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North American cold air outbreaks (CAOs) are large-scale temperature extremes that typically originate in the high latitudes and impact the midlatitudes in winter. As they transit southward, they can have significant socioeconomic consequences. CAOs from winter (DJF) 1979 to 2020 were identified in the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) using an automated feature tracking approach (TempestExtremesV2.1). This allowed for the systematic identification of a large number of cases without using predetermined, Eulerian regions. Another important advantage of this approach was the ability to compute a feature tracked thermodynamic energy budget in a nonfixed domain for every identified CAO event. As an example, the thermodynamic energy budget analysis was used to quantify important processes for the 18–23 January 1985 CAO. The dominant mechanisms of cooling and warming as well as lysis locations (i.e., eastern or western) were then used to generalize detected CAO events into subcategories. The associated statistics, spatial footprints, and composites of 500-hPa height, sea level pressure, and temperature and winds at 850 hPa were analyzed for three subcategories that contained the majority of events. This analysis revealed that CAO events that form and dissipate through different mechanisms occur in different regions, have different intensities, and are associated with different large-scale circulation patterns. Finally, the analysis of associated North Atlantic Oscillation (NAO) and Pacific–North American (PNA) teleconnection pattern revealed that the PNA is typically in a positive phase for eastern CAO events and in a negative phase for western events resulting primarily from horizontal advection, whereas the NAO did not have any significant relationship.

 

The higher‐order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models. This study investigates an underexplored aspect of CLUBB: its capacity to simulate near‐surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux. Using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB's stability‐driven substepping performed at each atmospheric time step. The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB‐computed zonal and meridional wind speed tendencies. Our 30‐year present‐day climate simulations (1980–2010) show that static coupling overestimates 10‐m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10‐m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind‐speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near‐surface wind speeds and associated turbulent momentum transport structure. Notably, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns. 

Improving the prediction of clouds in shallow-cumulus regimes via turbulence parameterization in the planetary boundary layer (PBL) will likely increase the global skill of global climate models (GCMs) because this cloud regime is common over tropical oceans where low-cloud fraction has a large impact on Earth's radiative budget. This study attempts to improve the prediction of PBL structure in tropical trade wind regimes in the Community Atmosphere Model (CAM) by updating its formulation of momentum flux in CLUBB (Cloud Layers Unified by Binormals), which currently does not by default allow for upgradient momentum fluxes. Hindcast CAM output from custom CLUBB configurations which permit countergradient momentum fluxes are compared to in situ observations from weather balloons collected during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte and Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (EUREC4A/ATOMIC) field campaign in the tropical Atlantic in early 2020. Comparing a version with CAM–CLUBB with a prognostic treatment of momentum fluxes results in vertical profiles that better match large-eddy simulation results. Countergradient fluxes are frequently simulated between 950 and 850 hPa over the EUREC4A/ATOMIC period in CAM–CLUBB. Further modification to the planetary boundary layer (PBL) parameterization by implementing a more generalized calculation of the turbulent length scale reduces model bias and root mean squared error (RMSE) relative to sounding data when coupled with the prognostic momentum configuration. Benefits are also seen in the diurnal cycle, although more systematic model errors persist. A cursory budget analysis suggests the buoyant production of momentum fluxes, both above and below the jet maximum, significantly contributes to the frequency and depth of countergradient vertical momentum fluxes in the study region. This paper provides evidence that higher-order turbulence parameterizations may offer pathways for improving the simulation of trade wind regimes in global models, particularly when evaluated in a process study framework. 

With general circulation models (GCMs) being increasingly used to explore extreme events over short temporal and small spatial scales, understanding how design choices in model configuration impact simulation results is critical. This research shows that the number of spontaneously generated tropical cyclones (TCs) in a version of the Community Atmosphere Model can be controlled by changing the coupling frequency between the dynamical core and physical parameterizations. More frequent coupling (i.e., shorter physics timesteps), even in the presence of an otherwise identical model, leads to large increases in TC activity. It is suggested that this arises due to competition within moist physics subroutines. Simulations with reduced physics timesteps preferentially eliminate instantaneous atmospheric instability via grid‐scale motions, even while producing mean climates similar to those with longer timesteps. These small‐scale variability increases lead to more tropical “seeds,” which are converted to full‐fledged TCs. This behavior is confirmed through a set of sensitivity experiments and highlights the caution needed in studying and generalizing phenomena that depend on both resolved and sub‐grid scales in GCMs and the need for targeting physics‐dynamics coupling as a model improvement strategy.

 

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection. 

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE valuesd_maxwere reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.

 

Nudging is a ubiquitous capability of numerical weather and climate models that is widely used in a variety of applications (e.g., crude data assimilation, “intelligent” interpolation between analysis times, constraining flow in tracer advection/diffusion simulations). Here, the focus is on the momentum nudging tendencies themselves, rather than the atmospheric state that results from application of the method. The initial intent was to interpret these tendencies as a quantitative estimate of model error (net parameterization error in particular). However, it was found that nudging tendencies depend strongly on the nudging time scale chosen, which is the primary result presented here. Reducing the nudging time scale reduces the difference between the model state and the target state, but much less so than the reduction in the nudging time scale, resulting in increased nudging tendencies. The dynamical core, in particular, appears to increasingly oppose nudging tendencies as the nudging time scale is reduced. A heuristic analysis suggests such a result should be expected as long as the state the model is trying to achieve differs from the target state, regardless of the type of target state (e.g., a reanalysis, another model). These results suggest nudging tendencies cannot bequantitativelyinterpreted as model error. Still, two experiments aimed at seeing how nudging can identify a withheld parameterization suggest nudging tendencies do contain some information on model errors and/or missing physical processes and still might be useful in model development and tuning, even if only qualitatively.