Earth and Environmental Sciences

Despite extensive global progress monitoring under the 2030 Agenda, existing Sustainable Development Goal (SDG) assessment frameworks remain structurally blind to within-country distributional disparities. This study addresses this gap by developing a methodologically transparent composite SDG index for multi-level governance assessment, applying it to 218 Nomenclature of Territorial Units for Statistics (NUTS 2) regions across the European Union over the period 2015–2022 (1744 region-year observations). In this context, the term “region-year observations” refers strictly to the balanced panel data structure, which is calculated by observing 218 distinct sub-national regions continuously over an 8-year period (218 regions × 8 years The index aggregates four dimensions—social, economic, educational, and institutional—using min-max normalization. The analysis yields three main results: (1) Spatial econometric analysis reveals strong, persistent positive spatial autocorrelation, with high-performing clusters concentrated in Northern and Western Europe and lagging clusters in Eastern and Southern peripheries. (2) A spatial error model identifies institutional governance quality as a consistent statistical predictor of sub-national SDG performance. The significance of the spatial error parameter (λ = 0.497) suggests that unobservable institutional and geographical common shocks systematically link neighboring regions. (3) Cluster analysis further distinguishes four regional archetypes: Disadvantaged, Leaders, Educated, and Transitional. These findings underscore the need for spatially aware SDG monitoring infrastructure and investment in institutional capacity as prerequisites for equitable governance, as integrating spatial dependencies is crucial to prevent national averages from masking severe regional developmental traps.

To better capture ecological quality under aerosol pollution stress, an AOD-integrated Remote Sensing Ecological Index (ARSEI) was developed for the Shenyang Metropolitan Area (2000–2025). Using Google Earth Engine, multi-source MODIS products were compiled to generate an annual growing-season ARSEI through PCA, combining PC1 and PC2 by variance-weighted contributions. Long-term trends were assessed with Theil–Sen slope estimation and the Mann–Kendall test, future persistence with the Hurst index, and drivers with an optimal parameter geographical detector. ARSEI closely matched conventional RSEI in multi-year pixel means (R2 = 0.98, p < 0.001) but identified larger “poor” (+0.4%) and “moderate” (+3.4%) areas from 2000 to 2025, indicating higher sensitivity to pollution-related stress. Ecological quality improved overall, with high grades in eastern mountainous forests and low grades in the central built-up core and surrounding croplands. Improvement was dominant (31.08% significant, 38.27% slight), while degradation was limited (4.27% significant, 13.92% slight) and concentrated in peri-urban expansion belts. Elevation was the strongest natural control, whereas land use and population were the leading socioeconomic drivers with increasing influence over time. Finally, we delineated differentiated management zones based on current status and projected trajectories to support targeted regional governance.

Forest restoration depends on producing seedlings able to convert nursery inputs into functional traits that persist after outplanting. This study evaluated whether contrasting nursery resource-management profiles, derived from container volume, fertilization, and irrigation, shaped seedling quality and field establishment of Pinus devoniana. Seedlings were conditioned for six months under eight profiles and validated during one year under field conditions. Nursery evaluation included morphology, biomass allocation, Dickson Quality Index (DQI), nutrient status, and proline; field validation included survival, growth, ectomycorrhization, stomatal density, and lignification. Profiles differed significantly in root collar diameter, height, root biomass, total biomass, root–shoot ratio, and DQI. The 5 L fertilized and irrigated profile produced the highest integrated quality, with 140.9% more root biomass than the weakest root profile and 144.3% higher DQI than the lowest-quality profile. Nitrogen- and proline-separated nutrient and stress responses showed that higher nutrient status did not always imply lower stress. Field survival reached its highest value under the 5 L fertilized and irrigated profile, exceeding several 1 L profiles by 74.8%. DQI was positively associated with field survival (r = 0.71, p = 0.048), supporting a nursery-to-field carry-over effect. The findings highlight rooting space as a leverage point for improving reforestation outcomes.

