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Published online by Cambridge University Press: 02 November 2021
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- The Climate DemonPast, Present, and Future of Climate Prediction, pp. 340 - 371Publisher: Cambridge University PressPrint publication year: 2021
References
Abatzoglou, J. T., and Williams, A. P., 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci., 113(42), 11770–11775, doi:10.1073/pnas.1607171113Google Scholar
Agrawala, S., 1998: Context and early origins of the Intergovernmental Panel on Climate Change. Clim. Change, 39, 605–620, doi:10.1023/A:1005315532386CrossRefGoogle Scholar
AIP–Bryan, 1989: Interview of Kirk Bryan by Spencer Weart on December 20, 1989, College Park, MD, Niels Bohr Library & Archives, American Institute of Physics, www.aip.org/history-programs/niels-bohr-library/oral-histories/5068. College Park, MD, Niels Bohr Library & Archives, American Institute of Physics, www.aip.org/history-programs/niels-bohr-library/oral-histories/33642Google Scholar
AIP-Farman, 2009: Interview of Joseph Farman by Keynyn Brysse on March 16, 2009, Niels Bohr Library & Archives, American Institute of Physics, College Park, MD, www.aip.org/history-programs/niels-bohr-library/oral-histories/33642Google Scholar
AIP–Kasahara, 1998: Interview of Akira Kasahara by Paul Edwards on November 2, 1998, College Park, MD, Niels Bohr Library & Archives, American Institute of Physics, www.aip.org/history-programs/niels-bohr-library/oral-histories/32440-1Google Scholar
AIP–Manabe, 1989: Interview of Syukuro Manabe by Spencer Weart on December 20, 1989, College Park, MD, Niels Bohr Library & Archives, American Institute of Physics, www.aip.org/history-programs/niels-bohr-library/oral-histories/5040Google Scholar
AIP–Manabe, 1998a: Interview of Syukuro Manabe by Paul Edwards on March 14, 1998, College Park, MD, Niels Bohr Library & Archives, American Institute of Physics, www.aip.org/history-programs/niels-bohr-library/oral-histories/32158-1Google Scholar
AIP–Manabe, 1998b: Interview of Syukuro Manabe by Paul Edwards on March 15, 1998, College Park, MD, Niels Bohr Library & Archives, American Institute of Physics, www.aip.org/history-programs/niels-bohr-library/oral-histories/32158-2Google Scholar
AIP–Washington, 1998: Interview of Warren Washington by Paul Edwards on October. 28 and 29, 1998, College Park, MD, Niels Bohr Library & Archives, American Institute of Physics, www.aip.org/history-programs/niels-bohr-library/oral-histories/33098Google Scholar
Albritton, D. L., Meira Filho, L. G., Cubasch, U., et al., 2001: Technical summary. In: Houghton, J. T., Ding, Y., Griggs, D. J., et al. (eds.), Climate change 2001: the scientific basis. Cambridge University Press, 21–83.Google Scholar
Alcamo, J., Moreno, J. M., Nováky, B., et al., 2007: Europe. Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University PressGoogle Scholar
Allen, M., 2019: Why protesters should be wary of “12 years to climate breakdown” rhetoric. TheConversation.com, https://theconversation.com/why-protesters-should-be-wary-of-12-years-to-climate-breakdown-rhetoric-115489Google Scholar
Allen, M., and Stainforth, D., 2002: Towards objective probabilistic climate forecasting. Nature, 419, 228, doi:10.1038/nature01092aGoogle Scholar
Allen, M. R., and Ingram, W. J., 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419(6903), 224–232Google Scholar
Ambrose, J., 2020: “Hijacked by anxiety”: how climate dread is hindering climate action. The Guardian, October 8, www.theguardian.com/environment/2020/oct/08/anxiety-climate-crisis-trauma-paralysing-effect-psychologistsGoogle Scholar
Amos, J., 2019: UK’s Halley Antarctic base in third winter shutdown. BBC News, February 28, www.bbc.com/news/science-environment-47408249Google Scholar
Andrijevic, M., Schleussner, C. F., Gidden, M. J., McCollum, D. L., and Rogelj, J., 2020: COVID-19 recovery funds dwarf clean energy investment needs. Science, 370, 298–300Google Scholar
APS, 2015: American Physical Society, Statement on Earth’s changing climate, aps.org/policy/statements/15_3.cfmGoogle Scholar
Arrhenius, S., 1908: Worlds in the making: the evolution of the universe. Harper, https://archive.org/details/worldsinmakingev00arrhrich/mode/2upGoogle Scholar
Ayala, F. J., 2009: Darwin and the scientific method. Proc. Natl. Acad. Sci., 106(Supplement 1), 10033–10039Google Scholar
Baker, M., and Roe, G., 2009: The shape of things to come: why is climate change so predictable? J. Clim., 22(17), 4574–4589Google Scholar
Balaji, V., 2021: Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science. Phil. Trans. R. Soc. A, 379, 20200085, doi:10.1098/rsta.2020.0085Google Scholar
Ball, P., 2016: The tyranny of simple explanations. The Atlantic, August 11, www.theatlantic.com/science/archive/2016/08/occams-razor/495332/Google Scholar
Barnston, A. G., Tippett, M. K., Ranganathan, M., et al., 2019: Deterministic skill of ENSO predictions from the North American Multimodel Ensemble. Clim. Dynam., 53, 7215–7234Google Scholar
Bastien-Olvera, B. A., and Moore, F. C., 2020: Use and non-use value of nature and the social cost of carbon. Nat. Sustain., 4, 101–108, doi:10.1038/s41893-020-00615-0CrossRefGoogle Scholar
Batterson, S., 2007: The vision, insight, and influence of Oswald Veblen. Notices of the AMS, 54, 606–618Google Scholar
Bauer, P., Thorpe, A., and Brunet, G., 2015: The quiet revolution of numerical weather prediction. Nature, 525, 47–57, doi:10.1038/nature14956Google Scholar
Baumberger, C., Knutti, R., and Hirsch Hadorn, G., 2017: Building confidence in climate model projections: an analysis of inferences from fit. WIREs Clim. Change, 8, e454Google Scholar
Beckage, B., Lacasse, K., Winter, J. M., et al., 2020: The earth has humans, so why don’t our climate models? Clim. Change, 163, 181–188, doi:10.1007/s10584-020-02897-xGoogle Scholar
Benestad, R., 2016: Downscaling climate information. Oxford research encyclopedia of climate science, doi:10.1093/acrefore/9780190228620.013.27Google Scholar
Benjamin, S. G., Brown, J. M., Brunet, G., Lynch, P., Saito, K., and Schlatter, T. W., 2019: 100 years of progress in forecasting and NWP applications. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc., 13.1–13.67Google Scholar
Berardelli, J., 2020: Some new climate models are projecting extreme warming. Are they correct? Yale Climate Connections, July 1, https://yaleclimateconnections.org/2020/07/some-new-climate-models-are-projecting-extreme-warming-are-they-correct/Google Scholar
Betts, R., 2018: Is our planet headed toward a “Hothouse”? Here’s what the science does – and doesn’t – say. Washington Post, August 10, www.washingtonpost.com/news/capital-weather-gang/wp/2018/08/10/hothouse-earth-heres-what-the-science-actually-does-and-doesnt-say/Google Scholar
Betts, R. A., Lorenzo, A., Catherine, B., et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Phil. Trans. R. Soc. A., 37620160452, doi:10.1098/rsta.2016.0452Google Scholar
Betz, G., 2010: What’s the worst case? The methodology of possibilistic prediction. Analyse and Kritik, 01, 87–106Google Scholar
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P., 2021: Enforcing analytic constraints in neural-networks emulating physical systems. Phys. Rev. Lett., 126, 098302Google Scholar
Bindoff, N. L., Stott, P. A., AchutaRao, K. M., et al., 2013: Detection and attribution of climate change: from global to regional. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 867–952.Google Scholar
Bintanja, R., Wal, R. S. W. v. d., and Oerlemans, J., 2005: A new method to estimate ice age temperatures. Clim. Dynam., 24, 197–211, doi:10.1007/s00382-004-0486-xGoogle Scholar
Bjordal, J., Storelvmo, T., Alterskjær, K., et al., 2020: Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback. Nat. Geosci., 13, 718–721, doi:10.1038/s41561-020-00649-1Google Scholar
Blackport, R., and Screen, J. A., 2020: Weakened evidence for mid-latitude impacts of Arctic warming. Nat. Clim. Change, 10, 1065–1066, doi:10.1038/s41558-020-00954-yGoogle Scholar
Blair, W. M., 1957: President draws planning moral. The New York Times, November 15, www.nytimes.com/1957/11/15/archives/president-draws-planning-moral-recalls-army-days-to-show-value-of.htmlGoogle Scholar
Blake, E. S., and Zelinsky, D. A., 2018: Hurricane Harvey. Tropical Cyclone Report, National Hurricane Center. May 8, www.nhc.noaa.gov/data/tcr/AL092017_Harvey.pdfGoogle Scholar
Bock, L., Lauer, A., Schlund, M., et al., 2020: Quantifying progress across different CMIP phases with the ESMValTool. J. Geophys. Res. – Atmos., 125, e2019JD032321Google Scholar
Bojkov, R. D., and Balis, D. S., 2009: The history of total ozone measurements: the early search for signs of a trend and an update. In: Zerefos, C., Contopoulos, G., and Skalkeas, G. (eds.), Twenty years of ozone decline. Springer, 73–110Google Scholar
Bony, S., Stevens, B., Held, I., et al., 2013: Carbon dioxide and climate: perspectives on a scientific assessment. In: Hurrell, J. W., and Asrar, G. (eds.), Climate science for serving society. Springer Netherlands, 391–413, https://library.wmo.int/doc_num.php?explnum_id=7660Google Scholar
Boos, W. R., and Storelvmo, T., 2016: Near-linear monsoon response to range of forcings. Proc. Nat. Acad. Sci., 113(6), 1510–1515Google Scholar
Borenstein, S., and Johnson, C. K., 2020: Modeling coronavirus: “uncertainty is the only certainty.” Associated Press, April 7, https://apnews.com/article/public-health-health-us-news-ap-top-news-virus-outbreak-88866498ff5c908e5f28f7b5b5e5b695Google Scholar
Borges, J. L., 1975: On exactitude in science. In: A universal history of infamy (trans. Norman Thomas de Giovanni). London, Penguin Books, 325Google Scholar
Borland, J. B., Jamiolkowski, M., and Viggiani, C., 2003: The stabilisation of the Leaning Tower of Pisa. Soils and Foundations – Tokyo, 43(5), 63–80Google Scholar
Borowski, S., 2012: The origin and popular use of Occam’s razor. American Association for the Advancement of Science, June 12, www.aaas.org/origin-and-popular-use-occams-razorGoogle Scholar
Bostrom, N., 2003: Are you living in a computer simulation? Philosophical Quarterly, 53(211), 243–255Google Scholar
Bova, S., Rosenthal, Y., Liu, Z., et al. 2021: Seasonal origin of the thermal maxima at the Holocene and the last interglacial. Nature, 589, 548–553Google Scholar
Box, G. E. P., 1976: Science and statistics. Journal of the American Statistical Association, 71(356), 791–799, doi:10.1080/01621459.1976.10480949Google Scholar
Brown, C., and Wilby, R., 2012: An alternate approach to assessing climate risks. Eos, 93(41), 401–402Google Scholar
Brown, P. T., Stolpe, M. B., and Caldeira, K., 2018: Assumptions for emergent constraints. Nature, 563, E1–E3Google Scholar
Bryan, K., 2006: Modeling ocean circulation. In: Jochum, M. and Murtugudde, R. (eds.), Physical oceanography. New York, Springer, 29–44. doi:10.1007/0-387-33152-2_3Google Scholar
Burgess, M., Ritchie, J., Shapland, J., and Pielke, R. Jr, 2020: IPCC baseline scenarios have over-projected CO2 emissions and economic growth. Environ. Res. Lett., 16, 014016CrossRefGoogle Scholar
Burrakoff, M., 2019: NASA’s study of astronaut twins creates a portrait of what a year in space does to the human body. Smithsonian Magazine, April 11, www.smithsonianmag.com/science-nature/nasas-twins-study-creates-portrait-human-body-after-year-space-180971945/Google Scholar
Caldeira, K., and Bala, G., 2017: Reflecting on 50 years of geoengineering research. Earth’s Future, 5, 10–17, doi:10.1002/2016EF000454Google Scholar
Calel, R., Chapman, S. C., Stainforth, D. A., et al., 2020: Temperature variability implies greater economic damages from climate change. Nat. Commun., 11, 5028Google Scholar
Calel, R., Stainforth, D. A., and Dietz, S., 2015: Tall tales and fat tails: The science and economics of extreme warming. Clim. Change, 132, 127–141Google Scholar
Canales, J., 2020: Bedeviled: a shadow history of demons in science. Princeton University PressGoogle Scholar
Carbon Brief, 2018: Q&A: How “integrated assessment models” are used to study climate change. CarbonBrief.org, October 2Google Scholar
