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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.

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We evaluated how different configurations of a large-scale river basin model affect simulations of streamflow, evaporation, soil moisture, and groundwater in the Po River Basin in Italy. We tested alternative soil depths and the inclusion or removal of subsurface flow pathways, and compared results with observations and with an established long-term water balance relationship. Setups that best matched river flow often underestimated evaporation and overestimated deep groundwater recharge.

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This study examines a multi-layer snow scheme in seasonal forecasts. Compared to a single-layer scheme, it better captures snow insulation, delaying spring snowmelt by 1–2 weeks. This postpones evaporation and slows soil moisture depletion, which promotes evaporative cooling due to increasing energy partitioning into latent heat flux and enhances precipitation occurrence. This leads to realistic land-atmosphere interactions and reduced biases across Northern Hemisphere mid- and high-latitudes.

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We evaluate a new model simulating soil uptake of methane and hydrogen from the atmosphere. Coupled to an atmospheric chemistry and land surface model, it accounts for weather and soil conditions. Our results match observations, showing accurate removal depending on soil properties, temperature, moisture, and atmospheric conditions. This work improves the model’s ability to represent natural cycles of methane and hydrogen.

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Predicting hydrological runoff in Arctic permafrost regions is difficult due to limited observations and complex terrain. We used a detailed physics-based model simulations to improve runoff estimates in a Earth system land model. Our method improved runoff accuracy and worked well across two different Arctic regions. This helps make runoff parameterization schemes more reliable for understanding water flow in permafrost areas under a changing climate.

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Many climate models struggle to represent marine stratocumulus clouds. In contrast, the Taiwan Earth System Model version 1 reproduces them realistically, yet the reasons are unclear. Using short-term forecast simulations and process-based analysis, we reveal how individual physical processes affect these clouds and identify both strengths and weaknesses in the model. This analysis framework can be applied to understand other phenomena in climate models.

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We studied how vegetation and soil roughness shape released dust. Conceptualising light reflected from the land surface to represent wind sheltering, we improved a global dust model. This approach removes the need for guesswork about where dust comes from and still matches observed dust in the atmosphere. It also shows that different paths can lead to similar dust levels, which encourages better ways to track how often dust is lifted. This helps guide efforts to predict dust and its impacts.

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Understanding how rainfall may change in the future is vital for managing floods and water resources in Australia. We tested different ways of constraining a regional climate model so it better matched observed rainfall during the extreme 2010–11 La Niña wet event. The most effective settings produced much more realistic rainfall, increasing confidence in using the model to explore future rainfall patterns and extreme weather risks.

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We develop a regional ocean model of the Amundsen and Bellingshausen Seas in Antarctica. Differences in model setups and parameter choices often limit usability and broader scientific application, especially for non-ocean modellers. We carefully evaluate the model outputs and establish a common control experiment that can be shared and applied across studies with tracer and particle applications. This effort aims to support wide community use and improve understanding of ice–ocean interactions.

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We successfully represented hydrology and carbon cycle associated with different forestry managements (Rotational and continuous cover forestry) for a drained peatland ecosystem using the processed based model LDNDC. This provides a robust framework for investigating future management scenarios and develop forest management strategies that supports climate neutrality in peatland ecosystems.

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This study evaluated Noah-MP performance in simulating gross primary productivity (GPP) across China and analyzed the sensitivity of key parameterization schemes. The modified two-stream radiation scheme (RAD01) shows superior performance, especially in grassland and shrubland ecosystems, while the BTR03 β-factor performs better in croplands. Surface exchange and runoff schemes systematically overestimate GPP, indicating structural biases in energy–carbon and hydro–vegetation coupling.

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We developed a high-resolution ocean model to study marine debris dispersion across the Indian Ocean, from coastal scales to the open sea. Results show that model resolution and wave forcing, through both the Stokes-Coriolis force and Stokes drift, play a key role in shaping ocean circulation, seasonal energy budgets, and floating debris trajectories. High-resolution currents and wave forcing, especially during monsoons, increase the anisotropic spread and travel distances of drifting material.

