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. 2003 ), and the seasonal forcing of the Asian–Australian monsoon, which is the largest contributor to SST variability within the Indonesian Seas ( Qu et al. 2005 ; Kida and Richards 2009 ; Halkides et al. 2011 ). Furthermore, the Maritime Continent falls along the pathway of the Madden–Julian oscillation (MJO), an intraseasonal tropical atmospheric phenomenon consisting of convective and subsidence cells propagating eastward from the Indian Ocean to the Pacific Ocean affecting weather across the
. 2003 ), and the seasonal forcing of the Asian–Australian monsoon, which is the largest contributor to SST variability within the Indonesian Seas ( Qu et al. 2005 ; Kida and Richards 2009 ; Halkides et al. 2011 ). Furthermore, the Maritime Continent falls along the pathway of the Madden–Julian oscillation (MJO), an intraseasonal tropical atmospheric phenomenon consisting of convective and subsidence cells propagating eastward from the Indian Ocean to the Pacific Ocean affecting weather across the
largely attributed to the long-term trends in high-latitude summertime atmospheric circulation. Yu and Zhong (2018) suggested that the anomalous autumn AD and AO modes could explain as much as 50% of autumn sea ice decline between 1979 and 2016. Aside from the remarkable long-term variability, the intraseasonal variability of the Arctic sea ice was also highlighted in previous studies. Indeed, Fang and Wallace (1994) have already found that the Arctic sea ice concentration responds to
largely attributed to the long-term trends in high-latitude summertime atmospheric circulation. Yu and Zhong (2018) suggested that the anomalous autumn AD and AO modes could explain as much as 50% of autumn sea ice decline between 1979 and 2016. Aside from the remarkable long-term variability, the intraseasonal variability of the Arctic sea ice was also highlighted in previous studies. Indeed, Fang and Wallace (1994) have already found that the Arctic sea ice concentration responds to
1. Introduction The eastern Pacific warm pool is a region of strong intraseasonal variability (ISV) during boreal summer. The 30–50 days (hereafter 40 day) ISV mode of the eastern Pacific (EPAC) is largely characterized by eastward propagation of convective anomalies ( Knutson and Weickmann 1987 ; Kayano and Kousky 1999 ; Maloney and Hartmann 2000a ; and many others). Besides the 40-day mode, a quasi-biweekly mode of about 16-day periodicity is also prominent over the EPAC domain ( Jiang and
1. Introduction The eastern Pacific warm pool is a region of strong intraseasonal variability (ISV) during boreal summer. The 30–50 days (hereafter 40 day) ISV mode of the eastern Pacific (EPAC) is largely characterized by eastward propagation of convective anomalies ( Knutson and Weickmann 1987 ; Kayano and Kousky 1999 ; Maloney and Hartmann 2000a ; and many others). Besides the 40-day mode, a quasi-biweekly mode of about 16-day periodicity is also prominent over the EPAC domain ( Jiang and
used to obtain robust estimates. In particular, it is shown that the detection and discrimination of the intraseasonal variability modes can be used to construct monitoring tools and objective forecasting models. This method is particularly effective for time intervals in which the analyzed variable exhibits a substantial number of quasi periodicities ( Mo 2001 ). The different modes that define the temporal evolution of temperature can also be used to improve the forecast by fitting time series
used to obtain robust estimates. In particular, it is shown that the detection and discrimination of the intraseasonal variability modes can be used to construct monitoring tools and objective forecasting models. This method is particularly effective for time intervals in which the analyzed variable exhibits a substantial number of quasi periodicities ( Mo 2001 ). The different modes that define the temporal evolution of temperature can also be used to improve the forecast by fitting time series
QuikSCAT winds, validated against available data, to study the basic dynamics of intraseasonal zonal current in the upper 200 m of the EqIO. Although the emphasis is on intraseasonal variability, we revisit some questions related to the dynamics of the seasonal cycle. a. Seasonal jets and undercurrents The Gan data showed that eastward equatorial jets ( Wyrtki 1973 ; Shenoi et al. 1999 ) accelerate to about 1 m s −1 when a westerly wind stress abruptly increases in spring and fall, but they
QuikSCAT winds, validated against available data, to study the basic dynamics of intraseasonal zonal current in the upper 200 m of the EqIO. Although the emphasis is on intraseasonal variability, we revisit some questions related to the dynamics of the seasonal cycle. a. Seasonal jets and undercurrents The Gan data showed that eastward equatorial jets ( Wyrtki 1973 ; Shenoi et al. 1999 ) accelerate to about 1 m s −1 when a westerly wind stress abruptly increases in spring and fall, but they
