Influence of large-scale climate signals on the precipitation variability over Iran

Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pac...
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Autor*in:	Javanshiri, Zohreh [verfasserIn] Babaeian, Iman Pakdaman, Morteza

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Atmosphere-ocean circulation Climate teleconnections Iran precipitation Wavelet coherence Persian Gulf

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 37(2022), 5 vom: 25. Dez., Seite 1745-1762
Übergeordnetes Werk:	volume:37 ; year:2022 ; number:5 ; day:25 ; month:12 ; pages:1745-1762

Links:	Volltext

DOI / URN:	10.1007/s00477-022-02363-3

Katalog-ID:	SPR050116835

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allfields	10.1007/s00477-022-02363-3 doi (DE-627)SPR050116835 (SPR)s00477-022-02363-3-e DE-627 ger DE-627 rakwb eng Javanshiri, Zohreh verfasserin aut Influence of large-scale climate signals on the precipitation variability over Iran 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. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran’s monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran’s precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5%$^\circ%$S-5%$^\circ%$N, 170%$^\circ%$-120%$^\circ%$W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran’s precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. Atmosphere-ocean circulation (dpeaa)DE-He213 Climate teleconnections (dpeaa)DE-He213 Iran precipitation (dpeaa)DE-He213 Wavelet coherence (dpeaa)DE-He213 Persian Gulf (dpeaa)DE-He213 Babaeian, Iman aut Pakdaman, Morteza aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 5 vom: 25. Dez., Seite 1745-1762 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:5 day:25 month:12 pages:1745-1762 https://dx.doi.org/10.1007/s00477-022-02363-3 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_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_4277 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 37 2022 5 25 12 1745-1762
spelling	10.1007/s00477-022-02363-3 doi (DE-627)SPR050116835 (SPR)s00477-022-02363-3-e DE-627 ger DE-627 rakwb eng Javanshiri, Zohreh verfasserin aut Influence of large-scale climate signals on the precipitation variability over Iran 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. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran’s monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran’s precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5%$^\circ%$S-5%$^\circ%$N, 170%$^\circ%$-120%$^\circ%$W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran’s precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. Atmosphere-ocean circulation (dpeaa)DE-He213 Climate teleconnections (dpeaa)DE-He213 Iran precipitation (dpeaa)DE-He213 Wavelet coherence (dpeaa)DE-He213 Persian Gulf (dpeaa)DE-He213 Babaeian, Iman aut Pakdaman, Morteza aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 5 vom: 25. Dez., Seite 1745-1762 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:5 day:25 month:12 pages:1745-1762 https://dx.doi.org/10.1007/s00477-022-02363-3 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_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_4277 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 37 2022 5 25 12 1745-1762
allfields_unstemmed	10.1007/s00477-022-02363-3 doi (DE-627)SPR050116835 (SPR)s00477-022-02363-3-e DE-627 ger DE-627 rakwb eng Javanshiri, Zohreh verfasserin aut Influence of large-scale climate signals on the precipitation variability over Iran 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. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran’s monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran’s precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5%$^\circ%$S-5%$^\circ%$N, 170%$^\circ%$-120%$^\circ%$W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran’s precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. Atmosphere-ocean circulation (dpeaa)DE-He213 Climate teleconnections (dpeaa)DE-He213 Iran precipitation (dpeaa)DE-He213 Wavelet coherence (dpeaa)DE-He213 Persian Gulf (dpeaa)DE-He213 Babaeian, Iman aut Pakdaman, Morteza aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 5 vom: 25. Dez., Seite 1745-1762 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:5 day:25 month:12 pages:1745-1762 https://dx.doi.org/10.1007/s00477-022-02363-3 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_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_4277 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 37 2022 5 25 12 1745-1762
allfieldsGer	10.1007/s00477-022-02363-3 doi (DE-627)SPR050116835 (SPR)s00477-022-02363-3-e DE-627 ger DE-627 rakwb eng Javanshiri, Zohreh verfasserin aut Influence of large-scale climate signals on the precipitation variability over Iran 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. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran’s monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran’s precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5%$^\circ%$S-5%$^\circ%$N, 170%$^\circ%$-120%$^\circ%$W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran’s precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. Atmosphere-ocean circulation (dpeaa)DE-He213 Climate teleconnections (dpeaa)DE-He213 Iran precipitation (dpeaa)DE-He213 Wavelet coherence (dpeaa)DE-He213 Persian Gulf (dpeaa)DE-He213 Babaeian, Iman aut Pakdaman, Morteza aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 5 vom: 25. Dez., Seite 1745-1762 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:5 day:25 month:12 pages:1745-1762 https://dx.doi.org/10.1007/s00477-022-02363-3 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_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_4277 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 37 2022 5 25 12 1745-1762
