The DOI matches its Crossref record; 62 of the 69 references it cites were found in a registry or an index.
Probabilistic climate change projections using neural networks
Open accessAbstract
Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.
Overview
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
Verification
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?
In the citing paper
“Calculations from this model revealed that almost half the members surpassed the surface heating range that the IPCC estimated earlier.”
From Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action (2022) · cites this paper as Knutti et al., 2003
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Climate change 2001 : the scientific basis
Houghton, J., Ding, Y., Griggs, D., Noguer, M., van der Linden, P. V. D., Dai, X., Maskell, K., & Johnson, C. (2001) · Foreign Affairs · doi:10.2307/20033020 · Cited by 14,162
Training feedforward networks with the Marquardt algorithm
Hagan, M., & Menhaj, M. B. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329697 · Cited by 7,778
Special report on emissions scenarios
Nakićenović, N., Alcamo, J., Davis, G. R., de Vries, B. J. M., Fenhann, J. V., Gaffin, S., Gregory, K., Grübler, A., Jung, T. Y., Kram, T., et al. (2000) · Cited by 6,089
Hagan, M., Demuth, H. B., Beale, M. H., & Jes?s, O. D. (2016) · Institutional Repository of the National University Open and Distance UNAD (Universidad Nacional Abierta y a Distancia) · Cited by 5,133
Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model
Cox, P. M., Betts, R., JONES, C. D., Spall, S. A., & Totterdell, I. J. (2000) · Nature · doi:10.1038/35041539 · Cited by 4,169
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Crämer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., et al. (2003) · Global Change Biology · doi:10.1046/j.1365-2486.2003.00569.x · Cited by 3,343
Perceptrons: An Introduction to Computational Geometry
Minsky, M., & Papert, S. A. (1969) · Cited by 3,273
Mullin, A., & Rosenblatt, F. F. (1963) · American Mathematical Monthly · doi:10.2307/2312103 · Cited by 2,211
Causes of Climate Change Over the Past 1000 Years
Crowley, T. J. (2000) · Science · doi:10.1126/science.289.5477.270 · Cited by 2,068
Research works printed but not linked: 3
- Church JA, Gregory JM (2001) Changes in sea level. Climate change 2001: the scientific basis. In: Houghton JT et al. (eds) Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, pp 639-693
- Legget J, Pepper WJ, Swart RJ (1992) Climate change 1992. The Supplementary Report to the IPCC Scientific Assessment, Ch Emissions Scenarios for IPCC: an Update. Cambridge Uni- versity Press, Cambridge, UK, pp 69-95
- Shine KP, Derwent RG, Wuebbles DJ, Morcrette JJ (1995) Radi- ative forcing of climate. In: Houghton JY, Jenkins GJ, Ephraums JJ (eds) Climate change, the IPCC scientific assess- ment, Cambridge University Press, Cambridge, UK
Cited by
Sorted by citations
Greenhouse-gas emission targets for limiting global warming to 2 °C
Meinshausen, M., Meinshausen, N., Hare, W., Raper, S. C. B., Frieler, K., Knutti, R., Frame, D. J., & Allen, M. R. (2009) · Nature · doi:10.1038/nature08017 · Cited by 2,775
Self-citationFowler, H. J., Blenkinsop, S., & Tebaldi, C. (2007) · International Journal of Climatology · doi:10.1002/joc.1556 · Cited by 2,160
The use of the multi-model ensemble in probabilistic climate projections
Tebaldi, C., & Knutti, R. (2007) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2076 · Cited by 1,862
Self-citationChallenges in combining projections from multiple climate models.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. A. (2010) · Journal of Climate · doi:10.1175/2009jcli3361.1 · Cited by 1,270
Self-citationDwivedi, Y. K., Hughes, L., Kar, A. K., Baabdullah, A. M., Grover, P., Abbas, R., Andreini, D., Abumoghli, I., Barlette, Y., Bunker, D., et al. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102456 · Cited by 761
Metadata
- Authors
- Knutti, R., Stocker, T. F., Joos, F., & Plattner, G.-K.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Knutti, R.
- Venue
- Climate Dynamics
- Published
- 2003-09-01
- Volume
- 21
- Issue
- 3-4
- Pages
- 257-272
- ISSN
- 0930-7575 (ISSN-L)
- 1432-0894
- OpenAlex
- W2167220187
- Cited by
- 197
- References
- 69
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 6 · 2020: 6 · 2019: 5 · 2018: 6 · 2017: 7 · 2016: 8 · 2015: 6 · 2014: 8 · 2013: 9 · 2012: 8
- FWCI
- 15.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Climate Dynamics (publishedVersion, cc-by)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Geoengineering
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Keywords
- climate change projections, neural network surrogate model, climate model ensembles, climate sensitivity, radiative forcing, carbon cycle uncertainty, ocean heat uptake, surface warming, probability density function, emission scenarios, IPCC projections, model parameter uncertainty
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