Abstract. Solar-induced chlorophyll fluorescence (SIF) is a small light signal emitted during the initial steps of photosynthesis and can be observed across scales (from photosystem level to satellite observation footprints). To be able to model SIF, we need to understand the mechanistic processes (including both physical and biological) leading to the observed SIF signal. In this study, we implemented a representation of SIF emission and transmission processes into the terrestrial biosphere model QUINCY (“QUantifying Interactions between terrestrial Nutrient CYcles and the climate system”). We tested the model across three different boreal coniferous forests located in North America and Europe that have eddy covariance derived CO2 fluxes and tower-based SIF observations. We found that different SIF radiative transfer approaches (one based on mSCOPE, one on two-stream radiative transfer model L2SM, and one empirically based) overestimated the SIF signal, but showed no large differences in the timing of their seasonal and diurnal predictions. The two-stream radiative transfer model approach, L2SM, provided stable performance while being comparatively computationally efficient. Our parameterization for sustained non-photochemical quenching was important for successfully simulating the timing of the SIF seasonal cycle. However, our parameterization did not perform equally well at all three sites, likely because of different temperature regimes at each site. We further evaluated the potential of remote sensing-based SIF from TROPOMI (the TROPOspheric Monitoring Instrument) to provide accurate information on SIF and found that it could potentially be used in model development. This study demonstrated the usefulness of observations at various spatial scales and the linkages between SIF and GPP and their seasonal cycle at three different evergreen forest sites.

Nutrient pollution in freshwater systems poses major ecological challenges, requiring robust modelling tools to support effective water management. However, catchment-scale water quality modelling is often constrained by sparse monitoring networks and high parameter uncertainty associated with complex biogeochemical systems. This study presents an integrated modelling framework combining the MOBIDIC–ADR hydrological model with a newly developed BIO–ALGAE reactive component to simulate nutrient dynamics across large catchments. The framework employs spatially regularized ensemble calibration using PEST++ iterative ensemble smoother to estimate distributed pollutant loads while quantifying parameter and predictive uncertainty. The model was applied to the Arno River catchment (7990 km 2 ) in Italy, simulating 8 water quality constituents including dissolved oxygen, nitrogen species, phosphorus compounds, and algal biomass over a ten-year period (2011–2020). Using 8151 observations from sparse sampling locations, the calibration demonstrated the model's ability to reproduce observed patterns across multiple constituents. The model proved effective in identifying pollution hotspots, highlighting strong associations between urban areas and elevated carbonaceous biochemical oxygen demand and ammonium loads, whereas phosphorus displayed a more heterogeneous spatial distribution indicative of multiple source contributions. Despite limitations under low dissolved oxygen conditions, the approach effectively captured first-order reactive processes and provided spatially explicit load estimates with uncertainty bounds. This framework offers a practical decision-support tool for targeted water quality management in data-scarce environments.

Rainfall variability contributes to crop failures and an increase in food insecurity in regions vulnerable to climate change, such as East Africa. Although the impact of growing period rainfall variability on crop yields has been well studied in East Africa, characterizing rainfall anomalies during the crop harvesting season and their effect on crop yields remains unexplored. Here, we evaluated the phenomenon of unexpected excessive rainfall anomalies during the dry season (RAD) and its impact on cereal crop yields in Ethiopia from 1996 to 2022. For the evaluation, we used the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), in situ meteorological stations, and crop yield data. Our results showed that the frequency and severity of RAD events vary across Ethiopia. On average, RAD events occurred 14 times during 1992-2022. Among regions in Ethiopia, Gambela, Southern Nations, Nationalities, and Peoples', and Oromiya experienced the highest frequency and severity of RAD events. During RAD events, most cereal crops showed an average reduction in yields compared to yields during normal rainfall events in the dry season. However, significant (P < 0.05) reductions in crop yields occurred mainly in the Oromiya and Southern Nations, Nationalities, and Peoples' regions. Maize showed the highest yield reduction compared to other cereal crops. Our findings underscore the need for regularly monitoring RAD events to mitigate their impacts on food security in the region.

