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influence regional hydrology. This challenge is further exacerbated by the persistent precipitation distribution biases in generations of climate models, undermining our confidence in model projections of future changes in precipitation. The biases that are well documented in the simulations from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) include excessive precipitation over oceanic regions off of the equator ( Hirota and Takayabu 2013 ; Fiedler et al
influence regional hydrology. This challenge is further exacerbated by the persistent precipitation distribution biases in generations of climate models, undermining our confidence in model projections of future changes in precipitation. The biases that are well documented in the simulations from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) include excessive precipitation over oceanic regions off of the equator ( Hirota and Takayabu 2013 ; Fiedler et al
, baroclinic waves and frontal systems provide strong lifting mechanisms and the Great Plains low-level jet (LLJ) provides anomalous moisture for favorable dynamical and thermodynamical environments for MCS development. In contrast, during summer, favorable environments featuring significantly weaker baroclinic lifting and thermodynamic instability suggest much lower predictability of MCSs compared to spring ( Song et al. 2019 ). Besides limitations in physics parameterizations, it is unclear if GCMs are
, baroclinic waves and frontal systems provide strong lifting mechanisms and the Great Plains low-level jet (LLJ) provides anomalous moisture for favorable dynamical and thermodynamical environments for MCS development. In contrast, during summer, favorable environments featuring significantly weaker baroclinic lifting and thermodynamic instability suggest much lower predictability of MCSs compared to spring ( Song et al. 2019 ). Besides limitations in physics parameterizations, it is unclear if GCMs are
1. Introduction The Madden–Julian oscillation (MJO) ( Madden and Julian 1971 , 1972 ) is the dominant mode of tropical intraseasonal variability. It is characterized by a convection–circulation coupled system propagating eastward from the Indian Ocean to the Pacific with periods ranging from approximately 30 to 60 days. The MJO modulates atmospheric (e.g., tropical cyclones), oceanic (e.g., chlorophyll), and ocean–atmosphere coupled [e.g., El Niño–Southern Oscillation (ENSO)] disturbances
1. Introduction The Madden–Julian oscillation (MJO) ( Madden and Julian 1971 , 1972 ) is the dominant mode of tropical intraseasonal variability. It is characterized by a convection–circulation coupled system propagating eastward from the Indian Ocean to the Pacific with periods ranging from approximately 30 to 60 days. The MJO modulates atmospheric (e.g., tropical cyclones), oceanic (e.g., chlorophyll), and ocean–atmosphere coupled [e.g., El Niño–Southern Oscillation (ENSO)] disturbances
, these are regions where Rossby waves cannot propagate. Areas where β M < 0 are shaded in black. These regions indicate that the meridional gradient of absolute vorticity is reversed, and stationary Rossby waves must turn before these latitudes (e.g., Hoskins and Ambrizzi 1993 ). A reversal of the absolute vorticity gradient is often observed on the poleward flank of the subtropical jet, so that Rossby waves emitted by Indian Ocean or western Pacific heating must travel east before they can
, these are regions where Rossby waves cannot propagate. Areas where β M < 0 are shaded in black. These regions indicate that the meridional gradient of absolute vorticity is reversed, and stationary Rossby waves must turn before these latitudes (e.g., Hoskins and Ambrizzi 1993 ). A reversal of the absolute vorticity gradient is often observed on the poleward flank of the subtropical jet, so that Rossby waves emitted by Indian Ocean or western Pacific heating must travel east before they can
. Since for in (2b) , is the depth-integrated pressure anomaly relative to its value off Sumatra. In Fig. 6b , we used ERA-Interim winds to evaluate (2). Sverdrup (1947) derived solution (2) from depth-integrated equations, and in so doing lost all information about the vertical structure of the flow. In a model that allows for vertical structure, baroclinic adjustments (namely, the radiation of baroclinic Rossby waves across the basin) tend to trap the Sverdrup flow in the upper ocean (e
. Since for in (2b) , is the depth-integrated pressure anomaly relative to its value off Sumatra. In Fig. 6b , we used ERA-Interim winds to evaluate (2). Sverdrup (1947) derived solution (2) from depth-integrated equations, and in so doing lost all information about the vertical structure of the flow. In a model that allows for vertical structure, baroclinic adjustments (namely, the radiation of baroclinic Rossby waves across the basin) tend to trap the Sverdrup flow in the upper ocean (e