Carrington, D., 2020: Climate “apocalypse” fears stopping people having children. The Guardian, November 27, www.theguardian.com/environment/2020/nov/27/climate-apocalypse-fears-stopping-people-having-children-studyGoogle Scholar
Carroll, S., 2013: What is science? Preposterous Universe blog, July 3, www.preposterousuniverse.com/blog/2013/07/03/what-is-science/Google Scholar
Carroll, S., 2016: Maybe we do not live in a simulation: the resolution conundrum. Preposterous Universe blog, August 22, www.preposterousuniverse.com/blog/2016/08/22/maybe-we-do-not-live-in-a-simulation-the-resolution-conundrum/Google Scholar
Carton, W., 2020: Carbon unicorns and fossil futures. Whose emission reduction pathways is the IPCC performing? In: Sapinski, J. P., Buck, H., and Malm, A. (eds.), Has it come to this? The promises and perils of geoengineering on the brink. Rutgers University PressGoogle Scholar
Charney, J. G., Arakawa, A., Baker, D. J., et al., 1979: Carbon dioxide and climate: a scientific assessment. National Academy of SciencesGoogle Scholar
Cho, A., 2012: Once again, physicists debunk faster-than-light neutrinos. Science, June 8, www.sciencemag.org/news/2012/06/once-again-physicists-debunk-faster-light-neutrinosGoogle Scholar
Christie, M., 2000: The ozone layer: a philosophy of science perspective. Cambridge University PressGoogle Scholar
Clark, D., 2020: Japanese supercomputer is crowned world’s speediest. The New York Times, June 22, www.nytimes.com/2020/06/22/technology/japanese-supercomputer-fugaku-tops-american-chinese-machines.htmlGoogle Scholar
Clark, P., 2013: Scientist who beat NASA to the ozone hole. Financial Times, May 17, www.ft.com/content/4084f0b8-bd77-11e2-890a-00144feab7deGoogle Scholar
Clement, A., Bellomo, K., Murphy, L. N., et al., 2015: The Atlantic Multidecadal Oscillation without a role for ocean circulation. Science, 350(6258), 320, doi:10.1126/science.aab3980Google Scholar
Cohen, J., Zhang, X., Francis, J., et al., 2020: Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather. Nat. Clim. Change, 10, 20–29, doi:10.1038/s41558-019-0662-yGoogle Scholar
Collins, M., Knutti, R., Arblaster, J., et al., 2013: Long-term climate change: projections, commitments and irreversibility. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 1029–1136Google Scholar
Colman, Z., 2021: “Garbage” models and black boxes? The science of climate disaster planning. Politico.com, March 16, www.politico.com/news/2021/03/16/climate-change-murky-models-476316Google Scholar
Conway, E., 2008: What’s in a name? Global warming vs. climate change. NASA, December 5, www.nasa.gov/topics/earth/features/climate_by_any_other_name.htmlGoogle Scholar
Copeland, B. J., and Proudfoot, D., 2011: Alan Turing: father of the modern computer. The Rutherford Journal, 4(1), https://espace.library.uq.edu.au/view/UQ:347665Google Scholar
Cox, P., Huntingford, C., and Williamson, M., 2018a: Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature, 553, 319–322Google Scholar
Cox, P., and Stephenson, D., 2007: Climate change – a changing climate for prediction. Science, 317, 207–208Google Scholar
Cox, P. M., Williamson, M. S., Nijsse, F. J. M. M., et al., 2018b: Cox et al. reply. Nature, 563, E10–E15Google Scholar
Crane, L., 2018: Terraforming Mars might be impossible due to a lack of carbon dioxide. New Scientist, July 30, www.newscientist.com/article/2175414-terraforming-mars-might-be-impossible-due-to-a-lack-of-carbon-dioxide/Google Scholar
Crowe, K., 2019: How “organized climate change denial” shapes public opinion on global warming. Canadian Broadcasting Corporation, Canada, September 27, www.cbc.ca/news/science/climate-change-denial-fossil-fuel-think-tank-sceptic-misinformation-1.5297236Google Scholar
Crutzen, P., 2006: Albedo enhancement by stratospheric sulfur injections: a contribution to resolve a policy dilemma? Clim. Change, 77, 211–219Google Scholar
Cubasch, U., Wuebbles, D., Chen, D., et al., 2013: Introduction. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 119–158Google Scholar
Curry, J. A., and Webster, P. J., 2011: Climate science and the uncertainty monster. Bull. Am. Meteorol. Soc., 92, 1667–1682Google Scholar
Dabberdt, W. F., Shellhorn, R., Cole, H., et al., 2003: Radiosondes. Encyclopedia of atmospheric sciences. Elsevier, 1900–1913Google Scholar
Das, S. R., 2008: The chip that changed the world. The New York Times, September 19, www.nytimes.com/2008/09/19/opinion/19iht-eddas.1.16308269.htmlGoogle Scholar
Dayton, L., 2016: Research chief cuts climate studies, sets new priorities. Science, 351(6274), 649, doi:10.1126/science.351.6274.649Google Scholar
Deser, C., 2020: Certain uncertainty: the role of internal climate variability in projections of regional climate change and risk management. Earth’s Future, doi:10.1029/2020EF001854Google Scholar
Deser, C., Knutti, R., Solomon, S., et al. 2012: Communication of the role of natural variability in future North American climate. Nat. Clim. Change, 2, 775–779, doi:10.1038/nclimate1562Google Scholar
Deser, C., Lehner, F., Rodgers, K. B., et al., 2020: Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change, 10, 277–286, doi:10.1038/s41558-020-0731-2Google Scholar
Dessai, S., and Hulme, M., 2003: Does climate policy need probabilities? Tyndall Centre working paper no. 34. Norwich, Tyndall Centre for Climate Change ResearchGoogle Scholar
Dessler, A. E., 2015: Introduction to modern climate change, 2nd ed.. Cambridge University PressGoogle Scholar
Dessler, A. E., 2018: The influence of internal variability on Earth’s energy balance framework and implications for estimating climate sensitivity. Atmos. Chem. Phys., 18, 5147–5155, doi:10.5194/acp-18-5147-2018Google Scholar
Dessler, A. E., and Cohan, D., 2018: We’re scientists. We know the climate’s changing. And we know why. Houston Chronicle, October 22, www.houstonchronicle.com/local/gray-matters/article/science-climate-change-combustion-fossil-fuels-13327165.phpGoogle Scholar
Dongarra, J., Heroux, M. A., and Luszczek, P., 2016: High-performance conjugate-gradient benchmark: a new metric for ranking high-performance computing systems. Int. J. High Perform. Comput. Appl., 30(1), 3–10, doi:10.1177/1094342015593158Google Scholar
Donnelly, J. P., 2009: Paleotempestology, the sedimentary record of intense hurricanes. In: Gornitz, V. (ed.), Encyclopedia of paleoclimatology and ancient environments, Encyclopedia of Earth Sciences Series. Dordrecht, Springer, 763–766. doi:10.1007/978-1-4020-4411-3_181Google Scholar
Doyle, A., 2013: Experts surer of manmade global warming but local predictions elusive. Reuters, August 16, www.reuters.com/article/us-climate-report/experts-surer-of-manmade-global-warming-but-local-predictions-elusive-idUSBRE97F0KM20130816Google Scholar
Duben, P. D., Russell, F. P., Niu, X., Luk, W., and Palmer, T. N., 2015: On the use of programmable hardware and reduced numerical precision in earth- system modeling. J. Adv. Model. Earth Syst., 7, 1393–1408, doi:10.1002/2015MS000494.Google Scholar
Dudney, J., and Suding, K. N., 2020: The elusive search for tipping points. Nat. Ecol. Evol., 4, 1449–1450, doi:10.1038/s41559-020-1273-8Google Scholar
Dunne, D., 2017: Hyperthermals: what can they tell us about modern global warming? CarbonBrief.org, October 9, www.carbonbrief.org/hyperthermals-what-can-they-tell-us-about-modern-global-warmingGoogle Scholar
Dyson, F. J., 2007: Heretical thoughts about science and society. Edge, August 7, edge.org/conversation/freeman_dyson-heretical-thoughts-about-science-and-societyGoogle Scholar
Easterbrook, S., 2010: What’s the pricetag on a Global Climate Model? Serendipity blog, September 3, www.easterbrook.ca/steve/2010/09/whats-the-pricetag-on-a-global-climate-model/Google Scholar
Easterbrook, S., 2011: One model to rule them all? Serendipity blog, November 6, www.easterbrook.ca/steve/2011/11/one-model-to-rule-them-all/Google Scholar
Edwards, J. R., 2012a: An early history of computing at Princeton. Princeton Alumni Weekly, April 4, https://paw.princeton.edu/article/early-history-computing-princetonGoogle Scholar
Edwards, P. N., 1999: Global climate science, uncertainty and politics: data-laden models, model-filtered data. Science as Culture, 8(4), 437–472Google Scholar
Edwards, P. N., 2010: A vast machine: computer models, climate data, and the politics of global warming. MIT PressGoogle Scholar
Edwards, P. N., 2012b: Entangled histories: climate science and nuclear weapons research. Bull. At. Sci., 68(4), 28–40, doi:10.1177/0096340212451574Google Scholar
Emanuel, K. A., 1999: The power of a hurricane: an example of reckless driving on the information superhighway. Weather, 54, 107–108Google Scholar
Emanuel, K. A., 2011: Edward Norton Lorenz 1917–2008: a biographical memoir. National Academy of Sciences, www.nasonline.org/publications/biographical-memoirs/memoir-pdfs/lorenz-edward.pdfGoogle Scholar
Emanuel, K. A., 2020: The relevance of theory for contemporary research in atmospheres, oceans, and climate. AGU Advances, 1, e2019AV000129Google Scholar
Eyring, V., Bony, S., Meehl, G. A., et al., 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958, doi:10.5194/gmd-9-1937-2016Google Scholar
Farman, J. C., Gardiner, B. G., and Shanklin, J. D., 1985: Large losses of total ozone in Antarctica reveal seasonal CIOx/NOx interaction. Nature, 315, 207–210Google Scholar
Fasullo, J. T., 2020: Evaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate Model Assessment Tool (CMATv1). Geosci. Model Dev., 13, 3627–3642Google Scholar
Feldman, M., 2019a: Dennard Scaling demise puts permanent dent in supercomputing. The Next Platform, June 18, www.nextplatform.com/2019/06/18/dennard-scaling-demise-puts-permanent-dent-in-supercomputing/Google Scholar
Feldman, M., 2019b: Exascale density pushes the boundaries of cooling. The Next Platform, November 26, www.nextplatform.com/2019/11/26/exascale-density-pushes-the-boundaries-of-cooling/Google Scholar
Feldman, M., 2020: HPC in 2020: AI is no longer an experiment. The Next Platform, January 9, www.nextplatform.com/2020/01/09/hpc-in-2020-ai-is-no-longer-an-experiment/Google Scholar
Feldstein, S. B., 2000: The timescale, power spectra, and climate noise properties of teleconnection patterns. J. Clim., 13(24), 4430–4440Google Scholar
Ferreira, A. P., Nieto, R., and Gimeno, L., 2019: Completeness of radiosonde humidity observations based on the IGRA. Earth Syst. Sci. Data, 11, 603–627, doi:10.5194/essd-11-603-2019Google Scholar
Fiedler, S., Crueger, T., D’Agostino, R., et al., 2020: Simulated tropical precipitation assessed across three major phases of the Coupled Model Intercomparison Project (CMIP). Mon. Wea. Rev., 148, 3653–3680, doi:10.1175/MWR-D-19-0404.1Google Scholar
Fiedler, T., Pitman, A. J., Mackenzie, K., et al. 2021: Business risk and the emergence of climate analytics. Nat. Clim. Change, doi:10.1038/s41558-020-00984-6Google Scholar
Fischer, E., and Knutti, R., 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change, 5, 560–564Google Scholar
Flato, G., Marotzke, J., Abiodun, B., et al., 2013: Evaluation of climate models. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 741–866Google Scholar
Fleming, J. R., 2010: Fixing the sky: the checkered history of weather and climate control. Columbia University PressGoogle Scholar
Fleurbaey, M., Ferranna, M., Budolfson, M., et al., 2019: The social cost of carbon: valuing inequality, risk, and population for climate policy. The Monist, 102, 84–109, doi:10.1093/monist/ony023Google Scholar
Flynn, C., 2018: Forecasts in retrospect: a history of numerical weather prediction. Metservice blog, January 26, https://blog.metservice.com/HistoryNWPGoogle Scholar
Foote, E., 1856: Circumstances affecting the heat of the sun’s rays: art. XXXI. Am. J. Sci. Arts, 2nd series, XXII(LXVI), November 1856, 382–383Google Scholar
Forster, P. M., Maycock, A. C., McKenna, C. M., et al., 2020: Latest climate models confirm need for urgent mitigation. Nat. Clim. Change, 10, 7–10Google Scholar
Fountain, H., 2017: Scientists link Hurricane Harveyʼs record rainfall to climate change. The New York Times, December 13, www.nytimes.com/2017/12/13/climate/hurricane-harvey-climate-change.htmlGoogle Scholar