Abstract Snow depth remains one of the largest sources of uncertainty in satellite‐derived sea ice thickness (SIT). Here, we introduce the novel N a dir R adiometer and R adar S ynergy ( NaRRS ) method that combines data from Sentinel‐3's Microwave Radiometer (MWR) and Synthetic Aperture Radar Altimeter (SRAL) to retrieve Arctic snow depth on sea ice. The resultant snow depths are co‐located with SRAL‐derived radar freeboard, reducing spatio‐temporal mismatches in SIT processing. NaRRS achieves an of 0.72 and RMSE of 0.05 m in cross‐validation against Operation IceBridge snow depth data, and better matches IceBird observations than the modified Warren Climatology (mW99). Ice drafts estimated from coupled snow depth and freeboard align with mW99 against Beaufort Sea moorings but reduce bias by up to 50% against Fram Strait moorings. This work provides a proof‐of‐concept for simultaneous, co‐located snow depth and SIT retrievals, paving the way for next‐generation satellite missions and retrieval frameworks.

Abstract. East Asia is a major source of fossil fuel emissions and strongly influences regional and global CO2 concentrations. Quantifying natural carbon sinks in this region is therefore essential for improving climate projections and informing mitigation strategies. We estimated the Net Ecosystem Exchange (NEE) and ocean carbon fluxes over East Asia (18.5–54° N, 73–146° E) during 2010–2019 using a Bayesian inversion framework. The GEOS-Chem chemical transport model was combined with GOSAT ACOS v9 XCO2 retrievals, and region-specific prior uncertainties were assigned using standard deviations from land and ocean models. Posterior estimates show enhanced carbon uptake relative to the prior, with NEE increasing from −0.17 ± 0.08 to −0.31 ± 0.06 PgC yr−1 and ocean uptake changing slightly from −0.20 ± 0.03 to −0.21 ± 0.03 PgC yr−1. Simulated CO2 concentrations based on posterior fluxes agreed better with independent observations than those from prior fluxes. East Asia's terrestrial ecosystems exhibited net carbon uptake during 2010–2019, consistent with increasing Enhanced Vegetation Index (EVI) trends. However, several regions showed temporary positive NEE during 2015–2016, likely linked to the strong 2015/2016 El Niño. When fossil fuel and biomass burning are included, East Asia released a net flux of +3.45 PgC yr−1 to the atmosphere during 2010–2019. Natural sinks offset only ∼ 13.6 % of fossil fuel emissions, leaving a substantial residual source. Despite increased posterior sinks, they remain insufficient to counter regional emissions, sustaining elevated CO2 levels and continued outflow from East Asia.

Abstract The mechanisms governing ENSO’s growth, decay, and phase transitions are encapsulated by the recharge oscillator model (ROM), a coupled pair of differential equations for equatorial sea surface temperature (SST) and thermocline depth. While the ROMcan explain many of ENSO’s key characteristics, its representation of asymmetry is ambiguous owing to a lack of consensus about what processes are most important for observed ENSO asymmetry. For example, a key source of structural uncertainty in the ROM is whether drivers of ENSO asymmetry should be parameterized as stochastic or deterministic processes. In this work, we demonstrate how to (partially) sidestep this issue by treating SST and thermocline depth as observables of a dynamical system, rather than state variables, and modeling their evolution with the Koopman operator. While in both the ROM and the Koopman framework ENSO is represented by a single eigenmode, the Koopman eigenmode is a (learned) nonlinear function of the observables, permitting ENSO to evolve asymmetrically. We show that a simple model based on this eigenmode, analogous to the ROM, can capture many leading-order features of El Niño – La Niña asymmetry in the CESM2 pre-industrial simulation, including the faster springtime decay of El Niños, the higher prevalence of multi-year La Niñas, and the westward displacement of anomalies during La Niñas. We anticipate this Koopman framework may be useful for studying inter-model differences in ENSO’s projected future changes, which are not well-predicted by the ROM and appear related to climate models’ varied representation of ENSO asymmetry.

