The other climate crisis – Nature
Manabe, S. & Weatherald, R. T. Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmos. Sci. 24, 241–259 (1967).
Google Scholar
Manabe, S. & Weatherald, R. T. On the distribution of climate change resulting from an increase of CO2 content of the atmosphere. J. Atmos. Sci. 37, 99–118 (1980).
Google Scholar
Stouffer, R. J., Manabe, S. & Bryan, K. Interhemispheric asymmetry in climate response to a gradual increase of atmospheric CO2. Nature 342, 660–662 (1989).
Google Scholar
Held, I. M. Simplicity amid complexity. Science 343, 1206–1207 (2014).
Google Scholar
Hasselmann, K. Stochastic climate models Part I. Theory. Tellus 28, 473–485 (1976).
Google Scholar
Hasselmann, K. in Meteorology of Tropical Oceans (ed. Shaw, D. B.) 251–259 (Royal Meteorological Society, 1979).
Stouffer, R. & Manabe, S. Assessing temperature pattern projections made in 1989. Nat. Clim. Change 7, 163-165, (2017). This paper summarizes the successful climate predictions that underlie the 2021 Nobel Prize in Physics.
Google Scholar
Ravishankara, A. R., Randall, D. A. & Hurrell, J. W. Complex and yet predictable: the message of the 2021 Nobel Prize in Physics. Proc. Natl Acad. Sci. USA 119, e2120669119 (2021).
Google Scholar
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–2016 (2016).
Google Scholar
Meehl, G. A. in Oxford Research Encyclopedia of Climate Science https://doi.org/10.1093/acrefore/9780190228620.013.933 (Oxford Univ. Press, 2023).
Held, I. M. Large-scale dynamics and climate change. Bull. Am. Meteorol. Soc. 74, 228–242 (1993).
Google Scholar
IPCC Climate Change 2001: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).
Shaw, T. A. et al. Regional climate change: consensus, discrepancies, and ways forward. Front. Clim. https://doi.org/10.3389/fclim.2024.1391634 (2024).
Kuhn, T. S. The Structure of Scientific Revolutions (Univ. Chicago Press, 1962). This book describes the stages of scientific revolutions, including a crisis and paradigm shift.
Wolchover, N. A deepening crisis forces physicists to rethink structure of nature’s laws. Quanta Magazine https://www.quantamagazine.org/crisis-in-particle-physics-forces-a-rethink-of-what-is-natural-20220301/ (2022).
Challenging the standard cosmological model. The Royal Society https://royalsociety.org/science-events-and-lectures/2024/04/cosmological-model/ (2024).
Arrhenius, S. Ueber die Wärmeabsorption durch Kohlensäure. Ann. Phys. https://doi.org/10.1002/andp.19013090404 (1901).
Callendar, G. S. The artificial production of carbon dioxide and its influence on temperature. Q. J. R. Meteorol. Soc. 64, 223–240 (1938).
Google Scholar
Wilson, D. & Gea-Banacloche, J. Simple model to estimate the contribution of atmospheric CO2 to the Earth’s greenhouse effect. Am. J. Phys. 80, 306–315 (2012).
Google Scholar
Jeevanjee, N., Seeley, J. T., Paynter, D. & Fueglistaler, S. An analytical model for spatially varying clear-sky CO2 forcing. J. Clim. 34, 9463–9480 (2021).
Google Scholar
Koll, D. D. B., Jeevanjee, N. & Lutsko, N. J. An analytic model for the clear-sky longwave feedback. J. Atmos. Sci. 80, 1923–1951 (2023).
Google Scholar
Stevens, B. & Kluft, L. A colorful look at climate sensitivity. Atmos. Chem. Phys. 23, 14673D14689 (2023).
Google Scholar
Wordsworth, R., Seeley, J. T. & Shine, K. P. Fermi resonance and the quantum mechanical basis of global warming. Planet. Sci. J. 5, 67 (2024).
Google Scholar
Gregory, J. M. et al. A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett. 31, L03205 (2004).
