Journal of Advances in Modeling Earth Systems最新文献

This study utilizes the wealth of observational data collected during the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift experiment to constrain and evaluate close to two-hundred daily Large-Eddy Simulations (LES) of Arctic boundary layers and clouds at high resolutions. A standardized approach is adopted to tightly integrate field measurements into the experimental configuration. Covering the full drift represents a step forward from single-case LES studies, and allows for a robust assessment of model performance against independent data under a range of atmospheric conditions. A homogeneously forced domain is simulated in a Lagrangian frame of reference, initialized with radiosonde and value-added cloud profiles. Prescribed boundary conditions include various measured surface characteristics. Time-constant composite forcing is applied, primarily consisting of subsidence rates sampled from reanalysis data. The simulations run for 3 hours, allowing turbulence and clouds to spin up while still facilitating direct comparison to MOSAiC data. Key aspects such as the vertical thermodynamic structure, cloud properties, and surface energy fluxes are well reproduced and maintained. The model captures the bimodal distribution of atmospheric states that is typical of Arctic climate. Selected days are investigated more closely to assess the model's skill in maintaining the observed boundary layer structure. The sensitivity to various aspects of the experimental configuration and model physics is tested. The model input and output are available to the scientific community, supplementing the MOSAiC data archive. The close agreement with observed meteorology justifies the use of LES for gaining further insight into Arctic boundary layer processes and their role in Arctic climate change.

This work integrates machine learning into an atmospheric parameterization to target uncertain mixing processes while maintaining interpretable, predictive, and well-established physical equations. We adopt an eddy-diffusivity mass-flux (EDMF) parameterization for the unified modeling of various convective and turbulent regimes. To avoid drift and instability that plague offline-trained machine learning parameterizations that are subsequently coupled with climate models, we frame learning as an inverse problem: Data-driven models are embedded within the EDMF parameterization and trained online in a one-dimensional vertical global climate model (GCM) column. Training is performed against output from large-eddy simulations (LES) forced with GCM-simulated large-scale conditions in the Pacific. Rather than optimizing subgrid-scale tendencies, our framework directly targets climate variables of interest, such as the vertical profiles of entropy and liquid water path. Specifically, we use ensemble Kalman inversion to simultaneously calibrate both the EDMF parameters and the parameters governing data-driven lateral mixing rates. The calibrated parameterization outperforms existing EDMF schemes, particularly in tropical and subtropical locations of the present climate, and maintains high fidelity in simulating shallow cumulus and stratocumulus regimes under increased sea surface temperatures from AMIP4K experiments. The results showcase the advantage of physically constraining data-driven models and directly targeting relevant variables through online learning to build robust and stable machine learning parameterizations.

A simple analytical model, the zero-buoyancy plume (ZBP) model, has been proposed to understand how small-scale processes such as plume-environment mixing and evaporation affect the steady-state structure of the atmosphere. In this study, we refine the ZBP model to achieve self-consistent analytical solutions for convective mass flux, addressing the inconsistencies in previous solutions. Our refined ZBP model reveals that increasing plume-environment mixing can increase upper-troposphere mass flux through two pathways: increased cloud evaporation or reduced atmospheric stability. To validate these findings, we conducted small-domain convection-permitting Radiative-Convective Equilibrium simulations with horizontal resolutions ranging from 4 km to 125 m. As a proxy for plume-environment mixing strength, the diagnosed entrainment rate increases with finer resolution. Consistent with a previous study, we observed that both anvil cloud fraction and upper-troposphere mass flux increase with higher resolution. Analysis of the clear-sky energy balance in the simulations with two different microphysics schemes identified both pathways proposed by the ZBP model. The dominant pathway depends on the relative strengths of evaporation cooling and radiative cooling in the environment. Our work provides a refined simple framework for understanding the interaction between small-scale convective processes and large-scale atmospheric structure.

