In Part 2 of this two‐part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part 1. Part 2 provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.

In this two‐part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100 km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part 1, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode—with prescribed sea surface temperatures (SSTs) and sea‐ice distribution—is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top‐of‐atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part 2, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.

We present the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), an atmosphere model developed by the Geophysical Fluid Dynamics Laboratory (GFDL) coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short‐to‐medium‐range and subseasonal‐to‐seasonal prediction, global‐to‐regional convective‐scale hurricane and contiguous U.S. precipitation forecasts, and global cloud‐resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System or FV3‐based models, including operational implementations of the Unified Forecast System. Continued development of SHiELD has shown improvement upon existing models. The flagship 13‐km SHiELD demonstrates steadily improved large‐scale prediction skill and precipitation prediction skill. SHiELD and the coarser‐resolution S‐SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden‐Julian Oscillation. The global‐to‐regional nested configurations T‐SHiELD (tropical Atlantic) and C‐SHiELD (contiguous United States) show significant improvement in hurricane structure from a new tracer advection scheme and promise for medium‐range prediction of convective storms.

Planetary boundary layer (PBL) parameterizations in mesoscale models have been developed for horizontal resolutions that cannot resolve any turbulence in the PBL, and evaluation of these parameterizations has been focused on profiles of mean and parameterized flux. Meanwhile, the recent increase in computing power has been allowing numerical weather prediction (NWP) at horizontal grid spacings finer than 1 km, at which kilometer-scale large eddies in the convective PBL are partly resolvable. This study evaluates the performance of convective PBL parameterizations in the Weather Research and Forecasting (WRF) Model at subkilometer grid spacings. The evaluation focuses on resolved turbulence statistics, considering expectations for improvement in the resolved fields by using the fine meshes. The parameterizations include four nonlocal schemes—Yonsei University (YSU), asymmetric convective model 2 (ACM2), eddy diffusivity mass flux (EDMF), and total energy mass flux (TEMF)—and one local scheme, the Mellor–Yamada–Nakanishi–Niino (MYNN) level-2.5 model. Key findings are as follows: 1) None of the PBL schemes is scale-aware. Instead, each has its own best performing resolution in parameterizing subgrid-scale (SGS) vertical transport and resolving eddies, and the resolution appears to be different between heat and momentum. 2) All the selected schemes reproduce total vertical heat transport well, as resolved transport compensates differences of the parameterized SGS transport from the reference SGS transport. This interaction between the resolved and SGS parts is not found in momentum. 3) Those schemes that more accurately reproduce one feature (e.g., thermodynamic transport, momentum transport, energy spectrum, or probability density function of resolved vertical velocity) do not necessarily perform well for other aspects.

Structural and parametric problems associated with physical parameterizations are often tied together in weather and climate models. This study examines the sensitivities of turbine‐height wind speeds to structural and parametric uncertainties associated with the planetary boundary layer (PBL) parameterizations in the Weather Research and Forecasting model over an area of complex terrain. The sensitivity analysis is based on experiments from two perturbed parameter ensembles using the Mellor‐Yamada‐Nakanishi‐Niino (MYNN) and Yonsei University (YSU) PBL schemes, respectively. In each scheme, most of the intermember variances can be explained by a few parameters. Compared to the YSU parameters, the MYNN parameters induce relatively weaker (stronger) impacts on wind speeds during daytime (nighttime). The two schemes can overall reproduce the observed diurnal features of turbine‐height wind speeds. Differences in the daytime wind speeds are evident between the two ensembles. The daytime biases exist even with well‐tuned parameter values in MYNN, indicating the structural error. The YSU scheme better matches monthly mean daytime observations, partly due to the compensation among the biases in different wind strengths. Compared to YSU, MYNN generally better agrees with observations in both weak and strong wind conditions. However, the improvements accomplished for one condition by parameter tuning may degrade model performances for others, suggesting the relationships that link different conditions are not accurately represented in the parameterizations. Simulated biases due to structural errors are further identified by evaluating them for different time of day and locations. Ultimately, this study improves understanding of structural limitations in the PBL schemes and provides insights on further parameterization development.

Over the last few years, the concept of incorporating aerial vehicles into the urban environment for diverse purposes has attracted ample interest and investment. These purposes cover a broad spectrum of applications, from larger vehicles designed for passenger transport, to package delivery and inspection/surveillance missions performed by small unmanned drones. While these Advanced Air Mobility (AAM) operations have the potential to alleviate bottlenecks arising from saturated surface transportation networks, there are a number of challenges that need to be addressed to make these operations safe and viable. One challenge is predicting weather effects within the urban environment with the required level of spatiotemporal fidelity, which current operational weather models fail to provide due to the use of coarse grid spacings (a few kilometers) constrained by the predictive performance limitations of traditional computer architectures. Herein, we demonstrate how FastEddy®, a microscale model that exploits the accelerated nature of graphics processing units for high‐performance computing, can be used to understand and predict urban weather impacts from seasonal, day‐to‐day, diurnal, and sub‐hourly scales. To that end, we efficiently perform more than 50 telescoped simulations of microscale urban effects at street‐scale (5 m grid spacing) driven by realistic weather over a 20 km² region centered at the downtown area of Dallas, Texas. Our analyses demonstrate that urban‐weather interactions at the street‐scale are complex and tightly connected, which is of utmost relevance to AAM operations. These demonstrations reveal the capability of such models to provide real‐time weather hazard avoidance products tailored to capture microscale urban effects.