Errors in Simple Climate Model Emulations of Past and Future Global Temperature Change

Jackson, Lawrence S.; Maycock, Amanda C.; Andrews, Timothy; Fredriksen, Hege‐Beate; Smith, Christopher J.; Forster, Piers M.

doi:10.1029/2022gl098808

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…Emulators can incorporate model spreads similar to output from classical MIPs with ESMs but with substantially less computing costs. Although the difficulty of accounting for non-parametric biases of CMIP models in emulators remains (Jackson et al, 2022), emulators are seen as useful to sample parametric differences and explore climate responses to different forcing agents.…”

Section: Methodological Opportunitiesmentioning

confidence: 99%

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

Fiedler

Naik

O’Connor

et al. 2023

Preprint

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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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Section: Methodological Opportunitiesmentioning

confidence: 99%

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

Fiedler

Naik

O’Connor

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, both SCMs and data‐driven emulators display fundamental limitations. Whilst SCMs provide a robust physical framework to reason about the climate system, they remain a simplistic model which may display a poor fit to ESM's outputs in certain scenarios (Geoffroy et al., 2013; Jackson et al., 2022; Meinshausen et al., 2011) and can only operate at the global level. Reasoning only in terms of global mean temperatures fails to capture the difference in exposure of different world regions and limits use for scientific applications that require regional projections.…”

Section: Introductionmentioning

confidence: 99%

FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation

Bouabid,

Sejdinovic,

Watson‐Parris

2024

J Adv Model Earth Syst

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Emulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation.

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“…However, both SCMs and data-driven emulators display fundamental limitations. Whilst EBMs provide a robust physical framework to reason about the climate system, they remain a simplistic model which may display a poor fit to ESM's outputs in certain scenarios (Meinshausen et al, 2011;Geoffroy et al, 2013;Jackson et al, 2022) and can only operate at the global level. Reasoning only in terms of global mean temperatures fails to capture the difference in exposure of different world regions and limits use for scientific applications that require regional projections.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic climate emulation with physics-constrained Gaussian processes

Bouabid¹,

Sejdinović²,

Watson-Parris³

2023

Preprint

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Emulators, or reduced complexity climate models, are surrogate Earth system models that produce projections of key climate quantities with minimal computational resources. Using time-series modeling or more advanced machine learning techniques, statistically-driven emulators have emerged as a promising venue, producing spatially resolved climate responses that are visually indistinguishable from state-of-the-art Earth system models. Yet, their lack of physical interpretability limits their wider adoption. In fact, the exploration of future emission scenarios still relies on one-dimensional energy balance models which can lack the flexibility to capture certain climate responses. Here, we propose a statistically-driven emulator that hinges on an energy balance model. Using Gaussian processes, we formulate an emulator of temperature response that exactly satisfies the physical thermal response equations of an energy balance model. The result is an emulator that (1) enjoys the flexibility of statistical machine learning models and can be fitted against observations to produce spatially-resolved predictions (2) has physically-interpretable parameters that can be used to make inference about the climate system. This model shows skilfull prediction of annual mean global distribution of temperature, even over scenarios outside the range of observed emissions of greenhouse gases and aerosols during training &#8212; improvement in RMSE of 27% against the energy balance model and of 60% against a physics-free machine learning model. In addition, the Bayesian nature of our formulation provides a principled way to perform uncertainty quantification on the predictions. We outline how the probabilistic nature of this emulator makes it a natural candidate for detection and attribution studies and discuss extension to a precipitation emulator also incorporating physical constraints. We hope that by combining the ability of machine learning techniques to capture complex patterns with the interpretability of energy balance models, our work will produce a reliable tool for efficiently and accurately exploring future climates.

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Errors in Simple Climate Model Emulations of Past and Future Global Temperature Change

Cited by 8 publications

References 42 publications

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

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

FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation

Probabilistic climate emulation with physics-constrained Gaussian processes

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