U.K. Community Earth System Modeling for CMIP6.

We describe the approach taken to develop the United Kingdom's first community Earth system model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the U.K. academic community. We document our model development procedure and the subsequent U.K. submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well-established, world-leading HadGEM models of the physical climate system and incorporates cutting-edge new representations of aerosols, atmospheric chemistry, terrestrial carbon, and nitrogen cycles and an advanced model of ocean biogeochemistry. A high-level metric of overall performance shows that both models, HadGEM3-GC3.1 and UKESM1, perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3-GC3.1 and UKESM1 include the emergent climate sensitivity (5.5 and 5.4 K, respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud-aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role, is highlighted in other papers in this special issue. We place our findings within the broader modeling landscape indicating how our understanding of key processes driving higher sensitivity in the two U.K. models seems to align with results from a number of other CMIP6 models.

Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models.

For the current generation of earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the range of equilibrium climate sensitivity (ECS, a hypothetical value of global warming at equilibrium for a doubling of CO2) is 1.8°C to 5.6°C, the largest of any generation of models dating to the 1990s. Meanwhile, the range of transient climate response (TCR, the surface temperature warming around the time of CO2 doubling in a 1% per year CO2 increase simulation) for the CMIP6 models of 1.7°C (1.3°C to 3.0°C) is only slightly larger than for the CMIP3 and CMIP5 models. Here we review and synthesize the latest developments in ECS and TCR values in CMIP, compile possible reasons for the current values as supplied by the modeling groups, and highlight future directions. Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6.

Enhanced future changes in wet and dry extremes over Africa at convection-permitting scale.

African society is particularly vulnerable to climate change. The representation of convection in climate models has so far restricted our ability to accurately simulate African weather extremes, limiting climate change predictions. Here we show results from climate change experiments with a convection-permitting (4.5 km grid-spacing) model, for the first time over an Africa-wide domain (CP4A). The model realistically captures hourly rainfall characteristics, unlike coarser resolution models. CP4A shows greater future increases in extreme 3-hourly precipitation compared to a convection-parameterised 25 km model (R25). CP4A also shows future increases in dry spell length during the wet season over western and central Africa, weaker or not apparent in R25. These differences relate to the more realistic representation of convection in CP4A, and its response to increasing atmospheric moisture and stability. We conclude that, with the more accurate representation of convection, projected changes in both wet and dry extremes over Africa may be more severe.

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change.

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a 'storyline' approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change.