Quantifying uncertainty from aerosol and atmospheric parameters and their impact on climate sensitivity
Open accessPublished version of a preprint
Abstract
Climate sensitivity in Earth system models (ESMs) is an emergent property that is affected by structural (missing or inaccurate model physics) and parametric (variations in model parameters) uncertainty. This work provides the first quantitative assessment of the role of compensation between uncertainties in aerosol forcing and atmospheric parameters, and their impact on the climate sensitivity of the Community Atmosphere Model, Version 4 (CAM4). Running the model with prescribed ocean and ice conditions, we perturb four parameters related to sulfate and black carbon aerosol radiative forcing and distribution, as well as five atmospheric parameters related to clouds, convection, and radiative flux. In this experimental setup where aerosols do not affect the properties of clouds, the atmospheric parameters explain the majority of variance in climate sensitivity, with two parameters being the most important: one controlling low cloud amount, and one controlling the timescale for deep convection. Although the aerosol parameters strongly affect aerosol optical depth, their impacts on climate sensitivity are substantially weaker than the impacts of the atmospheric parameters, but this result may depend on whether aerosol–cloud interactions are simulated. Based on comparisons to inter-model spread of other ESMs, we conclude that structural uncertainties in this configuration of CAM4 likely contribute 3 times more to uncertainty in climate sensitivity than parametric uncertainties. We provide several parameter sets that could provide plausible (measured by a skill score) configurations of CAM4, but with different sulfate aerosol radiative forcing, black carbon radiative forcing, and climate sensitivity.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
[247] use Gaussian processes to emulate climate model outputs based on nine possible aerosol parameter settings, allowing them to establish plausible parameter ranges with only 350 climate model runs instead of >100,000.
“As a recent example, [247] use Gaussian processes to emulate climate model outputs based on nine possible aerosol parameter settings, allowing them to establish plausible parameter ranges (and thus much better calibrated error-bars) with only 350 climate model runs instead of >100,000.”
From Tackling climate change with machine learning (2023) · cites this paper as [247]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 8,270 · see citing works
Bounding the role of black carbon in the climate system: A scientific assessment
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., et al. (2013) · Journal of Geophysical Research: Atmospheres · doi:10.1002/jgrd.50171 · Cited by 6,385 · see citing works
McKay, M. D., Beckman, R., & Conover, W. J. (1979) · Technometrics · doi:10.2307/1268522 · Cited by 3,834 · see citing works
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979) · Technometrics · doi:10.1080/00401706.1979.10489755 · Cited by 3,323 · see citing works
The Community Climate System Model Version 4
Gent, P. R., Gokhan Danabasoglu, C., Donner, L. J., Holland, M. M., Hunke, E., Jayne, S. R., Lawrence, D. M., Neale, R., Rasch, P. J., Vertenstein, M., et al. (2011) · Journal of Climate · doi:10.1175/2011jcli4083.1 · Cited by 3,138 · see citing works
Kay, J. E., Deser, C., Phillips, A. S., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S. C., Gokhan Danabasoglu, C., Edwards, J., et al. (2014) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-13-00255.1 · Cited by 2,496 · see citing works
A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output
Saltelli, A., Tarantola, S., & Chan, K. P.-S. (1999) · Technometrics · doi:10.1080/00401706.1999.10485594 · Cited by 2,275 · see citing works
Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., et al. (2010) · Atmospheric Chemistry and Physics · doi:10.5194/acp-10-7017-2010 · Cited by 2,105 · see citing works
Change, I. P. O. C. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781107415324.020 · Cited by 1,836 · see citing works
Change, I. P. O. C. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781107415324.016 · Cited by 1,482 · see citing works
