This study proposes a performance-based method to improve WRF climate simulation evaluation by addressing uncertainty in selecting scheme combinations. The method gives greater importance to difficult-to-simulate variables, such as precipitation and wind speed. Tested in two contrasting climate regions, it identified interpretable and well-performing model settings compared with equal weighting and established statistical weighting methods, including in the independent extreme-year test.

This research introduces a new method that combines model simulations with satellite observations to attribute the influence of wildfire smoke on an airmass. By dynamically determining the height of smoke plumes, we overcome a key limitation of earlier fixed reception-height approaches. This advancement is crucial for improving our understanding of how wildfire emissions influence cloud formation and the broader Earth's climate system.

We implemented water stable isotopologues in the non-hydrostatic mesoscale atmospheric model Meso-NH. We validated this isotopic version (Meso-NH-ISO) with a simulation of a well-documented squall line in the Sahel region. In future works, simulations with Meso-NH-ISO will be done on real cases of cyclones or squall lines. The goal is to better interpret and quantify isotopic observations on the field in terms of atmospheric processes that drive development and intensity of those thunderstorms.

Ground-based microwave instruments can continuously observe the lower atmosphere, but clouds make their measurements harder to use. We improved a computer model so that it represents how cloud and rain particles affect these observations, and tested it with six months of measurements from two sites in China. The updated model performed better under cloudy conditions without rain, supporting more reliable use of these observations in atmospheric monitoring and weather forecasting.

This study improves the Agro-C model to better represent how crops respond to changing soil moisture. The updated model adjusts carbon allocation between roots and leaves according to soil moisture and growth stage, and also accounts for leaf aging under water stress. Tests with field data for maize and wheat showed better predictions of plant growth above and below ground. The results can improve estimates of crop production and soil carbon storage under future drought and climate change.

We developed and tested a way to trace selected cloud particles in a simulation, including how they grow and merge. One run identifies particles that meet chosen conditions, and a second reconstructs only their saved histories. In a three-dimensional test, the saved data were estimated to be one-fifth of what would be needed to keep all particle histories. Larger reductions are possible when fewer particles are selected, making such studies more practical in large simulations.

Air pollution travels across continents, meaning emissions in one region can affect air quality far away. To better understand this, scientists from many groups are planning to run coordinated computer simulations of the atmosphere. By comparing results across models and emission scenarios, the planned study will show how pollution moves between regions and which sources matter most, helping governments design more effective air quality policies.

Accurate soil moisture data is essential for predicting weather. This study examined how observations from three satellites can be combined to improve land-surface simulations. While each satellite helps, their value changes with vegetation type. Merging these data sources gives a more reliable estimate of soil wetness, especially in central and western China. This approach strengthens soil-water monitoring and supports more dependable climate forecasting.

To enhance atmospheric transmittance accuracy in strong water vapor absorption bands, this study proposes an optimized scheme for the fast transmittance algorithm applied to the Hyperspectral Infrared Atmospheric Sounder-II. It introduces a transmittance threshold for sample selection and a weighted least squares regression with transmittance weighting. Validation against line-by-line models and observations shows significant improvements in forward model accuracy and stability at 6.7 μm.

Green roofs can help cool hot cities, and the Weather Research and Forecasting model lets us test how much. We upgraded its multi-layer green-roof scheme, adding plant shading, realistic heat flow in wet soil, evaporation from soil and wet leaves, and water uptake from deeper soil. Tested on a green roof in London, Canada, the new scheme cut errors in heat entering the roof by about three-quarters and captured bursts of evaporation after rain, improving how we study green-roof cooling.

Satellites can observe fires globally, but sensitivity of instruments in space is limited: they miss small fires and cannot see through clouds. We analyzed fire observations of two instruments and obtained their sensitivity to small fires, which depends on the resolution of the instrument, timing of the observation (day or night), and details of the data processing. We developed a procedure for comparing fire model predictions with satellite observations accounting for their limited sensitivity.

We introduce a new method to more precisely date ancient sediment layers using astronomical time cycles recorded in the rock. Unlike previous tools, it handles complex, multi-channel data without oversimplifying and gives clear estimates of dating uncertainty. This helps future research in better understand Earth's past climate, biological changes, and geologic events with greater confidence and accuracy.

We document the historical (1750–2023) volcanic sulfur dioxide emission dataset created for phase 7 of the Coupled Model Intercomparison Project, which is a set of coordinated climate model experiments run by modelling center worldwide. Our dataset underpins the stratospheric aerosol optical property dataset which will be used as input by most climate models. However, models with interactive stratospheric aerosol capability can directly input our emission dataset to run CMIP7 experiments.

Homogeneous freezing describes the spontaneous freezing of supercooled droplets, with the probability depending on temperature, droplet size, and elapsed time. Droplets freeze across a range of temperatures rather than all at once, and faster updrafts and larger droplets lead to freezing at warmer temperatures. Once some droplets freeze, the growing ice crystals deplete the surrounding vapour, causing the remaining droplets to evaporate. This affects the number of ice crystals in the cloud.

The triple isotopic composition of atmospheric oxygen (¹⁷Δ) is used to reconstruct past global biospheric productivity. We present the first implementation of the oceanic contribution (¹⁷Δ_ocean) in the intermediate-complexity model iLOVECLIM. Photosynthesis, respiration, and air–sea gas exchange are represented under preindustrial conditions. Model results agree with observations, providing a future key tool to study marine biogeochemical processes and past ocean biospheric productivity.

Ocean currents transport everything from pollution to marine larvae, but tracking millions of virtual particles to understand these movements typically requires weeks of computer processing. OceanTracker solves this bottleneck by being over ten times faster than existing tools, completing million-particle simulations in under an hour on standard computers. This speed breakthrough enables scientists to generate heat maps and analyze connectivity patterns between ocean regions.

Lake models are powerful tools for disentangling complex past climate data preserved in sediments. These models can be hard to set-up for lakes that are not well-observed. We improve an existing model with code to simulate water runoff and establish guidelines for setting-up the model when data are scarce. Using our workflow, more scientists can apply this tool to more lakes and gain a better understanding of the climate histories stored in their sediments.

Our study pioneers a PyTorch-based atmospheric pollutant assimilation system that leverages automatic differentiation and Graphics Processing Unit (GPU) acceleration to achieve order-of-magnitude speedups, while maintaining analysis accuracy consistent with the traditional Fortran-based system.

EnsAI is a newly developed artificial intelligence based program for efficiently generating ensembles of atmospheric surface ammonia concentrations that can be used in emissions inversions systems. Ensemble-based data assimilation methods are widely used for assimilation and emissions inversions, but are usually very computationally demanding. Once trained, EnsAI can run thousands of times faster than the physics-based models when run on graphics processing units.

We implemented a two-stage Rosenbrock solver (ROS2) in E3SMv1 and evaluated it with two atmospheric chemistry mechanisms. At a 180 s timestep, ROS2 reduced chemistry integration time by ~33% versus the original implicit solver on Cori/KNL CPUs, while maintaining stable one-year simulations and small ozone differences (≤1.03 ppb). Species-dependent differences in ozone, NO, NO₂, and OH were small. Results support ROS2 as a lower-cost option, with further convergence and CPU/GPU testing needed.