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The development of any atmosphere or ocean model warrants a suite of test cases (TCs) to verify its spatial and temporal discretizations, order of accuracy, stability, reproducibility, portability, scalability, etc. In this paper, we present a suite of shallow water TCs designed to verify the barotropic solver of atmosphere and ocean models. These include the non‐dispersive coastal Kelvin wave; the dispersive inertia‐gravity wave; the dispersive planetary and topographic Rossby waves; the barotropic tide; and a non‐linear manufactured solution. These TCs check the implementation of the linear pressure gradient term; the linear constant or variable‐coefficient Coriolis and bathymetry terms; and the non‐linear advection terms. Simulation results are presented for a variety of time‐stepping methods as well as two spatial discretizations: a mimetic finite volume method based on the TRiSK scheme, and a high‐order discontinuous Galerkin spectral element method. The experimental procedure for conducting these numerical experiments is detailed. It underscores several key considerations that vary depending on the chosen spatial discretization method. Finally, convergence studies of every TC are conducted with refinement in both space and time, only in space, and only in time. The convergence slopes match the expected theoretical predictions. Plain Language Summary: Before running an atmosphere, ocean, or a coupled climate simulation, every model developer should ensure the correct implementation of each term in the governing equations that drive the models forward in time. This motivates the development of idealized test cases (TCs), each of which verifies a subset of terms in the governing equations with different initial and boundary conditions. Here we present a suite of six TCs for the momentum equation and sea surface height equation for ocean models in a single‐layer configuration. The computed results from the ocean model can be compared to exact solutions. The computed solution always has a small error, but is said to converge to the exact solution with reduction in grid cell size and time step. If the model converges at the expected rate, then we know that it is solving the governing equations correctly. We show results of convergence tests from two models, and share the full specifications of these TCs so that other ocean modelers may reproduce them. Key Points: A suite of test cases is presented for the verification of barotropic dynamics of ocean models, with exact and manufactured solutionsSpecifications are provided for coastal Kelvin wave, inertia‐gravity wave, planetary and topographic Rossby waves, barotropic tide, and non‐linear casesResults are presented for a variety of time‐stepping methods and two types of spatial discretizations: TRiSK and discontinuous Galerkin spectral element method [ABSTRACT FROM AUTHOR]

A reference or "no‐feedback" radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan‐Boltzmann law as −4σTe‾3≈−3.8 ${-}4\sigma {\overline{{T}_{e}}}^{3}\approx -3.8$ W m−2 K−1 for Earth's present climate, where Te‾ $\overline{{T}_{e}}$ is a global effective emission temperature. The comparable radiative response in climate models, widely called the "Planck feedback," averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non‐negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan‐Boltzmann feedbacks arise from temperature‐dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long‐overlooked but notable gap in our understanding of Earth's reference radiative response to warming. Plain Language Summary: Earth's climate is stable because a warmer planet loses more energy to space, at infrared wavelengths invisible to the naked eye. The rate of change of this energy loss as the planet warms provides an estimate how Earth's energy balance responds to warming, which is simple enough to write on a small piece of paper. When scientists investigate the warming predicted by climate models, they often start from a similar but not identical calculation of how Earth's energy balance responds to warming. This calculation, based on model output, is about 15% less stabilizing than the simple pencil‐and‐paper estimate. In this paper, we explore the causes of this 15% difference between the pencil‐and‐paper estimate and the calculations using climate models. We show that the difference is primarily caused by the lack of assumed warming in climate models high in Earth's atmosphere, where temperatures are not closely linked to surface warming. This lack of warming acts as a hidden destabilizing feedback in current analysis of climate models. Key Points: Earth's reference radiative response, or "Planck feedback," is ∼0.5 W m−2 K−1 less stabilizing than a Stefan‐Boltzmann estimateWe find this deviation is mostly due to the assumed lack of stratospheric warming in calculations of the Planck feedbackThe lack of stratospheric warming serves as an implicit positive feedback in analysis of climate model warming [ABSTRACT FROM AUTHOR]

Broadband (spectrally‐integrated) radiation calculations are dominated by the expense of spectral integration, and many applications require fast parameterizations for computing radiative flux. Here we describe a novel approach using a linear weighted sum of monochromatic calculations at a small set of optimally‐chosen frequencies. The empirically‐optimized quadrature method is used to compute atmospheric boundary fluxes, net flux profiles throughout the atmosphere, heating rate profiles, and top‐of‐the‐atmosphere forcing by CO2, in the longwave for clear skies. We evaluate the method against two modern correlated k‐distribution models and find that we can achieve comparable errors with 32 spectral points. We also examine the effect of minimizing different cost functions, and find that in order to accurately represent heating rates and CO2 forcing, these quantities must be included in the cost function. Plain Language Summary: Quantifying the way radiation flows through the atmosphere is computationally expensive, and most applications require fast approximation. In this paper, we develop an empirically‐optimized quadrature method that can compute quantities of interest in climate science and weather prediction such as radiative flux throughout the atmosphere, heating rates, and forcing by carbon dioxide. The method uses a simple optimization algorithm, combined with a linear model, to achieve accuracy and computational cost of similar order of magnitude to the modern correlated k‐distribution models. Depending on the focus of the application, the algorithm can easily be adapted to prioritize desired quantities. Key Points: Spectrally integrated radiative quantities can be estimated from a small weighted set of monochromatic calculationsTradeoffs between computational cost and accuracy are commensurate with correlated k‐distribution modelsOptimization priorities can be tailored to a specific applications [ABSTRACT FROM AUTHOR]

The mixing of tracers by mesoscale eddies, parameterized in many ocean general circulation models (OGCMs) as a diffusive‐advective process, contributes significantly to the distribution of tracers in the ocean. In the ocean interior, diffusive contribution occurs mostly along the direction parallel to local neutral density surfaces. However, near the surface of the ocean, small‐scale turbulence and the presence of the boundary itself break this constraint and the mesoscale transport occurs mostly along a plane parallel to the ocean surface (horizontal). Although this process is easily represented in OGCMs with geopotential vertical coordinates, the representation is more challenging in OGCMs that use a general vertical coordinate, where surfaces can be tilted with respect to the horizontal. We propose a method for representing the diffusive horizontal mesoscale fluxes within the surface boundary layer of general vertical coordinate OGCMs. The method relies on regridding/remapping techniques to represent tracers in a geopotential grid. Horizontal fluxes are calculated on this grid and then remapped back to the native grid, where fluxes are applied. The algorithm is implemented in an ocean model and tested in idealized and realistic settings. Horizontal diffusion can account for up to 10% of the total northward heat transport in the Southern Ocean and Western boundary current regions of the Northern Hemisphere. It also reduces the vertical stratification of the upper ocean, which results in an overall deepening of the surface boundary layer depth. Finally, enabling horizontal diffusion leads to meaningful reductions in the near‐surface global bias of potential temperature and salinity. Plain Language Summary: Mesoscale ocean eddies, which are analogous to the weather systems in the atmosphere, are crucial to the distribution of heat, salt, carbon, and nutrients throughout the global ocean. Most of the ocean models used in climate simulations do not have enough horizontal resolution to resolve these eddies and, therefore, their effects must be parameterized. In the ocean interior, where no appreciable heat or mass is exchanged across density surfaces, the mixing of tracers due to mesoscale eddies occurs along surfaces of constant density. However, as ocean boundaries are approached, mixing occurs on a plane parallel to the boundary. For example, near the surface of the ocean, which is where the scheme presented here is designed to work, this plane is mostly horizontal. There is a class of ocean models that use a vertical coordinate system whose layer thicknesses can vary horizontally, thus complicating the implementation of horizontal diffusive parameterizations. This paper presents and evaluates a method that allows horizontal fluxes to be calculated and applied within the surface layer of this class of ocean models. Key Points: General vertical coordinate ocean models lack the option to shift diffusive fluxes from neutral surfaces to a purely horizontal directionRegridding/remapping techniques are used to represent tracers in z*‐coordinates, where horizontal fluxes are easily appliedThe method reduces global tracer biases in forced simulations, regardless of the coordinate system employed [ABSTRACT FROM AUTHOR]

This study further evaluates the modeling approach of Jia et al. (2019), https://doi.org/10.1029/2019gl081960 (JEA19) to investigate the potential effects of mesoscale sea surface temperature (SST) variability on the atmospheric circulation. The approach employs a global atmospheric circulation model coupled to a slab ocean model to produce two ensembles of simulations: one in which the SST exhibits realistic mesoscale variability, and another in which the mesoscale SST variability is suppressed. The results of the present study suggest that the modeling approach yields the desired SST differences between the two ensembles at the mesoscales. They also show, however, that the approach can lead to undesirable SST differences at the large scales, if the prescribed pair of oceanic heat flux fields have large scale differences. In the experiments of JEA19, such forced, large scale SST differences dominate over the large scale differences that may develop in response to changes in the large scale atmospheric circulation by nonlinear interactions with the SST. This result suggests that finding a proper pair of estimates of the oceanic heat flux fields is necessary for the investigation of the potential upscale impact of mesoscale SST variability on the atmosphere by the approach of JEA19. The paper concludes with proposing a potential improved strategy to obtain such a pair of estimates. Plain Language Summary: This study evaluates a modeling approach to investigate the potential effects of mesoscale (scales smaller than 100 km) ocean sea surface temperature (SST) variability on the atmospheric circulation. The approach employs a global atmospheric circulation model coupled to a thermodynamical model of the ocean. Two ensembles of model simulations are prepared: one in which the ocean exhibits realistic mesoscale SST variability, and another in which that variability is suppressed. The results suggest that the modeling approach yields the desired mesoscale SST differences. They also show, however, that the approach can lead to undesirable large scale SST differences, if there are large scale differences between the prescribed oceanic heat transport fields of the two ensembles. In the experiments of JEA19, such forced, large scale SST differences dominate over the large scale differences that may develop in response to changes in the large scale atmospheric circulation by two‐way interactions with the SST. This result suggests that finding a proper pair of estimates of the oceanic heat transport fields is necessary for the investigation of the potential upscale impact of mesoscale SST variability on the atmosphere by the approach of JEA19. The paper concludes with proposing a potential improved strategy to obtain such a pair of estimates. Key Points: The large scale response in an uncoupled atmospheric simulation is the response of the model rather than that of the atmosphereA coupled modeling approach is necessary to investigate the large scale atmospheric response to mesoscale sea surface temperature variabilityA pair of oceanic heat fluxes that do not have large scale differences is critical to produce a realistic large scale atmospheric response [ABSTRACT FROM AUTHOR]

