Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6

By Flavio Lehner, C. Deser, Nicola Maher, Jochem Marotzke, Erich Fischer, L. Brunner, Reto Knutti, Ed Hawkins. Earth System Dynamics, 2020

521 citations recorded by the source corpus.

Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple single-model initial-condition large ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method bias < 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method bias (up to 50 %), and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f6c211567cd6d54f15836b98c5d592e27b5bbedd

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In Atmospheric Science

1. L. Brunner
2. R. Lorenz
3. A. Merrifield
4. Flavio Lehner
5. A. Pendergrass
6. I. Medhaug
7. Reto Knutti
8. B. Sanderson

In Global and Planetary Change

1. L. Brunner
2. R. Lorenz
3. A. Merrifield
4. Flavio Lehner
5. A. Pendergrass
6. I. Medhaug
7. Reto Knutti
8. B. Sanderson

Citations in the corpus

• Reduced global warming from CMIP6 projections when weighting models by performance and independence (2020, background): “Recently, efforts have been made to use so-called single model initial-condition large ensembles (SMILEs) to investigate internal variability in the climate projections more comprehensively (e.g., Kay et al., 2015; Maher et al., 2019; Lehner et al., 2020; Merrifield et al., 2020).”
• An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles (2020, background): “We find that large-scale, long-term climatological averages are the most suitable predictors for this purpose because, in general, the influence of internal variability increases on smaller spatial scales and shorter timescales (Hawkins and Sutton, 2009; Lehner et al., 2020).”