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1. Introduction Deep convective structures populate the tropics, provide the energetics that drive the large-scale tropical circulation, and interact with superimposed atmospheric waves ( Riehl and Malkus 1957 ; Lorenz 1969 ; Hendon and Liebmann 1991 ; Kiladis and Weickmann 1992 ; Chang 1995 ; Lane et al. 2001 ; Fierro et al. 2009 ). The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 , 1994 ; Zhang 2005 ) is one such disturbance, and while the MJO is commonly defined
1. Introduction Deep convective structures populate the tropics, provide the energetics that drive the large-scale tropical circulation, and interact with superimposed atmospheric waves ( Riehl and Malkus 1957 ; Lorenz 1969 ; Hendon and Liebmann 1991 ; Kiladis and Weickmann 1992 ; Chang 1995 ; Lane et al. 2001 ; Fierro et al. 2009 ). The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 , 1994 ; Zhang 2005 ) is one such disturbance, and while the MJO is commonly defined
1. Introduction The Maritime Continent (MC) plays an important role as a heat and moisture source that can impact global circulation and modulate planetary-scale variability ( Neale and Slingo 2003 ). However, despite its importance, large errors are commonly found in the MC region in global and regional climate and weather models (e.g., Gianotti et al. 2012 ; Holloway et al. 2012 ; Nguyen et al. 2015 ; Dirmeyer et al. 2012 ; and others). One likely source of these errors arises from the
1. Introduction The Maritime Continent (MC) plays an important role as a heat and moisture source that can impact global circulation and modulate planetary-scale variability ( Neale and Slingo 2003 ). However, despite its importance, large errors are commonly found in the MC region in global and regional climate and weather models (e.g., Gianotti et al. 2012 ; Holloway et al. 2012 ; Nguyen et al. 2015 ; Dirmeyer et al. 2012 ; and others). One likely source of these errors arises from the
, 2019 : Interannual variability of the Sulawesi Sea circulation forced by Indo-Pacific planetary waves . J. Geophys. Res. Oceans , 124 , 1616 – 1633 , https://doi.org/10.1029/2018JC014356 . 10.1029/2018JC014356 Koch-Larrouy , A. , A. Atimadipoera , P. van Beek , G. Madec , J. Aucan , F. Lyard , J. Grelet , and M. Souhaut , 2015 : Estimates of tidal mixing in the Indonesian archipelago from multidisciplinary INDOMIX in-situ data . Deep-Sea Res. I , 106 , 136 – 153
, 2019 : Interannual variability of the Sulawesi Sea circulation forced by Indo-Pacific planetary waves . J. Geophys. Res. Oceans , 124 , 1616 – 1633 , https://doi.org/10.1029/2018JC014356 . 10.1029/2018JC014356 Koch-Larrouy , A. , A. Atimadipoera , P. van Beek , G. Madec , J. Aucan , F. Lyard , J. Grelet , and M. Souhaut , 2015 : Estimates of tidal mixing in the Indonesian archipelago from multidisciplinary INDOMIX in-situ data . Deep-Sea Res. I , 106 , 136 – 153
1. Introduction The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) is a primary source of predictability of the Earth system on subseasonal (3–6 weeks) time scales ( Waliser et al. 2003 ). As the MJO moves eastward, its influences on many environmental hazards (e.g., tropical cyclones, cold surges, heat waves, lightning, and flood) and climate modes [e.g., Indian Ocean dipole (IOD), ENSO, and NAO] depend on whether its convection center is over the Indian Ocean, the Indo
1. Introduction The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) is a primary source of predictability of the Earth system on subseasonal (3–6 weeks) time scales ( Waliser et al. 2003 ). As the MJO moves eastward, its influences on many environmental hazards (e.g., tropical cyclones, cold surges, heat waves, lightning, and flood) and climate modes [e.g., Indian Ocean dipole (IOD), ENSO, and NAO] depend on whether its convection center is over the Indian Ocean, the Indo
et al. 2013 ; Narsey et al. 2020 ). Regional climate models (RCMs), which operate at higher spatial resolution, have contributed to improve our understanding and simulation of the mechanisms underlying rainfall in the Maritime Continent ( Vincent and Lane 2018 ; Ruppert and Zhang 2019 ; Li et al. 2020 ) through better representation of fine-scale processes (i.e., sea breeze, gravity waves, interaction across scales, air–ocean fine-scale interactions). However, RCMs are still prone to
et al. 2013 ; Narsey et al. 2020 ). Regional climate models (RCMs), which operate at higher spatial resolution, have contributed to improve our understanding and simulation of the mechanisms underlying rainfall in the Maritime Continent ( Vincent and Lane 2018 ; Ruppert and Zhang 2019 ; Li et al. 2020 ) through better representation of fine-scale processes (i.e., sea breeze, gravity waves, interaction across scales, air–ocean fine-scale interactions). However, RCMs are still prone to