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Accurate operational forecasts of temperature and wind in the coastal marine boundary layer are important for a wide range of applications. Leveraging data that were collected along the U.S. northeast coast during a multi-year period for the Third Wind Forecast Improvement project, we investigated the performance of the operational forecast model and identified systematic errors in wind and temperature forecasts that are now being addressed by the model developers.

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This study evaluates how horizontal resolution affects ENSO simulation in the CAS FGOALS-f3 climate model by comparing its ~25 km and ~100 km configurations. Using a reproducible, process-based diagnostic framework, we identify the structural origins of ENSO biases and show that they stem from resolution-dependent air-sea feedbacks and high-frequency atmospheric variability. This work informs future development for the FGOALS-f3 family and serves as a reference for CMIP6/CMIP7 model evaluation.

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This study evaluates the impact of grid resolution on long-term present and future climate runs using the Alfred Wegener Institute global model AWI-CM3. A higher-resolution setup (35 km atmosphere, eddy-permitting/resolving ocean) improves small-scale processes and long-term variability, across variables and regions, and can capture nonlinear ocean–atmosphere interactions often missed by Coupled Model Intercomparison Project (CMIP) models. Simulation data will be made publicly available.

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We examined the two-moment cloud microphysics sedimentation schemes of the ICON (ICOsahedral Nonhydrostatic) weather model, comparing the default semi-implicit with an explicit method faster on graphics processing units. Using idealized setups and thunderstorm case studies, we find differences in numerical diffusion and extreme precipitation rates due to changed coupling with the remaining microphysics. Neither method develops alarming instabilities in full model setups; both can be safely used.

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We use a state-of-the-art numerical chemistry-climate model to study the atmospheric sulfur dioxide budget. We simulate the atmospheric concentration of sulfur dioxide (SO₂) and corresponding sulfur deposition fluxes and compare the results with observational data from a satellite instrument and with ground-based in-situ measurements. For the evaluation of the simulated atmospheric lifetime of SO₂, we also simulate the fate of SO₂ emitted by two volcanic eruptions that happened in 2019.

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With CORDEX-CMIP6, ensembles of regional climate projections enable analyses on regional climate change. We present a regional coupled ocean-atmosphere model setup for Europe, tailored to provide consistent climate change information for the North and Baltic Seas. The simulation effectively captures the mean climate, variability, and extremes such as storm surges and marine heatwaves. Using this setup, we will contribute climate projections to EURO-CORDEX.

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Fast-changing currents shape surface energy and drive interior mixing of heat and salt. Because they are hard to observe globally, we use numerical models to quantify their impacts. We evaluate seven North and Equatorial Atlantic simulations with varying parameterizations, comparing modeled currents with those from observed surface buoy tracks. We show results are sensitive to model grid and seafloor resolution, tides and wind variability, with contrasting offshore and nearshore responses.

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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 anywhere 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.

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This study addresses a gap in understanding how turbulent mixing closure schemes and convective adjustments interplay in the Mediterranean Sea. Coupled ocean-wave simulations were performed with different mixing parameterizations and model results were compared against Argo float observations across different space and time scales. Results show that the Generalised Length Scale closure scheme best reproduces observed mixed layer properties and variability, without needing convective adjustment.

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Here we present an evaluation of 5 annual runs made with the Regional Air Quality Deterministic Prediction System, an operational chemical weather forecast system for North America. Measurements included NO₂, O₃, and PM_2.5, 8 other gas-phase species, 7 PM_2.5 species, and 3 ions in precipitation. Routine scores were augmented by many stratified analyses, and the results point to some model components where improvements are desirable. A companion paper provides a full description of the system.

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We evaluated how operating wind farms influence the atmosphere in numerical weather prediction using two wind farm parameterizations in the HARMONIE-AROME model, applied by over 10 European weather services. Accurate yield forecasts require including both onshore and offshore turbines. Wind turbines slightly alter near-surface temperature (<1 K on average). We also present an open-access European wind turbine dataset combining multiple data sources.

