Abstract
The term “Digital Twins of the Earth” has rocketed into scientific use and policymaker discourse by promising a virtual replica of our planet. While the potential of a digital representation of reality is captivating for environmental monitoring, decision-making, and scientific inquiry, the term lacks a clear and shared definition and may be misleading. It conceals that all digital representations are models and, as such, will always be detached from reality. Detailed simulation models are excellent digital laboratories that allow us to interrogate our theories about the world in ways otherwise not possible, given the limited scales at which we can run real-world experiments, yet a perfect representation of reality is impossible as it would be exactly as complex. As we embark on the journey of building such detailed models, one question we must ask is, "How can we ensure that they can be explored with scientific rigor?" Here, we discuss possible ways to utilize a model's internal variability to understand its dominant controls to increase our understanding of both the models we build and the world that they represent.
References
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access, 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499
Bauer, P., Stevens, B., & Hazeleger, W. (2021). A digital twin of Earth for the green transition. Nature Climate Change, 11(2), 80–83. https://doi.org/10.1038/s41558-021-00986-y
Beven, K. J. (2012). Rainfall-runoff modelling: The primer (2nd ed.). Chichester, West Sussex, Hoboken, NJ: Wiley-Blackwell.
BKG (2022). Digitaler Zwilling Deutschland. Bundesamt für Kartographie und Geodäsie. Retrieved from https://www.bkg.bund.de/DE/Forschung/Projekte/Digitaler-Zwilling/Digitaler-Zwilling_cont.html
Blair, G. S. (2021). Digital twins of the natural environment. Patterns, 2(10), 100359. https://doi.org/10.1016/j.patter.2021.100359
Borges, J. L. (1981). A universal history of infamy. Harmondsworth, Middlesex: Penguin Books.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification And Regression Trees: Routledge.
Carroll, L. (1893). Sylvie and Bruno concluded. Macmillan.
European Commission. (2019). The European Green Deal. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2019:640:FIN
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Ferretti, F., Saltelli, A., & Tarantola, S. (2016). Trends in sensitivity analysis practice in the last decade. The Science of the Total Environment, 568, 666–670. https://doi.org/10.1016/j.scitotenv.2016.02.133
Fischhoff, B., & Davis, A. L. (2014). Communicating scientific uncertainty. Proceedings of the National Academy of Sciences of the United States of America, 111 (Suppl 4), 13664–13671. https://doi.org/10.1073/pnas.1317504111
Gaiman, N. (2006). Fragile things. London: Headline Review.
Gleeson, T., Wagener, T., Döll, P., Zipper, S. C., West, C., & Wada, Y., et al. (2021). GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models. Geoscientific Model Development, 14(12), 7545–7571. https://doi.org/10.5194/gmd-14-7545-2021
Gnann, S., Reinecke, R., Stein, L., Wada, Y., Thiery, W., Müller Schmied, H., Satoh, Y., Pokhrel, Y., Ostberg, S., Koutroulis, A., Hanasaki, N., Grillakis, M., Gosling, S. N., Burek, P., Bierkens, M. F. P., & Wagener, T. (2023). Functional relationships reveal differences in the water cycle representation of global water models. Nature Water, 1(12), 1079–1090. https://doi.org/10.1038/s44221-023-00160-y
Grieves, M. (2014). Digital twin: manufacturing excellence through virtual factory replication. White Paper, 1(2014), 1–7.
Grimm, V., Johnston, A. S. A., Thulke, H.-H., Forbes, V. E., & Thorbek, P. (2020). Three questions to ask before using model outputs for decision support. Nature Communications, 11(1), 4959. https://doi.org/10.1038/s41467-020-17785-2
Hazeleger, W., Aerts, J.P.M., Bauer, P. et al. (2024) Digital twins of the Earth with and for humans. Commun Earth Environ 5, 463. https://doi.org/10.1038/s43247-024-01626-x
Lesk, C., Coffel, E., Winter, J., Ray, D., Zscheischler, J., Seneviratne, S. I., & Horton, R. (2021). Stronger temperature-moisture couplings exacerbate the impact of climate warming on global crop yields. Nature Food, 2(9), 683–691. https://doi.org/10.1038/s43016-021-00341-6
Li, X., Feng, M., Ran, Y., Su, Y., Liu, F., Huang, C., Shen, H., Xiao, Q., Su, J., Yuan, S., & Guo, H. (2023). Big Data in Earth system science and progress towards a digital twin. Nature Reviews Earth & Environment, 4(5), 319–332. https://doi.org/10.1038/s43017-023-00409-w
MacDonald, A. M., Lark, R. M., Taylor, R. G., Abiye, T., Fallas, H. C., Favreau, G., Goni, I., Kebede, S., Scanlon, B., Sorensen, J. P. R., Tijani, M., Upton, K. A., & West, C. (2021). Mapping groundwater recharge in Africa from ground observations and implications for water security. Environmental Research Letters, 16(3), 34012. https://doi.org/10.1088/1748-9326/abd661
Mu, M., Kauwe, M. G. de, Ukkola, A. M., Pitman, A. J., Guo, W., Hobeichi, S., & Briggs, P. R. (2021). Exploring how groundwater buffers the influence of heatwaves on vegetation function during multi-year droughts. Earth System Dynamics, 12(3), 919–938. https://doi.org/10.5194/esd-12-919-2021
