Evidence indicates that since convective-scale simulations can explicitly resolve motion around deep convection, they may improve representation of coupling between small-scale moist convection and upscale modes of atmospheric variability.  Prior studies indicate that there can be a shift in the mean state of large-scale tropical circulations in convective-scale simulations relative to models with parameterised deep convection. However, it is uncertain whether this shift is systematic in convective-scale simulations or simply the response in a single model realisation.

To resolve this uncertainty, we run a 9-member ensemble of simulations over tropical southern and eastern Africa, using the Met Office Unified Model on a 2.2km grid with no deep convection parameterisation. ERA5 forces the lateral boundaries and simulations use FLake, a lake scheme to reduce over-lake biases in precipitation. The ensemble will be compared to a similar configuration which uses a deep convection parameterisation and a 12km grid.

Our ensemble experiments quantify the internal variability associated with varying initial conditions in the tropics and subtropics, relative to the variability induced by lateral boundary forcings. The ensemble divergence will be compared for the simulations with and without convection parameterisations to explore implications of ensemble design for high-resolution simulations of large domains. Furthermore, the hypothesis that there is a systematic mean-state shift in large-scale tropical circulations in kilometre-scale simulations relative to coarser GCMs will be evaluated using the two ensembles.

Effects of incorporating FLake and the role of soil moisture in initialisations will also be discussed, as well as their implications for predictability in kilometre-scale simulations. Model outputs will be compared to in-situ observations over northwest Zambia obtained during the 2022 DRYCAB field campaign, and we will outline how these results inform the design of planned further simulations to investigate monsoon onset predictability on subseasonal-to-seasonal timescales.

How to cite: Morris, F., Zilli, M., Hart, N., and Samuel, J.: Quantifying Ensemble Divergence in Large-Domain Convective-Scale Simulations over Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4489, https://doi.org/10.5194/egusphere-egu25-4489, 2025.

The impact of the resolution on the large-scale features in an ocean-sea ice coupled model is represented in this paper through three aspects. Firstly, refined resolution accelerates temperature and salinity drifts at a basin-averaged scale by facilitating exchanges among basins, subsequently reducing global-averaged drifts. This amplification of basin-scale exchanges is associated with an accelerated large-scale circulation, leading to a more rapid equilibration of temperature and salinity above 300 meters. Secondly, the refined resolution yields improved simulations of large-scale temperature, salinity, and currents, particularly evident in regions such as the Gulf Stream and its extension. Enhanced current simulations and corresponding temperature distributions contribute to more accurate representations of wind stress through ocean currents and sea surface temperature feedback. This feedback, in turn, influences wind-driven currents, establishing positive feedback loops. Despite little impact on the temporal variability of phenomena such as ENSO, IOD, PDO, and AMO, the refined resolution enhances the strengths of their variabilities. However, spatial patterns of PDO and AMO do not exhibit improvement with refined resolution, potentially attributed to the coarse resolution of the reference dataset.

How to cite: Li, Y., Liu, H., Lin, P., Ding, M., and Yu, Z.: Improvement of large-scale circulation simulation in an ocean-sea ice model with high-resolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5848, https://doi.org/10.5194/egusphere-egu25-5848, 2025.

