Model Resolution Research Articles

Monte Carlo post-processing for radiation hydro simulations of accreting planets in protoplanetary disks

This paper is part of a series investigating the observational appearance of planets accreting from their nascent protoplanetary disk (PPD). We evaluate the differences between gas temperature distributions determined in our radiation hydrodynamical (RHD) simulations and those recalculated via post-processing with a Monte Carlo (MC) radiative transport (RT) scheme. Our MCRT simulations were performed for global PPD models, each composed of a local 3D high-resolution RHD model embedded in an axisymmetric global disk simulation. We report the level of agreement between the two approaches and point out several caveats that prevent a perfect match between the temperature distributions with our respective methods of choice. Overall, the level of agreement is high, with a typical discrepancy between the RHD and MCRT temperatures of the high-resolution region of only about 10 percent. The largest differences were found close to the disk photosphere, at the transition layer between optically dense and thin regions, as well as in the far-out regions of the PPD, occasionally exceeding values of 40 percent. We identify several reasons for these discrepancies, which are mostly related to general features of typical radiative transfer solvers used in hydrodynamical simulations (angle- and frequency-averaging and ignored scattering) and MCRT methods (ignored internal energy advection and compression and expansion work). This provides a clear pathway to reduce systematic temperature inaccuracies in future works. Based on MCRT simulations, we finally determined the expected error in flux estimates, both for the entire PPD and for planets accreting gas from their ambient disk, independently of the amount of gas piling up in the Hill sphere and the used model resolution.

Stable Boundary Layers in an Arctic Fjord‐Valley System: Evaluation of Temperature Profiles Observed From Fiber‐Optic Distributed Sensing and Comparison to Numerical Weather Prediction Systems at Different Resolutions

AbstractStable boundary layers (SBLs) commonly form during the Arctic polar night, but their correct representation poses a major challenge for numerical weather prediction (NWP) systems. To enable detailed model verification, we performed measurements of the lower atmospheric boundary layer with airborne fiber‐optic distributed sensing (FODS), a tethered sonde and ground‐based eddy‐covariance (EC) measurements during contrasting synoptic forcings in a fjord‐valley system in Svalbard. The FODS‐derived temperature variances and static stability profiles are used to investigate the spatial and temporal evolution of different inversion types. The strong gradients of the inversions are accompanied by an increased temperature variance, which is related to enhanced buoyancy fluctuations. The observed vertical temperature and wind speed profiles are compared to two configurations of the HARMONIE‐AROME system with different horizontal resolutions at 2.5 and 0.5 km. The higher‐resolution model captures cold pool and low level jet formation during weak synoptic forcing, resulting in a well‐represented vertical temperature profile, while the coarser model exhibits a warm bias in near‐surface temperatures of up to 8 K due to underestimated inversion strength. During changing background flow, the higher‐resolution model is more sensitive to misrepresented fjord‐scale wind directions and performs less well. The results indicate the importance of the ratio between nominal horizontal model resolution and valley width to represent SBL features. Our results underline the substantial benefit of spatially resolving FODS measurements for model verification studies as well as the importance of model and topography resolution for accurate representation of SBLs in complex terrain.

A hybrid ice-mélange model based on particle and continuum methods

Abstract. Ice mélange, a composite of sea ice and icebergs, can have a major influence on sea-ice–ocean interactions. However, ice mélange has not been represented in climate models because numerically efficient realizations do not exist. This motivates the development of a prototype of a dynamic hybrid ice-mélange model that we present in this paper. In our approach, icebergs are included as particles, while sea ice is treated as a continuum. To derive a joint continuum for ice mélange, we integrate particle properties into the sea-ice continuum. Thus, icebergs are viewed as thick, compact pieces of sea ice. The ice-mélange formulation is based on the viscous–plastic sea-ice rheology, which is currently the most widely used material law for sea ice in climate models. Starting from the continuum mechanical formulation, we modify the rheology such that icebergs are held together by a modified tensile strength in the material law. Due to the particle approach, we do not need highly resolved spatial meshes to represent the typical size of icebergs in ice mélange (< 300 m). Instead, icebergs can be tracked on a subgrid level, while the typical resolution of the sea-ice model can be maintained (≥10 km). This is an appealing property for computational efficiency and for an inclusion within large-scale models. In idealized test cases, we demonstrate that the proposed changes in the material law allow for a realistic representation of icebergs within the viscous–plastic sea-ice rheology. Furthermore, we show that subgrid dynamics, such as polynya formation due to grounded icebergs, can be modelled with the hybrid approach. Overall, this suggested extension of the viscous–plastic sea-ice model is a promising path towards the integration of ice mélange into climate models.