Abstract Extratropical cyclones are among the most damaging weather systems in midlatitudes; however, quantifying the influence of anthropogenic climate change (ACC) on individual storms remains challenging due to the interplay between thermodynamic and dynamical processes. Here, we assess the capacity of AI-based weather prediction (AIWP) models to both forecast and attribute two high-impact European extratropical cyclones with contrasting characteristics: storm Ciarán (November 2023), which underwent explosive cyclogenesis and extreme winds, and storm Claudia (November 2025), characterized by an intense atmospheric river.&amp;#xD;&amp;#xD;We evaluate four state-of-the-art AIWP models and benchmark them against operational forecasts from the ECMWF Integrated Forecast System. All AIWP systems skillfully reproduce the large-scale evolution of both storms several days in advance, albeit with event-dependent performance. Building on this skill, we apply a forecast-based storyline attribution framework comparing factual forecasts initialized from ERA5 with counterfactual simulations generated with a pseudo–global warming perturbation derived from CMIP6 historical simulations.&amp;#xD;&amp;#xD;The attribution analysis reveals physically coherent ACC fingerprints across sea-level pressure, low-level winds, moisture, and precipitation. Moisture-related signals are robust across models for both storms, while wind and circulation responses show greater model dependence, reflecting the differing levels of robustness of thermodynamic and dynamic responses to ACC. Precipitation attribution using the ECMWF Artificial Intelligence Forecasting System (AIFS) indicates regional ACC-induced increases consistent with near–Clausius–Clapeyron thermodynamic scaling, modulated by event-dependent dynamical responses that shape moisture availability and transport. These results demonstrate that AIWP models can support event-specific, computationally efficient ACC attribution within the forecast window, offering strong potential for assessing ACC influences on high-impact extratropical cyclones.&amp;#xD;

Abstract. Great attention has been paid to the short-term climate response following explosive volcanic eruptions, in order to understand effects on zonal winds, the polar vortex, and surface temperature across latitude. In contrast, several works have shown that evidence of volcanic forcing can persist for much longer in the stratosphere's chemical composition, even after the instigating aerosol population has dissipated. Heating by volcanic aerosols accelerates tropical upwelling, and thus drives an acceleration of the Brewer–Dobson Circulation (BDC), and enhances troposphere–stratosphere mass exchange. Even after tropical motion returns to its climatological mean, the anomalous mass exchange remains detectable in the stratosphere for several years. In this work, we use an age-of-air (AoA) tracer to diagnose stratospheric composition changes following the simulated 1991 Mt. Pinatubo eruption. Specifically, we employ simulation ensembles from the E3SMv2 climate model to identify statistically significant effects on zonal-mean AoA. In addition, we use the Residual Circulation Transit Time (RCTT) diagnostic to separate the effects of advective transport and mixing. We find that the Mt. Pinatubo eruption lowers AoA in the middle-to-upper stratosphere globally, primarily due to an accelerated residual meridional circulation. We also observe a localized increase of AoA near 20–100 hPa in the southern hemisphere (the hemisphere opposite the eruption), which we attribute to a damping of the seasonal BDC cycle by the volcanic aerosols. We suggest that a dampened seasonal BDC cycle is perhaps a generic result of any heating process driven by aerosols that evolve on timescales beyond seasonal in the meridional plane.

Abstract. Modelling the climate system on multi-millennial timescales is challenging when slow-response components, such as the deep ocean, vegetation, and ice sheets, must evolve alongside fast-response components such as atmospheric weather systems. This is crucial for investigating, for example, the dynamical structure of Earth's climate, including steady states, mapping the attractor of those states in a multi-dimensional phase space, and their response to external forcing and internal variability. Earth system models, such as those used in the Coupled Model Intercomparison Project (CMIP), are often too computationally expensive for simulations spanning many thousands of years. Moreover, simplified parameterizations and coarse resolutions typically employed in Earth Models of Intermediate Complexity (EMICs) can adversely affect the nonlinear interactions among the various climate components. Here, we describe a new tool, Biogeodynamics-Ice sheet-Geneva-MITgcm – or BIG-MITgcm for short – which attempts to fill in the hierarchy between these two classes of model. The core of BIG-MITgcm is a coupled MITgcm setup that includes atmospheric, ocean, thermodynamic sea ice, and land modules. To this, we asynchronously couple a vegetation model (BIOME4), a hydrological model (pysheds), and a new global-scale ice sheet model (MITgcmIS). The latter is implemented on the same cubed-sphere grid as MITgcm, using the shallow-ice approximation, and driven by a modified Positive Degree Day method to evaluate the ice-sheet surface mass balance. Here, we present a detailed description of the new ice sheet model and the coupling procedure employed. We evaluate BIG-MITgcm using a pre-industrial simulation initialized from observations of bedrock topography, together with a forced simulation over the 1979–2009 period. The model spontaneously grows plausible ice sheets. These two experiments allow us to assess the model's performance against CMIP-class models, as well as a combination of reanalyses and observations. To evaluate the ability of our model to represent completely different climate conditions and continental configurations, we also discuss a Permian-Triassic solution with a small ice sheet in the Northern Hemisphere. In summary, BIG-MITgcm successfully captures many large-scale properties of the current climate, suggesting that it will be a very useful tool for exploring current, past, and future climates. We conclude by discussing potential applications and future developments.