A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output
Saltelli, A., Tarantola, S., & Chan, K. (1999) · Technometrics · doi:10.2307/1270993 · Cited by 1,435 · see citing works
Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate Sensitivity
Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., Takemura, T., Chikira, M., Ogura, T., Sekiguchi, M., et al. (2010) · Journal of Climate · doi:10.1175/2010jcli3679.1 · Cited by 1,336 · see citing works
Large contribution of natural aerosols to uncertainty in indirect forcing
Carslaw, K. S., Lee, L. A., Reddington, C. L. S., Pringle, K. J., Rap, A., Forster, P. M. D. F., Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre, L. A., et al. (2013) · Nature · doi:10.1038/nature12674 · Cited by 1,117 · see citing works
Clouds, circulation and climate sensitivity
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R. L., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., et al. (2015) · Nature Geoscience · doi:10.1038/ngeo2398 · Cited by 987 · see citing works
The Formulation and Atmospheric Simulation of the Community Atmosphere Model Version 3 (CAM3)
Collins, W. D., Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J. R., Williamson, D. L., Briegleb, B. P., Bitz, C. M., Lin, S., & Zhang, M. (2006) · Journal of Climate · doi:10.1175/jcli3760.1 · Cited by 922 · see citing works
Global aerosol climatology from the MODIS satellite sensors
Remer, L. A., Kleidman, R. G., Levy, R. C., Kaufman, Y. J., Tanré, D., Mattoo, S., Martins, J. V., Ichoku, C. M., Koren, I., Yu, H., et al. (2008) · Journal of Geophysical Research Atmospheres · doi:10.1029/2007jd009661 · Cited by 807 · see citing works
Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere‐ocean climate models
Andrews, T., Gregory, J. M., Webb, M. J., & Taylor, K. E. (2012) · Geophysical Research Letters · doi:10.1029/2012gl051607 · Cited by 781 · see citing works
Spread in model climate sensitivity traced to atmospheric convective mixing
Sherwood, S. C., Bony, S., & Dufresne, J.-L. (2014) · Nature · doi:10.1038/nature12829 · Cited by 762 · see citing works
Selecting global climate models for regional climate change studies
Pierce, D. W., Barnett, T. P., Santer, B. D., & Gleckler, P. J. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0900094106 · Cited by 641 · see citing works
The Art and Science of Climate Model Tuning
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., et al. (2017) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-15-00135.1 · Cited by 611 · see citing works
Roustant, O., Ginsbourger, D., & Deville, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v051.i01 · Cited by 595 · see citing works
Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric General Circulation Models
CESS, R. D., Potter, G. L., Blanchet, J., Boer, G. J., Ghan, S. J., Kiehl, J. T., Treut, H. L., Li, L., Liang, X., Mitchell, J. F. B., et al. (1989) · Science · doi:10.1126/science.245.4917.513 · Cited by 562 · see citing works
Tuning the climate of a global model
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M. A., Helmuth Haak, G., Jungclaus, J. H., Klocke, D., Matei, D., et al. (2012) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2012ms000154 · Cited by 530 · see citing works
Papale, D., Heiskanen, J., Brümmer, C., Buchmann, N., Calfapietra, C., Carrara, A., Chen, H., Gielen, B., Gkritzalis, T., Hammer, S., et al. (2023) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-23-0216.1 · Cited by 436 · see citing works
Zhao, M., Golaz, J., Held, I. M., Guo, H., Balaji, V., BENSON, R., Chen, J., Chen, X., Donner, L. J., Dunne, J. P. P., et al. (2018) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2017ms001209 · Cited by 365 · see citing works
Forster, P. M. D. F., Andrews, T., Good, P., Gregory, J. M., Jackson, L., & Zelinka, M. D. (2013) · Journal of Geophysical Research Atmospheres · doi:10.1002/jgrd.50174 · Cited by 344 · see citing works
Twentieth century climate model response and climate sensitivity
Kiehl, J. T. (2007) · Geophysical Research Letters · doi:10.1029/2007gl031383 · Cited by 329 · see citing works
Hourdin, F., Foujols, M.-A., Codron, F., Guemas, V., Dufresne, J.-L., Bony, S., Denvil, S., Guez, L., Lott, F., Ghattas, J., et al. (2013) · Climate Dynamics · doi:10.1007/s00382-012-1411-3 · Cited by 302 · see citing works