1. Introduction The dependence of agriculture, drinking water, and energy production on the Indian summer monsoon (ISM) rainfall makes it the lifeline for a large fraction of the world’s population. The economy, life, and property in the region are vulnerable to significant variability of the ISM on intraseasonal, interannual, and interdecadal time scales ( Webster et al. 1998 ; Krishnamurthy and Goswami 2000 ; Goswami et al. 2006b ). Hence, predicting the seasonal mean ISM rainfall is of
1. Introduction The dependence of agriculture, drinking water, and energy production on the Indian summer monsoon (ISM) rainfall makes it the lifeline for a large fraction of the world’s population. The economy, life, and property in the region are vulnerable to significant variability of the ISM on intraseasonal, interannual, and interdecadal time scales ( Webster et al. 1998 ; Krishnamurthy and Goswami 2000 ; Goswami et al. 2006b ). Hence, predicting the seasonal mean ISM rainfall is of
satellite-altimetry data averaged over the Persian Gulf during 2002–15 shown in Fig. 2 . Filters are applied to the data to emphasize variability on different time scales, and global-mean sea level and the inverted-barometer effect are removed. Nonseasonal fluctuations explain 52% of the monthly data variance, and intraseasonal fluctuations (with ~2–6-month periods) alone account for 46% of the overall data variance. The altimetric time series of intraseasonal sea level averaged over the Persian Gulf
satellite-altimetry data averaged over the Persian Gulf during 2002–15 shown in Fig. 2 . Filters are applied to the data to emphasize variability on different time scales, and global-mean sea level and the inverted-barometer effect are removed. Nonseasonal fluctuations explain 52% of the monthly data variance, and intraseasonal fluctuations (with ~2–6-month periods) alone account for 46% of the overall data variance. The altimetric time series of intraseasonal sea level averaged over the Persian Gulf
tropical intraseasonal variability (TISV); see Lau and Waliser (2012) for a comprehensive review. While the strongest convective activity associated with the TISV is confined to the deep tropics, profound impacts of the TISV are detected over vast extratropics and mid to high latitudes through relaxation and enhancement of the Walker circulation and/or excitation of Rossby wave trains by diabatic heating associated with the TISV convection (e.g., Knutson and Weickmann 1987 ; Ferranti et al. 1990
tropical intraseasonal variability (TISV); see Lau and Waliser (2012) for a comprehensive review. While the strongest convective activity associated with the TISV is confined to the deep tropics, profound impacts of the TISV are detected over vast extratropics and mid to high latitudes through relaxation and enhancement of the Walker circulation and/or excitation of Rossby wave trains by diabatic heating associated with the TISV convection (e.g., Knutson and Weickmann 1987 ; Ferranti et al. 1990
IS and the TP exhibits a significant dipole pattern. The rainfall anomalies in the TP and the IS are negatively correlated. As both the 10–20- and 30–60-day ISOs can cause out-of-phase convection anomalies between central India and the southern slopes of TP ( Suhas et al. 2013 ; Murata et al. 2017 ), it is thus interesting to examine whether the dipole rainfall pattern exists on intraseasonal time scales. The interannual variability of seasonal-mean rainfall is governed by both the slowly
IS and the TP exhibits a significant dipole pattern. The rainfall anomalies in the TP and the IS are negatively correlated. As both the 10–20- and 30–60-day ISOs can cause out-of-phase convection anomalies between central India and the southern slopes of TP ( Suhas et al. 2013 ; Murata et al. 2017 ), it is thus interesting to examine whether the dipole rainfall pattern exists on intraseasonal time scales. The interannual variability of seasonal-mean rainfall is governed by both the slowly
( Ingram et al. 2002 ), as the occurrence of dry spells can strongly impact yields of rain-fed crops ( Sultan et al. 2005 ). Although there is more and more evidence of specific intraseasonal variability in convective activity during the West African monsoon ( Janicot and Sultan 2001 ; Sultan and Janicot 2003 ; Matthews 2004 ; Mounier and Janicot 2004 ; Mounier et al. 2008 ), no study has investigated its predictability. Nevertheless, there are many examples of skillful forecasts of intraseasonal
( Ingram et al. 2002 ), as the occurrence of dry spells can strongly impact yields of rain-fed crops ( Sultan et al. 2005 ). Although there is more and more evidence of specific intraseasonal variability in convective activity during the West African monsoon ( Janicot and Sultan 2001 ; Sultan and Janicot 2003 ; Matthews 2004 ; Mounier and Janicot 2004 ; Mounier et al. 2008 ), no study has investigated its predictability. Nevertheless, there are many examples of skillful forecasts of intraseasonal
