1,2,1,4,3,5,6,7,- 1Laboratoire des Sciences du Climat et de l’Environnement (LSCE), IPSL, CEA/CNRS/UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
- 2Earth System Division, National Institute for Environmental Studies (NIES), Tsukuba, Japan
- 3School of Geography and the Environment and Department of Physics, University of Oxford, Oxford, United Kingdom
- 4Cranfield Environment Centre, Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield, United Kingdom
- 5Met Office Hadley Centre, Exeter, United Kingdom
- 6School of Geography, University of Leeds, Leeds, United Kingdom
- 7Energy, Climate and Environment Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
- 8Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
Integrating physical, socio-economic, and technological perspectives is indispensable for addressing climate mitigation challenges. While directly coupling state-of-the-art Earth System Models (ESMs) and Integrated Assessment Models (IAMs) offers a way to explore feedbacks between these domains, doing so with full-complexity models remains computationally prohibitive. This is particularly true for cost-effective intertemporal optimization IAMs due to fundamental operational differences: while ESMs perform forward simulations, such IAMs optimize over time. Consequently, direct coupling would require numerous computationally intensive iterations to converge, a complication further compounded by the stochastic nature of ESMs.
To overcome the barriers to coupling ESMs and IAMs, we employ their reduced-complexity representations (i.e., emulators). We couple an IAM emulator representing 9 distinct IAMs (Xiong et al. 2025) with an ESM emulator, FaIR, representing 66 ESM configurations (Smith et al. 2024a). Using this coupled ESM-IAM emulator framework in an optimization setting, we calculate cost-effective pathways that achieve the temperature targets of the Paris Agreement with and without overshoot.
Our preliminary results indicate that the uncertainty ranges for such pathways are significantly larger than previously estimated. Our results also have implications for target setting; we show how pathways differ when IAMs optimize directly for a temperature target – a capability IAMs traditionally lack. Instead, IAMs typically rely on temperature proxies, such as carbon budgets (or their corresponding carbon price pathways), which do not necessarily provide an accurate representation of the temperature target. Furthermore, this study offers advanced insights into the dynamics of climate-economy interactions, providing a roadmap for future efforts to couple full-complexity models.
References
Xiong, W., Tanaka, K., Ciais, P., Johansson, D. J. A., & Lehtveer, M. (2025). emIAM v1.0: an emulator for integrated assessment models using marginal abatement cost curves. Geosci. Model Dev., 18(5), 1575-1612. doi:10.5194/gmd-18-1575-2025
Smith, C., Cummins, D. P., Fredriksen, H. B., Nicholls, Z., Meinshausen, M., Allen, M., . . . Partanen, A. I. (2024). fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections. Geosci. Model Dev., 17(23), 8569-8592. doi:10.5194/gmd-17-8569-2024
How to cite: Tanaka, K., Weiwei, X., Allen, M., Cain, M., Jenkins, S., Mathison, C., Patel, V., Smith, C., and Tachiiri, K.: Coupled ESM-IAM Emulator: Exploring Uncertainties in Temperature Target Pathways, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14741, https://doi.org/10.5194/egusphere-egu26-14741, 2026.
