Oceanic Stochastic Parameterizations in a Seasonal Forecast System

Andrejczuk, Mirosław; Cooper, Fenwick; Juricke, Stephan; Palmer, Tim; Weisheimer, Antje; Zanna, Laure

doi:10.1175/mwr-d-15-0245.1

Cited by 26 publications

(35 citation statements)

References 45 publications

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“…First test experiments have recently been run with the coupled ECMWF seasonal forecasting system based on CY41R1 and horizontal resolution TL255/1 • NEMO. As in the uncoupled case, the stochastic perturbations increase the ensemble spread (see also Andrejczuk et al, 2016), especially in the eddy-active regions of the Southern Ocean and the western boundary currents. Williams et al (2016) report the reduction of biases in a noneddy-permitting ocean model (FAMOUS) due to the introduction of stochastic perturbations to the temperature in the ocean and improved variability of the strength of the thermohaline circulation.…”

Section: Ocean Uncertaintiesmentioning

confidence: 90%

“…First test experiments have recently been run with the coupled ECMWF seasonal forecasting system based on CY41R1 and horizontal resolution TL255/1° NEMO. As in the uncoupled case, the stochastic perturbations increase the ensemble spread (see also Andrejczuk et al , 2016), especially in the eddy‐active regions of the Southern Ocean and the western boundary currents.…”

Section: Unrepresented Uncertainties In the Earth Systemmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Leutbecher

Lock

Ollinaho

et al. 2017

Quart J Royal Meteoro Soc

214

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Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.

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Section: Ocean Uncertaintiesmentioning

confidence: 90%

Section: Unrepresented Uncertainties In the Earth Systemmentioning

confidence: 99%

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Leutbecher

Lock

Ollinaho

et al. 2017

Quart J Royal Meteoro Soc

214

250

View full text Add to dashboard Cite

show abstract

“…In particular, in this paper we have only tested our hypothesis in terms of the fast processes occurring in the atmosphere. For example, the application of this methodology to include uncertainties in slower oceanic processes (e.g., Andrejczuk et al 2016) would require a study with coupled ocean-atmosphere models-this is work for the future. Also, it is important to note that we are not proposing to calibrate climate change projections based on the skill of the seasonal forecast results: a seasonal forecast system can be perfectly reliable and yet show little or no skill.…”

Section: Discussionmentioning

confidence: 99%

Calibrating Climate Change Time-Slice Projections with Estimates of Seasonal Forecast Reliability

2016

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In earlier work, it was proposed that the reliability of climate change projections, particularly of regional rainfall, could be improved if such projections were calibrated using quantitative measures of reliability obtained by running the same model in seasonal forecast mode. This proposal is tested for fast atmospheric processes (such as clouds and convection) by considering output from versions of the same atmospheric general circulation model run at two different resolutions and forced with prescribed sea surface temperatures and sea ice. Here output from the high-resolution version of the model is treated as a proxy for truth. The reason for using this approach is simply that the twenty-first-century climate change signal is not yet known and, hence, no climate change projections can be verified using observations. Quantitative assessments of reliability of the low-resolution model, run in seasonal hindcast mode, are used to calibrate climate change time-slice projections made with the same low-resolution model. Results show that the calibrated climate change probabilities are closer to the proxy truth than the uncalibrated probabilities. Given that seasonal forecasts are performed operationally already at several centers around the world, in a seamless forecast system they provide a resource that can be used without cost to help calibrate climate change projections and make them more reliable for users.

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“…It gives clear improvements in the skill of probabilistic predictions of precipitation (Buizza et al 1999(Buizza et al , 2005. Recently, there has been a growing interest in applying stochastic techniques to ocean models of the type used for longer-term seasonal forecasts and climate predictions (e.g., Sura and Penland 2002;Berloff 2005a;Berloff et al 2007;Li and von Storch 2013;Porta Mana and Zanna 2014;Jansen and Held 2014;Kitsios et al 2014;Andrejczuk et al 2016). Dawson and Palmer (2015) have shown that it may not be necessary to represent the small scales accurately, or even explicitly, in order to improve the simulation of the large-scale climate.…”

Section: Introductionmentioning

confidence: 99%

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

et al. 2016

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In climate simulations, the impacts of the subgrid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the subgrid variability in a computationally inexpensive manner. This study shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a highresolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a nonzero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the highresolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations.

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Oceanic Stochastic Parameterizations in a Seasonal Forecast System

Cited by 26 publications

References 45 publications

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Calibrating Climate Change Time-Slice Projections with Estimates of Seasonal Forecast Reliability

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

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