Fox, C. R., and Ulkümen, G., 2009: Distinguishing two dimensions of uncertainty. In: Brun, W., Keren, G., Kirkebøen, G., and Montgomery, H. (eds.), Perspectives on thinking, judging, and decision making. Oslo, Universitetsforlaget.Google Scholar
Freedman, A., 2016: Nearly 3,000 climate scientists condemn Australia’s dramatic research cuts. Mashable, February 10, https://mashable.com/2016/02/10/climate-scientists-australia-csiro-cuts/Google Scholar
Fried, B., 2014: What you missed in we the geeks: “weather is your mood and climate is your personality,” The White House, January 10, https://obamawhitehouse.archives.gov/blog/2014/01/10/what-you-missed-we-geeks-weather-your-mood-and-climate-your-personalityGoogle Scholar
Frigg, R., Bradley, S., Du, H., and Smith, L. A., 2014: Laplace’s demon and the adventures of his apprentices. Philos. Sci., 81, 31–59Google Scholar
Frisinger, H. H., 1973: Aristotle’s legacy in meteorology. Bull. Am. Meteorol. Soc., 54, 198–204Google Scholar
Fuhrer, O., Chadha, T., Hoefler, T., et al., 2018: Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0. Geosci. Model Dev., 11, 1665–1681, doi:10.5194/gmd-11-1665-2018Google Scholar
Funtowicz, S., and Ravetz, J., 1990: Uncertainty and quality in science for policy. Kluwer Academic PublishersGoogle Scholar
Gardiner, L. S., 2019: What does climate sound and look like? AGU Blogosphere, March 11, https://blogs.agu.org/sciencecommunication/2019/03/11/what-does-climate-sound-and-look-like/Google Scholar
Ghosh, P., 2017: Hawking says Trump’s climate stance could damage earth. BBC, July 2, www.bbc.com/news/science-environment-40461726Google Scholar
Giorgi, F., and Bi, X., 2009: Time of emergence (TOE) of GHG-forced precipitation change hot-spots. Geophys. Res. Lett., 36, L06709, doi:10.1029/2009GL037593.Google Scholar
GISTEMP Team, 2021: GISS surface temperature analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies, dataset accessed January 28, 2021, https://data.giss.nasa.gov/gistemp/Google Scholar
Glato, G. M., 2011: Earth system models: an overview. WIREs Clim. Change, 2, 783–800, doi:10.1002/wcc.148Google Scholar
Gleckler, P. J., Taylor, K. E., and Doutriaux, C. E., 2008: Performance metrics for climate models. J. Geophys. Res. – Atmos., 113, D06104Google Scholar
Goddard, L., Baethgen, W., Kirtman, B., and Meehl, G., 2009: The urgent need for improved climate models and predictions. Eos, 90(39), 343–344Google Scholar
Godfrey-Smith, P., 2003: Theory and reality: an introduction to the philosophy of science. University of Chicago PressGoogle Scholar
Goenner, H., 1993: The reaction to relativity theory in Germany, III: “A hundred authors against Einstein.” In Earman, J., Janssen, M., and Norton, J. D. (eds.), The attraction of gravitation: new studies in the history of general relativity. Boston, BirkhäuserGoogle Scholar
Golaz, J. C., Horowitz, L., and Levy, H., 2013: Cloud tuning in a coupled climate model: impact on 20th century warming. Geophys. Res. Lett., 40, 2246–2251, doi:10.1002/grl.50232Google Scholar
Goodell, J., 2018: Hothouse earth is merely the beginning of the end. Rolling Stone, August 9, www.rollingstone.com/politics/politics-features/hothouse-earth-climate-change-709470/Google Scholar
Goodell, J., 2021: Now is our last best chance to confront the climate crisis. Rolling Stone, April 14, www.rollingstone.com/politics/politics-features/climate-crisis-2050-goals-biden-administration-1154528/Google Scholar
Grant, W. J., 2016: CSIRO needs to tackle the impact of climate change following its jobs shake-up. TheConversation.com, February 4, https://theconversation.com/csiro-needs-to-tackle-the-impact-of-climate-change-following-its-jobs-shake-up-54176Google Scholar
Griffies, S. M., Stouffer, R. J., Adcroft, A. J., et al., 2015: A historical introduction to MOM. NOAA/GFDL, www.gfdl.noaa.gov/wp-content/uploads/2019/04/mom_history_2017.09.19.pdfGoogle Scholar
Grossman, D., 2016: Why our intuition about sea-level rise is wrong. Nautilus, 33, February 18, http://nautil.us/issue/33/attraction/why-our-intuition-about-sea_level-rise-is-wrongGoogle Scholar
Grubler, A., and Nakicenovic, N., 2001: Identifying dangers in an uncertain climate. Nature, 412, 15Google Scholar
Guerrero, J. E., Sanguineti, M., and Wittkowski, K., 2020: Variable cant angle winglets for improvement of aircraft flight performance. Meccanica, 55, 1917–1947, doi:10.1007/s11012-020-01230-1Google Scholar
GWPF, 2020: Cost of “net zero” will be astronomical, new reports warn. The Global Warming Policy Foundation, February 24, www.thegwpf.org/cost-of-net-zero-will-be-ruinous-new-reports-warn/Google Scholar
Hagedorn, R., Doblas-Reyes, F., and Palmer, T. N., 2005: The rationale behind the success of multimodel ensembles in seasonal forecasting – I. Basic concept. Tellus A, 57, 219–233, doi:10.1111/j.1600-0870.2005.00103.xGoogle Scholar
Haigh, T., Priestley, M., and Rope, C., 2014: Los Alamos bets on ENIAC: nuclear Monte Carlo simulations, 1947–1948. IEEE Ann. Hist. Comput., 36(3), 42–63. doi:10.1109/MAHC.2014.40Google Scholar
Hallam, R., 2019: Common sense for the 21st century: only nonviolent rebellion can now stop climate breakdown and social collapse. Chelsea GreenGoogle Scholar
Halper, M., 2015: Supercomputing’s super energy needs, and what to do about them. Commun. ACM, September 24, https://m-cacm.acm.org/news/192296-supercomputings-super-energy-needs-and-what-to-do-about-them/fulltext?mobile=trueGoogle Scholar
Hansen, J., Fung, I., Lacis, A., et al., 1988: Global climate changes as forecast by Goddard Institute for Space Studies three-dimensional model. J. Geophys. Res., 93, 9341–9364Google Scholar
Hansen, J., Lacis, A., Ruedy, R., and Sato, M., 1992: Potential climate impact of Mount Pinatubo eruption. Geophys. Res. Lett., 19, 215–218Google Scholar
Hansen, J., Ruedy, R., Sato, M., and Lo, K., 2010: Global surface temperature change, Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345Google Scholar
Hargreaves, J. C., and Annan, J. D., 2014: Can we trust climate models? WIREs Clim. Change, 5, 435–440, doi:10.1002/wcc.288Google Scholar
Harper, K., Uccellini, L. W., Kalnay, E., Carey, K., and Morone, L., 2007: 50th anniversary of operational numerical weather prediction. Bull. Am. Meteorol. Soc., 88, 639–650, doi:10.1175/BAMS-88-5-639Google Scholar
Harris, D. C., 2010: Charles David Keeling and the story of atmospheric CO2 measurements. Anal. Chem., 82, 7865–7870Google Scholar
Hartnett, K., 2018: Machine learning confronts the elephant in the room. Quanta Magazine, September 20, www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/Google Scholar
Harvey, F., 2013: Joe Farman obituary. The Guardian, May 16, www.theguardian.com/environment/2013/may/16/joe-farmanGoogle Scholar
Hausfather, Z., 2017: Explainer: why the sun is not responsible for recent climate change. CarbonBrief.org, August 18, www.carbonbrief.org/why-the-sun-is-not-responsible-for-recent-climate-changeGoogle Scholar
Hausfather, Z., 2019: CMIP6: the next generation of climate models explained. CarbonBrief.org, December 2, www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explainedGoogle Scholar
Hausfather, Z., 2020a: CO2 emissions from fossil fuels may have peaked in 2019. The Breakthrough Institute, https://thebreakthrough.org/issues/energy/peak-co2-emissions-2019Google Scholar
Hausfather, Z., 2020b: Explainer: how the rise and fall of CO2 levels influenced the ice ages. CarbonBrief.org, July 2, www.carbonbrief.org/explainer-how-the-rise-and-fall-of-co2-levels-influenced-the-ice-agesGoogle Scholar
Hausfather, Z., and Betts, R., 2020: Analysis: how “carbon-cycle feedbacks” could make global warming worse. CarbonBrief.org, April 14, www.carbonbrief.org/analysis-how-carbon-cycle-feedbacks-could-make-global-warming-worseGoogle Scholar
Hausfather, Z., Drake, H. F., Abbott, T., and Schmidt, G. A., 2020: Evaluating the performance of past climate model projections. Geophys. Res. Lett., 47, e2019GL085378, doi:10.1029/2019GL085378Google Scholar
Hausfather, Z., and Peters, G. P., 2020: Emissions – the “business as usual” story is misleading. Nature, 577, 618–620Google Scholar
Hawkins, E., Smith, R. S., Gregory, J. M., and Stainforth, D. A., 2015: Irreducible uncertainty in near-term climate projections. Clim. Dynam., 46(11–12), 3807–3819, doi:10.1007/s00382-015-2806-8Google Scholar
Hawkins, E., and Sutton, R., 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc., 90, 1095–1107Google Scholar
Hawkins, E., and Sutton, R., 2012: Time of emergence of climate signals. Geophys. Res. Lett., 39, L01702Google Scholar
Hayhoe, K., 2015: Who brings what to the global potluck? Union of Concerned Scientists blog, December 15, https://blog.ucsusa.org/science-blogger/at-cop21-who-brings-what-to-the-global-potluckGoogle Scholar
Hayhoe, K., 2018: Does messaging with fear really work? Global Weirding Series, January 31, www.youtube.com/watch?v=AeqAoozVyfQGoogle Scholar
Heal, G., and Millner, A., 2014: Uncertainty and decision making in climate change economics. Rev. Environ. Econ. Pol., 8, 120–37Google Scholar
Held, I. M., 2005: The gap between simulation and understanding in climate modeling. Bull. Am. Meteorol. Soc., 86(11), 1609–1614Google Scholar
Held, I. M., 2011: Heat uptake and internal variability. Isaac Held’s Blog, August 23, gfdl.noaa.gov/blog_held/16-heat-uptake-and-internal-variabilityGoogle Scholar
Held, I. M., and Soden, B. J., 2006: Robust responses of the hydrological cycle to global warming. J. Clim., 19(21), 5686–5699CrossRefGoogle Scholar
Held, I. M., and Suarez, M. J., 1994: A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models. Bull. Am. Meteorol. Soc., 75(10), 1825–1830Google Scholar
Henderson, L., 2018: The problem of induction. Stanford encyclopedia of philosophy, https://plato.stanford.edu/entries/induction-problem/Google Scholar
Hennessy, J. L., and Patterson, D. A., 2019: A new golden age for computer architecture. Commun. ACM, 62(2), 48–60, doi:10.1145/3282307Google Scholar
Hersh, S., 1972: Rainmaking is used as a weapon by U.S. The New York Times, July 3, www.nytimes.com/1972/07/03/archives/rainmaking-is-used-as-weapon-by-us-cloudseeding-in-indochina-is.htmlGoogle Scholar
Hilborn, R. C., 2004: Sea gulls, butterflies, and grasshoppers: a brief history of the butterfly effect in nonlinear dynamics. Am. J. Phys., 72, 425, doi:10.1119/1.1636492Google Scholar
Hillebrand, H., Donohue, I., Harpole, W. S., et al., 2020: Thresholds for ecological responses to global change do not emerge from empirical data. Nat. Ecol. Evol., 4, 1502–1509, doi:10.1038/s41559-020-1256-9Google Scholar
Holloway, J. L., Holt, A. W., Mauchly, J. W., and Woodbury, M. A., 1955: Topics in statistical meteorology. Final report of the statistics project of the University of Pennsylvania. Office of Naval Research, contract Nonr 551(07), 24 pp.Google Scholar
Hora, S., 1996: Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliab. Eng. Syst. Safe., 54, 217–223Google Scholar
Hossenfelder, S., 2019: Has reductionism run its course? Backreaction blog, October 2, http://backreaction.blogspot.com/2019/10/has-reductionism-run-its-course.htmlGoogle Scholar
Hourdin, F., Mauritsen, T., Gettelman, A., et al., 2017: The art and science of climate model tuning. Bull. Am. Meteorol. Soc., 98, 589–602Google Scholar
Huddleston, A., 2019: Happy 200th birthday to Eunice Foote, hidden climate science pioneer. Climate.gov, July 17, www.climate.gov/news-features/features/happy-200th-birthday-eunice-foote-hidden-climate-science-pioneerGoogle Scholar
Hulme, M., 2006: Chaotic world of climate truth. BBC News, November 4, http://news.bbc.co.uk/2/hi/science/nature/6115644.stmGoogle Scholar
Hulme, M., 2007: The appliance of science. The Guardian, March 13, www.theguardian.com/society/2007/mar/14/scienceofclimatechange.climatechangeGoogle Scholar
Hulme, M., 2013: How climate models gain and exercise authority. In: Hastrup, K. and Skrydstrup, M., (eds.), The social life of climate change models: anticipating nature. New York, Routledge, 30–44Google Scholar
Hulme, M., 2019: Is it too late (to stop dangerous climate change)? An editorial. WIREs Clim. Change, 11, e619Google Scholar
