Drivers of coupled model ENSO error dynamics and the spring predictability barrier
Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Larson, Sarah M. [verfasserIn] Kirtman, Ben P. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Climate dynamics - Berlin : Springer, 1986, 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 |
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| Übergeordnetes Werk: |
volume:48 ; year:2016 ; number:11-12 ; day:27 ; month:07 ; pages:3631-3644 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00382-016-3290-5 |
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SPR004666119 |
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| 245 | 1 | 0 | |a Drivers of coupled model ENSO error dynamics and the spring predictability barrier |
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| 520 | |a Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. | ||
| 650 | 4 | |a ENSO |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Forecast errors |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Error dynamics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spring predictability barrier |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a ENSO predictability |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kirtman, Ben P. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Climate dynamics |d Berlin : Springer, 1986 |g 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 |w (DE-627)268128561 |w (DE-600)1471747-5 |x 1432-0894 |7 nnns |
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10.1007/s00382-016-3290-5 doi (DE-627)SPR004666119 (SPR)s00382-016-3290-5-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Larson, Sarah M. verfasserin aut Drivers of coupled model ENSO error dynamics and the spring predictability barrier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. ENSO (dpeaa)DE-He213 Forecast errors (dpeaa)DE-He213 Error dynamics (dpeaa)DE-He213 Spring predictability barrier (dpeaa)DE-He213 ENSO predictability (dpeaa)DE-He213 Kirtman, Ben P. verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:48 year:2016 number:11-12 day:27 month:07 pages:3631-3644 https://dx.doi.org/10.1007/s00382-016-3290-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_2070 GBV_ILN_2086 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_2116 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_4012 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 38.80 ASE AR 48 2016 11-12 27 07 3631-3644 |
| spelling |
10.1007/s00382-016-3290-5 doi (DE-627)SPR004666119 (SPR)s00382-016-3290-5-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Larson, Sarah M. verfasserin aut Drivers of coupled model ENSO error dynamics and the spring predictability barrier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. ENSO (dpeaa)DE-He213 Forecast errors (dpeaa)DE-He213 Error dynamics (dpeaa)DE-He213 Spring predictability barrier (dpeaa)DE-He213 ENSO predictability (dpeaa)DE-He213 Kirtman, Ben P. verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:48 year:2016 number:11-12 day:27 month:07 pages:3631-3644 https://dx.doi.org/10.1007/s00382-016-3290-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_2070 GBV_ILN_2086 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_2116 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_4012 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 38.80 ASE AR 48 2016 11-12 27 07 3631-3644 |
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10.1007/s00382-016-3290-5 doi (DE-627)SPR004666119 (SPR)s00382-016-3290-5-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Larson, Sarah M. verfasserin aut Drivers of coupled model ENSO error dynamics and the spring predictability barrier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. ENSO (dpeaa)DE-He213 Forecast errors (dpeaa)DE-He213 Error dynamics (dpeaa)DE-He213 Spring predictability barrier (dpeaa)DE-He213 ENSO predictability (dpeaa)DE-He213 Kirtman, Ben P. verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:48 year:2016 number:11-12 day:27 month:07 pages:3631-3644 https://dx.doi.org/10.1007/s00382-016-3290-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_2070 GBV_ILN_2086 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_2116 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_4012 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 38.80 ASE AR 48 2016 11-12 27 07 3631-3644 |
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10.1007/s00382-016-3290-5 doi (DE-627)SPR004666119 (SPR)s00382-016-3290-5-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Larson, Sarah M. verfasserin aut Drivers of coupled model ENSO error dynamics and the spring predictability barrier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. ENSO (dpeaa)DE-He213 Forecast errors (dpeaa)DE-He213 Error dynamics (dpeaa)DE-He213 Spring predictability barrier (dpeaa)DE-He213 ENSO predictability (dpeaa)DE-He213 Kirtman, Ben P. verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:48 year:2016 number:11-12 day:27 month:07 pages:3631-3644 https://dx.doi.org/10.1007/s00382-016-3290-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_2070 GBV_ILN_2086 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_2116 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_4012 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 38.80 ASE AR 48 2016 11-12 27 07 3631-3644 |
