Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3587-2022
© Author(s) 2022. 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-15-3587-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 May 2022
Development and technical paper |
| 05 May 2022
Earth system modeling of mercury using CESM2 – Part 1: Atmospheric model CAM6-Chem/Hg v1.0
Peng Zhang
School of Atmospheric Science, Nanjing University, Nanjing, China
Yanxu Zhang
CORRESPONDING AUTHOR
School of Atmospheric Science, Nanjing University, Nanjing, China
Related authors
No articles found.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Short summary
In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Xiaotian Xu, Xu Feng, Haipeng Lin, Peng Zhang, Shaojian Huang, Zhengcheng Song, Yiming Peng, Tzung-May Fu, and Yanxu Zhang
Geosci. Model Dev., 15, 3845–3859, https://doi.org/10.5194/gmd-15-3845-2022, https://doi.org/10.5194/gmd-15-3845-2022, 2022
Short summary
Short summary
Mercury is one of the most toxic pollutants in the environment, and wet deposition is a major process for atmospheric mercury to enter, causing ecological and human health risks. High-mercury wet deposition in the southeastern US has been a problem for many years. Here we employed a newly developed high-resolution WRF-GC model with the capability to simulate mercury to study this problem. We conclude that deep convection caused enhanced mercury wet deposition in the southeastern US.
Ruochong Xu, Joel A. Thornton, Ben H. Lee, Yanxu Zhang, Lyatt Jaeglé, Felipe D. Lopez-Hilfiker, Pekka Rantala, and Tuukka Petäjä
Atmos. Chem. Phys., 22, 5477–5494, https://doi.org/10.5194/acp-22-5477-2022, https://doi.org/10.5194/acp-22-5477-2022, 2022
Short summary
Short summary
Monoterpenes are emitted into the atmosphere by vegetation and by the use of certain consumer products. Reactions of monoterpenes in the atmosphere lead to low-volatility products that condense to grow particulate matter or participate in new particle formation and, thus, affect air quality and climate. We use a model of atmospheric chemistry and transport to evaluate the global-scale importance of recent updates to our understanding of monoterpene chemistry in particle formation and growth.
Shibao Wang, Yun Ma, Zhongrui Wang, Lei Wang, Xuguang Chi, Aijun Ding, Mingzhi Yao, Yunpeng Li, Qilin Li, Mengxian Wu, Ling Zhang, Yongle Xiao, and Yanxu Zhang
Atmos. Chem. Phys., 21, 7199–7215, https://doi.org/10.5194/acp-21-7199-2021, https://doi.org/10.5194/acp-21-7199-2021, 2021
Short summary
Short summary
Mobile monitoring with low-cost sensors is a promising approach to garner high-spatial-resolution observations representative of the community scale. We develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3) based on GIS technology. Our results demonstrate the sensing power of mobile monitoring for urban air pollution, which provides detailed information for source attribution and accurate traceability at the urban micro-scale.
Yanxu Zhang, Xingpei Ye, Shibao Wang, Xiaojing He, Lingyao Dong, Ning Zhang, Haikun Wang, Zhongrui Wang, Yun Ma, Lei Wang, Xuguang Chi, Aijun Ding, Mingzhi Yao, Yunpeng Li, Qilin Li, Ling Zhang, and Yongle Xiao
Atmos. Chem. Phys., 21, 2917–2929, https://doi.org/10.5194/acp-21-2917-2021, https://doi.org/10.5194/acp-21-2917-2021, 2021
Short summary
Short summary
Urban air quality varies drastically at street scale, but traditional methods are too coarse to resolve it. We develop a 10 m resolution air quality model and apply it for traffic-related carbon monoxide air quality in Nanjing megacity. The model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network and agrees well with a validation dataset. The model can be a vigorous part of the smart city system and inform urban planning and air quality management.
