Climate simulations help us understand the Earth system and its evolution. The models used to perform these simulations are highly complex, require significant programming expertise to build and consume a lot of energy. A key component of climate models is their sea ice components. In this work, we present a sea ice model that offers an easier development process while maintaining strong performance. The model is able to run on a computer's graphics card, which greatly reduces its energy usage.

To help environmental researchers better understand the sources of greenhouse gas measurements, we developed a practical method for field instruments to calculate the footprints. By using simplified math and efficient computing, our approach allows real-time analysis of measurement zones, which was previously too complex for on-site processing. This enables more accurate data collection worldwide, helping improve climate change monitoring and ecosystem studies.

We developed an improved model to better understand how water and energy move through natural landscapes (forest, grasslands, croplands, etc.) throughout the day. By using detailed data from study-site in Germany, we tested the model and found its good agreement with micro-meteorological measurements. Unlike many other tools, this model works without needing new adjustments and offers a powerful way to study fast-changing water processes in different environments.

We produce one of the world's most detailed global weather and climate simulations, spanning 4 years and enabling the direct representation of storms rather than approximations. This allows the capture of dangerous events such as strong wind gusts, heavy rain, and powerful tropical and mid-latitude storms everywhere on Earth. Our results show major improvements over traditional climate models, but also reveal remaining challenges in representing large, organized storm systems in the tropics.

We perform calculations of photolysis reactions using an existing model but including updated input data. These reactions are important in shaping the composition of our upper atmosphere and that of other planets, for example, controlling ozone formation and destruction. The results of our model are compared with those of previous benchmarks, and rates of various reactions provided to facilitate other researchers in developing accurate schemes to capture photolysis in planetary atmospheres.

Geodynamic modeling helps scientists understand how the Earth deforms. New computer methods make these simulations faster and more efficient, especially on powerful computers. They automatically adjust settings for better performance and can handle complex materials and flow types. This approach makes it easier to study large, detailed models of Earth processes.

In this paper, we examined the inter-model differences among five Earth System Models in simulating the impact of aerosols on plant productivity. All models showed that the impact of human-made aerosols on global plant productivity was negative, but with the divergence in the amount of reduction. We found that the divergence was mostly caused by the parameterization of model in simulating canopy photosynthesis, which determines how strongly plants react to changes in climatic factors.

This paper reviews how two major geoscience communities, the Community Surface Dynamics Modeling System (CSDMS) and the U.S. Geological Survey (USGS), are making scientific models more FAIR: Findable, Accessible, Interoperable, and Reusable. By comparing their approaches and lessons learned, it highlights practical steps that improve openness, collaboration, and transparency in Earth system modeling.

We introduce a novel strategy for bias correction of tropospheric ozone maxima based on parametric interpolation of quantile biases (PIQB) from stations into the model grid. Its performance is evaluated and compared to other strategies found in literature. The results show that PIQB performs very well on simulations with a relatively high horizontal resolution, preserving model-resolved features yet mitigating model errors. We conclude that PIQB is suitable for correcting future projections.

A second-order conservative remapping is a common and standard method for transformation of data from one grid system to another in climate studies. The author describes a fundamental problem in the derivation of the method in spherical coordinates proposed by a pioneer study, which is left unresolved and unrealized. It has a potential to damage the remapping, however no significant impacts are expected in the past studies, because of a formulation introduced for the other objective.

Complex models in environmental science typically have a lot of tuning parameters, which has to be set by the users depending on the application. This study presents a new method of objective tuning of a huge number of parameters, by combining expert judgement with automated tuning called Linear Meta-Model optimisation (LiMMo). The method is successfully applied to the regional climate model named ICON (Icosahedral Non-hydrostatic)-CLM (Climate Limited-area Modelling) over Europe.

We assessed the impact of ozone damage representations in a land-surface model on simulations of vegetation productivity. Results varied depending on how ozone effects were triggered and how vegetation recovery was modeled. Schemes that incorporated vegetation-specific thresholds and memory effects on photosynthesis and water loss more accurately reflected spatial patterns, indicating directions for enhancing model realism and improving projections of ecosystem responses to ozone pollution.

We present palm_csd version 25.10, the current default preprocessing tool for generating the static input data for the building-resolving large-eddy simulation model PALM (Parallelized Large-eddy Simulation Model). This paper focuses on the processing of buildings, vegetation, pavement, water bodies, terrain height and land cover. We demonstrate the application of palm_csd using publicly available geodata for the city of Berlin (Germany). Common data inconsistencies and sources of uncertainty in urban geodata are discussed.

