuration (e.g., numerical convergence, atmospheric representation, spatial resolution) and physical parameterizations (e.g., ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea-ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features in a SIREx Part 2 companion paper.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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