Stephan Juricke

Stochastic parameterization: Towards a new view of weather and climate models

AbstractThe last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stri...

Effects of Stochastic Ice Strength Perturbation on Arctic Finite Element Sea Ice Modeling

AbstractThe ice strength parameter P* is a key parameter in dynamic/thermodynamic sea ice models that cannot be measured directly. Stochastically perturbing P* in the Finite Element Sea Ice–Ocean Model (FESOM) of the Alfred Wegener Institute aims at investigating the effect of uncertainty pertaining to this parameterization. Three different approaches using symmetric perturbations have been applied: 1) reassignment of uncorrelated noise fields to perturb P* at every grid point, 2) a Markov chain time correlation, and 3) a Markov chain time correlation with some spatial correlation between nodes. Despite symmetric perturbations, results show an increase of Arctic sea ice volume and a decrease of Arctic sea ice area for all three approaches. In particular, the introduction of spatial correlation leads to a substantial increase in sea ice volume and mean thickness. The strongest response can be seen for multiyear ice north of the Greenland coast. An ensemble of eight perturbed simulations generates a spread ...

Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction

The influence of a stochastic sea ice strength parametrization on the mean climate is investigated in a coupled atmosphere–sea ice–ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic sea ice parametrization causes an effective weakening of the sea ice. In the uncoupled model this leads to an Arctic sea ice volume increase of about 10–20% after an accumulation period of approximately 20–30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic sea ice thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic sea ice quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of sea ice. However, stochastic sea ice perturbations affect regional sea ice characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic sea ice parametrization on the mean climate of non-polar regions were found to be small.

Oceanic Stochastic Parameterizations in a Seasonal Forecast System

AbstractStochastic parameterization provides a methodology for representing model uncertainty in ensemble forecasts. Here the impact on forecast reliability over seasonal time scales of three existing stochastic parameterizations in the ocean component of a coupled model is studied. 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 parameterization 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 where ensemble spread is increased. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error on seasonal time scales. While there are good ground...

Potential sea ice predictability and the role of stochastic sea ice strength perturbations

Ensemble experiments with a climate model are carried out in order to explore how incorporating a stochastic ice strength parameterization to account for model uncertainty affects estimates of potential sea ice predictability on time scales from days to seasons. The impact of this new parameterization depends strongly on the spatial scale, lead time and the hemisphere being considered: Whereas the representation of model uncertainty increases the ensemble spread of Arctic sea ice thickness predictions generated by atmospheric initial perturbations up to about 4 weeks into the forecast, rather small changes are found for longer lead times as well as integrated quantities such as total sea ice area. The regions where initial condition uncertainty generates spread in sea ice thickness on subseasonal time scales (primarily along the ice edge) differ from that of the stochastic sea ice strength parameterization (along the coast lines and in the interior of the Arctic). For the Antarctic the influence of the stochastic sea ice strength parameterization is much weaker due to the predominance of thinner first year ice. These results suggest that sea ice data assimilation and prediction on subseasonal time scales could benefit from taking model uncertainty into account, especially in the Arctic.

Stochastic Subgrid-Scale Ocean Mixing: Impacts on Low-Frequency Variability

AbstractIn 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 e...

Seasonal to annual ocean forecasting skill and the role of model and observational uncertainty

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.