Hulme, M., 2020: Fetishising “the number”: how not to govern pandemics, climate and biodiversity. MikeHulme.org, July 9, https://mikehulme.org/fetishising-the-number-how-not-to-govern-pandemics-climate-and-biodiversity/Google Scholar
Hunter, J., 2020: The “climate doomers” preparing for society to fall apart, BBC News, March 16, https://notalotofpeopleknowthat.wordpress.com/2020/03/17/the-climate-doomers-preparing-for-society-to-fall-apart/Google Scholar
Hutson, M., 2020: Why modeling the spread of COVID-19 is so damn hard, IEEE Spectrum, September 22, https://science.slashdot.org/story/20/09/25/2359231/why-modeling-the-spread-of-covid-19-is-so-damn-hardGoogle Scholar
Huybers, P., 2010: Compensation between model feedbacks and curtailment of climate sensitivity. J. Clim., 23, 3009–3018Google Scholar
IAS, 2013: Institute for Advanced Study: an introduction. Princeton University Press, www.ias.edu/files/pdfs/publications/IASBook.pdfGoogle Scholar
Ingleby, B., Rodwell, M., and Isaksen, L., 2016: Global radiosonde network under pressure. ECMWF Newsletter, 149, 25–30, www.ecmwf.int/en/newsletter/149/meteorology/global-radiosonde-network-under-pressureGoogle Scholar
Ip, G., 2020: Business shifts from resistance to action on climate. The Wall Street Journal, September 16, www.wsj.com/articles/business-shifts-from-resistance-to-action-on-climate-11600233503Google Scholar
IPCC, 1990: Climate change: the IPCC scientific assessment, Houghton, J. T., Jenkins, G. J., and Ephraums, J. J. (eds.). Cambridge University PressGoogle Scholar
IPCC, 1996: Summary for Policymakers. In: Climate change 1995 – the science of climate change. Contribution of working group I to the second assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 1–8Google Scholar
IPCC, 2001: Summary for Policymakers. In: Climate change 2001: the scientific basis, Houghton, J. T., Ding, Y., Griggs, D. J., et al. (eds.). Cambridge University Press, 1–20Google Scholar
IPCC, 2007: Summary for policymakers. In: Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 1–18Google Scholar
IPCC, 2013: Summary for policymakers. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 3–29Google Scholar
IPCC, 2014a: Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Geneva, IPCCGoogle Scholar
IPCC, 2014b: Summary for policymakers. In: Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 1–32Google Scholar
Jackson, C. S., Sen, M. K., Huerta, G., Deng, Y., and Bowman, K. P., 2008: Error reduction and convergence in climate prediction. J. Clim., 21, 6698–6709Google Scholar
Jackson, R., 2020: Eunice Foote, John Tyndall and a question of priority. Notes Rec., 74(1), 105–118Google Scholar
Jeevanjee, N., Hassanzadeh, P., Hill, S., and Sheshadri, A., 2017: A perspective on climate model hierarchies. J. Adv. Model. Earth Syst., 9, 1760–1771, doi:10.1002/2017MS001038Google Scholar
Jehl, D., 2004: Judging intelligence: the report; senators assail C.I.A. judgments on Iraq’s arms as deeply flawed. The New York Times, July 10, www.nytimes.com/2004/07/10/world/judging-intelligence-report-senators-assail-cia-judgments-iraq-s-arms-deeply.htmlGoogle Scholar
Kahn, D., 2020: California thought it could delay climate disaster. Now millions of acres are burning. Politico.com, October 8, www.politico.com/states/california/story/2020/10/08/california-thought-it-could-delay-climate-disaster-now-millions-of-acres-are-burning-1317641Google Scholar
Kandlikar, M., Risbey, J., and Dessai, S., 2005: Representing and communicating deep uncertainty in climate change assessments. C. R. Geosci., 337(4), 443–455Google Scholar
Karmalkar, A. V., Sexton, D. M. H., Murphy, J. M., Booth, B. B. B., Rostron, J. W., and McNeall, D. J., 2019: Finding plausible and diverse variants of a climate model. Part II: development and validation of methodology. Clim. Dynam., 53, 847–877, doi:10.1007/s00382-019-04617-3Google Scholar
Kaufman, N., Barron, A. R., Krawczyk, W., et al., 2020: A near-term to net zero alternative to the social cost of carbon for setting carbon prices. Nat. Clim. Change, 10, 1010–1014Google Scholar
Kay, J., and King, M., 2020: Radical uncertainty: decision-making beyond the numbers. NortonGoogle Scholar
Kay, J. E., Wall, C., Yettella, V., et al., 2016: Global climate impacts of fixing the Southern Ocean shortwave radiation bias in the Community Earth System Model (CESM). J. Clim., 29, 4617–4636, doi:10.1175/JCLI-D-15-0358.1Google Scholar
Keeling, C. D., 1979: The Suess Effect: 13carbon–14carbon interrelations. Environ. Int., 2, 229–300Google Scholar
Keeling, C. D., Bacastow, R. B., Bainbridge, A. E., et al., 1976: Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii. Tellus, 28, 6, 538–551Google Scholar
Keeling, C. D., Piper, S. C., Bacastow, R. B., et al., 2001: Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and oceans from 1978 to 2000. I. Global aspects. SIO reference series, No. 01–06. San Diego, Scripps Institution of OceanographyGoogle Scholar
Keith, D. W., 2000: Geoengineering the climate: history and prospect. Annu. Rev. Energy Environ., 25, 245–284, doi:10.1146/annurev.energy.25.1.245Google Scholar
Kerr, R. A., 1994: Climate modeling’s fudge factor comes under fire. Science, 265(5178), 1528Google Scholar
Kerr, R. A., 1997: Climate change: model gets it right – without fudge factors. Science, 276(5315), 1041, doi:10.1126/science.276.5315.1041Google Scholar
Kiehl, J., 2006: Geoengineering climate change: treating the symptom over the cause? Clim. Change, 77, 227–228Google Scholar
King, D., Schrag, D., Dadi, Z., Qui, Y., and Ghosh, A., 2015: Climate change: a risk assessment. Cambridge, Cambridge University Centre for Science and PolicyGoogle Scholar
Kirtman, B., Power, S. B., Adedoyin, J. A., et al., 2013: Near-term climate change: projections and predictability. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 953–1028Google Scholar
Knutson, T. R., and Tuleya, R. E., 2005: Reply to comments on “Impacts of CO2-induced warming on simulated hurricane intensity and precipitation: sensitivity to the choice of climate model and convective scheme.” J. Clim., 18, 5183–5187Google Scholar
Knutti, R., 2008a: Should we believe model predictions of future climate change? Philos. T. R. Soc. A, 366, 4647–4664Google Scholar
Knutti, R., 2008b: Why are climate models reproducing the observed global surface warming so well? Geophys. Res. Lett., 35, L18704Google Scholar
Knutti, R., 2018: Climate model confirmation: from philosophy to predicting climate in the real world. In: Lloyd, E. A. and Winsberg, E. (eds.), Climate modelling: philosophical and conceptual issues, Springer, 325–359, doi:10.1007/978-3-319-65058-6_11Google Scholar
Knutti, R., Baumberger, C., and Hirsch Hadorn, G., 2019: Uncertainty quantification using multiple models – prospects and challenges. In: Beisbart, C. and Saam, N. J. (eds.), Computer simulation validation: fundamental concepts, methodological frameworks, and philosophical perspectives. Springer, 835–855Google Scholar
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G., 2010: Challenges in combining projections from multiple climate models. J. Clim., 23(10), 2739–2758Google Scholar
Knutti, R., Rogelj, J., Sedlaček, J., et al., 2016: A scientific critique of the two-degree climate change target. Nat. Geosci., 9, 13–18Google Scholar
Knutti, R., Rugenstein, M., and Hegerl, G., 2017: Beyond equilibrium climate sensitivity. Nat. Geosci., 10, 727–736Google Scholar
Knutti, R., and Sedlaček, J., 2013: Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Clim. Change, 3, 369–373Google Scholar
Kolbert, E., 2005: The climate of man – II. The New Yorker, May 2, 64, www.newyorker.com/magazine/2005/05/02/the-climate-of-man-iiGoogle Scholar
Kolbert, E., 2006: Field notes from a catastrophe: man, nature, and climate change. BloomsburyGoogle Scholar
Koonin, S. E., 2014: Climate science is not settled. The Wall Street Journal, September 19, www.wsj.com/articles/climate-science-is-not-settled-1411143565Google Scholar
Korten, T., 2015: In Florida, officials ban term “climate change.” Miami Herald, March 8, www.miamiherald.com/news/state/florida/article12983720.htmlGoogle Scholar
Kozlov, M., 2021: The arctic has a cloud problem. The Atlantic, February 27, www.theatlantic.com/science/archive/2021/02/arctic-has-cloud-problem/618159/Google Scholar
Kunreuther, H., Gupta, S., Bosetti, V., et al, 2014: Integrated risk and uncertainty assessment of climate change response policies. In: Climate change 2014: mitigation of climate change. Contribution of working group III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 151–205Google Scholar
Kusunoki, S., and Arakawa, O., 2015: Are CMIP5 models better than CMIP3 models in simulating precipitation over East Asia? J. Clim., 28, 5601–5621, doi:10.1175/JCLI-D-14-00585.1Google Scholar
Lahsen, M., 2005: Seductive simulations? Uncertainty distribution around climate models. Soc. Stud. Sci., 35(6), 895–922Google Scholar
Lahsen, M., 2013: Anatomy of dissent: a cultural analysis of climate skepticism. Am. Behav. Sci., 57(6), 732–753Google Scholar
Lane, A., 2004: Cold comfort – The Day after Tomorrow. The New Yorker, June 7, www.newyorker.com/magazine/2004/06/07/cold-comfort-4Google Scholar
Lawrence, J., Haasnoot, M., and Lempert, R., 2020: Climate change: making decisions in the face of deep uncertainty. Nature, 580, 456Google Scholar
Lawrence, M. G., and Schäfer, S., 2019: Promises and perils of the Paris Agreement. Science, 364, 829–830Google Scholar
Le Treut, H., Somerville, R., Cubasch, U., et al., 2007: Historical overview of climate change. In: Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 93–127Google Scholar
Legates, D., 2020: How the EPA’s “endangerment finding” endangers you. Townhall.com, July 10, https://townhall.com/columnists/davidlegates/2020/07/10/how-the-epas-endangerment-finding-endangers-you-n2572275Google Scholar
Lenhard, J., and Winsberg, E., 2010: Holism, entrenchment, and the future of climate model pluralism. Stud. Hist. Philos. M. P., 41, 253–262Google Scholar
Lenssen, N., Schmidt, G., Hansen, J., et al., 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. – Atmos., 124, 12, 6307–6326, doi:10.1029/2018JD029522Google Scholar
Leonhardt, F., 1997: The committee to save the Tower of Pisa: a personal report. Struct. Eng. Int., 7(3), 201 – 212, doi:10.2749/101686697780494734Google Scholar
Lewandowsky, S., Ballard, T., and Pancost, R. D., 2015: Uncertainty as knowledge. Phil. Trans. R. Soc. A, 373, 20140462Google Scholar
Lewis, J. M., 2008: Smagorinsky’s GFDL: building the team. Bull. Am. Meteorol. Soc., 89(9), 1339–1353Google Scholar
Lewis, N., and Curry, J. A., 2015: The implications for climate sensitivity of AR5 forcing and heat uptake estimates. Clim. Dynam., 45, 1009–1023, doi:10.1007/s00382-014-2342-yGoogle Scholar
Lipman, D., 2014: D-Day at 70: the most important weather forecast in history. Washington Post, June 6, www.washingtonpost.com/news/capital-weather-gang/wp/2014/06/06/d-day-at-70-the-most-important-weather-forecast-in-the-history-of-the-world/Google Scholar
Live Science, 2020: How accurate are Punxsutawney Phil’s Groundhog Day forecasts? Live Science, www.livescience.com/32974-punxsutawney-phil-weather-prediction-accuracy.htmlGoogle Scholar
Lloyd, E. A., 2010: Confirmation and robustness of climate models. Philos. Sci., 77(5), 971–984Google Scholar
Loft, R., 2020: Earth system modeling must become more energy efficient. Eos, July 28, doi:10.1029/2020EO147051Google Scholar
Lohr, S., 2018: Move over, China: U.S. is again home to world’s speediest supercomputer. The New York Times, June 8, www.nytimes.com/2018/06/08/technology/supercomputer-china-us.htmlGoogle Scholar
Lomborg, B., 2020a: The Lockdown’s lessons for climate activism. The Wall Street Journal, July 11, www.wsj.com/articles/the-lockdowns-lessons-for-climate-activism-11594440061Google Scholar
Lomborg, B., 2020b: Welfare in the 21st century: increasing development, reducing inequality, the impact of climate change, and the cost of climate policies. Technol. Forecast. Soc., 156, 119981Google Scholar
Lombrana, L. M., Rathi, A., and Warren, H., 2020: It’s a race against heat, and humanity is losing. Bloomberg Green, September 8, www.bloomberg.com/graphics/2020-race-against-heat/Google Scholar
