Journal of Advances in Modeling Earth Systems最新文献

Microbial processes are crucial in producing and oxidizing biological methane (CH₄) in natural wetlands. Therefore, modeling methanogenesis and methanotrophy is advantageous for accurately projecting CH₄ cycling. Utilizing the CLM-Microbe model, which explicitly represents the growth and death of methanogens and methanotrophs, we demonstrate that genome-enabled model parameterization improves model performance in four natural wetlands. Compared to the default model parameterization against CH₄ flux, genomic-enabled model parameterization added another contain on microbial biomass, notably enhancing the precision of simulated CH₄ flux. Specifically, the coefficient of determination (R²) increased from 0.45 to 0.74 for Sanjiang Plain, from 0.78 to 0.89 for Changbai Mountain, and from 0.35 to 0.54 for Sallie's Fen, respectively. A drop in R² was observed for the Dajiuhu nature wetland, primarily caused by scatter data points. Theil's coefficient (U) and model efficiency (ME) confirmed the model performance from default parameterization to genome-enabled model parameterization. Compared with the model solely calibrated to surface CH₄ flux, additional constraints of functional gene data led to better CH₄ seasonality; meanwhile, genome-enabled model parameterization established more robust associations between simulated CH₄ production rates and environmental factors. Sensitivity analysis underscored the pivotal role of microbial physiology in governing CH₄ flux. This genome-enabled model parameterization offers a valuable promise to integrate fast-cumulating genomic data with CH₄ models to better understand microbial roles in CH₄ in the era of climate change.

Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance, which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban-PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators expecting better scoring models to capture the latent heat flux more accurately. We find that the water budget is only closed in 57% of the model-site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all water balance indicators for any site. Models passing more indicators do not capture the latent heat flux more accurately refuting our hypothesis. While output reporting inconsistencies may have negatively affected model performance, our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.

This paper introduces a unified model for layered rotating shallow-water (RSW) and quasi-geostrophic (QG) equations, based on the intrinsic relationship between these two sets of equations. We propose a novel formulation of the QG equations as a projection of the RSW equations. This formulation uses the same prognostic variables as RSW, namely velocity and layer thickness, thereby restoring the proximity of these two sets of equations. It provides direct access to the ageostrophic velocities embedded within the geostrophic velocities resolved by the QG equations. This approach facilitates the study of differences between QG and RSW using a consistent numerical discretization. We demonstrate the effectiveness of this formulation through examples including vortex shear instability, double-gyre circulation, and a simplified North Atlantic configuration.

Many chemical transport models treat mineral dust as spherical. Solar backscatter retrievals of trace gases (e.g., OMI and TROPOMI) implicitly treat mineral dust as spherical. The impact of the morphology of mineral dust particles is studied to assess its implications for global chemical transport model (GEOS-Chem) simulations and solar backscatter trace gas retrievals at ultraviolet and visible (UV-Vis) wavelengths. We investigate how the morphology of mineral dust particles affects the simulated dust aerosol optical depth; surface area, reaction, and diffusion parameters for heterogeneous chemistry; phase function, and scattering weights for air mass factor (AMF) calculations used in solar backscatter retrievals. We use a mixture of various aspect ratios of spheroids to model the dust optical properties and a combination of shape and porosity to model the surface area, reaction, and diffusion parameters. We find that assuming spherical particles can introduce size-dependent and wavelength-dependent errors of up to 14% in simulated dust extinction efficiency with corresponding error in simulated dust optical depth typically within 5%. We find that use of spheroids rather than spheres increases forward scattered radiance and decreases backward scattering that in turn decrease the sensitivity of solar backscatter retrievals of NO₂ to aerosols by factors of 2.0–2.5. We develop and apply a theoretical framework based on porosity and surface fractal dimension with corresponding increase in the reactive uptake coefficient driven by increased surface area and species reactivity. Differences are large enough to warrant consideration of dust non-sphericity for chemical transport models and UV-Vis trace gas retrievals.

All-sky radiance assimilation has been extensively developed to provide additional information for numerical weather prediction under cloudy conditions. Microwave radiances are particularly sensitive to hydrometeors, which can be used to initialize hydrometeor directly if the hydrometeor control variables (HCVs) are available. However, the effects of HCVs statistical structure and their multivariate correlation on all-sky radiance assimilation remain unclear. In this study, five HCVs are introduced into the variational assimilation system. The characteristics of hydrometeor background errors are analyzed, and the combined effect with the observation operator is discussed. Then a 3D Variational all-sky assimilation framework with HCVs is modified to assimilate Fengyun-3C/D Microwave Humidity Sounder-II radiance. It is shown that hydrometeors are initialized by radiance directly, and the thermodynamic fields are adjusted accordingly. The characteristics of multi-variables increments are associated with both the characteristics of HCVs in background error and the Jacobians in observation operator. Furthermore, cycle assimilation and forecast experiments for three typhoon cases are conducted. It is found that the difference between observed and analyzed brightness temperatures decreases when HCVs are activated, and the hydrometeors analysis fields are more consistent with observations. Additionally, the typhoon intensity forecasts are improved with enhanced double warm-core and the secondary circulation. This paper analyzes the characteristics of variational all-sky assimilation framework with HCVs, and demonstrates the potential value of HCVs for variational all-sky radiance assimilation.