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A new configuration of the Australian Community Climate and Earth System Simulator coupled model, ACCESS-CM2, with a higher resolution ocean-sea ice component is introduced. The new version of the coupled climate model was designed to better capture smaller-scale ocean motions. While this configuration improves the representation of many aspects of the climate system, some biases from the existing lower-resolution version persist.

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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.

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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.

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Clouds play a key role in Earth’s climate, but their representation in models remains uncertain. We use high-resolution simulations to examine how two statistical representations of cloud processes influence cloud and rain formation, and how these effects manifest in global models. We find that simulated clouds are highly sensitive to the chosen method, and that features such as rain, fog, and ice become even more variable at the global scale.

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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.

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We evaluated a high-resolution numerical weather prediction model in a small, semi-arid U.S. city using dense ground-based measurements. While the forecasts demonstrated good skill for temperature and humidity, they consistently overestimated wind and underestimated nighttime cooling, with inaccurate heat advection predictions. The results highlight the need for improved urban representation in forecast models to better support heat warning systems for small cities.

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The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering gamma-spectrometric and magnetic surveys on igneous rocks due to limited Antarctic surveys. Machine learning predicts radionuclide and magnetic content based on terrain features, linking their distribution to landscape processes, morphometrics, lithology, and pedogeomorphology. Inaccuracies arise due to complex periglacial processes and landscape complexities.

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We document the forecasts of the composition of the stratosphere by the Copernicus Atmosphere Monitoring Service. The model's predictions are compared with satellite measurements over a recent period, during polar ozone depletion events, and after the Mount Pinatubo volcanic eruption. The system performs well for sulfate aerosols, ozone and several other key gases but not as well for several nitrogen-containing gases. Chemical processes in aerosols and polar clouds should be improved.

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We evaluated Rossby waves in dynamical downscaling simulations over North America, and their connections to surface air temperature variability and heatwaves. Simulated Rossby wave propagation is distorted by flow discontinuities at lateral boundaries and by biased mean wind patterns, thereby breaking the region-specific connections between Rossby waves and surface temperature. Adjusting simulated large-scale winds to match the forcing data can reduce these biases.

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Climate model performance at the air–sea interface has long been overlooked across the Southeast Asian seas. We thus assess various regional model physics configurations in this regard. Finding one optimal configuration is challenging: reliable rainfall rarely coincides with correct radiative heating. Simulations of rainfall however yield more dissensus, suggesting that this variable should be prioritized, for which the best results are obtained with the cumulus convection scheme of Tiedtke.

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Our study completed an uncertainty analysis of a modelling experiment for multi-decade biophysical conditions (e.g., plant processes and hydrology) and carbon (C) flux simulations at a temperate swamp in Southern Ontario, Canada. The adopted uncertainty analysis technique (GLUE) improved the modelling outcomes of our study. Consequently, the findings of this research will help inform decision making on future C flux modelling experiments and peatland C management in temperate swamps.

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We present two forecasting methods for extreme precipitation and wind in Europe, using stochastic weather generators and past atmospheric patterns. One targets precipitation via weather model reforecasts; the other predicts wind from large-scale patterns. Both outperform standard weather models up to 10 days ahead, offering improved accuracy for both individual and compound extreme events.

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This study evaluates the performance of the varying-resolution mesh model, E3SMv2-MPAS, in simulating key Arctic processes—from sea ice distribution and surface properties to the complex, three-dimensional structures of the Atlantic Water layer. We also pinpoint specific areas where the model still struggles, providing valuable directions for future model development. This progress means scientists can now use this tool more confidently to understand how Arctic ocean layers work and change.

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Reliable climate prediction requires accurate initialization of the ocean state. We developed a new data assimilation system that incorporates ocean temperature and salinity observations into a fully coupled climate model. This system improves simulations of Earth system variability from years to decades, and enhances skills in simulating winter temperature and precipitation variability over the United States. The results advance more reliable and skillful climate predictions.

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This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.

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A comparison of simulated and observed overturning transports across the Overturning in the Subpolar North Atlantic Program sections for the 2014–2022 period is presented. Eighteen ocean simulations participate in the study. The simulated transports are in general agreement with observations. Analyzing overturning circulations in both depth and density space together provides a more complete picture of the overturning properties. The study serves as a benchmark for evaluation of ocean models.

