Articles | Volume 11, issue 4
https://doi.org/10.5194/gmd-11-1293-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-1293-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
11 Apr 2018
Development and technical paper |
| 11 Apr 2018
| 11 Apr 2018
Estimating criteria pollutant emissions using the California Regional Multisector Air Quality Emissions (CA-REMARQUE) model v1.0
Christina B. Zapata
Department of Civil and Environmental Engineering, University of California – Davis, Davis, California, USA
Chris Yang
Institute of Transportation Studies, University of California – Davis, Davis, California, USA
Sonia Yeh
Institute of Transportation Studies, University of California – Davis, Davis, California, USA
Joan Ogden
Institute of Transportation Studies, University of California – Davis, Davis, California, USA
Michael J. Kleeman
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of California – Davis, Davis, California, USA
Related authors
Christina B. Zapata, Chris Yang, Sonia Yeh, Joan Ogden, and Michael J. Kleeman
Atmos. Chem. Phys., 18, 4817–4830, https://doi.org/10.5194/acp-18-4817-2018, https://doi.org/10.5194/acp-18-4817-2018, 2018
Short summary
Short summary
California's greenhouse gas reduction programs will require adoption of low-carbon energy sources across all economic sectors. We selected the least-cost portfolio of new energy sources using an energy–economic model. We then specified new air pollution emissions and simulated air quality with 4 km spatial resolution across the entire state. We find that the adoption of low-carbon energy reduced air pollution deaths 24–26 %, providing USD 11.4–20.4 billion per year of economic benefits.
Qixiang Xu, Fangcheng Su, Ke Wang, Ruiqin Zhang, Qi Ying, and Michael J. Kleeman
EGUsphere, https://doi.org/10.5194/egusphere-2025-44, https://doi.org/10.5194/egusphere-2025-44, 2025
Short summary
Short summary
This manuscript introduces a novel approach for improving the computational efficiency and scalability of source-oriented chemical mechanisms by simplifying the representation of reactions involving source-tagged species and implementing a source-oriented Euler Backward Iterative (EBI) solver. These advancements reduce simulation times by up to 74 % while maintaining accuracy, offering significant practical benefits for long-term source apportionment studies.
Shenglun Wu, Hyung Joo Lee, Andrea Anderson, Shang Liu, Toshihiro Kuwayama, John H. Seinfeld, and Michael J. Kleeman
Atmos. Chem. Phys., 22, 4929–4949, https://doi.org/10.5194/acp-22-4929-2022, https://doi.org/10.5194/acp-22-4929-2022, 2022
Short summary
Short summary
An ozone control experiment usually conducted in the laboratory was installed in a trailer and moved to the outdoor environment to directly confirm that we are controlling the right sources in order to lower ambient ozone concentrations. Adding small amounts of precursor oxides of nitrogen and volatile organic compounds to ambient air showed that the highest ozone concentrations are best controlled by reducing concentrations of oxides of nitrogen. The results confirm satellite measurements.
Xin Yu, Melissa Venecek, Anikender Kumar, Jianlin Hu, Saffet Tanrikulu, Su-Tzai Soon, Cuong Tran, David Fairley, and Michael J. Kleeman
Atmos. Chem. Phys., 19, 14677–14702, https://doi.org/10.5194/acp-19-14677-2019, https://doi.org/10.5194/acp-19-14677-2019, 2019
Short summary
Short summary
Predictions and measurements of ultrafine particle number and mass concentrations were in overall good agreement at 14 sites across California in the years 2012, 2015, and 2016. On-road vehicles, food cooking, and aircraft were important sources of ultrafine particles as expected, but natural gas combustion was also a significant source at all locations across California. These results can be used to study the health effects of ultrafine particles.
Melissa A. Venecek, Xin Yu, and Michael J. Kleeman
Atmos. Chem. Phys., 19, 9399–9412, https://doi.org/10.5194/acp-19-9399-2019, https://doi.org/10.5194/acp-19-9399-2019, 2019
Short summary
Short summary
Atmospheric ultrafine particles with a diameter < 100 nm are more toxic than larger particles. There are no measurement networks for ultrafine particles, but concentrations can be predicted using models. On-road vehicles, cooking, and aircraft are important sources of ultrafine particles as expected, but natural gas combustion was also found to be a significant source in cities across the United States. Results like this may support future health-effects studies on ultrafine particles.
Ali Akherati, Christopher D. Cappa, Michael J. Kleeman, Kenneth S. Docherty, Jose L. Jimenez, Stephen M. Griffith, Sebastien Dusanter, Philip S. Stevens, and Shantanu H. Jathar
Atmos. Chem. Phys., 19, 4561–4594, https://doi.org/10.5194/acp-19-4561-2019, https://doi.org/10.5194/acp-19-4561-2019, 2019
Short summary
Short summary
Unburned and partially burned organic compounds emitted from fossil fuel and biomass combustion can react in the atmosphere in the presence of sunlight to form particles. In this work, we use an air pollution model to examine the influence of these organic compounds released by motor vehicles and fires on fine particle pollution in southern California.