This study investigates the emergence of hurricane‐like vortices in idealized simulations of rotating moist convection. A Boussinesq atmosphere with simplified thermodynamics for phase transitions is forced by prescribing the temperature and humidity at the upper and lower boundaries. The governing equations are solved numerically using a variable‐density incompressible Navier‐Stokes solver with adaptive mesh refinement to explore the behavior of moist convection under a broad range of conditions. In the absence of rotation, convection aggregates into active patches separated by large unsaturated regions. Rotation modulates this statistical equilibrium state so that the self‐aggregated convection organizes hurricane‐like vortices. The warm and saturated air converges to the center of the vortices, and the latent heat released through the upwelling, forms the warm core structure. These hurricane‐like vortices share characteristics similar to tropical cyclones in the earth's atmosphere. The hurricane‐like vortices occur under conditionally unstable conditions where the potential energy given at the boundaries is large enough, corresponding to a moderate rate of rotation. This regime shares many similar characteristics to the tropical atmosphere indicating that the formation of intense meso‐scale vortices is a general characteristic of rotating moist convection. The model used here does not include any interactions with radiation, wind‐evaporation feedback, or cloud microphysics, indicating that, while these processes may be relevant for tropical cyclogenesis in the Earth atmosphere, they are not its primary cause. Instead, our results confirm that the formation and maintenance of hurricane‐like vortices involve a combination of atmospheric dynamics under the presence of rotation and of phase transitions. Plain Language Summary: This paper investigates the emergence of hurricane‐like vortices in a numerical simulation of an idealized atmosphere system. The complicated thermodynamics for water vapor is simplified. The remaining system can still produce a conditionally unstable atmosphere in which unsaturated air parcels experience a stable stratification and unsaturated parcels experience an unstable one. We find hurricane‐like vortices in the presence of rotation. The warm and saturated air converges to the center of the vortices, and the latent heat released through the upwelling forms the warm core structure and easily observed eyewall. The structure of hurricane‐like vortices is analogous to tropical cyclones in the earth's atmosphere. This idealized configuration produces characteristics similar to the tropical atmosphere so that the hurricane‐like vortices can be considered as the outcome of the tropical cyclogenesis, with reasonable simplifications. However, the tropical cyclogenesis here does not require any interactions with radiation, surface flux feedback, or precipitation. Our results confirm that the formation and maintenance of hurricane‐like vortices involve a combination of rotation and thermodynamic forcing. Key Points: The impact of rotation on simulations of idealized simulations of moist convection is studiedThe formation and maintenance of hurricane‐like vortices involve a combination of conditional instability and rotationRotating moist convection in a conditionally unstable environment should spontaneously generate hurricane‐like vortices [ABSTRACT FROM AUTHOR]

It is crucially important to estimate unknown parameters in process-based models by integrating observation and numerical simulation. For many applications in earth system sciences, a parameter estimation method which allows parameters to temporally change is required. In the present paper, an efficient and practical method to estimate time-varying parameters of relatively low dimensional models is presented. In the newly proposed method, called Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE-PF), an inflation method to maintain the spread of ensemble members in a Sampling-Importance-Resampling Particle Filter (SIRPF) is improved using a non-parametric posterior probabilistic distribution of time-invariant parameters obtained by comparing simulated and observed climatology. HOOPE-PF outperforms the original SIRPF in synthetic experiments with toy models and a real-data experiment with a conceptual hydrological model when an ensemble size is small. The advantage of HOOPE-PF is that its performance is not greatly affected by the size of perturbation to be added to ensemble members to maintain their spread while it is important to get the optimal performance in the original particle filter. Since HOOPE-PF is the extension of the existing particle filter which has been extensively applied to many models in earth system sciences such as land, ecosystem, hydrology, and paleoclimate reconstruction, HOOPE-PF can be applied to improve the simulation of these process-based models by considering time-varying model parameters. Plain Language Summary Computer simulation is widely used to understand earth systems such as atmosphere, ocean, land, ecosystem, and society. Many computer simulation models in earth system sciences inevitably have unknown coefficients of equations, which are called model parameters. It is important for accurate simulation of earth systems to tune these model parameters by comparing the results of computer simulation with observation. Although many previous works assumed that model parameters do not change over time, it is necessary to allow them to change over time in some applications, which makes it more difficult to estimate model parameters. In this study, a new method to estimate time-varying model parameters is proposed. The key idea of this paper is to combine two existing methods which integrate observations into computer simulation models. It is found that the proposed method works better than the existing method in three case studies. The proposed method can contribute to improving many simulations in earth system sciences by efficiently tuning time-varying model parameters. [ABSTRACT FROM AUTHOR]

Machine learning (ML) parameterizations of subgrid physics is a growing research area. A key question is whether traditional ML methods such as feed‐forward neural networks (FNNs) are better suited for representing only specific processes. Radiation schemes are an interesting example, because they compute radiative flows through the atmosphere using well‐established physical equations. The sequential aspect of the problem implies that FNNs may not be well‐suited for it. This study explores whether emulating the entire radiation scheme is more difficult than its components without vertical dependencies. FNNs were trained to replace a shortwave radiation scheme, its gas optics component, and its reflectance‐transmittance computations. In addition, a novel recurrent NN (RNN) method was developed to structurally incorporate the vertical dependence and sequential nature of radiation computations. It is found that a bidirectional RNN with an order of magnitude fewer model parameters than FNN is considerably more accurate, while offering a smaller but still significant 4‐fold speedup over the original code on CPUs, and a larger speedup on GPUs. The RNN predicts fluxes with less than 1% error, and heating rates computed from fluxes have a root‐mean‐square‐error of 0.16 K day−1 in offline tests using a year of global data. Finally, FNNs emulating gas optics are very accurate while being several times faster. As with RNNs emulating radiative transfer, the smaller dimensionality may be crucial for developing models that are general enough to be used as parameterizations. Plain Language Summary: Numerical weather and climate simulations are being performed at increasingly high resolution, making the energy cost of simulations significant. Computing how solar and terrestrial radiation interact with Earth's atmosphere, surface, and clouds is one of the most computationally expensive parts in climate models especially. This has invited efforts to replace these computations with predictions from a neural network, which is approximative but considerably faster than physical radiation computations. In this paper different ways of emulating a radiation code with neural networks have been explored. Its main contribution is developing a novel emulation method based on recurrent neural networks, which more closely resemble physical radiative transfer computations in their structure. The accuracy is found to be considerably higher than with traditional neural network approaches which use an order of magnitude more model parameters. Key Points: Feed‐forward and recurrent neural networks (NN) were developed to emulate a shortwave radiation scheme, as well as its componentsThe recurrent NN has far better accuracy than usual approaches, while offering a significant speedup especially on GPUsUsing NNs for gas optics is 3 times faster and does not sacrifice accuracy [ABSTRACT FROM AUTHOR]

In the event of an accidental or intentional contaminant release in the atmosphere, it is imperative, for managing emergency response, to diagnose the release parameters of the source from measured data. Reconstruction of the source information exploiting measured data is called an inverse problem. To solve such a problem, several techniques are currently being developed. The first part of this paper provides a detailed description of one of them, known as the renormalization method. This technique, proposed by Issartel (2005), has been derived using an approach different from that of standard inversion methods and gives a linear solution to the continuous Source Term Estimation (STE) problem. In the second part of this paper, the discrete counterpart of this method is presented. By using matrix notation, common in data assimilation and suitable for numerical computing, it is shown that the discrete renormalized solution belongs to a family of well-known inverse solutions (minimum weighted norm solutions), which can be computed by using the concept of generalized inverse operator. It is shown that, when the weight matrix satisfies the renormalization condition, this operator satisfies the criteria used in geophysics to define good inverses. Notably, by means of the Model Resolution Matrix (MRM) formalism, we demonstrate that the renormalized solution fulfils optimal properties for the localization of single point sources. Throughout the article, the main concepts are illustrated with data from a wind tunnel experiment conducted at the Environmental Flow Research Centre at the University of Surrey, UK. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to develop an optimized implicit‐explicit (IMEX) Runge‐Kutta scheme for atmospheric applications focusing on stability and accuracy. Following the common terminology, the proposed method is called IMEX‐SSP2(2,3,2), as it has second‐order accuracy and is composed of diagonally implicit two‐stage and explicit three‐stage parts. This scheme enjoys the Strong Stability Preserving (SSP) property for both parts. This new scheme is applied to nonhydrostatic compressible Boussinesq equations in two different arrangements, including (i) semiimplicit and (ii) Horizontally Explicit‐Vertically Implicit (HEVI) forms. The new scheme preserves the SSP property for larger regions of absolute monotonicity compared to the well‐studied scheme in the same class. In addition, numerical tests confirm that the IMEX‐SSP2(2,3,2) improves the maximum stable time step as well as the level of accuracy and computational cost compared to other schemes in the same class. It is demonstrated that the A‐stability property as well as satisfying “second‐stage order” and stiffly accurate conditions lead the proposed scheme to better performance than existing schemes for the applications examined herein. [ABSTRACT FROM AUTHOR]

Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America). Plain Language Summary: Gross primary productivity (GPP) is the rate at which plants remove carbon dioxide from the atmosphere during photosynthesis. Carbon dioxide is a greenhouse gas and excess in the atmosphere causes global warming and climate change. Changes in the amounts of atmospheric carbon dioxide will impact the entire Earth System. We therefore need the ability to accurately calculate GPP, especially for different possible carbon usage pathways in the future. Earth system models or ESMs allow us to simulate various processes happening in the earth's atmosphere and biosphere including photosynthesis and can help us estimate GPP changes for such different pathways. However, ESMs can vary significantly in their simulated GPP estimates making it difficult to have confidence in using these estimates. We describe a ML framework to better understand where ESMs differ in calculating GPP so that we can address knowledge gaps in models. This approach allows us to understand the processes involved without having to run computationally expensive simulations. With improved models, we can also improve our ability to predict climate change outcomes for the future. Key Points: A machine learning framework to advance our understanding of the terrestrial carbon cycle in earth system models or ESMs is proposedDifferences in the relative importance of atmospheric drivers of gross primary productivity highlights differences across modelsA method to attribute differences in productivity estimates from ESMs due to process representation versus atmospheric forcing is demonstrated [ABSTRACT FROM AUTHOR]

Convectively coupled Kelvin waves (CCKWs) are important drivers of tropical weather and may influence extreme rainfall and tropical cyclone formation. However, directly attributing these impacts to CCKWs remains a challenge. Numerical models also struggle to simulate the convective coupling of CCKWs. To address these gaps in understanding, this study examines a set of global simulations in which CCKW amplitudes are modified in the initial conditions. The Model for Prediction Across Scales –Atmosphere is used to simulate a time period in which several CCKWs coexisted around the globe, including an unusually strong CCKW located over the Atlantic. Prior to running the simulation, Kelvin‐filtered fields are identified in initial conditions and used to either amplify or dampen the initial wave amplitude. This method is effective at robustly changing the strength and structure of simulated CCKWs and can illuminate their convective coupling. Rainfall intensity within simulated CCKWs is shown to be partially controlled by column saturation fraction and deep convective inhibition. Despite the accurate depiction of most CCKWs during this time period, however, these experiments fail to simulate convective coupling in the strong Atlantic CCKW. This is true even after amplifying this wave at initialization. The cause of this failure is unclear and motivates additional work into the modeling and predictability of CCKW events. Overall, this study demonstrates that modifying CCKW amplitudes can serve as a useful tool for understanding CCKWs. This method may also be useful for future attributional work on the influence of CCKWs on other phenomena. Plain Language Summary: Convectively coupled Kelvin waves (CCKWs) are large waves in Earth's atmosphere that travel along the equator and can influence other weather features, like rainfall and tropical cyclones. However, it has been difficult to directly prove how CCKWs impact these other weather features. Weather forecasting models also struggle to accurately simulate CCKWs. To address these challenges, this study investigates a set of modeling experiments in which the strength of CCKWs has been changed. The Model for Prediction Across Scales—Atmosphere is used to produce simulations of a period for which several CCKWs existed around the world. The strength of these CCKW is changed in the initial conditions prior to running these simulations. Results show this adjustment method is effective at changing the strength and structure of simulated CCKWs. This is useful for identifying the connections between a wave's wind structure and rainfall. However, these experiments fail to accurately simulate a strong CCKW over the Atlantic. The cause of this failure is unclear, motivating additional work into modeling CCKW events. This study demonstrates how a method for changing CCKW strength could serve as a useful tool for understanding CCKWs and their influence on other weather features. Key Points: A method is presented to effectively adjust Kelvin wave amplitudes in simulations with parameterized convectionThis method can identify two‐way convective coupling in Kelvin waves and has potential use in attributional work on their impactsThe failure of numerical models to simulate a strong Kelvin wave motivates future work on the predictability of individual wave events [ABSTRACT FROM AUTHOR]