Google Scholar
Pierrehumbert, R. T. Fall Meeting 2012 Tyndall Lecture: Successful predictions. YouTube https://www.youtube.com/watch?v=RICBu_P8JWI (2012).
Phillips, N. A. The general circulation of the atmosphere: a numerical experiment. Q. J. R. Meteorol. Soc. 82, 123–164 (1956).
Google Scholar
Held, I. M. 100 years of progress in understanding the general circulation of the atmosphere. Meteorol. Monogr. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0017.1 (2018). This paper summarizes how the standard approach has been used to understand the general circulation of the atmosphere.
Shaw, T. A., Miyawaki, O. & Donohoe, A. Stormier Southern Hemisphere induced by topography and ocean circulation. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2123512119 (2022).
Shaw, T. A. Mechanisms of future predicted changes in the zonal mean mid-latitude circulation. Curr. Clim. Change Rep. https://doi.org/10.1007/s40641-019-00145-8 (2019).
Held, I. M. & Hou, A. Y. Nonlinear axially symmetric circulations in a nearly inviscid atmosphere. J. Atmos. Sci. 37, 515–533 (1980).
Google Scholar
Plumb, R. A. & Hou, A. Y. The response of a zonally symmetric atmosphere to subtropical thermal forcing: threshold behavior. J. Atmos. Sci. 49, 1790–1799 (1992).
Google Scholar
Xie, S.-P. & Philander, G. H. A coupled ocean–atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus A 46, 340–350 (1994).
Google Scholar
Frierson, D. M. W. et al. Contribution of ocean overturning circulation to tropical rainfall peak in the Northern Hemisphere. Nat. Geosci. https://doi.org/10.1038/NGEO1987 (2013).
Marshall, J., Donohoe, A., Ferreira, D. & McGee, D. The ocean’s role in setting the mean position of the Inter-Tropical Convergence Zone. Clim. Dyn. https://doi.org/10.1007/s00382-013-1767-z (2013).
Schneider, T., Bischoff, T. & Haug, G. H. Migrations and dynamics of the intertropical convergence zone. Nature https://doi.org/10.1038/nature13636 (2014).
Schneider, T. The general circulation of the atmosphere. Annu. Rev. Earth Planet. Sci. 34, 655–688 (2006).
Google Scholar
Vecchi, G. A., & Soden, B. J. Global warming and the weakening of the tropical circulation. J. Clim. 17, 4316–4340 (2007).
Google Scholar
Lu, J., Vecchi, G. A. & Reichler, T. A. Expansion of the Hadley cell under global warming. Geophys. Res. Lett. 8, 2181–2199 (2007).
Google Scholar
Hoskins, B. J. & Karoly, D. J. The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci. 38, 1179–1196 (1981).
Google Scholar
Held, I. M. in Large-Scale Dynamical Processes in the Atmosphere (eds Hoskins, B. J. & Pearce, R. P.) 127–168 (Academic Press, 1983).
Seager, R. et al. Is the Gulf Stream responsible for Europe’s mild winters? Q. J. R. Meteorol. Soc. 128, 2563–2586 (2002).
Google Scholar
Kaspi, Y. & Schneider, T. Winter cold of eastern continental boundaries induced by warm ocean waters. Nature 471, 621–624 (2011).
Google Scholar
Rodwell, M. J. & Hoskins, B. J. Monsoons and the dynamics of deserts. Q. J. R. Meteorol. Soc. 122, 1385–1404 (1996).
Google Scholar
Matsuno, T. Quasi-geostrophic motions in the equatorial area. J. Meteorol. Soc. Jpn 44, 25–43 (1966).
Google Scholar
Gill, A. E. Some simple solutions for heat-induced tropical circulation. Q. J. R. Meteorol. Soc. 106, 447–462 (1980).
Google Scholar
Knutson, T. & Manabe, S. Time-mean response over the tropical Pacific to increased CO2 in a coupled ocean–atmosphere model. J. Clim. 8, 2181–2199 (1995).
