A generic approach to explicit simulation of uncertainty  in the NEMO ocean model

Brankart, Jean-Michel; Candille, Guillem; Garnier, Florent; Calone, C.; Melet, Angélique; Bouttier, Pierre-Antoine; Brasseur, Pierre; Verron, Jacques

doi:10.5194/gmdd-8-615-2015

Cited by 22 publications

(47 citation statements)

References 27 publications

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“…Despite this, the impact of stochastic perturbations and their evolution depends strongly on the model horizontal resolution, the time‐scale considered, and the atmospheric forcing variability (for example, Williams, ; Brankart, ; Brankart et al. ; Andrejczuk et al. ; Williams et al.…”

Section: Discussionmentioning

confidence: 99%

“…), with a focus on the stochastic effect of unresolved scales in the computation of density (as in Brankart et al. ; see section 3.2 for results). The original purpose of these ensemble simulations was to investigate to what extent uncertain model operators can be made statistically consistent with observations (for example, satellite altimetry or Array for Real‐time Geostrophic Oceanography (ARGO) floats).…”

Section: Diversity Of Ocean Ensembles and Uncertaintymentioning

confidence: 99%

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Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions

Zanna

Brankart

Huber

et al. 2018

Quart J Royal Meteoro Soc

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The ocean plays an important role in the climate system on time-scales of weeks to centuries. Despite improvements in ocean models, dynamical processes involving multiscale interactions remain poorly represented, leading to errors in forecasts. We present recent advances in understanding, quantifying, and representing physical and numerical sources of uncertainty in novel regional and global ocean ensembles at different horizontal resolutions. At coarse resolution, uncertainty in 21st century projections of the upper overturning cell in the Atlantic is mostly a result of buoyancy fluxes, while the uncertainty in projections of the bottom cell is driven equally by both wind and buoyancy flux uncertainty. In addition, freshwater and heat fluxes are the largest contributors to Atlantic Ocean heat content regional projections and their uncertainties, mostly as a result of uncertain ocean circulation projections. At both coarse and eddy-permitting resolutions, unresolved stochastic temperature and salinity fluctuations can lead to significant changes in large-scale density across the Gulf Stream front, therefore leading to major changes in large-scale transport. These perturbations can have an impact on the ensemble spread on monthly time-scales and subsequently interact nonlinearly with the dynamics of the flow, generating chaotic variability on multiannual time-scales. In the Gulf Stream region, the ratio of chaotic variability to atmospheric-forced variability in meridional heat transport is larger than 50% on time-scales shorter than 2 years, while between 40 and 48 • S the ratio exceeds 50% on on time-scales up to 28 years. Based on these simulations, we show that air-sea interaction and ocean subgrid eddies remain an important source of error for simulating and predicting ocean circulation, sea level, and heat uptake on a range of spatial and temporal scales. We discuss how further refinement of these ensembles can help us assess the relative importance of oceanic versus atmospheric uncertainty in weather and climate.

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Section: Discussionmentioning

confidence: 99%

Section: Diversity Of Ocean Ensembles and Uncertaintymentioning

confidence: 99%

Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions

Zanna

Brankart

Huber

et al. 2018

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

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“…The study of Weisheimer et al [2011] compared the multi-model, perturbed parameter and atmospheric stochastic physics approach for monthly and seasonal forecasts showing the potential for stochastic parametrizations to outperform the multi-model ensemble.The motivation for stochastic parametrizations in the atmosphere originates to some degree from the existence of power law structures and the related rapid upscale error propagation [see Palmer , 2012, for a detailed discussion]. Since similar power law structures, associated with mesoscale eddies, can also be found in the ocean [LaCasce and Ohlmann, LaCasce, 2008] similar arguments for the potential role of stochastic parametrizations may therefore hold.Stochastic parametrizations have not only been introduced into the atmospheric component of NWP and seasonal forecast models [e.g.,Buizza et al, 1999; Palmer et al, 2009], but have more recently also been implemented into the sea ice[Juricke et al, 2013;Juricke and Jung, 2014], ocean [e.g.,Brankart, 2013;Brankart et al, 2015], land surface [MacLeod…”

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confidence: 99%

Oceanic Stochastic Parameterizations in a Seasonal Forecast System

et al. 2016

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We study the impact of three stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales.The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the Stochastically Perturbed Parametrization Tendency (SPPT) scheme -which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely the western boundary currents and the Southern Ocean. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error. Whilst there are good grounds for implementing stochastic schemes in ocean models, our results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.