Lorenz, E., 2006: Reflections on the conception, birth, and childhood of numerical weather prediction. Annu. Rev. Earth Pl. Sc., 34, 37– 45Google Scholar
Lorenz, E. N., 1963b: The predictability of hydrodynamic flow. Trans. N. Y. Acad. Sci., 25, 409–432Google Scholar
Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 3, 290–307Google Scholar
Lorenz, E. N., 1975: Climate predictability. In: Bolin, B., Döös, B., Godson, W., Hasselmann, K., Kutzbach, J., and Sawyer, J. (eds.), The physical basis of climate and climate modelling, vol. 16, GARP Publication Series. Geneva, WMO, 132–136Google Scholar
Luft, J., and Ingham, H., 1955: The Johari window, a graphic model of interpersonal awareness. Proceedings of the Western Training Laboratory in Group Development. Los Angeles, UCLAGoogle Scholar
Lynch, P., 2007: The origins of computer weather prediction and climate modeling. J. Comput. Phys., doi:10.1016/j.jcp.2007.02.034Google Scholar
Lynch, P., 2008: The ENIAC forecasts: a re-creation. Bull. Am. Meteorol. Soc., 89, 45–55Google Scholar
Lynch, P., 2011: From Richardson to early numerical weather prediction. In: Donner, L., Schubert, W., and Somerville, R. (eds.), The development of atmospheric general circulation models: complexity, synthesis and computation. Cambridge University Press, 3–17Google Scholar
MacDougall, A. H., 2020: Is there warming in the pipeline? A multi-model analysis of the Zero Emissions Commitment from CO2. Biogeosciences, 17, 2987–3016Google Scholar
MacKenzie, D., 1998: The certainty trough. In: Williams, R. and Faulkner, W. (eds.), Exploring expertise: issues and perspectives. Palgrave Macmillan, 325–329Google Scholar
Mackey, R., 2009: Why Ahmadinejad voted against occupying the U.S. embassy in 1979. The New York Times, November 8, https://thelede.blogs.nytimes.com/2009/11/08/why-ahmadinejad-voted-against-occupying-the-us-embassy-in-1979/Google Scholar
Macrae, N., 1999: John Von Neumann: the scientific genius who pioneered the modern computer, game theory, nuclear deterrence, and much more. American Mathematical SocietyGoogle Scholar
Maher, P., Gerber, E. P., Medeiros, B., et al., 2019: Model hierarchies for understanding atmospheric circulation. Rev. Geophys., 57, 250–280, doi:10.1029/2018RG000607Google Scholar
Maher, N., Lehner, F., and Marotzke, J., 2020: Quantifying the role of internal variability in the temperature we expect to observe in the coming decades. Environ. Res. Lett., 15, 054014Google Scholar
Manabe, S., 2019: Role of greenhouse gas in climate change. Tellus A: Dynamic Meteorology and Oceanography, 71, 1, 1620078, doi:10.1080/16000870.2019.1620078Google Scholar
Manabe, S., and Broccoli, A. J., 2020: Beyond global warming: how numerical models revealed the secrets of climate change. Princeton University PressGoogle Scholar
Manabe, S., Bryan, K., and Spelman, M. J., 1975: A global ocean-atmosphere climate model. Part I. The atmospheric circulation. J. Phys. Oceanogr., 5, 3–29Google Scholar
Mankin, J. S., Lehner, F., Coats, S., and McKinnon, K. A., 2020: The value of initial condition large ensembles to robust adaptation decision-making. Earth’s Future, 8, e2012EF001610Google Scholar
Mann, M. E., 2020: The story about the “business as usual” story is misleading. MichaelMann.net, January 29, https://michaelmann.net/content/story-about-%E2%80%98business-usual%E2%80%99-story-misleadingGoogle Scholar
Mann, M. E., Hassol, S. J., and Toles, T., 2017: Doomsday scenarios are as harmful as climate change denial. Washington Post, July 12, www.washingtonpost.com/opinions/doomsday-scenarios-are-as-harmful-as-climate-change-denial/2017/07/12/880ed002-6714-11e7-a1d7-9a32c91c6f40_story.htmlGoogle Scholar
Markow, T. A., 2015: The natural history of model organisms: the secret lives of Drosophila flies. eLife, 4, e06793, doi:10.7554/eLife.06793Google Scholar
Marvel, K., 2018: Thinking about climate on a dark, dismal morning. Scientific American blogs, December 25, https://blogs.scientificamerican.com/hot-planet/thinking-about-climate-on-a-dark-dismal-morning/Google Scholar
Marvel, K., Pincus, R., Schmidt, G. A., and Miller, R. L., 2018: Internal variability and disequilibrium confound estimates of climate sensitivity from observations. Geophys. Res. Lett., 45, 1595–1601, doi:10.1002/2017GL076468Google Scholar
Maslin, M. A., and Austin, P., 2012: Uncertainty: climate models at their limit? Nature, 486, 183–184Google Scholar
Matthews, H. D., and Caldeira, K., 2008: Stabilizing climate requires near-zero emissions. Geophys. Res. Lett., 35, L04705, doi:10.1029/2007GL032388Google Scholar
Mauchly, J. W., 1973: John W. Mauchly (1907–1980) interview: February 6, 1973, Computer Oral History Collection, Smithsonian Institution, https://amhistory.si.edu/archives/AC0196_mauc700622.pdfGoogle Scholar
Mauritsen, T., Stevens, B., Roeckner, E., et al., 2012: Tuning the climate of a global model. J. Adv. Model. Earth Syst., 4, M00A01, doi:10.1029/2012MS000154.4Google Scholar
McAlpine, F., 2015: Never mind the groundhogs: happy Hedgehog Day! BBC America, www.bbcamerica.com/anglophenia/2015/02/never-mind-groundhogs-happy-hedgehog-dayGoogle Scholar
McGrath, M., 2018: Caution urged over use of “carbon unicorns” to limit warming, BBC, October 5, www.bbc.com/news/science-environment-45742191Google Scholar
McKenna, S., Santoso, A., Sen Gupta, A., Taschetto, A. S., and Cai, W., 2020: Indian Ocean dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO. Sci. Rep., 10, 11500Google Scholar
McLaren, D., and Markusson, N., 2020: The co-evolution of technological promises, modelling, policies and climate change targets. Nat. Clim. Change, 10, 392–397Google Scholar
McSweeney, R., 2020: Explainer: nine “tipping points” that could be triggered by climate change. CarbonBrief.org, February 10, www.carbonbrief.org/explainer-nine-tipping-points-that-could-be-triggered-by-climate-changeGoogle Scholar
McWilliams, J. C., 2007: Irreducible imprecision in atmospheric and oceanic simulations. Proc. Nat. Acad. Sci., 104, 8709–8713, doi:10.1073/pnas.0702971104Google Scholar
Meehl, G. A., Stocker, T. F., Collins, W. D., et al., 2007: Global climate projections. In: Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 747–845Google Scholar
Meehl, G. A., Senior, C. A., Eyring, V., et al., 2020: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv., 6(26), eaba1981, doi:10.1126/sciadv.aba1981Google Scholar
Mengel, M. A. Nauels, J. Rogelj, , et al., 2018: Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action. Nat. Commun., 9, 601Google Scholar
Michaels, P., and Maue, R., 2018: Thirty years on, how well do global warming predictions stand up? The Wall Street Journal, June 21, www.wsj.com/articles/thirty-years-on-how-well-do-global-warming-predictions-stand-up-1529623442Google Scholar
Milinski, S., Maher, N., and Olonscheck, D., 2020: How large does a large ensemble need to be? Earth Syst. Dynam., 11, 885–901, doi:10.5194/esd-11-885-2020Google Scholar
Mill, J. S., 1843: A system of logic, ratiocinative and inductive. London, J. W. ParkerGoogle Scholar
Miller, R. L., Schmidt, G. A., Nazarenko, L. S., et al., 2014: CMIP5 historical simulations (1850–2012) with GISS ModelE2. J. Adv. Model. Earth Syst., 6, 441–478Google Scholar
Milly, P. C. D., Betancourt, J., Falkenmark, M., et al., 2008: Stationarity is dead: whither water management? Science, 319(5863), 573–574, doi:10.1126/science.1151915Google Scholar
Miner, S., 2018: Newton didn’t frame hypotheses. Why should we? Physics Today, April 24, doi:10.1063/PT.6.3.20180424aGoogle Scholar
Mirowski, P., 1992: Do economists suffer from physics envy? Finn. Econ. Pap., 5(1), 61–68Google Scholar
Mitchell, J. F. B., Manabe, S., Meleshko, V., and Tokioka, T., 1990: Equilibrium climate change – and its implications for the future. In: Houghton, J. T., Jenkins, G. J., and Ephraums, J. J. (eds.), Climate change: the IPCC scientific assessment. Cambridge University Press, 131–172Google Scholar
Molina, M., and Rowland, F., 1974: Stratospheric sink for chlorofluoromethanes: chlorine atom-catalysed destruction of ozone. Nature, 249, 810–812, doi:10.1038/249810a0Google Scholar
Moore, S., 2018: Follow the (climate change) money. The Heritage Foundation, Commentary, December 18, www.heritage.org/environment/commentary/follow-the-climate-change-moneyGoogle Scholar
Moss, R., Edmonds, J., Hibbard, K., et al., 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747–756Google Scholar
Nakićenović, N., Alcamo, J., Davis, G., et al., 2000: Special report on emissions scenarios: a special report of the Intergovernmental Panel on Climate Change. Cambridge, Cambridge University PressGoogle Scholar
NASA, 2004: How did navigators hit their precise landing target on mars? NASA Mars Exploration Rovers Spotlight, https://mars.nasa.gov/mer/spotlight/navTarget01.htmlGoogle Scholar
NASEM, 2016: Attribution of extreme weather events in the context of climate change. National Academies PressGoogle Scholar
NASEM, 2019: Negative emissions technologies and reliable sequestration: a research agenda. National Academies PressGoogle Scholar
NCEI, 2017: Groundhog Day forecasts and climate history. National Centers for Environmental Information, February 2, www.ncei.noaa.gov/news/groundhog-day-forecasts-and-climate-historyGoogle Scholar
Nielsen-Gammon, J., Escobedo, J., Ott, C., Dedrick, J., and Van Fleet, A., 2020: Assessment of historic and future trends of extreme weather in Texas, 1900–2036. Office of the Texas State Climatologist, https://climatexas.tamu.edu/products/texas-extreme-weather-report/index.htmlGoogle Scholar
Nissan, H., Goddard, L., Coughlan de Perez, E., et al., 2019: On the use and misuse of climate change projections in international development. WIREs Clim. Change, 10, e579Google Scholar
Nissan, H., Muñoz, A. G., and Mason, S. J., 2020: Targeted model evaluations for climate services: a case study on heat waves in Bangladesh. Clim. Risk Manag., 28, 100213Google Scholar
Noerdlinger, P. D., and Brower, K. R., 2007: The melting of floating ice raises the ocean level. Geophys. J. Int., 170(1), 145–150, doi:10.1111/j.1365-246X.2007.03472.xGoogle Scholar
Nordhaus, W., 2007a: The challenge of global warming: economic models and environmental policy. New Haven, CT, Yale University PressGoogle Scholar
Nordhaus, W., 2007b: The Stern review on the economics of climate change. New Haven, CT, Yale University PressGoogle Scholar
Nordhaus, W., 2015: Climate clubs: overcoming free-riding in international climate policy. Am. Econ. Rev., 105(4), 1339–1370, doi:10.1257/aer.15000001Google Scholar
Nordhaus, W. D., 2017: Revisiting the social cost of carbon. Proc. Natl. Acad. Sci., 114(7), 1518–1523, doi:10.1073/pnas.1609244114Google Scholar
Norman, J., Read, R., Bar-Yam, Y., and Taleb, N. N., 2015: Climate models and precautionary measures. Issues Sci. Technol., 31(4), 1Google Scholar
NOVA, 1999: Fall of the leaning tower. NOVA, October 5, PBS.org, www.pbs.org/wgbh/nova/pisa/interventions2.htmlGoogle Scholar
Novak, M., 2011: Weather control as a cold war weapon. Smithsonian Magazine, December 5, www.smithsonianmag.com/history/weather-control-as-a-cold-war-weapon-1777409/Google Scholar
NRC, 2015: Climate intervention: reflecting sunlight to cool Earth. National Academies PressGoogle Scholar
Nuñez, R. E., and Sweetser, E., 2006: With the future behind them: convergent evidence from Aymara language and gesture in the crosslinguistic comparison of spatial construals of time. Cogn. Sci., 30, 401–450Google Scholar
O’Gorman, P. A., and Dwyer, J. G., 2018: Using machine learning to parameterize moist convection: potential for modeling of climate, climate change, and extreme events. J. Adv. Model. Earth Syst., 10, 2548–2563Google Scholar
O’Neill, S., and Nicholson-Cole, S., 2009: “Fear won’t do it”: promoting positive engagement with climate change through visual and iconic representations. Sci. Commun., 30, 355Google Scholar
O’Raifeartaigh, C., 2017: Einstein’s greatest blunder? Scientific American Guest Blog, February 21, https://blogs.scientificamerican.com/guest-blog/einsteins-greatest-blunder/Google Scholar
Oestreicher, C., 2007: A history of chaos theory. Dialogues Clin. Neurosci., 9(3), 279–289Google Scholar