Google Scholar
Wills, R. C. J., White, R. H. & Levine, X. J. Northern Hemisphere stationary waves in a changing climate. Curr. Clim. Change Rep. https://doi.org/10.1007/s40641-019-00147-6 (2019).
Nikurashin, M. & Vallis, G. K. A theory of deep stratification and overturning circulation in the ocean. J. Phys. Oceanogr. 41, 485–502 (2011).
Google Scholar
Stommel, H. The westward intensification of wind-driven ocean currents. Eos Trans. AGU 29, 202–206 (1948).
Google Scholar
Stommel, H. Thermohaline convection with two stable regimes of flow. Tellus 13, 224–230 (1961).
Google Scholar
Cane, M. A., Zebiak, S. & Dolan, S. Experimental forecasts of El Niño. Nature 321, 827–832 (1986).
Google Scholar
Battisti, D., Vimont, D. J. & Kirtman, B. P. 100 years of progress in understanding the dynamics of coupled atmosphere–ocean variability. Meteorol. Monogr. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0025.1 (2019). This paper summarizes our understanding of coupled atmosphere–ocean dynamics as represented by intermediate and hybrid models.
Shaw, T. A. et al. Storm track processes and the opposing influences of climate change. Nat. Geosci. https://doi.org/10.1038/NGEO2783 (2016).
Jeevanjee, N., Hassanzadeh, P., Hill, S. & Sheshadri, A. A perspective on climate model hierarchies. J. Adv. Model. Earth Syst. https://doi.org/10.1002/2017MS001038 (2017). This paper summarizes the numerical model hierarchy for the atmosphere.
Maher, P. et al. Model hierarchies for understanding atmospheric circulation. Rev. Geophys. https://doi.org/10.1029/2018RG000607 (2019).
Randall, D. A. et al. 100 years of earth system model development. Meteorol. Monogr. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0018.1 (2018). This paper summarizes the numerical implementation of the standard approach as represented by coupled comprehensive climate models.
Hohenegger, C. & Schar, C. in Clouds and Climate: Climate Science’s Greatest Challenge (eds Siebesma, A. P. et al.) 329–355 (Cambridge Univ. Press, 2020).
Fisher, R. A. & Koven, C. D. Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems. J. Adv. Model. Earth Syst. https://doi.org/10.1029/2018MS001453 (2020).
The Big Melt (Max Planck Research, 2023); https://www.mpg.de/21679192/MPR_2023_4.pdf.
Levermann, A., Schewe, J., Petoukhov, V. & Held, H. Basic mechanism for abrupt monsoon transitions. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.0901414106 (2009).
Boos, W. R. & Storelvmo, T. Near-linear response of mean monsoon strength to a broad range of radiative forcings. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1517143113 (2016). This paper demonstrates that the theory used to predict ‘tipping points’ for monsoons is not robust when physical complexity is added.
Marotzke, J. Abrupt climate change and thermohaline circulation: mechanisms and predictability. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.97.4.1347 (2000).
Morrison, T. H. et al. Radical interventions for climate-impacted systems. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01542-y (2022).
Kopp, R. E. et al. ‘Tipping points’ confuse and can distract from urgent climate action. ESS Open Archive https://doi.org/10.22541/essoar.170542965.59092060/v1 (2024).
Douville, H., Qasmi, S., Ribes, A. & Bock, O. Global warming at near-constant tropospheric relative humidity is supported by observations. Commun. Earth Environ. https://doi.org/10.1038/s43247-022-00561-z (2022).
Douville, H. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 1055–1210 (IPCC, Cambridge Univ. Press, 2021).
Allan, R. Amplified seasonal range in precipitation minus evaporation. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/acea36 (2023).
Shrestha, S. & Soden, B. J. Anthropogenic weakening of the atmospheric circulation during the satellite era. Geophys. Res. Lett. https://doi.org/10.1029/2023GL104784 (2023).
Chemke, R. & Yuval, J. Human-induced weakening of the Northern Hemisphere tropical circulation. Nature https://doi.org/10.1038/s41586-023-05903-1 (2023).