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“…In order to avoid uncertainties from employing deterministic parameter estimations, Brankart () showed that the application of stochastic parameterization in the seawater equation of state can produce a notable effect on the average large‐scale circulation of the ocean, with a more prominent signal in regions of high mesoscale activity. Next, Brankart et al () investigated unsolved processes and scales of the ocean model through stochastic temperature (T) and salinity (S) fluctuations and noted that the small scales constantly modify the structure of the large‐scale density, and thus the pathway of the large‐scale circulation, as a result of the nonlinearity of the equation of state. Brankart et al () also mentioned but did not investigate the uncertainties derived from external forcing such as the atmospheric forcing, river runoff, or lateral boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Ocean Model Uncertainties Through Ensemble Forecast Experiments in the Southwest Atlantic Ocean

et al. 2019

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Ocean general circulation models even with realistic behavior still incorporate large uncertainties from external forcing. This study involves the realization of ensemble experiments using a regional model configured for the Southwest Atlantic Ocean to investigate uncertainties derived from the external forcing such as the atmosphere and bathymetry. The investigation is based on perturbing atmospheric surface fluxes and bathymetry through a series of ensemble experiments. The results showed a strong influence of the South Atlantic Convergence Zone on the underlying ocean, 7 days after initialization. In this ocean region, precipitation and radiation flux perturbations notably impacted the sea surface salinity and sea surface temperature, by producing values of ensemble spread that exceeded 0.08 and 0.2°C, respectively. Wind perturbations extended the impact on currents at surface, with the spread exceeding 0.1 m/s. The ocean responded faster to the bathymetric perturbations especially in shallow waters, where the dynamics are largely dominated by barotropic processes. Ensemble spread was the largest within the thermocline layer and in ocean frontal regions after a few months, but by this time, the impact on the modeled ocean obtained from either atmospheric or bathymetric perturbations was quite similar, with the internal dynamics dominating over time. In the vertical, the sea surface temperature exhibited high correlation with the subsurface temperature of the shallowest model levels within the mixed layer. Horizontal error correlations exhibited strong flow dependence at specific points on the Brazil and Malvinas Currents. This analysis will be the basis for future experiments using ensemble-based data assimilation in the Southwest Atlantic Ocean.Plain Language Summary The numerical models are powerful tools to provide knowledge about the ocean state concerning currents eddies, meanders, and other ocean dynamic and thermodynamic processes on a range of temporal and spatial scales. An accurate numerical model makes possible to get a tridimensional ocean representation with some confidence during time. Even though the ocean numerical models have been incorporating improvements, mainly due to a growing evolution of the computational resources, they are still somewhat limited and bring uncertainties on their simulations due many reasons that are related to the applied physical parameterization, atmospheric forcing, bathymetry, and some other issues. It is crucial to investigate and to know these uncertainties. This study goes further on the uncertainty investigations in order to create the basis (prior step) for an ensemble-based data assimilation system for the Southwest Atlantic Ocean. Our results indicated that uncertainty in wind forcing plays a major role in the determination of uncertainty in the ocean state. Compared to atmospheric forcing, the uncertainty in bathymetry produced a larger impact on the ocean representation, especially in shallow waters, though this may be in part due to excited waves at the i...

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A generic approach to explicit simulation of uncertainty in the NEMO ocean model

Cited by 22 publications

References 27 publications

Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions

Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions

Oceanic Stochastic Parameterizations in a Seasonal Forecast System

An Investigation of Ocean Model Uncertainties Through Ensemble Forecast Experiments in the Southwest Atlantic Ocean

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