We report on the first multi-year kilometre-scale global coupled simulations using ECMWF's Integrated Forecasting System (IFS) coupled to both the NEMO and FESOM ocean–sea ice models, as part of the H2020 Next Generation Earth Modelling Systems (nextGEMS) project. We focus mainly on an unprecedented IFS-FESOM coupled setup, with an atmospheric resolution of 4.4 km and a spatially varying ocean resolution that reaches locally below 5 km grid spacing. A shorter coupled IFS-FESOM simulation with an atmospheric resolution of 2.8 km has also been performed. A number of shortcomings in the original numerical weather prediction (NWP)-focused model configurations were identified and mitigated over several cycles collaboratively by the modelling centres, academia, and the wider nextGEMS community. The main improvements are (i) better conservation properties of the coupled model system in terms of water and energy budgets, which also benefit ECMWF's operational 9 km IFS-NEMO model; (ii) a realistic top-of-the-atmosphere (TOA) radiation balance throughout the year; (iii) improved intense precipitation characteristics; and (iv) eddy-resolving features in large parts of the mid- and high-latitude oceans (finer than 5 km grid spacing) to resolve mesoscale eddies and sea ice leads. New developments at ECMWF for a better representation of snow and land use, including a dedicated scheme for urban areas, were also tested on multi-year timescales. We provide first examples of significant advances in the realism and thus opportunities of these kilometre-scale simulations, such as a clear imprint of resolved Arctic sea ice leads on atmospheric temperature, impacts of kilometre-scale urban areas on the diurnal temperature cycle in cities, and better propagation and symmetry characteristics of the Madden–Julian Oscillation.

How to cite: Rackow, T., Becker, T., Ghosh, R., Koldunov, A., Pedruzo-Bagazgoitia, X., and Takasuka, D.: Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6699, https://doi.org/10.5194/egusphere-egu25-6699, 2025.

General Circulation Models (GCMs) are widely used to understand our climate and to simulate and predict the effects of global warming. They have shown persistent biases in the large-scale features of the general circulation and basic climate statistics, which are attributed mainly to parameterizations, especially the convection parameterization. To address this, Global storm-resolving models (GSRMs) provide an alternative approach to parameterization by explicitly resolving convection and its interaction with other processes through the refinement of the horizontal gridIn a prior study, we showed the physical convergence of the tropical and general circulation structure at a horizontal grid spacing of 2.5 km using aquaplanets. However, questions linger: Does the response to climate warming converge in a simplified framework as aquaplanets? 

We will present the effect of increasing horizontal grid spacing on the convergence of the climate change response in aquaplanet experiments. We will focus on the convergence of the storm tracks and jet stream in terms of their location and intensity using the global storm-resolving model ICON. Control runs, and idealized climate change experiments (increasing sea-surface temperature by 4 Kelvin) were conducted at horizontal grid spacing from 160 km to 2.5 km using an aqua-planet configuration. We adopt an aquaplanet configuration to focus on atmospheric phenomena, specifically convection and cloud feedback while reducing the effect of complex interaction with land, topography, sea ice, and seasons. We will discuss the convergence rate of the large-scale circulation, the eddy-driven jet, the subtropical jet, and the storm track and their response to climate warming, characterized by the location, width, and intensity.

How to cite: Peinado Bravo, A., Klocke, D., and Stevens, B.: Convergent Response in Aquaplanet Climate Change Experiments with Increasing Horizontal Resolution , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7755, https://doi.org/10.5194/egusphere-egu25-7755, 2025.

Storm-resolving models, such as the ICON model at 5 km resolution, are transforming our understanding of the Earth’s climate system by explicitly resolving key small-scale processes. This study highlights the dual nature of this modeling revolution: the advantages of improved representation of subgrid-scale dynamics and the challenges posed by existing parameterizations in capturing air-sea interactions.
On the one hand, a detailed momentum analysis of equatorial boundary layer winds using the coupled storm-resolving model ICON reveals dynamics that deviate from traditional assumptions. We identify two persistent wind patterns—zonal and meridional—governed by SST-driven pressure gradients, vertical turbulent flux, and horizontal momentum transport. These transport terms, largely overlooked in conventional models, and resolving the fine-scale interaction between SST gradients and boundary layer dynamics play a decisive role in shaping surface winds. A revised wind model, incorporating these findings, demonstrates strong agreement with storm-resolving model outputs.
On the other hand, storm-resolving models expose limitations in parameterizations of small-scale processes at the air-sea interface. For instance, the surface exchange coefficients—such as drag (c_D) and heat exchange (c_H)—are shown to be inadequate under low-wind regimes, leading to biases in surface pressure distribution and convection patterns. Using the ICON atmosphere-land-only "OptiFlux" configuration, we demonstrate that even small adjustments to these coefficients can substantially improve the representation of surface fluxes, strengthen pressure gradients, and enhance atmospheric dynamics.
These two aspects of this study illustrate the transformative potential and pressing challenges of storm-resolving models in further research.