Abstract. Air-quality models frequently underestimate fine particle number concentration (PNC), particularly in the nucleation/Aitken range – while reproducing PM2.5 mass more accurately, suggesting that key number-forming processes are missing from current frameworks. We propose and investigate a physically motivated pathway, Recondensation-Induced Nucleation (RIN), in which pre-existing ambient aerosols are vaporized during combustion and subsequently re-nucleate as the exhaust cools, selectively boosting particle number with negligible impact on mass. Controlled four-stroke engine experiments demonstrate that a distinct nucleation mode (&lt; 30 nm) appears only when ambient aerosols are present in the intake air, providing direct laboratory evidence of RIN. Parcel-model simulations of H2SO4–H2O systems further examine particle evaporation under in-cylinder condition and the self-limiting nature of nucleation. A parameterized RIN module was implemented in the Community Multiscale Air Quality (CMAQ) model and evaluate under Taiwan/West-Pacific urban conditions. Without RIN, CMAQ underpredicted PNC by 75 % and overpredicted PM2.5 by 21 % at the Xitun urban site; incorporating RIN reduced the PNC bias to 22 % with negligible change in PM2.5. The RIN mechanism thus transfers accumulation-mode mass to Aitken-mode number, not only partially alleviates the low-PNC bias but also the low Aitken- to accumulation mode number ratio bias found at the Xitun site. While RIN improves PNC estimates, it also leads to overestimation at the smallest sizes, likely reflecting inherent limitations in the modal parameterization.

Vegetation plays a dual role in enhancing carbon sequestration while influencing ozone (O3) formation through biogenic volatile organic compounds (BVOCs). Yet, the relative contributions of these processes and their spatial coupling remain poorly quantified, limiting our understanding of the co-benefits and trade-offs between air quality improvement and carbon mitigation. In this study, we established an integrated modeling framework that couples land-use, vegetation dynamics, and atmospheric chemistry to quantify the co-effects of vegetation on air quality and carbon sequestration. The results show that vegetation dynamics enhanced China’s terrestrial carbon sink by 73.7–151.9 Tg C yr–1 and increased BVOC emissions by 13.6–17.5 Tg, leading to 9.1–10.5 μg m–3 increases in surface O3 concentrations. Both O3 and carbon sinks exhibited pronounced spatial heterogeneity. Regions where enhanced carbon sinks coincided with elevated O3 were mainly located in Northeast China, the North China Plain, and Central China. In contrast, co-beneficial regions, characterized by simultaneous increases in carbon sinks and decreases in O3, were concentrated in southeastern China. These findings provide the first spatially explicit evidence of heterogeneous trade-offs and synergies between air-quality improvement and carbon sequestration, offering scientific guidance for coordinated land-use planning and vegetation management under future climate scenarios.

Abstract A new wave of steel capacity additions in emerging economies threatens to lock in coal-based production for decades. By combining detailed steel production modelling with plant-level data in an integrated assessment model, we estimate that existing and planned coal-based steel plants could commit the world to nearly 60 GtCO 2 . If current policy and investment trends continue beyond current plans, committed emissions reach 114 GtCO 2 , consuming 20% of the remaining carbon budget for limiting peak warming to 1.7 °C. We show that 60% of this lock-in risk can be avoided at moderate average abatement costs of US$100–150 tCO 2 −1 . In India alone, 22 GtCO 2 of future emissions could be avoided by leveraging climate finance to redirect US$50 billion this decade towards hydrogen-ready direct reduction steel plants. Near-term investment decisions on new steelmaking capacity represent a critical opportunity to avert the carbon lock-in and align the sector with climate targets.