A comprehensive understanding of meteorological, emission and chemical influences on severe haze is essential for air pollution mitigation. However, the nonlinearity of the atmospheric system greatly hinders this understanding. In this study, we developed the quantitative decoupling analysis (QDA) method by applying the Factor Separation (FS) method into the model processes to quantify the effects of emissions (E), meteorology (M), chemical reactions (C), and their nonlinear interactions and impact on fine particulate matter (PM_2.5) pollution. Taking a heavy-haze episode in Beijing as an example, we show that different from the integrated process rate (IPR) and the scenario analysis approach (SAA) in previous studies, the QDA method explicitly demonstrate the nonlinear effects by decomposing the variation of PM_2.5 concentration into individual contributions of E, M and C terms as well as the contributions from interactions among these processes. Results showed that M dominated the hourly fluctuation of the PM_2.5 concentration. The C terms increase with increasing the level of haze, reaching maximum (0.37 μg $��·�$ m⁻³ $��·�$ h⁻¹) at the maintenance stage. Moreover, our method reveals that there are non-negligible non-linear effects of meteorological, emission, and chemical processes during pollution stage, with the mean accounting for 50% of the increase in PM_2.5 concentrations, which is often ignored in the current air pollution control strategies. This study highlights that the QDA approach can be used to gain insight into the formation of heavy pollution, and to identify uncertainty in numerical models.

This study demonstrates the advantages of scale- and variable-dependent localization (SDL and VDL) on three-dimensional ensemble variational data assimilation of the hourly-updated high-resolution regional forecast system, the Rapid Refresh Forecast System (RRFS). SDL and VDL apply different localization radii for each spatial scale and variable, respectively, by extended control vectors. Single-observation assimilation tests and cycling experiments with RRFS indicated that SDL can enlarge the localization radius without increasing the sampling error caused by the small ensemble size and decreased associated imbalance of the analysis field, which was effective at decreasing the bias of temperature and humidity forecasts. Moreover, simultaneous assimilation of conventional and radar reflectivity data with VDL, where a smaller localization radius was applied only for hydrometeors and vertical wind, improved precipitation forecasts without introducing noisy analysis increments. Statistical verification showed that these impacts contributed to forecast error reduction, especially for low-level temperature and heavy precipitation.

We examine the influence of convective organization on extreme tropical precipitation events using model simulation data from the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). At a given SST, simulations with convective organization have more intense precipitation extremes than those without it at all scales, including instantaneous precipitation at the grid resolution (3 km). Across large-domain simulations with convective organization, models with explicit convection exhibit better agreement in the response of extreme precipitation rates to warming than those with parameterized convection. Among models with explicit convection, deviations from the Clausius-Clapeyron scaling of precipitation extremes with warming are correlated with changes in organization, especially on large spatiotemporal scales. Though the RCEMIP ensemble is nearly evenly split between CRMs which become more and less organized with warming, most of the models which show increased organization with warming also allow super-CC scaling of precipitation extremes. We also apply an established precipitation extremes scaling to understand changes in the extreme condensation events leading to extreme precipitation. Increased organization leads to greater increases in precipitation extremes by enhancing both the dynamic and implied efficiency contributions. We link these contributions to environmental variables modified by the presence of organization and suggest that increases in moisture in the aggregated region may be responsible for enhancing both convective updraft area fraction and precipitation efficiency. By leveraging a controlled intercomparison of models with both explicit and parameterized convection, this work provides strong evidence for the amplification of tropical precipitation extremes and their response to warming by convective organization.

A linear non-diffusive algorithm for advective transport is developed that greatly improves the detail at which aerosols and clouds can be represented in atmospheric models. Linear advection schemes preserve tracer correlations but the most basic linear scheme is rarely used by atmospheric modelers on account of its excessive numerical diffusion. Higher-order schemes are in widespread use, but these present new problems as nonlinear adjustments are required to avoid occurrences of negative concentrations, spurious oscillations, and other non-physical effects. Generally successful at reducing numerical diffusion during the advection of individual tracers, for example, particle number or mass, the higher-order schemes fail to preserve even the simplest of correlations between interrelated tracers. As a result, important attributes of aerosol and cloud populations including radial moments of particle size distributions, molecular precursors related through chemical equilibria, aerosol mixing state, and distribution of cloud phase are poorly represented. We introduce a new transport scheme, minVAR, that is both non-diffusive and preservative of tracer correlations, thereby combining the best features of the basic and higher-order schemes while enabling new features such as the tracking of sub-grid information at arbitrarily fine scales with high computational efficiency.

Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference data sets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2% to 12% and 57% to 75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Redox processes, aqueous and solid-phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox-sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.