References

Foundations of Climate Modeling

General Circulation Models, Scenarios, and Scientific Consensus

• Randall, D. A., et al. (2007). Climate models and their evaluation. In S. Solomon et al. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 589–662). Cambridge University Press. https://www.ipcc.ch/report/ar4/wg1/chapter-8-climate-models-and-their-evaluation/
• Flato, G., et al. (2013). Evaluation of climate models. In T. F. Stocker et al. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 741–866). Cambridge University Press. https://www.ipcc.ch/report/ar5/wg1/chapter-9-evaluation-of-climate-models/
• Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958. https://doi.org/10.5194/gmd-9-1937-2016
• Hausfather, Z., & Peters, G. P. (2020). Emissions – the ‘business as usual’ story is misleading. Nature, 577(7792), 618–620. https://doi.org/10.1038/d41586-020-00177-3
• Meehl, G. A., & Washington, W. M. (1996). El Niño-like climate change in a model with increased atmospheric CO₂ concentrations. Nature, 382(6589), 56-60. https://doi.org/10.1038/382056a0
• Stocker, T. F., et al. (2013). Technical summary. In T. F. Stocker et al. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 33–115). Cambridge University Press. https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_TS_FINAL.pdf
• Cook, J., et al. (2013). Quantifying the consensus on anthropogenic global warming in the scientific literature. Environmental Research Letters, 8(2), 024024. https://doi.org/10.1088/1748-9326/8/2/024024
• Knutti, R., Masson, D., & Gettelman, A. (2013). Climate model genealogy: Generation CMIP5 and how we got there. Geophysical Research Letters, 40(6), 1194-1199. https://doi.org/10.1002/grl.50256
• World Climate Research Programme (WCRP). (2023). Coupled Model Intercomparison Project (CMIP). https://www.wcrp-climate.org/wgcm-cmip
• Intergovernmental Panel on Climate Change (IPCC). (2021). Sixth Assessment Report: The Physical Science Basis. https://www.ipcc.ch/report/ar6/wg1/

Equilibrium Climate Sensitivity and Model Overestimation

• Intergovernmental Panel on Climate Change (IPCC). (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://www.ipcc.ch/report/ar6/wg1/
• Zelinka, M. D., Myers, T. A., McCoy, D. T., Po‐Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., & Taylor, K. E. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47(1), e2019GL085782. https://doi.org/10.1029/2019GL085782
• Lewis, N. & Curry, J. (2018). The impact of recent forcing and ocean heat uptake data on estimates of climate sensitivity. Journal of Climate, 31(15), 6051-6071. https://doi.org/10.1175/JCLI-D-17-0667.1
• McKitrick, R. & Christy, J. R. (2020). Pervasive warming bias in CMIP6 tropospheric layers. Earth and Space Science, 7(9), e2020EA001281. https://doi.org/10.1029/2020EA001281
• Santer, B. D., Bonfils, C., Painter, J. F., Zelinka, M. D., Mears, C., Wentz, F., Taylor, K. E., Gleckler, P. J., Peters, G. P., & Solomon, S. (2017). Human influence on the seasonal cycle of tropospheric temperature. Science, 361(6399), 245-250. https://doi.org/10.1126/science.aas8806
• UAH Satellite Temperature Dataset, Version 6.1. (2024). https://www.nsstc.uah.edu/climate/
• RSS Satellite Data. (2024). https://www.remss.com/
• Gregory, J. M., et al. (2004). A new method for diagnosing radiative forcing and climate sensitivity. Geophysical Research Letters, 31(3), L03205. https://doi.org/10.1029/2003GL018747
• Otto, A., et al. (2013). Energy budget constraints on climate response. Nature Geoscience, 6(6), 415–416. https://doi.org/10.1038/ngeo1836
• Lewis, N. (2023). Empirical estimates of equilibrium climate sensitivity using updated energy balance models. ESS Open Archive. https://doi.org/10.22541/essoar.169654678.46944811/v1
• Lüdecke, H.-J., et al. (2015). The medieval climate anomaly and the little ice age in temperature reconstructions. Climate of the Past, 11(10), 1239–1250. https://doi.org/10.5194/cp-11-1239-2015
• Stott, L., Timmermann, A., & Thunell, R. (2007). Southern hemisphere and deep-sea warming led deglacial atmospheric CO₂ rise and tropical warming. Science, 318(5849), 435-438. https://doi.org/10.1126/science.1143791
• Shakun, J. D., et al. (2012). Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation. Nature, 484(7392), 49-54. https://doi.org/10.1038/nature10915
• Crowley, T. J., & Unterman, M. B. (2013). Technical details on volcanic forcing. Nature Geoscience, 6(8), 621–623. https://doi.org/10.1038/ngeo1928
• Lindzen, R. S., & Choi, Y. S. (2011). On the observational determination of climate sensitivity and its implications. Asia-Pacific Journal of Atmospheric Sciences, 47(4), 377-390. https://doi.org/10.1007/s13143-011-0023-x
• Spencer, R. W., & Braswell, W. D. (2011). On the misdiagnosis of surface temperature feedbacks from variations in Earth’s radiant energy balance. Remote Sensing, 3(8), 1603-1613. https://doi.org/10.3390/rs3081603
• IPCC. (2013). Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://www.ipcc.ch/report/ar5/wg1/