Rethinking the Lower Bound on Aerosol Radiative Forcing
Stevens, B. (2015) · Journal of Climate · doi:10.1175/jcli-d-14-00656.1 · Cited by 234 · see citing works
Interpretation of Snow-Climate Feedback as Produced by 17 General Circulation Models
CESS, R. D., Potter, G. L., Zhang, M., Blanchet, J., Chalita, S., Colman, R. A., Dazlich, D. A., C, A. D. D. G., Dymnikov, V., Galin, V. Y., et al. (1991) · Science · doi:10.1126/science.253.5022.888 · Cited by 176 · see citing works
Dependence of climate forcing and response on the altitude of black carbon aerosols
Ban‐Weiss, G., Cao, L., Bala, G., & Caldeira, K. (2011) · Climate Dynamics · doi:10.1007/s00382-011-1052-y · Cited by 171 · see citing works
Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters
Lee, L. A., Carslaw, K. S., Pringle, K. J., Mann, G. W., & Spracklen, D. V. (2011) · Atmospheric chemistry and physics · doi:10.5194/acp-11-12253-2011 · Cited by 165 · see citing works
Cloud tuning in a coupled climate model: Impact on 20th century warming
Golaz, J., Horowitz, L. W., & Levy, H., II (2013) · Geophysical Research Letters · doi:10.1002/grl.50232 · Cited by 138 · see citing works
Shields, C. A., Bailey, D. A., Danabasoglu, G., Jochum, M., Kiehl, J. T., Levis, S., & Park, S. (2012) · Journal of Climate · doi:10.1175/jcli-d-11-00260.1 · Cited by 135 · see citing works
Regayre, L. A., Johnson, J. S., Yoshioka, M., Pringle, K. J., Sexton, D. M. H., Booth, B. B. B., Lee, L. A., Bellouin, N., & Carslaw, K. S. (2018) · Atmospheric chemistry and physics · doi:10.5194/acp-18-9975-2018 · Cited by 122 · see citing works
Uncertainty analysis for estimates of the first indirect aerosol effect
Chen, Y., & Penner, J. E. (2005) · Atmospheric chemistry and physics · doi:10.5194/acp-5-2935-2005 · Cited by 107 · see citing works
Using aquaplanets to understand the robust responses of comprehensive climate models to forcing
Medeiros, B., Stevens, B., & Bony, S. (2014) · Climate Dynamics · doi:10.1007/s00382-014-2138-0 · Cited by 101 · see citing works
Yang, B., Qian, Y., Berg, L. K., Ma, P., Wharton, S., Bulaevskaya, V., Yan, H., Hou, Z., & Shaw, W. J. (2016) · Boundary-Layer Meteorology · doi:10.1007/s10546-016-0185-2 · Cited by 99 · see citing works
Climate response to externally mixed black carbon as a function of altitude
Samset, B. H., & Myhre, G. (2015) · Journal of Geophysical Research Atmospheres · doi:10.1002/2014jd022849 · Cited by 95 · see citing works
Mapping the uncertainty in global CCN using emulation
Lee, L. A., Carslaw, K. S., Pringle, K. J., & Mann, G. W. (2012) · Atmospheric chemistry and physics · doi:10.5194/acp-12-9739-2012 · Cited by 95 · see citing works
Constraints on Model Response to Greenhouse Gas Forcing and the Role of Subgrid-Scale Processes
Sanderson, B. M., Knutti, R., Aina, T., Christensen, C., Faull, N., Frame, D. J., Ingram, W., Piani, C., Stainforth, D. A., Stone, D. A., et al. (2008) · Journal of Climate · doi:10.1175/2008jcli1869.1 · Cited by 68 · see citing works
The impact of structural error on parameter constraint in a climate model
McNeall, D., Williams, J., Booth, B. B. B., Betts, R., Challenor, P., Wiltshire, A., & Sexton, D. M. H. (2016) · Earth System Dynamics · doi:10.5194/esd-7-917-2016 · Cited by 64 · see citing works
Siler, N., Po–Chedley, S., & Bretherton, C. S. (2017) · Climate Dynamics · doi:10.1007/s00382-017-3673-2 · Cited by 40 · see citing works
Efficient screening of climate model sensitivity to a large number of perturbed input parameters
Covey, C., Lucas, D. D., Tannahill, J., Garaizar, X., & Klein, R. I. (2013) · Journal of Advances in Modeling Earth Systems · doi:10.1002/jame.20040 · Cited by 31 · see citing works
Kim, H., Kang, S. M., Hwang, Y., & Yang, Y. (2015) · Journal of Climate · doi:10.1175/jcli-d-15-0037.1 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Yoshioka, M., Regayre, L. A., Pringle, K. J., Johnson, J. S., Mann, G. W., Partridge, D. G., Sexton, D. M. H., Lister, G. M. S., Schutgens, N. A. J., Stier, P., et al. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001628 · Cited by 75 · see citing works
Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)
Elsaesser, G. S., Elsaesser, G. S., van Lier‐Walqui, M., Yang, Q., Kelley, M., Ackerman, A. S., Fridlind, A. M., Cesana, G., Schmidt, G. A., Wu, J., et al. (2025) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2024ms004713 · Cited by 26 · see citing works