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10.1007/s00382-016-3290-5 doi (DE-627)SPR004666119 (SPR)s00382-016-3290-5-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Larson, Sarah M. verfasserin aut Drivers of coupled model ENSO error dynamics and the spring predictability barrier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. ENSO (dpeaa)DE-He213 Forecast errors (dpeaa)DE-He213 Error dynamics (dpeaa)DE-He213 Spring predictability barrier (dpeaa)DE-He213 ENSO predictability (dpeaa)DE-He213 Kirtman, Ben P. verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:48 year:2016 number:11-12 day:27 month:07 pages:3631-3644 https://dx.doi.org/10.1007/s00382-016-3290-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_2070 GBV_ILN_2086 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_2116 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_4012 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 38.80 ASE AR 48 2016 11-12 27 07 3631-3644 |
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Enthalten in Climate dynamics 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 volume:48 year:2016 number:11-12 day:27 month:07 pages:3631-3644 |
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Enthalten in Climate dynamics 48(2016), 11-12 vom: 27. Juli, Seite 3631-3644 volume:48 year:2016 number:11-12 day:27 month:07 pages:3631-3644 |
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ENSO Forecast errors Error dynamics Spring predictability barrier ENSO predictability |
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Larson, Sarah M. @@aut@@ Kirtman, Ben P. @@aut@@ |
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Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ENSO</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Forecast errors</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Error dynamics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spring predictability barrier</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ENSO predictability</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kirtman, Ben P.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Climate dynamics</subfield><subfield code="d">Berlin : Springer, 1986</subfield><subfield code="g">48(2016), 11-12 vom: 27. 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Larson, Sarah M. |
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Larson, Sarah M. ddc 550 bkl 38.80 misc ENSO misc Forecast errors misc Error dynamics misc Spring predictability barrier misc ENSO predictability Drivers of coupled model ENSO error dynamics and the spring predictability barrier |
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550 ASE 38.80 bkl Drivers of coupled model ENSO error dynamics and the spring predictability barrier ENSO (dpeaa)DE-He213 Forecast errors (dpeaa)DE-He213 Error dynamics (dpeaa)DE-He213 Spring predictability barrier (dpeaa)DE-He213 ENSO predictability (dpeaa)DE-He213 |
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ddc 550 bkl 38.80 misc ENSO misc Forecast errors misc Error dynamics misc Spring predictability barrier misc ENSO predictability |
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ddc 550 bkl 38.80 misc ENSO misc Forecast errors misc Error dynamics misc Spring predictability barrier misc ENSO predictability |
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Drivers of coupled model ENSO error dynamics and the spring predictability barrier |
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Drivers of coupled model ENSO error dynamics and the spring predictability barrier |
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Larson, Sarah M. |
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Larson, Sarah M. Kirtman, Ben P. |
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550 ASE 38.80 bkl |
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Elektronische Aufsätze |
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10.1007/s00382-016-3290-5 |
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drivers of coupled model enso error dynamics and the spring predictability barrier |
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Drivers of coupled model ENSO error dynamics and the spring predictability barrier |
| abstract |
Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. |
| abstractGer |
Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. |
| abstract_unstemmed |
Abstract Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations %$(\tau_{x}^{\prime } )%$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic %$\tau_{x}^{\prime }%$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic %$\tau_{x}^{\prime }%$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall. |
| collection_details |
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| container_issue |
11-12 |
| title_short |
Drivers of coupled model ENSO error dynamics and the spring predictability barrier |
| url |
https://dx.doi.org/10.1007/s00382-016-3290-5 |
| remote_bool |
true |
| author2 |
Kirtman, Ben P. |
| author2Str |
Kirtman, Ben P. |
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c |
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false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00382-016-3290-5 |
| up_date |
2024-07-04T02:06:06.494Z |
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| score |
7.3995905 |