Journal of Advances in Modeling Earth Systems最新文献

Forecasting solar radiation is critical for balancing the electricity grid due to increasing production from solar energy. To this end, we need precise simulation of clouds, which is traditionally done by numerical weather prediction. However, these large-scale (LS) models struggle especially with forecasting stratocumulus clouds because their coarse vertical resolution cannot capture the sharp inversion present at stratocumulus cloud top. To address this issue, we employ large eddy simulation (LES), which operates at high resolution and has demonstrated superior accuracy in simulating stratocumulus clouds. However, LES relies on input data from a LS model, which is imperfect. To reduce the uncertainty caused by the LS data, we integrate a single ensemble Kalman filter step at the start of simulation in the LES model, utilizing local observations. Our results show that this approach is computationally feasible, robust, and reduces prediction error at assimilation by 50%. The improvement diminishes after approximately 1 hour of simulation due to the influence of large-scale forcing. Future work will focus on enhancing the LS inflow through nested simulations with realistic lateral boundary conditions to sustain the improvements in forecasting accuracy.

Identifying urban air pollution sources is essential for public health and environmental sustainability. In this study, we propose a novel hierarchical method for urban air pollution source identification, leveraging deep learning (DL) within an efficient Bayesian inference framework. We rely on observations in the form of two-dimensional (2D) pollutant concentration distributions, and adopt the Wasserstein $��\left(W_2\right)�$ distance to model the likelihood probability distribution. The hierarchical nature of the framework stems from the integration of two neural networks (NNs). The first one acts as an emulator that replicates the physical dispersion model to predict future pollution observations recursively over a defined timeframe. These predictions are then used as inputs for the second NN that approximates the $��W_2�$ distance between predicted and observed pollutant concentration distributions to rapidly compute the likelihood probability. The approach adopts a multi-model strategy to mitigate the accumulation of errors, particularly those arising from the recursive prediction steps across multiple time intervals, ensuring the reliability of predictions over extended periods. The proposed framework is implemented on graphics processing units (GPUs), enabling scalable computations for real-world applications and rapid decision making. Through extensive numerical experiments, we demonstrate the suggested method's effectiveness in accurately estimating pollution source parameters, including location, emission rate, and duration, using synthetic observational data. Sensitivity analyses further explore the impact of observational horizons and sampling on solution convergence and accuracy. Numerical results demonstrate robust performances and computational efficiency compared to the conventional approach, particularly in scenarios with limited computational resources and observations availability.

Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply-demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi-arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices.

Fluxes of carbon dioxide (CO₂) and methane (CH₄) from open water bodies are critical components of carbon-climate feedbacks in high latitudes. Processes governing the spatial and temporal variability of these aquatic greenhouse gas (GHG) fluxes are still highly uncertain due to limited observational data sets and lack of modeling studies incorporating comprehensive thermal and biochemical processes. This research investigates how slight variations in climate propagate through the biogeochemical cycles of ponds and resulting impacts on GHG emissions. We examine the thermal and biogeochemical dynamics of two ponds in the Yukon–Kuskokwim Delta, Alaska, under varying climatic conditions to study the impacts on CO₂, CH₄, and oxygen (O₂) concentrations and fluxes. We performed multiple numerical experiments, using the LAKE process-based model and field measurements, to analyze how these ponds respond to variations in air temperature, shortwave radiation, and snow cover. Our study demonstrates that ice cover duration and water temperature are primary climatic drivers of GHG fluxes. Climate experiments led to reductions in ice cover duration and increased water temperatures, which subsequently enhanced CH₄ and CO₂ gas emissions from two study ponds. On average, cumulative CH₄ and CO₂ emissions were 5% and 10% higher, respectively, under increases in air temperature and shortwave radiation. Additionally, we uncovered a need to incorporate groundwater influxes of dissolved gases and nutrients in order to fully represent processes governing aquatic biochemical activity. Our work highlights the importance of understanding local-scale processes in predicting future Arctic contributions to GHG emissions.