Kang, J., Shaw, T. A. & Sun, L. Arctic sea ice loss weakens Northern Hemisphere summertime storminess but not until the late 21st century. Geophys. Res. Lett. https://doi.org/10.1029/2022GL102301 (2023).
Chemke, R. & Coumou, D. Human influence on the recent weakening of storm tracks in boreal summer. npj Clim. Atmos. Sci. https://doi.org/10.1038/s41612-024-00640-2 (2024).
Woollings, T., Drouard, M., O’Reilly, C. H., Sexton, D. M. H. & McSweeney, C. Trends in the atmospheric jet streams are emerging in observations and could be linked to tropical warming. Commun. Earth Environ. https://doi.org/10.1038/s43247-023-00792-8 (2023).
Po-Chedley, S. et al. Internal variability and forcing influence model’s satellite differences in the rate of tropical tropospheric warming. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2209431119 (2022).
Olonscheck, D. & Rugenstein, M. Coupled climate models systematically underestimate radiation response to surface warming. Geophys. Res. Lett. https://doi.org/10.1029/2023GL106909 (2024).
Rantanen, M. et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ. https://doi.org/10.1038/s43247-022-00498-3 (2022).
Patterson, M. North-West Europe hottest days are warming twice as fast as mean summer days. Geophys. Res. Lett. https://doi.org/10.1029/2023GL102757 (2023).
Vautard, R. et al. Heat extremes in Western Europe increasing faster than simulated due to atmospheric circulation trends. Nat. Commun. https://doi.org/10.1038/s41467-023-42143-3 (2023).
Diaz, L. B., Saurral, R. I. & Vera, C. S. Assessment of South America summer rainfall climatology and trends in a set of global climate models large ensembles. Int. J. Climatol. 41, E59–E77 (2021).
Google Scholar
Varuolo-Clarke, A. M., Smerdon, J. R., Williams, A. P. & Seager, R. Gross discrepancies between observed and simulated twentieth-to-twenty-first-century precipitation trends in southeastern South America. J. Clim. 34, 6441–6457 (2021).
Google Scholar
Chemke, R., Ming, Y. & Yuval, J. The intensification of winter mid-latitude storm tracks in the Southern Hemisphere. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01368-8 (2022).
Kang, J. M., Shaw, T. A., Kang, S. M., Simpson, I. R. & Yu, Y. Revisiting the reanalysis-model discrepancy in Southern Hemisphere winter storm track trends. npj Clim. Atmos. Sci. 7, 252 (2024).
Google Scholar
Wills, R. C. J., Dong, Y., Proistosecu, C., Armour, K. C. & Battisti, D. S. Systematic climate model biases in the large-scale patterns of recent sea-surface temperature and sea-level pressure change. Geophys. Res. Lett. https://doi.org/10.1029/2022GL100011 (2022).
Seager, R., Henderson, N. & Cane, M. A. Persistent discrepancies between observed and modeled trends in the tropical Pacific Ocean. J. Clim. 35, 4571–4584 (2022).
Google Scholar
Lee, S. et al. On the future zonal contrasts of equatorial Pacific climate: perspectives from observations, simulations, and theories. npj Clim. Atmos. Sci. https://doi.org/10.1038/s41612-022-00301-2 (2022).
Chung, E.-S. et al. Reconciling opposing Walker circulation trends in observations and model projections. Nat. Clim. Change https://doi.org/10.1029/2023GL105332 (2019).
Watanabe, M., Iwakiri, T., Dong, Y. & Kang, S. M. Two competing drivers of the recent Walker circulation trend. Geophys. Res. Lett. https://doi.org/10.1029/2023GL105f332 (2023).
Kang, S. M. et al. Global impacts of recent Southern Ocean cooling. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2300881120 (2023).
Douville, H. & Willett, K. M. A drier than expected future, supported by near-surface relative humidity observations. Sci. Adv. https://doi.org/10.1126/sciadv.ade625 (2023).
Simpson, I. R. et al. Observed humidity trends in dry regions contradict climate models. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2302480120 (2023). This paper demonstrates a severe hydroclimate discrepancy over arid and semi-arid regions.