The gravity wave drag parametrization of the Whole Atmosphere Community Climate Model (WACCM) has been modified to include the wave‐driven atmospheric vertical mixing caused by propagating, non‐breaking, gravity waves. The strength of this atmospheric mixing is represented in the model via the "effective wave diffusivity" coefficient (Kwave). Using Kwave, a new total dynamical diffusivity (KDyn) is defined. KDyn represents the vertical mixing of the atmosphere by both breaking (dissipating) and vertically propagating (non‐dissipating) gravity waves. Here we show that, when the new diffusivity is used, the downward fluxes of Fe and Na between 80 and 100 km largely increase. Larger meteoric ablation injection rates of these metals (within a factor 2 of measurements) can now be used in WACCM, which produce Na and Fe layers in good agreement with lidar observations. Mesospheric CO2 is also significantly impacted, with the largest CO2 concentration increase occurring between 80 and 90 km, where model‐observations agreement improves. However, in regions where the model overestimates CO2 concentration, the new parametrization exacerbates the model bias. The mesospheric cooling simulated by the new parametrization, while needed, is currently too strong almost everywhere. The summer mesopause in both hemispheres becomes too cold by about 30 K compared to observations, but it shifts upward, partially correcting the WACCM low summer mesopause. Our results highlight the far‐reaching implications and the necessity of representing vertically propagating non‐breaking gravity waves in climate models. This novel method of modeling gravity waves contributes to growing evidence that it is time to move away from dissipative‐only gravity wave parametrizations. Plain Language Summary: Atmospheric gravity waves are generated in the lowest layers (∼bottom 10 km) of the atmosphere by processes such as weather systems and air masses interacting with the topography, and can propagate upward to ∼120 km. In this work, the representation of atmospheric gravity waves in a state‐of‐the‐art chemistry climate model, the Whole Atmosphere Community Climate Model (WACCM), has been updated. In the new model version, the mixing of the atmosphere caused by gravity waves that propagate upwards above 80 km and do not break is part of the total mixing of the atmosphere, which hitherto was considered to be caused by the turbulence created by gravity wave breaking. We show here that when this additional source of atmospheric mixing is taken into account, the WACCM model is better able to simulate the sodium and iron atom densities in the upper layers of the atmosphere (between ∼80 and 100 km), created by the ablation of cosmic dust. Additionally, gravity waves significantly affect the representation of CO2 mixing ratios and air temperature within the model. Our work is important because it shows that propagating gravity waves have non‐negligible impacts on basic properties of the Earth's atmosphere such as temperature, winds, and chemical species like CO2, and that the lack of their representation in current climate models needs to be addressed. Key Points: A parametrization representing vertical mixing by non‐breaking gravity waves has been implemented in a chemistry‐climate modelMesospheric CO2 and temperature are significantly impacted by the additional source of vertical mixingWave‐induced constituent transport largely reconciles the modeled mesospheric Na and Fe layers with the estimated meteoric injection rates [ABSTRACT FROM AUTHOR]

Global Climate Model tuning (calibration) is a tedious and time‐consuming process, with high‐dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand‐picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere‐only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient‐based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly‐tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high‐dimensional output and operating in a fraction of the time. Plain Language Summary: The systems governing the atmosphere and other physical processes on Earth are complex, and climate scientists use Earth Systems Models (ESMs) based on physical and computational representations of these processes to create simulations that mimic the behavior of Earth's climate. Since ESMs are defined at a lower resolution than some processes require, parameters are introduced to address the uncertainty in the approximation. The traditional approach is to hand‐tune these parameters using expert knowledge, a time‐consuming process that involves matching climate model output to observational data on Earth, as closely as possible. Instead, we introduce an alternative "automated calibration" framework that chooses such parameters automatically and in a fraction of the time. This is done by building a very fast machine‐learning‐based substitute (surrogate) for the ESM to predict variability in the climatology for 11 different spatial fields over four seasons, and then using this surrogate to search for the parameters that best match climate observations. We apply our framework to the atmospheric component of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2, and demonstrate an improvement of 2.7% in root‐mean‐squared‐error on average across the different variables and seasons, compared to the hand‐tuned solution. Key Points: We apply an autocalibration method to uncertain parameters in the Energy Exascale Earth System Model version 2 (E3SMv2) atmosphere modelWe use a 250‐member perturbed parameter E3SMv2 ensemble to train a polynomial chaos surrogate model and optimize input parametersThe autocalibrated parameter set improves E3SMv2's root‐mean‐squared‐error against observations by 2.7% [ABSTRACT FROM AUTHOR]

Coupled data assimilation (CDA) has been attracting researchers' interests to improve Earth system modeling. The CDA methods are classified into two: weakly coupled data assimilation (wCDA), which considers cross‐compartment interaction only in a forecast phase, and strongly coupled data assimilation (sCDA), which additionally uses other compartment's information in an analysis phase. Although sCDA can theoretically provide better estimates than wCDA since sCDA fully uses inter‐compartment covariances, the potential of sCDA in practice is still in debate. We investigate conditions under which sCDA is effective by applying Local Ensemble Transform Kalman Filter (LETKF) to a coupled Lorenz96 model. Especially, we aim to generalize CDA's behavior on coupling strength basis. By continuously changing a cross‐compartment interaction in the coupled Lorenz96 model, we find that sCDA's superiority over wCDA is particularly evident with large cross‐compartment interactions which attenuates dynamics of each compartment (the increase of variables in one compartment induces the reduction of those in the other compartment) and does not intensify the chaoticity of the whole system. The performance of sCDA is more sensitive to the LETKF's hyperparameters (i.e., localization and inflation) than wCDA. Furthermore, through two imperfect model experiments in which model parameters are uncertain, we quantify sCDA's vulnerability to model inaccuracies. The findings of the relationship between the skill of sCDA and inter‐compartment interactions give implications for future CDA strategies. Plain Language Summary: Data assimilation (DA) integrates computer simulation and observation to improve the estimate of system states. Since Earth is a coupled system in which different compartments such as atmosphere and ocean interact with each other, DA methods for coupled systems, namely coupled data assimilation (CDA), have been attracting researchers' interests in Earth system sciences. The CDA methods are classified into two: weakly coupled DA (wCDA) and strongly coupled DA (sCDA). Although sCDA has a higher potential than wCDA as it explicitly uses the information of cross‐compartment interactions, previous studies have mixed results regarding its accuracy and efficiency. Thus, we aimed to reveal conditions under which sCDA is effective. Especially, we focused on the intensity of compartment interactions. We found that the superiority of sCDA against wCDA depends heavily on the cross‐compartment interaction. When the cross‐compartment interaction is large, while it attenuates dynamics of each compartment and does not increase the chaoticity of the system, sCDA works more efficiently than wCDA. Furthermore, sCDA is more vulnerable to model inaccuracy than wCDA. The findings can give the insights to design a DA method for coupled systems. Key Points: Weakly and strongly coupled data assimilation (sCDA) are compared by applying Local Ensemble Transform Kalman Filter to the coupled‐Lorenz96 modelsCDA performs better when cross‐compartment interaction is large, but it does not increase system's chaoticitysCDA is vulnerable to inaccuracy of modeled processes and modeled cross‐compartment interactions [ABSTRACT FROM AUTHOR]

Rainfall in the tropics has been shown to be produced either by isolated but intense convective systems (showers regime) or widespread but weaker systems (rains regime). We examine significant rainfall systems observed in the OTREC project (Organization of Tropical East Pacific Convection) in order to tease out the physical mechanisms differentiating these two regimes. We find that rains occur in very moist environments, typically with weak conditional instability. In contrast, showers develop in drier environments with larger instability. Spectral weak temperature gradient numerical calculations show that showers are associated with episodic rainfall separated by significant quiescent periods, whereas rains produce continuous simulated rainfall after a spinup period. Mass flux profiles of showers and rains are very different, resulting in different effects on the large scale environment. Plain Language Summary: Rain can occur in intense bursts (showers) or in extended periods of less intense rainfall (rains). The latter regime often produces more total rain than the former. Using observations obtained over the tropical east Pacific and the southwest Caribbean, we have isolated the atmospheric conditions that occur for each of these rainfall regimes. In particular, rains require a very moist atmosphere but can occur with weak atmospheric instability, whereas showers occur in drier atmospheres but require stronger instability. Numerical simulations of rainfall systems confirm these results and reproduce the intense bursts of rain in showers versus the steady but weaker rainfall in rains. Key Points: "Showers" produce intense but localized rainfall; "rains" are weaker but widespread, producing more average precipitationRains occur in very moist environments with weak instability while showers favor drier but more unstable environmentsShowers are associated with periodic bursts of rain in numerical simulations, rains produce continuous precipitation [ABSTRACT FROM AUTHOR]

High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation. Plain Language Summary: Stratocumulus clouds cover a large fraction of the globe but are very challenging to reproduce in computer simulations of Earth's atmosphere because of their unique complexity. Previous studies find the model produces too few Stratocumulus clouds as we increase the model resolution, which, in theory, should improve the simulation of important motions for the clouds. This is because the clouds are exposed to more conditions that make them evaporate away. On Earth, stratocumulus clouds reflect a lot of sunlight. In the computer model of Earth, too much sunlight reaches the surface because of too few stratocumulus clouds, which makes it warmer. This study tests two methods to thicken Stratocumulus clouds in the computer model Earth. The first method smooths out some winds, which helps reduce the exposure of clouds to the conditions that make them evaporate. The second method moves water droplets in the cloud away from the conditions that would otherwise make them evaporate. In long simulations, combining these methods helps the model produce thicker stratocumulus clouds with more water. Key Points: We improve a long‐standing stratocumulus (Sc) dim bias in a high‐resolution Multiscale Modeling FrameworkIncorporating intra‐CRM hyperviscosity hedges against the numerics of its momentum solver, reducing entrainment vicinityFurther adding sedimentation boosts Sc brightness close to observed, opening path to more faithful low cloud feedback analysis [ABSTRACT FROM AUTHOR]

Positive feedbacks in climate processes can make it difficult to identify the primary drivers of climate phenomena. Some recent global climate model (GCM) studies address this issue by controlling the wind stress felt by the surface ocean such that the atmosphere and ocean become mechanically decoupled. Most mechanical decoupling studies have chosen to override wind stress with an annual climatology. In this study we introduce an alternative method of interannually varying overriding which maintains higher frequency momentum forcing of the surface ocean. Using a GCM (NCAR CESM1), we then assess the size of the biases associated with these two methods of overriding by comparing with a freely evolving control integration. We find that overriding with a climatology creates sea surface temperature (SST) biases throughout the global oceans on the order of ±1°C. This is substantially larger than the biases introduced by interannually varying overriding, especially in the tropical Pacific. We attribute the climatological overriding SST biases to a lack of synoptic and subseasonal variability, which causes the mixed layer to be too shallow throughout the global surface ocean. This shoaling of the mixed layer reduces the effective heat capacity of the surface ocean such that SST biases excite atmospheric feedbacks. These results have implications for the reinterpretation of past climatological wind stress overriding studies: past climate signals attributed to momentum coupling may in fact be spurious responses to SST biases. Plain Language Summary: Because the ocean influences the atmosphere and vice versa, chicken‐or‐egg type problems abound throughout the climate system. Some studies have addressed this by controlling the wind stress field felt by the ocean in climate models in order to mechanically decouple the ocean from the atmosphere and thus determine the surface ocean response to a change in momentum forcing. Most previous studies that override wind stress have fed the ocean a mean annual cycle; however, this method removes the effect of shorter‐term events like storms. We compare how well overriding experiments, forced either with the mean annual cycle of wind stress or with year‐to‐year varying wind stress, agree with a freely evolving control simulation. We find substantially larger sea surface temperature biases in the simulation forced with the mean annual cycle of wind stress. We attribute these biases to the lack of short‐term weather events which mix the surface ocean. Key Points: Most previous wind stress overriding simulations have disabled momentum feedbacks in global climate models by overriding with a climatologyWe introduce a protocol to override with interannually varying wind stress, which leads to smaller biases than climatological overridingWe attribute this difference to a lack of synoptic variability in climatological overriding which shoals the mixed layer [ABSTRACT FROM AUTHOR]