Christina B. Zapata, Chris Yang, Sonia Yeh, Joan Ogden, and Michael J. Kleeman
Atmos. Chem. Phys., 18, 4817–4830, https://doi.org/10.5194/acp-18-4817-2018, https://doi.org/10.5194/acp-18-4817-2018, 2018
Short summary
Short summary
California's greenhouse gas reduction programs will require adoption of low-carbon energy sources across all economic sectors. We selected the least-cost portfolio of new energy sources using an energy–economic model. We then specified new air pollution emissions and simulated air quality with 4 km spatial resolution across the entire state. We find that the adoption of low-carbon energy reduced air pollution deaths 24–26 %, providing USD 11.4–20.4 billion per year of economic benefits.
Shantanu H. Jathar, Christopher Heppding, Michael F. Link, Delphine K. Farmer, Ali Akherati, Michael J. Kleeman, Joost A. de Gouw, Patrick R. Veres, and James M. Roberts
Atmos. Chem. Phys., 17, 8959–8970, https://doi.org/10.5194/acp-17-8959-2017, https://doi.org/10.5194/acp-17-8959-2017, 2017
Short summary
Short summary
Our work makes novel emissions measurements of isocyanic acid, a toxic gas, from a modern-day diesel engine and finds that diesel engines emit isocyanic acid but the emissions control devices do not enhance or destroy the isocyanic acid. Air quality model calculations suggest that diesel engines are possibly important sources of isocyanic acid in urban environments although the isocyanic acid levels are ten times lower than levels linked to adverse human health effects.
Jianlin Hu, Shantanu Jathar, Hongliang Zhang, Qi Ying, Shu-Hua Chen, Christopher D. Cappa, and Michael J. Kleeman
Atmos. Chem. Phys., 17, 5379–5391, https://doi.org/10.5194/acp-17-5379-2017, https://doi.org/10.5194/acp-17-5379-2017, 2017
Short summary
Short summary
Organic aerosol is a major constituent of ultrafine particulate matter (PM0.1). In this study, a source-oriented air quality model was used to simulate the concentrations and sources of primary and secondary organic aerosols in PM0.1 in California for a 9-year modeling period to provide useful information for epidemiological studies to further investigate the associations with health outcomes.
Hsiang-He Lee, Shu-Hua Chen, Michael J. Kleeman, Hongliang Zhang, Steven P. DeNero, and David K. Joe
Atmos. Chem. Phys., 16, 8353–8374, https://doi.org/10.5194/acp-16-8353-2016, https://doi.org/10.5194/acp-16-8353-2016, 2016
Short summary
Short summary
A source-oriented CCN module was implemented in a source-oriented chemistry model to study the effect of aerosol mixing state on fog formation. The fraction of aerosols activating into CCN at a supersaturation of 0.5 % in the Central Valley decreased from 94 % in the internal mixture model to 80 % in the source-oriented model. The internal mixture model predicted greater CCN activation than the source-oriented model due to artificial coating of hydrophobic particles with hygroscopic components.
Christopher D. Cappa, Shantanu H. Jathar, Michael J. Kleeman, Kenneth S. Docherty, Jose L. Jimenez, John H. Seinfeld, and Anthony S. Wexler
Atmos. Chem. Phys., 16, 3041–3059, https://doi.org/10.5194/acp-16-3041-2016, https://doi.org/10.5194/acp-16-3041-2016, 2016
Short summary
Short summary
Losses of vapors to walls of chambers can negatively bias SOA formation measurements, consequently leading to low predicted SOA concentrations in air quality models. Here, we show that accounting for such vapor losses leads to substantial increases in the predicted amount of SOA formed from VOCs and to notable increases in the O : C atomic ratio in two US regions. Comparison with a variety of observational data suggests generally improved model performance when vapor wall losses are accounted for.
S. H. Jathar, C. D. Cappa, A. S. Wexler, J. H. Seinfeld, and M. J. Kleeman
Atmos. Chem. Phys., 16, 2309–2322, https://doi.org/10.5194/acp-16-2309-2016, https://doi.org/10.5194/acp-16-2309-2016, 2016
Short summary
Short summary
Multi-generational chemistry schemes applied in regional models do not increase secondary organic aerosol (SOA) mass production relative to traditional "two-product" schemes when both models are fitted to the same chamber data. The multi-generational chemistry schemes do change the predicted composition of SOA and the source attribution of SOA.
S. H. Jathar, C. D. Cappa, A. S. Wexler, J. H. Seinfeld, and M. J. Kleeman
Geosci. Model Dev., 8, 2553–2567, https://doi.org/10.5194/gmd-8-2553-2015, https://doi.org/10.5194/gmd-8-2553-2015, 2015
Short summary
Short summary
Multi-generational oxidation of organic vapors can significantly alter the mass, chemical composition and properties of secondary organic aerosol (SOA). Here, we implement a semi-explicit, constrained multi-generational oxidation model of Cappa and Wilson (2012) in a 3-D air quality model. When compared with results from a current-generation SOA model, we predict similar mass concentrations of SOA but a different chemical composition. O:C ratios of SOA are in line with those measured globally.