allfieldsSound	10.1007/s00477-022-02363-3 doi (DE-627)SPR050116835 (SPR)s00477-022-02363-3-e DE-627 ger DE-627 rakwb eng Javanshiri, Zohreh verfasserin aut Influence of large-scale climate signals on the precipitation variability over Iran 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. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran’s monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran’s precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5%$^\circ%$S-5%$^\circ%$N, 170%$^\circ%$-120%$^\circ%$W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran’s precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. Atmosphere-ocean circulation (dpeaa)DE-He213 Climate teleconnections (dpeaa)DE-He213 Iran precipitation (dpeaa)DE-He213 Wavelet coherence (dpeaa)DE-He213 Persian Gulf (dpeaa)DE-He213 Babaeian, Iman aut Pakdaman, Morteza aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 5 vom: 25. Dez., Seite 1745-1762 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:5 day:25 month:12 pages:1745-1762 https://dx.doi.org/10.1007/s00477-022-02363-3 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_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_4277 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 37 2022 5 25 12 1745-1762
language	English
source	Enthalten in Stochastic environmental research and risk assessment 37(2022), 5 vom: 25. Dez., Seite 1745-1762 volume:37 year:2022 number:5 day:25 month:12 pages:1745-1762
sourceStr	Enthalten in Stochastic environmental research and risk assessment 37(2022), 5 vom: 25. Dez., Seite 1745-1762 volume:37 year:2022 number:5 day:25 month:12 pages:1745-1762
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Atmosphere-ocean circulation Climate teleconnections Iran precipitation Wavelet coherence Persian Gulf
isfreeaccess_bool	false
container_title	Stochastic environmental research and risk assessment
authorswithroles_txt_mv	Javanshiri, Zohreh @@aut@@ Babaeian, Iman @@aut@@ Pakdaman, Morteza @@aut@@
publishDateDaySort_date	2022-12-25T00:00:00Z
hierarchy_top_id	27160235X
id	SPR050116835
language_de	englisch
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author	Javanshiri, Zohreh
spellingShingle	Javanshiri, Zohreh misc Atmosphere-ocean circulation misc Climate teleconnections misc Iran precipitation misc Wavelet coherence misc Persian Gulf Influence of large-scale climate signals on the precipitation variability over Iran
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topic_title	Influence of large-scale climate signals on the precipitation variability over Iran Atmosphere-ocean circulation (dpeaa)DE-He213 Climate teleconnections (dpeaa)DE-He213 Iran precipitation (dpeaa)DE-He213 Wavelet coherence (dpeaa)DE-He213 Persian Gulf (dpeaa)DE-He213
topic	misc Atmosphere-ocean circulation misc Climate teleconnections misc Iran precipitation misc Wavelet coherence misc Persian Gulf
topic_unstemmed	misc Atmosphere-ocean circulation misc Climate teleconnections misc Iran precipitation misc Wavelet coherence misc Persian Gulf
topic_browse	misc Atmosphere-ocean circulation misc Climate teleconnections misc Iran precipitation misc Wavelet coherence misc Persian Gulf
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title	Influence of large-scale climate signals on the precipitation variability over Iran
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title_full	Influence of large-scale climate signals on the precipitation variability over Iran
author_sort	Javanshiri, Zohreh
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container_start_page	1745
author_browse	Javanshiri, Zohreh Babaeian, Iman Pakdaman, Morteza
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author-letter	Javanshiri, Zohreh
doi_str_mv	10.1007/s00477-022-02363-3
title_sort	influence of large-scale climate signals on the precipitation variability over iran
title_auth	Influence of large-scale climate signals on the precipitation variability over Iran
abstract	Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran’s monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran’s precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5%$^\circ%$S-5%$^\circ%$N, 170%$^\circ%$-120%$^\circ%$W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran’s precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran’s monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran’s precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5%$^\circ%$S-5%$^\circ%$N, 170%$^\circ%$-120%$^\circ%$W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran’s precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran’s monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran’s precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5%$^\circ%$S-5%$^\circ%$N, 170%$^\circ%$-120%$^\circ%$W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran’s precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	5
title_short	Influence of large-scale climate signals on the precipitation variability over Iran
url	https://dx.doi.org/10.1007/s00477-022-02363-3
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author2	Babaeian, Iman Pakdaman, Morteza
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doi_str	10.1007/s00477-022-02363-3
up_date	2024-07-03T13:29:14.501Z
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Influence of large-scale climate signals on the precipitation variability over Iran

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