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arxiv: 2605.00717 · v2 · submitted 2026-05-01 · ⚛️ physics.ao-ph

Recognition: unknown

Leveraging Climate Services to Build Climate Resilient Power Systems

Laurent Dubus , Alberto Troccoli , Aron zuiker , Laurens Stoop
Authors on Pith no claims yet

Pith reviewed 2026-05-09 14:37 UTC · model grok-4.3

classification ⚛️ physics.ao-ph
keywords climate servicespower system planningrenewable energyclimate modelsenergy resilienceclimate database
0
0 comments X

The pith

Physical models in the Pan-European Climate Database provide more robust power system planning than historical machine learning methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper investigates the integration of climate services into power system planning to address the impacts of climate change on energy supply and demand. It highlights the Pan-European Climate Database (PECD4.2) as a key development that offers harmonized data from reanalysis and multiple climate models. The use of physical conversion models for wind and solar is presented as superior for capturing technological advancements and future climate scenarios. Remaining challenges include hydropower representation and inconsistent data standards, which the authors argue require enhanced collaboration and better tools to resolve.

Core claim

The Pan-European Climate Database version 4.2 integrates historical reanalysis data with outputs from six climate models under four different socioeconomic pathways. It employs physical models to convert climate variables into wind and solar power generation, which the authors claim better accounts for technological progression and yields more reliable results under non-stationary future conditions than machine learning techniques trained solely on past data.

What carries the argument

The Pan-European Climate Database (PECD4.2) and its associated physical conversion models for renewable energy sources.

If this is right

  • Energy planning can incorporate future climate risks more consistently.
  • Improved handling of spatial correlations and compound events in system adequacy assessments.
  • Shorter timelines for energy sector adoption of climate information.
  • Better representation of technological changes in renewable generation models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Development of similar databases for other continents could standardize global energy-climate integration.
  • Linking PECD data with economic models might reveal cost savings from climate-resilient designs.
  • User feedback loops could iteratively improve both climate projections and energy models.
  • This approach might extend to other infrastructure sectors vulnerable to climate extremes.

Load-bearing premise

Increased collaboration between climate service providers and energy stakeholders, supported by user-friendly tools, will result in standardized and consistent use of climate information in power system studies.

What would settle it

A direct comparison study finding that machine learning models trained on historical data outperform or match physical models in predicting power generation under projected future climates would undermine the preference for physical models.

read the original abstract

We explore the crucial interplay between climate change and power system planning, highlighting the urgent need to systematically integrate climate information into energy system studies. Climate change impacts the energy sector on multiple fronts. Short-term weather variability drives daily and seasonal fluctuations in supply and demand. Long-term trends and increased frequency of extremes pose risks to infrastructure performance, asset lifetimes, and system adequacy. Representing compound events and spatial correlations across borders is a complex challenge, and uncertainties persist due to uncertainties from different models, scenarios, and downscaling methodologies. The Pan-European Climate Database (PECD4.2), developed in partnership between ENTSO-E and C3S, marks a change in how energy system planning is conducted. The PECD4.2 integrates historical reanalysis and six climate models across four SSP's, providing harmonised, openly available datasets tailored for power system studies. The physical conversion models for wind and solar energy better reflect technological progression than machine learning methods trained on historical data, improving robustness under changing future conditions. Despite these advances, challenges remain. Particularly in hydropower modelling and the lack of public harmonised energy datasets that are required to train these models. Complex processing chains from raw climate data to actionable insights and the lack of standardized integration of climate information lengthen lead times for energy-sector adoption. This leads to diverging approaches and variable consideration of climate risks. Closer, more generalised collaboration and communication between climate service providers and energy stakeholders are therefore necessary, as are the development of user-friendly tools for data manipulation and analysis and robust feedback loops.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. This perspective manuscript argues for the systematic integration of climate services into power system planning to address climate change impacts on energy supply, demand, infrastructure, and system adequacy. It positions the Pan-European Climate Database (PECD4.2), developed jointly by ENTSO-E and C3S, as a key advance that provides harmonized historical reanalysis and climate model data across SSP scenarios, using physical conversion models for wind and solar that are asserted to outperform historical machine-learning approaches in robustness under future conditions. The paper identifies persistent challenges in hydropower modeling, lack of harmonized public energy datasets, complex processing chains, and inconsistent adoption, and calls for closer collaboration, user-friendly tools, and feedback mechanisms between climate service providers and energy stakeholders.