J. Hu, H. Zhang, Q. Ying, S.-H. Chen, F. Vandenberghe, and M. J. Kleeman
Atmos. Chem. Phys., 15, 3445–3461, https://doi.org/10.5194/acp-15-3445-2015, https://doi.org/10.5194/acp-15-3445-2015, 2015
Short summary
Short summary
Air quality model simulations have been conducted for California from 2000 to 2009 with 4km spatial resolution to provide exposure data for health effect studies. Comprehensive analysis shows that predicted concentrations for many pollutants are in agreement with measurements at monitoring stations, building confidence that the fields may be useful at times and locations where measurements are not available. Data can be downloaded for free at http://faculty.engineering.ucdavis.edu/kleeman/.
H. Zhang, S. P. DeNero, D. K. Joe, H.-H. Lee, S.-H. Chen, J. Michalakes, and M. J. Kleeman
Atmos. Chem. Phys., 14, 485–503, https://doi.org/10.5194/acp-14-485-2014, https://doi.org/10.5194/acp-14-485-2014, 2014
Related subject area
Atmospheric sciences
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models
Mitigating Hail Overforecasting in the 2-Moment Milbrandt-Yau Microphysics Scheme (v2.25.2_beta_04) in WRF (v4.5.1) by Incorporating the Graupel Spongy Wet Growth Process (MY2_GSWG v1.0)
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Comprehensive evaluation of iAMAS (v1.0) in simulating Antarctic meteorological fields with observations and reanalysis
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
Short summary
Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Short summary
This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Short summary
We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary
Short summary
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
Short summary
Short summary
In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
Short summary
Short summary
We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary
Short summary
This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
Short summary
Short summary
This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary
Short summary
We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Short summary
Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
Short summary
We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Short summary
This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary
Short summary
Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
Short summary
Short summary
The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
Short summary
Short summary
The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
Short summary
Short summary
This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
Short summary
The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Short summary
The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Short summary
The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
Short summary
Short summary
Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
Short summary
Short summary
Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
Short summary
Short summary
This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
Short summary
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
Short summary
Short summary
Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
Short summary
Short summary
Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
Short summary
Short summary
This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
Short summary
Short summary
This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
Short summary
Short summary
An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
Short summary
Short summary
As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
Short summary
Short summary
The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
Short summary
Short summary
In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
Short summary
Short summary
We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
Short summary
Short summary
Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
Short summary
Short summary
Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
Short summary
Short summary
We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Short summary
We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary
Short summary
This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
Short summary
Short summary
Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-229, https://doi.org/10.5194/gmd-2024-229, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
This study presents the first comprehensive evaluation of unstructured meshes using the iAMAS model over Antarctica, encompassing both surface and upper-level meteorological fields. Comparison with ERA5 and observational data reveals that the iAMAS model performs well in simulating the Antarctic atmosphere; iAMAS demonstrates comparable, and in some cases superior, performance in simulating temperature and wind speed in East Antarctica when compared to ERA5.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
Short summary
Short summary
Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
Short summary
Short summary
Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
Short summary
Short summary
The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Cited articles
Alleman, T. L., Eudy, L., Miyasato, M., Oshinuga, A., Allison, S., Corcoran, T., Chatterjee, S., Jacobs, T., Cherrillo, R. A., Clark, R., Virrels, I., Nine, R., Wayne, S., and Lansing, R.: Fuel Property, Emission Test, and Operability Results from a Fleet of Class 6 Vehicles Operating on Gas-To-Liquid Fuel and Catalyzed Diesel Particle Filters, SAE Technical Paper 2004-01-2959, https://doi.org/10.4271/2004-01-2959, 2004.
Alleman, T. L., Barnitt, R., Eudy, L., Miyasato, M., Oshinuga, A., Corcoran, T., Chatterjee, S., Jacobs, T., Cherrillo, R. A., Clark, N., and Wayne, W. S.: Final Operability and Chassis Emissions Results from a Fleet of Class 6 Trucks Operating on Gas-to-Liquid Fuel and Catalyzed Diesel Particle Filters, SAE Technical Paper 2005-01-3769, https://doi.org/10.4271/2005-01-3769, 2005.
Antanaitis, D. B.: Effect of Regenerative Braking on Foundation Brake Performance, SAE Int. J. Passeng. Cars – Mech. Syst., 3, 14–30, 2010.
Argonne National Laboratory Transportation Technology R&D Center: The VISION Model, available at: http://www.anl.gov/energy-systems/project/vision-model (last access: April 2013), 2012.
Argonne National Laboratory Transportation Technology R&D Center: GREET Model. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model, available at: https://greet.es.anl.gov/ (last access:d June 2015), 2014.
Bollen, J., van der Zwaan, B., Brink, C., and Eerens, H.: Local air pollution and global climate change: A combined cost-benefit analysis, Resour. Energy Econ., 31, 161–181, 2009.
California Air Resouces Board: Documentation of California's 2000–2012 GHG Inventory, 2014.
California Air Resources Board: Final Regulation Order – Amendments to Sects. 1900 and 1961 and Adoption of New Sects. 1961.1, Title 13, California Code of Regulations as Approved by OAL and filed with the Secretary of the State on 15 September 2005, California Air Resources Board, Sacramento, CA, 2005.
