Colin M. Zarzycki

The upscale influence of tropical cyclones (TCs) on the broader climate system remains poorly understood because of limitations in existing diagnostic and modeling methods. To address this, we present a method to modulate TC activity online in Earth-system models. Balanced, axisymmetric TC-like vortical perturbations derived from an analytical formulation with tunable parameters are superimposed on the atmospheric state. To add TCs to the model ("seeding"), cyclonic perturbations are introduced into quiescent environments. To remove TCs from the model ("unseeding"), anticyclonic perturbations are iteratively tuned to cancel existing TCs. During vortex insertion and removal, the balanced formulation prevents spurious wave generation while scalar global field corrections prevent climate drift. In fully coupled aquaplanet simulations, the method reliably modulates TC climatology: seeding increases TC frequency and accumulated cyclone energy well above baseline, while unseeding reduces them by up to 95%. TC tracking shows that seeding primarily enhances genesis, whereas unseeding suppresses TC frequency by predominantly targeting incipient storms. The resulting experiments produce systematic tropical mean-state and radiative responses that scale approximately monotonically with TC activity, including upper-ocean cooling and mid-tropospheric drying as TC activity increases. These large-scale atmospheric mean-state responses differ qualitatively from short-term local anomalies associated with individual TCs, providing strong evidence that TCs modify their surrounding environment. These results establish online vortex manipulation as a practical, climatologically stable framework for investigations of TC upscale effects in the coupled tropical atmosphere-ocean system.

Tropical cyclones (TCs) often undergo extratropical transition (ET) at the midlatitudes, becoming post‐tropical cyclones (PTCs). We combine a logistic regression PTC identification model with synthetic TC tracks and intensities from the Columbia HAZard model (CHAZ) to project future ET completion over the North Atlantic (NA). Projections are based on downscaled outputs from 12 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under three shared socioeconomic pathways (SSPs): SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. Future changes in PTC numbers largely follow projected TC frequency, while the ET fraction remains nearly unchanged. Despite this stability, the preferred ET completion region shifts northeastward. This shift likely results from weaker vertical wind shear, larger potential intensity, slower overall translation speed at the midlatitudes, and more northward TC genesis. Although storms move more slowly overall, an enhanced eastward storm‐motion component favors the northeastward shift, whereas a weaker northward component partially offsets the displacement.

Parameterizing boundary layer turbulence in Earth System Models (ESMs) introduces a key source of uncertainty. Here, we explore the impact of implementing a prognostic formulation (PMF) for momentum flux, which permits countergradient (CG) fluxes. We implement PMF in the turbulence parameterization in the Community Atmosphere Model version 6 (CAM6). CG fluxes in PMF reduce globally‐integrated momentum flux in CAM6. A similar response in globally‐integrated momentum flux is observed when reducing a tunable diffusivity parameter in the existing diagnostic formulation for momentum flux (DMF). Both methods of reducing effective eddy diffusivity produce qualitatively similar responses in the modeled general circulation, namely an enhanced upward branch of the Hadley cell, increased equatorward low‐level flow, and strengthened meridional temperature and moisture gradients. However, the reduction in effective eddy diffusivity from CG fluxes in PMF produces a higher‐magnitude response in the general circulation compared to reducing the diffusivity parameter in DMF.

In this study, we extend the Cloud Layers Unified by Binormals (CLUBB) turbulence scheme within the GFDL atmospheric model (AM4) by implementing direct momentum‐flux prognosis and a multiscale turbulent lengthscale, to improve the simulation of nocturnal precipitation and associated Low‐Level Jets (LLJs) over the Great Plains (GP). Toward this aim, we set up four AM4‐CLUBB configurations: diagnosed momentum flux, prognosed momentum flux, diagnosed momentum flux with a multiscale turbulent lengthscale, and prognosed momentum flux with a multiscale turbulent lengthscale. Simulations are evaluated against the AM4 control, the Integrated Multi‐satellitE Retrievals for GPM (IMERG), and the Doppler wind radar profiles from the Atmospheric Radiation Measurement program. Results show that all AM4‐CLUBB configurations improve the precipitation timing from the unrealistic midday peak seen in the AM4 control simulation toward the satellite‐observed nocturnal maximum. The configuration that prognoses momentum flux and uses a multi‐scale turbulent lengthscale, best matches the timing and intensity of GP precipitation rate. This configuration is also that which more accurately simulates the ARM‐observed nocturnal LLJ wind profiles, while increasing the frequency of counter‐gradient momentum fluxes near the LLJ core compared to prognosing momentum fluxes with the original AM4‐CLUBB turbulent lengthscale. Momentum budget analysis attributes this increase to a nearly fivefold enhancement in the buoyancy production term when using the multiscale formulation, and leads to stronger nocturnal convective activity, as diagnosed from the greater vertical velocity skewness and plume asymmetry.

