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The effect of modified equator‐to‐pole temperature gradients on the jet stream by low‐level polar warming and upper‐level tropical warming is not fully understood. We perform aquaplanet simulations to quantify the impact of different sea surface temperature distributions on jet stream strength, large wave amplitudes and extreme waviness. The responses to warming in the waviness metrics Sinuosity Index and Local Wave Activity are sensitive to the latitude range over which they are calculated. Therefore, we use a latitude range that accurately represents the position of the jet. The uniform warming scenario strengthens the jet and reduces large wave amplitudes. Reductions in meridional temperature gradients lead to weakened mid‐latitudinal jet strength and show significant decreases in large wave amplitudes and jet stream waviness. These findings contradict the mechanism that weakened jet streams increase wave amplitudes and extreme jet stream waviness. We conclude that weakened jet streams do not necessarily become wavier. Plain Language Summary: This research letter considers how different patterns of atmospheric warming, like low‐level warming at the poles and at high altitude in the tropics, impact the jet stream, which is a strong "river" of high‐altitude wind. We use numerical model simulations to mimic different scenarios of warming that maintain or reduce the temperature gradient between equator and poles. We find that when an Earth‐like planet completely covered by water warms in specific ways, it strengthens the jet stream when it is warmed uniformly or weakens the jet stream when only mid‐ and high latitudes are warmed. For the simulations with a reduced mid‐latitudinal temperature gradient, the size of largest jet stream waves reduces and makes the extreme waviness episodes less wavy. This research letter conclude that weakened jet streams do not necessarily become wavier, which opposes the popular paradigm that weakened jet streams become wavier due to warming in polar regions. Key Points: On an aquaplanet, weakened jet streams do not become wavier with reduced temperature gradients due to warming in mid‐ and high latitudesIn reduced temperature gradient simulations, the magnitude of large wave amplitudes and jet stream extreme waviness decrease robustlyThe Sinuosity Index and the Local Wave Activity waviness metrics are sensitive to the latitude range over which they are calculated [ABSTRACT FROM AUTHOR]

Many radiative transfer schemes approximate the spectral integration over ∼105 to ∼106 wavelengths with correlated k‐distributions methods that typically require only 101–102 spectral integration points (g‐points). The exact number of g‐points is then chosen as an optimal balance between computational costs and accuracy, normally assessed in terms of a number of radiative quantities. How this radiative accuracy propagates to simulation accuracy, however, is not straightforward. In this study, we therefore explore the sensitivity of cloud properties in large‐eddy simulations (LES) to the accuracy of radiative fluxes and heating rates. We first generate smaller sets of g‐points from existing k‐distributions by repeatedly combining adjacent g‐points while maintaining the highest possible accuracy on a chosen set of radiative metrics. Next, we perform three sets of LES with varying cloud—radiation coupling pathways, and therefore different requirements for the accuracy of the radiative transfer computations, to investigate how these smaller and thus less accurate k‐distributions affect simulation characteristics. The decrease in radiative accuracy with 3–4 times smaller k‐distributions results in biases in cloud properties that are relative small compared to their temporal fluctuations. These results show potential for speeding up radiative transfer computations in cloud‐resolving models by reducing the resolved spectral detail. However, more statistically converged simulations and a wider set of case studies is required to fully assess the robustness of our results. Plain Language Summary: Radiation emitted by the sun and the earth drives our weather and climate. Atmospheric models therefore need to compute how solar and thermal radiation interacts with cloud droplets, aerosols, and gas molecules. These computations can be performed very accurately by doing independent calculations for many wavelengths, but that is very time‐consuming. Here, we study to what extent the accuracy of the radiation computations affects the accuracy of simulations of the atmosphere. We find that the reduction in radiative accuracy corresponding to 3–4 times fewer representative computations results in relative small mean errors in cloud properties compared to the temporal fluctuations. In further research, we should aim to better understand whether the mean errors are small enough to represent accurate simulations. Key Points: We investigate the sensitivity of moist convection to the spectral detail of gas opticsWe perform multiple large‐eddy simulations, with progressively smaller k‐distributions, for three case studies of convective cloudsMean differences in simulated cloud properties are relatively small compared to temporal fluctuations [ABSTRACT FROM AUTHOR]