Oppenheimer, M., 2007: How the IPCC got started. Environmental Defense Fund blogs, November 1, http://blogs.edf.org/climate411/2007/11/01/ipcc_beginnings/Google Scholar
Oppenheimer, M., O’Neill, B. C., and Webster, M., 2008: Negative learning. Clim. Change, 89, 155–117Google Scholar
Oreskes, N., 2007: The scientific consensus on climate change: how do we know we’re not wrong? In: DiMento, J. F. and Doughman, P. (eds.), Climate change: what it means for us, our children, and our grandchildren. MIT Press, 90Google Scholar
Oreskes, N., and Belitz, K., 2001: Philosophical issues in model assessment. In: Anderson, M. and Bates, P. (eds.), Model validation: perspectives in hydrological science. West Sussex, Wiley, 23–42Google Scholar
Oreskes, N., and Conway, E. M., 2011: Merchants of doubt: how a handful of scientists obscured the truth on issues from tobacco smoke to climate change. Bloomsbury PublishingGoogle Scholar
Oreskes, N., Shrader-Frechette, K., and Belitz, K., 1994: Verification, validation, and confirmation of numerical models in earth sciences. Science, 263, 641–646Google Scholar
Oreskes, N., Smith, L., and Stainforth, D., 2010: Adaptation to global warming: do climate models tell us what we need to know? Philos. Sci., 77, 1012–1028Google Scholar
Ortiz, J. D., and Jackson, R., 2020: Understanding Eunice Foote’s 1856 experiments: heat absorption by atmospheric gases. Notes Rec., doi:10.1098/rsnr.2020.0031Google Scholar
Otto, F. E. L., Frame, D., Otto, A., and Allen, M. R., 2015: Embracing uncertainty in climate change policy. Nat. Clim. Change, 5, 917–920, doi:10.1038/nclimate2716Google Scholar
Overbye, D., 2017: The eclipse that revealed the universe. The New York Times, July 31, www.nytimes.com/2017/07/31/science/eclipse-einstein-general-relativity.htmlGoogle Scholar
Palm, R., Bolsen, T., and Kingsland, J. T., 2020: “Don’t tell me what to do”: resistance to climate change messages suggesting behavior changes. Weather Clim. Soc., 12, 827–835, doi:10.1175/WCAS-D-19-0141.1Google Scholar
Palmer, B., 2012: Global warming would harm the earth, but some areas might find it beneficial. Washington Post, January 23, www.washingtonpost.com/national/health-science/global-warming-would-harm-the-earth-but-some-areas-might-find-it-beneficial/2012/01/17/gIQAbXwhLQ_print.htmlGoogle Scholar
Palmer, T., 2008: Obituary – Edward Norton Lorenz. WMO Bulletin, 57(3), https://public.wmo.int/en/bulletin/obituary-1Google Scholar
Palmer, T., 2016: A personal perspective on modelling the climate system. Proc. R. Soc. A, 472, 20150772Google Scholar
Palmer, T., Döring, A., and Seregin, G., 2014: The real butterfly effect. Nonlinearity, 27, R123Google Scholar
Palmer, T., and Stevens, B., 2019: The scientific challenge of understanding and estimating climate change. Proc. Natl. Acad. Sci., 116 (49) 24390–24395Google Scholar
Palmer, T., Stevens, B., and Bauer, P., 2019: We need an international center for climate modeling. Scientific American, December 18, https://blogs.scientificamerican.com/observations/we-need-an-international-center-for-climate-modeling/Google Scholar
Palmer, T. N., 2009: Edward Norton Lorenz 23 May 1917 – 16 April 2008. Biogr. Mems. Fell. R. Soc., 55, 139–155, doi:10.1098/rsbm.2009.0004Google Scholar
Panosetti, D., Schlemmer, L., and Schär, C., 2019: Bulk and structural convergence at convection-resolving scales in real-case simulations of summertime moist convection over land. Q. J. R. Meteorol. Soc., 145, 1427–1443Google Scholar
Parker, W. S., 2006: Understanding pluralism in climate modeling. Found. Sci, 11, 349–368Google Scholar
Parker, W. S., 2010: Predicting weather and climate: uncertainty, ensembles and probability. Stud. Hist. Philos. M. P., 41, 263–272Google Scholar
Parker, W. S., and Risbey, J. S., 2015: False precision, surprise and improved uncertainty assessment. Phil. Trans. R. Soc. A, 373, 20140453Google Scholar
Pearce, F., 2010: Victory for openness as IPCC climate scientist opens up lab doors. The Guardian, February 9, www.theguardian.com/environment/2010/feb/09/ipcc-report-author-data-opennessGoogle Scholar
Persson, A., 2005: Early operational numerical weather prediction outside the USA: an historical introduction. Part 1: Internationalism and engineering NWP in Sweden, 1952–69. Meteorol. Appl., 12, 135–159, doi:10.1017/S1350482705001593Google Scholar
Peterson, T. C., Connolley, W. M., and Fleck, J., 2008: The myth of the 1970s global cooling consensus. Bull. Am. Meteorol. Soc., 89, 1325–1338Google Scholar
Philip, S., Kew, S., van Oldenborgh, G. J., et al., 2020: A protocol for probabilistic extreme event attribution analyses. Adv. Stat. Clim. Meterol. Oceanogr., 6, 177–203, doi:10.5194/ascmo-6-177-2020Google Scholar
Phillips, L., 2018: Turbulence, the oldest unsolved problem in physics. Ars Technica, October 10, https://arstechnica.com/science/2018/10/turbulence-the-oldest-unsolved-problem-in-physics/Google Scholar
Phillips, N. A., 1982: Jule Charney’s influence on meteorology. Bull. Am. Meteorol. Soc., 63(5), 492–498Google Scholar
Phillips, N. A., 2000: The start of numerical weather prediction in the United States. In: Spekat, A. (ed), 50th anniversary of numerical weather prediction. Commemorative symposium. Deutsche Meteorologische Gesellschaft, 13–28Google Scholar
Pierrehumbert, R. T, 2014: Climate science is settled enough. Slate, October 1, https://slate.com/technology/2014/10/the-wall-street-journal-and-steve-koonin-the-new-face-of-climate-change-inaction.htmlGoogle Scholar
Pierrehumbert, R. T., Brogniez, H., and Roca, R., 2007: On the relative humidity of the atmosphere. In: Schneider, T. and Sobel, A. H. (eds.), The global circulation of the atmosphere. Princeton University Press, 143–185Google Scholar
Pinker, S., 1994: The game of the name, op-ed. The New York Times, April 5, www.nytimes.com/1994/04/05/opinion/the-game-of-the-name.htmlGoogle Scholar
Pipitone, J., and Easterbrook, S., 2012: Assessing climate model software quality: a defect density analysis of three models. Geosci. Model Dev., 5, 1009–1022, doi:10.5194/gmd-5-1009-2012Google Scholar
Pisa Committee, 2002: Safeguard and stabilisation of the Leaning Tower of Pisa 1990–2001. The International Committee for the Safeguard of the Tower of Pisa. In: Estrategias relativas al patrimonio cultural mundial. La salvaguarda en un mundo globalizado. Principios, practicas y perspectivas. 13th ICOMOS General Assembly and Scientific Symposium. Actas. Comité Nacional Español del ICOMOS, Madrid, pp. 199–207, http://openarchive.icomos.org/id/eprint/576/Google Scholar
Platzman, G., 1987: Conversations with Jule Charney, NCAR Technical Note, NCAR/TN-298+PROC, November, 127Google Scholar
Platzman, G. W., 1979: The ENIAC computation of 1950 – gateway to numerical weather prediction. Bull. Am. Meteorol. Soc., 60(4), 302–312Google Scholar
Plumer, B., and Schwartz, J., 2020: These changes are needed amid worsening wildfires, experts say. The New York Times, September 10, www.nytimes.com/2020/09/10/climate/wildfires-climate-policy.htmlGoogle Scholar
Po-Chedley, S. C. Proistosescu, K. C. Armour, , et al., 2018: Climate constraint reflects forced signal. Nature, 563, E6–E9Google Scholar
Poincaré, H., 1899: Les Methodes Nouvelles de Ia Mécanique Celeste, vols. 1–3. Paris, Gauthier VillarsGoogle Scholar
Polvani, L. M., Clement, A. C., Medeiros, B., Benedict, J. J., and Simpson, I. R., 2017: When less is more: opening the door to simpler climate models. Eos, 98, doi:10.1029/2017EO079417Google Scholar
Popper, K., 1935: Logik der Forschung. Wien, J. Springer. Translated by Popper as The Logic of Scientific Discovery, London, Hutchinson, 1959Google Scholar
Prono, L., 2008: Smagorinsky, Joseph (1924–2005). In: Philander, S. G. (ed.), Encyclopedia of global warming and climate change. SAGE, 903–903, doi:10.4135/9781412963893.n583Google Scholar
Puko, T., 2020: EPA proposes emissions limits for jet aircraft. The Wall Street Journal, July 22, www.wsj.com/articles/epa-proposes-emissions-limits-for-jet-aircraft-11595429280Google Scholar
Qiu, L., 2018: The baseless claim that climate scientists are “driven” by money. The New York Times, November 27, www.nytimes.com/2018/11/27/us/politics/climate-report-fact-check.htmlGoogle Scholar
Rahmstorf, S., 2013: Sea level in the 5th IPCC report. RealClimate.org, October 15, www.realclimate.org/index.php/archives/2013/10/sea-level-in-the-5th-ipcc-report/Google Scholar
Rainey, J., 2017: How forecasters nailed Harvey’s massive rain dump. NBC News, August 31, www.nbcnews.com/news/us-news/how-forecasters-nailed-harvey-s-massive-rain-dump-n797506Google Scholar
Ramanathan, V., and Vogelmann, A. W., 1997: Greenhouse effect, atmospheric solar absorption and the earth’s radiation budget: from the Arrhenius/Langley era to the 1990s. Ambio, 26(1), 38–46Google Scholar
Ramaswamy, V., Boucher, O., Haigh, J., et al., 2001: Radiative forcing of climate change. In: Houghton, J. T., Ding, Y., Griggs, D. J., et al. (eds.), Climate change 2001: the scientific basis. Cambridge University Press, 349–416Google Scholar
Randers, J., and Goluke, U., 2020: An earth system model shows self-sustained melting of permafrost even if all man-made GHG emissions stop in 2020. Sci. Rep., 10, 18456, doi:10.1038/s41598-020-75481-zGoogle Scholar
Rasmussen, J. L., 1992: The world weather watch – a view of the future. Bull. Am. Meteorol. Soc., 73(4), 477–481Google Scholar
Rasool, S. I., and Schneider, S. H., 1971: Atmospheric carbon dioxide and aerosols: effects of large increases on global climate. Science, 173(3992), 138–141Google Scholar
Ravilious, K., 2018: Thirty years of the IPCC. Physics World, October 8, https://physicsworld.com/a/thirty-years-of-the-ipcc/Google Scholar
Rayner, S., 2012: Uncomfortable knowledge: the social construction of ignorance in science and environmental policy discourses. Econ. Soc., 41(1), 107–125Google Scholar
Readfearn, G., 2018: Earth’s climate monsters could be unleashed as temperatures rise. The Guardian, October 5, www.theguardian.com/environment/planet-oz/2018/oct/06/earths-climate-monsters-could-be-unleashed-as-temperatures-riseGoogle Scholar
Reed, B. C., 2015: Kilowatts to kilotons: wartime electricity use at Oak Ridge. History of Physics Newsletter, XII(6), Spring, https://engage.aps.org/fhp/resources/newsletters/newsletter-archive/spring-2015Google Scholar
Reichstein, M., Camps-Valls, G., Stevens, B., et al., 2019: Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195–204Google Scholar
Revkin, A., 2014: Certainties, uncertainties and choices with global warming. Dot Earth, New York Times blog, September 26, https://dotearth.blogs.nytimes.com/2014/09/26/certainties-uncertainties-and-choices-with-global-warming/Google Scholar
Revkin, A., 2017: There are lots of climate uncertainties. Let’s acknowledge and plan for them with honesty. Propublica.org, May 2, www.propublica.org/article/climate-change-uncertainties-bret-stephens-columnGoogle Scholar
Revkin, A. C., 2005: The daily planet: why the media stumble over the environment. In: Blum, D., Knudson, M., and Henig, R. M., (eds.), A field guide for science writers. Oxford University Press, 222–228Google Scholar
Riahi, K., van Vuuren, D. P., Kriegler, E., et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change, 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009Google Scholar
Rich, N., 2018: Losing Earth: the decade we almost stopped climate change. The New York Times Magazine, August 1, www.nytimes.com/interactive/2018/08/01/magazine/climate-change-losing-earth.html?mtrref=www.google.com&gwh=F6DE46AFCA439A96F422E63B47140895&gwt=regi&assetType=REGIWALLGoogle Scholar
Risbey, J., 2008: The new climate discourse: alarmist or alarming? Glob. Environ. Change, 18(1), 26–37Google Scholar
Risbey, J. S., 2004: Agency and the assignment of probabilities to greenhouse emissions scenarios. Clim. Change, 67, 37–42Google Scholar
Risbey, J. S., and Kandlikar, M., 2007: Expressions of likelihood and confidence in the IPCC uncertainty assessment process, Clim. Change, 85, 19–31Google Scholar
Roberts, D., 2012: In a climate-crazed world, how can we plan for the future? Grist, September 28, https://grist.org/climate-energy/in-a-climate-crazed-world-how-can-we-plan-for-the-future/Google Scholar
