An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts
Abstract The loss of autocorrelations of tropical sea surface temperatures (SST) during late spring, also called the spring predictability barrier (SPB), is a factor that strongly limits the predictability of El Nino Southern Oscillation (ENSO), and especially the statistical SST‐based ENSO forecast...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-03-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2020GL091287 |
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| author | Dmitry Mukhin Andrey Gavrilov Aleksei Seleznev Maria Buyanova |
| author_facet | Dmitry Mukhin Andrey Gavrilov Aleksei Seleznev Maria Buyanova |
| author_sort | Dmitry Mukhin |
| collection | DOAJ |
| description | Abstract The loss of autocorrelations of tropical sea surface temperatures (SST) during late spring, also called the spring predictability barrier (SPB), is a factor that strongly limits the predictability of El Nino Southern Oscillation (ENSO), and especially the statistical SST‐based ENSO forecasts starting from the winter‐spring season. Recent studies show that Pacific atmospheric circulation anomalies in winter‐spring may have a long‐term impact on the summer tropical climate via the SST footprint. Here, we infer an index based on sea level pressure (SLP) data from February to March in a single area surrounding Hawaii, and show that this area is the most informative part of the large SLP pattern initiating the SST footprinting mechanism. We then construct a statistically optimal linear model of the Nino 3.4 index taking this atmospheric index as a forcing. We find that this forcing efficiently lowers the SPB and provides significant improvements of interseasonal Niño 3.4 forecasts. |
| format | Article |
| id | doaj-art-ab8b9d117c7a4daab3384fd9fdd9d0a8 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-ab8b9d117c7a4daab3384fd9fdd9d0a82025-08-20T01:48:15ZengWileyGeophysical Research Letters0094-82761944-80072021-03-01486n/an/a10.1029/2020GL091287An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO ForecastsDmitry Mukhin0Andrey Gavrilov1Aleksei Seleznev2Maria Buyanova3Institute of Applied Physics of the RAS Nizhny Novgorod RussiaInstitute of Applied Physics of the RAS Nizhny Novgorod RussiaInstitute of Applied Physics of the RAS Nizhny Novgorod RussiaInstitute of Applied Physics of the RAS Nizhny Novgorod RussiaAbstract The loss of autocorrelations of tropical sea surface temperatures (SST) during late spring, also called the spring predictability barrier (SPB), is a factor that strongly limits the predictability of El Nino Southern Oscillation (ENSO), and especially the statistical SST‐based ENSO forecasts starting from the winter‐spring season. Recent studies show that Pacific atmospheric circulation anomalies in winter‐spring may have a long‐term impact on the summer tropical climate via the SST footprint. Here, we infer an index based on sea level pressure (SLP) data from February to March in a single area surrounding Hawaii, and show that this area is the most informative part of the large SLP pattern initiating the SST footprinting mechanism. We then construct a statistically optimal linear model of the Nino 3.4 index taking this atmospheric index as a forcing. We find that this forcing efficiently lowers the SPB and provides significant improvements of interseasonal Niño 3.4 forecasts.https://doi.org/10.1029/2020GL091287Data‐driven modelsENSOSST footprintstatistical forecasts |
| spellingShingle | Dmitry Mukhin Andrey Gavrilov Aleksei Seleznev Maria Buyanova An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts Geophysical Research Letters Data‐driven models ENSO SST footprint statistical forecasts |
| title | An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts |
| title_full | An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts |
| title_fullStr | An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts |
| title_full_unstemmed | An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts |
| title_short | An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts |
| title_sort | atmospheric signal lowering the spring predictability barrier in statistical enso forecasts |
| topic | Data‐driven models ENSO SST footprint statistical forecasts |
| url | https://doi.org/10.1029/2020GL091287 |
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