Emergent constraints on climate sensitivities

Abstract

Despite major advances in climate science over the last 30 years, persistent uncertainties in projections of future climate change remain. Climate projections are produced with increasingly complex models that attempt to represent key processes in the Earth system, including atmospheric and oceanic circulations, convection, clouds, snow, sea ice, vegetation, and interactions with the carbon cycle. Uncertainties in the representation of these processes feed through into a range of projections from the many state-of-the-art climate models now being developed and used worldwide. For example, despite major improvements in climate models, the range of equilibrium global warming due to doubling carbon dioxide still spans a range of more than 3. Here a promising way to make use of the ensemble of climate models to reduce the uncertainties in the sensitivities of the real climate system is reviewed. The emergent constraint approach uses the model ensemble to identify a relationship between an uncertain aspect of the future climate and an observable variation or trend in the contemporary climate. This review summarizes previous published work on emergent constraints and discusses the promise and potential dangers of the approach. Most importantly, it argues that emergent constraints should be based on well-founded physical principles such as the fluctuation-dissipation theorem. This review will stimulate physicists to contribute to the rapidly developing field of emergent constraints on climate projections, bringing to it much needed rigor and physical insights.

 RETRACTED ARTICLE: A constraint on historic growth in global photosynthesis due to increasing CO2

“…It is important to highlight, however, as with any application of the emergent constraint technique, multiple factors could lead to biases and undermine the robustness of the derived constraint. Of primary concern is the potential for emergent constraints to rely on spurious cross-model correlations that are not based on a clear physical relationship 46 . The constraint we identify is based on the known relationship between CO2 and the land-sink 7 , and tests suggest it is temporally robust (Extended Data Fig.…”

“…For example, if all models in the ensemble had the same missing or biased process representation, which led to systematic bias in the modeled relationship between the sensitivity of photosynthesis to CO2 and the land sink across models, that could bias the emergent constraint reported here. Systematic cross-model biases with shared structural similarity could also lead to an underestimation of the uncertainty associated with the values derived from the emergent constraint 46,47 . The models we examine represent the state-ofthe-science for land surface modeling, and have substantial diversity of process representations and responses to forcings 48 , even for well-studied processes such as photosynthesis.…”

Observation-constrained estimates of the global ocean carbon sink from Earth system models

“…Emergent constraints across large datasets such as an ensemble of ESMs with hundreds of variables can always be found and might not necessarily be reliable and robust (Caldwell et al, 2014;Brient, 2020;Sanderson et al, 2021;Williamson et al, 2021). To test the robustness of emergent constraints, three criteria were proposed (Hall et al, 2019).…”

Emergent constraints for the climate system as effective parameters of bulk differential equations

“…Yet despite recent scrutiny, there remains a basic, almost philosophical question: "What is an Emergent Constraint"?. While there are likely many perspectives on the answer to this question (see Nijsse and Dijkstra, 2018;Williamson et al, 2021, for example), here we suggest that one way to interpret many ECs is that they derive bulk parameters associated with differential equations that are valid at large spatial scales. Such equations are implicit in ESMs (i.e.…”

“…Some recent papers review the EC method, highlighting its capability and listing a set of potential pitfalls. For instance, Williamson et al (2021) identify a particularly broad range of discussion points related to ECs, all framed in their application to refining estimates of ECS.…”