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (∼28 km) and 0.125° (∼14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)-the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. With only prescribed sea surface temperatures, VR-CESM tended to produce a warmer summer (by about 1-3°C) and overestimated overall winter precipitation (about 25%-35%) compared to reference data sets. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ∼1-3°C colder than the reference data sets, underestimated precipitation by ∼20%-30% at 27 km resolution, and overestimated precipitation by ∼65-85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. This assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models. [ABSTRACT FROM AUTHOR]

This paper evaluates the numerical convergence of very short (1 h) simulations carried out with a spectral-element (SE) configuration of the Community Atmosphere Model version 5 (CAM5). While the horizontal grid spacing is fixed at approximately 110 km, the process-coupling time step is varied between 1800 and 1 s to reveal the convergence rate with respect to the temporal resolution. Special attention is paid to the behavior of the parameterized subgrid-scale physics. First, a dynamical core test with reduced dynamics time steps is presented. The results demonstrate that the experimental setup is able to correctly assess the convergence rate of the discrete solutions to the adiabatic equations of atmospheric motion. Second, results from full-physics CAM5 simulations with reduced physics and dynamics time steps are discussed. It is shown that the convergence rate is 0.4-considerably slower than the expected rate of 1.0. Sensitivity experiments indicate that, among the various subgrid-scale physical parameterizations, the stratiform cloud schemes are associated with the largest time-stepping errors, and are the primary cause of slow time step convergence. While the details of our findings are model specific, the general test procedure is applicable to any atmospheric general circulation model. The need for more accurate numerical treatments of physical parameterizations, especially the representation of stratiform clouds, is likely common in many models. The suggested test technique can help quantify the time-stepping errors and identify the related model sensitivities. [ABSTRACT FROM AUTHOR]

The ensemble Kalman particle filter (EnKPF) is a combination of two Bayesian-based algorithms, namely, the ensemble Kalman filter (EnKF) and the sequential importance resampling particle filter (SIR-PF). It was recently introduced to address non-Gaussian features in data assimilation for highly nonlinear systems, by providing a continuous interpolation between the EnKF and SIR-PF analysis schemes. In this paper, we first extend the EnKPF algorithm by modifying the formula for the computation of the covariance matrix, making it suitable for nonlinear measurement functions (we will call this extended algorithm nEnKPF). Further, a general form of the Kalman gain is introduced to the EnKPF to improve the performance of the nEnKPF when the measurement function is highly nonlinear (this improved algorithm is called mEnKPF). The Lorenz '63 model and Lorenz '96 model are used to test the two modified EnKPF algorithms. The experiments show that the mEnKPF and nEnKPF, given an affordable ensemble size, can perform better than the EnKF for the nonlinear systems with nonlinear observations. These results suggest a promising opportunity to develop a non-Gaussian scheme for realistic numerical models. [ABSTRACT FROM AUTHOR]

The effective climate sensitivity in the Department of Energy's Energy Exascale Earth System Model (E3SM) has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly due to a weaker positive cloud feedback that leads to a stronger negative radiative feedback. Present‐day atmosphere‐only experiments with uniform 4 K sea surface temperature warming are used to separate the contributions of individual model modifications to the reduced cloud feedback. We find that the reduced cloud feedback is mostly driven by changes over the tropical marine low cloud regime, mainly related to a new trigger function for the deep convection scheme and modifications in the cloud microphysics scheme. The new trigger function helps weaken the low cloud reduction by increasing the cloud water detrainment at low levels from deep convection under warming. Changes to the formula of autoconversion rate from liquid to rain and an introduced minimum cloud droplet number concentration threshold in cloud microphysical calculations help sustain clouds against dissipation by suppressing precipitation generation with warming. In the midlatitudes, the increased Wegener‐Bergeron‐Findeisen (WBF) efficiency strongly reduces present‐day liquid water and leads to a stronger negative cloud optical depth feedback. The reduced trade cumulus cloud feedback in v2 is closer to estimates from recent observational and large‐eddy modeling studies but might not be due to the right physical reasons. The reduced mid‐latitude cloud feedback may be more plausible because more realistic present‐day mixed‐phase clouds are produced through the change in the WBF efficiency. Plain Language Summary: Understanding how the Earth responds to greenhouse gas increases is important for climate change research. In the Department of Energy's Energy Exascale Earth System Model, the global temperature response to an abrupt quadrupling of atmospheric carbon dioxide has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly because low‐level clouds over the tropical oceans decrease less as the planet warms in version 2: a weaker amplifying cloud feedback. To understand the reasons behind this reduction, warming simulations were conducted to separate the contributions of individual model changes. We find that the reduced cloud feedback is primarily due to changes in the representation of the vertical movement of air through the depth of the lower atmosphere and of the microscopic properties of clouds. The findings highlight some unexpected impacts on cloud feedback resulting from modifications to the model's physics and emphasize the importance of monitoring and understanding changes in cloud feedback during model development. Key Points: E3SM's effective climate sensitivity is lower in version 2 mainly due to the reduced positive cloud feedback over marine low cloud regionsThe feedback reduction is primarily due to altered cloud microphysical parameters and a new deep convection trigger functionProcess‐level analysis is conducted to understand the impact of these model modifications on cloud feedbacks [ABSTRACT FROM AUTHOR]

The accuracy of initial conditions is an important driver of the forecast skill of numerical weather prediction models. Increases in the quantity of available measurements, particularly high‐resolution remote sensing observational data products from satellites, are valuable inputs for improving those initial condition estimates. However, the traditional data assimilation methods for integrating observations into forecast models are computationally expensive. This makes incorporating dense observations into operational forecast systems challenging, and it is often prohibitively time‐consuming. Additionally, high‐resolution observations often have correlated observation errors which are difficult to estimate and create problems for assimilation systems. As a result, large quantities of data are discarded and not used for state initialization. Using the Lorenz‐96 system for testing, we demonstrate that a simple machine learning method can be trained to assimilate high‐resolution data. Using it to do so improves both initial conditions and forecast accuracy. Compared to using the Ensemble Kalman Filter with high‐resolution observations ignored, our augmented method has an average root‐mean‐squared error reduced by 37%. Ensemble forecasts using initial conditions generated by the augmented method are more accurate and reliable at up to 10 days of forecast lead time. Plain Language Summary: Weather forecasts are highly sensitive to the estimate of the current state of the atmosphere, known as initial conditions. The atmosphere is chaotic, meaning that small errors in this estimate can grow quickly as the forecast model predicts events further into the future. The satellite era has contributed to large improvements in weather forecasts by providing additional data that allow for more accurate estimates of initial conditions. However, current methods for generating initial conditions are computationally time‐consuming, and as a result, large fractions of available measurements are not used for this purpose. In a proof‐of‐concept study using a simplified representation of the atmosphere for testing, we train a machine learning (ML) method to replicate the results of a traditional method. Once trained, ML models are usually very fast. Applying the trained model exclusively to measurements that would otherwise be too time‐consuming to use produces better initial conditions and more accurate forecasts. Key Points: Machine Learning (ML) augmented data assimilation (DA) of high‐resolution observations improves the analysis in a nonlinear dynamical modelExplainable Artificial Intelligence identifies system covariances to guide neural network training for analysis state reproductionShort‐term forecasts from the analysis generated by the ML augmented DA are more accurate and more reliable [ABSTRACT FROM AUTHOR]

In a "hothouse" climate, warm temperatures lead to a high tropospheric water vapor concentration. Sufficiently high water vapor levels lead to the closing of the water vapor infrared window, which prevents radiative cooling of the lower troposphere. Because water vapor also weakly absorbs solar radiation, hothouse climates feature radiative heating of the lower troposphere. In recent work, this radiative heating was shown to trigger a shift into a novel "episodic deluge" precipitation regime, where rainfall occurs in short, intense outbursts separated by multi‐day dry spells. Here, we further examine the role of the lower tropospheric radiative heating (LTRH) in the transition into the "episodic deluge" regime. We demonstrate that under high sea‐surface temperature the "episodic deluge" regime could be formed even before the LTRH turns positive. In addition, we examine whether these oscillations operate on larger scales and how these oscillations, which represent "temporal" convective self‐organization, would manifest in the presence of traditional "spatial" self‐ or forced‐aggregation in large‐domain convection‐permitting simulations. We find that the temporal oscillations become much less synchronized throughout a large domain (O $\mathcal{O}$ 1,000 km) because gravity waves cannot propagate fast enough to synchronize convection. We also show that temporal oscillations still dominate the rainfall distribution even when there is tropical convective self‐aggregation or a large‐scale overturning circulation. These results could have important implications for extreme precipitation events under a warming climate. Plain Language Summary: Water vapor is a strong greenhouse gas that closely follows surface temperature. Under very warm ("hothouse") conditions, which are believed to have existed in the Earth's distant past, temperature and water vapor are sufficiently high that the greenhouse effect of water vapor prevents heat from escaping out of the lower part of the atmosphere. This trapped heat makes the lower atmosphere even hotter, causing some unusual weather patterns. For example, it was recently shown to lead to short but intense bursts of rain, followed by several days of dry weather. However, it is unclear whether these patterns occur on larger scales and what their characteristic scale is. In this study, computer simulations were conducted to address these uncertainties and to further elucidate the role of the lower tropospheric radiative heating in the transition into this "episodic deluge" regime. We show that these temporal oscillations dominate the rainfall distribution even in the presence of other atmospheric factors such as tropical convective self‐aggregation or large‐scale overturning circulation. Key Points: We examine the temporal and spatial organization of convection in simulated hothouse conditionsWe demonstrate that lower tropospheric radiative heating is not necessary for the "episodic deluge" precipitation regime"Episodic deluge" precipitation regime occurs in self‐ or forced‐convective aggregated large domain simulations but is less synchronized [ABSTRACT FROM AUTHOR]

