Geoscientific Model Development最新文献

{"title":"Application and evaluation of CRACMM v1.0 mechanism in PM_2.5 simulation over China.","authors":"Qingfang Su, Yifei Chen, Yangjun Wang, David C Wong, Havala O T Pye, Ling Huang, Golam Sarwar, Benjamin Murphy, Bryan Place, Li Li","doi":"10.5194/gmd-19-2531-2026","DOIUrl":"10.5194/gmd-19-2531-2026","url":null,"abstract":"<p><p>Chemical mechanisms are one of the major sources of bias in chemical transport model simulations, making their improvement a critical step towards enhancing model performance and supporting air quality management and research. In this study, a newly developed chemical mechanism, the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM), integrated into the Community Multiscale Air Quality (CMAQ) modeling system, was evaluated through comparison with two traditional chemical mechanisms, Carbon Bond 6 version r3 with aero7 treatment of SOA (CB6r3_ae7) and State Air Pollution Research Center version 07tc with extended isoprene chemistry and aero7i treatment of SOA (Saprc07tic_ae7i), for China. Sensitivity simulations related to precursor reactive organic carbon (ROC) emissions were conducted to investigate the key driving factors of PM_2.5 formation. The results indicate that, when using the traditional primary organic aerosol (POA) inventory, the differences among the three chemical mechanisms are within 0-0.14 for the <i>R</i>, 0-10 μg m^-3 for the MB, and within 10 % for the NMB values. However, when the full-volatility emission inventory is applied in January, CRACMM exhibits improved performance in the Pearl River Delta (PRD) region. The MB is reduced by 3.0-7.8 μg m^-3. In addition, the NMB decreases by 17 %-23 %, and the root mean square error (RMSE) is reduced by 1-6 μg m^-3 compared with simulations using the traditional POA inventory across the four months. CRACMM predicts higher PM_2.5 concentrations during spring, summer and autumn, mainly due to enhanced secondary organic aerosol (SOA) formation driven by increased precursor emissions. Benzene-toluene-xylene (BTX) species and semi-volatile organic compound (SVOC) emissions significantly contributed to PM_2.5 formation in CRACMM. The SOA from BTX emissions accounts for nearly 50 % of the PM_2.5 changes, while intermediate-volatility organic compounds (IVOC) and SVOC emissions mainly affect PM_2.5 concentrations through SOA formation. These results indicate that CRACMM, when using the full-volatility inventory, can effectively compensate for the underestimation of PM_2.5 mass that may occur with traditional POA treatment, particularly in regions with high photochemical activity and abundant S/IVOC precursors.</p>","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"19 6","pages":"2531-2550"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13137371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining spin-up behaviour within WRF dynamical downscaling applications.","authors":"Megan S Mallard, Tanya L Spero, Jared H Bowden, Jeff Willison, Christopher G Nolte, Anna M Jalowska","doi":"10.5194/gmd-19-579-2026","DOIUrl":"10.5194/gmd-19-579-2026","url":null,"abstract":"<p><p>Spin-up is the period after initialization when a model transitions away from its dependence on initial conditions toward a dynamic equilibrium between driving boundary conditions and its own internal dynamics. Regional climate models (RCMs) are often used to simulate conditions over several decades to inform local adaptation and resilience activities. The spin-up period represents added cost to already resource-intensive simulations, and it is often infeasible to use a spin-up period that produces complete model equilibrium. Therefore, a pragmatic compromise is desired to minimize the effects of spin-up. Here, two overlapping dynamically downscaled simulations (31 year and 11 year integrations) using the Weather Research and Forecasting model over the contiguous US (CONUS) are used to explore convergence associated with model spin-up. The shorter simulation is initialized 20 years after initialization of the longer (reference) run, and the runs are analysed over the period covered by both simulations, giving the reference simulation a 20 year period to attain spin-up prior to comparison. After initialization, the shorter run features cooler surface and near-surface temperatures and greater soil moisture compared to the reference simulation. Differences between the runs decrease in magnitude over the first 3 months as autumn transitions to winter; however, these differences re-emerge and reach a secondary peak during the proceeding spring and summer. During this warm season, evaporation and accompanying evaporative cooling increase and temperature differences between the simulations re-emerge. These results support using at least one year of spin-up time in RCM applications to account for the seasonality of spin-up behaviour. Results from some regions of the CONUS indicate that spin-up durations of 1-4 years are needed to exclude spurious behavior in top-layer soil moisture, which exhibits prolonged spin-up compared with other near-surface variables examined here.</p>","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"19 2","pages":"579-594"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13266800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148264258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A regional physical–biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)","authors":"Elizabeth J. Drenkard, Charles A. Stock, Andrew Ross, Yi‐Cheng Teng, Theresa J. Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphaël Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert J. Hermann, Michael G. Jacox, Kelly Kearney, Rémi Pagès, Darren Pilcher, Mercedes Pozo Buil, Vivek Seelanki, Niki Zadeh","doi":"10.5194/gmd-18-5245-2025","DOIUrl":"https://doi.org/10.5194/gmd-18-5245-2025","url":null,"abstract":"Abstract. Regional ocean models enable the generation of computationally affordable and regionally tailored ensembles of near-term forecasts and long-term projections of sufficient resolution to serve marine resource management. Climate change, however, has created marine resource challenges, such as shifting stock distributions, that cut across domestic and international management boundaries and have pushed regional modeling efforts toward “coastwide” approaches. Here, we present and evaluate a multidecadal hindcast with a Northeast Pacific regional implementation of the Modular Ocean Model, version 6, with sea ice and biogeochemistry that extends from the Chukchi Sea to the Baja California Peninsula at 10 km horizontal resolution (MOM6-COBALT-NEP10k, or NEP10k). This domain includes an Arctic-adjacent system with a broad, shallow shelf seasonally covered by sea ice (the eastern Bering Sea), a sub-Arctic system with upwelling in the Alaska Gyre and predominant downwelling winds and large freshwater forcing along the coast (the Gulf of Alaska), and a temperate, eastern boundary upwelling ecosystem (the California Current Ecosystem). The coastwide model was able to recreate seasonal and cross-ecosystem contrasts in numerous ecosystem-critical properties including temperature, salinity, inorganic nutrients, oxygen, carbonate saturation states, and chlorophyll. Spatial consistency between modeled quantities and observations generally extended to plankton ecosystems, though small to moderate biases were also apparent. Fidelity with observed zooplankton biomass, for example, was limited to first-order seasonal and cross-system contrasts. Temporally, simulated monthly surface and bottom temperature anomalies in coastal regions (&lt;500 m deep) closely matched estimates from data-assimilative ocean reanalyses. Performance, however, was reduced in some nearshore regions coarsely resolved by the model's 10 km resolution grid and for point measurements. The time series of satellite-based chlorophyll anomaly estimates proved more difficult to match than temperature. System-specific ecosystem indicators were also assessed. In the eastern Bering Sea, NEP10k robustly matched observed variations, including recent large declines, in the area of the summer bottom water “cold pool” (&lt;2 °C), which exerts a profound influence on eastern Bering Sea fisheries. In the Gulf of Alaska, the simulation captured patterns of sea surface height variability and variations in thermal, oxygen, and acidification risk associated with local modes of interannual to decadal climate variability. In the California Current Ecosystem, the simulation robustly captured variations in upwelling indices and coastal water masses, though discrepancies in the latter were evident in the Southern California Bight. Enhanced model resolution may reduce such discrepancies, but any benefits must be carefully weighed against computational costs given the intended use of this system for ensemble pre","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"18 16","pages":"5245-5290"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://gmd.copernicus.org/articles/18/5245/2025/gmd-18-5245-2025.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147332156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RiverBedDynamics v1.0: a Landlab component for computing two-dimensional sediment transport and river bed evolution","authors":"Angel Monsalve, Samuel Anderson, N. M. Gasparini, Elowyn M. Yager","doi":"10.5194/gmd-18-3427-2025","DOIUrl":"https://doi.org/10.5194/gmd-18-3427-2025","url":null,"abstract":"Abstract. Computational landscape evolution models (LEMs) typically comprise at least two interacting components: a flow hydraulic solver that routes water across a landscape and a fluvial geomorphological model that modifies terrain properties, primarily bed surface elevation. LEMs used in long-term simulations over large watersheds, including some available in the Landlab library, often assume that only erosive processes occur in rivers and that terrain elevation increases solely due to tectonic uplift. Consequently, these models cannot capture the dynamics of gravel-bedded rivers, lacking the capacity to include sediment mixtures, simulate sediment deposition, and track textural changes in substrate stratigraphy that result from varying flow characteristics. To address this limitation, we developed, implemented, and tested RiverBedDynamics, a new Landlab component that simulates the evolution of bed surface elevation and grain size distribution in 2D grids based on the Exner equation for sediment mass balance. By dynamically coupling RiverBedDynamics with Landlab's hydrodynamic flow solver, OverlandFlow, we created a new LEM capable of simulating the dynamics of local shear stresses, bed load transport rates, and grain size distributions. Comparisons of our LEM results with analytical and previously reported solutions demonstrate its ability to accurately predict time-varying local changes in bed surface elevation, including erosion and deposition, as well as grain size distribution. Furthermore, application of our LEM to a synthetic watershed illustrates how spatially variable rainfall intensity leads to varying discharge patterns, which in turn drive changes in bed elevation and grain size distribution across the domain. This approach provides a more comprehensive representation of the complex interactions between flow dynamics and sediment transport in gravel-bedded rivers at timescales ranging from individual flood events to yearly morphological changes, enhancing our ability to model landscape evolution across diverse geomorphic settings.