Abstract. We present a new 12 km nested resolution capability in the GEOS-Chem global model of atmospheric composition. This capability can be applied to simulations for any user-selected domain worldwide from March 2021 onward by accessing a new hourly cubed-sphere C720 (approximately 12 × 12 km2 resolution) global wind archive from the NASA GEOS-FP meteorological data assimilation system. We regridded the archive to support rectilinear GEOS-Chem Classic nested grid simulations worldwide at 0.125° × 0.15625° resolution and denote this as the 12 km GEOS-Chem capability. We evaluate this 12 km configuration of GEOS-Chem by comparison with the standard 25 km (0.25° × 0.3125° resolution) nested configuration in simulations of transport tracers, oxidant-aerosol chemistry, and inversions of satellite data using the Integrated Methane Inversion (IMI). The 12 km simulation features stronger vertical transport (up to 20 % lower surface 222Rn concentrations) because it better captures horizontal convergence both spatially and temporally. Aerosol lifetimes against deposition are shorter by a few percent. The 12 km oxidant-aerosol chemistry can better simulate urban observations of NO2, and shows stronger ozone urban titration together with slightly higher surface ozone background due to enhanced vertical transport. 12 km and 25 km inversions using the IMI show highly consistent results on the regional scale, but the 12 km inversion provides greater information and improved spatial detail to resolve emissions from different sectors.

Abstract. Because of topography, climate change exhibits complex regional imprints in the Alps. This study aims at understanding the processes that link elevation-dependent warming (EDW) – i.e. the modulation of temperature trends with elevation – at seasonal scale in the Alps with the surface energy balance. We investigate projected EDW patterns in the Alps using 7 km resolution simulations spanning the period 1961–2100 made with the Modèle Atmosphérique Régional (MAR), exploring scenarios SSP2-4.5 and SSP5-8.5 and driven by two general circulation models, EC-Earth3 and MPI-ESM1-2-HR. We find a larger yearly warming signal at high elevations (1.2 to 1.5 °C °C−1 of global warming) than at low elevations (1.1 to 1.3 °C °C−1 of global warming), with contrasted seasonal patterns and intensities (up to 2 °C °C−1 of global warming at high elevations in summer). EDW profiles are found to be different near the surface than in the free atmosphere. Near the surface, a maximum warming is found in spring at mid-elevations that is migrating to higher elevations in summer and autumn. This signal is not found in the free atmosphere. The elevation of the maximum warming is moving upward, consistently with the snowline migration over the years in a warming climate. Investigating surface energy balance trends reveals a link between the profiles of EDW and those of net shortwave radiation and energy used to melt snow. The snow-albedo feedback linked to the net shortwave radiation trend is found to be responsible for two thirds of the impact of the snowline on warming, while snow melt accounts for the last third. Melting limits the warming at high elevation when snow is persisting. We suggest that snow melting is an important driver of EDW that should be considered in any EDW-snow investigations.

Abstract Chemical weathering of carbonate (CWC) minerals plays an important role in global carbon cycle, as it bridges the atmospheric, lithospheric and hydrospheric carbon pools. However, data limitations have hindered an accurate estimation of the carbon sink flux induced by global CWC (CSF CWC ), as well as its response to environmental change. Conventional hydrochemical methods, which infer CSF CWC indirectly from riverine hydrochemistry, provide only catchment-integrated signals, yet cannot resolve the specific contribution of CWC across different climate, pedological, and ecological settings within a catchment. Here, we synthesize 2,444 globally-distributed in-situ CWC rates measured by rock tablet tests, and investigate the magnitude, spatiotemporal variation and controlling factors of CSF CWC using a machine learning model. We find that soil physicochemical properties (e.g., pH and moisture) play a more important role in determining global CWC spatial variation than climate, hydrology and vegetation factors. The machine-learning model developed in this study explains 68% of the variance in globally observed CWC-induced carbon sink flux values. Global application of our model indicates that CWC generates a carbon sink of 0.27 Pg C yr -1 worldwide, which is comparable to previous catchment-integrated estimates derived using different approaches, accounting for approximately 8% of the total terrestrial carbon sink. Over the past two decades, global greening has significantly accelerated global total carbon sink induced by CWC, with this acceleration particularly pronounced in Asia. Overall, this study provides a benchmark estimate of global CSF CWC and advances the mechanistic understanding of carbonate weathering. Our findings contribute to improving existing weathering models and reducing uncertainties in future projections of the terrestrial carbon sink.