Ossing, F., Attinger, S., Jung, T., Visbeck, M., Brune, S., Cotton, F., & Teichmann, C. (2023). Synthesis paper Digital Twins of Planet Earth: First Draft for the General Assembly.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L.A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical Research Ed.), 372, n71. https://doi.org/10.1136/bmj.n71
Pattee, H. H. (1972). The nature of hierarchical controls in living matter. In Foundations of Mathematical Biology (pp. 1–22). Elsevier. https://doi.org/10.1016/B978-0-12-597201-7.50008-5
Pedersen, A. N., Pedersen, J. W., Borup, M., Brink-Kjær, A., Christiansen, L. E., & Mikkelsen, P. S. (2022). Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins. Water Science and Technology. https://doi.org/10.2166/wst.2022.059
Puy, A., Beneventano, P., Levin, S. A., Lo Piano, S., Portaluri, T., & Saltelli, A. (2022). Models with higher effective dimensions tend to produce more uncertain estimates. Science Advances, 8(42), eabn9450. https://doi.org/10.1126/sciadv.abn9450
Puy, A., Roy, P. T., & Saltelli, A. (2024). Discrepancy Measures for Global Sensitivity Analysis. Technometrics, 1–18. https://doi.org/10.1080/00401706.2024.2304341
Raj, P. (2021). Empowering digital twins with blockchain. In Advances in Computers. The Blockchain Technology for Secure and Smart Applications across Industry Verticals. Vol. 121, pp. 267–283. Elsevier. https://doi.org/10.1016/bs.adcom.2020.08.013
Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J. H. A., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini, N., Asadzadeh, M., Puy, A., Kucherenko, S. & Maier, H. R. (2021). The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. Environmental Modelling & Software, 137, 104954. https://doi.org/10.1016/j.envsoft.2020.104954
Reinecke, R., Gnann, S., Stein, L., Bierkens, M., de Graaf, I., Gleeson, T., Oude Essink, G., Sutanudjaja, E., Ruz-Vargas, C., Verkaik, J., & Wagener, T. (2023). Uncertainty in model estimates of global groundwater depth. Accepted in Environmental research letters. https://doi.org/10.31223/X5SM0R
Saltelli, A., Bammer, G., Bruno, I., Charters, E., Di Fiore, M., Didier, E., Nelson Espeland, W., Kay, J., Lo Piano, S., Mayo, D., & Pielke Jr, R., (2020). Five ways to ensure that models serve society: a manifesto. Nature, 582(7813), 482–484. https://doi.org/10.1038/d41586-020-01812-9
Saltelli, A., Jakeman, A., Razavi, S., & Wu, Q. (2021). Sensitivity analysis: A discipline coming of age. Environmental Modelling & Software, 146, 105226. https://doi.org/10.1016/j.envsoft.2021.105226
Saltelli, A., Gigerenzer, G., Hulme, M., Katsikopoulos, K. V., Melsen, L. A., Peters, G. P., Pielke, R. Jr, Robertson, S., Stirling, A., Tavoni, M., & Puy, A. (2024). Bring digital twins back to Earth. WIREs Climate Change, e915. https://doi.org/10.1002/wcc.915
Slingo, J., Bates, P., Bauer, P., Belcher, S., Palmer, T., Stephens, G., Stevens, B., Stocker, T., & Teutsch, G. (2022). Ambitious partnership needed for reliable climate prediction. Nature Climate Change, 12(6), 499–503. https://doi.org/10.1038/s41558-022-01384-8
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., & Xu, C. (2015). Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications. Journal of Hydrology, 523, 739–757. https://doi.org/10.1016/j.jhydrol.2015.02.013
Van de Schoot, R., Bruin, J. de, Schram, R., Zahedi, P., Boer, J. de, Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Hindriks, S., Tummers, L., & Oberski, D. L. (2021). An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence, 3(2), 125–133. https://doi.org/10.1038/s42256-020-00287-7
Vanrolleghem, P. A., Mannina, G., Cosenza, A., & Neumann, M. B. (2015). Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods. Journal of Hydrology, 522, 339–352. https://doi.org/10.1016/j.jhydrol.2014.12.056
Vereecken, H., Amelung, W., Bauke, S. L., Bogena, H., Brüggemann, N., Montzka, C., Vanderborght, J., Bechtold, M., Blöschl, G., Carminati, A., Javaux, M., Konings, A. G., Kusche, J., Neuweiler, I., Or, D., Steele-Dunne, S., Verhoef, A., Young, M. and Zhang, Y. (2022). Soil hydrology in the Earth system. Nature Reviews Earth & Environment, 3(9), 573–587. https://doi.org/10.1038/s43017-022-00324-6
Voosen, P. (2020). Europe builds ‘digital twin’ of Earth to hone climate forecasts. Science, 370(6512), 16–17. https://doi.org/10.1126/science.370.6512.16
Wagener, T., & Pianosi, F. (2019). What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling. Earth-Science Reviews, 194, 1–18. https://doi.org/10.1016/j.earscirev.2019.04.006
Wagener, T., Reinecke, R., & Pianosi, F. (2022). On the evaluation of climate change impact models. Wiley Interdisciplinary Reviews: Climate Change, 13(3), e772. https://doi.org/10.1002/wcc.772
Zehe, E., & Sivapalan, M. (2009). Threshold behaviour in hydrological systems as (human) geo-ecosystems: manifestations, controls, implications. Hydrology and Earth System Sciences, 13(7), 1273–1297. https://doi.org/10.5194/hess-13-1273-2009
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