[Stoch] Stochastic ocean parametrizations

[Juricke, Stephan]

Dear all,
There are two relatively new papers on stochastic ocean parametrizations, one in a NEMO ocean-only setup looking at low frequency variability and one on the impact of model and observational uncertainty on the skill of seasonal to annual ocean forecasts with the ECMWF seasonal forecasting system (see below).

Best wishes,
Stephan

Juricke, S., T.N. Palmer, and L. Zanna, 2017: Stochastic Subgrid-Scale Ocean Mixing: Impacts on Low-Frequency Variability.<https://owa.awi.de/owa/redir.aspx?C=AlRQkBnpWtaGOMMsv0KwReOoqOsgkPENh94GkWhgzeLfBaP8qTnWCA..&URL=https%3a%2f%2fjournals.ametsoc.org%2fdoi%2fabs%2f10.1175%2fJCLI-D-16-0539.1> J. Climate, 30, 4997–5019, https://doi.org/10.1175/JCLI-D-16-0539.1<https://owa.awi.de/owa/redir.aspx?C=c6kyp9Q7PFLV4ckbdjSE7jwb2yShREIxxtIsF7xptRnfBaP8qTnWCA..&URL=https%3a%2f%2fdoi.org%2f10.1175%2fJCLI-D-16-0539.1>

Abstract: In global ocean models, the representation of small-scale, high-frequency processes considerably influences the large-scale oceanic circulation and its low-frequency variability. This study investigates the impact of stochastic perturbation schemes based on three different subgrid-scale parameterizations in multidecadal ocean-only simulations with the ocean model NEMO at 1° resolution. The three parameterizations are an enhanced vertical diffusion scheme for unstable stratification, the Gent–McWilliams (GM) scheme, and a turbulent kinetic energy mixing scheme, all commonly used in state-of-the-art ocean models. The focus here is on changes in interannual variability caused by the comparatively high-frequency stochastic perturbations with subseasonal decorrelation time scales. These perturbations lead to significant improvements in the representation of low-frequency variability in the ocean, with the stochastic GM scheme showing the strongest impact. Interannual variability of the Southern Ocean eddy and Eulerian streamfunctions is increased by an order of magnitude and by 20%, respectively. Interannual sea surface height variability is increased by about 20%–25% as well, especially in the Southern Ocean and in the Kuroshio region, consistent with a strong underestimation of interannual variability in the model when compared to reanalysis and altimetry observations. These results suggest that enhancing subgrid-scale variability in ocean models can improve model variability and potentially its response to forcing on much longer time scales, while also providing an estimate of model uncertainty.


Juricke S, MacLeod D, Weisheimer A, Zanna L, Palmer TN. Seasonal to annual ocean forecasting skill and the role of model and observational uncertainty. Q J R Meteorol Soc. 2018;1–18. https://doi.org/10.1002/qj.3394<https://owa.awi.de/owa/redir.aspx?C=cftZRZ8PbpqVu5DlndficfJBoVDd39akemGLPwhIBbHfBaP8qTnWCA..&URL=https%3a%2f%2fdoi.org%2f10.1002%2fqj.3394>

Abstract: Accurate forecasts of the ocean state and the estimation of forecast uncertainties are crucial when it comes to providing skilful seasonal predictions. In this study we analyse the predictive skill and reliability of the ocean component in a seasonal forecasting system. Furthermore, we assess the effects of accounting for model and observational uncertainties. Ensemble forcasts are carried out with an updated version of the ECMWF seasonal forecasting model System 4, with a forecast length of ten months, initialized every May between 1981 and 2010. We find that, for essential quantities such as sea surface temperature and upper ocean 300 m heat content, the ocean forecasts are generally underdispersive and skilful beyond the first month mainly in the Tropics and parts of the North Atlantic. The reference reanalysis used for the forecast evaluation considerably affects diagnostics of forecast skill and reliability, throughout the entire ten‐month forecasts but mostly during the first three months. Accounting for parametrization uncertainty by implementing stochastic parametrization perturbations has a positive impact on both reliability (from month 3 onwards) as well as forecast skill (from month 8 onwards). Skill improvements extend also to atmospheric variables such as 2 m temperature, mostly in the extratropical Pacific but also over the midlatitudes of the Americas. Hence, while model uncertainty impacts the skill of seasonal forecasts, observational uncertainty impacts our assessment of that skill. Future ocean model development should therefore aim not only to reduce model errors but to simultaneously assess and estimate uncertainties.



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