This study introduces HyLake, a hybrid lake model that embeds a deep-learning surrogate for the water temperature module within a process-based backbone. HyLake simulates lake surface temperature and the latent and sensible heat fluxes in Lake Taihu more accurately than traditional process-based models and other hybrid experiments across different forcing datasets. The proposed coupling strategy provides a reliable tool for quantifying the impacts of climate change on aquatic ecosystems.

Regional analyses of atmospheric trace gases can improve knowledge of fluxes at high resolution but rely on specified boundary conditions (BCs) at the domain edges. Biases in the often-uncertain BCs propagate to the inferred fluxes. We develop a framework to explain how errors in the BCs influence the optimized fluxes, derive two metrics to estimate this influence, and compare two methods to correct for the biases. We demonstrate correcting BCs directly is more effective at reducing bias.

Predicting climate accurately is challenging due to uncertainties in model parameters. This study introduced an automated approach to refine key parameters, focusing on processes like cloud formation and atmospheric circulation. Testing adjustments to 10 and 20 parameters improved the model’s accuracy and stability, reducing errors in long-term simulations. This faster, more reliable method enhances climate models, supporting better future predictions and aiding global decision-making.

We describe a new model of coastal barrier ecogeomorphic change that operates over spatiotemporal scales congruous with effective management practices, incorporates key ecogeomorphic feedbacks, and provides probabilistic projections. The model skillfully captures important barrier dynamics through robust data integration and calibration of relatively simple model parameterizations, and can be used to understand and predict when, where, and how barriers evolve to inform decision-making processes.

This study resolves a key inconsistency in how Earth system models represent the physical properties of soil organic matter in land surface models. It introduces a new method to compute its volumetric fraction and physical effects using standard input data and soil mixture theory. Validated with experimental mixtures and field observations, the proposed framework improves the physical realism of soil property estimates.

We provide a new Earth System model configuration framed into the ICON architecture, which provides the baseline for the next generation of climate predictions and projections (hereafter ICON XPP). Two resolutions of ICON XPP are presented that show high runtime performances making it suitable to run long integrations and large-ensemble experiments. ICON XPP similarly perform to CMIP6-class of climate models making it a good basis for climate forecasts and projections, and climate research.

For air quality analyses, data assimilation models split available data into assimilation and validation data sets. The former is used to generate the analysis, the latter to verify the simulations. A preprocessor classifying the observations by the data characteristics is developed based on clustering algorithms. The assimilation and validation data sets are compiled by equally allocating data of each cluster. The resulting improvement of the analysis is evaluated with an air quality model.

The Gregory method is a common approach for calculating the equilibrium climate sensitivity (ECS). However, studies which apply this method lack transparency in how model data is processed prior to calculating the ECS, inhibiting replicability. Different choices of global weighting, net radiative flux variable, anomaly calculation, and linear regression fit can affect the ECS estimates. We investigate the impact of these choices and propose a standardised method for future ECS calculations.

In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.

Existing wave models are often quite heavy for coastal applications. The SnapWave model simulates wave refraction (turning towards the coast), shoaling (steepening up) and dissipation (loss of energy by friction and wave breaking), and it uses an efficient computational mesh that you can refine where you need it. In the paper we show that the model can reproduce time series of waves anywhere in the world, using a depth map and wave data from a global model (ERA5) or a local wave buoy.

This paper describes how the Coupled Model Intercomparison Project organized its 7th phase (CMIP7) to encourage the production of Earth system model outputs relevant for impacts and adaptation. Community engagement identified 13 opportunities for application across human and natural systems, 60 variable groups and 539 unique variables. We also show how simulations can more efficiently meet applications needs by targeting appropriate resolution, time slices, experiments and variable groups.

Agroforestry in the Sahel help store carbon and support food production, but land surface models struggle to capture their dynamics. We adapted the ORCHIDEE model to simulate Faidherbia albida, a tree that taps deep groundwater. This work highlights the need to integrate deep water uptake in land surface models for groundwater-dependent ecosystems, as it could enhance predictions, helping to sustain agroforestry in a changing climate.

We document the glacial system model (GSM), a 3D glaciological ice sheet systems model specifically designed for large ensemble modelling in glacial cycle contexts. The model is distinguished by the breadth of relevant processes represented for this context. This ranges from meltwater surface drainage with proglacial lake formation to state-of-the-art subglacial sediment production/transport/deposition. The other key distinguishing design feature is attention to addressing process uncertainties.

A new code, portUrb, is described and validated. portUrb is an atmospheric simulation code for turbulent boundary layers including flow through urban areas. The model is coded with an emphasis on robustness, simplicity, readability, portable performance on Graphics Processing Units (GPUs), and rapid prototyping of surrogate models through an ensemble capability where many different configurations can be run simultaneously to explore parameter choices.

While animal biodiversity is facing a global crisis as more and more species are becoming endangered or extinct, the role of animals for the functioning of ecosystems is still not fully understood. We contribute to bridging this gap by coupling a animal population model with a vegetation and thus enable future research in this topic.

This work presents a global groundwater modeling framework at 5 arcmin resolution, developed through an offline coupling of the H08 water resource model and MODFLOW6. The model includes a single-layer aquifer and is designed to capture long-term mean groundwater dynamics under varying climate types. The manuscript describes the model structure, input datasets, and evaluation against available observations.