Post‐tropical cyclones (PTCs) induce significant hazards in the mid‐latitudes, yet are often misrepresented in storm hazard models. This study introduces a new statistical model for assessing PTC hazard in the North Atlantic (NA). The model comprises three components: a logistic regression model for PTC identification, an auto‐regressive model for PTC intensity prediction, and a probability‐based PTC lysis model. The PTC identification component is adapted from a statistical model developed earlier by Bieli et al. (2020), https://doi.org/10.1175/waf‐d‐19‐0045.1 , while the PTC intensity and lysis components are newly developed. These components are coupled with synthetic tropical cyclone (TC) tracks and intensities generated by the Columbia Hazard Model (CHAZ) to simulate the full life cycle of PTCs, with flexibility for integration with other TC hazard models. The model reproduces key features of Atlantic PTC climatology, including timing of extratropical transition completion, intensity distributions, and spatial track density, and enables robust estimation of PTC return periods. It reveals lower risks of intense PTCs and higher risks of weak PTCs along the western NA near North America relative to the eastern NA near Europe. In some cases, the return periods of landfalling intense PTCs along the western side can be up to 4.4 times the eastern side, when PTCs stronger than 942 hPa are considered. Notably, comparable return periods between PTC‐only and storms at all phases near Europe and Canada underscore the importance of explicitly representing PTCs in hazard assessments for these regions. The framework is designed to support assessments of future Atlantic PTC hazard and risk.

UXarray is a community-developed Python package that extends the widely used Xarray ecosystem with native support for horizontally unstructured meshes, eliminating the need for costly, problematic regridding prior to visualization and analysis. Designed to meet the growing demands of kilometer-scale climate and weather models, UXarray aims to become a preeminent tool for the analysis, visualization, and postprocessing of Earth system data on irregular grids. It has been used in practice across a wide range of high-resolution atmospheric and ocean models, including MPAS, CAM-SE, E3SM, FESOM2, IFS, and ICON.Recently, UXarray played a key role in the 2025 WCRP Digital Earth – Global Hackathon (DEGH), where over 600 researchers, spanning four continents, collaborated to explore km-scale outputs, contributed from 11 different modeling centers from around the world. The use of UXarray was essential to fulfilling hackathon objectives, such as promoting global collaboration, sharing best-practice in process-based analysis of km-scale simulations, developing practical km-scale analysis workflows, and facilitating model intercomparison.This presentation will highlight UXarray’s current capabilities—including visualization tools and foundational analysis operators—share insights from the DEGH experience, outline future development plans, and highlight ways that the community can engage to shape the package moving forward.

Accurately simulating tropical cyclones (TCs) in global climate models remains a key challenge due to not only multiscale interactions that govern storm genesis, intensity, and structure, but computational constraints that limit model grid spacing. Here we evaluate TC representation in a development version of the Community Atmosphere Model, version 7 (CAM7), which introduces several major updates, including higher vertical resolution, a revised Zhang-McFarlane deep convection scheme, and a new prognostic formulation for turbulent momentum fluxes in the boundary-layer scheme CLUBB. Using a suite of globally-uniform and variable-resolution simulations at 0.25deg grid spacing, we assess both large-scale statistics (global and basinwise TC frequency) and storm-scale characteristics (inflow angle, inflow depth, and wind structure).CAM7 with prognostic momentum fluxes produces improved spatial patterns of TC activity and more realistic intensity metrics compared to prior CAM generations. However, default configurations overproduce storms by roughly a factor of two. By increasing the parameterized CAPE consumption by deep convection during TC genesis, we achieve a ~40% reduction in global TC counts and improved agreement with observed basin distributions. At the storm scale, we reduce boundary-layer diffusivity, leading to stronger tangential winds, larger inflow angles, and shallower inflow layers, consistent with idealized f-plane sensitivity experiments and more in line with both observations and large-eddy simulations.These results demonstrate that targeted parameter tuning in deep convection and boundary-layer turbulence schemes can substantially improve both the frequency and structure of simulated TCs in CAM7, advancing its capability for high-resolution climate and weather prediction applications.