We developed a new method to improve how a leading climate model simulates El Niño, a major driver of global weather extremes. By testing how the model responds to small changes in key atmospheric settings, we identified which processes matter most and adjusted them systematically. This approach makes the model’s behavior closer to observations and shows a promising path for building more reliable climate predictions.

This study demonstrates that short-period Global Storm Resolving Model (GSRM) simulations can inform long-term Global Climate Model (GCM) integrations through a machine-learning-based physics suite. With 80 d of GSRM-derived training data, the hybrid model achieves stable multiyear climate simulations and improved precipitation climatic characteristics.

Surface kinetic energy reflects the distribution of ocean circulation across temporal and spatial scales, shaping energy transfer and mixing in the upper ocean. In a series of North and Equatorial Atlantic numerical simulations, we show that the surface kinetic energy is sensitive to the model's choices in grid and seafloor resolution and to the tides and wind variability, offering guidance for the configuration of future ocean simulations.

The parameters that control a model's behavior determine its ability to represent a system. In this work, multiple cases test how to estimate the parameters of a model with components corresponding to both the physics and the chemical and biological processes (i.e. the biogeochemistry) of the ocean. While demonstrating how to approach this problem type, the results show estimating both sets of parameters simultaneously is better than estimating the physics then the biogeochemistry separately.

Global floods pose serious risks, but existing models are too slow for large-scale prediction. We redesigned the Catchment-based Macro-scale Floodplain (CaMa-Flood) model for graphics processing units (GPUs), reformulating irregular river networks, flux updates, and floodplain dynamics into highly parallel algorithms. CaMa-Flood-GPU runs global simulations in hours instead of days with the same accuracy, enabling larger ensembles, better flood-risk analysis, and improved preparedness worldwide.

Extratropical cyclones (ETCs) drive severe weather and cause significant socio-economic impacts. We present ImpactETC1.0, a framework that identifies ETC tracks and links them to local impacts, here storm surges. It uses global optimisation, BLOB analysis, and several post-processing options to improve tracking quality and identify impact-relevant tracks. Results show ImpactETC1.0 enables efficient, impact-focused ETC tracking.

MESMER is a Python-based climate emulator that provides spatially resolved realizations of multiple climate variables. Version 1.0.0 of MESMER consolidates previous emulation methods into one numerically stable, well-documented, and user-friendly software package. It can generate large ensembles of annual and monthly mean temperatures, as well as several climate extreme indicators, within minutes. The software is shared together with pre-calibrated parameters to enable broad community adoption.

This study introduces a new method to better understand extreme rainfall events by considering that heavy rain often occurs in clusters over time. Using rainfall records from northern Brazil, we developed and tested a statistical approach that improved prediction accuracy compared with existing methods. The results can support more reliable climate risk assessments and help improve planning for disaster prevention and climate change adaptation.

runoutSIM is a free R open-source software tool that helps map where landslides like debris flows might travel. It simulates, at regional scales, how far and fast material could move and whether it might reach roads, rivers, or other features. It also estimates how likely different source areas are to connect with these features – supporting hazard planning and enabling advances in modelling methods.

This study introduces a novel framework combining geodynamic and surface process models, enhancing our understanding of Earth's crust and upper mantle deformation. By integrating the codes Underworld 2 and Badlands within the Arbitrary Lagrangian-Eulerian with Internal Boundary (ALE-IB) scheme, our approach overcomes the limitations of previous methods. It maintains internal interface integrity and precise surface tracking, improving simulation fidelity.

Climate models still exhibit substantial cold biases over the Tibetan Plateau, with snow cover biases recognized as one of the major contributing factors. By incorporating the effects of standing dead grass stems and topographic relief on snow distribution and snow depletion processes, the snow cover fraction (SCF) parameterization was optimized, reducing positive SCF biases by 63 % and alleviating surface cold biases by approximately 1–2 °C in snow-affected regions.

The TOAR-classifier model is a data-driven tool that allows for an objective classification of air quality measuring stations as urban, rural, or suburban. Such classification is important in the analysis of air pollutant trends and regional signatures. The model is employed in the second Tropospheric Ozone Assessment Report but can also be used in other research work.

We explore how deep learning can improve local climate projections by adapting a national model to regional data. By relying on a paradigm called pre-training, we show that models can produce more consistent and physically aligned results, even when data is limited. This helps make future climate projections more reliable and supports better planning at both national and local levels.