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Simulations of convective precipitation events with microphysics schemes of varying complexity are statistically evaluated against polarimetric radar observations. Convective precipitation is potentially hazardous and difficult to predict. Cloud microphysics schemes contribute significantly to this uncertainty. Depending on the microphysics used, the simulated precipitation distribution varies significantly. The main reason for these differences are the underlying rain drop size distributions.

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This study compares the performance of the independently developed EPICC-Model with CAMx and CMAQ in simulating PM_2.5 and O₃ in China. It finds that EPICC-Model excels in simulating summer ozone peaks, accurately captures pollution characteristics in highly polluted areas, and better reproduces persistent compound pollution processes. Furthermore, this study reveals common issues among the models and directions for improvement, providing a basis for optimizing global air quality models.

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Land models need reliable soil properties to simulate water, but these settings are uncertain. We analyzed Community Land Model version 5 simulations for the United States from 1980 to 2010 to see how different soil settings shape patterns of soil moisture. Compared with an independent global land dataset, patterns align in many regions but differ in water-limited areas such as the Great Plains. Our maps show where to improve settings and guide future tests with observations.

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Both physical and ecosystem properties of the ocean are rapidly changing. These changes anticipating ecosystem responses to environmental change and effectively managing marine. The model-based predictions and their performance in the historical states of the ocean must be carefully evaluated against observations. In this study a coupled ocean and sea-ice simulation during 1993–2018 using observations. We focus on the Bering Sea shelf, which is the largest productive ecosystem in the US.

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The UVIOS2 model has been evaluated at Davos, Switzerland during the UVCIII campaign. The accuracy in the modelled UV indices has been assessed for different combinations of model inputs. A good overall agreement between UVIOS2 and the world reference spectroradiometer QASUME was found (average ratio of ~ 1 between the modelled and measured UV index), although the variability in the ratio can be large under cloudy conditions.

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We presented NAAC, a high-resolution, two-decade simulation of coastal hydrodynamics using the 3D unstructured-grid model SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model; v5.11.0). This model seamlessly integrates simulations from the North American Atlantic coastal ocean to tidal tributaries and wetlands. By bridging the gap between large-scale regional ocean models and fine-scale shallow water systems and intertidal zones, this work helps fill observational gaps and provides valuable insights into studies like saltwater intrusion.

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Ozone (O₃) pollution harms rice production and threatens food security. To understand these impacts, we calibrated a crop model using unique data from experiments where rice was grown in open fields under controlled O₃ exposure (free air). This is the first time such data have been used to improve a model's ability to predict how rice responds to O₃pollution. Our work provides a more accurate tool to study O₃'s effects and guide strategies to protect agriculture.

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Recent advancements in regional ocean modelling allow higher resolution simulations providing improved estimates of the large-scale ocean state, while also revealing new insights into the fine-scale processes connecting the open ocean to the continental shelf seas. Our study highlights the importance of increased model resolution in regions of the ocean that are particularly turbulent while in quasi-stable circulation regions (e.g., jets), the current state-of-the-art global models do suffice.

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This study evaluates the implementation of predicted rime mass distribution in the bin microphysics scheme DESCAM. Based on the ‘fill-in’ concept, the model allows a smooth transition between unrimed and graupel ice particle properties. The implementation is tested for a heavy snowfall event observed during the ICE-POP 2018 field campaign. The new version of DESCAM gives a better agreement with the observations with significant changes in the precipitation amount and spatial distribution.

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Debris flows are fast-flowing events that are saturated with granular material. They naturally occur in steep creeks and are a threat to local communities. Scientists have turned to numerical models to better understand how they behave. We investigate the accuracy of a numerical model that relies on modelling the debris flow as a mixture of a granular phase and a fluid phase. We focus on a demonstration of the capacity of the model to reliably represent the behaviour of the flow at different scales.