Significance. If the advocated integration occurs, the work could help standardize the use of climate information in energy studies, reducing variability in how risks are considered and supporting more resilient infrastructure planning. The open availability of PECD4.2 is a concrete strength that promotes accessibility and reproducibility. However, the manuscript functions primarily as expert framing rather than presenting new quantitative validation, comparisons, or case studies, so its significance lies in highlighting opportunities rather than demonstrating resolved technical improvements.

major comments (1)
  1. [Abstract / PECD4.2 description] Abstract and the section introducing PECD4.2: The central assertion that 'the physical conversion models for wind and solar energy better reflect technological progression than machine learning methods trained on historical data, improving robustness under changing future conditions' is presented without any quantitative comparison, error metrics, validation against observations, or citations to supporting studies. This claim is load-bearing for the argument that PECD4.2 marks a change in planning practices, yet remains qualitative and untested within the manuscript.
minor comments (2)
  1. [Abstract] The manuscript would benefit from explicit definitions or expansions of acronyms on first use (e.g., SSPs as Shared Socioeconomic Pathways) and a brief description of the six climate models referenced to improve accessibility for readers outside the immediate climate-energy community.
  2. [Challenges and recommendations section] Consider adding one or two concrete examples or references to existing power system studies that have already used PECD4.2 (or its predecessors) to illustrate the claimed benefits and reduce the lead time for adoption mentioned in the text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review, recognition of the manuscript's framing role, and recommendation for minor revision. As a perspective piece, the work aims to highlight opportunities for integrating climate services into power system planning rather than to deliver new quantitative benchmarks. We address the specific concern raised below.

read point-by-point responses

  1. Referee: Abstract and the section introducing PECD4.2: The central assertion that 'the physical conversion models for wind and solar energy better reflect technological progression than machine learning methods trained on historical data, improving robustness under changing future conditions' is presented without any quantitative comparison, error metrics, validation against observations, or citations to supporting studies. This claim is load-bearing for the argument that PECD4.2 marks a change in planning practices, yet remains qualitative and untested within the manuscript.

    Authors: We appreciate the referee drawing attention to this point. The manuscript is explicitly a perspective article whose purpose is to advocate for systematic adoption of harmonized climate datasets such as PECD4.2; it does not contain new model intercomparisons or validation experiments. The statement reflects the documented design choice in PECD4.2 (developed jointly by ENTSO-E and C3S) to employ physics-based conversion models that can be updated with projected technological parameters (e.g., future turbine power curves or PV temperature coefficients), in contrast to ML models whose training distributions are fixed to the historical period. This rationale is already implicit in the PECD4.2 documentation. Nevertheless, we agree that the claim would benefit from explicit support. In the revised version we will (i) qualify the wording to present the advantage as a methodological rationale rather than an empirically demonstrated superiority within the paper, and (ii) add citations to peer-reviewed studies that have compared physical versus data-driven approaches for long-term renewable resource assessment under non-stationary climate conditions. These additions will be confined to the abstract and the PECD4.2 introduction section. revision: yes

Circularity Check

0 steps flagged

No significant circularity: perspective piece with no derivations or self-referential predictions

full rationale

The paper is a perspective advocating systematic integration of climate services into power system planning. It describes the externally developed PECD4.2 database (partnership between ENTSO-E and C3S) and asserts that physical conversion models outperform historical ML methods, but presents no equations, fitted parameters, quantitative predictions, or derivation chain. No step reduces by construction to the paper's own inputs, self-citations, or ansatzes. Claims rest on external datasets and partnerships rather than internal definitions or fits. This is the expected outcome for a non-technical advocacy manuscript.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters or invented entities are introduced. One domain assumption is invoked regarding the superiority of physical conversion models.

axioms (1)
  • domain assumption Physical conversion models for wind and solar energy better reflect technological progression than machine learning methods trained on historical data under changing future conditions.
    Directly stated in the abstract as the basis for improved robustness of PECD4.2.

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Reference graph

Works this paper leans on

27 extracted references · 9 canonical work pages

  1. [1]

    On Climate Change (IPCC), I. P.Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Inter- governmental Panel on Climate Changeisbn: 9781009157896. doi:10.1017/ 9781009157896(Cambridge University Press, 2023)

    2021

  2. [2]

    iea.org/reports/world-energy-outlook-2021

    Internation Energy Agency.World Energy Outlook 20212021.https : / / www . iea.org/reports/world-energy-outlook-2021

    2021

  3. [3]

    United Nations, 2015.https://digitallibrary

    UNFCCC.Paris AgreementUN Treaty. United Nations, 2015.https://digitallibrary. un.org/record/831040?v=pdf

    2015

  4. [4]

