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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.

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.

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.

Prior research indicates that land use and land cover change (LULCC) in the central United States has led to significant changes in surface climate. The spatial resolution of simulations is particularly relevant in this region due to its influence on model skill in capturing mesoscale convective systems (MCSs) and on representing the spatial heterogeneity. Recent advances in Earth system models (ESMs) make it feasible to use variable resolution (VR) meshes to study regional impacts of LULCC while avoiding inconsistencies introduced by lateral boundary conditions typically seen in limited area models. Here, we present numerical experiments using the Community Earth System Model version 2–VR to evaluate (1) the influence of resolution and land use on model skill and (2) impacts of LULCC over the central United States at different resolutions. These simulations are configured either on the 1° grid or a VR grid with grid refinement to 1/8° over the contiguous United States for the period of 1984–2010 with two alternative land use data sets corresponding to the preindustrial and present day states. Our results show that skill in simulating precipitation over the central United States is primarily dependent on resolution, whereas skill in simulating 2‐m temperature is more dependent on accurate land use. The VR experiments show stronger LULCC‐induced precipitation increases over the Midwest in May and June, corresponding to an increase in the number of MCS‐like features and a more conductive thermodynamic environment for convection. Our study demonstrates the potential of using VR ESMs for hydroclimatic simulations in regions with significant LULCC.

Tropical cyclone intensification processes are explored in six high-resolution climate models. The analysis framework employs process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled. These diagnostics include budgets of column moist static energy and the spatial variance of column moist static energy, where the column integral is performed between fixed pressure levels. The latter allows for the quantification of the different feedback processes responsible for the amplification of moist static energy anomalies associated with the organization of convection and cyclone spinup, including surface flux feedbacks and cloud-radiative feedbacks. Tropical cyclones (TCs) are tracked in the climate model simulations and the analysis is applied along the individual tracks and composited over many TCs. Two methods of compositing are employed: a composite over all TC snapshots in a given intensity range, and a composite over all TC snapshots at the same stage in the TC life cycle (same time relative to the time of lifetime maximum intensity for each storm). The radiative feedback contributes to TC development in all models, especially in storms of weaker intensity or earlier stages of development. Notably, the surface flux feedback is stronger in models that simulate more intense TCs. This indicates that the representation of the interaction between spatially varying surface fluxes and the developing TC is responsible for at least part of the intermodel spread in TC simulation.

Characteristics of tropical cyclones (TCs) in global climate models (GCMs) are known to be influenced by details of the model configurations, including horizontal resolution and parameterization schemes. Understanding model-to-model differences in TC characteristics is a prerequisite for reducing uncertainty in future TC activity projections by GCMs. This study performs a process-level examination of TC structures in eight GCM simulations that span a range of horizontal resolutions from 1° to 0.25°. A recently developed set of process-oriented diagnostics is used to examine the azimuthally averaged wind and thermodynamic structures of the GCM-simulated TCs. Results indicate that the inner-core wind structures of simulated TCs are more strongly constrained by the horizontal resolutions of the models than are the thermodynamic structures of those TCs. As expected, the structures of TC circulations become more realistic with smaller horizontal grid spacing, such that the radii of maximum wind (RMW) become smaller, and the maximum vertical velocities occur off the center. However, the RMWs are still too large, especially at higher intensities, and there are rising motions occurring at the storm centers, inconsistently with observations. The distributions of precipitation, moisture, and radiative and surface turbulent heat fluxes around TCs are diverse, even across models with similar horizontal resolutions. At the same horizontal resolution, models that produce greater rainfall in the inner-core regions tend to simulate stronger TCs. When TCs are weak, the radial gradient of net column radiative flux convergence is comparable to that of surface turbulent heat fluxes, emphasizing the importance of cloud–radiative feedbacks during the early developmental phases of TCs.