Kathy Pegion

Summer rainfall over the southeastern United States (SEUS) is influenced by regional moisture transport, vertical motion, and large‐scale circulation variability, yet persistent wet periods can occur within different circulation configurations that are obscured by a mean wet composite. Here, we examine SEUS summer rainfall during 1979–2023 using a conditional weather‐regime framework in which persistent wet periods are first identified from 5‐day mean rainfall anomalies and the associated circulation states are subsequently classified using 850‐ and 200‐hPa geopotential height anomalies. Four conditional regimes (CRs) are identified, each characterized by distinct coupled lower‐ and upper‐tropospheric circulation anomalies. The composite western ridge of the North Atlantic Subtropical High (NASH) in CR A is located farther west, near 85° W, whereas CR B, CR C and CR D have closely clustered ridge locations near 80° W and 26°–27° N. Despite these similar western‐ridge locations, the regimes differ substantially in the surrounding NASH‐associated pressure field and moisture transport. In CR A and CR B, an anomalous ridge along the northern flank of the NASH is accompanied by stronger zonal moisture convergence, which provides the dominant contribution to net moisture convergence over the SEUS. In contrast, in CR C and CR D this ridge‐like anomaly is weaker or absent, zonal convergence is reduced, and the meridional contribution becomes relatively more important. All four regimes are also characterized by tropospheric ascent over the SEUS, indicating that the distinct moisture‐transport environments coincide with rainfall‐supporting vertical motion. Rossby wave source and wave activity flux diagnostics further indicate regime‐dependent upper‐level wave environments across the North Pacific and North America, with CR A exhibiting the clearest trans‐Pacific wave‐activity pathway. Overall, the results reveal distinct circulation–moisture pathways to persistent SEUS rainfall, underscoring that similar regional wet states can arise from fundamentally different large‐scale dynamical configurations.

This study investigates the sources of predictability underlying subseasonal precipitation skill over South America in existing subseasonal prediction systems. Using subseasonal re‐forecasts from the NCAR‐CESM2 model, we demonstrate that significant skill persists even when interannual variability is removed. The highest skill occurs during austral summer and spring. To isolate sources of predictability, we analyze a novel set of re‐forecast experiments initialized with climatological atmosphere, land and ocean states. Results indicate that atmospheric initial conditions are essential for achieving skill, while ocean initializations contribute to skill containing interannual variability and land initializations contribute minimally to skill. Canonical correlation analysis is used to identify the most skillful spatial patterns and associated time series during the high‐skill seasons. The most skillful patterns in December–February are not independent of the South American dipole but also contain additional skill beyond the dipole. This additional skill is not associated with propagating tropical convection or sea surface temperature anomalies indicating that the Madden–Julian Oscillation, El Niño–Southern Oscillation, or South Atlantic SST variability are not the sources of predictability. Instead, a wave‐train‐like structure extends across the South Pacific, resembling a Rossby wave response. Idealized experiments using a simplified atmospheric general circulation model show that this pattern can be reproduced by stationary tropical heating over the Maritime Continent, suggesting a dynamical link between tropical heating and South American precipitation variability on subseasonal timescales independent of ENSO and the MJO.

Two 2021 winter warm spell events experienced near record extreme surface temperatures, anomalies exceeding C, during the winter season in 2021 across the Southern Great Plains (SGP). Extreme heat during the winter season results in similar detrimental socioeconomic impacts compared to their counterpart summer heat wave events. Winter warm spell events across the SGP have been historically increasing over the last several decades, and as such, it is crucial to investigate the drivers of these extreme events. In this study, we use ERA‐5 reanalysis data to investigate the atmospheric and surface characteristics associated with these two extreme events (i.e., (a) 29 November–17 December 2021 and (b) 22 December–31 December 2021). A prolonged period of positive geopotential height anomalies amplified subsidence in combination with increased incoming solar radiation and surface heat fluxes aiding extreme surface temperatures during the first winter warm spell event. However, a more prominent atmospheric blocking high (i.e., Alaskan Ridge) initiated and intensified the extreme heat during the second winter warm spell. Increased incoming solar radiation and positive sensible heat flux due to a dry surface fostered extreme heat during the second winter warm spell event. Warm air advection throughout both winter warm spell events supported the extreme surface temperatures. Discovering potential crucial drivers to winter warm spells identifies the sources of predictability to improve prediction of these extreme heat events.