When a new, better-formulated physical parameterization is introduced into a global atmospheric model, aspects of the global model solutions are sometimes degraded. Then, in order to use the new global model to address science questions, there is an incentive to restore its accuracy. Oftentimes this restoration is achieved by tuning of model parameter values. Unfortunately, the retuning process is expensive because characterizing the parameter dependence requires numerous time-consuming global simulations. To reduce the cost of tuning, this manuscript introduces a “poor man's” model tuner, “QuadTune”. QuadTune carves the globe into regions and approximates the model parameter dependence through the use of an uncorrelated quadratic emulator (i.e., response surface). The simplicity of the emulator reduces the required number of global model simulations and aids explainability of tuner behavior. Tuning removes parametric error but leaves behind model structural error. Structural error manifests itself as regional residual biases, such as stubborn biases and tuning trade-offs. To visualize these residual biases, QuadTune's software includes a set of diagnostic plots. This paper illustrates the use of the plots for characterizing residual biases with an example tuning problem.

In this study, we extend the Cloud Layers Unified by Binormals (CLUBB) turbulence scheme within the GFDL atmospheric model (AM4) by implementing direct momentum-flux prognosis and a multiscale turbulent length scale, to improve the simulation of nocturnal precipitation and associated Low-Level Jets (LLJs) over the Great Plains (GP). Towards this aim, we set up four AM4-CLUBB configurations: diagnosed momentum flux, prognosed momentum flux, diagnosed momentum flux with a multiscale turbulent lengtshcale, and prognosed momentum flux with a multiscale turbulent lengtshcale. Simulations are evaluated against the AM4 control, the Integrated Multi-satellitE Retrievals for GPM (IMERG), and the Doppler wind radar profiles from the Atmospheric Radiation Measurement (ARM) program. Results show that all AM4-CLUBB configurations improve the precipitation timing from the unrealistic midday peak seen in the AM4 control simulation toward the satellite-observed nocturnal maximum. The configuration that prognoses momentum flux and uses a multi-scale turbulent length scale, best matches the timing and intensity of GP precipitation rate. This configuration is also that which more accurately simulates the ARM-observed nocturnal LLJ wind profiles, while increasing the frequency of counter-gradient momentum fluxes near the LLJ core compared to prognosing momentum fluxes with the original AM4-CLUBB turbulent lengthscale. Momentum budget analysis attributes this increase to a nearly fivefold enhancement in the buoyancy production term when using the multiscale formulation, and leads to stronger nocturnal convective activity, as diagnosed from the greater vertical velocity skewness and plume asymmetry.

When a new, better-formulated physical parameterization is introduced into a global atmospheric model, aspects of the global model solutions are sometimes degraded. Then, in order to use the new global model to address science questions, there is an incentive to restore its accuracy. Oftentimes this restoration is achieved by tuning of model parameter values. Unfortunately, the retuning process is expensive because characterizing the parameter dependence requires numerous time-consuming global simulations. To reduce the cost of tuning, this manuscript introduces a "poor man's'' model tuner, "QuadTune''. QuadTune carves the globe into regions and approximates the model parameter dependence by use of an uncorrelated quadratic emulator. The simplicity of the emulator reduces the required number of global model simulations and aids explainability. Tuning removes parametric error but leaves behind model structural error. Structural error manifests itself as regional residual biases, such as stubborn biases and tuning trade-offs. To visualize these residual biases, QuadTune's software includes a set of diagnostic plots. This paper illustrates the use of the plots for characterizing residual biases with an example tuning problem.