A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region
Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-h...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ivasić, Sara [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Boundary-forced predictability |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Climate dynamics - Berlin : Springer, 1986, 60(2022), 3-4 vom: 13. Juni, Seite 927-944 |
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| Übergeordnetes Werk: |
volume:60 ; year:2022 ; number:3-4 ; day:13 ; month:06 ; pages:927-944 |
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| DOI / URN: |
10.1007/s00382-022-06357-3 |
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| Katalog-ID: |
SPR049285599 |
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| 245 | 1 | 2 | |a A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region |
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| 520 | |a Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. | ||
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| 650 | 4 | |a Boundary-forced predictability |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tropical–extratropical teleconnections |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a North Atlantic–European climate |7 (dpeaa)DE-He213 | |
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10.1007/s00382-022-06357-3 doi (DE-627)SPR049285599 (SPR)s00382-022-06357-3-e DE-627 ger DE-627 rakwb eng Ivasić, Sara verfasserin (orcid)0000-0003-3840-0862 aut A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. Climate variability (dpeaa)DE-He213 Boundary-forced predictability (dpeaa)DE-He213 Tropical–extratropical teleconnections (dpeaa)DE-He213 North Atlantic–European climate (dpeaa)DE-He213 Herceg-Bulić, Ivana aut Enthalten in Climate dynamics Berlin : Springer, 1986 60(2022), 3-4 vom: 13. Juni, Seite 927-944 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:60 year:2022 number:3-4 day:13 month:06 pages:927-944 https://dx.doi.org/10.1007/s00382-022-06357-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 3-4 13 06 927-944 |
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10.1007/s00382-022-06357-3 doi (DE-627)SPR049285599 (SPR)s00382-022-06357-3-e DE-627 ger DE-627 rakwb eng Ivasić, Sara verfasserin (orcid)0000-0003-3840-0862 aut A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. Climate variability (dpeaa)DE-He213 Boundary-forced predictability (dpeaa)DE-He213 Tropical–extratropical teleconnections (dpeaa)DE-He213 North Atlantic–European climate (dpeaa)DE-He213 Herceg-Bulić, Ivana aut Enthalten in Climate dynamics Berlin : Springer, 1986 60(2022), 3-4 vom: 13. Juni, Seite 927-944 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:60 year:2022 number:3-4 day:13 month:06 pages:927-944 https://dx.doi.org/10.1007/s00382-022-06357-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 3-4 13 06 927-944 |
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10.1007/s00382-022-06357-3 doi (DE-627)SPR049285599 (SPR)s00382-022-06357-3-e DE-627 ger DE-627 rakwb eng Ivasić, Sara verfasserin (orcid)0000-0003-3840-0862 aut A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. Climate variability (dpeaa)DE-He213 Boundary-forced predictability (dpeaa)DE-He213 Tropical–extratropical teleconnections (dpeaa)DE-He213 North Atlantic–European climate (dpeaa)DE-He213 Herceg-Bulić, Ivana aut Enthalten in Climate dynamics Berlin : Springer, 1986 60(2022), 3-4 vom: 13. Juni, Seite 927-944 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:60 year:2022 number:3-4 day:13 month:06 pages:927-944 https://dx.doi.org/10.1007/s00382-022-06357-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 3-4 13 06 927-944 |
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10.1007/s00382-022-06357-3 doi (DE-627)SPR049285599 (SPR)s00382-022-06357-3-e DE-627 ger DE-627 rakwb eng Ivasić, Sara verfasserin (orcid)0000-0003-3840-0862 aut A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. Climate variability (dpeaa)DE-He213 Boundary-forced predictability (dpeaa)DE-He213 Tropical–extratropical teleconnections (dpeaa)DE-He213 North Atlantic–European climate (dpeaa)DE-He213 Herceg-Bulić, Ivana aut Enthalten in Climate dynamics Berlin : Springer, 1986 60(2022), 3-4 vom: 13. Juni, Seite 927-944 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:60 year:2022 number:3-4 day:13 month:06 pages:927-944 https://dx.doi.org/10.1007/s00382-022-06357-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 3-4 13 06 927-944 |
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10.1007/s00382-022-06357-3 doi (DE-627)SPR049285599 (SPR)s00382-022-06357-3-e DE-627 ger DE-627 rakwb eng Ivasić, Sara verfasserin (orcid)0000-0003-3840-0862 aut A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. Climate variability (dpeaa)DE-He213 Boundary-forced predictability (dpeaa)DE-He213 Tropical–extratropical teleconnections (dpeaa)DE-He213 North Atlantic–European climate (dpeaa)DE-He213 Herceg-Bulić, Ivana aut Enthalten in Climate dynamics Berlin : Springer, 1986 60(2022), 3-4 vom: 13. Juni, Seite 927-944 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:60 year:2022 number:3-4 day:13 month:06 pages:927-944 https://dx.doi.org/10.1007/s00382-022-06357-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 3-4 13 06 927-944 |
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The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. 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Ivasić, Sara |
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Ivasić, Sara misc Climate variability misc Boundary-forced predictability misc Tropical–extratropical teleconnections misc North Atlantic–European climate A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region |
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A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region Climate variability (dpeaa)DE-He213 Boundary-forced predictability (dpeaa)DE-He213 Tropical–extratropical teleconnections (dpeaa)DE-He213 North Atlantic–European climate (dpeaa)DE-He213 |
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modelling study of the impact of tropical ssts on the variability and predictable components of seasonal atmospheric circulation in the north atlantic–european region |
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A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region |
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Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Atmospheric variability and predictable components over the North Atlantic–European region (NAE) were analysed in the late-winter season using a general circulation model of intermediate complexity (ICTP AGCM). The method of empirical orthogonal functions (EOF) analysis was applied to 200-hPa geopotential heights to extract individual modes of variability occurring in the ensemble of numerical simulations. The same variable was selected for the signal-to-noise optimal patterns method, which identifies the patterns maximising the signal-to-noise ratio, following Straus et al. (J Clim 16(22):3629–3649, 2003). Six experiments based on a 35-member ensemble of 156-year long simulations were conducted to detect the potential impact of tropical sea surface temperatures. Each experiment was forced with observed sea surface temperature anomalies prescribed in different ocean areas: globally, in the entire tropical zone, the tropical Atlantic region, the tropical Pacific area, and the tropical Indian Ocean area. In late winter, the leading EOF pattern calculated for all individual ensemble members projects onto the North Atlantic Oscillation, while the second EOF pattern projects onto the East Atlantic pattern. However, EOF modes based on the ensemble mean, which should reflect the forced component of the signal, have different spatial characteristics. Alongside the classical analysis of signal and noise, results of the signal-to-noise optimal patterns method suggest that the optimal patterns and signal-to-noise ratio are affected by the boundary forcing of the oceans. Furthermore, the resemblance between the first optimal pattern and the EOF1 pattern based on the ensemble mean points toward the vital role of the lower-boundary-forced signal in establishing potential predictability in the NAE region. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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A modelling study of the impact of tropical SSTs on the variability and predictable components of seasonal atmospheric circulation in the North Atlantic–European region |
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https://dx.doi.org/10.1007/s00382-022-06357-3 |
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Herceg-Bulić, Ivana |
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| score |
7.398694 |