Emergent Constraints on the Large-Scale Atmospheric Circulation and Regional Hydroclimate: Do They Still Work in CMIP6 and How Much Can They Actually Constrain the Future?

Abstract

An ‘emergent constraint’ (EC) is a statistical relationship, across a model ensemble, between a measurable aspect of the present day climate (the predictor) and an aspect of future projected climate change (the predictand). If such a relationship is robust and understood, it may provide constrained projections for the real world. Here, Coupled Model Intercomparison Project 6 (CMIP6) models are used to revisit several ECs that were proposed in prior model intercomparisons with two aims: (1) to assess whether these ECs survive the partial out-of-sample test of CMIP6 and (2) to more rigorously quantify the constrained projected change than previous studies. To achieve the latter, methods are proposed whereby uncertainties can be appropriately accounted for, including the influence of internal variability, uncertainty on the linear relationship, and the uncertainty associated with model structural differences, aside from those described by the EC. Both least squares regression and a Bayesian Hierarchical Model are used. Three ECs are assessed: (a) the relationship between Southern Hemisphere jet latitude and projected jet shift, which is found to be a robust and quantitatively useful constraint on future projections; (b) the relationship between stationary wave amplitude in the Pacific-North American sector and meridional wind changes over North America (with extensions to hydroclimate), which is found to be robust but improvements in the predictor in CMIP6 result in it no longer substantially constrains projected change in either circulation or hydroclimate; and (c) the relationship between ENSO teleconnections to California and California precipitation change, which does not appear to be robust when using historical ENSO teleconnections as the predictor.

Revisiting the wintertime emergent constraint of the Southern Hemispheric midlatitude jet response to global warming

“…Furthermore, we show that the previously observed anchoring of the zonal-mean zonal wind response (i.e. the fact that the response structure does not follow the climatological jet latitude; Simpson and Polvani (2016); Simpson et al (2021)) is an artefact of the zonally asymmetric jet structure in CMIP5 and CMIP6 .…”

“…5a that, while the response trough latitude Φ T is clearly anchored and does not move with the jet latitude, the peak latitude Φ P is less clearly anchored even when discarding the low-latitude outlier model. We point out, however, that the anchoring was clearly present for Φ P in Simpson et al (2021), who used a larger model ensemble, including more low latitude models.…”

“…Empirically, Kidston and Gerber (2010) found an EC between climatological jet latitude and future jet shift in the Coupled Model Intercomparison Project Phase 3 (CMIP3). However, using CMIP5 data Simpson and Polvani (2016) showed that this EC only holds in the wintertime; more recently, Curtis et al (2020) and Simpson et al (2021) identified the same wintertime constraint in CMIP6. Simpson and Polvani (2016) proposed a possible explanation for the EC by observing that the zonal wind response does not vary with climatological jet latitude, as one would expect if the response were e.g.…”

“…We now turn to testing whether the toy model can reproduce the observed inter-model relationships in the zonal-mean jet response. For this we reproduce prior analyses from Simpson and Polvani (2016) and Simpson et al (2021) by plotting the response peak and trough locations (Φ P and Φ T ), as well as their strength (∆U P and ∆U T ), against the historical jet latitudesee Fig. 5.…”

Advancing our understanding of compound weather and climate events via large ensemble model simulations

“…Quantifying and understanding uncertainties in climate projections and identifying the potentially wide range of plausible climate futures is highly relevant for policymakers 23,58,59 . In particular, it is important to distinguish between uncertainty arising from internal climate variability, which is irreducible because of the inherently chaotic nature of the climate system, and uncertainty from structural differences amongst climate models, which could be reduced through, for instance, model improvements or emergent-constraints 60 . Uncertainty partitioning based on multi-model single-ensemble simulations 21 is expected to be biased, especially at regional scales and for variables with large variability 61 , which includes compound event frequencies (Section Estimating compound event likelihoods and associated uncertainties).…”