Authors

Malcolm J. Roberts, Met Office
Kevin A. Reed, Stony Brook University
Qing Bao, Institute of Atmospheric Physics Chinese Academy of Sciences
Joseph J. Barsugli, University of Colorado Boulder
Suzana J. Camargo, Lamont-Doherty Earth Observatory
Louis Philippe Caron, Consortium Ouranos, Canada
Ping Chang, Texas A&M University
Cheng Ta Chen, National Taiwan Normal University
Hannah M. Christensen, University of Oxford
Gokhan Danabasoglu, National Center for Atmospheric Research
Ivy Frenger, GEOMAR - Helmholtz-Zentrum für Ozeanforschung Kiel
Neven S. Fučkar, University of Oxford
Shabeh Ul Hasson, Universität Hamburg
Helene T. Hewitt, Met Office
Huanping Huang, Louisiana State University
Daehyun Kim, Seoul National University
Chihiro Kodama, Japan Agency for Marine-Earth Science and Technology
Michael Lai, Met Office
Lai Yung Ruby Leung, Pacific Northwest National Laboratory
Ryo Mizuta, Meteorological Research Institute
Paulo Nobre, Instituto Nacional de Pesquisas Espaciais
Pablo Ortega, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici
Dominique Paquin, Consortium Ouranos, Canada
Christopher D. Roberts, European Centre for Medium-Range Weather Forecasts
Enrico Scoccimarro, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici
Jon Seddon, Met Office
Anne Marie Treguier, Université de Bretagne Occidentale
Chia Ying Tu, Academia Sinica, Research Center for Environmental Changes
Paul A. Ullrich, Lawrence Livermore National Laboratory
Pier Luigi Vidale, University of Reading
Michael F. Wehner, Lawrence Berkeley National Laboratory
Colin M. Zarzycki, Pennsylvania State University

Document Type

Article

Publication Date

3-3-2025

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.

Publication Source (Journal or Book title)

Geoscientific Model Development

Number

504

First Page

1307

Last Page

1332

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