California Air Resources Board: Updated Informative Digest: Adoption of the Regulation to Reduce Emissions from Diesel Auxiliary Engines on Ocean-going Vessels while at Berth, California Air Resources Board, Sacramento, CA, 2007.
California Air Resources Board: CA-GREET version 1.8b, California Air Resources Board, Sacramento, CA, 2009a.
California Air Resources Board: Executive Order R-10-002. Relating to the Adoption of the Amendments to New Passenger Motor Vehicle Greenhouse Gas Emission Standards, California Air Resources Board, Sacramento, CA, 2009b.
California Air Resources Board: Final Regulation Order – Amendments to the Low Carbon Fuel Standard Regulation. Adopt Sects. 95480.2, 95480.3, 95480.4, and 95480.5; Amend Sects. 95480.1, 95481, 95482, 95484, 95485, 95486, 95488, and 95490, title 17, California Code of Regulations, California Air Resources Board, Sacramento, CA, 2009c.
California Air Resources Board: Exhaust Emission Standards for Compression Ignition (Diesel) Engines and Equipment, Off-Road Compression-Ignition (Diesel) Engine Standards (NMHC + NOx∕CO∕PM in g kW h), California Air Resources Board, Sacramento, CA, 2010a.
California Air Resources Board: Final Regulation Order – Amendments to Title 13, California Code of Regulations. Rulemaking to Consider Proposed Amendments to New Passenger Motor Vehicle Greenhouse Gas Emission Standards for Model Years 2012–2016 to Permit Compliance based on Federal Greenhouse Gas Emission Standards, California Air Resources Board, Sacramento, CA, 2010b.
California Air Resources Board: Staff Report: Initial Statment of Reasons for Proposed Rulemaking. Regulation for Energy Efficiency and Co-Benefits Assessment of Large Industrial Facilities, in: Stationary Source Division Emissions Assessment Branch (Ed.), California Air Resources Board, Sacramento, CA, 2010c.
California Air Resources Board: EMFAC2011 Technical Documentation, California Air Resources Board, Sacramento, CA, 2011a.
California Air Resources Board: Facility Search Engine Tool. California Environmental Protection Agency Air Rersources Board, p. Find criteria and toxics pollutant emissions data for facilities in California, California Air Resources Board, Sacramento, CA, 2011b.
California Air Resources Board: Final Regulation Order – Adopt Sects. 95480.2, 95480.3, 95480.4, and 95480.5; Amend Sects. 95480.1, 95481, 95482, 95484, 95485, 95486, 95488, and 95490, title 17, California Code of Regulations, California Air Resources Board, Sacramento, CA, 2011c.
California Air Resources Board: Final Regulation Order – Subchapter 10 Climate Change, Article 5, Sects. 95800 to 96023, Title 17, California Code of Regulations, California Air Resources Board, Sacramento, CA, 2011d.
California Air Resources Board: Final Regulation Order. Fuel Sulfur and Other Operational Requirements for Ocean-going Vessels within California Waters and 24 Nautical Miles of the California Baseline, in: California Air Resources Board (Ed.), 13 CCR, Sect. 2299.2, California Air Resources Board, Sacramento, CA, 2011e.
California Air Resources Board: ARB Vision Model Documentation, Appendix to the June 27, 2012 Draft Vision for Clean Air: A Framework for Air Quality and Climate Planning, Sacramento, CA, 2012a.
California Air Resources Board: Final Regulation Order – Part 1: Final Regulation Order: Amend Sect. 1962.1, Title 13, California Code of Regulations. Zero-Emission Vehicle Standards for 2009 through 2017 Model Year Passenger Cars, Light-Duty Trucks and Medium-Duty Vehicles, California Air Resources Board, Sacramento, CA, 2012b.
California Air Resources Board: Final Regulation Order – Part 2: California Exhaust Emission Standards and Test Procedures for 2009 through 2017 Model Zero-Emission Vehicles and Hybrid Electric Vehicles, in the Passenger Car, Light-Duty Truck and Medium-Duty Vehicle Classes, Adopted 17 December 2008, as last amended 22 March 2012, California Air Resources Board, Sacramento, CA, 2012c.
California Air Resources Board: Final Regulation Order – Part 3: Final Regulation Order: Adopt 1962.2, Title 13, California Code of Regulations. Zero-emission Vehicle Standards for 2018 and Subsequent Model Year Passenger Cars, Light-duty Trucks, and Medium-Duty Vehicles, California Air Resources Board, Sacramento, CA, 2012d.
California Air Resources Board: Final Regulation Order – Part 4: California Exhaust Emission Standards and Test Procedures for 2018 and Subsequent Model Zero-Emission Vehicles and Hybrid Electric Vehicles and Hybrid Electric Vehicles, in the Passenger Car, Light-Duty Truck and Medium-Duty Vehicle Classes, California Air Resources Board, Sacramento, CA, 2012e.
California Air Resources Board: Final Regulation Order – Part 5: Final Regulation Order: Amend 1962.3, Title 13, California Code of Regulations. Electric Vehicle Charging Requirements, California Air Resources Board, Sacramento, CA, 2012f.
