Constraining Future Temperature Projections using a Localized Spatiotemporal Emergent Constraint Approach

 

Accurate predictions of future climate change are vital to limit the harmful impacts of global warming on society and ecosystems, and to inform effective policymaking. Nevertheless, the inherent limitations of Earth System Models (ESMs), even when employing multi-model ensembles, continue to engender considerable uncertainties in future climate projections. The emergent constraint (EC) approach has the potential to assist in reducing these uncertainties by establishing a linkage between models and observational data of the current climate. However, the EC method remains imperfect, and predicting temperature continues to present significant challenges.

    Recent studies have demonstrated that transient Emergent Constraints (EC), particularly the Kriging for Climate Change (KCC), offer better predictive skill compared to traditional trend-based EC methods. However, existing KCC applications have largely been restricted to either Global Average Temperature (GAT) or simple joint GAT-local temperature predictions, often overlooking the complex spatial correlations inherent in climate data. The specific impact of spatial structure on future climate projections remains unexplored. To bridge this gap, this project introduces an innovative spatiotemporal EC approach.

    Building on this, we introduced the localized KCC (EC) method to minimize prediction uncertainty by leveraging regional observational data. Specifically, to mitigate biases arising from non-warming factors, we implemented a joint framework that integrates GAT with region-scale adjustments for future temperature projections.

   The validity of our approach was verified using an imperfect model test. We demonstrated that no matter which model is used as pseudo-observations, the bias in the posterior estimates is reduced in most regions compared to the prior. Overall, the global uncertainty is reduced by about 18% which is better than only using local temperature information. This enhanced robust method ultimately results in more reliable regional projections.  

    After validating the robustness of our method, we use HadCRUT5 observational data to primarily analyze and predict global temperature changes for a 20-year lead time (2040–2060) and a 50-year lead time (2070–2090).