How to cite: Winkler, M., Mellado, J. P., and Stevens, B.: Storm-Resolving Model ICON at the Air-Sea Interface: Insights into Momentum Dynamics and Parameterization Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8534, https://doi.org/10.5194/egusphere-egu25-8534, 2025.

This study evaluates the ability of global storm-resolving simulations in reproducing extreme precipitation events (EPEs) over the Mediterranean basin, with a specific focus on the Italian peninsula. We use multi-decadal simulations provided by two coupled models, ICON and IFS-FESOM, both developed under the EU’s Horizon 2020 Next Generation Earth Modelling Systems (NextGEMS) project. Thanks to the synergy between large-scale circulation patterns and km-scale atmospheric dynamics, it is expected that such models better represent precipitation distribution and intensity.

In this work we apply a classification of EPEs based on a set of thermodynamic parameters representative of the regional-scale environmental conditions, following  Grazzini et al. (2020), to classify EPEs over central-northern Italy in three main categories, based on the main uplift mechanism. 

We validate model simulations against the results of Grazzini et al. (2020) based on ArCIS/ERA5 data over central-northern Italy. The analysis is then extended to other Mediterranean regions, providing insights into the models’ capabilities and limitations in capturing extreme events under different large-scale conditions. 

How to cite: Lanteri, P. and Bordoni, S.: Mediterranean extreme precipitation events in storm-resolving NextGEMS Earth System Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9160, https://doi.org/10.5194/egusphere-egu25-9160, 2025.

The Climate Adaptation Digital Twin within the Destination Earth project represents an innovative initiative aimed at achieving operational kilometer-scale global climate simulations to support climate adaptation efforts. Three state-of-the-art Earth System Models (ESMs) are used separately and we are focusing on the scientific advancements and simulation results of the IFS-NEMO model throughout the project's duration.

During the first phase of the project, two main simulations were produced: a historical experiment (1990–2019) at 10 km resolution, and a SSP3-7.0 scenario (2020–2039) at 5 km resolution. Phase 2 aims to enable the operationalization of these simulations. Analysis of phase 1 IFS-NEMO results revealed a notable cold bias in the model’s mean state. To address this issue, a newly tuned version of the model was developed, significantly reducing the cooling trends. Key adjustments to achieve this improvement, first tested at a 25 km resolution version of the model, included refinements to sea-ice parameterization within the NEMO model,  and the introduction of MACv2-SP forcings in IFS, which enabled the representation of time-varying aerosols in the future scenarios. Additional enhancements were made to couple the river runoff to the ocean.

The outcomes of these efforts highlight the potential for substantial advancements in global climate modeling. Looking ahead, the integration of kilometer-scale simulations into operational workflows promises to deliver unprecedented detail and accuracy in climate projections. This will enable more precise assessments of climate impacts and provide critical insights for policymakers and stakeholders striving to implement effective climate adaptation strategies. The continued refinement of the IFS-NEMO model and its components will play a pivotal role in achieving these ambitious goals.

How to cite: Rocha, N., Ortega, P., Batlle, M., Wagner, I., Keller, K., Pelletier, C., Pedruzo, X., Rackow, T., Becker, T., Sidorenko, D., Nurisso, M., Caprioli, S., Nazarova, N., Ghosh, S., and Milinski, S.: Scientific developments of IFS-NEMO for Destination Earth’s Climate Adaptation Digital Twin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9386, https://doi.org/10.5194/egusphere-egu25-9386, 2025.

Aerosols influence Earth's climate directly by scattering or absorbing radiation and indirectly by serving as nuclei for cloud droplets or ice crystals. Earth system models have significantly improved our understanding of aerosols, clouds, and radiation. The resolution of these models has increased from above 100 kilometers to below 10 kilometers in recent years. With that, important atmospheric processes like deep convective motions are explicitly resolved.

