We study different aspects of the predictability of weather and climate as complex physical systems.

Research Areas

Development of stochastic parametrisations



  • Shutts, G., M. Leutbecher, A. Weisheimer, T. Stockdale, L. Isaksen and M. Bonavita (2011). Representing model uncertainty: stochastic parametrizations at ECMWF. ECMWF Newsletter, 129, 19-24. [pdf]
  • Palmer, T.N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G.J. Shutts, M. Steinheimer and A. Weisheimer (2009b). Stochastic parametrization and model uncertainty. ECMWF Tech. Memo. 598, 42pp. [pdf]
  • Berner, J., F.J. Doblas-Reyes, T.N. Palmer, G. Shutts and A. Weisheimer (2009). Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model. In "Stochastic Physics and Climate Modelling", by T.N. Palmer and P. Williams (Eds.), Chapter 15, 375-395, Cambridge University Press.

Assessing model uncertainty in weather and climate forecasts

All weather and climate forecasts are subject to uncertainties arising from imperfect knowledge of initial and boundary conditions, and from model errors associated with missing or poorly resolved processes. Handling the uncertainty that arises from model errors is very challenging and no widely accepted methodology exists. Here we develop and test different strategies to address model uncertainty in GCM-based weather and climate forecasts. We aim to identify, understand and constrain the physical processes that dominate forecast uncertainty. We particularly focus on uncertainties that are relevant to both weather and climate forecasting, e.g. by taking a seamless prediction approach. Our ultimate goal is to improve the prospects for providing more reliable probabilistic weather and climate forecasts to users.


High-resolution modelling

It is now understood that many small-scale processes have a profound influence on large-scale climate and climate variability. Including smaller scale processes in our numerical models of the weather and climate allows us to better understand the contribution of the smaller-scales to large-scale circulation. Smaller scale processes can be explicitly included in these models by increasing the horizontal resolution. High horizontal resolution is very expensive in terms of computation time and resources, and it is therefore important that we develop a good understanding of the impact of increased horizontal resolution in order to justify its use in operational models.

Horizontal Resolution: The four horizontal resolutions of the ECMWF IFS integrated under the Athena Project.Horizontal Resolution: The four horizontal resolutions of the ECMWF IFS integrated under the Athena Project.

Seamless prediction of weather and climate


Predictability on seasonal time scales

Atmospheric predictability on seasonal time scales arises from the slowly varying boundary conditions like the ocean or the land surface. The most prominent example of a coupled ocean-atmosphere process on these time scales is the El Nino Southern Oscillation (ENSO). ENSO is the single largest source for seasonal predictability in many regions of the world.

Our research interests include:

  • skill assessment of ECMWF's dynamical seasonal forecasting system S4
  • multi-model seasonal forecasts
  • forecast quality assessment of the ENSEMBLES seasonal-to-interannual forecasts
  • case studies


Predictability on decadal time scales

The climate system exhibits variability on a variety of timescales. Decadal predictability is a relatively new area of research trying to explore impacts from the atmospheric and oceanic initial conditions as well as from the boundary forcings on near-term climate predictions. The next Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5), due in 2013, will have in its Working Group 1 volume on the Physical Science Basis a dedicated chapter on "Near-Term Climate Change: Projections and Predictability".

Our latest study by Corti et al. (2012) we assess the reliability of decadal predictions of SST and 2m temperature based on a 54-member ensemble of the ECMWF coupled model. It was shown that the reliability from the ensemble system is good over global land areas, Europe and Africa and for the North Atlantic, Indian Ocean and, to a lesser extent, North Pacific basins for lead times up to 6–9 years. North Atlantic SSTs are reliably predicted even when the climate trend is removed, consistent with the known predictability for this region. By contrast, reliability in the Indian Ocean, where external forcing accounts for most of the variability, deteriorates severely after de-trending. More conventional measures of forecast quality, such as the anomaly correlation coefficient of the ensemble mean, are also considered, showing that the ensemble has significant skill in predicting multi-annual temperature averages.


Climate change



Inexact but efficient computing hardware

We investigate the use of stochastic processors and low precision arithmetic in atmospheric simulations. So called stochastic processors allow hardware induced faults in calculations, sacrificing bit-reproducibility in exchange for improvements in performance and/or a reduction in power consumption. A similar trade-off is achieved using low precision arithmetic, with improvements in computation and communication speed and savings in storage and memory requirements. As high-performance computing becomes more massively parallel and power hungry these two approaches may be important stepping stones in the pursuit of global cloud resolving atmospheric models.


Predictability of Weather and Climate group wiki page

We have a wiki page for internal use with information about

  • the cirrus cluster
  • the use of the ECMWF computing systems