1. Introduction The Madden–Julian oscillation (MJO) is the primary mode of tropical intraseasonal climate variability in the boreal winter and spring ( Madden and Julian 1971 ; Zhang 2005 ). It manifests as a planetary-scale system with organized multiscale convection and large-scale circulation and is featured by its eastward propagation along the equator. During a typical MJO event, a positive convection/rainfall anomaly develops over the western Indian Ocean, while convection tends to be
1. Introduction The Madden–Julian oscillation (MJO) is the primary mode of tropical intraseasonal climate variability in the boreal winter and spring ( Madden and Julian 1971 ; Zhang 2005 ). It manifests as a planetary-scale system with organized multiscale convection and large-scale circulation and is featured by its eastward propagation along the equator. During a typical MJO event, a positive convection/rainfall anomaly develops over the western Indian Ocean, while convection tends to be
1. Introduction The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) is the dominant component of intraseasonal (30–90 days) variability in the tropics. It consists of a circulation of planetary zonal scales coupled with organized deep convection. Its convection usually forms over the central Indian Ocean, propagates eastward across the Indo-Pacific Maritime Continent (MC) and warm pool along the equator at an average speed of about 5 m s −1 , and disappears over the central
1. Introduction The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) is the dominant component of intraseasonal (30–90 days) variability in the tropics. It consists of a circulation of planetary zonal scales coupled with organized deep convection. Its convection usually forms over the central Indian Ocean, propagates eastward across the Indo-Pacific Maritime Continent (MC) and warm pool along the equator at an average speed of about 5 m s −1 , and disappears over the central
1. Introduction The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) is a planetary-scale disturbance in the tropical atmosphere that propagates eastward with intraseasonal time scales of 30–90 days. As the dominant mode of tropical intraseasonal variability, the MJO affects global weather and climate (e.g., Zhang et al. 2013 ). The anomalously enhanced or suppressed convection associated with the MJO is tightly coupled to circulation anomalies in the tropics, through which
1. Introduction The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) is a planetary-scale disturbance in the tropical atmosphere that propagates eastward with intraseasonal time scales of 30–90 days. As the dominant mode of tropical intraseasonal variability, the MJO affects global weather and climate (e.g., Zhang et al. 2013 ). The anomalously enhanced or suppressed convection associated with the MJO is tightly coupled to circulation anomalies in the tropics, through which
-derived information is complemented with two monthly rain gauge analyses developed by the Global Precipitation Climatological Center and the U.S. Climate Prediction Center. The Global Precipitation Measurement (GPM) created with the Integrated Multisatellite Retrievals for GPM (GPM_3IMERGHH v05; Huffman 2017 ) is a global precipitation dataset at 0.1° (~11 km) spatial resolution and 30-min temporal resolution that builds upon TRMM. It generates rainfall estimates from intercalibrated spaceborne radio wave
-derived information is complemented with two monthly rain gauge analyses developed by the Global Precipitation Climatological Center and the U.S. Climate Prediction Center. The Global Precipitation Measurement (GPM) created with the Integrated Multisatellite Retrievals for GPM (GPM_3IMERGHH v05; Huffman 2017 ) is a global precipitation dataset at 0.1° (~11 km) spatial resolution and 30-min temporal resolution that builds upon TRMM. It generates rainfall estimates from intercalibrated spaceborne radio wave
-period planetary waves . J. Geophys. Res. Oceans , 119 , 3883 – 3908 , https://doi.org/10.1002/2014JC009935 . 10.1002/2014JC009935 Moon , J.-H. , and Y. T. Song , 2014 : Seasonal salinity stratifications in the near-surface layer from Aquarius, Argo, and an ocean model: Focusing on the tropical Atlantic/Indian Oceans . J. Geophys. Res. Oceans , 119 , 6066 – 6077 , https://doi.org/10.1002/2014JC009969 . 10.1002/2014JC009969 Nagura , M. , and S. Kouketsu , 2018 : Spiciness anomalies in the
-period planetary waves . J. Geophys. Res. Oceans , 119 , 3883 – 3908 , https://doi.org/10.1002/2014JC009935 . 10.1002/2014JC009935 Moon , J.-H. , and Y. T. Song , 2014 : Seasonal salinity stratifications in the near-surface layer from Aquarius, Argo, and an ocean model: Focusing on the tropical Atlantic/Indian Oceans . J. Geophys. Res. Oceans , 119 , 6066 – 6077 , https://doi.org/10.1002/2014JC009969 . 10.1002/2014JC009969 Nagura , M. , and S. Kouketsu , 2018 : Spiciness anomalies in the