To perform kilometer-scale (km-scale) simulations with the Earth system model ICON-MPIM, we developed the one-moment aerosol module HAM-lite. In HAM-lite, aerosols are represented as an ensemble of log-normal modes with prescribed properties. There are two pure modes, one composed of dust and one composed of sea salt, and two internally mixed modes, both composed of organic carbon, black carbon, and sulfate. The first mixed mode includes aerosols from biomass burning emissions and the second mixed mode includes aerosols from anthropogenic and volcanic emissions. The four modes are transported through the atmosphere and are coupled with various processes such as radiation, convection, and precipitation.

To assess the impact of anthropogenic aerosols, we performed two km-scale simulations over one year with different emission scenarios. The present-day scenario is based on emissions from the Community Emissions Data System (CEDS) and the Global Fire Assimilation System (GFAS). The pre-industrial scenario is based on the historic biomass burning emissions for CMIP6 (BB4CMIP). In both simulations, the sea surface temperature and sea ice are prescribed with the boundary conditions of AMIP, and the initial conditions of the atmosphere and land are derived from the operational analysis of ECMWF. Based on these two scenarios, we analyze how anthropogenic aerosols interact with radiation and clouds over one year. 

How to cite: Weiss, P. and Stier, P.: Assessing the impact of anthropogenic aerosols in a kilometer-scale Earth system model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10848, https://doi.org/10.5194/egusphere-egu25-10848, 2025.

Mesoscale convective systems are a crucial feature in Sahel, a water vulnerable semi-arid region in West Africa. Observational studies have shown that they are responsible for bringing approximately 90% of the rainfall during the summer monsoon season, and play an especially important role in extreme rainfall events. Despite of their important impacts on society and climate, traditional general circulation models, with their coarse horizontal resolution and parameterized convection schemes, struggle to properly simulate these organized convective systems. However, the newer generation of km-scale convection-permitting climate models have been shown to much more accurately capture the characteristics of mesoscale convective systems, showing great potential for studies of future climate change in vulnerable regions such as the Sahel.

In this study we analyze the latest simulations run with IFS and ICON within the NextGEMS project, with a horizontal resolution up to 9 km. Using a lagrangian tracking algorithm to identify the mesoscale convective systems, we investigate how they and their related weather are represented in the models, how well they scale in strength with known amplifying factors and if any trends can be identified in the simulation.

How to cite: Berntell, E.: Representation of West African mesoscale convective systems in NextGEMS km-scale simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10993, https://doi.org/10.5194/egusphere-egu25-10993, 2025.

3D prognostic groundwater flow on a global scale is currently lacking in Earth system models. In order to prepare Earth system models for kilometer-scale simulations with integrated continental hydrology, the ParFlow hydrological model has been coupled to the land model of the ICON modelling framework. Global simulations of atmosphere and land were conducted with a two-way coupling between ParFlow and the soil hydrological scheme of ICON-Land over the Pan-European region. In this first configuration, ParFlow and ICON-Land exchange surface moisture fluxes and liquid soil water. Analyzing simulations covering the extended summer months, it is found that the coupling with ParFlow significantly reduces the soil-water variability in the deeper soil layers by resolving actual shallow aquifers. In ParFlow, surface runoff and infiltration are more physical resulting in a more realistic response of soil moisture to weather patterns on longer time scales. Correlations of soil moisture with surface latent heat flux and atmospheric moisture transport show that this results regionally in an increased land-atmosphere coupling strength. Also, the lateral flow of near-surface groundwater, which is intrinsically linked to the formation of river networks, influences atmospheric variables related to cloud formation by increasing their horizontal heterogeneity. Apart from these results, which demonstrated the importance of an integrated hydrological model for shallow groundwater in Earth system modelling, first results of high-resolution coupled simulations with an extended ParFlow coverage on a latitude belt over the tropical zone at 1 km resolution are presented. 