    European Commission.European Green DealEuropean Union, 2019.https : //eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52019DC0640

    2019

  5. [5]

    Investing in a climate-neutral future for the benefit of our people.European Union, 2020

    European Commission.Stepping up Europe’s 2030 climate ambition. Investing in a climate-neutral future for the benefit of our people.European Union, 2020. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0562

    2030

  6. [6]

    European Commission.REPowerEU PlanEuropean Union, 2022.https://eur- lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2022%3A230%3AFIN

    2022

  7. [7]

    P., López-Romero, J

    Jerez, S., Tobin, I., Vautard, R., Montávez, J. P., López-Romero, J. M., Thais, F., Bar- tok, B., Christensen, O. B., Colette, A., Déqué, M., Nikulin, G., Kotlarski, S., van Meij- gaard, E., Teichmann, C. & Wild, M. The impact of climate change on photovoltaic power generation in Europe.Nature Communications.doi:10.1038/ncomms10014 (2015)

    work page doi:10.1038/ncomms10014 2015

  8. [8]

    Pancin, B

    Tobin, I., Jerez, S., Vautard, R., Thais, F., van Meijgaard, E., Prein, A., Déqué, M., Kot- larski, S., Maule, C. F., Nikulin, G., Noël, T. & Teichmann, C. Climate change im- pacts on the power generation potential of a European mid-century wind farms scenario.Environmental Research Letters.doi:10.1088/1748- 9326/11/3/034013 (2016)

    work page doi:10.1088/1748- 2016

  9. [9]

    Tobin, I., Greuell, W., Jerez, S., Ludwig, F., Vautard, R., van Vliet, M. T. H. & Bréon, F.-M. Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming.Environmental Research Letters.doi:10 . 1088 / 1748 - 9326 / aab211(2018)

    2018

  10. [10]

    & Troccoli, A

    Dubus, L., Muralidharan, S. & Troccoli, A. inWeather & Climate Services for the Energy Industry(Springer International Publishing, 2018).isbn: 9783319684185. doi:10.1007/978-3-319-68418-5_4. REFERENCES 10

    work page doi:10.1007/978-3-319-68418-5_4 2018

  11. [11]

    & Saint-Drenan, Y.-M

    Bloomfield, H., Brayshaw, D., Troccoli, A., Goodess, C., De Felice, M., Dubus, L., Bett, P. & Saint-Drenan, Y.-M. Quantifying the sensitivity of european power systems to energy scenarios and climate change projections.Renewable Energy.doi:10. 1016/j.renene.2020.09.125(2021)

    2020

  12. [12]

    T., Wohland, J., Stoop, L

    Craig, M. T., Wohland, J., Stoop, L. P., Kies, A., Pickering, B., Bloomfield, H. C., Brow- ell, J., De Felice, M., Dent, C. J., Deroubaix, A., Frischmuth, F., Gonzalez, P. L., Gro- chowicz, A., Gruber, K., Härtel, P., Kittel, M., Kotzur, L., Labuhn, I., Lundquist, J. K., Pflugradt, N., van der Wiel, K., Zeyringer, M. & Brayshaw, D. J. Overcoming the disco...

    2022

  13. [13]

    & Stoop, L

    Biewald, B., Cozian, B., Dubus, L., Zappa, W. & Stoop, L. P. Evaluation of ‘Dunkelflaute’ event detection methods considering grid operators’ needs.Environmental Re- search: Energy.doi:10.1088/2753-3751/adcf29(2025)

    work page doi:10.1088/2753-3751/adcf29(2025 2025

  14. [14]

    & van der Wiel, K

    Van Duinen, B., van der Most, L., Baatsen, M. & van der Wiel, K. Meteorological drivers of co-occurring renewable energy droughts in Europe.Renewable and Sustainable Energy Reviews.doi:10.1016/j.rser.2025.115993(2025)

    work page doi:10.1016/j.rser.2025.115993(2025 2025

  15. [15]

    J., Huertas-Hernando, D., Radu, D., Sharp, J., Zappa, W

    Dubus, L., Brayshaw, D. J., Huertas-Hernando, D., Radu, D., Sharp, J., Zappa, W. & Stoop, L. P. Towards a future-proof climate database for European energy sys- tem studies.Environmental Research Letters.doi:10 . 1088 / 1748 - 9326 / aca1d3 (2022)

    2022

  16. [16]

