Journal of Advances in Modeling Earth Systems最新文献

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.

The CNRM-Cerfacs Climate Prediction System (C3PS) is a new research modeling tool for performing climate reanalyzes and seasonal-to-multiannual predictions for a wide array of Earth system variables. C3PS is based on the CNRM-ESM2-1 model including interactive aerosols and stratospheric chemistry schemes as well as terrestrial and marine biogeochemistry enabling a comprehensive representation of the global carbon cycle. C3PS operates through a seamless coupled initialization for the atmosphere, land, ocean, sea ice and biogeochemistry components that allows a continuum of predictions across seasonal to multiannual time-scales. C3PS has also contributed to the Decadal Climate Prediction Project (DCPP-A) as part of the sixth Coupled Model Intercomparison Project (CMIP6). Here we describe the main characteristics of this novel Earth system-based prediction platform, including the methodological steps for obtaining initial states to produce forecasts. We evaluate the entire C3PS initialization procedure with the most up-to-date observations and reanalyzes over 1960–2021, and we discuss the overall performance of the system in the light of the lessons learned from previous and actual prediction platforms. Regarding the forecast skill, C3PS exhibits comparable seasonal predictive skill to other systems. At the multiannual scale, C3PS shows significant predictive skill in surface temperature during the first 2 years after initialization in several regions of the world. C3PS also exhibits potential predictive skill in Net primary production (NPP) and carbon fluxes several years in advance. This expands the possibility of applications of forecasting systems, such as the possibility of performing multiannual predictions of marine ecosystems and carbon cycle.