Communicating potentially large but non-robust changes in multi-model projections of future climate

The future climate projections in the IPCC reports are visually communicated via maps showing the mean response of climate models to alternative scenarios of socio-economic development. The presence of large changes is highlighted by stippling the maps where the mean climate response (the signal) is large compared to internal variability (the noise) and the response is robust, that is, consistent in sign, across the individual models. In addition, hatching is used to mark the regions with a small multi-model mean change. This approach may fail to recognize the risk of large changes in regions where the uncertainty is large and the response is not robust. Here, we present a more informative diagnostic to support risk assessment that is obtained by quantifying the mean forced signal-to-noise ratio of the individual model responses, rather than the signal-to-noise ratio of the mean response. This enables us to identify regions where a large future change compared to year-to-year variability is plausible, regardless of whether the signal is robust across the ensemble. For mean precipitation changes, we find that the majority (58% in surface area) of the unmarked regions and a sizeable portion (19%) of the hatched regions from the AR5 projections hid climate change responses to the RCP8.5 scenario that are on average large compared to the year-to-year variability. Based on the newer CMIP6 ensemble, a considerable potential for large annual-mean precipitation changes, despite the lack of a robust multi-model projection, exists over 22% of the surface land area, particularly in Central America, northern South America (including the Amazon), Central and West Africa (including parts of the Sahel), and the Maritime Continent.

Automated summary

The study proposes a more informative diagnostic approach by quantifying the mean forced signal-to-noise ratio of individual model responses to support risk assessment. This enables identification of regions where large future changes are possible, regardless of the robustness of the signal across the ensemble.