    & Tocquer, F.LES NOUVELLES PROJEC- TIONS CLIMATIQUES DE RÉFÉRENCE DRIAS 2020tech

    Soubeyroux, J.-M., Bernus, S., Corre, L., Drouin, A., Dubuisson, B., Etchevers, P., Josse, P., Kerdoncuff, M., Samacoits, R. & Tocquer, F.LES NOUVELLES PROJEC- TIONS CLIMATIQUES DE RÉFÉRENCE DRIAS 2020tech. rep. (Meteo-France, 2020)

    2020

  17. [17]

    copernicus.org/EGU2020/EGU2020-10375_presentation.pdf(2020)

    Hersbach, H., Bell, B., Berrisford, P., Dahlgren, P., Horányi, A., Munoz-Sebater, J., Nicolas, J., Radu, R., Schepers, D., Simmons, A.,et al.The ERA5 Global Reanal- ysis: achieving a detailed record of the climate and weather for the past 70 years.European geophysical union general assembly.https://presentations. copernicus.org/EGU2020/EGU2020-10375_prese...

    2020

  18. [18]

    T., Cohen, S., Macknick, J., Draxl, C., Guerra, O

    Craig, M. T., Cohen, S., Macknick, J., Draxl, C., Guerra, O. J., Sengupta, M., Haupt, S. E., Hodge, B.-M. & Brancucci, C. A review of the potential impacts of climate change on bulk power system planning and operations in the United States. Renewable and Sustainable Energy Reviews.doi:10.1016/j.rser.2018.09.022 (2018)

    work page doi:10.1016/j.rser.2018.09.022 2018

  19. [19]

    & Almond, S.ENTSO-E use of Pan European Climate Database (PECD)- Issues and SolutionC3S, 2021

    Troccoli, A. & Almond, S.ENTSO-E use of Pan European Climate Database (PECD)- Issues and SolutionC3S, 2021

    2021

  20. [20]

    rte - france

    RTE.Energy pathways to 2050RTE, 2021.https : / / www . rte - france . com / en / media/10390

    2021

  21. [21]

    com / eliagroup / docs / 20240924 _ belgianelectricitysystemblueprint2035 - 205

    Elia.Belgian electricity system blueprint for 2035-2050Elia, 2024.https : / / issuu . com / eliagroup / docs / 20240924 _ belgianelectricitysystemblueprint2035 - 205

    2035

  22. [22]

    E., Thornton, H., Ranchin, T., Wald, L., Saint-Drenan, Y.-M., De Felice, M., Brayshaw, D., Suckling, E., Percy, B

    Troccoli, A., Goodess, C., Jones, P., Penny, L., Dorling, S., Harpham, C., Dubus, L., Parey, S., Claudel, S., Khong, D.-H., Bett, P. E., Thornton, H., Ranchin, T., Wald, L., Saint-Drenan, Y.-M., De Felice, M., Brayshaw, D., Suckling, E., Percy, B. & Blower, J. Creating a proof-of-concept climate service to assess future renewable energy mixes in Europe: A...

    work page doi:10.5194/asr-15-191-2018(2018 2018

  23. [24]

    & Saint-Drenan, Y.-M

    Bartók, B., Tobin, I., Vautard, R., Vrac, M., Jin, X., Levavasseur, G., Denvil, S., Dubus, L., Parey, S., Michelangeli, P.-A., Troccoli, A. & Saint-Drenan, Y.-M. A climate pro- jection dataset tailored for the European energy sector.Climate Services.doi:10. 1016/j.cliser.2019.100138(2019)

    work page arXiv 2019

  24. [25]

    & Sanger, L

    Dubus, L., Saint-Drenan, Y.-M., Troccoli, A., De Felice, M., Moreau, Y., Ho-Tran, L., Goodess, C., Amaro e Silva, R. & Sanger, L. C3S Energy: A climate service for the provision of power supply and demand indicators for Europe based on the ERA5 reanalysis and ENTSO-E data.Meteorological Applications.doi:10.1002/met.2145 (2023)

    work page doi:10.1002/met.2145 2023

  25. [26]

    Copernicus C3S.Climate and energy related variables from the Pan-European Climate Database derived from reanalysis and climate projections2024. doi:10. 24381/CDS.F323C5EC

  26. [27]

    & Stoop, L

    Van Duinen, B., van der Wiel, K. & Stoop, L. Selecting representative climate years for national to continental-scale energy system studies.EGU2026.doi:10.5194/ egusphere-egu26-1873(2026)

    2026

  27. [28]

    Copernicus C3S.Explore and Analyse PECDv4.2 Climate and Energy Data — 2025 ENTSO-E ECMWF training2026.https : / / ecmwf - training . github . io / 2025-entsoe-training/intro.html

    2025