Abstract. The Laurentian Great Lakes share several physical characteristics with the coastal ocean, including atmosphere–water interactions, rotational dynamics, and ice cover processes. However, their weak density stratification, relatively small surface area, and distinct seasonal mixing cycles pose unique challenges for numerical modeling. Modeling approaches and parameterizations developed for global applications, however, may yet provide valuable pathways for addressing persistent biases in lake models. To examine these possibilities, we develop a 3D hydrodynamic model for Lake Michigan-Huron (LMH) using the Modular Ocean Model version 6.0 coupled with the Sea Ice Simulator version 2.0 (MOM6-SIS2). Originally designed for global ocean and earth system modeling, MOM6 offers flexible vertical coordinate systems (VCSs) to maintain density gradients and improved handling of complex bathymetry, both potential advantages for application in inland water bodies like the Great Lakes. This is the first study to investigate MOM6-SIS2's ability to simulate key features of hydrography and circulation in freshwater systems under different VCSs. This study tested z∗ (depth-based) and hybrid (depth and isopycnal) VCSs. Simulations were performed for the years 2017 and 2018 and evaluated against in situ and remote sensing observations, as well as outputs from a contemporary Finite Volume Community Ocean Model (FVCOM) of LMH (LMH-FVCOM), used in an operational forecast system. MOM6-SIS2-LMH skillfully simulated daily averaged lake surface temperature (LST), vertical thermal structure, and ice concentration, with biases in LST and ice concentration generally below 0.5 °C and 2 %, respectively. It also produced comparable results to LMH-FVCOM in terms of LST, vertical thermal structure, and ice concentration. Both VCSs (z∗ and hybrid) successfully captured large-scale circulation patterns and seasonal overturning. The hybrid VCS, reduced excessive thermocline diffusion in deep waters, observed in both FVCOM and MOM6-SIS2 with z∗ VCS and allowed the model to maintain ecologically important deep cold water in the summer months. These improvements highlight the potential of MOM6-SIS2 to successfully simulate lake dynamics and offer the potential to more accurately resolve the delicate balance of thermal structure and mixing in stratified lake environments. However, the limited nearshore resolution resulting from MOM6's structured grid degraded the simulation of flows through the Straits of Mackinac, as well as nearshore temperature and water level variability.

Abstract. Simulating the composition and evolution of organic aerosol (OA) in Earth System Models (ESMs) presents significant challenges due to the high computational demands of detailed chemical mechanisms. The computationally efficient ORACLE module employs the volatility basis set framework and can simulate secondary organic aerosol (SOA) formation from a range of precursors, including volatile (VOCs), intermediate-volatility (IVOCs), semi-volatile (SVOCs), and low-volatility organic compounds (LVOCs). In this study, a lite configuration of the ORACLE v1.0 module (ORACLE-lite) is implemented into the TM5-MP global chemical transport model (CTM), which represents the chemistry-transport component of the EC-Earth3-AerChem ESM. SOA formation from anthropogenic VOCs is neglected to reduce the number of surrogate species and further improve computational efficiency. For the standalone TM5-MP simulation, the global annual mean surface total OA concentration using ORACLE-lite is approximately 1.1 µg m−3, representing a 25 % increase compared to the previous version of the model. The annual atmospheric OA burden also increases by 50 %, reaching 3.67 Tg. Corresponding predictions from EC-Earth3-AerChem are slightly higher, with a surface total OA concentration of 1.16 µg m−3 and an atmospheric burden of 3.83 Tg, representing increases of 30 % and 60 %, respectively, compared to the previous version of the model. Comparison of monthly measured PM2.5 OA concentrations from Europe and the US with the corresponding predictions shows that the models bias is reduced by approximately half in the standalone TM5-MP simulation and by a factor of three in EC-Earth3-AerChem when ORACLE-lite is implemented. These enhancements enable more accurate and computationally feasible assessments of the climate impacts of individual organic aerosol components in future ESM studies.

