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Overcoming the Disconnect Between Energy System and Climate Modeling

  • Michael Craig
  • , Jan Wohland
  • , Laurens Stoop
  • , Alexander Kies
  • , Bryn Pickering
  • , Hannah Bloomfield
  • , Jethro Browell
  • , Matteo De Felice
  • , Chris Dent
  • , Adrien Deroubaix
  • , Felix Frischmuth
  • , Paula Gonzalez
  • , Aleksander Grochowicz
  • , Katharina Gruber
  • , Philipp Hartel
  • , Martin Kittel
  • , Leander Kotzur
  • , Inga Labuhn
  • , Julie Lundquist
  • , Noah Pflugradt
  • Karin van der Wiel, Marianne Zeyringer, David Brayshaw
  • University of Michigan, Ann Arbor
  • ETH Zurich
  • Climate Service Center Germany
  • Royal Netherlands Meteorological Institute
  • Tennet
  • Utrecht University
  • Frankfurt Institute for Advanced Studies
  • University of Bristol
  • University of Reading
  • University of Glasgow
  • Joint Research Centre
  • University of Edinburgh
  • Alan Turing Institute
  • Max Planck Institute for Meteorology
  • Fraunhofer Institute for Energy Economics & Energy System Technology
  • University of Oslo
  • University of Natural Resources & Life Sciences
  • German Institute for Economic Research
  • Forschungszentrum Julich
  • University of Bremen
  • National Renewable Energy Laboratory
  • University of Colorado Boulder

Research output: Contribution to journal › Article › peer-review

107 Scopus Citations

Abstract

Energy system models underpin decisions by energy system planners and operators. Energy system modeling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modeling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modeling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modeling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision-making.
Original languageAmerican English
Pages (from-to)1405-1417
Number of pages13
JournalJoule
Volume6
Issue number7
DOIs
StatePublished - 2022

NLR Publication Number

  • NREL/JA-5000-82991

Keywords

  • climate change
  • climate modeling
  • energy system
  • wind energy

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