Journal of Advances in Modeling Earth Systems最新文献

Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross-correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate-based inference at a fraction of the computational cost of full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi-criteria trade-off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models.

Numerical studies of submesoscale ocean dynamics are restricted by several challenges, including its vast range of scales, nonhydrostatic features, and strong anisotropy. The Stratified Ocean Model with Adaptive Refinement (SOMAR) was developed to address many of these issues. Recent improvements to SOMAR incorporate Runge-Kutta time integration, Arakawa-C grids, new grid transfer methods, and error controllers in an effort to increase the model's fidelity and stability. In this paper, we detail these recent improvements, establish SOMARv2's accuracy, and demonstrate its utility as an efficient submesoscale model.

This study investigates the effects of urbanization, specifically land use change and anthropogenic emissions (AE), on convection, lightning, and surface precipitation for a case of summertime sea-breeze convection observed over the Houston metropolitan area. The unique capabilities of the NASA-Unified Weather Research and Forecasting model allows us to conduct a series of sensitivity experiments with complex configurations, in particular including multi-year land model spin-up simulations, treatment of aerosols and their precursors, and explicit cloud charging and lightning. The simulation results show that urban land use primarily alters the temporal evolution of convection, lightning, and surface precipitation, leading to late afternoon thunderstorm development. The decrease in latent heat flux from the land surface caused by urbanization weakens convection in the early afternoon, while a condition suitable for convection development is maintained in the late afternoon due to less stabilization of the lower troposphere by the weaker convection development and high sensible heat flux from the surface. On the other hand, anthropogenic aerosols directly enhance convection, lightning, and surface precipitation by increasing convective updrafts due to the aerosol-induced convective invigoration. The combined effects of urban land use and AE lead to even stronger thunderstorms in the late afternoon, mostly consistent with observations. These results indicate that urbanization increases the probability of late afternoon thunderstorms over the Houston area during the summer season. Advanced weather forecasting models that incorporate these urbanization effects might support sustainable urban planning to better mitigate the impacts of urbanization on local weather and public safety.

The development of the Simplified Cloud Resolving Energy Exascale Earth System Atmosphere Model (SCREAMv1) enables global storm-resolving simulations on modern GPU-based supercomputers. However, the high computational cost of SCREAMv1 limits its routine use for process-level studies, creating a need for efficient proxy configurations. This study addresses this gap by introducing DP-SCREAMv1, a doubly periodic cloud-resolving model designed to be fully consistent with SCREAMv1 while enabling high-resolution, long-duration simulations at significantly reduced computational expense by simulating a limited doubly periodic domain rather than the entire globe. Built on a C++/Kokkos architecture, DP-SCREAMv1 achieves exceptional performance scalability on GPU systems and includes a rich library of cases for validation and scientific exploration. In this work, we demonstrate short wall-clock times at SCREAMv1's default resolution and show that DP-SCREAMv1 supports routine execution of large-domain, high-resolution experiments that were previously challenging in practice. Furthermore, we show that DP-SCREAMv1 enables routine execution of “Giga-LES” style simulations and facilitates large-domain, high-resolution simulations that were recently considered burdensome to perform. These results document an efficient, fully consistent process-level configuration for SCREAMv1 (DP-SCREAMv1) and illustrate its use for long-duration and large-domain experiments at cloud-resolving to eddy-permitting resolution.

Aerosol-cloud interactions are a persistent source of uncertainty in climate research. This study presents findings from a model intercomparison project examining the impact of aerosols on clouds and climate in convection-permitting radiative-convective equilibrium (RCE) simulations. Specifically, 11 different modeling teams conducted RCE simulations under varying aerosol concentrations, domain configurations, and sea surface temperatures (SSTs). We analyze the response of domain-mean cloud and radiative properties to imposed aerosol concentrations across different SSTs. Additionally, we explore the potential impact of aerosols on convective aggregation and large-scale circulation in large-domain simulations. The results reveal that the cloud and radiative responses to aerosols vary substantially across models. However, a common trend across models, SSTs, and domain configurations is that increased aerosol loading tends to suppress warm rain formation, enhance cloud water content in the mid-troposphere, and consequently increase mid-tropospheric humidity and upper-tropospheric temperature, thereby impacting static stability. The warming of the upper troposphere can be attributed to reduced lateral entrainment effects due to the higher environmental humidity in the mid-troposphere. However, models do not agree on aerosol impacts on convective updraft velocity based on the preliminary examination of high-percentiles of vertical velocity at a single mid-troposheric layer (500 hPa). In large-domain simulations, where convection tends to self-organize, aerosol loading does not consistently influence self-organization but tends to reduce the intensity of large-scale circulation forming between convective clusters and dry regions. This reduction in circulation intensity can be explained by the increase in static stability due to the upper tropospheric warming.

