UK community Earth system modelling for CMIP6Senior, C. A., Jones, C. G., Wood, R. A., Sellar, A., Belcher, S., Klein‐Tank, A., Sutton, R. ORCID: https://orcid.org/0000-0001-8345-8583, Walton, J., Lawrence, B. ORCID: https://orcid.org/0000-0001-9262-7860, Andrews, T. and Mulcahy, J. P. (2020) UK community Earth system modelling for CMIP6. Journal of Advances in Modeling Earth Systems, 12 (9). e2019MS002004. ISSN 1942-2466
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1029/2019MS002004 Abstract/SummaryWe describe the approach taken to develop the UK’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 UK academic community. We document our model development procedure and the subsequent UK 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 the physical model, 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 (5.5K and 5.4K 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 modelling landscape indicating how our understanding of key processes driving higher sensitivity in the two UK models seems to align with results from a number of other CMIP6 models.
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