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"18 11","pages":"3427-3451"},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://gmd.copernicus.org/articles/18/3427/2025/gmd-18-3427-2025.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147898455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study","authors":"Chayan Banerjee, Kien Nguyen, Clinton Fookes, G. R. Hancock, Tom Coulthard","doi":"10.5194/gmd-18-803-2025","DOIUrl":"https://doi.org/10.5194/gmd-18-803-2025","url":null,"abstract":"Abstract. In geosciences, including hydrology and geomorphology, the reliance on numerical models necessitates the precise calibration of their parameters to effectively translate information from observed to unobserved settings. Traditional calibration techniques, however, are marked by poor generalizability, demanding significant manual labor for data preparation and the calibration process itself. Moreover, the utility of machine-learning-based and data-driven approaches is curtailed by the requirement for the numerical model to be differentiable for optimization purposes, which challenges their generalizability across different models. Furthermore, the potential of freely available geomorphological data remains underexploited in existing methodologies. In response to these challenges, we introduce a generalizable framework for calibrating numerical models, with a particular focus on geomorphological models, named Iterative Model Calibration (IMC). This approach efficiently identifies the optimal set of parameters for a given numerical model through a strategy based on a Gaussian neighborhood algorithm. Through experiments, we demonstrate the efficacy of IMC in calibrating the widely used landscape evolution model CAESAR-Lisflood (CL). The IMC process substantially improves the agreement between CL predictions and observed data (in the context of gully catchment landscape evolution), surpassing both uncalibrated and manual approaches.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"18 3","pages":"803-818"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://gmd.copernicus.org/articles/18/803/2025/gmd-18-803-2025.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147905263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ.","authors":"T Nash Skipper, Christian Hogrefe, Barron H Henderson, Rohit Mathur, Kristen M Foley, Armistead G Russell","doi":"10.5194/gmd-17-8373-2024","DOIUrl":"10.5194/gmd-17-8373-2024","url":null,"abstract":"<p><p>United States (US) background ozone (O₃) is the counterfactual O₃ that would exist with zero US anthropogenic emissions. Estimates of US background O₃ typically come from chemical transport models (CTMs), but different models vary in their estimates of both background and total O₃. Here, a measurement-model data fusion approach is used to estimate CTM biases in US anthropogenic O₃ and multiple US background O₃ sources, including natural emissions, long-range international emissions, short-range international emissions from Canada and Mexico, and stratospheric O₃. Spatially and temporally varying bias correction factors adjust each simulated O₃ component so that the sum of the adjusted components evaluates better against observations compared to unadjusted estimates. The estimated correction factors suggest a seasonally consistent positive bias in US anthropogenic O₃ in the eastern US, with the bias becoming higher with coarser model resolution and with higher simulated total O₃, though the bias does not increase much with higher observed O₃. Summer average US anthropogenic O₃ in the eastern US was estimated to be biased high by 2, 7, and 11 ppb (11%, 32%, and 49%) for one set of simulations at 12, 36, and 108 km resolutions and 1 and 6 ppb (10% and 37%) for another set of simulations at 12 and 108 km resolutions. Correlation among different US background O₃ components can increase the uncertainty in the estimation of the source-specific adjustment factors. Despite this, results indicate a negative bias in modeled estimates of the impact of stratospheric O₃ at the surface, with a western US spring average bias of -3.5 ppb (-25%) estimated based on a stratospheric O₃ tracer. This type of data fusion approach can be extended to include data from multiple models to leverage the strengths of different data sources while reducing uncertainty in the US background ozone estimates.</p>","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"17 22","pages":"8373-8397"},"PeriodicalIF":4.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling.","authors":"David C Wong, Jeff Willison, Jonathan E Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, Hosein Foroutan","doi":"10.5194/gmd-17-7855-2024","DOIUrl":"10.5194/gmd-17-7855-2024","url":null,"abstract":"<p><p>The Community Multiscale Air Quality (CMAQ) model has been used for regulatory purposes at the U.S. EPA and in the research community for decades. In 2012, we released the Weather Research and Forecasting (WRF)-CMAQ coupled model that enables aerosol information from CMAQ to affect meteorological processes through direct effects on shortwave radiation. Both CMAQ and WRF-CMAQ are considered limited-area models. Recently, we have extended domain coverage to the global scale by linking the meteorological Model for Prediction Across Scales - Atmosphere (MPAS-A, hereafter referred simply to as MPAS) with CMAQ to form the MPAS-CMAQ global coupled model. To configure these three different models, i.e., CMAQ (offline), WRF-CMAQ, and MPAS-CMAQ, we have developed the Advanced Air Quality Modeling System (AAQMS) for constructing each of them effortlessly. We evaluate this newly built MPAS-CMAQ coupled model using two global configurations: a 120 km uniform mesh and a 92-25 km variable mesh with the finer area over North America. Preliminary computational tests show good scalability and model evaluation, when using a 3-year simulation (2014-2016) for the uniform mesh case and a monthly simulation of January and July 2016 for the variable mesh case, on ozone and PM_2.5 and show reasonable performance with respect to observations. The 92-25 km configuration has a high bias in winter-time surface ozone across the United States, and this bias is consistent with the 120 km result. Summertime surface ozone in the 92-25 km configuration is less biased than the 120 km case. The MPAS-CMAQ system reasonably reproduces the daily variability of daily average PM from the Air Quality System (AQS) network.