Related subject area
Atmospheric sciences
Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 1: Understanding expressiveness of schemes for different regions from the mechanism perspective
Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model
Implementation of a satellite-based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data
The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data
Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020
Description and evaluation of the new UM–UKCA (vn11.0) Double Extended Stratospheric–Tropospheric (DEST vn1.0) scheme for comprehensive modelling of halogen chemistry in the stratosphere
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, variational mode decomposition, principal component analysis, and random forest: VMD-PCA-RF (version 1.0.0)
Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2)
A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx
The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics
Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0
A standardized methodology for the validation of air quality forecast applications (F-MQO): lessons learnt from its application across Europe
Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa
A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application
Evaluation of vertically resolved longwave radiation in SPARTACUS-Urban 0.7.3 and the sensitivity to urban surface temperatures
Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning
QES-Plume v1.0: a Lagrangian dispersion model
A two-way coupled regional urban–street network air quality model system for Beijing, China
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
Emulating lateral gravity wave propagation in a global chemistry–climate model (EMAC v2.55.2) through horizontal flux redistribution
Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas
An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models
Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion
Improved representation of volcanic sulfur dioxide depletion in Lagrangian transport simulations: a case study with MPTRAC v2.4
Use of threshold parameter variation for tropical cyclone tracking
Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes
The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale
A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter
Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1
CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
A method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)
Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis
Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance
A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0
The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models
The development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)
GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)
Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models
Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method
An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters
Sensitivity of tropospheric ozone to halogen chemistry in the chemistry–climate model LMDZ-INCA vNMHC
Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants
Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models
The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6635–6670, https://doi.org/10.5194/gmd-16-6635-2023, https://doi.org/10.5194/gmd-16-6635-2023, 2023
Short summary
Short summary
Most current studies on planetary boundary layer (PBL) parameterization schemes are relatively fragmented and lack systematic in-depth analysis and discussion. In this study, we comprehensively evaluate the performance capability of the PBL scheme in five typical regions of China in different seasons from the mechanism of the scheme and the effects of PBL schemes on the near-surface meteorological parameters, vertical structures of the PBL, PBL height, and turbulent diffusion.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev., 16, 6531–6552, https://doi.org/10.5194/gmd-16-6531-2023, https://doi.org/10.5194/gmd-16-6531-2023, 2023
Short summary
Short summary
It is important to know how well atmospheric models do in mountains, but there are not very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado River basin against the available data. The model works rather well, but there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we could not do before.
Angel Liduvino Vara-Vela, Christoffer Karoff, Noelia Rojas Benavente, and Janaina P. Nascimento
Geosci. Model Dev., 16, 6413–6431, https://doi.org/10.5194/gmd-16-6413-2023, https://doi.org/10.5194/gmd-16-6413-2023, 2023
Short summary
Short summary
A 1-year simulation of atmospheric CH4 over Europe is performed and evaluated against observations based on the TROPOspheric Monitoring Instrument (TROPOMI). A good general model–observation agreement is found, with discrepancies reaching their minimum and maximum values during the summer peak season and winter months, respectively. A huge and under-explored potential for CH4 inverse modeling using improved TROPOMI XCH4 data sets in large-scale applications is identified.
Zhaojun Tang, Zhe Jiang, Jiaqi Chen, Panpan Yang, and Yanan Shen
Geosci. Model Dev., 16, 6377–6392, https://doi.org/10.5194/gmd-16-6377-2023, https://doi.org/10.5194/gmd-16-6377-2023, 2023
Short summary
Short summary
We designed a new framework to facilitate emission inventory updates in the adjoint of GEOS-Chem model. It allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 meteorological data; this allows us to perform long-term analysis with consistent meteorological data.
Rui Zhu, Zhaojun Tang, Xiaokang Chen, Xiong Liu, and Zhe Jiang
Geosci. Model Dev., 16, 6337–6354, https://doi.org/10.5194/gmd-16-6337-2023, https://doi.org/10.5194/gmd-16-6337-2023, 2023
Short summary
Short summary
A single ozone (O3) tracer mode was developed in this work to build the capability of the GEOS-Chem model for rapid O3 simulation. It is combined with OMI and surface O3 observations to investigate the changes in tropospheric O3 in China in 2015–2020. The assimilations indicate rapid surface O3 increases that are underestimated by the a priori simulations. We find stronger increases in tropospheric O3 columns over polluted areas and a large discrepancy by assimilating different observations.
Ewa M. Bednarz, Ryan Hossaini, N. Luke Abraham, and Martyn P. Chipperfield
Geosci. Model Dev., 16, 6187–6209, https://doi.org/10.5194/gmd-16-6187-2023, https://doi.org/10.5194/gmd-16-6187-2023, 2023
Short summary
Short summary
Development and performance of the new DEST chemistry scheme of UM–UKCA is described. The scheme extends the standard StratTrop scheme by including important updates to the halogen chemistry, thus allowing process-oriented studies of stratospheric ozone depletion and recovery, including impacts from both controlled long-lived ozone-depleting substances and emerging issues around uncontrolled, very short-lived substances. It will thus aid studies in support of future ozone assessment reports.
Shaohui Zhou, Chloe Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
Geosci. Model Dev., 16, 6247–6266, https://doi.org/10.5194/gmd-16-6247-2023, https://doi.org/10.5194/gmd-16-6247-2023, 2023
Short summary
Short summary
The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indices for 13 months remain relatively stable: the forecasting accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev., 16, 6087–6125, https://doi.org/10.5194/gmd-16-6087-2023, https://doi.org/10.5194/gmd-16-6087-2023, 2023
Short summary
Short summary
We implemented an alternative aerosol scheme in the high- and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. This development enables the comparison of different aerosol schemes with different complexity in the same model framework. It identifies improvements compared to a range of observations in both the troposphere and stratosphere.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
Geosci. Model Dev., 16, 6161–6185, https://doi.org/10.5194/gmd-16-6161-2023, https://doi.org/10.5194/gmd-16-6161-2023, 2023
Short summary
Short summary
To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and assess its performance against TROPOMI v2 over power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind directions and prior emissions.