Structural Uncertainty and Modeling Limits

• Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., & Zhang, X. Y. (2013). Clouds and aerosols. In Climate Change 2013: The Physical Science Basis (pp. 571-658). Cambridge University Press. https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter07_FINAL.pdf
• Zelinka, M. D., Myers, T. A., McCoy, D. T., Po‐Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., & Taylor, K. E. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47(1), e2019GL085782. https://doi.org/10.1029/2019GL085782
• Ceppi, P., & Nowack, P. (2021). Observational evidence that cloud feedback amplifies global warming. Proceedings of the National Academy of Sciences, 118(30), e2026290118. https://doi.org/10.1073/pnas.2026290118
• Schneider, T., Kaul, C. M., & Pressel, K. G. (2019). Possible climate transitions from breakup of stratocumulus decks under greenhouse warming. Nature Geoscience, 12(3), 163-167. https://doi.org/10.1038/s41561-019-0310-1
• Stevens, B., & Bony, S. (2013). What are climate models missing? Science, 340(6136), 1053-1054. https://doi.org/10.1126/science.1237554
• Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., Heydt, A. S., Knutti, R., Mauritsen, T., ... & Zelinka, M. D. (2020). An assessment of Earth's climate sensitivity using multiple lines of evidence. Reviews of Geophysics, 58(4), e2019RG000678. https://doi.org/10.1029/2019RG000678
• Stier, P., et al. (2013). Host of uncertainty: Aerosol-cloud interactions and climate sensitivity. Nature Geoscience, 6(3), 177–178. https://doi.org/10.1038/ngeo1735
• Hourdin, F., et al. (2017). The art and science of climate model tuning. Bulletin of the American Meteorological Society, 98(3), 589-602. https://doi.org/10.1175/BAMS-D-15-00135.1
• Mauritsen, T., & Stevens, B. (2015). Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nature Geoscience, 8(5), 346-351. https://doi.org/10.1038/ngeo2414
• IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://www.ipcc.ch/report/ar6/wg1/
• Sanderson, B. M., Knutti, R., & Caldwell, P. (2015). Addressing interdependency in a multimodel ensemble by interpolation of model properties. Journal of Climate, 28(13), 5150-5170. https://doi.org/10.1175/JCLI-D-14-00361.1
• Gettelman, A., & Sherwood, S. C. (2016). Processes responsible for cloud feedback. Current Climate Change Reports, 2(4), 179-189. https://doi.org/10.1007/s40641-016-0052-8
• Knutti, R., Masson, D., & Gettelman, A. (2013). Climate model genealogy: Generation CMIP5 and how models are related. Geophysical Research Letters, 40(6), 1194-1199. https://doi.org/10.1002/grl.50256

