Search Results
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
. 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
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
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
1. Introduction The equatorial Atlantic Ocean is characterized by energetic zonal currents that vary dominantly on seasonal to interannual time scales ( Brandt et al. 2016 ; Claus et al. 2016 ). In contrast, meridional velocity exhibits a spectral peak on intraseasonal time scales, that is, periods of 10–50 days ( Athie and Marin 2008 ; Bunge et al. 2008 ; Ascani et al. 2015 ). It has been shown that intraseasonal meridional velocity variability is forced either by the instability of the
1. Introduction The equatorial Atlantic Ocean is characterized by energetic zonal currents that vary dominantly on seasonal to interannual time scales ( Brandt et al. 2016 ; Claus et al. 2016 ). In contrast, meridional velocity exhibits a spectral peak on intraseasonal time scales, that is, periods of 10–50 days ( Athie and Marin 2008 ; Bunge et al. 2008 ; Ascani et al. 2015 ). It has been shown that intraseasonal meridional velocity variability is forced either by the instability of the
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
the seasonal mean monsoon is closely linked to the structure and amplitudes of intraseasonal variability (ISV) from 20 to 90 days [see Goswami (2005) for a review], at least some part of the shortcomings in the seasonal forecasts could arise because of the biases in representing the intraseasonal modes. In recent years there has been increased need for the prediction of intraseasonal spells that can directly benefit farmers and hydrologists ( Webster and Hoyos 2004 ; Xavier and Goswami 2007
the seasonal mean monsoon is closely linked to the structure and amplitudes of intraseasonal variability (ISV) from 20 to 90 days [see Goswami (2005) for a review], at least some part of the shortcomings in the seasonal forecasts could arise because of the biases in representing the intraseasonal modes. In recent years there has been increased need for the prediction of intraseasonal spells that can directly benefit farmers and hydrologists ( Webster and Hoyos 2004 ; Xavier and Goswami 2007
1. Introduction Over the Sahel, the monsoon brings most of the rainfall during a 3–4-month period, from June to September. This rainy season is punctuated by dry and wet periods occurring at various intraseasonal time scales ( Janicot et al. 2011 ), which can be dramatic for local populations and economies ( Sultan et al. 2005 ). During the last decade, considerable endeavors have been made to better document and understand the intraseasonal variability 1 (ISV). Three preferred time scales
1. Introduction Over the Sahel, the monsoon brings most of the rainfall during a 3–4-month period, from June to September. This rainy season is punctuated by dry and wet periods occurring at various intraseasonal time scales ( Janicot et al. 2011 ), which can be dramatic for local populations and economies ( Sultan et al. 2005 ). During the last decade, considerable endeavors have been made to better document and understand the intraseasonal variability 1 (ISV). Three preferred time scales