Global reanalyzes like ERA5 accurately capture atmospheric processes at spatial scales of O(10) $\mathcal{O}(10)$ km or larger. By downscaling ERA5 with large‐eddy simulation (LES), LES can provide details about processes at spatio‐temporal scales down to meters and seconds. Here, we present an open‐source Python package named the "Large‐eddy simulation and Single‐column model—Large‐Scale Dynamics," or (LS)2D in short, designed to simplify the downscaling of ERA5 with doubly‐periodic LES. A validation with observations, for several sensitivity experiments consisting of month‐long LESs over Cabauw (the Netherlands), demonstrates both its usefulness and limitations. The day‐to‐day variability in the weather is well captured by (LS)2D and LES, but the setup under‐performs in conditions with broken or near overcast clouds. As a novel application of this modeling system, we used (LS)2D to study surface solar irradiance variability, as this quantity directly links land‐surface processes, turbulent transport, and clouds, to radiation. At a horizontal resolution of 25 m, the setup reproduces satisfactorily the solar irradiance variability down to a timescale of seconds. This demonstrates that the coupled LES‐ERA5 setup is a useful tool that can provide details on the physics of turbulence and clouds, but can only improve on its host reanalysis when applied to meteorological suitable conditions. Plain Language Summary: Modern global weather models are accurate in predicting atmospheric processes at scales of around 10 km or larger, but are less good at predicting smaller scale processes, like for example, the interaction between solar radiation, individual clouds, and the resulting clouds shadows that are cast onto the land surface. High spatio‐temporal resolution research models are able to capture these smaller scale processes, but require a coupling to a weather model to account for the day‐to‐day variability in our weather. In this paper, we present a framework to couple large to small scale models, and demonstrate both the benefits and challenges of using this coupled model setup. The coupled setup excels in capturing the aforementioned high frequency interactions between small clouds and surface solar radiation. However, the chaotic nature of broken to overcast clouds is proven difficult to represent. The coupled model setup is published as open‐source code, and is therefore freely available to the research community. Key Points: We developed an open‐source Python package named (LS)2D, designed to downscale the ERA5 reanalysis with turbulence and cloud‐resolving large‐eddy simulation (LESs)One month long experiments with (LS)2D and MicroHH over the Netherlands demonstrate both the skill and limitations of the coupled setupCapturing high‐frequency interactions between clouds and surface solar irradiance requires high resolution (O $\mathcal{O}$(10) m) LES [ABSTRACT FROM AUTHOR]

We present simulations of cumulus convection over land with shortwave radiation computed by a Monte Carlo ray tracer coupled to the flow solver. Ray tracing delivers very realistic in‐cloud heating rates and global horizontal irradiance fields. The necessary performance of the ray tracer has been enabled by the raw power of graphics processing unit computing and from techniques for accelerating data lookup and ray tracer convergence. We used a case study over a grassland in the Netherlands to compare simulations with a coupled ray tracer to those with a conventional two‐stream solver, and to test ray tracer convergence. We demonstrate that the simulated cloud evolution is insensitive to the convergence of the ray tracing across a wide range of samples per pixel per spectral quadrature point. Furthermore, simulations with a coupled ray tracer produce surface irradiance patterns that resemble observations and that strongly feed back to the evolution of clouds via locally enhanced surface heat fluxes. Plain Language Summary: Clouds absorb and reflect solar radiation and create spatial patterns at the land surface of cloud shadows interspersed with sunny regions. These patterns are currently largely simplified in most weather models because radiation computations cost a lot of computer power. We have developed a fast and realistic numerical radiation model that runs on a modern computer graphics card. By doing so, we can do simulations that produce radiation patterns that closely resemble reality and study how these patterns affect clouds. This enables us to improve our understanding of the complex interactions between clouds, solar radiation, and the Earth's surface. Key Points: We present graphics processing unit‐accelerated cloud‐resolving simulations with a coupled ray tracerSimulated cloud statistics are insensitive to ray tracer convergence across a wide range of sample sizesStrong coupling between surface irradiance patterns and cloud evolution underlines need for coupled 3D radiative transfer [ABSTRACT FROM AUTHOR]