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Creators/Authors contains: "Zarzycki, Colin M."

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  1. Abstract

    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.

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  2. Abstract

    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.

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  3. Abstract

    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.

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  4. Abstract

    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.

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  5. Abstract

    A new configuration of the Community Earth System Model (CESM)/Community Atmosphere Model with full chemistry (CAM‐chem) supporting the capability of horizontal mesh refinement through the use of the spectral element (SE) dynamical core is developed and called CESM/CAM‐chem‐SE. Horizontal mesh refinement in CESM/CAM‐chem‐SE is unique and novel in that pollutants such as ozone are accurately represented at human exposure relevant scales while also directly including global feedbacks. CESM/CAM‐chem‐SE with mesh refinement down to ∼14 km over the conterminous US (CONUS) is the beginning of the Multi‐Scale Infrastructure for Chemistry and Aerosols (MUSICAv0). Here, MUSICAv0 is evaluated and used to better understand how horizontal resolution and chemical complexity impact ozone and ozone precursors over CONUS as compared to measurements from five aircraft campaigns, which occurred in 2013. This field campaign analysis demonstrates the importance of using finer horizontal resolution to accurately simulate ozone precursors such as nitrogen oxides and carbon monoxide. In general, the impact of using more complex chemistry on ozone and other oxidation products is more pronounced when using finer horizontal resolution where a larger number of chemical regimes are resolved. Large model biases for ozone near the surface remain in the Southeast US as compared to the aircraft observations even with updated chemistry and finer horizontal resolution. This suggests a need for adding the capability of replacing sections of global emission inventories with regional inventories, increasing the vertical resolution in the planetary boundary layer, and reducing model biases in meteorological variables such as temperature and clouds.

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