The Laurentian Great Lakes are the world's largest freshwater system and regulate the climate of the Great Lakes region, which has been increasingly experiencing climatic, hydrological, and ecological changes. An accurate mechanistic representation of the Great Lakes thermal structure in Regional Climate Models (RCMs) is paramount to studying the climate of this region. Currently, RCMs have primarily represented the Great Lakes through coupled one‐dimensional (1D) column lake models; this approach works well for small inland lakes but is unable to resolve the realistic hydrodynamics of the Great Lakes and leads to inaccurate representations of lake surface temperature (LST) that influence regional climate and weather patterns. This work overcomes this limitation by developing a fully two‐way coupled modeling system using the Weather Research and Forecasting model and a three‐dimensional (3D) hydrodynamic model. The coupled model system resolves the interactive physical processes between the atmosphere, lake, and surrounding watersheds; and validated against a range of observational data. The model is then used to investigate the potential impacts of lake‐atmosphere coupling on the simulated summer LST of Lake Superior. By evaluating the difference between our two‐way coupled modeling system and our observation‐driven modeling system, we find that coupled‐lake atmosphere dynamics can lead to a higher LST during June‐September through higher net surface heat flux entering the lake in June and July and a lower net surface heat flux entering the lake in August and September. The unstratified water in June distributes the entering surface heat flux throughout the water column leading to a minor LST increase, while the stratified waters of July create a conducive thermal structure for the water surface to warm rapidly under the higher incoming surface heat flux. This research provides insight into the coupled modeling system behavior, which is critical for enhancing our predictive understanding of the Great Lakes climate system. Plain Language Summary: The Laurentian Great Lakes, a tremendously large body of freshwater, have a profound effect on the regional climate. Therefore, climate simulations for the Great Lakes region, including climate projections, rely on an accurate simulation of the Great Lakes. However, traditionally, the Great Lakes within Regional Climate Models have been simulated using a one‐dimensional (1D) lake model. This 1D approach leads to a poor simulation of the lake surface temperature (LST) and eventually affects the climate simulation. In this study, we introduce a newly developed regional climate modeling system that allows for a two‐way exchange of information between the Weather Research and Forecasting (WRF) model and a three‐dimensional (3D) model of the Great Lakes. This system is able to accurately reproduce the historical conditions of the Great Lakes. Additionally, to improve our understanding of coupled model behavior, we investigated how the two‐way interaction between WRF and the lake model affects the simulation of summer LST in Lake Superior. We discovered that the two‐way interaction between the models can lead to higher estimates of summer LST through changes in the surface heat fluxes. Key Points: A regional climate modeling system for the Great Lakes using a two‐way coupled atmosphere‐3D hydrodynamic lake model (WF2C) is developedWF2C allows the lake‐atmospheric conditions to evolve and interact freely with each otherLake‐atmosphere coupling affects simulated lake surface temperature by modifying heat fluxes through influencing atmosphere variables [ABSTRACT FROM AUTHOR]

In the Earth Sciences, the 3D radiative transfer equation is often solved for by Monte Carlo (MC) methods. They can, however, be computationally taxing, and that can narrow their range of application and limit their use in explorations of model parameter spaces. A novel family of MC algorithms is investigated here in which single simulations provide estimates of both radiative quantities A for a set of parameters x^ $\hat{x}$, as usual, as well as the overarching functional A $\mathcal{A}$(x) that can be evaluated, extremely efficiently, at any x. One such algorithm is developed and demonstrated for horizontally averaged broadband solar radiative fluxes as functions of surface albedo for uniform Lambertian surfaces beneath inhomogeneous cloudy atmospheres. Simulations for a high‐resolution synthetic cloud field, at various solar zenith angles, illustrate the potential of the method to gain insights into the nature of 3D radiative effects for complicated atmosphere‐surface conditions using information specially derived from the MC simulation. For simulations performed with a single surface albedo it is found that as surface albedo increases, 3D radiative effects increase, too, with maxima occurring at middling to large values, and then decrease. By utilizing the derived coefficients that describe A(x) $\mathcal{A}(x)$ it was established that these 3D effects stem from differences in fractions of radiation entrapped at successive orders of internal multiple reflections for 1D and 3D transfer. Plain Language Summary: The general aim of this study is to improve our understanding of a particular process that occurs in the atmosphere, that is, how radiation (light) is affected by the presence of clouds. It focuses on the role that the ground reflectivity plays on the radiative effect of cumulus clouds. To that end, we extend the so‐called MC methods—which are often used in the community to simulate light–cloud interactions and estimate the resulting radiative fluxes—so that one simulation yields not only the result for a given surface reflectivity, but also the overarching functional that expresses the full dependency of the flux on the surface reflectivity. It is much faster than running one simulation per value of surface reflectivity, and also gives access to new information usually not available in standard MC simulations. This modified "Functional" MC method is applied to solar radiation in a virtual 3D cloud field. By analysis of the resulting functionals, it is demonstrated that surface reflectivity plays a major role in the intensity of a process called entrapment of light. Key Points: Functionalized Monte Carlo methods that estimate functional forms instead of scalar quantities are presentedSolar fluxes are estimated as polynomial functions of surface albedo with a single simulationSurface albedo plays a major role in 3D effects of cumulus clouds through the entrapment process [ABSTRACT FROM AUTHOR]

Whereas it is now widely accepted that cumulus cloud sizes are power‐law distributed, characteristic exponents reported in the literature vary greatly, generally taking values between 1 and >3. Although these differences might be explained by variations in environmental conditions or physical processes organizing the cloud ensembles, the use of improper fitting methods may also introduce large biases. To address this issue, we propose to use a combination of maximum likelihood estimation and goodness‐of‐fit tests to provide more robust power‐law fits while systematically identifying the size range over which these fits are valid. The procedure is applied to cloud size distributions extracted from two idealized high‐resolution simulations displaying different organization characteristics. Overall, power‐laws are found to be outperformed by alternative distributions in almost all situations. When clouds are identified based on a condensed water path threshold, using power‐laws with an exponential cutoff yields the best results as it provides superior fits in the tail of the cloud size distributions. For clouds identified using a combination of water content and updraft velocity thresholds in the free troposphere, no substantial improvement over pure power‐laws can be found when considering more complex two‐parameter distributions. In this context however, exponential distributions provide results that are as good as, if not better than power‐laws. Finally, it is demonstrated that the emergence of scale free behaviors in cloud size distributions is related to exponentially distributed cloud cores merging as they are brought closer to each other by underlying organizing mechanisms. Plain Language Summary: Clouds constitute an important element of the climate system reflecting incoming solar radiation and emitting infra‐red radiation that heats the atmosphere. The net radiative impact of clouds however depends on many factors including their size. It is thus of prime importance to characterize the size of clouds, in particular convective clouds, and understand the underlying processes controlling them. In this study, a numerical model is used to simulate two convective situations at horizontal resolutions providing a fine description of cloud processes. After identifying individual clouds and calculating their size, statistical methods are employed to characterize the cloud size distributions. Depending on the situation, cloud size distributions are found to be best represented by either power‐laws with an exponential cutoff or exponential functions. Pure power‐laws, which constitute the most popular model used to represent cloud size distributions, are generally found to yield poorer fits. Finally, it is demonstrated that power‐laws in cloud size distributions emerge when individual cloud cores, that are exponentially distributed in size, are brought closer to each other and merge as the cloud ensemble organizes. Key Points: A combination of statistical methods is used to fit cloud size distributions from two simulated convective cloud ensemblesDepending on the situation, exponential distributions and power‐laws with an exponential cutoff may constitute superior alternatives to pure power‐lawsThe merging of individual cloud cores is found to control the emergence of power‐law cloud size distributions [ABSTRACT FROM AUTHOR]

Dynamical cores used to study the circulation of the atmosphere employ various numerical methods ranging from finite‐volume, spectral element, global spectral, and hybrid methods. In this work, we explore the use of Flux‐Differencing Discontinuous Galerkin (FDDG) methods to simulate a fully compressible dry atmosphere at various resolutions. We show that the method offers a judicious compromise between high‐order accuracy and stability for large‐eddy simulations and simulations of the atmospheric general circulation. In particular, filters, divergence damping, diffusion, hyperdiffusion, or sponge‐layers are not required to ensure stability; only the numerical dissipation naturally afforded by FDDG is necessary. We apply the method to the simulation of dry convection in an atmospheric boundary layer and in a global atmospheric dynamical core in the standard benchmark of Held and Suarez (1994, https://doi.org/10.1175/1520-0477(1994)075〈1825:apftio〉2.0.co;2). Plain Language Summary: Numerical models cannot explicitly represent all degrees of freedom that characterize atmospheric flows due to limitations in computing power. One must allocate the available computational degrees of freedom to reduce the degradation of the solution. In this work, we explore the use of the discontinuous Galerkin numerical method, a hybrid approach that combines the accuracy of spectral methods with the flexibility of finite volume methods. We apply it to idealized dry atmospheric simulations and show that the method is robust and incorporates physical principles to best account for unresolved processes. Key Points: The Flux‐Differencing Discontinuous Galerkin (FDDG) method is a robust numerical discretization for geophysically relevant configurationsFDDG allows for a computationally stable total energy formulation of the compressible Euler equations with gravity and rotationFDDG simulates a dry convective boundary layer and the atmospheric general circulation using only numerical dissipation [ABSTRACT FROM AUTHOR]

The oceanic Meridional Overturning Circulation (MOC) plays a key role in the climate system, and monitoring its evolution is a scientific priority. Monitoring arrays have been established at several latitudes in the Atlantic Ocean, but other latitudes and oceans remain unmonitored for logistical reasons. This study explores the possibility of inferring the MOC from globally‐available satellite measurements via machine learning (ML) techniques, using the ECCOV4 state estimate as a test bed. The methodological advantages of the present approach include the use purely of available satellite data, its applicability to multiple basins within a single ML framework, and the ML model simplicity (a feed‐forward fully connected neural network (NN) with small number of neurons). The ML model exhibits high skill in MOC reconstruction in the Atlantic, Indo‐Pacific, and Southern Oceans. The approach achieves a higher skill in predicting the model Southern Ocean abyssal MOC than has previously been achieved via a dynamically‐based approach. The skill of the model is quantified as a function of latitude in each ocean basin, and of the time scale of MOC variability. We find that ocean bottom pressure generally has the highest reconstruction skill potential, followed by zonal wind stress. We additionally test which combinations of variables are optimal. Furthermore, ML interpretability techniques are used to show that high reconstruction skill in the Southern Ocean is mainly due to (NN processing of) bottom pressure variability at a few prominent bathymetric ridges. Finally, the potential for reconstructing MOC strength estimates from real satellite measurements is discussed. Plain Language Summary: The Meridional Overturning Circulation (MOC) plays a key role in the exchange of heat and chemical constituents between oceans globally, and between the atmosphere and the deep ocean. However, it is currently directly monitored only at a handful of different latitudes in the Atlantic Ocean. Using an ocean simulation constrained by available measurements, we examine the feasibility of monitoring the MOC indirectly using measurements made by satellites. We train a so‐called "neural network" to learn relations between the MOC and satellite‐observable ocean properties, such as pressure at the sea floor. These relations are used to produce reconstructions of the simulated MOC derived from the satellite‐observable ocean properties alone, and to test which satellite‐observable ocean properties and regions may be most impactful in MOC reconstruction. We find that this approach yields high skill in reconstructing the MOC over much of the ocean, suggesting that this approach could be transferred to indirectly monitor the MOC in nature. Key Points: The Meridional Overturning Circulation (MOC) is a key ocean circulation pattern, which is under‐observedWe use an ocean state estimate to test the feasibility of inferring the MOC based solely on satellite observationsA unified machine learning methodology shows high skill in MOC reconstruction as a function of latitude and basin [ABSTRACT FROM AUTHOR]