Abstract Pyrocumulonimbus (pyroCb) represents an extreme manifestation of wildfire-atmosphere interactions, capable of intensifying fire behaviour and generating severe atmospheric pollution. Although previous studies have suggested that climate change may increase environmental conditions favourable for pyroCb occurrence, a quantitative assessment of how these changes translate into future pyrocumulonimbus occurrence probabilities remains lacking. In this study, we combine statistical modelling of pyroCb occurrence with climate projections to conduct a probabilistic assessment of future pyroCb risk across temperate southeast Australia. Using output from of the NSW and ACT Regional Climate Modelling project, Phase 2 (NARCliM2.0), we examine future changes in two key environmental indices associated with pyroCb-the Continuous Haines Index (C-Haines) and the Fuel Moisture Index (FMI)-under a low-emission scenario (SSP1-2.6) and a high-emission scenario (SSP3-7.0). Then, a previously developed statistical model is used to estimate changes in pyroCb occurrence probability driven by C-Haines and FMI. The results indicate an overall increase in pyroCb probability under future climate conditions, with larger increases projected for the near future (2025-2059) than for the far future (2065-2099). The most pronounced probability increases are found in northern New South Wales. This study provides methodological support for the quantitative assessment of long-term extreme wildfire risk.

Local policymakers require spatially detailed climate projections to plan heat adaptation strategies, yet regional climate models (RCMs) at 5–12 km resolution cannot resolve the microclimatic heterogeneity that governs urban heat exposure. We present a statistical-dynamical downscaling method that combines bias-corrected EURO-CORDEX ensemble output with the mesoscale urban climate model FITNAH-3D to produce 50 m resolution maps of heat-related characteristic days across the German federal state of Baden-Württemberg (~36,000 km 2 , 24.7 million grid cells). The method transfers the spatial temperature pattern simulated by FITNAH-3D under idealized autochthonous conditions to the full temporal variability of the RCM ensemble through a validated empirical correction factor. Validation against 66 stations over the reference period (1971–2000) yields mean absolute errors (MAE) of 6 days/year for summer days (≥25 °C) and 2 days/year for hot days (≥30 °C). The downscaled ensemble mean projects, under +2 K summer warming (approximately RCP 4.5 mid-century), an average of 55 summer days and 17 hot days per year, with intra-domain ranges of 13–97 and 2–42, respectively. Under +3 K warming (approximately RCP 8.5 mid-century), tropical nights increase to a domain-averaged 3 per year, with maxima of 51 in densely built urban cores. A sensitivity analysis shows that the correction factor is robust: ±10% perturbation changes MAE by less than 2 days/year. We discuss the effective resolution after post-processing, the physical basis for the spatial transferability of the correction factor, and the implications of ensemble spread for adaptation planning. The methodology has been operationalized in Baden-Württemberg's Climate Atlas.

Abstract Layer Precipitable Water (LPW) characterizes the vertical structure of atmospheric moisture and is essential for accurate weather forecasts. China's FY‐4B satellite delivers near‐real‐time LPW products, but is constrained by large uncertainties. To address these limitations, we first integrated spherical cap harmonic analysis with extreme gradient boosting to enhance the Total Precipitable Water (TPW), and then calibrated the LPW by proposing a novel proportional allocation model considering spatiotemporal variability. Validation against radiosonde indicates that the root‐mean‐square errors of the FY‐4B LPW were reduced from 3.0 to 1.9 mm in the low layer, 4.2 to 2.3 mm in the mid layer, and 2.5 to 1.8 mm in the high layer, with the bias reduced from a maximum of −1.5 mm to near zero. Further evaluation with ERA5 demonstrates enhanced spatial consistency of the modified product. This work fills the gap of high‐accuracy LPW data, and supports more accurate forecasting and early‐warning applications.