A comparison of the regional Arctic System Reanalysis and the global ERA‐Interim Reanalysis for the Arctic

Bromwich, David H.; Wilson, Aaron B.; Bai, Le Sheng; Moore, G. W. K.; Bauer, Péter

doi:10.1002/qj.2527

Cited by 146 publications

(148 citation statements)

References 69 publications

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“…As a follow up of this study, it would be interesting to investigate the causes of the missing high values of fluctuating velocities and the overestimation of the integral timescale, first by examining the impact of the atmospheric forcing resolution and second by checking how the inertial/tidal oscillations are represented by the two modeling platforms used here. To better asses the quality of the simulated sea-ice dynamics, it would be interesting to also perform a dispersion analysis as in Rampal et al (2008) or to specifically study sea-ice deformation as in Bouillon and Rampal (2015) with data from models and observations. Finally, it is worth noting that the representation of the mean sea-ice circulation depends on many processes (mean circulation of the atmosphere and ocean, spatial and temporal variation of the ocean-ice and air-ice drag coefficients as a function of the ice age/type, representation of the ocean-ice and atmosphere-ice boundary layers (McPhee, 2012), etc.…”

Section: Discussionmentioning

confidence: 99%

“…Using such approaches to simulate the spread of sea ice would be inappropriate because sea-ice motion fields are intermittent in time and discontinuous in space, but are valid for reproducing the statistics of individual sea-ice trajectories. The "random walk" model was, for example, used in Colony and Thorndike (1985) with a mean field and statistics on the fluctuations derived by Colony and Thorndike (1984).…”

Section: Introductionmentioning

confidence: 99%

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Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories

et al. 2016

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Abstract. We characterize sea-ice drift by applying a Lagrangian diffusion analysis to buoy trajectories from the International Arctic Buoy Programme (IABP) dataset and from two different models: the standalone Lagrangian sea-ice model neXtSIM and the Eulerian coupled ice-ocean model used for the TOPAZ reanalysis. By applying the diffusion analysis to the IABP buoy trajectories over the period 1979-2011, we confirm that sea-ice diffusion follows two distinct regimes (ballistic and Brownian) and we provide accurate values for the diffusivity and integral timescale that could be used in Eulerian or Lagrangian passive tracers models to simulate the transport and diffusion of particles moving with the ice. We discuss how these values are linked to the evolution of the fluctuating displacements variance and how this information could be used to define the size of the search area around the position predicted by the mean drift. By comparing observed and simulated sea-ice trajectories for three consecutive winter seasons (2007)(2008)(2009)(2010)(2011), we show how the characteristics of the simulated motion may differ from or agree well with observations. This comparison illustrates the usefulness of first applying a diffusion analysis to evaluate the output of modeling systems that include a sea-ice model before using these in, e.g., oil spill trajectory models or, more generally, to simulate the transport of passive tracers in sea ice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories

et al. 2016

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“…The temperature difference, T a − T s is taken between the ice surface and the atmosphere at 2 m, the same as the difference in specific humidity, q a − q s . We calculate the specific humidity using the formulation of Buck (1981). The drag coefficients C t and C q are set to 1.3 × 10 −3…”

Section: Thermodynamical Componentmentioning

confidence: 99%

neXtSIM: a new Lagrangian sea ice model

et al. 2016

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Abstract. The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multiscale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales.

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“…After adding the extra 10-m level, we find that on average there are eight or nine levels located below 1500 m. We analyse the 11-year period from 2000 to 2010 but we only consider winter months (October-March). Bromwich et al (2015) compared the near-surface and upper-level analyses from ASR-Interim and ERA-Interim with surface station data and soundings made in the Arctic region. They found that the correlation between the observed and analysed 10-m wind speed was higher in ASR-Interim than in ERA-Interim during every month (during winter months the correlation for ASR-Interim was 0.72 and for ERA-Interim 0.67) and that ASR-Interim showed slightly negative bias whereas ERA-Interim showed positive bias.…”

Section: Arctic System Reanalysis -Interim Data Setmentioning

confidence: 99%

A climatology of low‐level jets in the mid‐latitudes and polar regions of the Northern Hemisphere

Tuononen

Sinclair

Vihma

2015

Atmospheric Science Letters

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A wintertime climatology of the occurrence and characteristics of low-level jets (LLJs) in the Northern Hemisphere mid-latitudes and polar regions was developed. A LLJ detection algorithm was applied to 11 years of Arctic system reanalysis data. The highest occurrence of LLJs was associated with strong gradients in topography and with the sea-ice edge. Sea areas fully covered with sea ice also favoured the occurrence of LLJs, however, these areas, including the central Arctic, had fewer LLJs than along the sea-ice edge. LLJs also occurred frequently in the seldom studied Sea of Okhotsk area.

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A comparison of the regional Arctic System Reanalysis and the global ERA‐Interim Reanalysis for the Arctic

Cited by 146 publications

References 69 publications

Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories

Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories

neXtSIM: a new Lagrangian sea ice model

A climatology of low‐level jets in the mid‐latitudes and polar regions of the Northern Hemisphere

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