Differences in multi‐model ensembles of CMIP5 and CMIP6 projections for future droughts in South Korea
Abstract
Abstract This study quantified the uncertainties of future drought projections in the South Korea case by means of the reliability ensemble averaging (REA) of 10 GCM equivalents of the Coupled Model Intercomparison Project, Phases 5 and 6 (CMIP5 and CMIP6). Two meteorological drought indices, the Standardized Precipitation Index (SPI) and the Standardized Precipitation‐Evapotranspiration Index (SPEI) for Representative Concentration Pathway (RCP) 4.5 and RCP8.5 of CMIP5 and Shared Socioeconomic Pathway (SSP) 2‐4.5 and SSP5‐8.5 of CMIP6 were considered. The GCMs' performances for the historical period were evaluated, and their biases were corrected using quantile mapping. The multi‐model ensemble (MME) means of the GCMs were generated using the entropy and TOPSIS methods. The results showed that the TOPSIS MME estimated more intense droughts than the entropy MME. The levels of SPI and SPEI severity estimated using the entropy MME were higher than those from the TOPSIS MME. The SPI and SPEI outcomes of RCP4.5 were much more robust than those of SSP2‐4.5. The projected drought severity in the near future was much greater than in the far future, while the reliability of drought projections in the far future was much higher than in the near future. The reliability levels of drought projections for the SSP scenarios were higher than for the RCPs for most durations. This study can support planning and management of future droughts considering uncertainty variables.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
A non-parametric smoothing spline method is more reliable for correcting daily precipitation and temperature data in South Korea.
“This study used a non-parametric smoothing spline method as a transformation function, found to be more reliable for correcting daily precipitation and temperature data in South Korea in a previous study (Song et al. 2021).”
From Future Hydrological Drought Analysis Considering Agricultural Water Withdrawal Under SSP Scenarios (2022) · cites this paper as Song et al. 2021
Cites
The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., et al. (2016) · Geoscientific Model Development · doi:10.5194/gmd-9-3461-2016 · Cited by 5,241 · cited 5× · see citing works
“The combination of RCP and SSP can improve knowledge of future climate change (Eyring et al., 2016; O'Neill et al., 2016).”
BackgroundInfluentialDrought under global warming: a review
Dai, A. (2011) · WIREs Climate Change · doi:10.1002/wcc.81 · Cited by 3,523 · cited 1× · see citing works
“Some studies comparing drought predictions obtained using different drought indices found that the estimation method for drought characteristics can lead to uncertainty in drought predictions for future periods (Keyantash and Dracup, 2002; Mo, 2008; Dai, 2010).”
BackgroundGlobal warming and changes in drought
Trenberth, K. E., Dai, A., van der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., & Sheffield, J. (2014) · Nature Climate Change · doi:10.1038/nclimate2067 · Cited by 3,382 · see citing works
O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., et al. (2017) · Global Environmental Change · doi:10.1016/j.gloenvcha.2015.01.004 · Cited by 3,359 · see citing works
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012) · Hydrology and Earth System Sciences · doi:10.5194/hess-16-3383-2012 · Cited by 1,318 · see citing works
The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., et al. (2019) · Geoscientific Model Development · doi:10.5194/gmd-12-1573-2019 · Cited by 1,069 · see citing works
Riahi, K., Kriegler, E., Johnson, N., Bertram, C., den Elzen, M., Eom, J., Schaeffer, M., Edmonds, J., Isaac, M., Krey, V., et al. (2015) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2013.09.016 · Cited by 360 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Future Hydrological Drought Analysis Considering Agricultural Water Withdrawal Under SSP Scenarios
Kim, J. H., Sung, J. H., Shahid, S., & Chung, E.-S. (2022) · Water Resources Management · doi:10.1007/s11269-022-03116-1 · Cited by 38 · see citing works
Self-citation
- Authors
- Song, Y. H., Shahid, S., & Chung, E.
- Affiliations
- Seoul National University of Science and Technology (KR)
- University of Technology Malaysia (MY)
- Corresponding author
- Chung, Eun‐Sung
- Venue
- International Journal of Climatology
- Publisher
- Wiley
- Published
- 2021-09-23
- Volume
- 42
- Issue
- 5
- Pages
- 2688-2716
- ISSN
- 0899-8418 (ISSN-L)
- 1097-0088
- OpenAlex
- W3200701321
- Cited by
- 45
- References
- 7
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 10 · 2023: 16 · 2022: 4
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Climatology (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Keywords
- drought projections, multi-model ensemble, CMIP5, CMIP6, reliability ensemble averaging, Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, entropy weight method, TOPSIS, quantile mapping, RCP scenarios, Shared Socioeconomic Pathways, drought severity, South Korea
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