Journal of Advances in Modeling Earth Systems最新文献

This study presents the first attempt to simulate a full cycle of the quasi-biennial oscillation (QBO) in a global storm-resolving model (GSRM) that explicitly simulates deep convection and gravity waves instead of parameterizing them. Using the Icosahedral Nonhydrostatic (ICON) model with horizontal and vertical resolutions of about $��5��k�m�$ and $��400��m�$, respectively, we show that an untuned state-of-the-art GSRM is already on the verge of simulating a QBO-like oscillation of the zonal wind in the tropical stratosphere for the right reasons. ICON shows overall good fidelity in simulating the QBO momentum budget and the downward propagation of the QBO jets in the upper QBO domain (25–35 km). In the lowermost stratosphere, however, ICON does not simulate the downward propagation of the QBO jets to the tropopause. This is the result of a pronounced lack of QBO wave forcing, mainly on planetary scales. The lack of planetary-scale wave forcing in the lowermost stratosphere is caused by an underestimation of planetary-scale wave momentum fluxes entering the stratosphere. We attribute this lack of planetary-scale wave momentum fluxes to a substantial lack of convectively coupled equatorial waves (CCEWs) in the tropical troposphere. Therefore, we conclude that in ICON, simulating a realistic spatio-temporal variability of tropical deep convection, in particular CCEWs, is currently the main roadblock toward simulating a reasonable QBO. To overcome this intermediate situation, we propose to aim at an improved explicit simulation of tropical deep convection by retuning the remaining parameterizations of cloud microphysics and vertical diffusion, and by increasing the horizontal resolution.

Distribution and change of freshwater resources is often simulated with global hydrological models. However, owing to process representation limitations and forcing data uncertainties, these model simulations have shortcomings. Combining them with observations via data assimilation, for example, with data from the Gravity Recovery and Climate Experiment (GRACE) mission or streamflow measured at in situ stations is considered to improve the realism of the simulations. We assimilate gridded total water storage anomaly (TWSA) from GRACE into the WaterGAP Global Hydrology Model (WGHM) over the Mississippi River basin via an Ensemble Kalman Filter. Our results agree with previous studies where assimilating GRACE observations nudges TWSA simulations closer to the observations, reducing the root mean square error (RMSE) by 21% compared to an uncalibrated model. However, simulations of streamflow show degeneration at more than 90% of all gauge stations for metrics such as RMSE and correlations; only the annual phase of simulated streamflow improves at half the stations. Therefore, for the first time, we instead assimilated streamflow observations into the WGHM, which improved simulated streamflow at up to nearly 80% of the stations, with normalized RMSE showing improvements of up to 0.1, while TWSA was well-simulated in all metrics. Combining both approaches, that is, jointly assimilating GRACE-derived TWSA and streamflow observations, leads to a trade-off between a good fit of both variables albeit skewed to the GRACE observations. Overall, we speculate that our findings point to limitations of process representation in WGHM hindering consistent flux simulation from the storage history, especially in dry regions.

This study presents the development of a so-called Turbulent Kinetic Energy (TKE)-l, or TKE-l, parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large-scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and the number of parameters has been minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has been paid to the numerical stability and convergence of the TKE equation at large time steps, an essential prerequisite for capturing stable boundary layers in General Circulation Models (GCMs). Tests, parametric sensitivity assessments and preliminary tuning are performed on single-column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are assessed through its implementation in the Laboratoire de Météorologie Dynamique Zoom GCM and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers.