Graph Model for Conflict Resolution Considering Heterogeneous Behavior Based on Hesitant Fuzzy Preference and Social Network Analysis

Graph Model for Conflict Resolution Considering Heterogeneous Behavior Based on Hesitant Fuzzy Preference and Social Network Analysis

A diffusion-based super resolution model for enhancing sonar images.

Improved hardware and processing techniques such as synthetic aperture sonar have led to imaging sonar with centimeter resolution. However, practical limitations and old systems limit the resolution in modern and legacy datasets. This study proposes using single image super resolution based on a conditioned diffusion model to map between images at different resolutions. This approach focuses on upscaling legacy, low-resolution sonar datasets to enable backward compatibility with newer, high-resolution datasets, thus creating a unified dataset for machine learning applications. The study demonstrates improved performance for classifying upscaled images without increasing the probability of false detection. The increased probability of detection was 7% compared to bicubic interpolation, 6% compared to convolutional neural networks, and 2% compared to generative adversarial networks. The study also proposes two sonar specific evaluation metrics based on acoustic physics and utility to automatic target recognition.

Performance of the medium and high horizontal resolution models from HighResMIP-CMIP6 in simulating synoptic-scale cyclones over South America

Performance of the medium and high horizontal resolution models from HighResMIP-CMIP6 in simulating synoptic-scale cyclones over South America

CRFS: A Decision Conflict Resolution Model Based on Human–Machine Coordination in Equipment Manufacturing

CRFS: A Decision Conflict Resolution Model Based on Human–Machine Coordination in Equipment Manufacturing

Refining the role of bathymetry, hydrodynamics and upwelling at various scales along the coral reefs at Sodwana Bay, South Africa

Hydrodynamics and physical processes that occur at various length and time scales strongly influence coral reefs. Therefore, understanding the interactions between reefs, hydrodynamics and other physical processes is crucial for the maintenance and survival of reef systems. Coral reefs around the world are under increasing threat to global climate change, and additionally coral bleaching is a major concern for the health and survival of these reefs. Some marginal coral reefs are situated in areas where the complex ocean flow patterns interact with topographical features, providing possible refuges to rising ocean temperatures and coral bleaching. A prominent example is the Sodwana Bay coral reef system which has shown resilience to coral bleaching. This resilience has been attributed to cold water temperature anomalies that cause short-term temperature fluctuations on the reefs. This study explores hydrodynamics at various scales around the Sodwana Bay coral reefs and associated short-term temperature anomalies using a flexible mesh hydrodynamic model of the southwest region of the Indian Ocean, nested within a global ocean model. The nested hydrodynamic model better replicates the observed temperature anomalies when compared to the the reanalysis NEMO global ocean model. The higher model resolution around Sodwana results in less numerical mixing and smoothing of the temperature fields in the nearshore region when compared to the reanalysed NEMO global ocean model leading to a better replication of the local hydrodynamics around the Sodwana region. The anomalies investigated were associated with remote upwelling of cold water near the Delagoa Peninsula, followed by advection from the Delagoa Bight towards the Sodwana region. The separation of the strong intermittent southward stream from the Delagoa Peninsula is strongly linked to the upwelling at the Delagoa Peninsula. An analysis of the hydrodynamic patterns during the anomaly periods reveal that when the strong southward stream reattaches to the coastline, it typically does so south of Sodwana. The reattachment of the stream has an inertial effect and pushes the flow of water against the coastline which deflects the flow northwards up past Sodwana resulting in a northward current reversal along the Sodwana coastline which agrees with observed current reversals during the anomaly periods by insitu measurements taken on the Sodwana reefs. The model also revealed that local upwelling occurs within the Sodwana canyons during this event, making the water in the canyons colder than the surrounding water. When the locally upwelled water spreads over the reef system, the anomaly amplitude is enhanced by approximately 20 %.

Sources of PM2.5 exposure and health benefits of clean air actions in Shanghai.

