TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Sakov, Pavel; Counillon, François; Bertino, Laurent; Lisæter, Knut Arild; Oke, Peter R.; Korablev, Alexander

doi:10.5194/os-8-633-2012

Cited by 339 publications

(312 citation statements)

References 71 publications

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“…The sea-ice-ocean model of TOPAZ (hereafter called the TOPAZ model) is used here in free-run mode (i.e., no data assimilation is applied) in the same configuration as in Sakov et al (2012). The model grid covers the Arctic and North Atlantic oceans with a mean resolution of approximately 12 km over the Arctic.…”

Section: Topaz Trajectories Datasetmentioning

confidence: 99%

“…The TOPAZ model in free-run mode has been evaluated in Sakov et al (2012) and was found to overestimate seaice drift by about 3 km day −1 compared to buoy data. To try to solve this issue, the frictional drag parameters for the atmosphere-ice stress has been reduced to c a = 0.0016 in the TOPAZ operational platform (not yet documented).…”

Section: Topaz Trajectories Datasetmentioning

confidence: 99%

“…The model grid covers the Arctic and North Atlantic oceans with a mean resolution of approximately 12 km over the Arctic. The three TOPAZ simulations analyzed in the following start on 15 September and finish on 15 May for three consecutive winters from 2007 to 2010 with initial conditions extracted from the free-run simulation described in Sakov et al (2012). The applied atmospheric forcing fields are the 10 m wind velocity, the 2 m temperature and mixing ratio, mean sea level pressure, total precipitation and the fraction of that which is snow, and the incoming short-wave and long-wave radiation from the ERA interim reanalysis (ERAi) distributed at 80 km spatial resolution and 6 h temporal resolution (http://www.ecmwf.int/en/ research/climate-reanalysis/era-interim, ECMWF, 2011).…”

Section: Topaz Trajectories Datasetmentioning

confidence: 99%

“…TOPAZ is a coupled sea-ice-ocean data assimilation system (Sakov et al, 2012) used in the operational Arctic Ocean forecast platform of the European Copernicus Marine Environment Monitoring service (http://marine.copernicus.eu). It has also been used to build a 23-year reanalysis , also distributed by the Copernicus marine service.…”

Section: Topaz Trajectories Datasetmentioning

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: Topaz Trajectories Datasetmentioning

confidence: 99%

Section: Topaz Trajectories Datasetmentioning

confidence: 99%

Section: Topaz Trajectories Datasetmentioning

confidence: 99%

Section: Topaz Trajectories Datasetmentioning

confidence: 99%

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Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories

et al. 2016

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“…For the model evaluation we force our model with the ocean state of the TOPAZ4 reanalysis (see Sakov et al, 2012), and with the atmospheric state of the Arctic System Reanalysis, Interim version (ASR-Interim hereafter) (http://rda.ucar. edu/datasets/ds631.4/, Byrd Polar Research Centre/The Ohio State University (2012).…”

Section: Simulation Set-upmentioning

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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An intercomparison of Arctic ice drift products to deduce uncertainty estimates

et al. 2014

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An intercomparison of four low‐resolution remotely sensed ice‐drift products in the Arctic Ocean is presented. The purpose of the study is to examine the uncertainty in space and time of these different drift products. The comparison is based on monthly mean ice drifts from October 2002 to December 2006. The ice drifts were also compared with available buoy data. The result shows that the differences of the drift vectors are not spatially uniform, but are covariant with ice concentration and thickness. In high (low) ice‐concentration areas, the differences are small (large), and in thick (thin) ice‐thickness areas, the differences are small (large). A comparison with the drift deduced from buoys reveals that the error of the drift speed depends on the magnitude of the drift speed: larger drift speeds have larger errors. Based on the intercomparison of the products and comparison with buoy data, uncertainties of the monthly mean drift are estimated. The estimated uncertainty maps reasonably reflect the difference between the products in relation to ice concentration and the bias from the buoy drift in relation to drift speed. Examinations of distinctive features of Arctic sea ice motion demonstrate that the transpolar drift speed differs among the products by 13% (0.32 cm s−1) on average, and ice drift curl in the Amerasian Basin differs by up to 24% (3.3 × 104 m2 s−1). These uncertainties should be taken into account if these products are used, particularly for model validation and data assimilation within the Arctic.

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TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Cited by 339 publications

References 71 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

An intercomparison of Arctic ice drift products to deduce uncertainty estimates

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