The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
Radiative forcing in the ACCMIP historical and future climate simulations
Open accessAbstract
Abstract. The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) examined the short-lived drivers of climate change in current climate models. Here we evaluate the 10 ACCMIP models that included aerosols, 8 of which also participated in the Coupled Model Intercomparison Project phase 5 (CMIP5). The models reproduce present-day total aerosol optical depth (AOD) relatively well, though many are biased low. Contributions from individual aerosol components are quite different, however, and most models underestimate east Asian AOD. The models capture most 1980–2000 AOD trends well, but underpredict increases over the Yellow/Eastern Sea. They strongly underestimate absorbing AOD in many regions. We examine both the direct radiative forcing (RF) and the forcing including rapid adjustments (effective radiative forcing; ERF, including direct and indirect effects). The models' all-sky 1850 to 2000 global mean annual average total aerosol RF is (mean; range) −0.26 W m−2; −0.06 to −0.49 W m−2. Screening based on model skill in capturing observed AOD yields a best estimate of −0.42 W m−2; −0.33 to −0.50 W m−2, including adjustment for missing aerosol components in some models. Many ACCMIP and CMIP5 models appear to produce substantially smaller aerosol RF than this best estimate. Climate feedbacks contribute substantially (35 to −58%) to modeled historical aerosol RF. The 1850 to 2000 aerosol ERF is −1.17 W m−2; −0.71 to −1.44 W m−2. Thus adjustments, including clouds, typically cause greater forcing than direct RF. Despite this, the multi-model spread relative to the mean is typically the same for ERF as it is for RF, or even smaller, over areas with substantial forcing. The largest 1850 to 2000 negative aerosol RF and ERF values are over and near Europe, south and east Asia and North America. ERF, however, is positive over the Sahara, the Karakoram, high Southern latitudes and especially the Arctic. Global aerosol RF peaks in most models around 1980, declining thereafter with only weak sensitivity to the Representative Concentration Pathway (RCP). One model, however, projects approximately stable RF levels, while two show increasingly negative RF due to nitrate (not included in most models). Aerosol ERF, in contrast, becomes more negative during 1980 to 2000. During this period, increased Asian emissions appear to have a larger impact on aerosol ERF than European and North American decreases due to their being upwind of the large, relatively pristine Pacific Ocean. There is no clear relationship between historical aerosol ERF and climate sensitivity in the CMIP5 subset of ACCMIP models. In the ACCMIP/CMIP5 models, historical aerosol ERF of about −0.8 to −1.5 W m−2 is most consistent with observed historical warming. Aerosol ERF masks a large portion of greenhouse forcing during the late 20th and early 21st century at the global scale. Regionally, aerosol ERF is so large that net forcing is negative over most industrialized and biomass burning regions through 1980, but remains strongly negative only over east and southeast Asia by 2000. Net forcing is strongly positive by 1980 over most deserts, the Arctic, Australia, and most tropical oceans. Both the magnitude of and area covered by positive forcing expand steadily thereafter.
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.
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
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052
The representative concentration pathways: an overview
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., et al. (2011) · Climatic Change · doi:10.1007/s10584-011-0148-z · Cited by 7,741
Self-citationThe MODIS Aerosol Algorithm, Products, and Validation
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R.-R., Ichoku, C., Levy, R. C., Kleidman, R. G., et al. (2005) · Journal of the Atmospheric Sciences · doi:10.1175/jas3385.1 · Cited by 3,122
Hansen, J., Sato, M., Ruedy, R., Lo, K., Lea, D. W., & Medina-Elizade, M. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606291103 · Cited by 2,134
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
Self-citationUncertainty estimates in regional and global observed temperature changes: A new data set from 1850
Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B., & Jones, P. D. (2006) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2005jd006548 · Cited by 2,040
Development and evaluation of an Earth-System model – HadGEM2
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., et al. (2011) · Geoscientific Model Development · doi:10.5194/gmd-4-1051-2011 · Cited by 1,516
Self-citationHansen, J., Sato, M., Ruedy, R., Nazarenko, L., Lacis, A., Schmidt, G. A., Russell, G., Aleinov, I., Bauer, M., Bauer, S., et al. (2005) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2005jd005776 · Cited by 1,486
Self-citationMIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., et al. (2011) · Geoscientific Model Development · doi:10.5194/gmd-4-845-2011 · Cited by 1,395
Self-citationSimultaneously Mitigating Near-Term Climate Change and Improving Human Health and Food Security
Shindell, D., Kuylenstierna, J. C. I., Vignati, E., van Dingenen, R., Amann, M., Klimont, Z., Anenberg, S. C., Muller, N., Janssens-Maenhout, G., Raes, F., et al. (2012) · Science · doi:10.1126/science.1210026 · Cited by 1,382
Self-citation
Cited by
(2019) · doi:10.5194/gmd-12-1443-2019 · Cited by 1,093
Young, P. J., Archibald, A. T., Bowman, K. W., Lamarque, J.-F., Naik, V., Stevenson, D. S., Tilmes, S., Voulgarakis, A., Wild, O., Bergmann, D., et al. (2013) · Atmospheric Chemistry and Physics · doi:10.5194/acp-13-2063-2013 · Cited by 677
Self-citationScattering and absorbing aerosols in the climate system
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., et al. (2022) · Nature Reviews Earth & Environment · doi:10.1038/s43017-022-00296-7 · Cited by 561
Air Quality and Climate Connections
Fiore, A. M., Naik, V., & Leibensperger, E. M. (2015) · Journal of the Air & Waste Management Association · doi:10.1080/10962247.2015.1040526 · Cited by 537
Chemistry and the Linkages between Air Quality and Climate Change
von Schneidemesser, E., Monks, P. S., Allan, J. D., Bruhwiler, L., Forster, P., Fowler, D., Lauer, A., Morgan, W. T., Paasonen, P., Righi, M., et al. (2015) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00089 · Cited by 522
Metadata
- Authors
- Shindell, D. T., Lamarque, J.-F., Schulz, M., Flanner, M., Jiao, C., Chin, M., Young, P. J., Lee, Y. H., Rotstayn, L., Mahowald, N., Milly, G., Faluvegi, G., Balkanski, Y., Collins, W. J., Conley, A. J., Dalsoren, S., Easter, R., Ghan, S., Horowitz, L., Liu, X., Myhre, G., Nagashima, T., Naik, V., Rumbold, S. T., Skeie, R., Sudo, K., Szopa, S., Takemura, T., Voulgarakis, A., Yoon, J.-H., & Lo, F.
- Venue
- Atmospheric Chemistry and Physics
- Publisher
- Copernicus GmbH
- Published
- 2013-03-15
- Volume
- 13
- Issue
- 6
- Pages
- 2939-2974
- ISSN
- 1680-7316 (ISSN-L)
- 1680-7324
- OpenAlex
- W1968340782
- Cited by
- 447
- References
- 83
- FWCI
- 41.30
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Radiative forcing in the ACCMIP historical and future climate simulations. https://app.cochat.ai/papers/10.5194/acp-13-2939-2013
- The paper
- Shindell, D. T., Lamarque, J.-F., Schulz, M., Flanner, M., Jiao, C., Chin, M., Young, P. J., Lee, Y. H., Rotstayn, L., Mahowald, N., Milly, G., Faluvegi, G., Balkanski, Y., Collins, W. J., Conley, A. J., Dalsoren, S., Easter, R., Ghan, S., Horowitz, L., . . . Lo, F. (2013). Radiative forcing in the ACCMIP historical and future climate simulations. Atmospheric Chemistry and Physics. https://doi.org/10.5194/acp-13-2939-2013
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.