Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America

Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forci...
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Autor*in:	Faranda, Davide [verfasserIn] Messori, Gabriele [verfasserIn] Yiou, Pascal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Weather extremes Analogues Climate dynamics Dynamical systems theory

Übergeordnetes Werk:	Enthalten in: Climate dynamics - Berlin : Springer, 1986, 54(2020), 3-4 vom: 21. Jan., Seite 2187-2201
Übergeordnetes Werk:	volume:54 ; year:2020 ; number:3-4 ; day:21 ; month:01 ; pages:2187-2201

Links:	Volltext

DOI / URN:	10.1007/s00382-019-05106-3

Katalog-ID:	SPR004687787

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520			\|a Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes.
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allfields	10.1007/s00382-019-05106-3 doi (DE-627)SPR004687787 (SPR)s00382-019-05106-3-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Faranda, Davide verfasserin aut Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes. Weather extremes (dpeaa)DE-He213 Analogues (dpeaa)DE-He213 Climate dynamics (dpeaa)DE-He213 Dynamical systems theory (dpeaa)DE-He213 Messori, Gabriele verfasserin aut Yiou, Pascal verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 54(2020), 3-4 vom: 21. Jan., Seite 2187-2201 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:54 year:2020 number:3-4 day:21 month:01 pages:2187-2201 https://dx.doi.org/10.1007/s00382-019-05106-3 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_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 38.80 ASE AR 54 2020 3-4 21 01 2187-2201
spelling	10.1007/s00382-019-05106-3 doi (DE-627)SPR004687787 (SPR)s00382-019-05106-3-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Faranda, Davide verfasserin aut Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes. Weather extremes (dpeaa)DE-He213 Analogues (dpeaa)DE-He213 Climate dynamics (dpeaa)DE-He213 Dynamical systems theory (dpeaa)DE-He213 Messori, Gabriele verfasserin aut Yiou, Pascal verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 54(2020), 3-4 vom: 21. Jan., Seite 2187-2201 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:54 year:2020 number:3-4 day:21 month:01 pages:2187-2201 https://dx.doi.org/10.1007/s00382-019-05106-3 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_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 38.80 ASE AR 54 2020 3-4 21 01 2187-2201
allfields_unstemmed	10.1007/s00382-019-05106-3 doi (DE-627)SPR004687787 (SPR)s00382-019-05106-3-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Faranda, Davide verfasserin aut Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes. Weather extremes (dpeaa)DE-He213 Analogues (dpeaa)DE-He213 Climate dynamics (dpeaa)DE-He213 Dynamical systems theory (dpeaa)DE-He213 Messori, Gabriele verfasserin aut Yiou, Pascal verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 54(2020), 3-4 vom: 21. Jan., Seite 2187-2201 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:54 year:2020 number:3-4 day:21 month:01 pages:2187-2201 https://dx.doi.org/10.1007/s00382-019-05106-3 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_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 38.80 ASE AR 54 2020 3-4 21 01 2187-2201
allfieldsGer	10.1007/s00382-019-05106-3 doi (DE-627)SPR004687787 (SPR)s00382-019-05106-3-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Faranda, Davide verfasserin aut Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes. Weather extremes (dpeaa)DE-He213 Analogues (dpeaa)DE-He213 Climate dynamics (dpeaa)DE-He213 Dynamical systems theory (dpeaa)DE-He213 Messori, Gabriele verfasserin aut Yiou, Pascal verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 54(2020), 3-4 vom: 21. Jan., Seite 2187-2201 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:54 year:2020 number:3-4 day:21 month:01 pages:2187-2201 https://dx.doi.org/10.1007/s00382-019-05106-3 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_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 38.80 ASE AR 54 2020 3-4 21 01 2187-2201
allfieldsSound	10.1007/s00382-019-05106-3 doi (DE-627)SPR004687787 (SPR)s00382-019-05106-3-e DE-627 ger DE-627 rakwb eng 550 ASE 38.80 bkl Faranda, Davide verfasserin aut Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes. Weather extremes (dpeaa)DE-He213 Analogues (dpeaa)DE-He213 Climate dynamics (dpeaa)DE-He213 Dynamical systems theory (dpeaa)DE-He213 Messori, Gabriele verfasserin aut Yiou, Pascal verfasserin aut Enthalten in Climate dynamics Berlin : Springer, 1986 54(2020), 3-4 vom: 21. Jan., Seite 2187-2201 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:54 year:2020 number:3-4 day:21 month:01 pages:2187-2201 https://dx.doi.org/10.1007/s00382-019-05106-3 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_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 38.80 ASE AR 54 2020 3-4 21 01 2187-2201
language	English
source	Enthalten in Climate dynamics 54(2020), 3-4 vom: 21. Jan., Seite 2187-2201 volume:54 year:2020 number:3-4 day:21 month:01 pages:2187-2201
sourceStr	Enthalten in Climate dynamics 54(2020), 3-4 vom: 21. Jan., Seite 2187-2201 volume:54 year:2020 number:3-4 day:21 month:01 pages:2187-2201
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Weather extremes Analogues Climate dynamics Dynamical systems theory
dewey-raw	550
isfreeaccess_bool	false
container_title	Climate dynamics
authorswithroles_txt_mv	Faranda, Davide @@aut@@ Messori, Gabriele @@aut@@ Yiou, Pascal @@aut@@
publishDateDaySort_date	2020-01-21T00:00:00Z
hierarchy_top_id	268128561
dewey-sort	3550
id	SPR004687787
language_de	englisch
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author	Faranda, Davide
spellingShingle	Faranda, Davide ddc 550 bkl 38.80 misc Weather extremes misc Analogues misc Climate dynamics misc Dynamical systems theory Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America
authorStr	Faranda, Davide
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topic_title	550 ASE 38.80 bkl Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America Weather extremes (dpeaa)DE-He213 Analogues (dpeaa)DE-He213 Climate dynamics (dpeaa)DE-He213 Dynamical systems theory (dpeaa)DE-He213
topic	ddc 550 bkl 38.80 misc Weather extremes misc Analogues misc Climate dynamics misc Dynamical systems theory
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title	Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America
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title_full	Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America
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author_browse	Faranda, Davide Messori, Gabriele Yiou, Pascal
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title_sort	diagnosing concurrent drivers of weather extremes: application to warm and cold days in north america
title_auth	Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America
abstract	Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes.
abstractGer	Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes.
abstract_unstemmed	Abstract A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes.
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container_issue	3-4
title_short	Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America
url	https://dx.doi.org/10.1007/s00382-019-05106-3
remote_bool	true
author2	Messori, Gabriele Yiou, Pascal
author2Str	Messori, Gabriele Yiou, Pascal
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doi_str	10.1007/s00382-019-05106-3
up_date	2024-07-04T02:11:58.347Z
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Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America

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