We carry out a comprehensive atmospheric transport model (ATM) intercomparison project. This project aims to evaluate errors in ATMs and three air-sea O₂ flux products by comparing model simulations with observations collected from surface stations, ships, and aircraft. We also present a model evaluation framework to independently quantify transport-related and flux-related biases that contribute to model-observation discrepancies in atmospheric tracer distributions.

Understanding how clouds evolve is important for improving weather predictions, but existing tools for tracking cloud changes are complex and difficult to compare. To address this, we developed the Community Cloud Model Evaluation Toolkit (CoCoMET) that makes it easier to analyze clouds in both models and observations. By simplifying data processing, standardizing results, and introducing new analysis features, CoCoMET helps researchers better evaluate cloud behavior and improve models.

Climate models often exclude human behaviour. We introduce a model that includes economic, social, and environmental factors that influence dietary choices. This helps us understand how behaviour shifts impact future emissions and climate conditions. By considering a range of plausible behaviours, we provide a more accurate picture of potential outcomes, improving representations in climate models.

Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.

Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it is absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 Pg C in present-day Yedoma deposits and a 1–5 m shallower peat depth and 43 % less passive soil carbon in peatlands compared to the conventional protocol.

This study presents a toolkit to simplify input data creation for an urban microclimate model. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Our validation indicates that the automated methods can yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.

We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century, with a mean precipitation reduction accompanied, over the Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn.

This study improves how we predict ice formation in clouds by accounting for variable ice sizes and different weather conditions. Using simulations, we developed a more accurate method that works efficiently, making it suitable for application in weather and climate prediction models. The new approach is numerically verified and provides precise predictions of ice formation events and reliable estimates of key parameters.

This study aims to assess the capabilities of the advanced two-dimensional water quality and hydrodynamic model CE-QUAL-W2 v4.5 sediment diagenesis module, focusing on its application to a reservoir in Portugal over a six-year period (2016–2021). Overall, the results suggest that the diagenesis model is better suited for detailed process-based dynamics over extended timeframes, whereas simpler models such as the Hybrid model (combining the zero- and first-order models), are more appropriate for short- to medium-term applications or situations with limited data availability.

A new method has been developed to more accurately adjust atmospheric delay data for use in satellite positioning, especially in areas with large height differences. By using long-term weather data and testing with global observation stations, the new method significantly improves accuracy compared to traditional approaches. This can benefit applications such as precise positioning and weather monitoring using navigation satellite signals.

This study introduces a new method that helps improve the accuracy of climate models by automatically selecting the best parameters to match real-world observations. Instead of manually adjusting many parameters, the method uses a mathematical tool to find the most appropriate settings for the model. It can be especially helpful for researchers who introduce new physical parameters into climate models to assess their impact on model results and optimize them along with the old ones.

Satellite gravimetry enables direct measurement of total water storage (TWS), a capability that was previously unattainable. In this study, we present an open-source land data assimilation system with global hydrological model, which temporally, vertically, and laterally dis-aggregates satellite-based TWS. This study provides a practical framework establishing operational water management with current and future satellite gravity missions.

Magmatic dykes transport magma to the Earth's surface, sometimes causing eruptions. We advanced a model of dyking, treating it as plastic deformation in a porous medium, unlike the classic model that treats dykes as fractures in elastic solids. Comparing the two, we found the plastic model aligns with the fracture model in dyke speed and energy consumption, despite quantitative differences. This new method could be a powerful tool for understanding volcanic processes during tectonic activity.

Large volcanic eruptions are unpredictable and can have significant climatic impacts. If one occurs, operational decadal forecasts will become invalid and must be rerun including the volcanic forcing. By analyzing the climate response in EC-Earth3 retrospective predictions, we show that idealised forcings produced with two simple models could be used in operational decadal forecasts to account for the radiative impacts of the next major volcanic eruption.

To realistically simulate small-scale processes in the atmosphere and ocean, such as clouds or mixing, high-resolution numerical models are needed. However, these models are computationally very demanding. Here, we present a recently developed atmosphere–ocean model which is able to resolve most of these processes and is less expensive to run due to its computational design. Our model can be used for a wide range of applications like the investigation of marine heatwaves or future projections.

