This study evaluates how horizontal resolution (~ 25 vs. ~ 100 km) affects El Niño–Southern Oscillation (ENSO) simulation in the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System (CAS FGOALS-f3) climate model. A reproducible, process-based framework reveals ENSO biases stem from resolution-dependent air–sea feedbacks and high-frequency atmospheric variability. This work informs future development for the FGOALS-f3 family and serves as a reference for CMIP6/CMIP7 evaluation.

Wildfires pose serious risks to natural environments and cities, making it essential to predict how smoke travels through the air. We studied how wind patterns affect computer models of fire spread. We simulated fires under both smooth and turbulent airflows. Our results show that while assuming smooth wind is acceptable for light breezes, it causes errors in moderate winds. Therefore, models must account for natural turbulence to accurately predict fire plume in realistic weather.

This study tested using machine learning to speed up detailed simulations in the SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) model. By training ML (machine learning) models to correct a simpler version of SCREAM, some results improved, but others did not. Technical challenges were addressed, and new tools were developed. The work shows promise for making simulations more efficient, though further improvements are needed.

Wet soil are difficult to model because water changes how sunlight interacts with soil surfaces and alters reflectance in different viewing directions. We developed a unified physical model to simulate soil reflectance under varying moisture conditions. The results show that soil moisture not only darkens soil but also changes their directional reflectance, especially in the near-infrared region. This work can improve soil moisture retrieval and future remote sensing applications.

Bidirectional flux models give a unified model for emission and dry deposition, but few studies have been conducted in which satellite observations are used to refine the parameters in these models. A new bidirectional flux model for ammonia was developed that was designed specifically for use with satellite observations. Ammonia satellite observations were used to refine bidirectional flux model parameters, which improved the agreement of the model with ammonia surface observations.

This study highlights challenges in estimating seasonal climate predictability using the perfect model framework, which assumes only initial conditions cause error. We show that forecasts can exceed the predicted limit, known as the Potential Predictability Limit (PPL), due to model imperfections in simulating physical processes. A new method is proposed to estimate PPL more accurately and avoid such paradoxes.

We develop a novel data assimilation system by integrating incremental machine learning with a localized ensemble Kalman filter to enhance the simulation and reanalysis of particulate matter < 2.5 µm chemical components. Compared to traditional chemical transport model-based data assimilation, our assimilation system has superior computational efficiency and simulation improvements. Comparisons with independent observations and reanalysis datasets validate the robust performance of our system.

We introduce a Lagrangian atmospheric transport model (AgPaDS) that complements existing approaches by providing an efficient massively parallelized implementation and a unique option for advanced live 3-D visualization of simulation data on global scales for supporting exploratory analyses. The tool is tailored to applications in crop epidemiology and can be used to improve assessment of risks posed to food production by windborne crop disease epidemics.

A microscale, obstacle-resolving meteorological model has been extended with a snow cover and precipitation scheme making it the first model of its kind that includes rain and snow. The model allows first estimates on the influence of different city characteristics on precipitation heterogeneities. The performance of the model extension is assessed by comparing the results of different model versions. For the comparisons, threshold values were derived based on computational accuracy.

Although under huge pressure and with permanent darkness, the seafloor has a diversity of organisms. They are almost entirely dependent on food sinking from the ocean surface onto the seafloor. This model allows us to study how these organisms survive in this hostile environment. Making use of evidence that biological characteristics, like lifetime, vary with size of organism and temperature, this model can simulate the fate of seafloor creatures from bacteria to large sea cucumbers.

Vegetation plays a fundamental role in regulating Earth’s climate on time scales ranging from seconds to millions of years. Here, we develop and test a new vegetation model that uses evolutionary principles to predict vegetation structure, functioning, and diversity under environmental conditions fundamentally different from the present. Using the model in combination with fossil data from Earth's past may help to better understand the response of vegetation systems to environmental change.

Although the scientific community has made much progress over the last decades, climate models still do not perfectly simulate the present climate. Therefore, the model outputs are usually corrected for these errors. This article presents a method to apply successive stages of repeated error correction that lead to a better simulation of the present climate than in previous studies, in which the same correction method had been applied only once.