Estimating PM2.5 exposure and its health impacts in cities involves large uncertainty due to the limitations of model resolutions. Consequently, attributing the sources of PM2.5-related health impacts at the city level remains challenging. We characterize the health impacts associated with chronic PM2.5 exposure and anthropogenic emissions in Shanghai using a chemical transport model (GEOS-Chem) and its adjoint. By incorporating high-resolution satellited-derived PM2.5 estimates into the calculation, we investigate the response of PM2.5 exposure and its related health impacts in Shanghai to changes in anthropogenic emissions from each individual region, species, sector, and month. We estimate that a 10% decrease in anthropogenic emissions throughout China avoids over 752 (506-1,044) PM2.5-related premature deaths in Shanghai, with changes in local emissions potentially saving 241 (161-334) lives. Ammonia (NH3) emissions are identified as the marginal dominant contributor to the health impacts due to the NH3-limited PM2.5 formation within the city, thus controlling NH3 emissions at both the local and regional scales are effective at reducing the population's exposure to PM2.5. A negative response of the PM2.5 exposure to local nitrogen oxides (NOx) emission changes is detected in winter. Even so, controlling NOx emissions is still justified since the negative impacts decrease as anthropogenic emissions decline and NOx emission reductions benefit the public health on average. The anthropogenic emission changes due to Clean Air Actions helped avoid 3,132 (2,108-4,346) PM2.5-related premature deaths in 2019 relative to 2013, most of which are associated with emission reductions in the agricultural and industrial sectors.

Impacts of model resolution on the simulation of sea-level variability by a global ocean-sea ice model

The effects of model resolution on the simulation of sea-level variability were analyzed based on the second-generation climate system ocean model from the State Key Laboratory of Numerical Modeling for Atmospheric Science and Geophysical Fluid Dynamics, Institute of Atmosphere Physics (LICOM2) with resolutions of 1° (LICOM2-L) and 0.1° (LICOM2-H).The interannual variability, decadal variability, and long-term trends of the dynamic sea level (DSL) are estimated using a multivariate linear regression model based on the LICOM2-L and LICOM2-H datasets during 1958–2007. The analysis reveals that the distributions of interannual and decadal variability, as well as long-term trends, are consistent between the LICOM2-L and LICOM2-H simulations in the tropics and mid-latitudes. However, differences in these variabilities are most pronounced in the regions of the western boundary currents and Antarctic Circumpolar Current, primarily due to variations in thermosteric sea level (TSSL) and halosteric sea level. In contrast, the DSL variability differences in the Southern Ocean are mainly due to the TSSL. Analyses of ocean heat content (OHC) budgets suggest that the differences between the LICOM2-L and LICOM2-H simulations are mainly in decadal variability and long-term trends. The interannual and decadal variabilities of OHC are significantly influenced by both large-scale mean advection and eddy-induced transport. The latter plays a more pronounced role in high-latitude regions and contributes notably to decadal variability and trend differences. At the equator, eddy-induced transport is the primary driver of long-term trends, accounting for 80% of the total contribution, while the large-scale mean advection contributes the remaining 20%. These findings underscore the complex interplay between mean advection and eddy processes in shaping the thermohaline structure and sea-level variability in the ocean models.

Sub-daily scale rainfall extremes in India and incongruity between hourly rain gauges data and CMIP6 models

Self-recording rain gauges hourly rainfall data from 1969 to 2010 have been utilized to identify rain events at a sub-daily scale. At the sub-daily scale, a significant decrease in the frequency of heavy rainfall events (HREs) is observed over central India and northeast India, while an increase is observed over the northern west coast of India. Frequency of short-duration HREs over central India and long duration HREs over northern west coast of India is increased in the recent decades than in earlier decades. Incongruity with the observations, CMIP6 historical and AMIP high temporal resolution models are not able to simulate the short-duration HREs and, in turn, the observed trends at a sub-daily scale over the India landmass. The inability of CMIP6 models to predict short-duration HREs suggests caution in predicting future projections of extreme precipitation at a sub-daily scale and highlights the need for further improvements in climate models.

Dispute Resolution Model in Sewangi Island Village Communities Based on Wetlands

Dispute Resolution Model in Sewangi Island Village Communities Based on Wetlands

Assessing the impact of digital elevation model resolution on hypsometric analysis in large river Basins (India): a non-parametric statistical approach

Assessing the impact of digital elevation model resolution on hypsometric analysis in large river Basins (India): a non-parametric statistical approach

Impact of data structure types and spatial resolution on landslide volumetric change measurements

Terrain is a dynamic component of the landscape, subject to rapid changes, particularly in scenarios such as landslides. This study investigates how the spatial resolution and data structure of digital terrain models (DTMs) influence the estimation of landslide volume changes. We selected a landslide formed by the undercutting action of the Belá River in Slovakia as our research site. Our findings indicate that raster data structures, across various spatial resolutions, generally yield more consistent volume estimates compared to 3D mesh data structures. Nonetheless, at higher spatial resolutions (0.1 m and 0.25 m), the 3D mesh data structure demonstrates superior capability in capturing detailed terrain features, resulting in more precise volume estimations of the landslide.

Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning

Methane emissions from dairy farms are a significant driver of climate change, yet their relationship with farm-specific practices remains poorly understood. This study employs Sentinel-5P satellite-derived methane column concentrations as a proxy to examine emission dynamics across 11 dairy farms in Eastern Canada, using data collected between January 2020 and December 2022. By integrating advanced analytics, we identified key drivers of methane concentrations, including herd genetics, feeding practices, and management strategies. Statistical tools such as Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) addressed multicollinearity, stabilizing predictive models. Machine learning approaches—Random Forest and Neural Networks—revealed a strong negative correlation between methane concentrations and the Estimated Breeding Value (EBV) for protein percentage, demonstrating the potential of genetic selection for emissions mitigation. Our approach refined concentration estimates by integrating satellite data with localized atmospheric modeling, enhancing accuracy and spatial resolution. These findings highlight the transformative potential of combining satellite observations, machine learning, and farm-level characteristics to advance sustainable dairy farming. This research underscores the importance of targeted breeding programs and management strategies to optimize environmental and economic outcomes. Future work should expand datasets and apply inversion modeling for finer-scale emission quantification, advancing scalable solutions that balance productivity with ecological sustainability.

Contrasting Southern Ocean Sea Level Responses to Surface Flux Changes in Eddy-Rich and Eddy-Parameterized Climate Model Configurations

Abstract The impact of ocean model resolution on sea level projections in the Southern Ocean is investigated using eddy-rich (ER) and eddy-parameterized configurations of the Max Planck Institute Earth System Model under the Shared Socioeconomic Pathway (SSP) 5-8.5 scenario. We employ the Flux-Anomaly Forced Model Intercomparison Project (FAFMIP) experiment—heat, stress, and freshwater perturbations—at both resolutions to pinpoint the sources of these differences. South of 55°S, we found that the changes in thermosteric and halosteric sea levels vary substantially between resolutions due to different responses to freshwater perturbations. In the eddy-parameterized model, the resulting increase in stratification suppresses the mixing of salt and heat from the Circumpolar Deep Water with surface layers. These cause differences in the response of surface fluxes and meridional transports yielding an increase in thermosteric sea levels and a decrease in halosteric sea levels. In the eddy-rich configuration, the main driver of eddy-induced warming and salinification between 40° and 44°S is wind stress perturbations. The efficiency of direct eddy effects in ER is restricted to small areas such as the Agulhas Retroflection, the Brazil–Malvinas confluence zone, the Tasman Sea, and, to some extent, the Antarctic Circumpolar Current (ACC). Contrary to expectations, ACC transport increases in the eddy-rich model while decreasing in the eddy-parameterized model under the SSP5-8.5 scenario. FAFMIP results reveal that this decrease is a result of the overcompensation of wind-induced changes by freshwater flux forcing. These results underscore the critical importance of high-resolution models for capturing the processes in sea level projections in the Southern Ocean and beyond. Significance Statement We studied how ocean model resolution affects sea level projections in the Southern Ocean using Max Planck Institute Earth System Model simulations. Higher-resolution models provide a more accurate representation of ocean circulation and its response to changing forcings. We examined how surface heat, momentum, and water fluxes, both separately and combined, shape ocean dynamics. In a strong global warming scenario, significant differences in steric sea level change were observed south of 55°S between the model that simulates eddies and the one that has their effects parameterized. The response to surface freshwater forcing is the primary cause of these differences. Our findings emphasize the critical role of ocean model resolution in accurately understanding and predicting future sea level changes, which is essential for effectively addressing our needs for adaptation.

Droughts in Wind and Solar Power: Assessing Climate Model Simulations for a Net‐Zero Energy Future

AbstractUnderstanding and predicting “droughts” in wind and solar power availability can help the electric grid operator planning and operation toward deep renewable penetration. We assess climate models' ability to simulate these droughts at different horizontal resolutions, ∼100 and ∼25 km, over Western North America and Texas. We find that these power droughts are associated with the high/low pressure systems. The simulated wind and solar power variabilities and their corresponding droughts during historical periods are more sensitive to the model bias than to the model resolution. Future climate simulations reveal varied future change of these droughts across different regions. Although model resolution does not affect the simulation of historical droughts, it does impact the simulated future changes. This suggests that regional response to future warming can vary considerably in high‐ and low‐resolution models. These insights have important implications for adapting power system planning and operations to the changing climate.