Makula, E. K. & Zhou, B. Coupled Model Intercomparison Project Phase 6 evaluation and projection of East African precipitation. Int. J. Climatol. 42, 2398–2412 (2022).
Google Scholar
Maddison, J. M., Catto, J., Hanna, E., Luu, L. N. & Screen, J. A. Missing increase in summer greenland blocking in climate models. Geophys. Res. Lett. https://doi.org/10.1029/2024GL108505 (2024).
Blackport, R. & Fyfe, J. Climate models fail to capture strengthening wintertime North Atlantic jet and impacts on Europe. Sci. Adv. 8, eabn3112 (2022).
Google Scholar
Gensini, V. A. & Brooks, H. E. Spatial trends in United States tornado frequency. npj Clim. Atmos. Sci. https://doi.org/10.1038/s41612-018-0048-2 (2018).
Tang, B. A., Gensini, V. A. & Homeyer, C. R. Trends in United States large hail environments and observations. npj Clim. Atmos. Sci. 2, 45 (2019).
Google Scholar
Prein, A. F. Thunderstorm straight line winds intensify with climate change. Nat. Clim. Change https://doi.org/10.1038/s41558-023-01852-9 (2023). This paper demonstrates a signal (thunderstorm straight line winds) for which we have no expectations and is therefore outside the scope of the standard approach.
Jain, S. et al. Importance of internal variability for climate model assessment. npj Clim. Atmos. Sci. https://doi.org/10.1038/s41612-023-00389-0 (2023).
Kosaka, Y. & Xie, S.-P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).
Google Scholar
Fyfe, J. C. et al. Making sense of the early-2000s warming slowdown. Nat. Clim. Change 6, 224–228 (2016).
Google Scholar
Rugenstein, M., Zelinka, M., Karnauskas, K. B., Ceppi, P. & Andrews, T. Patterns of surface warming matter for climate sensitivity. Eos https://doi.org/10.1029/2023EO230411 (2023).
Dunn, R. J. H., Willett, K. M., Ciavarella, A. & Stott, P. A. Comparison of land surface humidity between observations and CMIP5 models. Earth Syst. Dyn. https://doi.org/10.5194/esd-8-719-2017 (2017).
Schmidt, G. Climate models can’t explain 2023’s huge heat anomaly: we could be in uncharted territory. Nature 627, 46 (2024).
Google Scholar
Esper, J., Torbenson, M. & Buntgen, U. 2023 summer warmth unparalleled over the past 2,000 years. Nature https://doi.org/10.1038/s41586-024-07512-y (2024).
Kuhlbrodt, T., Swaminathan, R., Ceppi, P. & Wilder, T. A glimpse into the future: the 2023 ocean temperature and sea ice extremes in the context of longer-term climate change. Bull. Am. Meteorol. Soc. 627, 46 (2024).
Purich, A. & Doddridge, E. W. Record low Antarctic sea ice coverage indicates a new sea ice state. Commun. Earth Environ. 4, 314 (2023).
Google Scholar
Dong, B., Sutton, R. T., Shaffrey, L. & Harvey, B. Recent decadal weakening of the summer Eurasian westerly jet attributable to anthropogenic aerosol emissions. Nat. Commun. https://doi.org/10.1038/s41467-022-28816-5 (2022).
Hodnebrog, O. et al. Recent reductions in aerosol emissions have increased Earth’s energy imbalance. Commun. Earth Environ. https://doi.org/10.1038/s43247-024-01324-8 (2024).
Kang, J., Shaw, T. A. & Sun, L. Anthropogenic aerosol forcing has significantly weakened regional summertime storminess in the Northern Hemisphere in the satellite era. AGU Adv. 5, e2024AV001318 (2024).
Google Scholar
Schumacher, D. L. et al. Exacerbated summer European warming not captured by climate models neglecting long-term aerosol changes. Commun. Earth Environ. https://doi.org/10.1038/s43247-024-01332-8 (2024).