California Air Resources Board: Energy Efficiency and Co-Benefits Assessment of Large Industrial Sources. Hydrogen Sector Public Report, California Air Resources Board, Sacramento, CA, 2014.
California Air Resources Board: CA-GREET 2.0 Model and Documentation, California Air Resources Board, Sacramento, CA, 2015.
California Air Resources Board: Final Regulation Order – California Cap on Greenhouse Gas Emissions and Market-based Compliance Mechanisms, California Air Resources Board, Sacramento, CA, 2017.
California Department of Food and Agriculture: California Dairy Statistics 2010, in: California Department of Food and Agriculture (Ed.), 1220 N Street, Sacramento, CA 95814, 2011.
Cheung, K. L., Polidori, A., Ntziachristos, L., Tzamkiozis, T., Samaras, Z., Cassee, F. R., Gerlofs, M., and Sioutas, C.: Chemical Characteristics and Oxidative Potential of Particulate Matter Emissions from Gasoline, Diesel, and Biodiesel Cars, Environ. Sci. Technol., 43, 6334–6340, 2009.
Cheung, K. L., Ntziachristos, L., Tzamkiozis, T., Schauer, J. J., Samaras, Z., Moore, K. F., and Sioutas, C.: Emissions of Particulate Trace Elements, Metals and Organic Species from Gasoline, Diesel, and Biodiesel Passenger Vehicles and Their Relation to Oxidative Potential, Aerosol Sci. Tech., 44, 500–513, 2010.
Cooper, E., Arioli, M., Carrigan, A., and Jain, U.: Exhaust Emissions of Transit Buses. Sustainable Urban transportation fuels and Vehicles, Working Paper, EMBARQ, EMBARQ Global, 10 G Street NE, Suite 800, Washington DC 20002, USA, 1(202)729-7600, 2012.
Delfino, R. J., Sioutas, C., and Malik, S.: Potential Role of Ultrafine Particles in Associations between Airborne Particle Mass and Cardiovascular Health, Environ. Health Persp., 113, 934–946, 2005.
Donaldson, K., Stone, V., Clouter, A., Renwick, L., and MacNee, W.: Ultrafine particles, Occup. Environ. Med., 58, 211, https://doi.org/10.1136/oem.58.3.211, 2001.
Donaldson, K., Brown, D., Clouter, A., Duffin, R., MacNee, W., Renwick, L., Tran, L., and Stone, V.: The pulmonary toxicology of ultrafine particles, J. Aerosol. Med., 15, 213–220, 2002.
Durbin, T. D., Cocker, D. R., Sawant, A. A., Johnson, K., Miller, J. W., Holden, B. B., Helgeson, N. L., and Jack, J. A.: Regulated emissions from biodiesel fuels from on/off-road applications, Atmos. Environ., 41, 5647–5658, 2007.
Elder, A., Gelein, R., Silva, V., Feikert, T., Opanashuk, L., Carter, J., Potter, R., Maynard, A., Finkelstein, J., and Oberdorster, G.: Translocation of inhaled ultrafine manganese oxide particles to the central nervous system, Environ. Health Persp., 114, 1172–1178, 2006.
Environmental Protection Agency: Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2008, in: Environmental Protection Agency (Ed.), Office of Atmospheric Programs (6207J), 1200 Pennsylvania Avenue, N. W. Washington, DC 20460 USA, 2010.
Ferreira da Silva, M., Vicente de Assuncao, J., de Fatima Andrade, M., and Pesquero, C. R.: Characterization of metal and trace element contents of particulate matter (PM10) emitted by vehicles running on Brazilian fuels-hydrated ethanol and gasoline with 22 % of anhydrous ethanol, J. Toxicol. Env. Heal. A, 73, 901–909, 2010.
Frank, B. P., Tang, S., Lanni, T., Grygas, J., Rideout, G., Meyer, N., and Beregszaszy, C.: The Effect of Fuel Type and Aftertreatment Method on Ultrafine Particle Emissions from a Heavy-Duty Diesel Engine, Aerosol Sci. Tech., 41, 1029–1039, 2007.
Fripp, M.: Switch: a planning tool for power systems with large shares of intermittent renewable energy, Environ. Sci. Technol., 46, 6371–6378, 2012.
Garcia-Menendez, F., Saari, R. K., Monier, E., and Selin, N. E.: US Air Quality and Health Benefits from Avoided Climate Change under Greenhouse Gas Mitigation, Environ. Sci. Technol., 49, 7580–7588, 2015.
Gautam, M.: Testing of Volatile and Nonvolatile Emissions from Advanced Technology Natural Gas Vehicles. Final Report. Center for Alternative Fuels, Engines & Emissions West Virginia University, Prepared for John Collins State of California Air Resources Board, CARB Final Report for contract No. 07-430, Sacramento California, 2011.
Gilbreath, J., Rose, T., and Thong, F. F.: California Natural Gas Pipelines, in: Map of Major Natural Gas Pipelines in California (Ed.), California Energy Maps, California Energy Comission, Sacramento California, 2014.
