Abstract
AbstractThe 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.
Highlights
A central part of the Intergovernmental Panel on Climate Change (IPCC) assessment reports describes the response of the physical climate system to the anthropogenic forcing due to greenhouse gas and aerosol emissions, and land use change (Collins et al, 2013)
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
We show the utility of the method by applying it to examine future projections of precipitation change from the previous (CMIP5) and current (CMIP6) generation of Comparison Project (CMIP) climate models
Summary
A central part of the Intergovernmental Panel on Climate Change (IPCC) assessment reports describes the response of the physical climate system to the anthropogenic forcing due to greenhouse gas and aerosol emissions, and land use change (Collins et al, 2013). Open stippling, that is, open circles, marks regions where less than 90% of models agree on the direction of change, but the mean forced signal-to-noise relative to the year-to-year variability is greater than unity (γforced ≥ 1) This is interpreted as a plausibly large response in the presence of a non-robust projection. Our attention shifts to quantifying how the forced changes compare with the amplitude of year-to-year variability, which is a useful quantity to assess the potential for impacts If such signal-to-noise is on average greater than or equal to unity (γforced ≥ 1), it becomes important to communicate the risk of a large change, which is what the open stippling portrays. Unmarked regions show where the risk of a large response compared to inter-annual variability is small Overall, these conditions define a set of three mutually exclusive and exhaustive categories to classify and communicate the future changes projected from multi-model ensembles.
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