Climate models initialized near the observed state typically drift toward their own climatology as the forecast evolves. This drift is commonly corrected through a lead-time and start-month dependent bias adjustment, derived from a hindcast data set. While widely used, this traditional correction has well-known limitations: it is statistically inefficient, prone to introducing artificial discontinuities, and offers little insight into the underlying causes of forecast error. This paper presents an alternative framework that addresses these limitations and provides a more process-oriented diagnostic. The proposed method fits separate autoregressive models with exogenous input (ARX models) to both forecasts and observations. Forecast errors are then predicted and removed using the difference between the two ARX models. The method is demonstrated and compared to traditional methods using seasonal forecasts of global mean temperature from the SPEAR model, a contributor to the North American Multi-Model Ensemble (NMME). The ARX approach outperforms traditional methods in independent data even when traditional approaches include a linear trend correction. The analysis reveals that SPEAR exhibits an exaggerated response to radiative forcing, leading to significant trend errors. Notably, these errors are already present in the first month. These initial trend errors can be reproduced by a one-dimensional data assimilation system, indicating that they originate from SPEAR's exaggerated response to radiative forcing, which is carried forward into the first-guess fields used in the data assimilation system.

Land-atmosphere coupling involves multiple processes occurring across different temporal and spatial scales. These complex processes are not yet fully represented in climate or numerical weather prediction models. To evaluate these coupled models and improve or develop new parameterizations, four different single-column model setups are proposed. These setups vary in the type of spatially homogeneous land cover: grassland, wheat field, corn field, and pine forest. They are based on the single-column version of the CNRM-CM6-1 model, which couples the ARPEGE-Climat atmospheric component with the SURFEX surface modeling platform. First, the simulation results are compared with observational data, focusing on the radiative balance, surface fluxes, soil temperature and moisture, as well as air temperature, specific humidity, and wind speed near the surface. The evaluation shows that the ARPEGE–SURFEX single-column model can accurately replicate both surface and atmospheric conditions. Next, the same modeling framework is applied to the Meso-NH–SURFEX large-eddy simulation model. These simulations provide a consistent reference for evaluating the boundary-layer in CNRM-CM6-1, particularly in terms of potential temperature and specific humidity throughout the day. Finally, a sensitivity study on the impact of surface parameters on land-atmosphere coupling reveals that the hydric stress parameterization, by modifying canopy conductance and transpiration, is the most influential driver of temperature and moisture in the mixed layer. This work highlights the advantages of using the ARPEGE–SURFEX single-column configuration as a reliable platform for experimentation and parameterization improvement, and local climate modeling. The study cases developed are made available for further evaluation of land-atmosphere coupling.

Organic aerosol (OA) is an important constituent of the Earth's atmosphere, yet the extent of its destruction by photolysis remains an active research question. Recent laboratory studies reveal evidence for rapid short-term photolysis for secondary OA, but the rate declines to negligible levels over time. Here we use the stratosphere to investigate long-term OA photolysis because of the relatively simple sources and sinks of OA in this region. Airborne campaign observations show that the organic content in organic-sulfate aerosols remains stable with altitude and time in the stratosphere, indicating no significant photolysis. Satellite observations of the 2020 Australian wildfires reveal OA persists over a year in the stratosphere, consistent with model simulations excluding long-term photolysis. These findings suggest long-term OA photolysis is negligible in the real atmosphere. The current Community Earth System Model (CESM) significantly underestimates the abundance of stratospheric OA due to assumed rapid photolysis. We add this well-validated mechanism into CESM by turning off secondary OA photolysis after it is 50 days old, effectively simulating stratospheric OA consistent with observations. In summary, multiple lines of evidence confirm that the long-term photolysis of OA is negligible or extremely slow. Incorporating this mechanism into CESM addresses a key model deficiency, improving simulation of stratospheric OA.

The differential cycling of marine macromolecules, such as dissolved carbohydrates, proteins, and lipids play a fundamental role in marine microbial metabolisms, ultimately regulating the ocean's capacity to sequester carbon downwards via the biological pump or move carbon up the food chain, supporting marine food webs. To date, their representation in global scale models of marine biogeochemistry and ecosystems is lacking. Here we add explicit representation of marine macromolecular cycling for dissolved polysaccharides, lipids, amino polysaccharides, and proteins, within the dissolved organic matter pools of the Marine Biogeochemistry Library (MARBL) ecosystem model, implemented within the ocean circulation model of the Community Earth System Model. The resulting dissolved macromolecule distributions identify polysaccharide and amino polysaccharide accumulation within the upper ocean of the subtropics owing to longer lifetimes than dissolved lipids and proteins which are diagnosed with shorter lifetimes and accumulate in more biologically productive regions. Representation of marine macromolecules is found to better match observed constraints of dissolved organic carbon to nitrogen stoichiometry patterns with depth than its MARBL predecessor which considers only bulk pools. This implementation of macromolecular cycling within the marine dissolved organic matter pool represents an important next step in better characterizing the complexity of natural organic matter within the marine ecosystem.