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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.

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.

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.

Midlatitude stationary waves are relatively persistent large‐scale longitudinal variations in atmospheric circulation. Although recent case studies have suggested a close connection between stationary waves and extreme weather events, little is known about the global‐scale linkage between stationary waves and wildfire activity, as well as the potential changes in this relationship in a warmer climate. Here, by analyzing the Community Earth System Model version 2 large ensemble, we show that a zonal wavenumber 5–6 stationary wave pattern tends to synchronize wildfire occurrences across the Northern Hemisphere midlatitudes. The alternation of upper‐troposphere ridges and troughs creates a hemispheric‐scale spatial pattern of alternating hot/dry and cold/wet conditions, which increases or decreases wildfire occurrence, respectively. More persistent high‐pressure conditions drastically increase wildfire probabilities. Even though the dynamics of these waves change little in response to anthropogenic global warming, the corresponding midlatitude wildfire variability is projected to intensify due to changes in climate background conditions.

Boreal‐wintertime hindcasts in the Unified Forecast System with the tropics nudged toward reanalysis improve United States (US) West Coast precipitation forecasts at Weeks 3–4 lead times when compared to those without nudging. To diagnose the origin of these improvements, a multivariate k‐means clustering method is used to group hindcasts into subsets by their initial conditions. One cluster characterized by an initially strong Aleutian Low demonstrates larger improvements at Weeks 3–4 with nudging compared to the others. The greater improvements with nudging for this cluster are related to model errors in simulating the interaction between the Aleutian Low and the teleconnection patterns associated with the Madden‐Julian oscillation (MJO) and El Niño‐Southern Oscillation (ENSO). Improving forecasts of tropical intraseasonal precipitation, especially during early MJO phases under non‐cold ENSO, may be important for producing better Weeks 3–4 precipitation forecasts for the US West Coast.

Recent work using CMIP5 models under RCP8.5 suggests that individual multimodel mean changes in precipitation and wind variability associated with the Madden‐Julian oscillation (MJO) are not detectable until the end of the 21st century. However, a decrease in the ratio of MJO circulation to precipitation anomaly amplitude is detectable as early as 2021–2040, consistent with an increase in dry static stability as predicted by weak temperature gradient balance. Here, we examine MJO activity in multiple reanalyses (ERA5, MERRA‐2, and ERA‐20C) and find that MJO wind and precipitation anomaly amplitudes have a complicated time evolution over the record. However, a decrease in the ratio of MJO circulation to precipitation anomaly amplitude is detected over the observational period, consistent with the change in dry static stability. These results suggest that weak temperature gradient theory may be able to help explain changes in MJO activity in recent decades.

With widespread influence on global climate and weather extremes, the Madden-Julian Oscillation (MJO) plays a crucial role in subseasonal prediction. Our latest global climate models (GCMs), however, have great difficulty in realistically simulating the MJO. This model inability is largely due to problems in representation of MJO’s cumulus organization. This study, based on a series of idealized aqua-planet model experiments using an atmospheric-only GCM, clearly demonstrates that MJO propagation is strongly modulated by the large-scale background state in which the lower-tropospheric mean moisture gradient and zonal winds are critical. Therefore, when tuning climate models to achieve improved MJO simulations, particular attention needs to be placed on the model large-scale mean state that is also significantly affected by cumulus parameterizations. This study indicates that model biases in representing MJO propagation may be related to the widely reported double-ITCZ (intertropical convergence zone) problem in climate models.

The impacts of rising carbon dioxide (CO₂) concentration and ocean feedbacks on the Madden‐Julian Oscillation (MJO) are investigated with the Community Atmospheric Model Version 5 (CAM5) in an idealized aquaplanet configuration. The climate response associated with quadrupled CO₂concentrations and sea surface temperature (SST) warming are examined in both the uncoupled CAM5 and a version coupled to a slab ocean model. Increasing CO₂concentrations while holding SST fixed produces only small impacts to MJO characteristics, while the SST change resulting from increased CO₂concentrations produces a significant increase in MJO precipitation anomaly amplitude but smaller increase in MJO circulation anomaly amplitude, consistent with previous studies. MJO propagation speed increases in both coupled simulations with quadrupling of CO₂and uncoupled simulations with the same climatological surface temperature warming imposed, although propagation speed is increased more with coupling. While climatological SST changes are identical between coupled and uncoupled runs, other aspects of the basic state such as zonal winds do not change identically. For example, climate warming produces stronger superrotation and weaker mean lower tropospheric easterlies in the coupled run, which contributes to greater increases in MJO eastward propagation speed with warming through its effect on moisture advection. The column process, representing the sum of vertical moist static energy (MSE) advection and radiative heating anomalies, also supports faster eastward propagation with warming in the coupled run. How differing basic states between coupled and uncoupled runs contribute to this behavior is discussed in more detail.

Since its discovery in the early 1970s, the crucial role of the Madden‐Julian Oscillation (MJO) in the global hydrological cycle and its tremendous influence on high‐impact climate and weather extremes have been well recognized. The MJO also serves as a primary source of predictability for global Earth system variability on subseasonal time scales. The MJO remains poorly represented in our state‐of‐the‐art climate and weather forecasting models, however. Moreover, despite the advances made in recent decades, theories for the MJO still disagree at a fundamental level. The problems of understanding and modeling the MJO have attracted significant interest from the research community. As a part of the AGU's Centennial collection, this article provides a review of recent progress, particularly over the last decade, in observational, modeling, and theoretical study of the MJO. A brief outlook for near‐future MJO research directions is also provided.