Ozone source attribution in polluted European areas during summer 2017 as simulated with MECO(n)

Abstract. Emissions of land transport and anthropogenic non-traffic emissions (e.g. industry, households and power generation) are significant sources of nitrogen oxides, carbon monoxide and volatile organic compounds (VOCs). These emissions are important precursors of tropospheric ozone and affect air quality. The contribution of the emission sectors to ozone cannot be measured directly but can only be calculated using sophisticated atmospheric chemistry models. For this study we apply the MECO(n) model system (MESSy-fied ECHAM and COSMO models nested n times) equipped with a source attribution method to investigate the contribution of various sources to ground-level ozone in Europe. Compared to previous source apportionment studies for Europe, for the first time we apply a combined NOx–VOC tagging implemented in an online nested global–regional chemistry–climate model to achieve a finer resolution over central Europe (12 km) but concurrently incorporating the effect of long-range transport. We distinguish 10 different source sectors and 4 geographical source regions, analysing especially the contribution from the land transport sector. Our analysis focuses on large ozone events during summer in four different regions, two major polluted regions (Po Valley and Benelux) and two more remote regions (Iberian Peninsula and Ireland). The analysis concentrates on results for summer 2017, during which measurement campaign EMeRGe took place. Measurement data from this campaign are used for model evaluation. Our analysis shows that European land transport emissions contribute largely (42 % and 44 %, respectively) to ground-level NOy mixing ratios over Benelux and the Po Valley. Due to the overall lower ozone production efficiency over Benelux compared to the Po Valley, however, the contributions to ground-level ozone are larger in the Po Valley (12 %) compared to Benelux (8 %). In line with previous publications using different source apportionment methods, our results underline the large importance of long-range transport of ozone, especially from North America (Benelux, Ireland), but also from Africa (Iberian Peninsula), and provide additional information about the sectoral contribution not available before. Our analysis shows that the contributions of European emissions from land transport and anthropogenic non-traffic sectors strongly increase with increasing values of MDA8 (daily maximum 8 h average) ozone over the Po Valley and in the Benelux region. Accordingly, these two sectors drive large MDA8 values in these regions. Inter-comparisons of results for 2018 and with a coarser model resolution (50 instead of 12 km) show that these results are robust with respect to inter-annual variability and model resolution. Comparing our results with results from other source attribution methods we find that the contributions to ozone from individual sectors, which have large NOx but rather low VOC emissions, are estimated to be lower, if their emissions of NOx and VOCs are regarded concurrently.

A Critical Analysis of Family Dispute Resolution Practices in Pakistan

Informal practices that exist within family relations are as ubiquitous as formal legal mechanisms widely used to resolve family disputes in Pakistan. To achieve this, this study critically examined the FDR Practices in Pakistan in light of the existing legal framework highlighting its strengths and gaps and proffering recommendation to improve upon the same. Legal provisions under the Muslim Family Laws Ordinance of 1961 and the West Pakistan Family Court Act of 1964 exist, but operate in spite of these, obstacles like procedural anomalies and Jirga and Panchayat systems continue to exist and human rights violations are not rare. The study was done using a qualitative research methodology which used a review of legal provisions, court practices and alternative dispute resolution models. Moreover, results proved that current frameworks do not sufficiently take care of family related disputes and that informal methods are not fair and accountable. The results of this study suggest that the formation of regulated FDR centers promotes relatively efficient resolution of disputes, reduces the burden placed on courts and protects family relationships.

Constructing Predictive Implicit Solvent Models: Insights from Exploring Potential Parameter Space and Property Predictions.

Implicit solvent (IS) models enhance the efficiency of simulating liquid systems, but those based on the potential of mean force calculations using explicit solvent (ES) models often fail to capture the solute structure and assembly dynamics under various conditions. This study examines the relationships between the parameter space of IS models and their predictive capabilities regarding solute structure and assembly dynamics, focusing on NaCl solutions and protein-bound silica colloidal nanoparticles. For NaCl solutions, models developed using concentration-dependent dielectric constants generally lack efficacy, and their effectiveness in predicting high-concentration liquid structures varies based on the ES model used. Adjusting the dielectric constant value or the shape of the short-range potentials can improve the accuracy of these models in predicting liquid structures. For protein-bound silica colloidal nanoparticles, we find that interaction site size and model resolution significantly influence the prediction of nanoparticle assembly dynamics, with different resolutions producing aggregates with distinct structures and connection modes. This study elucidates the complex relationship between parameter space in IS models and liquid structure and assembly kinetics, providing crucial insights for developing more accurate and predictive models.