How to cite: Weinkaemmerer, J., Schnur, R., Goergen, K., and Kollet, S.: The ICON-ParFlow coupling: Integrating a continental-scale hydrological model into an Earth system model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11236, https://doi.org/10.5194/egusphere-egu25-11236, 2025.

Here we investigate tropical precipitation biases in the novel kilometer-scale Earth system models (ICON and IFS) developed by the EU-funded H2020 nextGEMS project. Despite the much higher resolution, these km-scale models still feature biases that are common to CMIP models: first, tropical precipitation is systematically overestimated. Second, the double ITCZ (Intertropical Convergence Zone) bias is not ameliorated, with too little rain falling close to the Equator and too much rain in the southern branch relative to the northern branch. The double ITCZ bias is consistent with Hadley circulations that feature secondary cells close to the equator. Third, both the northern and the southern ITCZ branches are displaced poleward relative to observations. 

Focusing on the tropical precipitation distribution, we more explicitly quantify existing biases through a symmetric and an antisymmetric precipitation index. Leveraging the well-established atmospheric energy balance framework, we show how hemispherically symmetric biases are positively corellated with biases in the equatorial net energy input (NEI), once any residual in its global average is removed. In both models, equatorial NEI biases primarily arise from surface latent heat fluxes. Hemispherically antisymmetric biases are instead negatively correlated with the cross-equatorial atmospheric energy transport, which is in turn linked to biases in the NEI hemispheric asymmetry. The leading sources of asymmetric biases are top-of-atmosphere radiative fluxes in IFS and surface radiative fluxes in ICON.

Finally, although we find that notorious GCM precipitation biases are not mitigated when employing km-scale grids, we also see that the atmospheric energy balance holds great potential for improving tropical precipitation patterns. In this regard key candidates for improving the energy balance are surface flux schemes, particularly for latent heat over the oceans, and cloud-radiative effects. 

How to cite: Müller, S. K. and Bordoni, S.: Understanding tropical precipitation biases in kilometer-scale global climate models using the atmospheric energy balance framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11818, https://doi.org/10.5194/egusphere-egu25-11818, 2025.

The resolution of climate models significantly influences their ability to simulate physical processes and reduce biases, especially in oceanic and atmospheric systems. The Eddy-Rich Earth System Models (EERIE) project focuses on developing next-generation Earth System models at kilometer-scale resolution. In this study, we compare the control simulations of one of the EERIE models, the ICOsahedral Non-hydrostatic Earth System Model (ICON-ESM-ER), with those of its eddy-rich predecessor, the Max Planck Institute Earth System Model (MPI-ESM-ER). The ICON-ESM-ER features a 5 km ocean resolution coupled with a 10 km atmospheric resolution, while the MPI-ESM-ER employs a 10 km ocean resolution and a 100 km atmospheric resolution. Additionally, the ICON-ESM-ER uses an unstructured icosahedral grid, whereas the MPI-ESM-ER is based on a tripolar curvilinear grid. As models gradually move to finer spatial resolution, we naturally expect to improve simulations of atmospheric and oceanic flows. However, things become particularly interesting when new thresholds are crossed, as it enables the explicit simulation of previously unresolved phenomena. This can also introduce new complexities and challenges. The analysis reveals distinct differences in biases between the two models. For instance, focusing on the Southern Ocean, ICON-ESM-ER exhibits overall warmer biases than its predecessor MPI-ESM-ER and shows very large positive dynamic sea level biases. Additionally, ICON-ESM-ER produces large positive zonal surface wind biases in this region. On a more positive note, the sea surface salinity biases in the South Atlantic and Indian Ocean are negligible in ICON-ESM-ER. The ICON-ESM-ER does not outperform MPI-ESM-ER and, in some cases, introduces larger biases in key climate variables. Understanding these biases, particularly in comparison to its predecessor, is essential to guide future model development and improve the representation of critical processes in the Earth system.