Understanding fluid flow through fractured porous rocks is crucial for groundwater and geothermal energy management. Existing tools lack integrated workflows. We developed UpsFrac, an open-source software that integrates fracture modeling and permeability upscaling. It handles complex fracture patterns and rock properties. The software enables uncertainty quantification, helping scientists make accurate and efficient predictions for groundwater protection and renewable energy development.

Earth System Model simulations are typically run on large, highly congested flagship computers using workflows. These workflows can consist of thousands of tasks. If these tasks are queued individually, the wait time can add up, resulting in a long response time. In this paper, we explore a technique for aggregating tasks into a single submission. We found that this simple technique reduced the time spent in the queue by up to 7 %.

We developed a deep learning model based on Generative Adversarial Networks (GANs) to improve rainfall forecasts in northern China. Traditional models struggle with accuracy, especially for heavy rain. Our model merges data from multiple forecasts, capturing detailed rainfall patterns and offering more reliable short-term predictions.

We present MET-AICE, a sea ice prediction system based on artificial intelligence techniques that has been running operationally since March 2024. The forecasts are produced daily and provide sea ice concentration predictions for the next 10 days. We evaluate the MET-AICE forecasts from the first year of operation, and we compare them to forecasts produced by three physically-based models. We show that MET-AICE is skillful and provides more accurate forecasts than the physically-based models.

Global models of the atmosphere contain errors that lead to inaccurate simulations. A software tool ("QuadTune") is presented that attempts to mitigate errors related to suboptimal parameter values. It also displays diagnostic plots that provide hints about where structural errors might lie in the model.

Regional Climate Model version 5 (RegCM5) reasonably reproduced the dominant spatiotemporal pattern of annual precipitation over the basin using the Emanuel convective scheme with the Nogherotto–Tompkins (NoTo) large-scale scheme. Using the MOLOCH non-hydrostatic dynamical core is recommended in future research for improving precipitation simulation and assessing the impacts of land-use change, due to dam-induced reservoirs on local climate.

Volcanic eruptions are natural hazards impacting aviation, the environment, and climate. Here, we improve the simulation of volcanic material transport using the Weather Research and Forecasting (WRF-Chem) version 4.8. Analysis of ash, sulfate, and SO₂ mass budgets was performed. The direct radiative effect of volcanic aerosols was implemented. A preprocessor, PrepEmisSources, was developed to streamline the preparation of volcanic emissions.

LISFLOOD-FP 8.2 is a framework for running real-world simulations of rapid, multiscale floods driven by impact events like tsunamis. It builds on the LISFLOOD-FP 8.0 and 8.1 papers published in GMD: whereas LISFLOOD-FP 8.0 focussed on GPU-parallelisation, and LISFLOOD-FP 8.1 focussed on static mesh adaptivity of (multi)wavelets, LISFLOOD-FP 8.2 combines GPU (graphics processing unit)-parallelisation with multiwavelet dynamic mesh adaptivity to drastically reduce simulation runtimes, achieving up to a 4.5-fold speedup.

Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse conditions. Here, we demonstrate and evaluate the rsofun R package, which provides a computationally efficient implementation of the P-model for site-scale simulations of ecosystem photosynthesis. Bayesian model fitting to observed fluxes and traits and evaluation on an independent test data set indicated robust calibration and unbiased prediction capabilities.

We present a new version of the landslide model r.avaflow. It includes a model where different materials move on top of each other instead of mixing; a model supporting the entire range from block sliding to flowing; a model for slow-moving processes; and an interface for virtual reality visualization. Based on the results for four case studies we conclude that, at the moment, our enhancements are very useful for visualization of landslides for awareness building and environmental education.

We present the Potsdam Integrated Assessment Modeling validation tool, piamValidation, an open-source R package for validating climate scenarios. The tool enables comparison of model outputs with historical data, feasibility constraints and alternative scenarios. Designed as a community resource, validation configuration files can serve as a knowledge-sharing platform. The main objective is to improve the credibility of Integrated Assessment Models by promoting standardized validation practices.

One of the main constituents of Particulate Matter at the surface are Secondary Inorganic Aerosols (SIA) which are influenced by both anthropogenic emissions and the acidity of clouds and aerosols. This study shows improvements in introduced into the IFS-COMPO (Integrated Forecast System-COMPOsition) simulating the surface concentrations of SIA and the resulting changes in the total wet deposition for Europe, the US and South-East Asia.

Each vehicle from road traffic is a source of heat and an obstacle that induce wind when it passes. It directly impacts the local atmospheric conditions and the road surface temperature. These impacts are included in the numerical model of the Town Energy Balance, used to simulate local conditions in urbanised environments. Simulations show that road traffic has a significant impact on the road surface temperature up to a few degrees, and on local variables.

We present DRIVE v1.0, a data-driven framework to estimate road transport emissions, their temporal profiles, and the associated uncertainties. The method was applied to the city of Munich, where we present bottom-up emission estimates for the years 2019 to 2022. The estimates are compared against official municipal reports as well as national and European downscaled inventories.