Abstract
An emerging body of literature highlights diverse threats that climate change might pose to reliable, resilient, affordable, and clean energy provision. The potential consequences of these threats are underscored by recent real-world events, like rolling blackouts in California and Texas. In recognition of these threats, a community of practice in energy-climate modeling has started to form that aims to better coordinate two types of models: (1) energy system models and (2) weather and climate models. Several disconnects between these two modeling communities hinder the use of the full potential of climate expertise and information in energy system modeling.
To overcome these disconnects, we propose a research agenda consisting of near-term interdisciplinary activities and long-term transdisciplinary activities among the energy and climate modeling communities. In the near-term, our proposed interdisciplinary activities aim to expedite the use of climate data in energy system modeling, generating much-needed insights for decision-makers. In the long-term, our proposed transdisciplinary activities aim to enable two developments: energy-system-tailored climate datasets for historical and future meteorological conditions and energy system models that can effectively leverage those datasets. Achieving this research agenda will require global energy and climate modeling communities and their funders to reframe and reconsider their methods and processes.
To overcome these disconnects, we propose a research agenda consisting of near-term interdisciplinary activities and long-term transdisciplinary activities among the energy and climate modeling communities. In the near-term, our proposed interdisciplinary activities aim to expedite the use of climate data in energy system modeling, generating much-needed insights for decision-makers. In the long-term, our proposed transdisciplinary activities aim to enable two developments: energy-system-tailored climate datasets for historical and future meteorological conditions and energy system models that can effectively leverage those datasets. Achieving this research agenda will require global energy and climate modeling communities and their funders to reframe and reconsider their methods and processes.
| Original language | English |
|---|---|
| Pages (from-to) | 1405-1417 |
| Number of pages | 13 |
| Journal | Joule |
| Volume | 6 |
| Issue number | 7 |
| Early online date | 2 Jun 2022 |
| DOIs | |
| Publication status | Published - 20 Jul 2022 |
Bibliographical note
Funding Information:The hosting of the NextGenEC workshops—which led to the initiation and progression of this paper—was enabled with the kind support of the University of Reading and the National Centre for Atmospheric Science (e.g. through access to conferencing software), and the initial workshop concept was developed during the PRIMAVERA project (which received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement no. 641727). The authors would like to acknowledge the following sources of funding. During large parts of this work, J.W. was funded through an ETH Postdoctoral Fellowship and acknowledges support from the ETH and Uniscientia foundations. B.P. was funded by the Swiss Federal Office of Energy (SFOE) under grant number SI/502229. L.P.S. received funding from the Netherlands Organisation for Scientific Research (NWO) under grant number 647.003.005 and is part of the IS-ENES3 project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 824084. C.J.D. is part of the projects “Managing Uncertainty in Government Modeling” (sponsored by the Alan Turing Institute) and “Decision support under climate uncertainty for energy security and net zero” (sponsored by the Alan Turing Institute and EPSRC). A.G. acknowledges UiO:Energi Thematic Research Group Spatial-Temporal Uncertainty in Energy Systems (SPATUS). K.G. is funded through the reFUEL project, an ERC grant with no. ERC2017-STG 758149. P.H. is part of the PROGRESS project funded by the Federal Ministry for Economic Affairs and Energy (BMWi.IIC5, funding ref. 03EI1027). J.K.L. has funding provided by the US Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under contract no. DE-AC36-08GO28308. D.J.B. was a co-principal investigator on the EU H2020 PRIMAVERA project (grant number 641727) and is the lead-convenor of the Next Generation Challenges in Energy Climate Modeling workshops from which this perspective was developed. Conceptualization, D.J.B. J.W. M.T.C. and L.P.S.; original draft, J.W. M.T.C. and L.P.S.; revision, review, and editing core group, D.J.B. M.T.C. A.K. B.P. L.P.S. and J.W.; revision and review, all authors. All authors (except L.K.) were present at the Next Generation Energy Climate Modeling 2021 workshop that led to the conceptualization of this paper and was organized by D.J.B. The ordering of the first two authors was decided by a coin toss; these co-first authors can prioritize their names as first authors when adding this paper's reference to their résumés. The authors declare no competing interests.
Funding Information:
The hosting of the NextGenEC workshops—which led to the initiation and progression of this paper—was enabled with the kind support of the University of Reading and the National Centre for Atmospheric Science (e.g., through access to conferencing software), and the initial workshop concept was developed during the PRIMAVERA project (which received funding from the European Union’s Horizon 2020 Research and Innovation Program under grant agreement no. 641727 ). The authors would like to acknowledge the following sources of funding. During large parts of this work, J.W. was funded through an ETH Postdoctoral Fellowship and acknowledges support from the ETH and Uniscientia foundations . B.P. was funded by the Swiss Federal Office of Energy (SFOE) under grant number SI/502229 . L.P.S. received funding from the Netherlands Organisation for Scientific Research (NWO) under grant number 647.003.005 and is part of the IS-ENES3 project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 824084 . C.J.D. is part of the projects “Managing Uncertainty in Government Modeling” (sponsored by the Alan Turing Institute ) and “Decision support under climate uncertainty for energy security and net zero” (sponsored by the Alan Turing Institute and EPSRC ). A.G. acknowledges UiO:Energi Thematic Research Group Spatial-Temporal Uncertainty in Energy Systems (SPATUS). K.G. is funded through the reFUEL project, an ERC grant with no. ERC2017-STG 758149 . P.H. is part of the PROGRESS project funded by the Federal Ministry for Economic Affairs and Energy (BMWi.IIC5, funding ref. 03EI1027 ). J.K.L. has funding provided by the US Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office . This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under contract no. DE-AC36-08GO28308 . D.J.B. was a co-principal investigator on the EU H2020 PRIMAVERA project (grant number 641727 ) and is the lead-convenor of the Next Generation Challenges in Energy Climate Modeling workshops from which this perspective was developed.
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