Analysis and Forecasting of Sea Ice Conditions with Three-Dimensional Variational Data Assimilation and a Coupled Ice–Ocean Model

Caya, Alain; Buehner, Mark; Carrières, Tom

doi:10.1175/2009jtecho701.1

Cited by 58 publications

(61 citation statements)

References 28 publications

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“…Initialized experiments can additionally or alternatively assimilate salinity (Servonnat et al 2015), sea ice (e.g. Caya et al 2010), land soil moisture (e.g. Han et al 2012), as well as different atmospheric fields, such as wind-stress (Ding et al 2013;Thoma et al 2015) or the 3D winds (Stewart and Haine 2013).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing extreme AMOC events through nudging of the ocean surface: a perfect model approach

et al. 2017

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mixed layer depth, which, in essence, keeps the restoring time scale constant. This new technique substantially improves water mass transformation in the regions of convection, and in particular, the formation of the densest waters, which are key for the representation of the AMOC extreme. It is therefore a promising strategy that may help to better constrain the AMOC variability and other ocean features in the models. As this restoring technique only uses surface data, for which better and longer observations are available, it opens up opportunities for improved reconstructions of the AMOC over the last few decades.

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Section: Introductionmentioning

confidence: 99%

Reconstructing extreme AMOC events through nudging of the ocean surface: a perfect model approach

et al. 2017

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“…In contrast, areas near the ice edge, where the ice concentration is between 0 and 100 %, have larger values of ensemble spread. A diffusion operator is used for representing the spatial background-error correlations within this sea-ice 3DVar data assimilation system and not a spectral transform (Caya et al, 2010). Shlyaeva et al (2015) also used the diffusion operator to represent the spatial localisation function in preliminary experiments with EnVar.…”

Section: Results From Applying Scale-dependent Localisation To An Envmentioning

confidence: 99%

Scale-dependent background-error covariance localisation

Buehner¹,

Shlyaeva

2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TA new approach is presented and evaluated for efficiently applying scale-dependent spatial localisation to ensemble background-error covariances within an ensemble-variational data assimilation system. The approach is primarily motivated by the requirements of future data assimilation systems for global numerical weather prediction that will be capable of resolving the convective scale. Such systems must estimate the global and synoptic scales at least as well as current global systems while also effectively making use of information from frequent and spatially dense observation networks to constrain convective-scale features. Scale-dependent covariance localisation allows a wider range of scales to be efficiently estimated while simultaneously assimilating all available observations. In the context of an idealised numerical experiment, it is shown that using scaledependent localisation produces an improved ensemble-based estimate of spatially varying covariances as compared with standard spatial localisation. When applied to an ensemble of Arctic sea-ice concentration, it is demonstrated that strong spatial gradients in the relative contribution of different spatial scales in the ensemble covariances result in strong spatial variations in the overall amount of spatial localisation. This feature is qualitatively similar to what might be expected when applying an adaptive localisation approach that estimates a spatially varying localisation function from the ensemble itself. When compared with standard spatial localisation, scale-dependent localisation also results in a lower analysis error for sea-ice concentration over all spatial scales.

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“…The largest errors in the ice cover are found in the St Lawrence Estuary and in the northeast gulf and coincide with areas of lowest Radarsat coverage. A 3DVAR sea-ice assimilation system based on Caya et al (2010) is currently in development to permit the use of additional ice datasets.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an operational ice–ocean analysis and forecasting system for the Gulf of St Lawrence

Smith

Roy

Brasnett

2012

Quart J Royal Meteoro Soc

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Our ability to simulate and understand oceanic conditions of the Gulf of St Lawrence (GSL) has significantly increased in the last decade with the development of regional ocean models combining scientific knowledge, oceanographic data, and computing techniques. This has created opportunities for their integration with meteorological forecast models where the importance of sea ice-ocean dynamics in air-sea exchanges was recently demonstrated. In order to produce accurate initial conditions for coupled weather forecasts, a GSL ice-ocean 'pseudo' analysis (IOPA) system has been developed and implemented operationally at the Canadian Meteorological Centre (CMC). Based on prognostic ice-ocean model solutions the IOPA system has sufficient skill to produce realistic daily fields without flux correction or data assimilation of temperature and salinity. Using only a simple assimilation method for sea-ice data, daily oceanic fields are obtained and can be used to initialize ice-ocean or coupled atmospheric forecasting systems.A 3-year retrospective evaluation of this operational method is presented focusing on sea-surface temperature (SST) and sea-ice conditions. Model estimates are compared with in situ and satellite-derived data. Results show that the IOPA system has an accuracy equivalent to or better than the current operational SST and ice analyses produced at the CMC. Together with preceding modelling efforts, this work lays the foundation for the first state-of-the-art GSL ocean analysis and forecast systems combining ocean modelling and real-time three-dimensional data assimilation. Coupled atmosphere-ice-ocean forecasts produced with the same ice-ocean model and initialized with the IOPA system are presented in a companion paper.

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Analysis and Forecasting of Sea Ice Conditions with Three-Dimensional Variational Data Assimilation and a Coupled Ice–Ocean Model

Cited by 58 publications

References 28 publications

Reconstructing extreme AMOC events through nudging of the ocean surface: a perfect model approach

Reconstructing extreme AMOC events through nudging of the ocean surface: a perfect model approach

Scale-dependent background-error covariance localisation

Evaluation of an operational ice–ocean analysis and forecasting system for the Gulf of St Lawrence

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