Natural Variability and Non-CO₂ Drivers

• Gray, L. J., Beer, J., Geller, M., Haigh, J. D., Lockwood, M., Matthes, K., Cubasch, U., Fleitmann, D., Harrison, G., Hood, L., Luterbacher, J., Meehl, G. A., Shindell, D., van Geel, B., & White, W. (2010). Solar influences on climate. Reviews of Geophysics, 48(4), RG4001. https://doi.org/10.1029/2009RG000282
• Meehl, G. A., Hu, A., & Tebaldi, C. (2021). Decadal climate prediction: Assessment and lessons for future efforts. Current Climate Change Reports, 7, 181–193. https://doi.org/10.1007/s40641-021-00181-w
• McGregor, S., Timmermann, A., & England, M. H. (2014). ENSO modulation of the Pacific decadal oscillation. Journal of Climate, 27(2), 712–725. https://doi.org/10.1175/JCLI-D-13-00296.1
• Mann, M. E., & Emanuel, K. A. (2006). Atlantic multidecadal oscillation and the likelihood of late 20th century Northern Hemisphere temperature trends. Geophysical Research Letters, 33(17), L17706. https://doi.org/10.1029/2006GL026747
• Xie, S.-P., & Kosaka, Y. (2017). What caused the global warming hiatus of 1998–2013? Current Climate Change Reports, 3, 128–140. https://doi.org/10.1007/s40641-017-0063-0
• Newman, M., Alexander, M. A., & Ault, T. R. (2016). The Pacific decadal oscillation, revisited. Journal of Climate, 29(12), 4399–4427. https://doi.org/10.1175/JCLI-D-15-0508.1
• Seager, R., Ting, M., Davis, M., Cane, M., Naik, N., & Cook, E. (2010). Tropical Pacific influence on North American climate during the 2011–2014 California drought. Geophysical Research Letters, 41(24), 8749–8757. https://doi.org/10.1002/2014GL062447
• Timmermann, A., & Jin, F.-F. (2002). A nonlinear mechanism for decadal El Niño amplitude changes. Geophysical Research Letters, 29(1), 3-1–3-4. https://doi.org/10.1029/2001GL013369
• Scaife, A. A., & Smith, D. (2018). A signal of the North Atlantic oscillation in the global mean surface temperature. Nature Geoscience, 11, 12-17. https://doi.org/10.1038/s41561-017-0036-2
• Trenberth, K. E., & Shea, D. J. (2006). Atlantic hurricanes and natural variability in 2005. Geophysical Research Letters, 33(12), L12704. https://doi.org/10.1029/2006GL026894
• Delworth, T. L., Zeng, F., & Vecchi, G. A. (2022). Improved simulation of Atlantic multidecadal variability in the latest generation of CMIP models. Nature Communications, 13, 4307. https://doi.org/10.1038/s41467-022-32007-0
• Benestad, R. E. (2016). Empirical-statistical downscaling in climate modeling. Eos, 97. https://doi.org/10.1029/2016EO055403
• Bell, T. L., & Chelliah, M. (2006). Leading modes of interannual variability in the global ocean. Journal of Climate, 19(19), 4923-4940. https://doi.org/10.1175/JCLI3908.1
• Lean, J. L., & Rind, D. H. (2008). How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophysical Research Letters, 35(18), L18701. https://doi.org/10.1029/2008GL034864
• Hegerl, G. C., & Zwiers, F. W. (2011). Use of models in detection and attribution of climate change. WIREs Climate Change, 2(4), 570-591. https://doi.org/10.1002/wcc.121

Regional Failures and Secondary Effect Miscalculations

Regional Precipitation Bias and Model Error

• Giorgi, F., & Gutowski, W. J. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40, 467-490. https://doi.org/10.1146/annurev-environ-102014-021217
• Dirmeyer, P. A., et al. (2018). Challenges in understanding and predicting regional precipitation. Earth System Dynamics, 9(2), 545-564. https://doi.org/10.5194/esd-9-545-2018
• Liu, P., et al. (2022). Quantifying uncertainty in regional precipitation projections. Nature Communications, 13, 3257. https://doi.org/10.1038/s41467-022-30923-9

Monsoon Simulation Limitations

• Turner, A. G., & Annamalai, H. (2012). Climate change and the South Asian summer monsoon. Nature Climate Change, 2, 587-595. https://doi.org/10.1038/nclimate1495
• Ramesh, K. V., et al. (2021). Limitations of CMIP6 models in simulating South Asian summer monsoon. Scientific Reports, 11, 16290. https://doi.org/10.1038/s41598-021-95813-1
• Ashfaq, M., et al. (2022). Evaluation of regional monsoon representation in CMIP6 models. Climate Dynamics, 58, 2671-2686. https://doi.org/10.1007/s00382-021-06038-2

