Levi Glenn Silvers

Estimating the range of possible tropical cyclone (TC) activity for a given season is not possible from observations. The envelope of possible activity must be derived from an ensemble of simulations for that season. This manuscript estimates the range of possible Atlantic TC activity during the 2005, 2013 and 2024 seasons using the ACE2 deep-learning model. Comparing the observed TCs with the ensemble mean number reveals that the 2005 season overperformed relative to the expected TC number, while both 2013 and 2024 underperformed. In particular, 90\% of the 2024 ensembles had more than the 18 named TCs observed during the 2024. When El Niño--Southern Oscillation is relatively weak, mean TC activity seems to be set by tropical Atlantic SST. While the Madden Julian Oscillation (MJO) does modulate TC activity, the range of TC variability among ensembles for a given year is not driven by MJO timing.

The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) phase I provided insights into the tropical radiative‐convective equilibrium state across an unprecedented ensemble of models that includes both those with parameterized and explicit convection. RCEMIP phase II (RCEMIP‐II) introduces mock‐Walker simulations to examine how sea surface temperature (SST) gradients and the forced circulation they theoretically induce interact with the structure of convection and mean climate state. This overview paper presents descriptions of and initial results from 17 numerical models regarding clouds, convective organization, and circulation structure. Results regarding warming the mean SST in the presence of an SST gradient generally mirror those from RCEMIP‐I but a larger range of dynamical circulations, along with additional phenomena, are possible in RCEMIP‐II. Increases in the SST gradient while the mean SST is held constant lead to more convective organization across many models, as measured by a number of organization metrics. Increases in the SST gradient also tend to decrease the frequency of high, thin clouds while increasing the frequency of low clouds, including the possibility of stratocumulus, which were not seen in RCEMIP‐I. RCEMIP‐II also provides an estimate of the influence of a pattern effect, suggesting increased negative climate feedbacks if increases in the SST gradient occur alongside mean warming. However, the SST gradient's inability to fully constrain the circulation structure allows simulations to show varying importance of self‐aggregation and forced clustering over the warm pool, resulting in extreme variety in the circulation structures and important climate variables in the organized state across the model ensemble.

October–November Caribbean tropical cyclone (TC) activity has significant impacts for both the Caribbean islands and Central America (e.g., Hurricanes Eta and Iota in 2020). October–November Caribbean TCs can also track northward and make continental United States landfall, resulting in substantial damage and fatalities (e.g., Hurricane Michael in 2018 and Delta and Zeta in 2020). We find significant increasing trends in October–November Caribbean hurricanes, rapidly intensifying hurricanes (winds increasing by ≥15 m s ⁻¹ within 24 hr), and landfalling hurricanes during the global satellite era (1979–present). Since 1979, we also observe significant warming trends in the western Atlantic Warm Pool and anomalous relative cooling in the eastern Pacific during October–November. These trends yield a more conducive dynamic and thermodynamic environment for Caribbean TCs, including reductions in Caribbean vertical wind shear, increases in Caribbean potential intensity, and a more TC‐conducive African easterly jet.

Tropical cyclone (TC) rapid intensification (RI) is a major source of uncertainty in TC prediction. Here we examine observed basin‐specific relationships between RI and El Niño‐Southern Oscillation (ENSO), where RI is defined as a TC strengthening by ≥30 kt within 24 hr. During El Niño, the number of RI events significantly increases in the eastern North Pacific, western North Pacific and South Pacific, with the opposite behavior in the North Atlantic. During La Niña, changes in RI occurrence in the Atlantic and Pacific are approximately opposite as in El Niño. The ENSO–Indian Ocean RI relationship is weak. These changes are consistent with environmental conditions modulating RI, including mid‐level moisture, vertical wind shear, sea surface temperatures and potential intensity. Because of disagreement between the recently observed La Nina‐like trend and an El Niño‐like trend simulated by climate models, improved model representation of ENSO could reduce future uncertainty in RI projections.

The 2024 North Atlantic (hereafter Atlantic) hurricane season started quickly, with the earliest Category 5 on record (Beryl) and three hurricanes forming through 14 August. Following Ernesto's dissipation on 20 August, the Atlantic hurricane season became extremely quiet during the climatological peak of hurricane season, with only one Category 2 hurricane (Francine) and one tropical storm through 23 September. Several environmental factors likely contributed to this unexpected, prolonged lull. During mid‐to‐late August, subseasonal conditions were broadly favorable for Atlantic hurricanes, but a northward shift in African easterly wave emergence latitude yielded fewer tropical cyclone seed disturbances that also traversed unfavorably cool ocean water. During early‐to‐mid September, subseasonal variability driven by the Madden‐Julian oscillation was less conducive to hurricane activity, with several bouts of increased vertical wind shear across the central Atlantic. Throughout most of the lull, the tropical Atlantic was anomalously dry and subsident, suppressing hurricane formation chances.