How to cite: Wickramage, C., Kröger, J., and Wachsmann, F.: Comparing biases in the earth system model ICON-ESM-ER with its predecessor MPI-ESM-ER, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12756, https://doi.org/10.5194/egusphere-egu25-12756, 2025.

Convective clouds are a key component of the climate system, impacting the hydrological cycle, and leading to the redistribution of heat, moisture, and momentum. Traditional low-resolution climate models rely on parameterisations to represent convection and thus struggle to realistically capture convective processes. In contrast, km-scale models can directly simulate deep convection, improving the accuracy of cloud and precipitation fields. However, significant uncertainties remain, due to parameterisations of remaining unresolved subgrid-scale processes, which must be addressed.

Traditional model evaluation methods rely on aggregated spatial and temporal statistics, which overlook the fine-grained details critical to understanding the physical processes underlying convection. In addition, conventional dimensionality reduction techniques (e.g., principal component analysis) cannot capture the non-linear relationships of small-scale physical processes.

To address these limitations, we use computer vision models to learn meaningful low-dimensional embeddings of outgoing longwave radiation (OLR) fields and evaluate km-scale models in this new embedding space. More specifically, we use contrastive learning, a self-supervised technique that trains machine learning models to distinguish between similar and dissimilar data points, to train a deep neural network to generate compact representations of OLR fields.

We present results from a case study evaluation of two km-scale models, the Integrated Forecasting System (IFS) and the Icosahedral Nonhydrostatic Model (ICON), developed as part of the nextGEMS project. The simulations are compared to observations from the Geostationary Operational Environmental Satellites (GOES-16). We quantitatively assess the realism of km-scale models by comparing the embedding distributions of models and observations using vector quantisation. Finally, we use explainability methods to identify key factors influencing the accuracy of simulated convection. Our results highlight the value of our approach in understanding and improving the performance of high-resolution climate models, contributing to more reliable climate projections at finer spatial scales.

How to cite: Freischem, L., Weiss, P., Christensen, H., and Stier, P.: Discovering convection biases in global km-scale climate models using computer vision, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13120, https://doi.org/10.5194/egusphere-egu25-13120, 2025.

As the climate continues to warm, hydrometeorological extremes are extracting a greater toll from society both economically and socially. The need for accurate extreme event projections during acute dry spells was recently highlighted by the January 2025 devastating wildfires in the Los Angeles region. Current CMIP-style global climate models broadly project an increasing frequency and intensity of heavy precipitation and drought. However, the relatively coarse resolution, lack of ocean-atmosphere coupling, and parameterization of convection means they do not capture the spatial heterogeneity and mesoscale processes of complex coasts and topography relevant for simulating extreme events which often introduces model biases.

The ongoing H2020 Next Generation Earth Modelling Systems (nextGEMS) project aims to address these issues with the development of convection-permitting, fully-coupled, Earth-system models. Using the ECMWF Integrated Forecast System (IFS) and Icosahedral Nonhydrostatic Weather and Climate Model (ICON), we examine detailed dry spell characteristics in the Mediterranean region of Europe and then expand our analysis globally. These results are compared against a suite of observations (station and satellite based), reanalysis datasets, and CESM2 simulations.

Using ICON and IFS with about 6 km and 4 km spatial resolution, respectively over a five-year period in the Mediterranean, we find the increased resolution and hybrid/explicit representation of convection improves the representation of dry hour frequency and alleviated the long-standing drizzle bias observed in many GCMs, here illustrated for CESM2. For simulating the maximum length of dry spells over land, switching off the convective parameterization scheme in ICON improves accuracy with similar dry spell lengths as observations and reanalysis. However, the annual maximum length of dry spells over the sea for both ICON and IFS is excessive by 30-50 days. The depiction of dry spells in the Mediterranean region is representative of the nextGEMS’ models performance across the whole mid-latitudes. Ongoing research using recently developed 30-year transient ICON and IFS simulations (2020-2050) looks to investigate how dry extremes evolve globally in a warming world.