Graboski, M. S., McCormick, R. L., Alleman, T. L., and Herring, A. M.: The Effect of Biodiesel Composition on Engine Emissions from a DDC Series 60 Diesel Engine: Final Report, Report 2 in a series of 6. National Renewable Energy Laboratory, Colorado Institute for Fuels and Engine Research, Colorado School of Mines, Golden Colorado, 2003.
Graham, L. A., Belisle, S. L., and Baas, C.-L.: Emissions from light duty gasoline vehicles operating on low blend ethanol gasoline and E85. Atmos. Environ., 42, 4498–4516, 2008.
Hasegawa, M., Sakurai, Y., Kobayashi, Y., Oyama, N., Sekimoto, M., and Watanabe, H.: Effects of Fuel Properties (Content of FAME or GTL) on Diesel Emissions under Various Driving Modes, SAE International, https://doi.org/10.4271/2007-01-4041, 2007.
Haskew, H. M. and Liberty, T. F.: Exhaust and Evaporative Emissions Testing of Flexible-Fuel Vehicles, Coordinating Research Council, Inc., 3650 Mansell Road Suite 140 Alpharetta, GA 30022, p. 473, 2011.
Hays, M. D., Preston, W., George, B. J., Schmid, J., Baldauf, R., Snow, R., Robinson, J. R., Long, T., and Faircloth, J.: Carbonaceous aerosols emitted from light-duty vehicles operating on gasoline and ethanol fuel blends, Environ. Sci. Technol., 47, 14502–14509, 2013.
Hixson, M., Mahmud, A., Hu, J. L., Bai, S., Niemeier, D. A., Handy, S. L., Gao, S. Y., Lund, J. R., Sullivan, D. C., and Kleeman, M. J.: Influence of regional development policies and clean technology adoption on future air pollution exposure, Atmos. Environ., 44, 552–562, 2010.
Hoek, G., Boogaard, H., Knol, A., de Hartog, J., Slottje, P., Ayres, J. G., Borm, P., Brunekreef, B., Donaldson, K., Forastiere, F., Holgate, S., Kreyling, W. G., Nemery, B., Pekkanen, J., Stone, V., Wichmann, H. E., and van der Sluijs, J.: Concentration Response Functions for Ultrafine Particles and All-Cause Mortality and Hospital Admissions: Results of a European Expert Panel Elicitation, Environ. Sci. Technol., 44, 476–482, 2010.
Jayaram, V., Agrawal, H., Welch, W. A., Miller, J. W., and Cocker III, D. R.: Real-time gaseous, PM and ultrafine particle emissions from a modern marine engine operating on biodiesel, Environ. Sci. Technol., 45, 2286–2292, 2011.
Johnston, J., Mileva, A., Nelson, J. H., and Kammen, D. M.: SWITCH-WECC. Data, Assumptions, and Model Formulation, Renewable and Appropriate Energy Laboratory, Berkeley, California, 2013.
Keshavarzmohammadian, A., Henze, D. K., and Milford, J. B.: Emission Impacts of Electric Vehicles in the US Transportation Sector Following Optimistic Cost and Efficiency Projections, Environ. Sci. Technol., 51, 6665–6673, 2017.
Knol, A. B., de Hartog, J. J., Boogaard, H., Slottje, P., van der Sluijs, J. P., Lebret, E., Cassee, F. R., Wardekker, J. A., Ayres, J. G., Borm, P. J., Brunekreef, B., Donaldson, K., Forastiere, F., Holgate, S. T., Kreyling, W. G., Nemery, B., Pekkanen, J., Stone, V., Wichmann, H. E., and Hoek, G.: Expert elicitation on ultrafine particles: likelihood of health effects and causal pathways, Part. Fibre Toxicol., 6, 19, https://doi.org/10.1186/1743-8977-6-19, 2009.
Kreyling, W. G., Semmler, M., and Moller, W.: Dosimetry and toxicology of ultrafine particles, J. Aerosol Med., 17, 140–152, 2004.
Lobo, P., Hagen, D. E., and Whitefield, P. D.: Comparison of PM emissions from a commercial jet engine burning conventional, biomass, and Fischer-Tropsch fuels, Environ. Sci. Technol., 45, 10744–10749, 2011.
Lobo, P., Rye, L., Williams, P. I., Christie, S., Uryga-Bugajska, I., Wilson, C. W., Hagen, D. E., Whitefield, P. D., Blakey, S., Coe, H., Raper, D., and Pourkashanian, M.: Impact of alternative fuels on emissions characteristics of a gas turbine engine – part 1: gaseous and particulate matter emissions, Environ. Sci. Technol., 46, 10805–10811, 2012.
Loughlin, D. H., Benjey, W. G., and Nolte, C. G.: ESP v1.0: methodology for exploring emission impacts of future scenarios in the United States, Geosci. Model Dev., 4, 287–297, https://doi.org/10.5194/gmd-4-287-2011, 2011.
Loulou, R., Goldstein, G., Kanudia, A., Lettila, A., and Remme, U.: Documentation for the TIMES Model. Part I: Times Concepts and Theory, in: (IEA-ETSAP), I.E.A.-E.T.S.A.P. (Ed.), Energy Technology Systems Analysis Programme, available at: http://www.iea-etsap.org/web/Documentation.asp (last access: September 2017), 2016.
