On the link between mean state biases and prediction skill in the tropics: an atmospheric perspective.
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While most prediction models drop well below an anomaly correlation coefficient of 0.55 by lead month 3, some promising systems show skill in predicting the AZM in summer when initialized on May 1.
“While most prediction models drop well below that by lead month 3 (Richter et al., 2017), some promising systems show skill in predicting the AZM in summer when initialized on May 1 (Prodhomme et al., 2016).”
From The tropical atlantic observing system (2019) · cites this paper as Richter et al., 2017
Predicting the AZM is particularly challenging, with dynamical forecasts often matched or even outperformed by persistence forecasts.
“Predicting the AZM is particularly challenging, with dynamical forecasts often matched or even outperformed by persistence forecasts (Stockdale et al., 2006;Richter et al., 2017).”
From The tropical atlantic observing system (2019) · cites this paper as Richter et al., 2017
Cites
On the origin of equatorial Atlantic biases in coupled general circulation models.
Richter, I., & Xie, S.-P. (2008) · Climate Dynamics · doi:10.1007/s00382-008-0364-z · Cited by 294 · see citing works
Self-citationClimate model biases in the eastern tropical oceans: causes, impacts and ways forward.
Richter, I. (2015) · WIREs Climate Change · doi:10.1002/wcc.338 · Cited by 253 · see citing works
Self-citation
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Initialized Earth System prediction from subseasonal to decadal timescales
Meehl, G. A., Richter, J. H., Teng, H., Capotondi, A., Cobb, K., Doblas-Reyes, F., Donat, M. G., England, M. H., Fyfe, J. C., Han, W., et al. (2021) · Nature Reviews Earth & Environment · doi:10.1038/s43017-021-00155-x · Cited by 231 · see citing works
The tropical atlantic observing system
Foltz, G. R., Brandt, P., Richter, I., Rodríguez-Fonseca, B., Hernandez, F., Dengler, M., Rodrigues, R. R., Schmidt, J. O., Yu, L., Lefevre, N., et al. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00206 · Cited by 150 · see citing works
“Predicting the AZM is particularly challenging, with dynamical forecasts often matched or even outperformed by persistence forecasts (Stockdale et al., 2006; Richter et al., 2017).”
BackgroundSelf-citation
- Authors
- Richter, I., Doi, T., Behera, S. K., & Keenlyside, N.
- Affiliations
- Japan Agency for Marine-Earth Science and Technology (JP)
- University of Bergen (NO)
- Corresponding author
- Richter, Ingo
- Venue
- Climate Dynamics
- Published
- 2017-08-21
- Volume
- 50
- Issue
- 9-10
- Pages
- 3355-3374
- ISSN
- 0930-7575 (ISSN-L)
- 1432-0894
- OpenAlex
- W2745406122
- Cited by
- 53
- References
- 2
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 1 · 2022: 6 · 2021: 9 · 2020: 20 · 2019: 4 · 2018: 2
- FWCI
- 4.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Climate Dynamics (publishedVersion)
- Funders
- U.S. Department of Energy
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Keywords
- forecast skill, tropical climate, sea surface temperature bias, surface wind anomalies, precipitation anomalies, anomaly correlation coefficient, root mean square error, signal-to-noise ratio, CMIP5, equatorial Pacific, equatorial Atlantic
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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.
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