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Tropical easterly waves (TEWs) are westward-propagating off-equatorial waves that are typically convectively coupled. TEWs make significant contributions to the annual rainfall in many regions of the tropics, and often seed tropical cyclones. Climatologies of TEWs exist regionally and hemispherically, however, none exist at the global scale. The climatology in this study is the first attempt to study TEWs globally, applying a combination of the TRACK algorithm and objective criteria to all basins to identify TEW activity at both 850 and 700 hPa. In addition to areas of TEW activity in previously studied regions such as the North Atlantic and eastern North Pacific Ocean basins, this study has identified TEW activity in every other tropical ocean basin in both hemispheres. On average across the globe, the methods employed tracked 380 waves per year at 850 hPa and 638 waves per year at 700 hPa. There were no significant linear trends globally or hemispherically over the 41 years analyzed, but large interannual variability. Despite the variety of regions the TEWs occur in, the distribution of average speeds agrees with studies using other data and tracking methods, with averages between 7.5–8 m s −1 depending on the level and hemisphere. TEW activity shows a strong preference to the warm season, with approximately double the number of TEWs occurring in the warm season compared to the cold season, a pattern that is observed in both the northern and southern hemispheres. This database is publicly available to enable further work in understanding TEW behavior and predictability globally.

Tropical easterly waves (TEWs) play a critical role in regulating convection and precipitation across the global tropics. TEWs act as seed disturbances for tropical cyclogenesis, serve as an essential component in monsoon precipitation, and produce large amounts of rainfall and diabatic heating that can strongly affect the large-scale circulation. To help improve our knowledge of a more elusive type of tropical wave, we use satellite and reanalysis estimates of the diabatic heating associated with TEWs that are identified by a tracking algorithm based on low-level curvature vorticity. This study uses the Tropical Rainfall Measuring Mission (TRMM) version 6 convective–stratiform heating (CSH) and spectral latent heating (SLH) orbital products to create a global climatology (1998–2015) of TEW diabatic heating. TEW-specific composites for the satellite-observed vertical structure of diabatic heating are compared to similar terms from MERRA-2 across a variety of tropical regions. There are striking differences between the reanalysis and satellite heating with MERRA-2 having much stronger background heating, especially at low levels. Both the satellite-observed and reanalysis heating profiles show stronger midlevel heating associated with TEWs relative to the unconditional background. Similar patterns of mid- and bottom-heaviness emerge in two-dimensional composites of TEW latent heating as stronger heating rates and percent contributions to the background are generally higher at 500 hPa than at 850 hPa. Although TEWs only represent a few percent of the background heating across the global tropics, they comprise 30%–50% of the heating in the prominent TEW tracks over the northeastern Atlantic and Pacific Oceans.

Western U.S. (WUS) rainfall and snowpack vary greatly on interannual and decadal timescales. This combined with their importance to water resources makes future projections of these variables highly societally relevant. Previous studies have shown that precipitation events in the WUS are influenced by the timing, positioning, and duration of extreme integrated water vapor transport (IVT) events (e.g., atmospheric rivers) along the coast. We investigate end-of-21st-century projections of WUS precipitation and IVT in a collection of regional climate models (RCMs) from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX). Several of the NA-CORDEX RCMs project a decrease in cool season precipitation at high elevation (e.g., across the Sierra Nevada) with a corresponding increase in the Great Basin of the U.S. We explore the larger-scale controls on this terrain-related precipitation change in a subset of the NA-CORDEX RCMs through an examination of IVT-events. Projected changes in frequency and duration of IVT-events depend on the event’s extremity: by the end of the century extreme IVT-events increase in frequency whereas moderate IVT-events decrease in frequency. Furthermore, in the future, total precipitation across the WUS generally increases during extreme IVT-events, whereas total precipitation from moderate IVT-events decreases across higher elevations. Thus, we argue that the mean cool season precipitation decreases at high elevations and increases in the Great Basin are largely determined by changes in moderate IVT-events which are projected to be less frequent and bring less high-elevation precipitation.

