Estimating Uncertainty in Simulated ENSO Statistics Article Swipe
Yann Planton
,
Jiwoo Lee
,
Andrew T. Wittenberg
,
Peter J. Gleckler
,
Éric Guilyardi
,
Shayne McGregor
,
Michael J. McPhaden
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2023ms004147
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2023ms004147
Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1029/2023ms004147
- OA Status
- gold
- Cited By
- 2
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402346496
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4402346496Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1029/2023ms004147Digital Object Identifier
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Estimating Uncertainty in Simulated ENSO StatisticsWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-09-01Full publication date if available
- Authors
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Yann Planton, Jiwoo Lee, Andrew T. Wittenberg, Peter J. Gleckler, Éric Guilyardi, Shayne McGregor, Michael J. McPhadenList of authors in order
- Landing page
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https://doi.org/10.1029/2023ms004147Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1029/2023ms004147Direct OA link when available
- Concepts
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Skewness, Statistics, Statistic, Variance (accounting), Ensemble forecasting, Precipitation, Climatology, Environmental science, Limit (mathematics), Climate model, Econometrics, Computer science, Meteorology, Mathematics, Climate change, Geology, Geography, Mathematical analysis, Business, Accounting, OceanographyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.members | 32 |
| abstract_inverted_index.optimal | 28 |
| abstract_inverted_index.priori, | 104 |
| abstract_inverted_index.project | 87 |
| abstract_inverted_index.regions | 247 |
| abstract_inverted_index.results | 125, 239 |
| abstract_inverted_index.signals | 41 |
| abstract_inverted_index.surface | 62 |
| abstract_inverted_index.theory, | 95 |
| abstract_inverted_index.trends. | 25 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.computed | 158, 182 |
| abstract_inverted_index.describe | 73 |
| abstract_inverted_index.duration | 197 |
| abstract_inverted_index.ensemble | 31, 106, 144, 199 |
| abstract_inverted_index.estimate | 176 |
| abstract_inverted_index.existing | 185, 213 |
| abstract_inverted_index.expected | 178 |
| abstract_inverted_index.internal | 14, 43, 236 |
| abstract_inverted_index.metrics. | 49 |
| abstract_inverted_index.obtained | 79 |
| abstract_inverted_index.required | 33, 111, 201 |
| abstract_inverted_index.skewness | 57 |
| abstract_inverted_index.suggests | 167 |
| abstract_inverted_index.variance | 55 |
| abstract_inverted_index.Moreover, | 146 |
| abstract_inverted_index.assessing | 21 |
| abstract_inverted_index.decreases | 133 |
| abstract_inverted_index.ensemble, | 189 |
| abstract_inverted_index.ensembles | 2, 82, 226 |
| abstract_inverted_index.equations | 100 |
| abstract_inverted_index.estimate, | 102 |
| abstract_inverted_index.generally | 155 |
| abstract_inverted_index.performed | 220 |
| abstract_inverted_index.simulated | 235 |
| abstract_inverted_index.sometimes | 172 |
| abstract_inverted_index.variables | 245 |
| abstract_inverted_index.Leveraging | 92 |
| abstract_inverted_index.adequately | 203 |
| abstract_inverted_index.applicable | 243 |
| abstract_inverted_index.comparable | 156 |
| abstract_inverted_index.diagnostic | 231 |
| abstract_inverted_index.equatorial | 67 |
| abstract_inverted_index.evaluating | 17 |
| abstract_inverted_index.frequently | 7 |
| abstract_inverted_index.historical | 164, 186 |
| abstract_inverted_index.simulation | 109, 194 |
| abstract_inverted_index.statistic. | 206 |
| abstract_inverted_index.statistics | 118, 132, 153, 181 |
| abstract_inverted_index.tolerance. | 123 |
| abstract_inverted_index.Oscillation | 77 |
| abstract_inverted_index.demonstrate | 148 |
| abstract_inverted_index.distinguish | 35 |
| abstract_inverted_index.established | 93 |
| abstract_inverted_index.simulations | 5, 214 |
| abstract_inverted_index.statistical | 94 |
| abstract_inverted_index.temperature | 63 |
| abstract_inverted_index.uncertainty | 129, 179 |
| abstract_inverted_index.variability | 15, 44 |
| abstract_inverted_index.characterize | 204 |
| abstract_inverted_index.development) | 223 |
| abstract_inverted_index.simulations. | 91 |
| abstract_inverted_index.sufficiently | 229 |
| abstract_inverted_index.variability. | 237 |
| abstract_inverted_index.precipitation | 59 |
| abstract_inverted_index.uncertainties | 115, 150, 232 |
| abstract_inverted_index.intercomparison | 86 |
| abstract_inverted_index.Niño–Southern | 76 |
| abstract_inverted_index.pre‐industrial | 161, 169 |
| abstract_inverted_index.sampling‐based | 114 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.71532773 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |