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Colin M. Zarzycki

Abstract

Tropical cyclones (TCs), particularly those that are intense and/or slow moving, induce sea surface temperature (SST) reductions along their tracks (commonly referred to as cold wakes) that provide a negative feedback on storm energetics by weakening surface enthalpy fluxes. While computing gains have allowed for simulated TC intensity to increase in global climate models as a result of increased horizontal resolution, many configurations utilize prescribed, noninteractive SSTs as a surface boundary condition to minimize computational cost and produce more accurate TC climatologies. Here, an idealized slab ocean is coupled to a 0.25° variable-resolution version of the Community Atmosphere Model (CAM) to improve closure of the surface energy balance and reproduce observed Northern Hemisphere cold wakes. This technique produces cold wakes that are realistic in structure and evolution and with magnitudes similar to published observations, without impacting large-scale SST climatology. Multimember ensembles show that the overall number of TCs generated by the model is reduced by 5%–9% when allowing for two-way air–sea interactions. TC intensity is greatly impacted; the strongest 1% of all TCs are 20–30 hPa (4–8 m s−1) weaker, and the number of simulated Saffir–Simpson category 4 and 5 TCs is reduced by 65% in slab ocean configurations. Reductions in intensity are in line with published thermodynamic theory. Additional offline experiments and sensitivity simulations demonstrate this response is both significant and robust. These results imply caution should be exercised when assessing high-resolution prescribed SST climate simulations capable of resolving intense TCs, particularly if discrete analysis of extreme events is desired.

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Colin M. Zarzycki and Christiane Jablonowski

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Tropical cyclone (TC) forecasts at 14-km horizontal resolution (0.125°) are completed using variable-resolution (V-R) grids within the Community Atmosphere Model (CAM). Forecasts are integrated twice daily from 1 August to 31 October for both 2012 and 2013, with a high-resolution nest centered over the North Atlantic and eastern Pacific Ocean basins. Using the CAM version 5 (CAM5) physical parameterization package, regional refinement is shown to significantly increase TC track forecast skill relative to unrefined grids (55 km, 0.5°). For typical TC forecast integration periods (approximately 1 week), V-R forecasts are able to nearly identically reproduce the flow field of a globally uniform high-resolution forecast. Simulated intensity is generally too strong for forecasts beyond 72 h. This intensity bias is robust regardless of whether the forecast is forced with observed or climatological sea surface temperatures and is not significantly mitigated in a suite of sensitivity simulations aimed at investigating the impact of model time step and CAM’s deep convection parameterization. Replacing components of the default physics with Cloud Layers Unified by Binormals (CLUBB) produces a statistically significant improvement in forecast intensity at longer lead times, although significant structural differences in forecasted TCs exist. CAM forecasts the recurvature of Hurricane Sandy into the northeastern United States 60 h earlier than the Global Forecast System (GFS) model using identical initial conditions, demonstrating the sensitivity of TC forecasts to model configuration. Computational costs associated with V-R simulations are dramatically decreased relative to globally uniform high-resolution simulations, demonstrating that variable-resolution techniques are a promising tool for future numerical weather prediction applications.

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Colin M. Zarzycki, Christiane Jablonowski, and Mark A. Taylor

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A statically nested, variable-mesh option has recently been introduced into the Community Atmosphere Model’s (CAM's) Spectral Element (SE) dynamical core that has become the default in CAM version 5.3. This paper presents a series of tests of increasing complexity that highlight the use of variable-resolution grids in CAM-SE to improve tropical cyclone representation by dynamically resolving storms without requiring the computational demand of a global high-resolution grid. As a simplified initial test, a dry vortex is advected through grid transition regions in variable-resolution meshes on an irrotational planet with the CAM subgrid parameterization package turned off. Vortex structure and intensity is only affected by grid resolution and no spurious artifacts are observed. CAM-SE model simulations using an idealized tropical cyclone test case on an aquaplanet show no numerical distortion or wave reflection when the cyclone interacts with an abrupt transition region. Using the same test case, the authors demonstrate that a regionally refined mesh with significantly fewer degrees of freedom can produce the same local results as a globally uniform grid. Additionally, the authors discuss a more complex aquaplanet experiment with meridionally varying sea surface temperatures that reproduces a quasi-realistic global climate. Tropical cyclogenesis is facilitated without the need for vortex bogusing in a high-resolution patch embedded within a global grid that is otherwise too coarse to resolve realistic tropical cyclones in CAM.

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Colin M. Zarzycki, Paul A. Ullrich, and Kevin A. Reed

Abstract

This article describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products with one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or for regional domains. Simple-to-visualize “scorecards” allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher-resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic Ocean TC frequency. Upstream refinement over northern Africa offers little benefit in simulating storm occurrence, but spatial genesis patterns are improved. We also show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on the version of the Morrison–Gettelman microphysics package that is used.

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Fei He, Derek J. Posselt, Colin M. Zarzycki, and Christiane Jablonowski

Abstract

This paper presents a balanced tropical cyclone (TC) test case designed to improve current understanding of how atmospheric general circulation model (AGCM) configurations affect simulated TC development and behavior. It consists of an analytic initial condition comprising two independently balanced components. The first provides a vortical TC seed, while the second adds a planetary-scale zonal flow with height-dependent velocity and imposes background vertical wind shear (VWS) on the TC seed. The environmental flow satisfies the steady-state hydrostatic primitive equations in spherical coordinates and is in balance with other background field variables (e.g., temperature, surface geopotential). The evolution of idealized TCs in the test case framework is illustrated in 10-day simulations performed with the Community Atmosphere Model, version 5.1.1 (CAM 5.1.1). Environmental wind profiles with different magnitudes, directions, and vertical inflection points are applied to ensure that the technique is robust to changes in the VWS characteristics. The well-known shear-induced intensity change and structural asymmetry in tropical cyclones are well captured. Sensitivity of TC evolution to small perturbations in the initial vortex is also quantitatively addressed to validate the numerical robustness of the technique. It is concluded that the enhanced TC test case can be used to evaluate the impact of model choice (e.g., resolution, physical parameterizations) on the simulation and representation of TC-like vortices in AGCMs.

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Kevin Reed, Michael F. Wehner, Alyssa M. Stansfield, and Colin M. Zarzycki
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Alan M. Rhoades, Xingying Huang, Paul A. Ullrich, and Colin M. Zarzycki

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The location, timing, and intermittency of precipitation in California make the state integrally reliant on winter-season snowpack accumulation to maintain its economic and agricultural livelihood. Of particular concern is that winter-season snowpack has shown a net decline across the western United States over the past 50 years, resulting in major uncertainty in water-resource management heading into the next century. Cutting-edge tools are available to help navigate and preemptively plan for these uncertainties. This paper uses a next-generation modeling technique—variable-resolution global climate modeling within the Community Earth System Model (VR-CESM)—at horizontal resolutions of 0.125° (14 km) and 0.25° (28 km). VR-CESM provides the means to include dynamically large-scale atmosphere–ocean drivers, to limit model bias, and to provide more accurate representations of regional topography while doing so in a more computationally efficient manner than can be achieved with conventional general circulation models. This paper validates VR-CESM at climatological and seasonal time scales for Sierra Nevada snowpack metrics by comparing them with the “Daymet,” “Cal-Adapt,” NARR, NCEP, and North American Land Data Assimilation System (NLDAS) reanalysis datasets, the MODIS remote sensing dataset, the SNOTEL observational dataset, a standard-practice global climate model (CESM), and a regional climate model (WRF Model) dataset. Overall, given California’s complex terrain and intermittent precipitation and that both of the VR-CESM simulations were only constrained by prescribed sea surface temperatures and data on sea ice extent, a 0.68 centered Pearson product-moment correlation, a negative mean SWE bias of <7 mm, an interquartile range well within the values exhibited in the reanalysis datasets, and a mean December–February extent of snow cover that is within 7% of the expected MODIS value together make apparent the efficacy of the VR-CESM framework.

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Alyssa M. Stansfield, Kevin A. Reed, Colin M. Zarzycki, Paul A. Ullrich, and Daniel R. Chavas

Abstract

Tropical cyclones (TCs) can subject an area to heavy precipitation for many hours, or even days, worsening the risk of flooding, which creates dangerous conditions for residents of the U.S. East and Gulf Coasts. To study the representation of TC-related precipitation over the eastern United States in current-generation global climate models, a novel analysis methodology is developed to track TCs and extract their associated precipitation using an estimate of their dynamical outer size. This methodology is applied to three variable-resolution (VR) configurations of the Community Atmosphere Model, version 5 (CAM5), with high-resolution domains over the North Atlantic and one low-resolution conventional configuration, as well as to a combination of reanalysis and observational precipitation data. Metrics and diagnostics such as TC counts, intensities, outer storm sizes, and annual mean total and extreme precipitation are compared between the CAM5 simulations and reanalysis/observations. The high-resolution VR configurations outperform the global low-resolution configuration for all variables in the North Atlantic. Realistic TC intensities are produced by the VR configurations. The total North Atlantic TC counts are lower than observations but better than reanalysis.

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Fei He, Derek J. Posselt, Naveen N. Narisetty, Colin M. Zarzycki, and Vijayan N. Nair

Abstract

This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

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Xingchao Chen, Robert G. Nystrom, Christopher A. Davis, and Colin M. Zarzycki

Abstract

Understanding the dynamics of the flow-dependent forecast error covariance across the air–sea interface is beneficial toward revealing the potential influences of strongly coupled data assimilation on tropical cyclone (TC) initialization in coupled models, and the fundamental dynamics associated with TC air–sea interactions. A 200-member ensemble of convection-permitting forecasts from a coupled atmosphere–ocean regional model is used to investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018). Forecast uncertainties in both atmospheric and oceanic domains, from an Eulerian perspective, increase with forecast lead time, mainly from TC displacement errors. In a storm-relative framework, the ensemble forecast uncertainties in both domains are predominantly caused by differences in the simulated storm intensity and structure. The largest ensemble spread in the atmospheric pressure, temperature, and wind fields can be found within the TC inner-core region. Alternatively, the largest ensemble spread in the upper-ocean currents and temperature fields are located along the cold wake behind the storm. Cross-domain ensemble correlations between simulated atmospheric (oceanic) observations and oceanic (atmospheric) state variables in the storm-relative coordinates are highly anisotropic, variable dependent, and ultimately driven by the dynamics of TC air–sea interactions. Meaningful and dynamically consistent cross-domain ensemble correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update state variables associated with the coupled ocean–atmosphere prediction of TCs using strongly coupled data assimilation. Sensitivity experiments demonstrate that at least 60–80 ensemble members are required to represent physically consistent cross-domain correlations and minimize sampling errors.

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