Langmuir turbulence, a dominant process in the ocean surface boundary layer, drives substantial vertical mixing that influences temperature, salinity, mixed layer depth, and biogeochemical tracer distributions. While direct resolution of Langmuir turbulence in ocean and climate models remains computationally prohibitive, its effects are commonly parameterized, frequently within established turbulent mixing frameworks like the K-profile parameterization (KPP). This study utilizes a modified KPP that determines boundary layer depth through an integral criterion, diverging from the conventional KPP's dependence on the bulk Richardson number. The modified KPP demonstrates markedly lower sensitivity to model vertical resolution than its conventional counterpart. Building upon this modified KPP framework, we introduce an innovative parameterization scheme for Langmuir mixing effects. We evaluate the performance of this new scheme against existing approaches using a one-dimensional (1D) column model across four different scenarios, incorporating validation against both large eddy simulation (LES) results and field measurements. Our analysis reveals that the new Langmuir mixing scheme, explicitly designed for the modified KPP framework, performs competitively while maintaining reduced sensitivity to vertical resolution.

The vertical transport (VT) of soil organic carbon (SOC) mixes carbon pools of varying depth-origin and decomposability, regulating whole-profile SOC dynamics through altered carbon pool interactions, such as the priming effect (PE). However, quantifying this process in situ is challenging. Using global data sets on SOC stocks and carbon inputs, we trained a depth-resolved SOC model incorporating VT and PE to assess the vertical gradient of VT and PE, and explore their roles in regulating whole-profile SOC dynamics in response to climate change. The results indicate that VT-induced redistribution of SOC is essential for capturing observed profile distribution of SOC stocks. Transported carbon from neighboring layers accounted for 8%–27% of total layer-specific carbon inputs, varying by depth and ecosystem type, and regulated SOC turnover behavior via the PE, especially in deeper layers. Precipitation emerged as the most important factor influencing layer-specific VT. While the PE was higher in upper layers, it was far from its maximum potential in deeper layers, making SOC dynamics in these layers more sensitive to carbon input changes. If VT and PE gradients are not considered, the sensitivity of whole-profile SOC to warming will be underestimated, and the impact of carbon input changes will be overestimated, particularly in deeper layers. Our findings highlight the critical role of VT and PE in controlling whole-profile SOC dynamics, underscoring the need to explicitly include these processes in Earth system models for reliable whole-profile SOC predictions under climate change.

Atmospheric gravity waves can play a significant role on atmospheric chemistry through temperature fluctuations. A recent modeling study introduced a method to implement subgrid-scale orographic gravity-wave-induced temperature perturbations in the Whole Atmosphere Community Climate Model (WACCM). The model with a wave-induced temperature parameterization was able to reproduce for example, the influence of mountain wave events on atmospheric chemistry, as highlighted in previous literature. Here we extend the subgrid-scale wave-induced temperature parameterization to also include non-orographic gravity waves arising from frontal activity and convection. We explore the impact of these waves on middle atmosphere chemistry, particularly focusing on reactions that are strongly sensitive to temperature. The non-orographic gravity waves increase the variability of chemical reaction rates, especially in the lower mesosphere. As an example, we show that this, in turn, leads to increases in the daytime ozone variability. To demonstrate another impact, we briefly investigate the role of non-orographic gravity waves in cirrus cloud formation in this model. Consistent with findings from the previous study focusing on orographic gravity waves, non-orographic waves also enhance homogeneous nucleation and increase cirrus clouds. The updated method used enables the global chemistry-climate model to account for both orographic and non-orographic gravity-wave-induced subgrid-scale dynamical perturbations in a consistent manner.

The reduced sensitivity of mean Southern Ocean zonal transport with respect to surface wind stress magnitude changes, known as eddy saturation, is studied in an idealized analytical model. The model is based on the assumption of a balance between surface wind stress forcing and bottom dissipation in the planetary geostrophic limit, coupled to the GEOMETRIC form of the Gent–McWilliams eddy parameterization. The assumption of a linear stratification, together with an equation for the parameterized domain integrated total eddy energy, enables the formulation of a two component dynamical system, which reduces to the non-linear oscillator of Ambaum and Novak (2014, https://doi.org/10.1002/qj.2352) in a Hamiltonian limit. The model suggests an intrinsic oscillatory time scale for the Southern Ocean, associated with a combination of mean shear erosion by eddies and eddy energy generation by the mean shear. For Southern Ocean parameters the model suggests that perturbing the system via stochastic wind forcing may lead to relatively large excursions in eddy energy.