Fiedler, Stephanie , Naik, Vaishali, O'Connor, Fiona M., Smith, Christopher J., Pincus, Robert, Griffiths, Paul, Kramer, Ryan, Takemura, Toshihiko, Allen, Robert J., Im, Ulas, Kasoar, Matthew, Modak, Angshuman, Turnock, Steven, Voulgarakis, Apostolos, Watson-Parris, Duncan, Westervelt, Daniel M., Wilcox, Laura J., Zhao, Alcide, Collins, William J., Schulz, Michael, Myhre, Gunnar and Forster, Piers M. (2024) Interactions between atmospheric composition and climate change – Progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP. Geoscientific Model Development, 7 (6). pp. 2387-2417. DOI 10.5194/gmd-17-2387-2024.

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Abstract

The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood and diversity in climate model experiments persists as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article synthesizes current challenges and emphasizes opportunities for advancing our understanding of climate change and model diversity. The perspective of this article is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol and Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specialisms across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation-response paradigm through multi-model ensembles of Earth System Models of varying complexity. It specifically facilitated contributions to the research field through sharing knowledge on best practices for the design of model diagnostics and experimental strategies across MIP boundaries, e.g., for estimating effective radiative forcing. We discuss the challenges of gaining insights from highly complex models that have specific biases and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible, and machine learning approaches for faster and better sub-grid scale parameterizations where they are needed. Both would improve our ability to adopt a smart experimental design with an optimal tradeoff between resolution, complexity and simulation length. Future experiments can be evaluated and improved with sophisticated methods that leverage multiple observational datasets, and thereby, help to advance the understanding of climate change and its impacts.

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