Submitted as: review and perspective paper
04 Apr 2023
Submitted as: review and perspective paper |  | 04 Apr 2023
Status: this preprint is currently under review for the journal GMD.

Interactions between atmospheric composition and climate change – Progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP

Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Robert Pincus, Paul Griffiths, Ryan Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster

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.

Stephanie Fiedler et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-29', Anonymous Referee #1, 02 May 2023
  • RC2: 'Comment on gmd-2023-29', Anonymous Referee #2, 29 May 2023

Stephanie Fiedler et al.

Stephanie Fiedler et al.


Total article views: 507 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
382 114 11 507 2 4
  • HTML: 382
  • PDF: 114
  • XML: 11
  • Total: 507
  • BibTeX: 2
  • EndNote: 4
Views and downloads (calculated since 04 Apr 2023)
Cumulative views and downloads (calculated since 04 Apr 2023)

Viewed (geographical distribution)

Total article views: 469 (including HTML, PDF, and XML) Thereof 469 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 05 Jun 2023
Short summary
Climate scientists want to better understand modern climate change. To that end, climate model experiments are performed and compared. The results of climate model experiments differ as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition – climate interactions.