UKCM2-LL: a new low-resolution GC5 configuration with constrained climate sensitivity – methodology and development

Abstract. The Global Coupled model version 5 (GC5) of the Met Office Unified Model incorporates substantial developments across its model components and shows improved performance for a range of applications. However, it exhibits a very high effective climate sensitivity (EffCS) of 6.7 K and an excessive rise in recent global-mean surface air temperatures (GMSAT), limiting its suitability for some climate applications. This motivated the development of an alternative GC5-based configuration with EffCS constrained to lie within the IPCC Sixth Assessment Report “very likely” range, improved simulation of historical temperatures, and acceptable climatological performance. This new configuration, called UKCM2-LL, will form part of the UK’s submission to CMIP7.

We describe a two-stage methodology used to develop UKCM2-LL. First, a 503-member atmosphere-only perturbed parameter ensemble (PPE) of GC5 variants was used to train statistical emulators that predict climatological performance metrics and atmosphere-only feedbacks. These emulators were then used to generate 41 candidate configurations predicted to have substantially reduced EffCS relative to GC5. Second, coupled preindustrial control and abrupt 4×CO₂ experiments were used to evaluate the candidates against large-scale climatological metrics and to diagnose EffCS, progressively narrowing the set of viable configurations through an expert-led evaluation process. Final fine-tuning of a single candidate was performed manually using the coupled experiments and was informed by parameter sensitivity information derived from the PPE. The resulting UKCM2-LL configuration has an EffCS of 3.6 K and exhibits an improved simulation of historical temperatures relative to GC5. However, achieving this lower EffCS required a degradation in climatological performance, reflecting a structural constraint of the GC5 model.

This work demonstrates the value of PPE-based approaches for systematically exploring such structural constraints, and for parameter tuning during model development. We discuss potential improvements to the methodology and consider the implications of explicitly constraining climate sensitivity for future model development and multi-model ensemble diversity.