Pollen, one type of primary biological aerosol particle (PBAP), is emitted from the terrestrial biosphere and can undergo physical changes in the atmosphere via particle rupture. To examine the fate of pollen and its atmospheric processing, a pollen emission and transport scheme is coupled to the Weather Research and Forecasting Model with Chemistry (WRF‐Chem). We simulate the emission of pollen and its impacts on the cloud properties and precipitation in the Southern Great Plains from 12 to 19 April 2013, a period with both high pollen emissions and convective activity. We conduct a suite of ensemble runs that simulate primary pollen and three different pollen rupture mechanisms that generate subpollen particles, including (a) high humidity‐induced surface rupture, (b) high humidity‐induced in‐atmosphere plus surface rupture, and (c) lightning‐induced rupture, where in‐cloud and cloud‐to‐ground lightning strikes trigger pollen rupture events. When relative humidity is high (>80%), coarse primary pollen (∼1 μg m−3) is converted into fine subpollen particles (∼1.2e−4 μg m−3), which produces 80% more subpollen particles than lightning‐induced rupture. The in‐atmosphere humidity‐driven rupture predominantly produces subpollen particles, which is further enhanced during a frontal thunderstorm. During strong convection, vertical updrafts lift primary pollen and subpollen particles (∼0.5e−4 μg m−3) to the upper troposphere (∼12 km) and laterally transports the ruptured pollen in the anvil top outflow. In regions of high pollen and strong convection, ruptured pollen can influence warm cloud formation by decreasing low cloud (<4 km) cloud water mixing ratios and increasing ice phase hydrometeors aloft (>10 km). Plain Language Summary: Biological aerosols like pollen are released from the terrestrial biosphere into the atmosphere and affect atmospheric processes, hydrology, and climate. For example, large primary pollen ruptures in different atmospheric conditions to produce multiple small‐sized pollen fragments. Moreover, these ruptured particles can trigger thunderstorm asthma. In this study, a Weather Research and Forecasting Model with Chemistry was used to evaluate the fate of pollen in the atmosphere. Model simulations indicate that the three pollen rupture mechanisms, including high humidity‐induced surface rupture, in‐atmosphere plus surface rupture, and lightning‐induced rupture, influence the overall pollen load over the Southern Great Plains during convective days with high pollen emissions. The SPP are produced in the highest concentrations in the atmosphere and on the surface due to the high relative humidity‐induced‐rupture. Even though in‐cloud and cloud‐to‐ground lightning strikes trigger pollen rupture events, they cannot produce as much as humidity‐induced ruptures. The ruptured pollen is transported to higher altitudes by vertical updrafts, horizontally through the outflows from the top of the clouds, and finally to the surface by the downdraft. In regions of high pollen and strong convection, ruptured particles can further alter the hydrometeors and influence cloud formation. Key Points: The greatest amount of subpollen particles is formed from surface and in‐atmosphere humidity‐driven primary pollen ruptureVertical updrafts lift pollen to the upper troposphere (∼12 km) during convective eventsRuptured pollen can reduce cloud water mixing ratios and slightly increase ice phase hydrometeors aloft [ABSTRACT FROM AUTHOR]

Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed. Plain Language Summary: Land‐atmosphere interactions such as soil moisture‐precipitation feedback occur at a wide range of spatial scales. For example, spatial variability of surface fluxes such as radiation and precipitation can influence surface latent and sensible heat fluxes, which further influence turbulent mixing processes in the boundary layer and cloud and precipitation. Current global climate models (GCMs) have difficulties in representing land‐atmosphere interactions at scales smaller than the GCM grid (subgrid scales). To meet this challenge, we employ a novel framework, called multiple atmosphere multiple land (MAML), in a superparamaterized version of E3SM (SP‐E3SM) in which a cloud resolving model is embedded within each GCM grid to better resolve clouds and convection. MAML explicitly simulates the subgrid variabilities of land states and fluxes at scale of ∼1 km and provides feedback to the atmosphere. The use of MAML is shown to have a limited effect on the strength of land‐atmosphere coupling due to the current one‐way subgrid land‐atmosphere interaction setup. Despite the limited effect, MAML increases the rainfall in India, Amazon, and Central Africa, which reduces the dry bias in the model. Key Points: A multiple atmosphere multiple land (MAML) framework of land‐atmosphere coupling is implemented in the super‐parameterized E3SM (SP‐E3SM)MAML increases precipitation in India, Central Africa, and Amazon by increasing water vapor and hence large‐scale moisture convergenceMAML's impact on land‐atmosphere interactions is limited by the current setup of one‐way coupling on cloud‐resolving scales [ABSTRACT FROM AUTHOR]

The meandering jet streams of the Northern Hemisphere influence the weather for more than half of Earth's population, so it is imperative that we improve our understanding of their behavior and how they respond to climate change. Here, we describe a novel laboratory model for a meandering zonal jet. This model comprises a large rotating annulus with a series of topographic ridges, and an imposed radial vorticity flux. Flow interactions with the topographic ridges operate to concentrate the zonal transport into a narrow jet, which supports the development and propagation of Rossby waves. We investigate the dynamics of the jet for a range of rotation rates, imposed radial vorticity fluxes, and topographic ridge configurations. The circulations are classified into two distinct regimes: predominantly zonal or predominantly meandering. The flow regime can be quantified by the ratio of the Ekman dissipation and jet advection timescales, which gives an indication of whether disturbances arising from the flow‐topography interaction are dissipated faster than the time taken to circuit the annulus; if not, these disturbances will reencounter the topography, and thus be reinforced and amplified. For predominantly zonal flows, the radial vorticity flux is split equally between the standing meanders and transient eddies. For predominantly meandering flows, standing meanders perform 79% of the radial vorticity flux, with 18% accommodated by the transient eddies. Our experiments indicate that the Arctic amplification associated with climate change will tend to favor predominantly zonal flow conditions, suggesting a reduced occurrence of atmospheric blocking events caused by the jet streams. Plain Language Summary: Jet streams are narrow, meandering bands of intense eastward winds circling Earth in the upper atmosphere. They are a prominent dynamical feature of Earth's climate system and have major implications for navigation and weather prediction. Modeling the jet streams is important for understanding their dynamics and how they might be affected by climate change. Here, we describe a new approach for laboratory experiments to model meandering jets like the jet streams. The laboratory circulations can be classified into two distinct flow states: one which is predominantly eastward or one which has substantial north‐south meanders in addition to its eastward flow. The flow state of a particular experiment is shown to depend on the relative strength of the forcing and the rate at which the jet variability is dissipated. Key Points: We use a novel laboratory experiment to investigate zonal jet dynamics and distinguish between standing meanders and transient eddiesFlows occupy two distinct regimes; predominantly zonal, or predominantly meandering, depending on the timescales of forcing and dissipationFor predominantly meandering flow, the standing meanders perform 79% of meridional tracer transport, with 18% by transient eddies [ABSTRACT FROM AUTHOR]

This article introduces the second generation CMCC Earth System Model (CMCC‐ESM2) that extends a number of marine and terrestrial biogeochemical processes with respect to its CMIP5 predecessor. In particular, land biogeochemistry was extended to a wider set of carbon pools and plant functional types, along with a prognostic representation of the nitrogen cycle. The marine ecosystem representation was reshaped toward an intermediate complexity of lower trophic level interactions, including an interactive benthic compartment and a new formulation of heterotrophic bacterial population. Details are provided on the model setup and implementation for the different experiments performed as contribution to the sixth phase of the Coupled Model Intercomparison Project. CMCC‐ESM2 shows an equilibrium climate sensitivity of 3.57°C and a transient climate response of 1.97°C which are close to the CMIP5 and CMIP6 multi‐model averages. The evaluation of the coupled climate‐carbon response in the historical period against available observational datasets show a consistent representation of both physical and biogeochemical quantities. However, the land carbon sink is found to be weaker than the current global carbon estimates and the simulated marine primary production is slightly below the satellite‐based average over recent decades. Future projections coherently show a prominent global warming over the northern hemisphere with intensified precipitations at high latitudes. The expected ranges of variability for oceanic pH and oxygen, as well as land carbon and nitrogen soil storage, compare favorably with those assessed from other CMIP6 models. Plain Language Summary: Earth System Models integrate our knowledge on the underlying physical and biogeochemical mechanisms that drive or influence the global climate and the biosphere over the land and in the ocean. These models are used to provide realistic estimates of climate variability and its response to perturbations in the chemical constituents of the atmosphere and modifications of the terrestrial surface. This work describes the science at the base of the second generation Earth System Model developed at the Euro‐Mediterranean Centre on Climate Change and the major results obtained from the simulation of historical (from the pre‐industrial period until present) and different future scenarios up to 2100 in the context of the sixth Coupled Model Intercomparison Project. The model provides a solid representation of the present‐day physical climate and biosphere dynamics in comparison to available observations and data reconstruction of the recent past. The projected global warming signal and carbon accumulation within terrestrial and oceanic systems under future climate scenarios are comparable to the findings of other models involved in the sixth intercomparison project. Key Points: This work introduces the second generation CMCC Earth System Model (CMCC‐ESM2) and its configuration for CMIP6Estimated climate sensitivity and carbon‐climate feedbacks are similar to average of CMIP5 and locate in the lower end of CMIP6Both climate and biogeochemical dynamics are assessed through the comparison with observations and previous literature findings [ABSTRACT FROM AUTHOR]

Tropical anvil clouds are an important player in Earth's climate and climate sensitivity, but simulations of anvil clouds are uncertain. Here we identify and investigate one source of uncertainty by demonstrating a marked increase of anvil cloud fraction with resolution in cloud‐resolving simulations of radiative‐convective equilibrium. This increase in cloud fraction can be traced back to the resolution dependence of horizontal mixing between clear and cloudy air. A mixing timescale is diagnosed for each simulation using the cloud fraction theory of Seeley, Jeevanjee, Langhans, and Romps (2019) (https://doi.org/10.1029/2018GL080747) and is found to scale linearly with grid spacing, as expected from a simple scaling law. Thus mixing becomes more efficient with increasing resolution, generating more evaporation in middle and lower tropospheric updrafts. This decreases their precipitation efficiency (PE), thereby increasing their overall mass flux, leading to greater detrainment at the anvil level and hence higher anvil cloud fraction. The decrease in PE also yields a marked increase in relative humidity with resolution. Plain Language Summary: High anvil‐shaped clouds occurring in tropical thunderstorms are an important player in the climate system, but our understanding and simulations of this phenomena is uncertain. Here we show that the areal coverage of such "anvil" clouds in idealized simulations of the tropics is highly dependent on how finely the tropical atmosphere is represented in the simulation. Finer resolutions yield more evaporation and less rainfall per cloud, so then more clouds are required to provide the rainfall which the atmosphere requires, leading to an increase of cloudiness with resolution. Key Points: Cloud‐resolving simulations of radiative‐convective equilibrium exhibit a marked increase of anvil cloud fraction with resolutionThis sensitivity is closely related to the resolution‐dependence of evaporation and precipitation efficiencyThe root of these sensitivities is the resolution‐dependence of mixing between clear and cloudy air [ABSTRACT FROM AUTHOR]

A new framework for the development of global high resolution models using a vertical‐meridional, two‐dimensional pole‐to‐pole domain is proposed. Compared with the three‐dimensional global model, the two‐dimensional framework can simulate cloud‐scale convection at significantly reduced computational cost, data volume, and turn‐around time, and accelerate the development phase. Although idealized, it allows for simulation of various clouds and scale interactions under a wide range of environmental conditions, even though zonal waves such as mid latitude storms cannot be simulated. Physics schemes can be tested and assessed over a full array of simultaneously occurring cloud regimes. The framework can serve as an intermediate step between a one‐dimensional simulation and a full three‐dimensional global model. Using this framework, we analyze the resolution dependencies of simulated clouds and the Hadley circulation in a range of global high resolution models by performing multiple 1000‐day simulations; for some resolutions tested, for example, 2 km horizontal resolution, three‐dimensional simulation is not feasible at the present time. Analysis shows that both horizontal and vertical resolution determine the properties of the Hadley circulation and clouds, and that the differences among the simulated Hadley circulations are understood as differences in scale interactions influenced by resolution. Plain Language Summary: The resolution of global climate models has increased dramatically in recent years. While an increase in resolution usually provides improvements in the numerical solution, it imposes an enormous increase in computational cost. This becomes more serious in the model development phase because numerous tests are required to assess the performance, ideally in a similar computational domain and resolution as the target simulations. We approach the problem by applying a vertical‐meridional, two‐dimensional framework with a computational domain that extends from the south‐pole to the north‐pole and includes the entire troposphere. By removing the zonal extension, we can significantly reduce computation time, and easily increase model resolution and the number of test cases. This simplified framework is capable of representing the Hadley circulation, which is a fundamental feature of Earth's atmosphere, and allows us to study internal feedbacks in the Hadley circulation as well as effects of model configurations on the simulated atmosphere. Key Points: New framework simulates clouds and Hadley circulation in a vertical‐meridional, two‐dimensional domainUsefulness of the framework is presented by a resolution sensitivity study with multiple 1000‐day simulationsScale interactions differ between high and low resolution resulting in single or split Hadley circulations [ABSTRACT FROM AUTHOR]