Laboratory earthquakes are an important means to understand natural earthquakes. While previous work focused on transient prediction, lacking future prediction capability, we propose a method and evaluate on data from laboratory experiments with different slip behaviours. It shows stable predictions in modelling slip moments, intervals, and predictions beyond trained horizons, especially for challenging slip scenarios, which is crucial for cyclic geophysical process such as seismicity.

FastCTM is a neural network model to simulate key criteria air pollution levels, offering an efficient alternative to traditional chemical transport models. Its structure is informed by the physical and chemical principles of the atmosphere, allowing it to learn and replicate complex atmospheric processes. FastCTM demonstrated matching accuracy to traditional models with less computational demand. It also provides analysis of how different atmospheric processes contribute to air quality changes.

We use total freeboard data from NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) across both hemispheres and estimates of winter Arctic sea ice thickness to evaluate climate model simulations of sea ice, providing constraints beyond the traditional sea ice area metric. ICESat-2 provides accurate freeboard data, but its short observational record requires careful consideration of natural variability.

We present the new Multi-Model Large Ensemble Archive (MMLEAv2) and introduce the newly updated Climate Variability Diagnostics Package version 6 (CVDPv6), which is designed specifically for use with large ensembles. For highly variable quantities, we demonstrate that a model might perform evaluation poorly or favourably compared to the single realisation of the world that the observations represent, highlighting the need for large ensembles for model evaluation.

A new version (v7) of the numerical model GEOCLIM is presented here. GEOCLIM models the evolution of ocean and atmosphere chemical composition on multi-million-year timescales, including carbon and oxygen cycles, CO_2, and climate. GEOCLIM is associated with a climate model, and a new procedure to link the climate model to GEOCLIM is presented here. GEOCLIM is applied here to investigate the evolution of ocean oxygenation following Earth's orbital parameter variations around 94 million years ago.

Given the serious health risks of urban air pollution, monitoring local pollution levels is crucial. The Retina v2 algorithm creates high-resolution pollution maps by integrating satellite and local measurements with an air quality model. Easily portable to other cities, it balances accuracy with low computational demands, matching or outperforming complex dispersion models and data-heavy machine learning. Satellite data proves especially valuable in cities with sparse or no monitoring networks.

We introduce a new high-resolution model that couples the atmosphere and ocean to better simulate extreme weather events. It combines the Geophysical Fluid Dynamics Laboratory (GFDL) advanced atmospheric and ocean models with a powerful coupling system that enables robust and efficient two-way interactions. Simulations show that the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model represents a key step toward improving extreme weather forecasts.

The Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) inorganic aerosol sub-model has been implemented in the Global Environmental Multiscale – Modeling Air Quality and Chemistry (GEM-MACH) air quality model. MOSAIC includes metal cation reactions and is a non-equilibrium, double-moment scheme that conserves aerosol number. Compared to the current aerosol sub-model, MOSAIC produces a more accurate size distribution and aerosol number concentration. It also improves the simulated nitrate and ammonium distribution. This work serves to expand the capacity of GEM-MACH for chemistry and weather coupling.

Land surface models are used for simulating how Earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes the evaluation process more comprehensive and efficient, stands out by incorporating various factors and working with data at any scale, enabling scientists to incorporate new types of models and measurements as our understanding of Earth's systems evolves.

A new method called coarse-graining scale analysis is gaining traction as an alternative to Fourier analysis. However, it requires data to be on a regular grid. To address this, we present a high-performance Python package of the coarse-graining technique using discrete Laplacians. This method can handle any mesh type and is ideal for processing output directly from unstructured-mesh models. Computation is split into preparation and solving phases, with GPU acceleration ensuring fast processing.

The northern Indian Ocean is central to the livelihoods and economies of countries that comprise about one-third of the world's population. We present a high-resolution (~10 km) ocean model that simulates seasonal and year-to-year variability in ocean, including currents, oxygen levels, and phytoplankton growth. This model is a powerful tool to study how climate change and human activities influence the northern Indian Ocean, which can be used for marine resource applications and management.

This study shows that an alternate snow cover fraction parameterization significantly improves snow simulation by CLASSIC in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and snow cover fraction.