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Abstract The Madden-Julian oscillation (MJO) has profound impacts on weather and climate phenomena, and thus changes in its activity have important implications under human-induced global climate change. Here, the time at which the MJO change signal emerges from natural variability under anthropogenic warming is investigated. Using simulations of the Community Earth System Model version 2 large ensemble forced by the shared socioeconomic pathways SSP370 scenario, an increase in ensemble mean MJO precipitation amplitude and a smaller increase in MJO circulation amplitude occur by the end of the 21 st century, consistent with previous studies. Notably, the MJO precipitation amplitude change signal generally emerges more than a decade earlier than that of MJO wind amplitude. MJO amplitude changes also emerge earlier over the eastern Pacific than other parts of the tropics. Our findings provide valuable information on the potential changes of MJO variability with the aim of improving predictions of the MJO and its associated extreme events. 

The mechanisms regulating the relationship between the tropical island diurnal cycle and large-scale modes of tropical variability such as the boreal summer intraseasonal oscillation (BSISO) are explored in observations and an idealized model. Specifically, the local environmental conditions associated with diurnal cycle variability are explored. Using Luzon Island in the northern Philippines as an observational test case, a novel probabilistic framework is applied to improve the understanding of diurnal cycle variability. High-amplitude diurnal cycle days tend to occur with weak to moderate offshore low-level wind and near to above average column moisture in the local environment. The transition from the BSISO suppressed phase to the active phase is most likely to produce the wind and moisture conditions supportive of a substantial diurnal cycle over western Luzon and the South China Sea (SCS). Thus, the impact of the BSISO on the local diurnal cycle can be understood in terms of the change in the probability of favorable environmental conditions. Idealized high-resolution 3D Cloud Model 1 (CM1) simulations driven by base states derived from BSISO composite profiles are able to reproduce several important features of the observed diurnal cycle variability with BSISO phase, including the strong, land-based diurnal cycle and offshore propagation in the transition phases. Background wind appears to be the primary variable controlling the diurnal cycle response, but ambient moisture distinctly reduces precipitation strength in the suppressed BSISO phase and enhances it in the active phase.

 

A multiscale analysis of the environment supporting tornadoes in southeast South America (SESA) was conducted based on a self-constructed database of 74 reports. Composites of environmental and convective parameters from ERA5 were generated relative to tornado events. The distribution of the reported tornadoes maximizes over the Argentine plains, while events are rare close to the Andes and south of Sierras de Córdoba. Events are relatively common in all seasons except in winter. Proximity environment evolution shows enhanced instability, deep-layer vertical wind shear, storm-relative helicity, reduced convective inhibition, and a lowered lifting condensation level before or during the development of tornadic storms in SESA. No consistent signal in low-level wind shear is seen during tornado occurrence. However, a curved hodograph with counterclockwise rotation is present. The Significant Tornado Parameter (STP) is also maximized prior to tornadogenesis, most strongly associated with enhanced CAPE. Differences in the convective environment between tornadoes in SESA and the U.S. Great Plains are discussed. On the synoptic scale, tornado events are associated with a strong anomalous trough crossing the southern Andes that triggers lee cyclogenesis, subsequently enhancing the South American low-level jet (SALLJ) that increases moisture advection to support deep convection. This synoptic trough also enhances vertical shear that, along with enhanced instability, sustains organized convection capable of producing tornadic storms. At planetary scales, the tornadic environment is modulated by Rossby wave trains that appear to be forced by convection near northern Australia. Madden–Julian oscillation phase 3 preferentially occurs 1–2 weeks ahead of tornado occurrence.

The main goal of this study is to describe what atmospheric conditions (from local to global scales) are present prior to and during tornadic storms impacting southeast South America (SESA). Increasing potential for deep convection, wind shear, and potential for rotating updrafts, as well as reducing convective inhibition and cloud-base height, are predominant a few hours before and during the events in connection to low-level northerly winds enhancing moisture transport to the region. Remote convective activity near northern Australia appears to influence large-scale atmospheric circulation that subsequently triggers convective storms supporting tornadogenesis 1–2 weeks later in SESA. Our findings highlight the importance of accounting for atmospheric processes occurring at different scales to understand and predict tornado occurrences.

 

Organized deep convective activity has been routinely monitored by satellite precipitation radar from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM). Organized deep convective activity is found to increase not only with sea surface temperature (SST) above 27°C, but also with low-level wind shear. Precipitation shows a similar increasing relationship with both SST and low-level wind shear, except for the highest low-level wind shear. These observations suggest that the threshold for organized deep convection and precipitation in the tropics should consider not only SST, but also vertical wind shear. The longwave cloud radiative feedback, measured as the tropospheric longwave cloud radiative heating per amount of precipitation, is found to generally increase with stronger organized deep convective activity as SST and low-level wind shear increase. Organized deep convective activity, the longwave cloud radiative feedback, and cirrus ice cloud cover per amount of precipitation also appear to be controlled more strongly by SST than by the deviation of SST from its tropical mean. This study hints at the importance of non-thermodynamic factors such as vertical wind shear for impacting tropical convective structure, cloud properties, and associated radiative energy budget of the tropics.