Lundqvist, R. G.: The IGCC demonstration plant at Värnamo, Bioresource Technol., 46, 49–53, 1993.
Mann, M. K. and Spath, P. L.: Life Cycle Assessment of a Biomass Gasifiction Combined-Cycle System. National Renewable Energy Laboratory, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, 303-275-3000, 1997.
McCollum, D., Yang, C., Yeh, S., and Ogden, J.: Deep greenhouse gas reduction scenarios for California – Strategic implications from the CA-TIMES energy-economic systems model, Energy Strateg. Rev., 1, 19–32, 2012.
Nelson, D. J., Mileva, A., Johnston, J., and Kammen, P. D.: Scenarios for Deep Carbon Emission Reductions from Electricity by 2050 in Western North America Using the SWITCH Power Electric Power Sector Planning Model. California's Carbon Challenge Phase II, in: California Energy Commission (Ed.). Renewable and Appropriate Energy Laboratory Energy and Resources Group, 310 Barrows Hall Berkeley, CA 94720-3050, University of California, Berkeley, p. 142, 2013.
Oberdorster, G., Sharp, Z., Atudorei, V., Elder, A., Gelein, R., Lunts, A., Kreyling, W., and Cox, C.: Extrapulmonary translocation of ultrafine carbon particles following whole-body inhalation exposure of rats, J. Toxicol. Env. Heal. A, 65, 1531–1543, 2002.
Osborne, D., Fritz, S., and Glenn, D.: The Effects of Biodiesel Fuel Blends on Exhaust Emissions from a General Electric Tier 2 Line-Haul Locomotive, ASME 2010 Internal Combustion Engine Division Fall Technical Conference. ASME, San Antonio, Texas, USA, 2010.
Ostro, B., Hu, J., Goldberg, D., Reynolds, P., Hertz, A., Bernstein, L., and Kleeman, M. J.: Associations of mortality with long-term exposures to fine and ultrafine particles, species and sources: results from the California Teachers Study Cohort, Environ. Health Persp., 123, 549–556, 2015.
Parker, N.: Spatially Explicit Projection of Biofuel Supply for Meeting Renewable Fuel Standard, Transport Res. Rec.: J. Transport. Res. Board, 2287, 72–79, 2012.
Petzold, A., Lauer, P., Fritsche, U., Hasselbach, J., Lichtenstern, M., Schlager, H., and Fleischer, F.: Operation of marine diesel engines on biogenic fuels: modification of emissions and resulting climate effects, Environ. Sci. Technol., 45, 10394–10400, 2011.
Rafaj, P., Schöpp, W., Russ, P., Heyes, C., and Amann, M.: Co-benefits of post-2012 global climate mitigation policies, Mitig. Adapt. Strat. Gl., 18, 801–824, 2012.
Ran, L., Loughlin, D. H., Yang, D., Adelman, Z., Baek, B. H., and Nolte, C. G.: ESP v2.0: enhanced method for exploring emission impacts of future scenarios in the United States – addressing spatial allocation, Geosci. Model Dev., 8, 1775–1787, https://doi.org/10.5194/gmd-8-1775-2015, 2015.
Rounce, P., Tsolakis, A., and York, A. P. E.: Speciation of particulate matter and hydrocarbon emissions from biodiesel combustion and its reduction by aftertreatment, Fuel, 96, 90–99, 2012.
Rudokas, J., Miller, P. J., Trail, M. A., and Russell, A. G.: Regional air quality management aspects of climate change: impact of climate mitigation options on regional air emissions, Environ. Sci. Technol., 49, 5170–5177, 2015.
Shindell, D., Kuylenstierna, J. C. I., Vignati, E., van Dingenen, R., Amann, M., Klimont, Z., Anenberg, S. C., Muller, N., Janssens-Maenhout, G., Raes, F., Schwartz, J., Faluvegi, G., Pozzoli, L., Kupiainen, K., Höglund-Isaksson, L., Emberson, L., Streets, D., Ramanathan, V., Hicks, K., Oanh, N. T. K., Milly, G., Williams, M., Demkine, V., and Fowler, D.: Simultaneously Mitigating Near-Term Climate Change and Improving Human Health and Food Security, Science, 335, 183, 2012.
Ståhl, K. and Neergaard, M.: IGCC power plant for biomass utilisation, Värnamo, Sweden, Biomass Bioenerg., 15, 205–211, 1998.
Starcrest Consulting Group, L.: San Pedro Bay Ports Clean Air Action Plan: 2010 Update. Appendix A: San Pedro Bay Ports Emissions Forecasting Methodology & Results. The Port of Los Angeles, The Port of Long Beach, P. O. Box 434, Poulsbo, WA 98370, 2009.
State of California, D.o. F.: Report P-1 (County): State and County Total Population Projections, 2010–2060, Sacramento, California, 2013.
Szybist, J. P., Youngquist, A. D., Barone, T. L., Storey, J. M., Moore, W. R., Foster, M., and Confer, K.: Ethanol Blends and Engine Operating Strategy Effects on Light-Duty Spark-Ignition Engine Particle Emissions, Energ. Fuel., 25, 4977–4985, 2011.