Accurate soil moisture is crucial for weather prediction, but traditional methods often miss correct spatial patterns. We addressed this by treating moisture data as cohesive images rather than isolated points. Using image processing, we optimized both the location and intensity of moisture anomalies. This approach doubled the accuracy of spatial patterns and reduced errors in China and the United States.

Solar power generation depends on weather conditions and photovoltaic modules, making accurate forecasts crucial for reliable grid operation. We combined weather prediction and artificial intelligence to improve the solar power prediction at different time scales for a plant. By improving sunlight predictions and incorporating physical constraints into the model, our approach reduced errors significantly. This can help integrate clean energy into power grids safely and efficiently.

Accurate simulations of ice-sheet evolution require a multi-physics approach that captures the interaction between glaciers and their subglacial environment. For this purpose, we have extended ice sheet and subglacial hydrology models using a coupling library. This simplifies coupled Earth systems simulations by allowing models to interact with minimal effort and computational expense. We have evaluated the solution regarding performance and choice of coupling library.

Tropical cyclones come in various size and shape, which smoothes out key storm features in composite analyses. To address this, we developed a compositing method that symmetrizes storms and better aligns their eyewalls and horizontal extents prior to compositing. This approach preserves mesoscale features in the composites, reduces within-group variance, and enhances the power of statistical testing. The method facilitates the investigation and understanding of tropical cyclone development.

A comparison of simulated and observed overturning transports across the Overturning in the Subpolar North Atlantic Program sections is presented. Eighteen ocean simulations participate in the study with resolutions ranging from coarse to eddy-resolving. The simulated transports are in general agreement with observations, with better agreement at high resolution. Analyzing overturning circulations in both depth and density space together provides a more complete picture.

Mesoscale Convective Systems cause extreme weather and flash floods, yet they remain difficult to simulate in climate models. We developed a tracking tool to identify these storms using standard model data. Our 27-year record for Europe shows these systems drive over 60 percent of heavy hourly rain in the warm-season. This algorithm allows us to evaluate climate model performance and investigate how these intense storms will change in a warming climate.

High-resolution atmospheric simulations can become numerically unstable over steep terrain. Traditional terrain smoothing approaches modify the domain globally, reducing terrain detail. We developed a local smoothing method that improves simulation stability while only modifying steep-slope points, helping to retain the benefits of high-resolution modeling. Tested in a mesoscale and a microscale atmospheric model, it is computationally efficient, easy to implement, and adaptable to other models.

SIM-HOM (Sesquiterpene, Isoprene, and Monoterpene-derived Highly Oxygenated organic Molecules) is a mechanistic module simulating HOM formation from primary biogenic precursors. By providing quasi-molecular resolution, it quantifies the low-volatility compounds that drive secondary organic aerosol formation, robustly linking complex gas-phase chemistry to aerosol dynamics and climate impacts.

We developed G&M3D 1.0, a user-friendly software package that enables users to build, visualize, and analyze 3D models of subsurface geological structures. We tested the software using a Louisiana salt dome case study, demonstrating its practical value for geological interpretation. By providing both open-source code and a ready-to-use application, this work offers an accessible platform for education, research, and professional geophysical analysis.

This study presents a novel scheme for simulating Earth's free surface. Traditional methods like 'Sticky Air' in the Eulerian scheme face limitations such as increased computational costs and marker fluctuation issues. Our approach integrates the 'Sticky Air' concept into an Arbitrary Lagrangian–Eulerian framework using an internal boundary enabling a true free surface simulation, which reduces marker noise and enhances numerical stability.

This study presents a series of modifications to the Tiedtke convection scheme, aiming to improve summer rainfall predictions in the 4.8-km-resolution nested grid of the Taiwan Global Forecast System (TGFS). The modifications improve the spatial distribution of rainfall and reduce the heavy rainfall bias in five-day forecast, as demonstrated by case studies and evaluations over a two-month period.