</p>","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"17 21","pages":"7855-7866"},"PeriodicalIF":4.9,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11960730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community.","authors":"Christos I Efstathiou, Elizabeth Adams, Carlie J Coats, Robert Zelt, Mark Reed, John McGee, Kristen M Foley, Fahim I Sidi, David C Wong, Steven Fine, Saravanan Arunachalam","doi":"10.5194/gmd-17-7001-2024","DOIUrl":"10.5194/gmd-17-7001-2024","url":null,"abstract":"<p><p>The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures.</p>","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"17 18","pages":"7001-7027"},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry.","authors":"Kelvin H Bates, Mathew J Evans, Barron H Henderson, Daniel J Jacob","doi":"10.5194/gmd-17-1511-2024","DOIUrl":"10.5194/gmd-17-1511-2024","url":null,"abstract":"<p><p>We updated the chemical mechanism of the GEOS-Chem global 3-D model of atmospheric chemistry to include new recommendations from the NASA Jet Propulsion Laboratory (JPL) chemical kinetics Data Evaluation 19-5 and from the International Union of Pure and Applied Chemistry (IUPAC) and to balance carbon and nitrogen. We examined the impact of these updates on the GEOS-Chem version 14.0.1 simulation. Notable changes include 11 updates to reactions of reactive nitrogen species, resulting in a 7% net increase in the stratospheric NO_<i>x</i> (NO + NO₂) burden; an updated CO + OH rate formula leading to a 2.7% reduction in total tropospheric CO; adjustments to the rate coefficient and branching ratios of propane + OH, leading to reduced tropospheric propane (-17%) and increased acetone (+3.5%) burdens; a 41% increase in the tropospheric burden of peroxyacetic acid due to a decrease in the rate coefficient for its reaction with OH, further contributing to reductions in peroxyacetyl nitrate (PAN; -3.8%) and acetic acid (-3.4%); and a number of minor adjustments to halogen radical cycling. Changes to the global tropospheric burdens of other species include -0.7% for ozone, +0.3% for OH (-0.4% for methane lifetime against oxidation by tropospheric OH), +0.8% for formaldehyde, and -1.7% for NO_<i>x</i>. The updated mechanism reflects the current state of the science, including complex chemical dependencies of key atmospheric species on temperature, pressure, and concentrations of other compounds. The improved conservation of carbon and nitrogen will facilitate future studies of their overall atmospheric budgets.</p>","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"7 4","pages":"1511-1524"},"PeriodicalIF":4.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10953788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140174205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)","authors":"Han Qiu, G. Bisht, Lingcheng Li, D. Hao, Donghui Xu","doi":"10.5194/gmd-17-143-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-143-2024","url":null,"abstract":"Abstract. The lateral transport of water in the subsurface is important in modulating terrestrial water energy distribution. Although a few land surface models have recently included lateral saturated flow within and across grid cells, it is not a default configuration in the Climate Model Intercomparison Project version 6 experiments. In this work, we developed the lateral subsurface flow model within both unsaturated and saturated zones in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2.0). The new model, called ELMlat, was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model, for three idealized hillslopes that included a convergent hillslope, divergent hillslope, and tilted V-shaped hillslope with variably saturated initial conditions. ELMlat showed comparable performance against PFLOTRAN in terms of capturing the dynamics of soil moisture and groundwater table for the three benchmark hillslope problems. Specifically, the mean absolute errors (MAEs) of the soil moisture in the top 10 layers between ELMlat and PFLOTRAN were within 1 %±3 %, and the MAEs of water table depth were within ±0.2 m. Next, ELMlat was applied to the Little Washita experimental watershed to assess its prediction of groundwater table, soil moisture, and soil temperature. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset generated from a global model calibrated with long-term observations. The effects of lateral groundwater flow on the energy flux partitioning were more prominent in lowland areas with shallower groundwater tables, where the difference in simulated annual surface soil temperature could reach 0.3–0.4 ∘C between ELMv2.0 and ELMlat. Incorporating lateral subsurface flow in ELM improves the representation of the subsurface hydrology, which will provide a good basis for future large-scale applications.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"74 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}