Global climate scenarios and model development

The climate is a global system; its understanding and possible projections into the future need to build on a range of spatial scales including the global dimension.


Global climate models are developed under the rationale that coupled representations of climate-relevant processes can describe the combined effects that constitute the overall climate system. When development is focusing on individual processes and their interactions within the climate system, the resulting overall performance will improve. SMHI is leading the development of the global climate model and Earth System Model EC-Earth, based on the world-leading weather forecast model of the ECMWF (European Centre of Medium Range Weather Forecast) in its seasonal prediction configuration.


The major tasks in this research area are:

  • to develop an Earth system model in several configurations for usage in climate change projections, predictions and process studies
  • to generate climate change scenario simulations in the context of several Model Intercomparison projects (MIPs) as part of the 6th phase of the Climate Model Intercomparison Project (CMIP6)
  • to provide global scale climate change information for regional downscaling and impact research.

Climate change projections are the base for assessment of possible future climate change and for climate services supporting climate change adaptation and estimating the consequences of climate change mitigation.

Computational science for climate models

Numerical modeling of the Earth's climate uses complex computer programs as its main tool. These often consist of millions of lines of code and numerical experiments include calculations that can last for several weeks on supercomputers. The model code is developed in interaction between different research areas in climate science and will handle increasing complexity and data volume, at the same time as the computer architecture and software environment are constantly changing.

In this research area, modern methods and tools from computer science are used to improve the development of the global climate model EC-Earth and to make large amounts of data from numerical experiments available for analysis. In order to meet a continuing increased need for computing capacity and the ability to handle large amounts of data, development must be driven forward in community with European networks such as ENES (European Network for Earth System Modeling) and contribute to a sustainable infrastructure for climate modeling.

Modelling climate change and its governing processes

This research area includes performing and analyzing global climate simulations with the current EC-Earth3 and the upcoming EC-Earth4 model, contributing to model development and in particular working for the integration of more Earth system model components in the EC-Earth model. Environmental monitoring of climate modeling and simulation of climate change are also included in the assignment, e.g. by following activity in CMIP.

Climate change is a major challenge for society. Climate modeling provides important knowledge about the dynamics and variability of the climate system, and can help provide a better basis for decisions on future climate issues. Climate adaptation and mitigation are two important users of data that have been produced using global climate models, and therefore it is important to maintain and develop our understanding of climate modeling.


The major tool for global climate modelling is the EC-Earth model in different configurations. For improved model evaluation, SMHI participates in the development of standard analysis metrics and analysis software that covers a range of climate-related processes. For improved technical sustainability of the model, SMHI participates in climate model infrastructure efforts.

SMHI collaborates with Swedish and European partners to develop the EC-Earth Earth System Model, and with the CMIP6-ScenarioMIP (and related MIPs) to carry out simulations.

Recent Publications

Wyser, K.,  Koenigk, T., Fladrich, U., Fuentes-Franco, R., Karami, M. P. and Kruschke, T. (2021) The SMHI Large Ensemble (SMHI-LENS) with EC-Earth3.3.1. Geoscientific Model Development, 14, 4781–4796,

Jones, C. D., Hickman, J. E., Rumbold, S. T., Walton, J., Lamboll, R. D., Skeie, R. B., et al. (2021). The climate response to emissions reductions due to COVID-19: Initial results from CovidMIP. Geophysical Research Letters, 48,

Wyser, K., van Noije, T., Yang S., von Hardenberg, J., O'Donnell, D. Ralf Döscher, R. (2020) On the increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6. Geosci. Model Dev., 13, 3465–3474.

Wyser, W., Kjellström, E., Koenigk, T., Martins, H. and Döscher, R. (2020)Warmer climate projections in EC-Earth3-Veg: the role of changes in the greenhouse gas concentrations from CMIP5 to CMIP6. Environ. Res. Lett. 15, doi:10.1088/1748-9326/ab81c2

Smith, D. M. et al., 2018: Predicted Chance That Global Warming Will Temporarily Exceed 1.5 degrees. Geophysical Research Letters, Vol. 45, no 21, 11895-11903 p., 

Caian, M. et al., 2018: An interannual link between Arctic sea-ice cover and the North Atlantic Oscillation, Climate Dynamics, Vol. 50, no 1-2, 443-443 p.,

Berg, P., R. Döscher, T. Koenigk, 2016. On the effects of constraining atmospheric circulation in a coupled atmosphere-ocean Arctic regional climate model. Clim Dyn 46: 3499, doi:10.1007/s00382-015-2783-y.

Brodeau, L., & Koenigk, T. (2016). Extinction of the northern oceanic deep convection in an ensemble of climate model simulations of the 20th and 21st centuries. Climate Dynamics, 46(9-10), 2863-2882. Clim Dyn (2016) 46: 2863. doi:10.1007/s00382-015-2736-5.

Caian, M., Koenigk, T., Döscher, R., Devasthale, A., accepted: An inter-annual link between Arctic sea-ice cover and North Atlantic Oscillation. Climate Dynamics.

Döscher, R., Vihma, T., & Maksimovich, E. (2014). Recent advances in understanding the Arctic climate system state and change from a sea ice perspective: a review. Atmospheric Chemistry and Physics, 14(24), 13571-13600. doi:10.5194/acp-14-13571-2014.

Döscher, R. and Koenigk, T. (2013): Arctic rapid sea ice loss events in regional coupled climate scenario experiments, Ocean Sci., 9, 217-248, doi:10.5194/os-9-217-2013.

van den Hurk, B. J., Bouwer, L. M., Buontempo, C., Döscher, R., Ercin, E., Hananel, C., ... & Pappenberger, F. (2016). Improving predictions and management of hydrological extremes through climate services: www. imprex. eu. Climate Services, 1, 6-11. doi:10.1016/j.cliser.2016.01.001.