Publications by Stephan Juricke

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

Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain) 144 (2018) 1947-1964

S Juricke, D MacLeod, A Weisheimer, L Zanna, TN Palmer

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.

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


M Leutbecher, S-J Lock, P Ollinaho, STK Lang, G Balsamo, P Bechtold, M Bonavita, HM Christensen, M Diamantakis, E Dutra, S English, M Fisher, RM Forbes, J Goddard, T Haiden, RJ Hogan, S Juricke, H Lawrence, D MacLeod, L Magnusson, S Malardel, S Massart, I Sandu, PK Smolarkiewicz, A Subramanian, F Vitart, N Wedi, A Weisheimer

A simulation of small to giant Antarctic iceberg evolution: Differential impact on climatology estimates


T Rackow, C Wesche, R Timmermann, HH Hellmer, S Juricke, T Jung

Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model


P Davini, J von Hardenberg, S Corti, HM Christensen, S Juricke, A Subramanian, PAG Watson, A Weisheimer, TN Palmer

STOCHASTIC PARAMETERIZATION Toward a New View of Weather and Climate Models


J Berner, U Achatz, L Batte, L Bengtsson, A de la Camara, HM Christensen, M Colangeli, DRB Coleman, D Crommelin, SI Dolaptchiev, CLE Franzke, P Friederichs, P Imkeller, H Jarvinen, S Juricke, V Kitsios, F Lott, V Lucarini, S Mahajan, TN Palmer, C Penland, M Sakradzija, J-S von Storch, A Weisheimer, M Weniger, PD Williams, J-I Yano

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

JOURNAL OF CLIMATE 30 (2017) 4997-5019

S Juricke, TN Palmer, L Zanna

Oceanic Stochastic Parameterizations in a Seasonal Forecast System


M Andrejczuk, FC Cooper, S Juricke, TN Palmer, A Weisheimer, L Zanna

Towards multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model formulation and mean climate

Climate Dynamics Springer Science and Business Media LLC 44 (2015) 757-780

D Sidorenko, T Rackow, T Jung, T Semmler, D Barbi, S Danilov, K Dethloff, W Dorn, K Fieg, HF Goessling, D Handorf, S Harig, W Hiller, S Juricke, M Losch, J Schröter, DV Sein, Q Wang

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

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences The Royal Society 372 (2014) 20130283-20130283

S Juricke, T Jung

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


S Juricke, HF Goessling, T Jung

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

Journal of Climate American Meteorological Society 26 (2013) 3785-3802

S Juricke, P Lemke, R Timmermann, T Rackow

<jats:p> The 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 in the multiyear ice comparable to the interannual variability of the model. Results cannot be reproduced by a simple constant global modification of P*. </jats:p>