In this study, we find significant sensitivity to the choice of time step for the Energy Exascale Earth System Model's atmospheric component, leading to large decreases in the magnitude of cloud forcing when the time step is reduced to 10 s. Reducing the time step size for the microphysics increases precipitation, leading to a drying of the atmosphere and an increase in surface evaporation. This effect is amplified when the microphysics is substepped together with other cloud physics processes. Coupling the model's dynamics and physics more frequently reduces cloud fraction at lower altitudes, while producing more cloud liquid at higher altitudes. Reducing the deep convection time step also reduces low cloud mass and cloud fraction. Together, these results suggest that cloud physics in a global circulation model can depend strongly on time step and, in particular, on the frequency with which cloud‐related processes are coupled with each other and with the model dynamics. Plain Language Summary: Computer simulations of the Earth's atmosphere take the state of the atmosphere at one point in time, then predict the state of the atmosphere a short interval of time into the future. The length of this time interval is known as the "time step". By doing this repeatedly, models can produce a simulated history of the atmosphere for years or even centuries. A smaller time step size requires more computer time but should ideally lead to more accurate results. In this study, we reduce the time step for the atmosphere in the Energy Exascale Earth System Model from half an hour to 10 s. The simulation with a smaller time step has more rain, which removes water from the atmosphere and reduces the fraction of the Earth's surface that is covered by clouds. We also experiment with changing the time step for only some parts of the model and not others. We find that the effects of the time step size on the model are related mostly to the frequency of coupling between processes rather than the time step used for any individual processes. Key Points: Changing time step has as large an effect on Energy Earth System Model, version 1's climate as doubling horizontal resolutionShorter time steps dry the atmosphere and increase surface precipitationCoupling between processes is the most important source of model time step sensitivity [ABSTRACT FROM AUTHOR]

In an attempt to advance the understanding of the Earth's weather and climate by representing deep convection explicitly, we present a global, four‐month simulation (November 2018 to February 2019) with ECMWF's hydrostatic Integrated Forecasting System (IFS) at an average grid spacing of 1.4 km. The impact of explicitly simulating deep convection on the atmospheric circulation and its variability is assessed by comparing the 1.4 km simulation to the equivalent well‐tested and calibrated global simulations at 9 km grid spacing with and without parametrized deep convection. The explicit simulation of deep convection at 1.4 km results in a realistic large‐scale circulation, better representation of convective storm activity, and stronger convective gravity wave activity when compared to the 9 km simulation with parametrized deep convection. Comparison of the 1.4 km simulation to the 9 km simulation without parametrized deep convection shows that switching off deep convection parametrization at a too coarse resolution (i.e., 9 km) generates too strong convective gravity waves. Based on the limited statistics available, improvements to the Madden‐Julian Oscillation or tropical precipitation are not observed at 1.4 km, suggesting that other Earth system model components and/or their interaction are important for an accurate representation of these processes and may well need adjusting at deep convection resolving resolutions. Overall, the good agreement of the 1.4 km simulation with the 9 km simulation with parametrized deep convection is remarkable, despite one of the most fundamental parametrizations being turned off at 1.4 km resolution and despite no adjustments being made to the remaining parametrizations. Plain Language Summary: We present the world's first global simulation of an entire season of the Earth's atmosphere with 1.4 km average grid spacing and the top of the modeled atmosphere as high as 80 km. Albeit only a single realization due to its considerable computational cost, the resulting model output provides a reference and guidance for future simulations. For illustration we compare to simulations at 9 km grid spacing that represent the state of the art in numerical weather prediction and are still considerably finer when compared to models that are used for climate projections today. Thanks to its unprecedented detail, the simulation output will support future model development and satellite mission planning and may be seen as a prototype contribution to a future digital twin of our Earth. Key Points: A unique simulation with 1.4 km average grid spacing is presented for model development and process evaluationThe 1.4 km simulation shows remarkable fidelity with respect to the well‐calibrated simulation at 9 km with parametrized deep convectionSwitching off deep convection at a too coarse resolution (9 km) generates too strong convective gravity waves [ABSTRACT FROM AUTHOR]

Abstract The objective of this paper is to develop an optimized implicit‐explicit (IMEX) Runge‐Kutta scheme for atmospheric applications focusing on stability and accuracy. Following the common terminology, the proposed method is called IMEX‐SSP2(2,3,2), as it has second‐order accuracy and is composed of diagonally implicit two‐stage and explicit three‐stage parts. This scheme enjoys the Strong Stability Preserving (SSP) property for both parts. This new scheme is applied to nonhydrostatic compressible Boussinesq equations in two different arrangements, including (i) semiimplicit and (ii) Horizontally Explicit‐Vertically Implicit (HEVI) forms. The new scheme preserves the SSP property for larger regions of absolute monotonicity compared to the well‐studied scheme in the same class. In addition, numerical tests confirm that the IMEX‐SSP2(2,3,2) improves the maximum stable time step as well as the level of accuracy and computational cost compared to other schemes in the same class. It is demonstrated that the A‐stability property as well as satisfying “second‐stage order” and stiffly accurate conditions lead the proposed scheme to better performance than existing schemes for the applications examined herein.

This study describes the formulation and application of a partial coupling method that disentangles the coupling between the atmosphere and ocean and isolates the atmosphere‐ and ocean‐driven components of the coupled climate interactions. In contrast to strategies using stand‐alone simulations with prescribed atmosphere or ocean states, the climate components in the partially coupled method remain coupled, but the impact of ocean circulation changes is removed from the air‐sea interaction using temperature‐like tracers. The partially coupled simulation thereby suppresses the ocean‐driven interaction and isolates an atmosphere‐driven interaction only. The ocean‐driven component can be inferred by comparing climate response in the partially coupled simulation with that of a standard fully coupled one. The partial coupling approach is applied to decompose the fully coupled climate response to CO2 quadrupling into atmosphere‐ and ocean‐driven components. The linearity of the decomposition is validated by simulating the ocean‐driven response using another complimentary partially coupled simulation forced only with the atmosphere‐driven anomalous surface fluxes. A comparison of the two partially coupled simulations with the fully coupled simulation indicates that the sum of the atmosphere‐ and ocean‐driven components accurately describes the fully coupled response. The decomposition identifies several robust atmosphere‐ and ocean‐driven features of the global warming and provides new insights into the impacts of atmospheric feedbacks on the Atlantic overturning circulation and sea ice response to CO2 increase. Plain Language Summary: Climate change and variability include components that are produced by both atmospheric and ocean changes. This study describes a method that disentangles the interaction between the atmosphere and ocean and separates out the components of climate change that are caused by atmospheric and oceanic changes. Two climate experiments with quadrupled CO2 concentration are used to demonstrate this method. In one experiment the component of the ocean temperature change that is produced by ocean circulation changes is prevented from interacting with the atmosphere at the surface, so that the climate change in this experiment is caused by the atmospheric CO2 increase alone. Typical coupling strategies allow both the interactions caused by atmospheric CO2 increases and ocean circulation changes to occur. The difference between the two experiments isolate the climate change component that is caused by ocean circulation changes. An additional experiment was used to reproduce the climate change component caused by the ocean circulation changes, in order to show that the two components sum up to the total climate change when all interactions caused by the atmosphere and ocean are allowed. Key Points: A new method is described providing insight into the role of the atmosphere and ocean in the coupled climate response or variabilityA careful verification shows the ocean/ atmosphere‐driven decomposition of the fully coupled response to CO2 forcing is largely linearThe decomposition improves attribution and provides time evolution and spatial distribution of atmosphere‐ and ocean‐driven responses [ABSTRACT FROM AUTHOR]

Simultaneous calculation of atmospheric processes is faster than calculating processes one at a time. This type of parallelism is beneficial or perhaps even necessary to provide good performance on modern supercomputers, which achieve faster performance through increased processor count rather than improved clock speed. The scalability of the Energy Exascale Earth System Model (E3SM) Atmosphere Model (EAM) is limited by the fluid dynamics which scales up to the number of mesh cells in the global mesh. In contrast, the suite of physics parameterizations in EAM is scalable up to the total number of physics columns, which is an order of magnitude greater than the number of mesh cells. A proposed solution to unlocking the greater potential performance from the physics suite is to solve the physics and dynamics in parallel. This work represents a first attempt at parallel splitting of the grid‐scale fluid dynamics model and the subgrid‐scale physics parameterizations in a global atmosphere model. We will demonstrate that switching to parallel physics‐dynamics coupling extends the scalability of the EAM to up to 3 times the previous peak scalability limit and is up to 20% faster than the sequentially split coupling at the highest core counts and the same time step. Decadal simulations of both coupling approaches show very little impact to the model climate. This improved performance does not come without drawbacks, however. Parallel splitting requires a shorter time step and other modifications which largely offset performance gains. A mass fixer is required for conservation. Techniques for mitigating these issues are also discussed. Plain Language Summary: In order to improve the computational performance of global atmosphere models, the community must look at the impact of switching to more parallel splitting of individual processes, thus increasing the amount of work that can be distributed over a large number of computational units. This work looks at the impact to both accuracy and computational performance when the two most expensive components in the Energy Exascale Earth System Model (E3SM) atmosphere model (EAM) are coupled in parallel instead of sequentially. The results are mixed: Model climate does not appear to be impacted by the switch in coupling; however, the performance gains were modest and fall short of the predicted improvements. This work discusses exactly why the performance gains fell short and points out the major infrastructure changes that would be required to extract the maximum performance gains. The results here will be helpful to any effort to adopt parallel splitting in any multiphysics model. Key Points: Parallel coupling of physics and dynamics improves model scalabilityMass conservation and stability are major concerns for parallel splitting implementationParallel splitting may require a more restrictive model time step [ABSTRACT FROM AUTHOR]

Plant leaves play an important role in water, carbon, and energy exchanges between terrestrial ecosystems and atmosphere. Assimilating remotely sensed leaf area index (LAI) into land surface models is a promising approach to improve our understanding of those processes. Toward this goal, this study uses the Community Land Model with carbon and nitrogen components (CLM4CN) coupled with the Data Assimilation Research Testbed (DART). Global Land Surface Satellite (GLASS) LAI data are assimilated via the Ensemble Adjustment Kalman Filter. A random 40‐member atmospheric forcing ensemble is used to drive the CLM4CN to provide background error covariance. The results show that assimilating GLASS LAI and updating both LAI and leaf C/N is an effective method to provide a high‐accuracy estimate of LAI. The simulations always systematically overestimate LAI, especially in low‐latitude regions, with the largest bias up to 5 m2/m2, which are effectively corrected in the analyzed LAI, with the bias reduced to ±1 m2/m2. Significantly improved regions are located in central Africa, Amazonia, southern Eurasia, northeastern China, and western Europe, where evergreen/deciduous forests and mixed forests are dominant. Except for the temperate zone in the Southern Hemisphere, the analyzed LAI can well represent seasonal variations. The most pronounced assimilation impact in low‐latitude regions is attributed to large initial forecast error covariance and sufficient background errors. The MOD 16 evapotranspiration estimates and upscaled gross primary production have been used to evaluate the assimilation impact, which highlight neutral to highly positive improvement. Key Points: Assimilating GLASS LAI and updating LAI and leaf C/N is an effective method to provide a high‐accuracy estimate of LAI using DART/CLM4CNAssimilation is more effective in growing season due to large initial forecast error covariances and sufficient background errorsA clear added value of the assimilation has been highlighted based on GPP and evapotranspiration observation‐based estimates [ABSTRACT FROM AUTHOR]

The Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy's Energy Exascale Earth System Model is described. The model began as a fork of the well‐known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of light‐absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and ground‐based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosol‐cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to "brute force" tune the high‐resolution configurations, so short‐term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models. Plain Language Summary: This study provides an overview of a new computer model of the Earth's atmosphere that is used as one component of the Department of Energy's latest Earth system model. The model can be used to help understand past, present, and future changes in Earth's behavior as the system responds to changes in atmospheric composition (like pollution and greenhouse gases), land, and water use and to explore how the atmosphere interacts with other components of the Earth system (ocean, land, biology, etc.). Physical, chemical, and biogeochemical processes treated within the atmospheric model are described, and pointers to previous and recent work are listed to provide additional information. The model is compared to present‐day observations and evaluated for some important tests that provide information about what could happen to clouds and the environment as changes occur. Strengths and weaknesses of the model are listed, as well as opportunities for future work. Key Points: A brief description and evaluation is provided for the atmospheric component of the Department of Energy's Energy Exascale Earth System ModelModel fidelity has generally improved compared to predecessors and models participating in past international model evaluationsStrengths and weaknesses of the model, as well as opportunities for future work, are described [ABSTRACT FROM AUTHOR]

We demonstrate that two‐way nesting significantly improves the structure of simulated hurricane in an atmospheric general circulation model. Two sets of 30‐day hindcast experiments are conducted, one with the global‐uniform‐resolution (approximately 25‐km nominal horizontal resolution) and the other with a regionally refined two‐way nest (approximately 8 km over the tropical North Atlantic). The increase in the horizontal resolution on the nested grid improves the representation of storm intensity and intensification rate. When normalized by the radius of maximum wind (RMW), composite hurricane structures are generally similar in both simulations and compare well to observations. However, the hurricanes in the globally uniform configuration have much larger RMWs than observed, while those in the two‐way‐nested configuration have more realistic RMWs. We also find that the representation of the RMW has a critical impact on the simulation of inertial stability and boundary‐layer convergence in the inner‐core region. The more realistic inner‐core size (indicated by RMW) and structure are possible reasons for the improved intensification rates in the two‐way‐nested configuration. Key Points: Two‐way nesting is an efficient way to better‐resolve mesoscale features over a selected region in global modelsThe regionally refined 8‐km grid substantially improves the hurricane inner‐core size and structure representationsThe realistic representation of inner‐core inertial stability helps improve the simulated hurricane intensification processes [ABSTRACT FROM AUTHOR]

A model for the spread of a wildfire is developed within the U.K. Met Office Unified Model (UM) and used to simulate the Kilmore East fire complex (in southeastern Australia) on Black Saturday (7 February 2009). The UM is configured with four nests with horizontal grid spacings of 4 km, 1.5 km, 444 m, and 144 m. In the first simulation, the UM simply provides predictions of the near‐surface conditions for the wildfire model with no feedbacks to the atmosphere from the fire. In the second, the atmosphere and fire are coupled, allowing the fire to affect the local‐scale weather. The agreement between the coupled simulation and the observed fire behavior is reasonably good. For example, the area burnt is approximately 80% of the actual area burnt. However, such agreement is achieved only by coupling the fire to the atmosphere and, importantly, by igniting 18 additional fires at the times and places the long‐range transport of burning material (spotting) was observed. Without coupling the burnt area is about half of that observed. The calculations reported here suggest that the behavior and spread of fires like the Kilmore East fire are predictable but only when long‐range spotting is included and the atmosphere and fires are coupled. Additional numerical experiments with coarser grids suggest that, although the details of the fire spread are lost, a grid spacing of 1.5 km may be sufficient to simulate the main features of the fire spread. Key Points: A wildfire spread model is coupled to the U.K. Met Office Unified Model and used to simulate the Kilmore East fireThere is reasonable agreement between the simulation and the actual Kilmore East fireSuch agreement relies on the atmosphere and the fire being coupled and accurate but unforecastable, long‐range spotting [ABSTRACT FROM AUTHOR]

Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability and with respect to the temporal evolution at the 26° N latitude. Key Points: Five initialization and ensemble generation methods are tested with respect to their impact on the skill of a decadal prediction systemResults show that the tested methods provide an added value for the prediction skill as compared to the reference prediction systemThe study deals with dynamical consistency during initialization and ocean initial state uncertainty [ABSTRACT FROM AUTHOR]

The representation of stratocumulus by the atmospheric component of the Centre National de Recherches Météorologiques model version 6 (CNRM‐CM6‐1) is assessed. An Atmospheric Model Intercomparison Project‐type simulation is first used to document the main model errors, namely, a large lack of stratocumulus over the eastern part of tropical ocean basins. Short‐term hindcasts, following the Transpose‐Atmospheric Model Intercomparison Project framework, are then used to better assess the timescales associated with the cloud bias growth and to highlight the processes leading to them. These biases are shown to appear within only a few hours, independently of errors in the large‐scale circulation that set up within a few days. Key processes underlying the low‐cloud formation are thus mainly local and, to the first order, do not imply any feedback between the model physics and the large‐scale dynamics. As a consequence, short‐term hindcasts provide a relevant framework to investigate whether the low‐cloud underestimate is related to errors in the large‐scale state variables or to errors in the model parameterizations. Sensitivity tests highlight that the involved processes arise (1) mostly from misrepresentation of subgrid effects on cloud formation and (2) partly from biases in drying induced by cloud‐top entrainment mixing. Improvements in the representation of stratocumulus in the CNRM‐CM6‐1 model might thus be expected by including a more realistic subgrid‐scale temperature and moisture distribution, that would link convective and turbulence processes. Finally, this study confirms the potential of short‐term hindcasts, which provide a trustworthy framework to evaluate and develop climate model parameterizations. Key Points: Short‐term hindcasts help to disentangle timescales of marine stratocumulus cloud bias growth in the CNRM‐CM6‐1 modelBiases in low clouds are mostly related to errors in the subgrid distribution of temperature and humidity and the cloud‐top entrainmentAlong with single‐columns models, short‐term hindcasts are a complementary testbed for cloud parameterization development [ABSTRACT FROM AUTHOR]

We proposed a surface flux scheme based on the Monin‐Obukhov similarity for finite volume models. The scheme solves a negative bias existing in conventional surface flux schemes. In the conventional schemes, the model prognostic variables are used as the center point value in the model grid to estimate the surface fluxes even though the variables are volume‐averaged values in finite volume models. The substitutional use of the volume‐averaged model variables for the point value results in underestimation of friction velocity and temperature scale and consequent surface fluxes. The underestimation is more remarkable for a thinner layer, namely, higher resolution, and larger roughness length. In the proposed scheme, the model variables are used as volume‐averaged quantities. Large‐eddy simulation and single‐column model experiments on a planetary boundary layer show that the surface fluxes calculated with the conventional scheme are, in fact, smaller compared to those with the proposed scheme. The proposed scheme allows better estimation of not only surface fluxes but also surface atmospheric diagnostics. Improved estimation of the surface flux with this scheme will contribute to improving the reliability of several kinds of simulations (e.g., those related to the atmosphere, ocean, ice sheet, and sea ice). Importantly, its impact becomes even more significant for higher‐resolution simulations, which are likely to be more sought after in the future. Key Points: Surface fluxes are underestimated by a conventional similarity scheme in finite volume modelsWe propose a surface flux scheme using volume averaged valuesThe proposed scheme improves surface flux estimation, especially in higher resolution simulations [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory has recently developed two global coupled general circulation models, the Forecast‐oriented Low Ocean Resolution (FLOR) model and the High atmospheric resolution Forecast‐oriented Low Ocean Resolution (HiFLOR) model, which are now being utilized for climate research and seasonal predictions. Compared to their predecessor Coupled Model version 2.1 (CM2.1), the new versions have improved ocean/atmosphere physics and numerics and refinement of the atmospheric horizontal grid from 220 km (CM2.1) to 55 km (FLOR) and 26 km (HiFLOR). Both FLOR and HiFLOR demonstrate greatly improved simulations of the tropical Pacific annual‐mean climatology, with FLOR practically eliminating any equatorial cold bias in sea surface temperature. An additional model experiment (Low Ocean Atmosphere Resolution version 1) using FLOR's ocean/atmosphere physics, but with the atmospheric grid coarsened toward that of CM2.1, is used to further isolate the impacts of the refined atmospheric grid versus the improved physics and numerics. The improved ocean/atmosphere formulations are found to produce more realistic tropical Pacific patterns of sea surface temperature and rainfall, surface heat fluxes, ocean mixed layer depths, surface currents, and tropical instability wave activity; enhance the near‐surface equatorial upwelling; and reduce the intercentennial warm drift of the tropical Pacific upper ocean. The atmospheric grid refinement further improves these features and also improves the tropical Pacific surface wind stress, implied Ekman and Sverdrup transports, subsurface temperature and salinity structure, and heat advection in the equatorial upper ocean. The results highlight the importance of nonlocal air‐sea interactions in the tropical Pacific climate system, including the influence of off‐equatorial surface fluxes on the equatorial annual‐mean state. Implications are discussed for improving future simulations, observations, and predictions of tropical Pacific climate. Key Points: New global models show greatly improved tropical Pacific annual‐mean climateBoth physics improvements and refined atmospheric grids aid the new modelsOff‐equatorial surface fluxes strongly affect the equatorial Pacific climatology [ABSTRACT FROM AUTHOR]

Abstract: The adjoint method is used to calibrate the medium complexity climate model “Planet Simulator” through parameter estimation. Identical twin experiments demonstrate that this method can retrieve default values of the control parameters when using a long assimilation window of the order of 2 months. Chaos synchronization through nudging, required to overcome limits in the temporal assimilation window in the adjoint method, is employed successfully to reach this assimilation window length. When assimilating ERA‐Interim reanalysis data, the observations of air temperature and the radiative fluxes are the most important data for adjusting the control parameters. The global mean net longwave fluxes at the surface and at the top of the atmosphere are significantly improved by tuning two model parameters controlling the absorption of clouds and water vapor. The global mean net shortwave radiation at the surface is improved by optimizing three model parameters controlling cloud optical properties. The optimized parameters improve the free model (without nudging terms) simulation in a way similar to that in the assimilation experiments. Results suggest a promising way for tuning uncertain parameters in nonlinear coupled climate models. [ABSTRACT FROM AUTHOR]

Abstract: An ever increasing community of earth system modelers is incorporating new physical processes into numerical models. This trend is facilitated by advancements in computational resources, improvements in simulation skill, and the desire to build numerical simulators that represent the water cycle with greater fidelity. In this quest to develop a state‐of‐the‐art water cycle model, we coupled HydroGeoSphere (HGS), a 3‐D control‐volume finite element surface and variably saturated subsurface flow model that includes evapotranspiration processes, to the Weather Research and Forecasting (WRF) Model, a 3‐D finite difference nonhydrostatic mesoscale atmospheric model. The two‐way coupled model, referred to as HGS‐WRF, exchanges the actual evapotranspiration fluxes and soil saturations calculated by HGS to WRF; conversely, the potential evapotranspiration and precipitation fluxes from WRF are passed to HGS. The flexible HGS‐WRF coupling method allows for unique meshes used by each model, while maintaining mass and energy conservation between the domains. Furthermore, the HGS‐WRF coupling implements a subtime stepping algorithm to minimize computational expense. As a demonstration of HGS‐WRF's capabilities, we applied it to the California Basin and found a strong connection between the depth to the groundwater table and the latent heat fluxes across the land surface. [ABSTRACT FROM AUTHOR]