Abstract
AbstractPolitical decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. To provide these estimates, different approaches to constrain, filter, or weight climate model projections into probabilistic distributions have been proposed. However, an assessment of multiple such methods to, for example, expose cases of agreement or disagreement, is often hindered by a lack of coordination, with methods focusing on a variety of variables, time periods, regions, or model pools. Here, a consistent framework is developed to allow a quantitative comparison of eight different methods; focus is given to summer temperature and precipitation change in three spatial regimes in Europe in 2041–60 relative to 1995–2014. The analysis draws on projections from several large ensembles, the CMIP5 multimodel ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods’ key features are summarized, assumptions are discussed, and resulting constrained distributions are presented. Method agreement is found to be dependent on the investigated region but is generally higher for median changes than for the uncertainty ranges. This study, therefore, highlights the importance of providing clear context about how different methods affect the assessed uncertainty—in particular, the upper and lower percentiles that are of interest to risk-averse stakeholders. The comparison also exposes cases in which diverse lines of evidence lead to diverging constraints; additional work is needed to understand how the underlying differences between methods lead to such disagreements and to provide clear guidance to users.
Highlights
Human-induced climate change calls for rapid cuts in anthropogenic greenhouse gas emissions to avoid increasingly negative impacts
For the model performance component, REA applies weights on a variable-by-variable basis while ClimWIP uses a set of six diagnostics based on a number of variables (Brunner et al 2019)
For the absolute values of the 10th and 90th percentiles agreement between methods is low and the full range of values can exceed 18C. This partly reflects the different components of uncertainty included in different methods, notably the additional treatment of carbon cycle uncertainty in U.K. Climate Projections (UKCP), which contributes to a wider uncertainty estimate than any other method
Summary
Human-induced climate change calls for rapid cuts in anthropogenic greenhouse gas emissions to avoid increasingly negative impacts. Even with such reductions, climate will continue to change over the decades, requiring reliable information about regional future changes for assessing impacts, identifying risks, and making adaptation decisions. The typical way of providing this information is by making estimates of the most likely change and known uncertainties based on an ensemble of climate models, often expressed as a probability. The uncertainties are primarily driven by three sources: uncertain future emissions, model uncertainty, and internal variability. The uncertainties associated with climate model responses to external forcings and internal variability, in turn, have been widely explored using ensemble modeling approaches
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