Global modeling of aerosol-particle number and size is important for understanding aerosol effects on Earth's climate and air quality. Fine-resolution global models are desirable for representing nonlinear aerosol-microphysical processes, their nonlinear interactions with dynamics and chemistry, and spatial heterogeneity. However, aerosol-microphysical simulations are computationally demanding, which can limit the achievable global horizontal resolution. Here, we present the first coupling of the TwO-Moment Aerosol Sectional (TOMAS) microphysics scheme with the High-Performance configuration of the GEOS-Chem model of atmospheric composition (GCHP), a coupling termed GCHP-TOMAS. GCHP's architecture allows massively parallel GCHP-TOMAS simulations including on the cloud, using hundreds of computing cores, faster runtimes, more memory, and finer global horizontal resolution (e.g., 25 km × 25 km, 7.8 × 10⁵ model columns) versus the previous single-node capability of GEOS-Chem-TOMAS (tens of cores, 200 km × 250 km, 1.3 × 10⁴ model columns). GCHP-TOMAS runtimes have near-ideal scalability with computing-core number. Simulated global-mean number concentrations increase (dominated by free-tropospheric over-ocean sub-10-nm-diameter particles) toward finer GCHP-TOMAS horizontal resolution. Increasing the horizontal resolution from 200 km × 200–50 km × 50 km increases the global monthly mean free-tropospheric total particle number by 18.5%, and over-ocean sub-10-nm-diameter particles by 39.8% at 4-km altitude. With a cascade of contributing factors, free-tropospheric particle-precursor concentrations increase (32.6% at 4-km altitude) with resolution, promoting new-particle formation and growth that outweigh coagulation changes. These nonlinear effects have the potential to revise current understanding of processes controlling global aerosol number and aerosol impacts on Earth's climate and air quality.

Through the implementation of a streaming filter, output of numerical ocean simulations can be band-pass filtered at tidal frequencies while the model is running, yielding time series of sinusoidal motions consisting of tidal signals in the filter's target frequency band. The filtering algorithm is developed from a system of two ordinary differential equations that represents the motion of a damped harmonic oscillator. The filter's response to a broadband input signal is unity at its target frequency but vanishes toward the low and high frequency limits. The decay of the filter response is controlled by a dimensionless parameter, which determines the filter's bandwidth. As a result, the filter allows signals within a small frequency band around its target frequency to pass through, while blocking signals outside of its target frequency band. In this work, the filtering algorithm is implemented into the barotropic solver of the Modular Ocean Model version 6 (MOM6) for determining the instantaneous tidal velocities of the semi-diurnal and diurnal tides. Utilizing the filters, the frequency-dependent internal wave drag is applied to the semi-diurnal and diurnal frequency bands separately. The simulation results suggest that the performance of the algorithm is consistent with the filter transfer function in Fourier space. Potential applications of the algorithm also include de-tiding the model output for nested regional ocean models, especially those for the purpose of operational forecasting.

The seabed and the water column are tightly coupled in shallow coastal environments. Numerical models of seabed-water interaction provide an alternative to observational studies that require concurrent measurements in both compartments, which are hard to obtain and rarely available. Here, we present a coupled model that includes water column biogeochemistry, seabed diagenesis, sediment transport and hydrodynamics. Our model includes realistic representations of biogeochemical reactions in both seabed and water column, and fluxes at their interface. The model was built on algorithms for seabed-water exchange in the Regional Ocean Modeling System and expanded to include carbonate chemistry in seabed. The updated model was tested for two sites where benthic flux and porewater concentration measurements were available in the northern Gulf of Mexico hypoxic zone. The calibrated model reproduced the porewater concentration-depth profiles and benthic fluxes of O₂, dissolved inorganic carbon (DIC), TAlk, NO₃ and NH₄. We used the calibrated model to explore the role of benthic fluxes in acidifying bottom water during fair weather and resuspension periods. Under fair weather conditions, model results indicated that bio-diffusion in sediment, labile material input and sediment porosity have a large control on the importance of benthic flux to bottom water acidification. During resuspension, the model indicated that bottom water acidification would be enhanced due to the sharp increase of the DIC/TAlk ratio of benthic fluxes. To conclude, our model reproduced the seabed-water column exchange of biologically important solutes and can be used for quantifying the role of benthic fluxes in driving bottom water acidification over continental shelves.

The ocean surface mixed layer plays a crucial role as an entry or exit point for heat, salt, momentum, and nutrients from the surface to the deep ocean. In this study, we introduce a framework to assess the evolution of the mixed layer depth (MLD) for realistic forcings and preconditioning conditions. Our approach involves a physically-based parameter space defined by three dimensionless numbers: λ_s representing the relative contribution of the buoyancy flux and the wind stress at the air-sea interface, R_h the Richardson number which characterizes the stability of the water column relative to the wind shear, and f/N_h which characterizes the importance of the Earth's rotation (ratio of the Coriolis frequency f and the pycnocline stratification N_h). Four MLD evolution regimes (“restratification,” “stable,” “deepening,” and “strong deepening”) are defined based on the values of the normalized temporal evolution of the MLD. We evaluate the 3D parameter space in the context of 1D simulations and we find that considering only the two dimensions (λ_s, R_h) is the best choice of 2D projection of this 3D parameter space. We then demonstrate the utility of this two-dimensional λ_s − R_h parameter space to compare 3D realistic ocean simulations: we discuss the impact of the horizontal resolution (1°, 1/12°, or 1/60°) and the Gent-McWilliams parameterization on MLD evolution regimes. Finally, a proof of concept of using observational data as a truth indicates how the parameter space could be used for model calibration.