Tropical Cyclone Modeling and Resolution Constraints

• Walsh, K. J. E., et al. (2016). Tropical cyclones and climate change. Wiley Interdisciplinary Reviews: Climate Change, 7(1), 65-89. https://doi.org/10.1002/wcc.371
• Roberts, M. J., et al. (2020). Benefits of high-resolution tropical cyclone modeling. Bulletin of the American Meteorological Society, 101(11), E1847-E1863. https://doi.org/10.1175/BAMS-D-19-0303.1
• Knutson, T. R., et al. (2019). Tropical cyclones and climate change assessment: Part II. Bulletin of the American Meteorological Society, 100(10), 1987-2000. https://doi.org/10.1175/BAMS-D-18-0189.1

Ocean-Atmosphere Coupling and Feedback Errors

• Jackson, L. C., et al. (2020). AMOC in CMIP6 models: historical and future changes. Geophysical Research Letters, 47(9), e2019GL086900. https://doi.org/10.1029/2019GL086900
• Zhang, L., & Wang, C. (2022). ENSO simulation drift in CMIP6 models. Journal of Climate, 35(13), 4083-4099. https://doi.org/10.1175/JCLI-D-21-0860.1
• Richter, I., et al. (2021). Systematic biases in eastern Pacific SSTs in CMIP-class models. Geophysical Research Letters, 48, e2021GL092496. https://doi.org/10.1029/2021GL092496

Land-Use, Carbon Sink, and Forest Neutrality Misrepresentation

• Bastos, A., et al. (2020). The global forest carbon sink is declining. Nature, 583, 524-529. https://doi.org/10.1038/s41586-020-2497-4
• Friedlingstein, P., et al. (2023). Global carbon budget 2023. Earth System Science Data, 15, 5301-5366. https://doi.org/10.5194/essd-15-5301-2023
• Anderegg, W. R. L., et al. (2020). Climate-driven risks to the climate mitigation potential of forests. Science, 368, eaaz7005. https://doi.org/10.1126/science.aaz7005

Antarctic Ice-Ocean Feedbacks and Sea Level Rise

• Rignot, E., et al. (2019). Four decades of Antarctic Ice Sheet mass balance. Proceedings of the National Academy of Sciences, 116(4), 1095-1103. https://doi.org/10.1073/pnas.1812883116
• Seroussi, H., et al. (2020). ISMIP6 Antarctic projections. The Cryosphere, 14, 3033-3070. https://doi.org/10.5194/tc-14-3033-2020
• Scambos, T. A., et al. (2021). Ice shelf collapse and glacier acceleration in Antarctica. Nature, 593, 383-389. https://doi.org/10.1038/s41586-021-03427-0

Systemic and Secondary Effects in Climate Risk

• IPCC. (2023). AR6 Synthesis Report: Climate Change 2023. Intergovernmental Panel on Climate Change. https://www.ipcc.ch/report/ar6/syr/
• Schellnhuber, H. J., et al. (2016). Why the right climate target was agreed in Paris. Nature Climate Change, 6, 649-653. https://doi.org/10.1038/nclimate3013
• Lenton, T. M., et al. (2019). Climate tipping points - too risky to bet against. Nature, 575, 592-595. https://doi.org/10.1038/d41586-019-03595-0

Forecast Failures and the Collapse of Policy Legitimacy

Arctic Sea Ice and Glacier Forecast Failures

• Serreze, M. C., & Stroeve, J. (2015). Arctic sea ice trends, variability and implications for seasonal ice forecasting. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2045), 20140159. https://doi.org/10.1098/rsta.2014.0159
• Stroeve, J., et al. (2012). Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophysical Research Letters, 39(16), L16502. https://doi.org/10.1029/2012GL052676
• Bolch, T., et al. (2012). The state and fate of Himalayan glaciers. Science, 336(6079), 310-314. https://doi.org/10.1126/science.1215828
• Scherler, D., Bookhagen, B., & Strecker, M. R. (2011). Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Nature Geoscience, 4(3), 156-159. https://doi.org/10.1038/ngeo1068
• IPCC. (2010). IPCC statement on the melting of Himalayan glaciers. https://www.ipcc.ch/site/assets/uploads/2018/05/ipccstatementonhimalayans-1.pdf