synoptic variability ( Blackmon 1976 ). Typical variables used to calculate storm tracks are meridional wind, eddy kinetic energy, or geopotential height, at a fixed vertical level. This metric represents the climatology of baroclinic wave activity (i.e., high and low pressure systems), but for historical reasons has been termed “storm track” [see Wallace et al. (1988) for more discussion]. Following Chang et al. (2002) , we consider each ocean basin as having its own storm track. Storm tracks offer
synoptic variability ( Blackmon 1976 ). Typical variables used to calculate storm tracks are meridional wind, eddy kinetic energy, or geopotential height, at a fixed vertical level. This metric represents the climatology of baroclinic wave activity (i.e., high and low pressure systems), but for historical reasons has been termed “storm track” [see Wallace et al. (1988) for more discussion]. Following Chang et al. (2002) , we consider each ocean basin as having its own storm track. Storm tracks offer
1997 ; Schumacher and Houze 2003 ) even though the latter is undoubtedly associated with the occurrence of buoyant convective rain. Neelin et al. (2009) found, over the tropical oceans, that the bulk environmental moisture value associated with onset is sensitive to column-averaged tropospheric temperature —a warmer troposphere will show onset at a higher value of column water vapor (CWV). Empirical evidence therefore suggests that two bulk thermodynamic variables—CWV and —are sufficient to
1997 ; Schumacher and Houze 2003 ) even though the latter is undoubtedly associated with the occurrence of buoyant convective rain. Neelin et al. (2009) found, over the tropical oceans, that the bulk environmental moisture value associated with onset is sensitive to column-averaged tropospheric temperature —a warmer troposphere will show onset at a higher value of column water vapor (CWV). Empirical evidence therefore suggests that two bulk thermodynamic variables—CWV and —are sufficient to
regulating this important form of variability. The MJO exhibits pronounced seasonality in its propagation characteristics. During boreal winter, the MJO is characterized by the equatorial eastward propagation ( Madden and Julian 1994 ). In contrast, it exhibits marked poleward movement over the Indian Ocean (IO) and western Pacific (WP) during summer with a relatively weak eastward-propagating component (e.g., Lau and Chan 1986 ; Hsu and Weng 2001 ; Jiang et al. 2004 ). Various theories have been
regulating this important form of variability. The MJO exhibits pronounced seasonality in its propagation characteristics. During boreal winter, the MJO is characterized by the equatorial eastward propagation ( Madden and Julian 1994 ). In contrast, it exhibits marked poleward movement over the Indian Ocean (IO) and western Pacific (WP) during summer with a relatively weak eastward-propagating component (e.g., Lau and Chan 1986 ; Hsu and Weng 2001 ; Jiang et al. 2004 ). Various theories have been
). The present study will focus on ENSO, the NAO, and midlatitude weather regimes and will evaluate their representations and impacts on the seasonal prediction skill in the Climate Forecast System, version 2 (CFSv2). ENSO is a prominent coupled mode involving the tropical atmosphere and ocean. Although the associated SST anomalies largely occur in the tropics, ENSO induces climate anomalies in many parts of the planet and plays an important role in seasonal prediction ( Power et al. 1999 ; Yuan
). The present study will focus on ENSO, the NAO, and midlatitude weather regimes and will evaluate their representations and impacts on the seasonal prediction skill in the Climate Forecast System, version 2 (CFSv2). ENSO is a prominent coupled mode involving the tropical atmosphere and ocean. Although the associated SST anomalies largely occur in the tropics, ENSO induces climate anomalies in many parts of the planet and plays an important role in seasonal prediction ( Power et al. 1999 ; Yuan
interesting to compare these budgets to those of easterly waves both in the Pacific and over Africa. Furthermore, incorporating these equations into a linear theoretical framework for monsoon depressions may also shed light on our understanding of these systems. Such a framework is presented in a companion paper ( Adames and Ming 2018 ). Acknowledgments This work was supported by the National Oceanic and Atmospheric Administration (NOAA) Grant NA15OAR4310099. We thank Isaac Held, Kuniaki Inoue, Brian
interesting to compare these budgets to those of easterly waves both in the Pacific and over Africa. Furthermore, incorporating these equations into a linear theoretical framework for monsoon depressions may also shed light on our understanding of these systems. Such a framework is presented in a companion paper ( Adames and Ming 2018 ). Acknowledgments This work was supported by the National Oceanic and Atmospheric Administration (NOAA) Grant NA15OAR4310099. We thank Isaac Held, Kuniaki Inoue, Brian
