Emergent Constraints in Climate Projections: A Case Study of Changes in High-Latitude Temperature Variability

Abstract

Climate projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensemble show a decrease in interannual surface temperature variability over high latitudes with a large intermodel spread, in particular over the areas of sea ice retreat. Here relationships are found between the models’ present-day performance in sea ice–related metrics and future changes in temperature variability. These relations, so-called emergent constraints, can produce ensembles of models calibrated with present-day observations with a narrower spread across their members than across the full ensemble. The underlying assumption is that models in better agreement with observations or reanalyses in a carefully selected metric probably have a more realistic representation of local processes, and therefore are more reliable for projections. Thus, the reliability of this method depends on the availability of high-quality observations or reanalyses. This work represents a step toward formalization of the emergent constraints framework, as so far there is no consensus on how the constraints should be best implemented. The authors quantify the reduction in spread from emerging constraints for various metrics and their combinations, different emission scenarios, and seasons. Some of the general features of emerging constraints are discussed, and how to effectively aggregate information across metrics and seasons to achieve the largest reduction in model spread. It is demonstrated, based on the case of temperature variability, that a robust constraint can be obtained by combining relevant metrics across all seasons. Such a constraint results in a strongly reduced spread across model projections, which is consistent with a process understanding of variability changes due to sea ice retreat.

Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America

“…Borodina et al () found that aggregating multiple diagnostics, also across seasons, helps to capture the relevant processes to constrain an ensemble. Hence, it is important to use a large enough but not too large number of diagnostics.…”

“…The numbers may be similar, but the interpretation of the spread is very different, and we should have more confidence in the latter. Borodina et al (2017) found that aggregating multiple diagnostics, also across seasons, helps to capture the relevant processes to constrain an ensemble. Hence, it is important to use a large enough but not too large number of diagnostics.…”

Detection of a Climate Change Signal in Extreme Heat, Heat Stress, and Cold in Europe From Observations

“…The differences between models and observations may partly result from unforced internal variability that affects even the distribution of multidecadal trends, from biased trends in the driving GCMs or in the RCMs or from observational uncertainties. Too little warming of hot extremes is in contrast to too much warming of hot extremes found in GCMs (Borodina et al, 2017;Zwiers et al, 2011). This may relate to the fact that most of the EURO-CORDEX models used a prescribed constant aerosol climatology, while there has been a substantial decline in aerosol forcing over Europe (Wild, 2009).…”

A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo Feedback

“…The EC approach has been applied to regional and global climate studies (e.g., Bracegirdle & Stephenson, , ; Borodina et al, ; Cox et al, ; Fasullo & Trenberth, ; Hall & Qu, ; Qu & Hall, ; Sherwood et al, ) and more broadly for Earth system studies (e.g., Bowman et al, ; Cox et al, ; Karpechko et al, ; Wenzel et al, ). Many of these studies compute correlations between z t + τ and x t where they identify the range of models whose x t are within the precision of the observations, y t .…”