This study uses tropical satellite observations to demonstrate that vertical wind shear affects the relationship between sea surface temperature and tropical organized deep convection and precipitation. Shear also affects associated cloud properties and how clouds affect the flow of radiation in the atmosphere. Although how vertical wind shear affects convective organization has long been studied in the mesoscale community, the study attempts to apply mesoscale theory to explain the large-scale mean organization of tropical deep convection, cloud properties, and radiative feedbacks. The study also provides a quantitative observational baseline of how vertical wind shear modifies cloud radiative effects and convective organization, which can be compared to numerical simulations.

 

Abstract This study investigates why the major convective envelope of the Madden–Julian oscillation (MJO) detours to the south of the Maritime Continent (MC) only during boreal winter [December–March (DJFM)]. To examine processes affecting this MJO detour, the MJO-related variance of precipitation and column-integrated moisture anomalies in DJFM are compared with those in the seasons before [October–November (ON)] and after [April–May (AM)]. While MJO precipitation variance is much higher in the southern MC (SMC) during DJFM than in other seasons, the MJO moisture variance is comparable among the seasons, implying that the seasonal locking of the MJO’s southward detour cannot be explained by the magnitude of moisture anomalies alone. The higher precipitation variance in the SMC region is partly explained by the much higher moisture sensitivity of precipitation in DJFM than in other seasons, resulting in a more efficient conversion of anomalous moisture to anomalous precipitation. DJFM is also distinguishable from the other seasons by stronger positive wind–evaporation feedback onto MJO precipitation anomalies due to the background westerly wind in the lower troposphere. It is found that the seasonal cycle of moisture–precipitation coupling and wind–evaporation feedback in the SMC region closely follows that of the Australian monsoon, which is active exclusively in DJFM. Our results suggest that the MJO’s southward detour in the MC is seasonally locked because it occurs preferentially when the Australian monsoon system produces a background state that is favorable for MJO development in the SMC. 

Abstract The impact of the environmental background wind on the diurnal cycle near tropical islands is examined in observations and an idealized model. Luzon Island in the northern Philippines is used as an observational test case. Composite diurnal cycles of CMORPH precipitation are constructed based on an index derived from the first empirical orthogonal function (EOF) of ERA5 zonal wind profiles. A strong precipitation diurnal cycle and pronounced offshore propagation in the leeward direction tends to occur on days with a weak, offshore prevailing wind. Strong background winds, particularly in the onshore direction, are associated with a suppressed diurnal cycle. Idealized high resolution 2-D Cloud Model 1 (CM1) simulations test the dependence of the diurnal cycle on environmental wind speed and direction by nudging the model base-state toward composite profiles derived from the reanalysis zonal wind index. These simulations can qualitatively replicate the observed development, strength, and offshore propagation of diurnally generated convection under varying wind regimes. Under strong background winds, the land-sea contrast is reduced, which leads to a substantial reduction in the strength of the sea-breeze circulation and precipitation diurnal cycle. Weak offshore prevailing winds favor a strong diurnal cycle and offshore leeward propagation, with the direction of propagation highly sensitive to the background wind in the lower free troposphere. Offshore propagation speed appears consistent with density current theory rather than a direct coupling to a single gravity wave mode, though gravity waves may contribute to a destabilization of the offshore environment. 

Changes to the tropical eastern North Pacific Intraseasonal Oscillation (ISO) at the end of the 21st century and implications for tropical cyclone (TC) genesis are examined in the Shared Socioeconomic Pathways (SSP585) scenario of the Coupled Model Intercomparison Project phase 6 (CMIP6) data set. Multimodel mean composite low-level wind and precipitation anomalies associated with the leading intraseasonal mode indicate that precipitation amplitude increases while wind amplitude weakens under global warming, consistent with previous studies for the Indo-Pacific warm pool. The eastern North Pacific intraseasonal precipitation/wind pattern also tends to shift southwestward in a warmer climate, associated with weaker positive precipitation anomalies near the coast of Mexico and Central America during the enhanced convection/westerly wind phase. Implications for the modulation of TC genesis by the leading intraseasonal mode are then explored using an empirical genesis potential index (GPI). In the historical simulation, GPI shows positive anomalies in the eastern North Pacific in the convectively enhanced phase of the ISO. The ISO’s modulation of GPI weakens near the coast of Mexico and Central America with warming, associated with a southward shift of GPI anomalies. Further examination of the contribution from individual environmental variables that enter the GPI shows that relative humidity and vorticity changes during ISO events weaken positive GPI anomalies near the Mexican coast with warming and make genesis more favorable to the southwest. The impact of vertical shear anomaly changes is also to favor genesis away from the coast. These results suggest a weaker modulation of TCs near the Mexican Coast by the ISO in a warmer climate. 

Abstract The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads. 

Few studies have utilized machine learning techniques to predict or understand the Madden‐Julian oscillation (MJO), a key source of subseasonal variability and predictability. Here, we present a simple framework for real‐time MJO prediction using shallow artificial neural networks (ANNs). We construct two ANN architectures, one deterministic and one probabilistic, that predict a real‐time MJO index using maps of tropical variables. These ANNs make skillful MJO predictions out to ∼18 days in October‐March and ∼11 days in April‐September, outperforming conventional linear models and efficiently capturing aspects of MJO predictability found in more complex, dynamical models. The flexibility and explainability of simple ANN frameworks are highlighted through varying model input and applying ANN explainability techniques that reveal sources and regions important for ANN prediction skill. The accessibility, performance, and efficiency of this simple machine learning framework is more broadly applicable to predict and understand other Earth system phenomena.