The quasi‐biennial oscillation (QBO) impacts the Madden‐Julian Oscillation (MJO) activity with a stronger MJO in QBO easterly (QBOE) than QBO westerly (QBOW) winters. However, this relationship is poorly represented in the current generation climate models. For the first time, this paper applies a stratospheric zonal‐mean nudging in a subseasonal prediction system to capture it. Two strong MJO cases in a QBO‐neutral winter are investigated. The QBO temperature and zonal wind anomalies are added separately as well as together to the stratosphere using nudging in MJO case hindcast. Only by nudging the QBO temperature anomalies while leaving the zonal wind free, can the prediction system capture the observed QBO‐MJO connection. The tropopause instability is found positively correlated to the MJO amplitude, but it cannot fully explain the captured connection. The free‐evolving zonal wind anomalies in the stratosphere due to the nudged QBO temperature are crucial for the captured connection.

We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer.

A recent study categorized the Madden-Julian Oscillation (MJO) during the boreal winter season into four types including stand, jump, slow eastward propagating, and fast eastward propagating MJO. This study focuses on the stand and jump MJO events. Based on whether their convection penetrates the Maritime Continent (MC), stand and jump MJO are combined as non-penetrating (NP) MJO, while the rest two types are combined as eastward-penetrating (EP) MJO. Results reveal the relative roles of the westward-propagating wave (WPW), as well as the QBO and ENSO, in limiting MJO propagation. Lack of the pre-moistening over the southern sea surface of MC is responsible for NP MJO’s failure to penetrate MC. The active convection of WPW hinders the NP MJO descending branch over the Pacific and therfore leads to the insufficient meridional advective moistening over the southern sea surface of the MC. The overall drying over the MC for jump MJO also comes from the intraseasonal dry amonalies induced by WPW over the western Pacific, which are then advected to MC by the superposed seasonal mean northern easterlies. The independent convection over the Pacific for jump MJO is influenced by a combined effect of the QBO and ENSO. Under the influence of the preferred QBO westerly (QBOW) phase, the tropopause instability of NP MJO is decoupled with its convection. The decoupled tropopause instability propagates eastward into the Pacific, and amplifies the local WPW convection there for jump MJO. For stand MJO, however, the La Nina-like seasonal mean cool sea surface temperature (SST) anmalies confine WPW within the western Pacific. Therefore, the decoupled tropopause instability of stand MJO is out phase of WPW over the central Pacific, and fails to induce an independent convection as a result. The similar diagnosis is further applied to the CESM2 and E3SM CMIP6 historical simulations to investigate the MJO diversity and propagation in climate models. Four MJO types as in the observation are successfully categorized in these two models. The accompanied westward propagating waves over the central-western Pacific for NP MJO events are also evident. However, models suffer from some biases in MJO propagation such as too many NP MJO events than the observation.

Most present forecast systems for estuaries predict conditions for only a few days into the future. However, there are many reasons to expect that skillful estuarine forecasts are possible for longer time periods, including increasingly skillful extended atmospheric forecasts, the potential for lasting impacts of atmospheric forcing on estuarine conditions, and the predictability of tidal cycles. In this study, we test whether skillful estuarine forecasts are possible for up to 35 days into the future by combining an estuarine model of Chesapeake Bay with 35‐day atmospheric forecasts from an operational weather model. When compared with both a hindcast simulation from the same estuarine model and with observations, the estuarine forecasts for surface water temperature are skillful up to about 2 weeks into the future, and the forecasts for bottom temperature, surface and bottom salinity, and density stratification are skillful for all or the majority of the forecast period. Bottom oxygen forecasts are skillful when compared to the model hindcast, but not when compared with observations. We also find that skill for all variables in the estuary can be improved by taking the mean of multiple estuarine forecasts driven by an ensemble of atmospheric forecasts. Finally, we examine the forecasts in detail using two case studies of extreme events, and we discuss opportunities for improving the forecast skill.

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

The North American Multi‐Model Ensemble (NMME) has become integral scientific infrastructure in subseasonal and seasonal climate prediction, advancing applied prediction and scientific understanding of the mechanisms and predictability of short‐term climate. The NMME was designed to evolve as old models were retired and new models joined the ensemble. This study examines the assumption that prediction skill will increase as the system evolves, focusing on 2 m temperature, precipitation rate, and sea surface temperature prediction. The common period of 1982–2010 is studied for four configurations of the NMME, approximately representing the operational model suites of 2011, 2012, 2014–2018, and 2019–present. Substantial improvement in temperature prediction over both land and ocean is observed, with little change in global precipitation prediction. Sea surface temperature prediction at longer leads has improved over much of the globe, with the notable exception of the central‐eastern tropical Pacific, where prediction skill has declined.