Scenario Misuse and RCP 8.5 Overapplication

• Hausfather, Z., & Peters, G. P. (2020). Emissions – the “business as usual” story is misleading. Nature, 577(7792), 618-620. https://doi.org/10.1038/d41586-020-00177-3
• Ritchie, J., & Dowlatabadi, H. (2017). Why do climate scenarios return to coal? Energy, 140, 1276-1291. https://doi.org/10.1016/j.energy.2017.08.083
• IPCC. (2021). AR6 Synthesis Report: Climate Change 2021. https://www.ipcc.ch/report/ar6/syr/

Carbon Pricing Model Fragility

• Pindyck, R. S. (2017). The use and misuse of models for climate policy. Review of Environmental Economics and Policy, 11(1), 100-114. https://doi.org/10.1093/reep/rew012
• Nordhaus, W. D. (2017). Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences, 114(7), 1518-1523. https://doi.org/10.1073/pnas.1609244114
• Barron, A. R., et al. (2018). Policy insights from the EMF 32 study on U.S. carbon tax scenarios. Climate Change Economics, 9(1), 1840001. https://doi.org/10.1142/S2010007818400015

Policy Laundering and Regulatory Overreach

• Rayner, S. (2010). How to eat an elephant: a bottom-up approach to climate policy. Climate Policy, 10(6), 615-621. https://doi.org/10.3763/cpol.2010.0138
• Beck, S., & Mahony, M. (2017). The IPCC and the politics of anticipation. Nature Climate Change, 7(5), 311-313. https://doi.org/10.1038/nclimate3264
• Grundmann, R. (2016). Climate change as a wicked social problem. Nature Geoscience, 9, 562-563. https://doi.org/10.1038/ngeo2780

ESG Modeling and CMIP Fallacies

• Stenek, V., et al. (2022). How to use climate scenarios for risk management and strategic planning. International Finance Corporation.https://www.ifc.org/wps/wcm/connect/industry_ext_content/ifc_external_corporate_site/financial+institutions/resources/publications/em-compass-note-109
• Dietz, S., et al. (2021). Financial markets and climate transition risk: Evidence from global physical climate risk. Review of Financial Studies, 34(7), 3103-3140. https://doi.org/10.1093/rfs/hhab041
• Tett, S. F. B., et al. (2017). Uncertainty and scenario-based planning for climate risk. Climatic Change, 145(1-2), 1-13. https://doi.org/10.1007/s10584-017-2065-3

The Consensus Deception and Manufactured Agreement

Cook et al. (2013) and the “97% Consensus”

• Cook, J., Nuccitelli, D., Green, S. A., Richardson, M., Winkler, B., Painting, R., ... & Skuce, A. (2013). Quantifying the consensus on anthropogenic global warming in the scientific literature. Environmental Research Letters, 8(2), 024024. https://doi.org/10.1088/1748-9326/8/2/024024
• Powell, J. L. (2015). Climate scientists virtually unanimous: Anthropogenic global warming is true. Bulletin of Science, Technology & Society, 35(5-6), 121–124. https://doi.org/10.1177/0270467616634958
• Tol, R. S. J. (2016). The structure of the climate change debate. Energy Policy, 98, 642–647. https://doi.org/10.1016/j.enpol.2016.09.009

Bray & von Storch Surveys and Climate Scientist Perspectives

• Bray, D., & von Storch, H. (2010). CliSci2008: A survey of the perspectives of climate scientists concerning climate science and climate change. Reports on Polar and Marine Research, 399, 1–146. https://epic.awi.de/id/eprint/30214/
• Bray, D., & von Storch, H. (2014). The Bray and von Storch 2015 survey of climate scientists. Meteorologische Zeitschrift, 23(6), 493–500. https://doi.org/10.1127/metz/2014/0616

On Consensus, Confirmation Bias, and Suppression

• Oreskes, N. (2004). The scientific consensus on climate change. Science, 306(5702), 1686. https://doi.org/10.1126/science.1103618
• Kahan, D. M., Jenkins‐Smith, H., & Braman, D. (2011). Cultural cognition of scientific consensus. Journal of Risk Research, 14(2), 147–174. https://doi.org/10.1080/13669877.2010.511246
• Hulme, M. (2013). Uncertainties in climate science and climate policy. In C. Heyward & D. Roser (Eds.), Climate justice in a non-ideal world (pp. 35–52). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199650568.003.0003