Climate model uncertainties ripe to be squeezed The latest climate models and observations offer unprecedented opportunities to reduce the remaining uncertainties in future climate change, according to a new study. Although the human impact of recent climate change is now clear, future climate change depends on how much additional greenhouse gas is emitted by humanity and also how sensitive the Earth System is to those emissions. Reducing uncertainty in the sensitivity of the climate to carbon dioxide emissions is necessary to work-out how much needs to be done to reduce the risk of dangerous climate change, and to meet international climate targets. The study, which emerged from an intense workshop at the Aspen Global Change Institute in August 2017, explains how new evaluation tools will enable a more complete comparison of models to ground-based and satellite measurements. Produced by a team of 29 international authors, the study is published in Nature Climate Change. Lead author Veronika Eyring, of DLR in Germany,said:

“We decided to convene a workshop at the AGCI to discuss how we can make the most of these new opportunties [sic] to take climate model evaluation to the next level”.

“When considered together, the latest models and observations can significantly reduce uncertainties in key aspects of future climate change”, said workshop co-organiser Professor Peter Cox of the University of Exeter in the UK.

“The pieces are now in place for us to make progress on that challenging scientific problem”, explained Veronika Eyring.

Taking climate model evaluation to the next level

Abstract

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil [sic] the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers. Fig. 1: Annual mean SST error from the CMIP5 multi-model ensemble. Fig. 2: Schematic diagram of the workflow for CMIP Evaluation Tools running alongside the ESGF.