We develop a novel approach to detect cloud-subcloud coupling during the cloud life cycle and analyze a large eddy simulation of marine shallow cumulus based on the Barbados oceanographic and meteorological experiment campaign. Our results demonstrate how the activity of sub-cloud coherent updrafts (SCUs) affect the evolution of shallow cloud properties during their life cycles, from triggering to development, and through to dissipation. Most clouds $��\left(��\sim �80�\%��\right)�$ are related to SCUs during their lifetime but not every SCU ($��\sim �20�\%�$ for short-lived ones) leads to cloud formation. The fastest growing SCUs in a relatively moist region are most likely to initiate clouds. The evolution of cloud base mass-flux depends on cloud lifetime. Compared with short-lived clouds, longer lived clouds have longer periods of development, even normalized by the full lifetime, and tend to increase their cloud base mass-flux to a stronger maximum. This is consistent with the evolution of mass flux near the top of SCU, indicating that the development of clouds is closely related to the sub-cloud activity. When the SCUs decay and detach from the lifting condensation level, the corresponding cloud base starts to rise, signifying the start of cloud dissipation, during which the cloud top lowers to approach the rising cloud base. Previous studies have described similar conceptual pieces of this relationship but here we provide a continuous framework to cover all the stages of cloud-subcloud coupling. Our findings provide quantitative evidence to supplement the conceptual model of shallow cloud life cycle and is critical to improve the steady-state assumption in parameterization.

To bridge the gaps between meteorological large-eddy simulation (LES) models and computational fluid dynamics (CFD) models for microscale urban climate simulations, the present study has developed a meteorological LES model for urban areas. This model simulates urban climates across both mesoscale (city scale) and microscale (city-block scale). The paper offers an overview of this LES model, which distinguishes itself from standard numerical weather prediction models by resolving buildings and trees at the microscale simulations. It also differs from standard CFD models by accounting for atmospheric stratification and physical processes. Noteworthy features of this model include: (a) the calculation of long- and short-wave radiations in three dimensions, incorporating multiple reflections within urban canopy layers using the radiosity method, and accounting for building and tree shadows in the simulations; (b) the provision of various heat stress indices (Universal Thermal Climate Index, Wet Bulb Globe Temperature, MRT, THI); (c) the assessment of the efficacy of heat stress mitigation measures such as dry-mist spraying, roadside trees, cool pavements, and green/cool roofs strategies; (d) the capability to run on supercomputers, with the code parallelized in a three-dimensional manner, and the model can also run on a graphics processing unit cluster. Following the introduction of this model, the study confirms its basic performance through various numerical experiments, including simulations of thermals in the convective boundary layer, coherent structure of turbulence over urban canopy, and thermal environment and heat stress indices in urban districts. The model developed in this study is intended to serve as a community tool for addressing both fundamental and applied studies in urban climatology.

Synthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long-term hazard of rare high-impact storm events. Existing downscaling approaches rely on statistical or statistical-deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two-way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post-processing to capture the asymmetries in the simulated surface wind. In this study, we present an innovative data-driven approach for TC synthetic downscaling. Using a machine learning-based high-resolution global weather model (ML-GWM), our approach can simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two-way interactions between the storm and its environment. This approach consists of multiple components: a data-driven model for generating synthetic TC seeds, a blending method that seamlessly integrates storm seeds into the surrounding while maintaining the seed structure, and a model based on a recurrent neural network to correct for biases in storm intensity. Compared to observations and synthetic storms simulated using existing statistical-deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Leveraging the computational efficiency of ML-GWM, our approach shows substantial potential for TC regional hazard and risk assessment.