The NARCCAP RCM–GCM ensemble is used to explore the uncertainty in midcentury projections of snow over North America that arise when multiple RCMs are used to downscale multiple GCMs. Various snow metrics are examined, including snow water equivalent (SWE), snow cover extent (SCE), snow cover duration (SCD), and the timing of the snow season. Simulated biases in baseline snow characteristics are found to be sensitive to the choice of RCM and less influenced by the driving GCM. By midcentury, domain-averaged SCE and SWE are projected to decrease in all months of the year. However, using multiple RCMs to downscale multiple GCMs inflates the uncertainty in future projections of both SCE and SWE, with projections of SWE being more uncertain. Spatially, the RCMs show winter SWE decreasing over most of North America, except north of the Arctic rim, where SWE is projected to increase. SCD is also projected to decrease with both a later start and earlier termination of the snow season. For all metrics considered, the magnitude of the climate change signal varies across the RCMs. The ensemble spread is large over the western United States, where the RCMs disagree on the sign of the change in SWE in some high-elevation regions. Future projections of snow (both magnitude and spatial patterns) are more similar between simulations performed with the same RCM than the simulations driven by the same GCM. This implies that climate change uncertainty is not sufficiently explored in experiments performed with a single RCM driven by multiple GCMs.

Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.

The release of global climate projections coupled with the demand for local-resolution climate-forced meteorology has prompted many research groups to downscale these projections using various statistical, dynamical, and current machine learning techniques. Such downscaled datasets are being used to plan infrastructure and other community needs over the coming decades. Faced with roughly a dozen available US downscaled datasets, many practitioners ask, ‘What are the relevant differences between datasets?’ This work highlights the difficulty of comparing downscaled datasets and illustrates ways in which datasets differ even when using identical climate model input data. We show that substantial variability in precipitation projections arises from downscaling alone and that the downscaled dataset agreement varies depending on global climate projection. This analysis emphasizes the need for greater coordination and movement toward rigorous benchmarking of downscaling strategies within the downscaling research community, à la the land-modeling community, to better quantify downscaling dataset differences, strengths, and weaknesses for practitioners.

The relationship between African easterly waves and convection is examined in two coupled general circulation models: the Community Climate System Model (CCSM) and the “superparameterized” CCSM (SP-CCSM). In the CCSM, the easterly waves are much weaker than observed. In the SP-CCSM, a two-dimensional cloud-resolving model replaces the conventional cloud parameterizations of CCSM. Results show that this allows for the simulation of easterly waves with realistic horizontal and vertical structures, although the model exaggerates the intensity of easterly wave activity over West Africa. The simulated waves of SP-CCSM are generated in East Africa and propagate westward at similar (although slightly slower) phase speeds to observations. The vertical structure of the waves resembles the first baroclinic mode. The coupling of the waves with convection is realistic. Evidence is provided herein that the diabatic heating associated with deep convection provides energy to the waves simulated in SP-CCSM. In contrast, horizontal and vertical structures of the weak waves in CCSM are unrealistic, and the simulated convection is decoupled from the circulation.

This study presents a process-informed framework for assessing the differential credibility of diverse downscaling methodologies, including both statistical (simple and complex) and dynamical approaches. The methods evaluated include a convolutional neural network (CNN), the Locally Constructed Analog Method (LOCA), the Statistical DownScaling Model (SDSM), quantile delta mapping (QDM), simple interpolation with bias correction, and two regional climate models. As proof of concept, we apply the framework to evaluate the physical consistency of processes associated with wet-day occurrence at a site in the southern USA Great Plains. Additionally, we introduce a relative credibility metric that quantifies cross-method performance and outlines how this framework can be extended to other variables, regions, and downscaling applications. Results show that all downscaling methods perform credibly when the parent global climate model (GCM) performs credibly. However, complex statistical methods (CNN, LOCA, SDSM) tend to exacerbate GCM errors, while simpler methods (QDM, interpolation + bias correction) generally preserve GCM credibility. Dynamical downscaling, by contrast, can mitigate inherited biases and improve overall process-level credibility. These findings underscore the importance of process-based evaluation in downscaling assessments and reveal how downscaling model complexity interacts with GCM quality.

This study presents climate change results from the North American Regional Climate Change Assessment Program (NARCCAP) suite of dynamically downscaled simulations for the North American monsoon system in the southwestern United States and northwestern Mexico. The focus is on changes in precipitation and the processes driving the projected changes from the regional climate simulations and their driving coupled atmosphere–ocean global climate models. The effect of known biases on the projections is also examined. Overall, there is strong ensemble agreement for a large decrease in precipitation during the monsoon season; however, this agreement and the magnitude of the ensemble-mean change is likely deceiving, as the greatest decreases are produced by the simulations that are the most biased in the baseline/current climate. Furthermore, some of the greatest decreases in precipitation are being driven by changes in processes/phenomena that are less credible (e.g., changes in El Niño–Southern Oscillation, when it is initially not simulated well). In other simulations, the processes driving the precipitation change may be plausible, but other biases (e.g., biases in low-level moisture or precipitation intensity) appear to be affecting the magnitude of the projected changes. The most and least credible simulations are clearly identified, while the other simulations are mixed in their abilities to produce projections of value.