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Abstract We characterise, and explore the drivers of, differences in the internal variability of the atlantic meridional overturning circulation (AMOC) across five NEMO-based CMIP6 class climate models. While the variability of AMOC variability is dominated by its lower dense limb in all models, there is large diversity in the timescale, multidecadal variability, and latitudinal coherence of AMOC across models. In particular, the UK models have much weaker AMOC multidecadal variability and latitudinal coherence. The model diversity is associated with differences in salinity-governed surface density variations which drive high-density water mass transformation (WMT) in the Greenland–Iceland–Norwegian Seas (GIN) and the Arctic. Specifically, GIN Seas WMT shows large multidecadal variability which has a major impact on AMOC variability in non-UK models. In contrast, the smaller variability in GIN Seas WMT in the UK models has limited impact on the lower latitude AMOC via the Denmark strait overflow mass transport. This leads to a latitudinally less coherent and weaker multidecadal variability of the AMOC lower limb. Such differences between UK and non-UK models are related to differences in model mean states and densification processes in the Arctic and GIN Seas. Consequently, we recommend further in-depth studies to better understand and constrain processes driving salinity changes in the Arctic and GIN Seas for more reliable representation of the AMOC in climate models. 

Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest the possibility of significant changes in both large-scale aspects of the ocean and atmospheric circulations and in the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of the High-Resolution Model Intercomparison Project (HighResMIP1) was successful at producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions on the order of 1° that are typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis of a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose the High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances, along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various digital twin initiatives. It also has the potential to be used as training and validation data for the fast-evolving machine learning climate models.