For modern land surface models (LSMs) representing a singular bulk vegetation layer, the longwave optical properties (i.e., emissivity, reflectivity, and transmittivity) of vegetation layer are derived with a simplified constraint of assuming zero leaf reflectivity. This constraint is necessary, for instance, to the Beer–Lambert (B–L) law to establish a relationship between the optical properties and leaf area index. However, the simplified constraint leads to an overestimation of land surface emissivity in the vegetated regions. In this study, we introduce a new scheme considering realistic leaf reflectivity values rather than assuming zero. This new scheme is based on the relationship derived from the B–L law, but it is statistically augmented to consider the effects of leaf reflections. It is designed to emulate a multi-vegetation-layer numerical model known as the Norman model, which is capable of numerical calculations of multi-reflections among leaves. This new method consists of only a couple of simple equations; despite its simplicity, it very closely mimics the Norman model; The discrepancy of the results between the new method and the Norman model is less than measurement uncertainties for any combination of input parameters. When the new scheme is implemented in the Community Land Model version 5 (CLM5), the land surface emissivity values are simulated much more consistently with global measurements, resulting in significant alterations of land surface energy budget. The enhanced realism through our new scheme is poised to contribute to more accurate numerical weather and climate simulations.

This study utilizes Deep Neural Networks (DNN) to improve the K-Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11-year turbulence-resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN-augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long-term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each differing in their input and output variables, have been developed and trained. The performance of models utilizing the KPP_DNN schemes is compared to those employing traditional deterministic first-order and second-moment closure turbulent mixing parameterizations. Solution comparisons indicate that the simulated mixed layer becomes cooler and deeper when wave effects are included in parameterizations, aligning closer with observations. In the KPP framework, the velocity scale of unresolved shear, which is used to calculate ocean surface boundary layer depth, has a greater impact on the simulated mixed layer than the magnitude of diffusivity does. In the KPP_DNN, unresolved shear depends not only on wave forcing, but also on the mixed layer depth and buoyancy forcing.

The Southern Ocean connects the ocean's major basins via the Antarctic Circumpolar Current (ACC), and closes the global meridional overturning circulation (MOC). Observing these transports is challenging because three-dimensional mesoscale-resolving measurements of currents, temperature, and salinity are required to calculate transport in density coordinates. Previous studies have proposed to circumvent these limitations by inferring subsurface transports from satellite measurements using data-driven methods. However, it is unclear whether these approaches can identify the signatures of subsurface transport in the Southern Ocean, which exhibits an energetic mesoscale eddy field superposed on a highly heterogeneous mean stratification and circulation. This study employs Deep Learning techniques to link the transports of the ACC and the upper and lower branches of the MOC to sea surface height (SSH) and ocean bottom pressure (OBP), using an idealized channel model of the Southern Ocean as a test bed. A key result is that a convolutional neural network produces skillful predictions of the ACC transport and MOC strength (skill score of $��\sim �$0.74 and $��\sim �$0.44, respectively). The skill of these predictions is similar across timescales ranging from daily to decadal but decreases substantially if SSH or OBP is omitted as a predictor. Using a fully connected or linear neural network yields similarly accurate predictions of the ACC transport but substantially less skillful predictions of the MOC strength. Our results suggest that Deep Learning offers a route to linking the Southern Ocean's zonal transport and overturning circulation to remote measurements, even in the presence of pronounced mesoscale variability.

The shallow-water analogue models for the tropical atmosphere are examined from a formulational point of view. The normal-mode approach provides a formal procedure to reduce the primitive equation system to a shallow-water analogue, although approaches based on vertical integrals of the primitive equation system may be more intuitively appealing. Under a general framework of the latter, classical models by Gill (1980, https://doi.org/10.1256/smsqj.44904) and Lindzen and Nigam (1987, 2.0.co;2>https://doi.org/10.1175/1520-0469(1987)044<2418:otross>2.0.co;2) are derived in a deductive manner, by elucidating their limitations, implications, as well physical processes assumed. Major advantage of shallow-water analogue models is that after a vertical integral, the determination of convective heating rate simply reduces to that of a precipitation rate. Consequently, the question of representing convection also almost reduces to that of precipitation. This fact leads to confusions in literature about distinction between large-scale precipitation and subgrid-scale convection. This framework further supports a popular notion of the moisture as a key variable for describing convection. By reviewing the existing formulations, it is shown that convection can be parameterized without moisture under the limit of the parcel-environment quasi-equilibrium.

While the impacts of climate change on global food security have been studied extensively, the capability of emerging tools that couple land surface processes and crop growth in reproducing inter-annual yield variability at regional scale remains to be tested rigorously. In this study, we analyzed the effects of weather variations between years (1999–2019) on regional crop productivity for two agriculturally managed regions with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC), using the latest version of the Community Land Model (CLM5) and the WFDE5 (WATCH Forcing Data methodology applied to ECMWF reanalysis version 5) reanalysis. Overall, the simulation results were able to reproduce the total annual crop yields of certain crops, while also capturing the differences in total yield magnitudes between the domains. However, the simulations showed limitations in correctly capturing inter-annual differences of crop yield compared to official yield records, which resulted in relatively low correlation coefficients between 0.07 and 0.39 in AUS-VIC and between 0.11 and 0.42 in DE-NRW. The mean absolute deviation of simulated winter wheat yields was up to 4.6 times lower compared to state-wide records from 1999 to 2019. Our results suggest the following limitations of CLM5: (a) limitations in simulating yield responses from plant hydraulic stress; (b) errors in simulating soil moisture contents compared to satellite-derived data; and (c) errors in the representation of cropland in general, for example, crop parameterizations and human influences.