How to cite: Wille, J. and Fischer, E.: Dry spell representation on regional and global scale using convection-permitting models within the nextGEMS project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17833, https://doi.org/10.5194/egusphere-egu25-17833, 2025.

Aerosol particles from natural and anthropogenic sources play an important role in the Earth's climate through their interactions with radiation and clouds. However, the underlying mechanisms and their climate impacts remain poorly understood. Kilometer-scale high-resolution climate simulations provide a powerful tool to tackle these uncertainties and reveal new details about the effects of aerosols, e.g., on moist convective clouds and fine-scale atmospheric dynamics. Recently, the reduced-complexity aerosol module HAM-lite was developed for global simulations within the ICON-MPIM Earth system model. While based on the proven but complex aerosol module HAM, HAM-lite represents aerosols as a group of logarithmic-normal modes with predefined sizes and compositions. It uses one mode each for pure dust and sea salt particles, and two internally mixed modes with organic carbon, black carbon, and sulfate. Now, this coupled model system has been further advanced to support limited-area mode (LAM) applications, enabling faster, targeted simulations of specific source and target regions and their associated aerosol processes.

We showcase the enhanced capability of ICON-MPIM and HAM-lite through LAM case studies. Regional simulations are performed at a resolution of approximately 2.5 kilometers over several months, using AMIP boundary conditions for sea surface temperature and sea ice. Initial and lateral boundary conditions for the atmosphere are sourced from ECMWF operational analysis, while aerosol boundary data are derived from either the Copernicus Atmosphere Monitoring Service reanalysis (EAC4 CAMS) or global ICON-MPIM-HAM-lite simulations. In this study, we present LAM applications for case studies of air pollution in Central Europe and Eastern Australia, densely populated regions with extensive aerosol measurement networks for model evaluation in the northern and southern hemispheres, respectively. Further analyses include aerosol processes at high-latitudes in the Fram Strait-Svalbard Arctic region, investigating the effects of sea ice on sea-spray emissions and polar air mass exchange; and low-latitude events in West Africa, focusing on the transport and impacts of dust and biomass burning smoke on regional climate and air quality.

How to cite: Kubin, A., Heinold, B., Weiss, P., Stier, P., and Tegen, I.: Regionally focused aerosol-climate modelling at kilometer scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20426, https://doi.org/10.5194/egusphere-egu25-20426, 2025.

We present the current IFS-FESOM model configuration of the Destination Earth (DestinE) project, which we used to compute a coupled climate projection (SSP-3.70) from 2020 to 2040 with unprecedented storm-resolving resolution. The atmospheric resolution of 4.4 km allows us to replace previously necessary parametrizations with explicitly resolved atmospheric dynamics. The unstructured NG5 ocean mesh, which locally reaches below 5 km resolution, resolves mesoscale ocean eddies and sea ice leads.

IFS-FESOM consists of the Integrated Forecasting System (IFS, developed by ECMWF) coupled to the Finite Volume Sea Ice-Ocean Model FESOM2. It utilizes the IO-server and post-processing toolkit multIO, providing hourly outputs and statistical processing of numerous variables. It also takes advantage of recent improvements to the IFS, including enhanced representations of snow and land use, as well as a dedicated scheme for urban areas and cities worldwide.

We present initial results from analyzing the simulation, addressing technical challenges and scientific questions related to running km-scale simulations over multiple decades.

How to cite: Beyer, S., Rackow, T., Sidorenko, D., Koldunov, N., John, A., Ghosh, R., Streffing, J., Cheedela, S. K., Rajput, M. M., Andrés-Martínez, M., Al Turjman, M. H., Aguridan, R., Nurisso, M., Wehner, J., and Jung, T.: 4.4-km global climate projections with IFS-FESOM, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18012, https://doi.org/10.5194/egusphere-egu25-18012, 2025.