Abstract
<p>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, we revisit the framework from Hawkins and Sutton (2009) for uncertainty partitioning for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. We also investigate forced changes in variability itself, something that is newly possible with SMILEs. 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.</p>
Highlights
The framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven single-model initial-condition large ensembles (SMILEs) and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives
The original approach is shown to work well at global scales, while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method bias, and more accurate partitioning of uncertainty is achieved through the use of SMILEs
Uncertainties in climate change projections can be attributed to different sources – in context of Coupled Model Intercomparison Projects (CMIPs) to three specific ones (Hawkins and Sutton, 2009), described as follows
Summary
Scenario uncertainty can be quantified by comparing a consistent and sufficiently large set of models run under different emissions scenarios This uncertainty is considered irreducible from a climate science perspective, as the scenarios are socioeconomic what-if scenarios and do not have any probabilities assigned (which does not imply they are likely in reality). A SMILE enables the robust quantification of a model’s forced response and internal variability via computation of ensemble statistics, provided the ensemble size is large enough Due to their computational costs, SMILEs have not been wide spread even in the latest CMIP6 archive. With multiple SMILEs, one can directly quantify the evolving fractional contributions of internal variability and model structural differences to the total projection uncertainty under a given emissions scenario. We revisit the HS09 approach using temperature and precipitation projections from multiple SMILEs, CMIP5 and CMIP6 to illustrate where it works, where it has limitations and how SMILEs can be used to complement the original approach
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