EGU2020-19202, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

What can we learn from single model initial-condition large ensembles (SMILEs)? A Comparison of Multiple SMILEs for Precipitation

Raul R. Wood1, Flavio Lehner2,3, Angeline Pendergrass3, Sarah Schlunegger4, and Keith Rodgers5
Raul R. Wood et al.
  • 1LMU Munich, Department of Geography, Munich, Germany (
  • 2Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
  • 3National Center for Atmospheric Research, USA
  • 4Princeton University, Atmospheric and Oceanic Sciences
  • 5IBS Center for Climate Physics, Busan, and Pusan National University, Republic of Korea

Identifying anthropogenic influences on climate amidst the “noise” of internal climate variability is a central challenge for the climate research community. In recent years, several modeling groups have produced single-model initial-condition large ensembles (SMILE) to analyze the interplay of the forced climate change and internal climate variability under current and future climate conditions. These simulations help to improve our understanding of climate variability, including extreme events, and can be employed as test-beds for statistical approaches to separate forced and internal components of climate variability.

So far, most studies have focused on either an individual or a  limited number of SMILEs. In this work we compare seven large ensembles to disentangle the influence of internal variability and model response uncertainty for multiple precipitation indices (e.g. wettest day of the year, precipitation with a return period of 20 years). What can we learn from intercomparison of SMILEs, how similar are they in terms of spatial patterns and forced response, and what if they aren’t? How does the forced response of an ensemble of SMILEs compare to the CMIP5 multi-model ensemble? By assessing multiple SMILEs we can identify robust signals for regional and global precipitation properties and revealing anthropogenic responses that are inherent to our current representations of the Earth system.

How to cite: Wood, R. R., Lehner, F., Pendergrass, A., Schlunegger, S., and Rodgers, K.: What can we learn from single model initial-condition large ensembles (SMILEs)? A Comparison of Multiple SMILEs for Precipitation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19202,, 2020