We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two‐moment Morrison‐Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4‐MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud‐to‐precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under‐prediction of total and low‐level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over‐prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low‐level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4‐MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high‐level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.

\papertype Original Article The 2024 North Atlantic (hereafter Atlantic) hurricane season started quickly, with the earliest Category 5 on record (Beryl) and three hurricanes forming through 14 August. Following Ernesto’s dissipation on 20 August, the Atlantic hurricane season became extremely quiet during the climatological peak of hurricane season, with only one Category 2 hurricane (Francine) and one tropical storm through 23 September. Several environmental factors likely contributed to this unexpected, prolonged lull. During mid-to-late August, subseasonal conditions were broadly favorable for Atlantic hurricanes, but a northward shift in African easterly wave emergence latitude yielded fewer TC seed disturbances that traversed unfavorably cool ocean water. During early-to-mid September, subseasonal variability driven by the Madden-Julian oscillation was less conducive to hurricane activity, with several bouts of increased vertical wind shear across the central Atlantic. Throughout most of the lull, the tropical Atlantic was anomalously dry and subsident, suppressing hurricane formation chances.

Canada experienced its most severe wildfire season on record in 2023, with nearly 5% of its forested land burned-almost four times the previous record set in 1995. Our analysis indicated that fire severity, strongly correlated with the monthly Fire Weather Index (FWI), was most intense in the western provinces and territories during May and July, whereas in the eastern provinces, it peaked in June, leading to a seasonal and areal average of more than 3.5 standard deviations (STD). This unprecedented fire activity was fueled by record-breaking, persistent high-pressure systems, with both their frequency and intensities surpassing 3 STD, along with variable winds. These abnormal atmospheric patterns exacerbated dry conditions, reduced cloud cover, and increased surface solar radiation, driving record-high temperatures and FWI values, all exceeding ±3 STD. The extreme high-pressure events were primarily linked to a combination of climatological standing waves and exceptionally strong, transient quasi-stationary waves. The dominant patterns in the mid-troposphere were characterized by large-scale planetary waves at low zonal wavenumbers (1–4). Long-term warming trends also contributed, though they played a lesser role, accounting for roughly 10–20% of the overall anomalies. These findings provide critical insights into the atmospheric dynamics driving Canada’s unprecedented wildfire season.

Tropical cyclone (TC) rapid intensification (RI) is a major source of uncertainty in TC prediction. Here we examine observed basin-specific relationships between RI and El Niño-Southern Oscillation (ENSO), where RI is defined as a TC strengthening by >=30 kt within 24 hr. During El Niño, there is a significant increase in RI in the eastern North Pacific, western North Pacific and South Pacific, with the opposite behavior in the North Atlantic. During La Niña, RI has an approximately opposite relationship in the Atlantic and Pacific. The relationship between ENSO and Indian Ocean RI is weak. These changes are closely linked to environmental conditions modulating RI, including mid-level moisture, vertical wind shear and sea surface temperatures. Because there is disagreement between the recently-observed La Nina-like trend and an El Niño-like trend simulated by climate models, anticipating future RI trends is conditional on improved model representation and physical understanding of ENSO.

We evaluate cloud simulations using satellite simulators against multiple observational datasets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory’s Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a two-moment Morrison-Gettelman bulk cloud microphysics with prognostic precipitation (MG2) is applied, denoted as AM4-MG2. The modelled cloud spatial distributions, vertical profiles, phase partitioning, cloud-to-precipitation transitions, and radiative effects from AM4.0 and AM4-MG2 compare reasonably well with satellite observations. Model biases include the underestimate of total and low-level clouds, especially optically thin/intermediate clouds, but the overestimate of optically thick clouds, indicating “too few, too bright’ biases. These biases compensate each other and result in reasonable estimates of cloud radiative effects. The underestimate of low-level clouds is associated with too frequent and too early drizzle/precipitation formation. The precipitation bias is improved in AM4-MG2, where the autoconversion scheme initiates the precipitation more realistically.