Gettelman, A. et al. Has reducing ship emissions brought forward global warming? Geophys. Res. Lett. https://doi.org/10.1029/2024GL109077 (2024).
Raghuraman, S. P. et al. The 2023 global warming spike was driven by El Niño/Southern Oscillation. EGUsphere https://doi.org/10.5194/egusphere-2024-1937 (2024).
Small, R. J., Bryan, F. O., Bishop, S. P. & Tomas, R. A. Air–sea turbulent heat fluxes in climate models and observational analyses: what drives their variability? J. Clim. https://doi.org/10.1175/JCLI-D-18-0576.1 (2019).
Busecke, J. J. M. et al. The overlooked sub-grid air–sea flux in climate models. Preprint at Earth ArXiv https://doi.org/10.31223/X5WQ47 (2024).
Seager, R. et al. Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases. Nat. Clim. Change 9, 517–522 (2019).
Google Scholar
Fiedler, S. et al. Simulated tropical precipitation assessed across three major phases of the Coupled Model Intercomparison Project (CMIP). Mon. Weather Rev. 148, 3653D3680 (2020).
Google Scholar
Marchau, V. A. W. J., Walker, W. E., Bloemen, P. J. T. M. & Popper, S. W. Decision Making under Deep Uncertainty (Springer, 2019). This book provides practical tools for making decisions under deep uncertainty.
Mauritsen, T. et al. Tuning the climate of a global model. J. Adv. Model. Earth Syst. https://doi.org/10.1029/2012MS000154 (2012).
Hourdin, F. et al. Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model. Clim. Dyn. https://doi.org/10.1007/s00382-012-1411-3 (2013).
Schmidt, G. H. et al. Practice and philosophy of climate model tuning across six US modeling centers. Geosci. Mod. Dev. 10, 3207–3223 (2017).
Google Scholar
Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).
Google Scholar
Vogel, R. et al. Strong cloud-circulation coupling explains weak trade cumulus feedback. Nature 612, 696–700 (2022).
Google Scholar
Watanabe, M. et al. Possible shift in controls of the tropical Pacific surface warming pattern. Nature https://doi.org/10.1038/s41586-024-07452-7 (2024).
Hahn, L. C., Armour, K. C., Zelinka, M. D., Bitz, C. M. & Donohoe, A. Contributions to polar amplification in CMIP5 and CMIP6 models. Front. Earth Sci. 9, 710036 (2021).
Google Scholar
Wendisch, M. et al. Overview: quasi-Lagrangian observations of Arctic air mass transformations: introduction and initial results of the HALO(AC)3 aircraft campaign. EGUsphere https://doi.org/10.5194/egusphere-2024-783 (2024).
Satoh, M. et al. Global cloud-resolving models. Curr. Clim. Change Rep. 5, 172–184 (2019).
Google Scholar
Schneider, T. et al. Harnessing AI and computing to advance climate modelling and prediction. Nat. Clim. Change 13, 887–889 (2023).
Google Scholar
Watt-Meyer, O. et al. ACE: a fast, skillful learned global atmospheric model for climate prediction. Preprint at https://arxiv.org/abs/2310.02074 (2023). This paper demonstrates a climate emulator with no a priori knowledge of physical constraints, representing a fundamentally new paradigm.
Bone, C., Gastineau, G., Thiria, S., Gallinari, P. & Mejia, C. Detection and attribution of climate change using a neural network. J. Adv. Model. Earth Syst. https://doi.org/10.1029/2022MS003475 (2023).
Kochkov, D. et al. Neural general circulation models for weather and climate. Nature 632, 1060–1066 (2024).
Google Scholar
Subel, A. & Zanna, L. Building ocean climate emulators. Preprint at https://arxiv.org/abs/2402.04342 (2024).
Swart, N. et al. The Southern Ocean Freshwater Release Model Experiments Initiative (SOFIA): scientific objectives and experimental design. EGUsphere https://doi.org/10.5194/egusphere-2023-198 (2023).
Roach, L. et al. Winds and meltwater together lead to Southern Ocean surface cooling and sea ice expansion Geophys. Res. Lett. https://doi.org/10.1029/2023GL105948 (2023).
