Reducing uncertainty in local temperature projections

Abstract

Planning for adaptation to climate change requires accurate climate projections. Recent studies have shown that the uncertainty in global mean surface temperature projections can be considerably reduced using historical observations. However, the transposition of these new results to the local scale is not yet available. Here, we adapt an innovative statistical method that combines the latest generation of climate model simulations, global observations, and local observations to reduce uncertainty in local temperature projections. By taking advantage of the tight links between local and global temperature, we can derive the local implications of global constraints. The model uncertainty is reduced by 30% up to 70% at any location worldwide, allowing to substantially improve the quantification of risks associated with future climate change. A rigorous evaluation of these results within a perfect model framework indicates a robust skill, leading to a high confidence in our constrained climate projections.

Robust and perfectible constraints on human-induced Arctic amplification

“…The observational constraint method, called Kriging for Climate Change (KCC), has been previously applied to global and local warming 31,32 , and can be easily applied to other climate variables 38,39 as long as their internal variability can be fitted with a simple mix of auto-regressive processes (Fig. S18).…”

“…Our KCC technique does not build on empirical linear regression schemes and has been tested successfully in a perfect model framework 31 . It has been Confidential manuscript already applied at both global 31 and local scales 32 . Beyond temperature, KCC has been also used to constrain other variables, such as global total precipitable water 38 or global land surface relative humidity 39 , leading to consistent results for both CMIP6 and CMIP5 models.…”

“…This result advocates for a better understanding and consideration of internal climate variability when constraining the Arctic climate projections with the observed trends 30 . Our analysis is based on the KCC statistical package [31][32] and on the combined use of GSAT and PSAT reconstructions (see Methods). This Bayesian technique allows us to derive a posterior distribution of the projected anomalies from the prior distribution derived from the raw model outputs.…”

Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6

“…Finally, we do not evaluate model outputs against historical observations and instead make an implicit assumption that the outputs from each scenario, GCM, and downscaling method represent equally plausible realizations of future climate. There is an increasing number of GCM weighting techniques [79,80] that account for historical performance while guarding against overfitting, some of which can induce significant changes in CMIP6 projections [81]. Future work might investigate how the application of such techniques alters the variance decompositions.…”

Origin, importance, and predictive limits of internal climate variability

“…These feedbacks allow us to rule out the highest emissions scenarios, thus substantially reducing the scenario uncertainty that often dominates total projection uncertainty later this century (figure 3(c)). Second, new physically-based constraints on global and regional climate projection uncertainty (Lorenz et al 2018, Sherwood et al 2020, Qasmi and Ribes 2022 promise to reduce response uncertainty, whose importance tends to peak mid-century when initial conditions are forgotten but scenarios have not yet diverged substantially (figure 3(c); see also Lehner et al 2020). Future work will seek to combine these two pathways to constrain projection uncertainty at regional scales.…”