Robock, A., 2008: 20 reasons why geoengineering may be a bad idea. Bull. Atomic Sci., 64(2), 14–18, doi:10.2968/064002006Google Scholar
Rodhe, H., Charlson, R., and Crawford, E., 1997: Svante Arrhenius and the greenhouse effect. Ambio, 26(1), 2–5Google Scholar
Roe, G., 2013: Costing the Earth: a numbers game or a moral imperative? Weather Clim. Soc., 5(4), 378–380, doi:10.1175/WCAS-D-12-00047.1Google Scholar
Roe, G. H., and Bauman, Y., 2012: Climate sensitivity: should the climate tail wag the policy dog? Clim. Change, 117, 647–662Google Scholar
Rogelj, J., Meinshausen, M., Sedlacek, J., and Knutti, R., 2014: Implications of potentially lower climate sensitivity on climate projections and policy. Environ. Res. Lett., 9, 031003Google Scholar
Rogers, J. S., 1984: Planning models are for insight not numbers: a complementary modelling approach. In: Lev, B., (ed.), Analytic techniques for energy planning. Elsevier Science, 67–81Google Scholar
Rohr, M., 2016: Great storm of 1900 brought winds of change. Houston Chronicle, May 19, www.houstonchronicle.com/local/history/article/Great-Storm-of-1900-brought-winds-of-change-7724171.phpGoogle Scholar
Roker, A., 2015: Blown away: Galveston hurricane, 1900. HistoryNet, October, www.historynet.com/blown-away.htmGoogle Scholar
Roston, E., and Migliozzi, B., 2015: What’s really warming the world? Bloomberg Businessweek, June 24, www.bloomberg.com/graphics/2015-whats-warming-the-world/Google Scholar
Rowland, F. S., 2009: Stratospheric ozone depletion. In: Zerefos, C., Contopoulos, G., and Skalkeas, G. (eds.), Twenty years of ozone decline. Springer, 23–66Google Scholar
Rypdal, M., Fredriksen, H., Rypdal, K., et al., 2018: Emergent constraints on climate sensitivity. Nature, 563, E4–E5Google Scholar
Salvia, S., 2017: From Archimedean hydrostatics to post-Aristotelian mechanics: Galileo’s early manuscripts De motu antiquiora (ca. 1590). Phys. Perspect., 19, 105–150, doi:10.1007/s00016-017-0202-yGoogle Scholar
Sanderson, B. M., and Knutti, R., 2012: On the interpretation of constrained climate model ensembles. Geophys. Res. Lett., 39(16), L16708Google Scholar
Sarewitz, D., and Pielke, R. Jr, 1999: Prediction in science and policy. Technol. Soc., 21, 121–133Google Scholar
Schär, C., Fuhrer, O., Arteaga, A., et al., 2020: Kilometer-scale climate models: prospects and challenges. Bull. Am. Meteorol. Soc., 101, E567–E587, doi:10.1175/BAMS-D-18-0167.1Google Scholar
Schmidt, G., 2007a: Hansen’s 1988 projections. RealClimate.org, May 15, www.realclimate.org/index.php/archives/2007/05/hansens-1988-projections/Google Scholar
Schmidt, G., 2007b: The physics of climate modeling. Physics Today, 60(1), 72, doi:10.1063/1.2709569Google Scholar
Schmidt, G., 2014: The emergent patterns of climate change. TED Talk, March, www.ted.com/talks/gavin_schmidt_the_emergent_patterns_of_climate_changeGoogle Scholar
Schmidt, G., 2017: What did NASA know? and when did they know it? RealClimate.org, December 24, www.realclimate.org/index.php/archives/2017/12/what-did-nasa-know-and-when-did-they-know-it/Google Scholar
Schmidt, G., 2018: 30 years after Hansen’s testimony. RealClimate.org, June 21, www.realclimate.org/index.php/archives/2018/06/30-years-after-hansens-testimony/Google Scholar
Schmidt, G., 2019: The best case for worst case scenarios. RealClimate.org, February 26, www.realclimate.org/index.php/archives/2019/02/the-best-case-for-worst-case-scenarios/Google Scholar
Schmidt, G., 2020: Unknowability in climate science: chaos, structure, and society. Social Research: An International Quarterly, 87(1), 133–149, www.muse.jhu.edu/article/758637Google Scholar
Schmidt, G., Bader, D., Donner, L. J., et al., 2017: Practice and philosophy of climate model tuning across six US modeling centers. Geosci. Model Dev., 10, 3207–3223, doi:10.5194/gmd-10-3207-2017Google Scholar
Schmidt, G., and Sherwood, S., 2015: A practical philosophy of complex climate modelling. Eur. J. Phil. Sci., 5(2), 149–169Google Scholar
Schmitt, H. H., and Happer, W., 2013: In defense of carbon dioxide. The Wall Street Journal, May 8, www.wsj.com/articles/SB10001424127887323528404578452483656067190Google Scholar
Schneider, S. H., 2002: Can we estimate the likelihood of climatic changes at 2100? Clim. Change, 52, 441–451Google Scholar
Schneider, T., Lan, S., Stuart, A., and Teixeira, J., 2017b: Earth system modeling 2.0: a blueprint for models that learn from observations and targeted high-resolution simulations. Geophys. Res. Lett., 44, 12396–12417Google Scholar
Schneider, T., Teixeira, J., Bretherton, C., et al., 2017a: Climate goals and computing the future of clouds. Nat. Clim. Change, 7, 3–5, doi:10.1038/nclimate3190Google Scholar
Schneider-Mayerson, M., and Leong, K. L., 2020: Eco-reproductive concerns in the age of climate change. Clim. Change, 163, 1007–1023Google Scholar
Schoeberl, M. R., and Rodriguez, J. M., 2009: The rise and fall of dynamical theories of the ozone hole. In: Zerefos, C., Contopoulos, G., and Skalkeas, G. (eds.), Twenty years of ozone decline. Springer, 263–272. doi:10.1007/978-90-481-2469-5_19Google Scholar
Schrödinger, E., 1935: Die gegenwärtige Situation in der Quantenmechanik. Naturwissenschaften, 23, 807–812Google Scholar
Schulthess, T. C., Bauer, P., Wedi, N., Fuhrer, O., Hoefler, T., and Schär, C., 2019: Reflecting on the goal and baseline for exascale computing: a roadmap based on weather and climate simulations. Comput. Sci. Eng., 21(1), 30–41, doi:10.1109/MCSE.2018.2888788Google Scholar
Schwalm, C. R., Glendon, S., and Duffy, P. B., 2020: RCP8.5 tracks cumulative CO2 emissions. Proc. Natl. Acad. Sci., 117(33), 19656–19657Google Scholar
Scientific American, 1856: Scientific ladies. – Experiments with condensed gases. Scientific American, 12(1), (Sep. 13, 1856), 5Google Scholar
Scientific Reports, 2020: Climate change: ending greenhouse gas emissions may not stop global warming. EurekaAlert! AAAS, November 12, www.eurekalert.org/pub_releases/2020-11/sr-cce110520.phpGoogle Scholar
Scientists, 2016: An open letter to the Australian Government and CSIRO, February 11, www.australasianscience.com.au/article/issue-janfeb-2016/open-letter-australian-government-and-csiro.htmlGoogle Scholar
Shackley, S., and Wynne, B., 1995: Integrating knowledges for climate change: pyramids, nets and uncertainties. Glob. Environ. Change, 5(2), 113–126Google Scholar
Shalett, B., 1946: Electronics to aid weather figuring. The New York Times, January 11, www.nytimes.com/1946/01/11/archives/electronics-to-aid-weather-figuring-scientists-tell-weather-bureau.htmlGoogle Scholar
Shaw, B., 2017: Weather forecasting: how does it work, and how reliable is it? PrecisionAg.com, November 14, www.precisionag.com/digital-farming/data-management/weather-forecasting-how-does-it-work-and-how-reliable-is-it/Google Scholar
Shaw, B., 2018: Betting on rain? The accuracy and reliability of precipitation forecasts. PrecisionAg.com, January 16, www.precisionag.com/digital-farming/data-management/betting-on-rain-the-accuracy-and-reliability-of-precipitation-forecasts/Google Scholar
Shaw, G. K., 2019: On ENIAC’s anniversary, a nod to its female “computers.” Penn Today, February 14, https://penntoday.upenn.edu/news/eniacs-anniversary-nod-its-female-computersGoogle Scholar
Shepherd, T. G., Boyd, E., Calel, R. A., et al., 2018: Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Clim. Change, 151, 555–557Google Scholar
Shepherd, T. G., and Sobel, A. G., 2020: Localness in climate change. Comparative Studies of South Asia, Africa and the Middle East, 40(1), 7–16, doi:10.1215/1089201X-8185983Google Scholar
Sherwood, S. C., Webb, M. J., Annan, J. D., et al., 2020: An assessment of Earth’s climate sensitivity using multiple lines of evidence. Reviews of Geophysics, 58, e2019RG000678, doi:10.1029/2019RG000678Google Scholar
Shirani-Mehr, H., Rothschild, D., Goel, S., and Gelman, A., 2018: Disentangling bias and variance in election polls. J. Am. Stat. Assoc., 113(522), 607–614, doi:10.1080/01621459.2018.1448823Google Scholar
Silver, N., 2012: The signal and the noise: why most predictions fail – but some don’t. PenguinGoogle Scholar
Silver, N., 2014: How FiveThirtyEight calculates pollster ratings. FiveThirtyEight.com, September 25, https://fivethirtyeight.com/features/how-fivethirtyeight-calculates-pollster-ratings/Google Scholar
Sima, R. J., 2020: Combining AI and analog forecasting to predict extreme weather. Eos, 101, March 4, doi:10.1029/2020EO140896Google Scholar
SIMIP Community, 2020: Arctic sea ice in CMIP6. Geophys. Res. Lett., 47, e2019GL086749Google Scholar
Smagorinsky, J., 1963: General circulation experiments with the primitive equations. I. The basic experiment. Mon. Weather Rev., 91, 99–164Google Scholar
Smagorinsky, J., 1983: The beginnings of numerical weather prediction and general circulation modeling: early recollections. Advances in geophysics, vol. 25. Academic PressGoogle Scholar
Smith, D. M., Scaife, A. A., Eade, R., et al., 2020: North Atlantic climate far more predictable than models imply. Nature, 583, 796–800Google Scholar
Smith, L. A., 2002: What might we learn from climate forecasts? Proc. Natl. Acad. Sci. USA, 99, 2487–2492Google Scholar
Smith, L. A., and Stern, N., 2011: Uncertainty in science and its role in climate policy. Phil. Trans. R. Soc. A, 369, 4818–4841Google Scholar
Smith, S., 2016: Unfriendly climate. Texas Monthly, May, www.texasmonthly.com/articles/katharine-hayhoe-lubbock-climate-change-evangelist/Google Scholar
Sokal, A., 2015: Physics envy in psychology: a cautionary tale. Seminar, https://physics.nyu.edu/sokal/CCNY_lecture_Nov_19_15.pdfGoogle Scholar
Sokal, J., 2019: The hidden heroines of chaos. Quanta Magazine, May 20, www.quantamagazine.org/hidden-heroines-of-chaos-ellen-fetter-and-margaret-hamilton-20190520/Google Scholar
Solomon, S., 1997: Transcript of interview of Susan Solomon. American Meteorological Society Oral History Project. NCAR Archives, http://n2t.net/ark:/85065/d7959fz0Google Scholar
Solomon, S., Qin, D., Manning, M., et al., 2007: Technical Summary. In: Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 19–91Google Scholar
Solomon, S., Ivy, D. J., Kinnison, D., Mills, M. J., Neely, R. R., and Schmidt, A., 2016: Emergence of healing in the Antarctic ozone layer. Science, 353, 269–274, doi:10.1126/science.aae0061Google Scholar
Somerville, R. C. J., 1996: The forgiving air. Berkeley, CA, University of California PressGoogle Scholar
Somerville, R. C. J., Stone, P. H., Halem, M., et al., 1974: The GISS model of the global atmosphere. J. Atmos. Sci., 31, 84–117Google Scholar
Soon, W. W. H., 2005: Variable solar irradiance as a plausible agent for multidecadal variations in the Arctic-wide surface air temperature record of the past 130 years. Geophys. Res. Lett., 32, L16712Google Scholar
Sorenson, R. P., 2011: Eunice Foote’s pioneering research on CO2 and climate warming. Search and Discovery, Article # 70092, www.searchanddiscovery.com/pdfz/documents/2011/70092sorenson/ndx_sorenson.pdf.htmlGoogle Scholar
Spade, P. V., and Panaccio, C., 2019: William of Ockham. Stanford encyclopedia of philosophy, https://plato.stanford.edu/entries/ockham/Google Scholar
Spencer, R. W., and Christy, J. R., 1990: Precise monitoring of global temperature trends from satellites. Science, 247, 1558Google Scholar
Spiegelhalter, D. J., and Riesch, H., 2011: “Don’t know, can’t know”: embracing deeper uncertainties when analysing risks. Phil. Trans. R. Soc. A, 369, 4730–4750Google Scholar
Stainforth, D., Aina, T., Christensen, C., et al., 2005: Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433, 403–406Google Scholar
Stainforth, D. A., Allen, M. R., Tredger, E. R., and Smith, L. A., 2007: Confidence, uncertainty and decision-support relevance in climate predictions. Phil. Trans. R. Soc. A, 3652145–3652161Google Scholar
Steffen, W., Rockström, J., Richardson, K., et al., 2018: Trajectories of the earth system in the anthropocene. Proc. Natl. Acad. Sci., 115(33), 8252–8259Google Scholar
Stephens, B., 2017: Climate of complete certainty. The New York Times, April 28, www.nytimes.com/2017/04/28/opinion/climate-of-complete-certainty.htmlGoogle Scholar
Stocker, T. F., Qin, D., Plattner, G. K., et al., 2013: Technical summary. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 33–115Google Scholar
