Status and future of global and regional ocean prediction systems

Tonani, Marina; Balmaseda, Magdalena; Bertino, Laurent; Blockley, Ed; Brassington, Gary B.; Davidson, Fraser; Drillet, Yann; Hogan, P. J.; Kuragano, Tsurane; Lee, Tong; Mehra, Avichal; Paranathara, Francis; Tanajura, Clemente Augusto Souza; Wang, Hui

doi:10.1080/1755876x.2015.1049892

Cited by 58 publications

(45 citation statements)

References 43 publications

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“…Significant incremental progress has been made in ocean forecasting since the inception of GOV in (Bell et al 2015Tonani et al 2015) and GOV has had a major impact on the development of the global operational 2 oceanography capability (Bell et al 2015). Global modelling and data-assimilation systems have been progressively developed (Martin submitted) implemented (Tonani et al 2015) and inter-compared (Ryan et al 2015), taking advantage of increased computing power and enhanced model resolution.…”

Section: Synthesis Of Achievementsmentioning

confidence: 99%

“…inclusion of tides, improved surface forcing/surface fields and waves/current interactions (Tonani et al 2015;Chassignet and Sandery 2013); . assimilation of new observations including sea-ice, salinity and biological/biogeochemical observations (Martin et al 2015); .…”

Section: Synthesis Of Achievementsmentioning

confidence: 99%

“…Global modelling and data-assimilation systems have been progressively developed (Martin submitted) implemented (Tonani et al 2015) and inter-compared (Ryan et al 2015), taking advantage of increased computing power and enhanced model resolution. Improvements include:…”

Section: Synthesis Of Achievementsmentioning

confidence: 99%

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Synthesis of new scientific challenges for GODAE OceanView

Schiller

Bell

Brassington

et al. 2015

Journal of Operational Oceanography

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Section: Synthesis Of Achievementsmentioning

confidence: 99%

Section: Synthesis Of Achievementsmentioning

confidence: 99%

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Synthesis of new scientific challenges for GODAE OceanView

Schiller

Bell

Brassington

et al. 2015

Journal of Operational Oceanography

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“…MFS (http://medforecast.bo.ingv.it/) is an operational forecasting system [13][14][15] consisting of a near real-time observation system with satellite and in situ elements, a numerical ocean forecasting model at a basin scale, based on a primitive equation model [16,17], and a data assimilation scheme [18]. The MFS spatial domain is shown in Fig.…”

Section: Cosmo and Mfsmentioning

confidence: 99%

“…Sensitivity experiments have been carried out using different SSTs surface boundary conditions for the COSMO forecasts. Four different experiments have been carried out: the first two using SST fields obtained from the OSTIA (operational sea surface temperature and sea ice analysis) system, while the other two using the SST analyses and forecasts from MFS (Mediterranean Forecasting System, Tonani et al, 2015;Pinardi and Coppini, 2010). The different boundary conditions determine differences in the trajectory, pressure minimum and wind intensity of the simulated Medicane.…”

mentioning

confidence: 99%

Impact of Sea Surface Temperature on COSMO Forecasts of a Medicane over the Western Mediterranean Sea

Romaniello¹,

Oddo²,

Tonani³

et al. 2015

JEASE

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Abstract:The paper describes and analyzes the sensitivity of an operational atmospheric model to different SST (sea surface temperature) estimates. The model's sensitivity has been analyzed in a Medicane (Mediterranean hurricane) test case. Numerical simulations have been performed using the COSMO (consortium for small-scale modeling) atmospheric model, in the COSMO-ME configuration. The model results show that the model is capable of capturing the position, timing and intensity of the cyclone. Sensitivity experiments have been carried out using different SSTs surface boundary conditions for the COSMO forecasts. Four different experiments have been carried out: the first two using SST fields obtained from the OSTIA (operational sea surface temperature and sea ice analysis) system, while the other two using the SST analyses and forecasts from MFS (Mediterranean Forecasting System, Tonani et al., 2015;Pinardi and Coppini, 2010). The different boundary conditions determine differences in the trajectory, pressure minimum and wind intensity of the simulated Medicane. The sensitivity experiments showed that a colder than real SST field determines a weakening of the minimum pressure at the vortex center. MFS SST analyses and forecasts allow the COSMO model to simulate more realistic minimum pressure values, trajectories and wind speeds. It was found that MFS SST forecast, as surface boundary conditions for COSMO-ME runs, determines a significant improvement, compared to ASCAT observations, in terms of wind intensity forecast as well as cyclone dimension and location.

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Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

et al. 2016

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Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

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Status and future of global and regional ocean prediction systems

Cited by 58 publications

References 43 publications

Synthesis of new scientific challenges for GODAE OceanView

Synthesis of new scientific challenges for GODAE OceanView

Impact of Sea Surface Temperature on COSMO Forecasts of a Medicane over the Western Mediterranean Sea

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

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