Yeager, S. G. et al. Reduced Southern Ocean warming enhances global skill and signal-to-noise in an eddy-resolving decadal prediction system. npj Clim. Atmos. Sci. https://doi.org/10.1038/s41612-023-00434-y (2023). This paper shows how including small-scale ocean eddies alleviates the sea surface temperature pattern discrepancy.
Boning, C. W., Dispert, A., Visbeck, M., Rintoul, S. R. & Schwarzkopf, F. U. The response of the Antarctic Circumpolar Current to recent climate change. Nat. Geosci. 1, 864–869 (2008).
Google Scholar
Stewart, A. L., Neumann, N. K. & Solodoch, A. Eddy saturation of the Antarctic Circumpolar Current by standing waves. J. Phys. Ocean. 53, 1161–1181 (2023).
Google Scholar
Takasuka, D. et al. How can we improve the seamless representation of climatological statistics and weather toward reliable global k-scale climate simulations? J. Adv. Model. Earth Syst. 16, e2023MS003701 (2024). This paper highlights the potential impacts of relaxing the LSD assumption through kilometre-scale modelling.
Google Scholar
Lee, J. & Hohenegger, C. Weaker land–atmosphere coupling in global storm-resolving simulation. Proc. Natl Acad. Sci. USA 121, e2314265121 (2024).
Google Scholar
Held, I. M. The gap between simulation and understanding in climate modeling. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-86-11-1609 (2005).
Emanuel, K. The relevance of theory for contemporary research in atmospheres, oceans, and climate. AGU Adv. https://doi.org/10.1029/2019AV000129 (2020).
Xie, S.-P. et al. Towards predictive understanding of regional climate change. Nat. Clim. Change https://doi.org/10.1038/NCLIMATE2689 (2015).
Byrne, M. P. et al. Theory and the future of land-climate science. Nat. Geosci. https://doi.org/10.1038/s41561-024-01553-8 (2024).
Polvani, L. M., Clement, A. C., Medeiros, B., Benedict, J. & Simpson, I. R. When less is more: opening the door to simpler climate models. Eos https://doi.org/10.1029/2017EO079417 (2017).
Hsu, T.-Y., Primeau, F. & Magnusdottir, G. A Hierarchy of global ocean models coupled to CESM1. J. Adv. Model. Earth Syst. https://doi.org/10.1029/2021MS002979 (2022).
Emanuel, K. A., Wing, A. A. & Vincent, E. M. Radiative–convective instability. J. Adv. Model. Earth Syst. https://doi.org/10.1002/2013MS000270 (2013).
Zhang, C., Adames, A. F., Khouider, B., Wang, B. & Yang, D. Four rheories of the Madden–Julian oscillation. Rev. Geophys. https://doi.org/10.1029/2019RG000685 (2020).
Bony, S. et al. Observed modulation of the tropical radiation budget by deep convective organization and lower-tropospheric stability. AGU Adv. https://doi.org/10.1029/2019AV000155 (2020). This paper shows how small-scale convective instabilities neglected by the standard approach destabilize the large-scale tropical circulation.
Klein, R. Scale-dependent models for atmospheric flows. Annu. Rev. Fluid Mech. 42, 249–274 (2010).
Google Scholar
Neal, E., Nakamura, N. & Huang, C. The 2021 Pacific northwest heat wave and associated blocking: meteorology and the role of an upstream cyclone as a diabatic source of wave activity. Geophys. Res. Lett. https://doi.org/10.1029/2021GL097699 (2022).
Rothlisberger, M. & Papritz, L. Quantifying the physical processes leading to atmospheric hot extremes at a global scale. Nat. Geosci. https://doi.org/10.1038/s41561-023-01126-1 (2023).
Zhang, Y. & Boos, W. R. An upper bound for extreme temperatures over midlatitude land. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.221527812 (2023).
Shaw, T. A. & Miyawaki, O. Fast upper-level jet stream winds get faster under climate change. Nat. Clim. Change 14, 61–67 (2023).
