Energy and CO2 Footprint of Climate Model Intercomparison Projects

• URL: http://arxiv.org/abs/2608.23509v1
• Date: Mon, 24 Aug 2026 17:13:29 GMT
• Title: Energy and CO2 Footprint of Climate Model Intercomparison Projects
• Abstract summary: This paper examines the evolution of climate model carbon accounting from voluntary post-hoc estimation to standardized accounting under the newly established CMIP7 Task Team on Energy Consumption.<n>Using high-resolution Destination Earth simulations on MareNostrum 5, we empirically evaluate how different accounting boundaries impact reported energy, carbon emissions, and financial costs.
• Score: 0.23332469289621785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Earth System Models (ESMs) rely heavily on High-Performance Computing (HPC) resources to simulate global climate. As these models evolve, their computational demands continue to grow, driven by three factors: (1) finer spatial grid resolutions, (2) the integration of complex biogeochemical processes (e.g., atmospheric chemistry, interactive vegetation, land use, and ice sheets), and (3) larger climate ensembles to manage uncertainty. Historically, growth in peak computing performance (FLOP/s) has outpaced improvements in energy efficiency (FLOP/Watt), increasing total HPC power consumption. Despite the central role of Model Intercomparison Projects (MIPs) in climate research, quantifying their computational and environmental costs has received limited systematic attention. This paper examines the evolution of climate model carbon accounting from voluntary post-hoc estimation in the Coupled Model Intercomparison Project phase 6 (CMIP6) to standardized accounting under the newly established CMIP7 Task Team on Energy Consumption. Using high-resolution Destination Earth simulations on MareNostrum 5, we empirically evaluate how different accounting boundaries (operational, active-only, and embodied carbon) impact reported energy, carbon emissions, and financial costs. Finally, we outline key methodological considerations for standardizing energy and carbon accounting for Model Intercomparison Projects (MIPs).

Related papers

• Energy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials [31.798185135917084]
  We develop a climate classification framework tailored to PV applications using a variety of machine learning (ML) techniques.<n>Our approach incorporates both energy yield, and for the first time, also the module lifetime with climate dependent degradation.<n>Results can support a wide range of applications, including PV module optimization, system siting decisions, and comparative performance studies.
  arXiv  Detail & Related papers  (2026-08-26T07:12:42Z)
• AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models [2.7946918847372277]
  We propose AI-CARE, an evaluation tool for reporting energy consumption, and carbon emissions of machine learning models.<n>We demonstrate, through theoretical analysis and empirical validation, that carbon-aware benchmarking changes the relative ranking of models.<n>Our proposal aims to shift the research community toward transparent, multi-objective evaluation and align ML progress with global sustainability goals.
  arXiv  Detail & Related papers  (2026-02-17T21:52:48Z)
• Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid [2.2999148299770047]
  GenAI represents a rapidly expanding digital infrastructure whose energy demand and associated CO2 emissions are emerging as a new category of climate risk.<n>This study introduces G-TRACE, a cross-modal, region-aware framework that quantifies training- and inference-related emissions.<n>We propose the AI Sustainability Pyramid, a seven-level governance model linking carbon accounting metrics with operational readiness, optimization, and stewardship.
  arXiv  Detail & Related papers  (2025-11-06T19:52:02Z)
• Building Interpretable Climate Emulators for Economics [38.16419395699246]
  We introduce a framework for developing efficient and interpretable climate emulators (CEs) for economic models of climate change.<n>We consider three versions of the CCE, which we evaluate within a simple representative agent economic model.<n>We show how regional baseline climates, non-uniform warming, and the associated uncertainties propagate into economic damages.
  arXiv  Detail & Related papers  (2024-11-16T10:22:23Z)
• Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
  A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility. We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL) Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
  arXiv  Detail & Related papers  (2024-06-12T05:08:51Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning [77.62876532784759]
  Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
  arXiv  Detail & Related papers  (2023-02-16T18:35:00Z)
• Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model [72.65502770895417]
  We quantify the carbon footprint of BLOOM, a 176-billion parameter language model, across its life cycle. We estimate that BLOOM's final training emitted approximately 24.7 tonnes ofcarboneqif we consider only the dynamic power consumption. We conclude with a discussion regarding the difficulty of precisely estimating the carbon footprint of machine learning models.
  arXiv  Detail & Related papers  (2022-11-03T17:13:48Z)
• Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI [47.130004596434816]
  In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
  arXiv  Detail & Related papers  (2022-07-31T09:34:53Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.