Subramanian, A., Juricke, S., Dueben, P., and Palmer, T., 2019: A stochastic representation of subgrid uncertainty for dynamical core development. Bull. Am. Meteorol. Soc., 100, 1091–1101, doi:10.1175/BAMS-D-17-0040.1Google Scholar
Supran, G., and Oreskes, N., 2017: Assessing ExxonMobil’s climate change communications (1977–2014). Environ. Res. Lett., 12, 084019Google Scholar
Sutton, R. T., 2019: Climate science needs to take risk assessment much more seriously. Bull. Am. Meteorol. Soc., 100 (9), 1637–1642Google Scholar
Sutton, R. T., and Hawkins, E., 2020: ESD ideas: global climate response scenarios for IPCC assessments. Earth Syst. Dynam., 11, 751–754, doi:10.5194/esd-11-751-2020Google Scholar
Taylor, K. E., Stouffer, R. J., and Meehl, G. A., 2012: An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc., 93, 485–498Google Scholar
The Economist, 2017: Language: finding a voice. The Economist, Technology Quarterly, May 1, www.economist.com/technology-quarterly/2017-05-01/languageGoogle Scholar
The Independent, 2019: Lloyds of London suffers £1bn loss after year of devastating hurricanes and wildfires. The Independent, March 27, www.independent.co.uk/news/business/news/lloyds-london-losses-hurricanes-wildfires-natural-disasters-a8841576.htmlGoogle Scholar
Thompson, C., 2019: The secret history of women in coding. The New York Times, February 13, www.nytimes.com/2019/02/13/magazine/women-coding-computer-programming.htmlGoogle Scholar
Thompson, E., 2013: Modelling North Atlantic storms in a changing climate, Ph.D. thesis. London, Imperial CollegeGoogle Scholar
Thompson, N. C., and Spanuth, S., 2018: The decline of computers as a general purpose technology: why deep learning and the end of Moore’s Law are fragmenting computing. Commun. ACM, 64(3), 64–72, doi:10.1145/3430936Google Scholar
Thompson, P. D., 1957: Uncertainty of initial state as a factor in the predictability of large scale atmospheric flow patterns. Tellus, 9(3), 275–295Google Scholar
Thompson, P. D., 1983: A history of numerical weather prediction in the United States. Bull. Am. Meteorol. Soc., 64, 755–769Google Scholar
Thorne, P. W., Lanzante, J. R., Peterson, T. C., Seidel, D. J., and Shine, K. P., 2011: Tropospheric temperature trends: history of an ongoing controversy. WIREs Clim. Change, 2, 66–88, doi:10.1002/wcc.80Google Scholar
Tierney, J. E., Zhu, J., King, J., et al., 2020: Glacial cooling and climate sensitivity revisited. Nature, 584, 569–573Google Scholar
Trenberth, K., 2010: More knowledge, less certainty. Nat. Clim. Change, 1, 20–21, doi:10.1038/climate.2010.06Google Scholar
Trenberth, K., and Knutti, R., 2017: Yes, we can do “sound” climate science even though it’s projecting the future. TheConversation.com, April 5, https://theconversation.com/yes-we-can-do-sound-climate-science-even-though-its-projecting-the-future-75763Google Scholar
Trenberth, K. E., 2007: Predictions of climate. Climate Feedback Blog, Nat. Clim. Change, June 4, http://blogs.nature.com/climatefeedback/2007/06/predictions_of_climate.htmlGoogle Scholar
Trenberth, K. E., Fasullo, J. T., and Kiehl, J., 2009: Earth’s global energy budget. Bull. Am. Meteorol. Soc., 90(3), 311–324Google Scholar
Trimmer, J. D., 1980: The present situation in quantum mechanics: a translation of Schrödinger’s “Cat Paradox” paper. Proc. Am. Philos. Soc., 124(5), 323–338Google Scholar
Tropp, H. S., 2003: Mauchly, John W. ACM Digital Library, https://dl.acm.org/doi/pdf/10.5555/1074100.1074582Google Scholar
Trumbla, R., 2007: The Great Galveston Hurricane of 1900. NOAA, https://celebrating200years.noaa.gov/magazine/galv_hurricane/welcome.htmlGoogle Scholar
Turing, A. M., 1950: I. – Computing machinery and intelligence. Mind, LIX(236), 433–460, doi:10.1093/mind/LIX.236.433Google Scholar
United Nations, 1992: Rio declaration on environment and development. The United Nations Conference on Environment and Development, www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_CONF.151_26_Vol.I_Declaration.pdfGoogle Scholar
Vallis, G. K., 2016: Geophysical fluid dynamics: whence, whither and why? Proc. R. Soc. A, 47220160140, doi:10.1098/rspa.2016.0140Google Scholar
van der Sluijs, J., van Eijndhoven, J., Shackley, S., and Wynne, B., 1998: Anchoring devices in science for policy: the case of consensus around climate sensitivity. Soc. Stud. Sci., 28, 291–323Google Scholar
van Oldenborgh, G. J., Mitchell-Larson, E., Vecchi, G. A., et al., 2019: Cold waves are getting milder in the northern midlatitudes. Environ. Res. Lett., 14, 114004Google Scholar
van Oldenborgh, G. J., van der Wiel, K., Sebastian, A., et al., 2017: Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environ. Res. Lett., 12, 124009Google Scholar
Veisdal, J., 2020: John von Neumann’s 1935 letter to Oswald Veblen. Medium.com/cantors-paradise, April 18, www.cantorsparadise.com/john-von-neumanns-1935-letter-to-oswald-veblen-3acbe1b69098Google Scholar
Vitello, P., 2013: Joseph Farman, 82, is dead; discovered ozone hole. The New York Times, May 18, www.nytimes.com/2013/05/19/science/earth/joseph-farman-82-is-dead-discovered-ozone-hole.htmlGoogle Scholar
Vlamis, K., 2021: After extreme cold events in 1989 and 2011, Texas was warned to winterize power plants – but many still froze in the latest storms. Yahoo! News, February 19, https://news.yahoo.com/extreme-cold-events-1989-2011-051906264.htmlGoogle Scholar
Voiland, A., 2010: Aerosols: tiny particles, big impact. NASA Earth Observatory, November 2, https://earthobservatory.nasa.gov/features/AerosolsGoogle Scholar
Voosen, P., 2016: Climate scientists open up their black boxes to scrutiny. Science, 354(6311), 401–402Google Scholar
Voosen, P., 2019: A world without clouds? Hardly clear, climate scientists say. Sciencemag.org, February 26, www.sciencemag.org/news/2019/02/world-without-clouds-hardly-clear-climate-scientists-sayGoogle Scholar
Voosen, P., 2020a: Europe is building a “digital twin” of Earth to revolutionize climate forecasts. Science, October 1, doi:10.1126/science.abf0687Google Scholar
Voosen, P., 2020b: Hidden predictability in winds could improve climate forecasts. Science, 369, 490–491Google Scholar
Wagner, G., and Weitzman, M. L., 2018: Potentially large equilibrium climate sensitivity tail uncertainty. Econ. Lett., 168, 144–146, doi:10.1016/j.econlet.2018.04.036Google Scholar
Wagner, G., and Zeckhauser, R. J., 2016: Confronting deep and persistent climate uncertainty. Discussion paper, 2016–84. Harvard project on climate agreements. Belfer Center, JulyGoogle Scholar
Wagner, T. J. W., and Eisenman, I., 2015: How climate model complexity influences sea ice stability. J. Clim., 28(10), 3998–4014Google Scholar
Waldman, S., 2019: Why a high-profile climate science opponent quit Trump’s White House. Sciencemag.org, September 12, doi:10.1126/science.aaz4845Google Scholar
Walker, J. C. G., Hays, P. B., and Kasting, J. F., 1981: A negative feedback mechanism for the long-term stabilization of the Earth’s surface temperature. J. Geophys. Res., 86(C10), 9776–9782Google Scholar
Wallace-Wells, D., 2017: The uninhabitable Earth. New York Magazine, July 9, https://nymag.com/intelligencer/2017/07/climate-change-earth-too-hot-for-humans.htmlGoogle Scholar
Wallace-Wells, D., 2019: We’re getting a clearer picture of the climate future – and it’s not as bad as it once looked. New York Magazine, December 20, https://nymag.com/intelligencer/2019/12/climate-change-worst-case-scenario-now-looks-unrealistic.htmlGoogle Scholar
Wallace-Wells, D., 2020: What climate alarm has already achieved. New York Magazine, August 14, https://nymag.com/intelligencer/2020/08/what-climate-alarm-has-already-achieved.htmlGoogle Scholar
Wang, C., Soden, B. J., Yang, W., and Vecchi, G. A., 2021: Compensation between cloud feedback and aerosol-cloud interaction in CMIP6 models. Geophys. Res. Lett., 48, e2020GL091024, doi:10.1029/2020GL091024Google Scholar
Washington, W. M., 1970: On the simulation of the Indian monsoon and tropical easterly jet stream with the NCAR general circulation model. Proc. Symp. Tropical Meteor., Honolulu, HI, Amer. Meteor. Soc., J VI 1–5Google Scholar
Washington, W. M., and Kasahara, A., 2011: The evolution and future research goals for general circulation models. In Donner, L., Schubert, W., and Somerville, R. (eds.), The development of atmospheric general circulation models. Cambridge University Press, 18–36Google Scholar
Weart, S. R., 2008: The discovery of global warming. Harvard University Press, https://history.aip.org/climate/index.htmGoogle Scholar
Weaver, C. P., Moss, R. H., Ebi, K. L., et al., 2017: Reframing climate change assessments around risk: recommendations for the US National Climate Assessment. Environ. Res. Lett., 12(8), 080201Google Scholar
Webster, B., 2020: Unhaltable global warming press release withdrawn by Scientific Reports journal. The Times, November 13, www.thetimes.co.uk/article/unhaltable-global-warming-claim-withdrawn-by-scientific-reports-journal-rtxjz9m6fGoogle Scholar
Weik, M. H., 1961: The ENIAC Story. Ordnance. Washington, DC, American Ordnance Association, January–February 1961Google Scholar
Weiss, E. B., 2009: The Vienna Convention for the protection of the ozone layer and the Montreal Protocol on substances that deplete the ozone layer, https://legal.un.org/avl/ha/vcpol/vcpol.htmlGoogle Scholar
Wentz, F. J., and Schabel, M., 1996: Effects of satellite orbital decay on MSU lower tropospheric temperature trends. Nature, 394, 661–664Google Scholar
Wheeling, K., 2020: The debate over the United Nations’ energy emissions projections. Eos.org, 101, December 18, https://eos.org/articles/the-debate-over-the-united-nations-energy-emissions-projectionsGoogle Scholar
Wild, M., 2020: The global energy balance as represented in CMIP6 climate models. Clim. Dynam., 55, 553–577Google Scholar
Winsberg, E., 2018b: Communicating uncertainty to policymakers: the ineliminable role of values. In: Lloyd, E. A. and Winsberg, E. (eds.), Climate modelling: philosophical and conceptual issues. Springer Verlag, 381–412Google Scholar
Winsberg, E., and Goodwin, W. M., 2016: The adventures of climate science in the sweet land of idle arguments. Stud. Hist. Philos. Sci. B: Stud. Hist. Philos. M. P., 54, 9–17, doi:10.1016/j.shpsb.2016.02.001Google Scholar
Witman, S., 2017: Meet the computer scientist you should thank for your smartphone’s weather app. Smithsonian Magazine, June 16, www.smithsonianmag.com/science-nature/meet-computer-scientist-you-should-thank-your-phone-weather-app-180963716/Google Scholar
WMO Bulletin, 1996: The Bulletin interviews Professor Edward N. Lorenz. WMO Bulletin, 45(2), AprilGoogle Scholar
Woetzel, J., Pinner, D., Samandari, H., et al., 2020: Climate risk and response: Physical hazards and socioeconomic impacts. McKinsey Global Institute
, www.mckinsey.com/business-functions/sustainability/our-insights/climate-risk-and-response-physical-hazards-and-socioeconomic-impactsGoogle Scholar
Wood, G., 2009: Re-engineering the earth. The Atlantic, July/August, www.theatlantic.com/magazine/archive/2009/07/re-engineering-the-earth/307552/Google Scholar
Worland, J., 2021: The Texas power grid failure is a climate change cautionary tale. Time, February 18, https://time.com/5940491/texas-power-outage-climate/Google Scholar
Yan, X. H., Boyer, T., Trenberth, K., et al., 2016: The global warming hiatus: slowdown or redistribution? Earth’s Future, 4, 472–482, doi:10.1002/2016EF000417Google Scholar
Yoder, K., 2019: Is it time to retire “climate change” for “climate crisis”? Grist.org, June 17, https://grist.org/article/is-it-time-to-retire-climate-change-for-climate-crisis/Google Scholar
Yohe, G., Andronova, N., and Schlesinger, M., 2004: To hedge or not against an uncertain climate future? Science, 306(5695), 416–417Google Scholar
Yong, E., 2015: How reliable are psychology studies? The Atlantic, August 27, www.theatlantic.com/science/archive/2015/08/psychology-studies-reliability-reproducability-nosek/402466/Google Scholar
Zworykin, V. K., 1945: Outline of weather proposal (with historical introduction by James Fleming). In: Fleming, J. R. (ed.), History of meteorology, vol. 4, 57–78, 2008Google Scholar