Braghiere, R. K., Yamasoe, M. A., Rosário, N. E., da Rocha, H. R., Nogueira, J. D. S., & de Araújo, A. C. (2020) · Atmospheric chemistry and physics · doi:10.5194/acp-20-3439-2020 · Cited by 24 · see citing works
Toward Efficient Calibration of Higher‐Resolution Earth System Models
Fletcher, C. G., McNally, W., Virgin, J. G., & King, F. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002836 · Cited by 14 · see citing works
Self-citationZhao, A., Bollasina, M. A., Crippa, M., & Stevenson, D. S. (2019) · Atmospheric chemistry and physics · doi:10.5194/acp-19-14517-2019 · Cited by 12 · see citing works
Amazonian aerosol size distributions in a lognormal phase space: characteristics and trajectories
Unfer, G. R., Machado, L. A. T., Artaxo, P. E., Franco, M. A., Kremper, L. A., Pöhlker, M. L., Pöschl, U., & Pöhlker, C. (2024) · Atmospheric chemistry and physics · doi:10.5194/acp-24-3869-2024 · Cited by 8 · see citing works
Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models
Fischer, M., Knippertz, P., van der Linden, R., Lemburg, A., Pante, G., Proppe, C., & Marsham, J. H. (2024) · Weather and Climate Dynamics · doi:10.5194/wcd-5-511-2024 · Cited by 7 · see citing works
An Investigation of Parameter Sensitivity of Minimum Complexity Earth Simulator
Chen, J., Cui, H., Xu, Y., & Ge, Q. (2020) · Atmosphere · doi:10.3390/atmos11010095 · Cited by 7 · see citing works
Jaoui, M., Nestorowicz, K., Rudzinski, K. J., Lewandowski, M., Kleindienst, T. E., Torres, J., Bulska, E., Danikiewicz, W., & Szmigielski, R. (2025) · Atmospheric chemistry and physics · doi:10.5194/acp-25-1401-2025 · Cited by 5 · see citing works
Different chlorine and hydroxyl radical environments impact m -xylene oxidation products
Bhattacharyya, N., Modi, M., Jahn, L. G., & Ruiz, L. H. (2023) · Environmental Science Atmospheres · doi:10.1039/d3ea00024a · Cited by 5 · see citing works
A New Approach to Sensible Heat Flux via CFD-Surface Renewal Integration
Li, Y., Hu, Y., Lu, Y., Fu, Y., & Wang, J. (2025) · Agronomy · doi:10.3390/agronomy15122708 · Cited by 1 · see citing works
Middle East dust as an important external driver of the Indian Ocean Dipole
Liu, G., Xie, S., Hansen, J. E., Wang, X., Dong, Y., & Li, J. (2026) · Nature Communications · doi:10.1038/s41467-026-68842-1 · Cited by 1 · see citing works
Chen, D., Ding, M., Sun, Q., & Zuo, T. (2026) · Chemosphere · doi:10.1016/j.chemosphere.2026.144967 · Cited by 1 · see citing works
Declining Geoengineering Efficacy Caused by Cloud Feedbacks in Transient Solar Dimming Experiments
Virgin, J. G., & Fletcher, C. G. (2023) · Journal of Climate · doi:10.1175/jcli-d-23-0333.1
Self-citationFeinberg, A. A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6237623
Global fully coupled climate-aerosol CMA-CPSv4 – Part 1: Aerosol simulation performance
Zheng, M., Wu, T., Lu, Y., Jie, W., Liu, Y., Xin, X., Zhang, J., Zhao, H., Zhang, X., & Yang, J. (2026) · Geoscientific model development · doi:10.5194/gmd-19-7279-2026
- Authors
- Fletcher, C. G., Kravitz, B., & Badawy, B.
- Affiliations
- University of Waterloo (CA)
- Pacific Northwest National Laboratory (US)
- Environment and Climate Change Canada (CA)
- Corresponding author
- Fletcher, Christopher G.
- Kravitz, Ben
- Badawy, Bakr
- Venue
- Atmospheric Chemistry and Physics
- Publisher
- Copernicus GmbH
- Published
- 2018-12-11
- Volume
- 18
- Issue
- 23
- Pages
- 17529-17543
- ISSN
- 1680-7316 (ISSN-L)
- 1680-7324
- OpenAlex
- W2888391110
- Cited by
- 20
- References
- 45
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 3 · 2020: 2 · 2019: 2 · 2018: 1
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Atmospheric chemistry and physics (publishedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Biogeosciences (European Geosciences Union) (submittedVersion, cc-by)
- Funders
- Natural Sciences and Engineering Research Council of Canada (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- climate sensitivity, uncertainty quantification, aerosol radiative forcing, sulfate aerosols, black carbon, aerosol-cloud interactions, structural uncertainty, low cloud fraction, Earth system models, inter-model spread
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
