Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over Japan
We present a skill assessment of 1-month lead deterministic predictions of monthly surface air temperature anomalies over most part of Japan based on a large-ensemble climate model, SINTEX-F. We found that September is the most predictable and the only month in which the prediction skill beats the p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Climate |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fclim.2022.887782/full |
| _version_ | 1818058432141328384 |
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| author | Takeshi Doi Masami Nonaka Swadhin Behera |
| author_facet | Takeshi Doi Masami Nonaka Swadhin Behera |
| author_sort | Takeshi Doi |
| collection | DOAJ |
| description | We present a skill assessment of 1-month lead deterministic predictions of monthly surface air temperature anomalies over most part of Japan based on a large-ensemble climate model, SINTEX-F. We found that September is the most predictable and the only month in which the prediction skill beats the persistence. Interestingly, however, prediction of December becomes skillful (correlation skill: 0.67) when we select only years in which the signal-to-noise ratio of the predictions is relatively high. This means that the signal-to-noise ratio can partly indicate the prediction skill. The inter-member co-variability suggests that a combination of the tropical Pacific and western Indian Ocean surface temperature is the key for the prediction. Although seasonal climate prediction in the mid-latitude regions, such as Japan, is still challenging in general, providing the signal-to-noise ratio and the inter-member co-variability in addition to the real-time prediction might be useful for stakeholders to know how confident the individual prediction is, as well as its potential sources of predictability. Such information can be helpful to take necessary mitigation measures to reduce socio-economic losses associated with extreme climate. |
| first_indexed | 2024-12-10T13:00:32Z |
| format | Article |
| id | doaj.art-4c0ff08df4404832aade3fc364138edc |
| institution | Directory Open Access Journal |
| issn | 2624-9553 |
| language | English |
| last_indexed | 2024-12-10T13:00:32Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Climate |
| spelling | doaj.art-4c0ff08df4404832aade3fc364138edc2022-12-22T01:47:59ZengFrontiers Media S.A.Frontiers in Climate2624-95532022-09-01410.3389/fclim.2022.887782887782Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over JapanTakeshi DoiMasami NonakaSwadhin BeheraWe present a skill assessment of 1-month lead deterministic predictions of monthly surface air temperature anomalies over most part of Japan based on a large-ensemble climate model, SINTEX-F. We found that September is the most predictable and the only month in which the prediction skill beats the persistence. Interestingly, however, prediction of December becomes skillful (correlation skill: 0.67) when we select only years in which the signal-to-noise ratio of the predictions is relatively high. This means that the signal-to-noise ratio can partly indicate the prediction skill. The inter-member co-variability suggests that a combination of the tropical Pacific and western Indian Ocean surface temperature is the key for the prediction. Although seasonal climate prediction in the mid-latitude regions, such as Japan, is still challenging in general, providing the signal-to-noise ratio and the inter-member co-variability in addition to the real-time prediction might be useful for stakeholders to know how confident the individual prediction is, as well as its potential sources of predictability. Such information can be helpful to take necessary mitigation measures to reduce socio-economic losses associated with extreme climate.https://www.frontiersin.org/articles/10.3389/fclim.2022.887782/fullsignal-to-noise ratiodeterministic prediction skill1-month lead predictionmonthly temperatureseasonal prediction |
| spellingShingle | Takeshi Doi Masami Nonaka Swadhin Behera Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over Japan Frontiers in Climate signal-to-noise ratio deterministic prediction skill 1-month lead prediction monthly temperature seasonal prediction |
| title | Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over Japan |
| title_full | Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over Japan |
| title_fullStr | Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over Japan |
| title_full_unstemmed | Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over Japan |
| title_short | Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over Japan |
| title_sort | can signal to noise ratio indicate prediction skill based on skill assessment of 1 month lead prediction of monthly temperature anomaly over japan |
| topic | signal-to-noise ratio deterministic prediction skill 1-month lead prediction monthly temperature seasonal prediction |
| url | https://www.frontiersin.org/articles/10.3389/fclim.2022.887782/full |
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