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We assess the implementation of additional organic carbon pathways into a global setup of a numerical model, which simulates the ocean circulation, sea ice, and biogeochemical processes. With a focus on the Arctic Ocean, this model tracks the temporal and spatial dynamics of phytoplankton, exudation of organic carbon, and its aggregation to so-called transparent exopolymer particles. We evaluate the simulation using measurements from ship-based and remote-sensing campaigns in the Arctic Ocean.

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The distribution of ozone (O₃) and its precursors (NO_x, VOCs) is explored using the chemistry–climate model MUSICAv0 and evaluated using measurements from the Michigan–Ontario Ozone Source Experiment. A custom grid of ~7 km was created over Michigan. A sector-based diurnal cycle for anthropogenic nitric oxide was included in the model. This work shows that grid resolution plays a more important role in relation to O₃ precursors and that the diurnal cycle significantly impacts nighttime O₃ formation.

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This study evaluated the PM_2.5 simulation by the latest CRACMM mechanism coupled with CMAQ, covering different seasons and specific regions over China. Results derived by CRACMM are compared with two well-established chemical mechanisms, Saprc07 and CB6. Differences in PM_2.5 and SOA drivers between CRACMM and the two existing mechanisms are further explored. Results provide a solid foundation for the further application of CRACMM in understanding and regulating air pollution globally.

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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.

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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.

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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.

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This study presents the Offline Fennel model, a tool designed to simulate ocean biogeochemical processes efficiently. By using existing hydrodynamic data, the model significantly reduces computation time from 6 h to just 30 min. We tested its accuracy in the northern Gulf of Mexico and found it closely matches physical–biogeochemical coupled simulations. This model allows researchers to conduct more tests and simulations without the need for extensive computational resources.

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The recent WeatherBench 2 provides a versatile framework for the verification of deterministic and ensemble forecasts. In this paper, we present an explicit extension to binary forecasts of hydroclimatic extremes. Seventeen verification metrics for binary forecasts are employed, and scorecards are generated to showcase the predictive performance. The extension facilitates more comprehensive comparisons of hydroclimatic forecasts and provides useful information for forecast applications.

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This study investigates how climate models warm in response to simplified carbon emissions trajectories, refining the understanding of climate reversibility and commitment. Metrics are defined for warming response to cumulative emissions and for the cessation of emissions or ramp-down to net-zero and net-negative levels. Results indicate that previous concentration-driven experiments may have overstated the Zero Emissions Commitment due to emissions rates exceeding historical levels.

Executive editor

As a core contribution to CMIP7, this paper offers an idealized yet insightful projection of climate system behavior during the net-zero transition. Its policy relevance is clear, as it effectively links human-driven emission mitigation efforts with their climatic consequences.

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We ran a convection-permitting model with regional mesh refinement (3.25 km and 800 m) to simulate present-day wind and solar capacity factors over California, coupling it to an energy generation model. The high-resolution models captured realistic seasonal and diurnal cycles, with wind markedly better than a 25 km model and solar outperforming a 3 km operational forecast. We highlight the critical role of resolution, modeling assumptions, and data reliability in renewable energy assessment.

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The impact of horizontal resolution and model time step on extreme precipitation over Europe is examined in OpenIFS. We find that the biases are reduced with higher horizontal resolution but not with a shorter time step. The large-scale precipitation is sensitive to the horizontal resolution and time step; however, the convective precipitation is sensitive to the model time step. Higher horizontal resolution is more important for extreme precipitation simulation than a shorter time step.

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This study provides a comprehensive evaluation of unstructured meshes using the integrated Atmospheric Model Across Scales (iAMAS) over Antarctica, encompassing both surface and upper-level meteorological fields. Comparisons with the fifth-generation reanalysis (ERA5) from the European Centre for Medium-Range Weather Forecasts and observational data indicate that iAMAS performs well in simulating the Antarctic atmosphere.

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We made a new regional ocean model to assist fisheries and ecosystem managers in making decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature and nutrient and oxygen levels and can even reproduce metrics used by, and important to, ecosystem managers.

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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.

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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity, dynamics, and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote sensing products. The model realistically predicts the structure and composition as well as the seasonality of carbon and water fluxes at both sites.