Workshop on Improving Modelling of the AMOC

Monday, September 23, 2024 to Wednesday, September 25, 2024
Event City: 
Event Description: 

Workshop on Improving Modelling of the AMOC

The representation of the Atlantic Meridional Overturning Circulation (AMOC) in climate models is very varied, with different models having different strengths, different variability, and different magnitudes of responses to increasing greenhouse gases. This is a problem for climate projections and predictions, because the uncertainty in the AMOC response causes additional uncertainty in future changes of the climate. We know that the representation of the AMOC in models has deficiencies due to missing or poorly represented processes, but the pathway to future improvements is not clear.

To address this issue, we would like to invite participation in a workshop to discuss how to improve the modelling of the AMOC in climate models. The goals of this workshop will be to provide recommendations to the community about how to improve our modelling of the AMOC, including suggestions of specific activities. Topics to be discussed include how to assess models’ fidelity, the role of resolution, and how to improve representation of important processes.

To enable focused discussion, in person participation will be limited in numbers (~50). Applicants should have expertise and/or be currently working on research addressing how the AMOC, or processes relevant to the AMOC (such as convection, overflows, the Gulf Stream, etc.), are sensitive to different model formulation, resolution, parameterizations, etc. This could include research based on coupled climate models, ocean-only models, or high-resolution global or regional models. 

The workshop is being organised by the CLIVAR AMOC Task Team and will take place at the Met Office, Exeter, UK on 23-25 September 2024. There will be no registration fee, however attendees will need to cover transport and accommodation themselves.

Applications should be received by 14 June 2024. We will send invitations based on the relevance of research and diversity of topics. Please use the following link:



Gokhan Danabasoglu

Laura Jackson

Eric Chassignet

Rong Zhang

Anne-Marie Treguier