Google Scholar
Lin, N. & Emanuel, K. A. Grey swan tropical cyclones. Nat. Clim. Change 6, 106–111 (2016).
Google Scholar
Kirtman, B. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 953–1028 (IPCC, Cambridge Univ. Press, 2013).
Zhao, S. et al. Explainable El Niño predictability from climate mode interactions. Nature 630, 891–898 (2024).
Google Scholar
Vallis, G. K. Atmospheric and Oceanic Fluid Dynamics Fundamentals and Large-scale Circulation (Cambridge Univ. Press, 2006).
Betts, A. K. & Miller, B. M. The representation of cumulus convection in numerical models. Meteorol. Monogr. 24, 107–121 (1993).
Google Scholar
Stevens, B. Quasi-steady analysis of a PBL model with an eddy-diffusivity profile and nonlocal fluxes. Mon. Weather Rev. 128, 13 (2000).
Google Scholar
Singh, M. S. & O’Gorman, P. A. Influence of entrainment on the thermal stratification in simulations of radiative–convective equilibrium. Geophys. Res. Lett. 40, 4398–4403 (2013).
Google Scholar
Rasp, S., Pritchard, M. & Gentine, P. Deep learning to represent subgrid processes in climate models. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1810286115 (2018).
Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).
Google Scholar
Maher, N. et al. The Max Planck Institute Grand Ensemble: enabling the exploration of climate system variability. J. Adv. Model. Earth Syst. 11, 2050–2069 (2019).
Google Scholar
Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020). This paper demonstrates the impact of noise (internal climate variability) on regional climate change using large ensembles of comprehensive climate models.
Google Scholar
Smith, D. et al. Attribution of multi-annual to decadal changes in the climate system: the Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP). Front. Clim. https://doi.org/10.3389/fclim.2022.955414 (2022).
Sexton, D. M. H. et al. A perturbed parameter ensemble of HadGEM3-GC3.05 coupled model projections: part 1: selecting the parameter combinations. Clim. Dyn. 56, 3395–3436 (2021).
Google Scholar
Eidhammer, T. et al. An extensible perturbed parameter ensemble (PPE) for the Community Atmosphere Model Version 6. EGUsphere https://doi.org/10.5194/egusphere-2023-2165 (2024).
Matsugishi, S., Ohno, T. & Satoh, M. Differences in the cloud, precipitation, and convection representation between the global sub-km mesh simulation and km simulations. EGUsphere https://doi.org/10.5194/egusphere-egu24-14676 (2024).
Hewitt, H., Fox-Kemper, B., Pearson, B., Roberts, M. & Klocke, D. The small scales of the ocean may hold the key to surprises. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01386-6 (2022).
Lorenz, E. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141 (1963).
Google Scholar
Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015). This paper reviews the factors contributing to the steady advances in numerical weather prediction skill.
Google Scholar
Smith, L. A. What might we learn from climate forecasts. Proc. Natl Acad. Sci. USA 99, 2487–2492 (2002).
Google Scholar
McWilliams, J. Irreducible imprecision in atmospheric and oceanic simulations. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.0702971104 (2007). This paper introduces the concept of structural instability and irreducible uncertainty for climate predictions.
Hawkins, E., Smith, R. S., Gregory, J. M. & Stainforth, D. M. Irreducible uncertainty in near-term climate projections. Clim. Dyn. 46, 3807–3819 (2016).
Schmidt, G. et al. CERESMIP: a climate modeling protocol to investigate recent trends in the Earth’s energy imbalance. Front. Clim. https://doi.org/10.3389/fclim.2023.1202161 (2023).
McKinnon, K. A. & Deser, C. Internal variability and regional climate trends in an observational large ensemble. J. Clim. 31, 6783–6802 (2018).
Google Scholar
Rodwell, M. J. et al. Characteristics of occasional poor medium-range weather forecasts for Europe. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-12-00099.1 (2013).
Mass, C. The uncoordinated giant II: why U.S. operational numerical weather prediction is still lagging and how to fix it. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-22-0037.1 (2023).