The Port of Los Angeles, The Port of Long Beach: San Pedro Bay Ports Clean Air Action Plan: 2010 Update, The Port of Los Angeles, Los Angeles, CA, USA, 2010.
Tittmann, P. W., Parker, N. C., Hart, Q. J., and Jenkins, B. M.: A spatially explicit techno-economic model of bioenergy and biofuels production in California, J. Transp. Geogr., 18, 715–728, 2010.
Trail, M. A., Tsimpidi, A. P., Liu, P., Tsigaridis, K., Hu, Y., Rudokas, J. R., Miller, P. J., Nenes, A., and Russell, A. G.: Impacts of potential CO2-reduction policies on air quality in the United States, Environ. Sci. Technol., 49, 5133–5141, 2015.
US Department of Agriculture Rural Development Agency: Cooperative Approaches for Implementation of Dairy Manure Digesters, in: Agency, R. D. (Ed.), STOP 3252, 1400 Independence Ave., S.W, Washington, DC 20250-3252, 2009.
US Department of Energy National Energy Technology Laboratory: Archived 2010 Worldwide Gasification Database, available at: www.netl.doe.gov/research/coal/energy-systems/gasification/gasification-plant-databases/2010-archive (last access: June 2015), 2010.
US Department of Energy National Energy Technology Laboratory: United States Proposed Gasification Plant Database, March 2015. ed. US Department of Energy, National Energy Technology Laboratory, available at: www.netl.doe.gov/File%20Library/Research/Coal/energy%20systems/gasification/worldwide%20database/US-Gasification-Database.xlsx, last access: June, 2015.
US Energy Information Administration Independent Statistics and Analysis: Electricity. Form EIA-860 detailed data, available at: www.eia.gov/electricity/data/eia860/ (last access: February 2014), 2012.
US Environmental Protection Agency AgSTAR Program: Market Opportunities for Biogas Recovery Systems at US Livestock Facilities, in: US Environmental Protection Agency (Ed.), US Environmental Protection Agency, Washington DC, USA, 2011.
US Environmental Protection Agency: eGRID. Nineth edition with year 2010 data (Version 1.0), 9th ed. Environmental Protection Agency, p. The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data on the environmental characteristics of almost all electric power generated in the United States, US Environmental Protection Agency, Washington DC, USA, 2014.
van Aardenne, J., Dentener, F., Van Dingenen, R., Maenhout, G., Marmer, E., Vignati, E., Russ, P., Szabo, L., and Raes, F.: Climate and air quality impacts of combined climate change and air pollution policy scenarios., JRC Scientific and Technical Reports. European Commission. Joint Research Centre. Institute for Environment and Sustainability, Luxembourg: Publications Office of the European Union, 2010.
West, J. J., Smith, S. J., Silva, R. A., Naik, V., Zhang, Y., Adelman, Z., Fry, M. M., Anenberg, S., Horowitz, L. W., and Lamarque, J. F.: Co-benefits of Global Greenhouse Gas Mitigation for Future Air Quality and Human Health, Nat. Clim. Change, 3, 885–889, 2013.
Yang, C., Yeh, S., Ramea, K., Zakerinia, S., McCollum, D., Bunch, D., and Ogden, J.: Modeling Optimal Transition Pathways to a Low Carbon Economy in California: California TIMES (CA-TIMES) Model, Institute of Transportation Studies, University of California, Davis, CA, 2014.
Yang, C., Yeh, S., Zakerinia, S., Ramea, K., and McCollum, D.: Achieving California's 80 % greenhouse gas reduction target in 2050: Technology, policy and scenario analysis using CA-TIMES energy economic systems model, Energ. Policy, 77, 118–130, 2015.
Yoon, S., Hu, S., Kado, N. Y., Thiruvengadam, A., Collins, J. F., Gautam, M., Herner, J. D., and Ayala, A.: Chemical and toxicological properties of emissions from CNG transit buses equipped with three-way catalysts compared to lean-burn engines and oxidation catalyst technologies, Atmos. Environ., 83, 220–228, 2014.
Zhang, H., Chen, G., Hu, J., Chen, S. H., Wiedinmyer, C., Kleeman, M., and Ying, Q.: Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States, Sci. Total Environ., 473–474, 275–285, 2014.
Zhang, Y., Bowden, J. H., Adelman, Z., Naik, V., Horowitz, L. W., Smith, S. J., and West, J. J.: Co-benefits of global and regional greenhouse gas mitigation for US air quality in 2050, Atmos. Chem. Phys., 16, 9533–9548, https://doi.org/10.5194/acp-16-9533-2016, 2016.
Short summary
The CA-REMARQUE emissions model translates policies designed for climate change mitigation into inputs needed for air pollution analysis in California. The model captures the complicated trade-offs associated with changing fuels and technologies that sometimes increase air pollution emissions in some areas while decreasing emissions in other areas. These detailed calculations are needed in highly populated regions like California where simple emissions controls have already been applied.
The CA-REMARQUE emissions model translates policies designed for climate change mitigation into...