Jiangshan Zhu and Ross Noel Bannister
Geosci. Model Dev., 16, 6067–6085, https://doi.org/10.5194/gmd-16-6067-2023, https://doi.org/10.5194/gmd-16-6067-2023, 2023
Short summary
Short summary
We describe how condensation and evaporation are included in the existing (otherwise dry) simplified ABC model. The new model (Hydro-ABC) includes transport of vapour and condensate within a dynamical core, and it transitions between these two phases via a micro-physics scheme. The model shows the development of an anvil cloud and excitation of atmospheric waves over many frequencies. The covariances that develop between variables are also studied together with indicators of convective motion.
Jiangyong Li, Chunlin Zhang, Wenlong Zhao, Shijie Han, Yu Wang, Hao Wang, and Boguang Wang
Geosci. Model Dev., 16, 6049–6066, https://doi.org/10.5194/gmd-16-6049-2023, https://doi.org/10.5194/gmd-16-6049-2023, 2023
Short summary
Short summary
Photochemical box models, crucial for understanding tropospheric chemistry, face challenges due to slow computational efficiency with large chemical equations. The model introduced in this study, ROMAC, boosts efficiency by up to 96 % using an advanced atmospheric solver and an adaptive optimization algorithm. Moreover, ROMAC exceeds traditional box models in evaluating the impact of physical processes on pollutant concentrations.
Lina Vitali, Kees Cuvelier, Antonio Piersanti, Alexandra Monteiro, Mario Adani, Roberta Amorati, Agnieszka Bartocha, Alessandro D'Ausilio, Paweł Durka, Carla Gama, Giulia Giovannini, Stijn Janssen, Tomasz Przybyła, Michele Stortini, Stijn Vranckx, and Philippe Thunis
Geosci. Model Dev., 16, 6029–6047, https://doi.org/10.5194/gmd-16-6029-2023, https://doi.org/10.5194/gmd-16-6029-2023, 2023
Short summary
Short summary
Air quality forecasting models play a key role in fostering short-term measures aimed at reducing human exposure to air pollution. Together with this role comes the need for a thorough assessment of the model performances to build confidence in models’ capabilities, in particular when model applications support policymaking. In this paper, we propose an evaluation methodology and test it on several domains across Europe, highlighting its strengths and room for improvement.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
Short summary
Short summary
The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
Short summary
Short summary
We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Megan A. Stretton, William Morrison, Robin J. Hogan, and Sue Grimmond
Geosci. Model Dev., 16, 5931–5947, https://doi.org/10.5194/gmd-16-5931-2023, https://doi.org/10.5194/gmd-16-5931-2023, 2023
Short summary
Short summary
Cities' materials and forms impact radiative fluxes. We evaluate the SPARTACUS-Urban multi-layer approach to modelling longwave radiation, describing realistic 3D geometry statistically using the explicit DART (Discrete Anisotropic Radiative Transfer) model. The temperature configurations used are derived from thermal camera observations. SPARTACUS-Urban accurately predicts longwave fluxes, with a low computational time (cf. DART), but has larger errors with sunlit/shaded surface temperatures.
Daehyeon Han, Jungho Im, Yeji Shin, and Juhyun Lee
Geosci. Model Dev., 16, 5895–5914, https://doi.org/10.5194/gmd-16-5895-2023, https://doi.org/10.5194/gmd-16-5895-2023, 2023
Short summary
Short summary
To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep learning (DL), we carried out a comprehensive evaluation and analysis. We compared four key factors: DL model, length of the input sequence, loss function, and ensemble approach. Generally, U-Net outperformed ConvLSTM. Loss function and ensemble showed potential for improving performance when they synergized well. The length of the input sequence did not significantly affect the results.
Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, and Rob Stoll
Geosci. Model Dev., 16, 5729–5754, https://doi.org/10.5194/gmd-16-5729-2023, https://doi.org/10.5194/gmd-16-5729-2023, 2023
Short summary
Short summary
The Quick Environmental Simulation (QES) tool is a low-computational-cost fast-response framework. It provides high-resolution wind and concentration information to study complex problems, such as spore or smoke transport, urban pollution, and air quality. This paper presents the particle dispersion model and its validation against analytical solutions and wind-tunnel data for a mock-urban setting. In all cases, the model provides accurate results with competitive computational performance.
Tao Wang, Hang Liu, Jie Li, Shuai Wang, Youngseob Kim, Yele Sun, Wenyi Yang, Huiyun Du, Zhe Wang, and Zifa Wang
Geosci. Model Dev., 16, 5585–5599, https://doi.org/10.5194/gmd-16-5585-2023, https://doi.org/10.5194/gmd-16-5585-2023, 2023
Short summary
Short summary
This paper developed a two-way coupled module in a new version of a regional urban–street network model, IAQMS-street v2.0, in which the mass flux from streets to background is considered. Test cases are defined to evaluate the performance of IAQMS-street v2.0 in Beijing by comparing it with that simulated by IAQMS-street v1.0 and a regional model. The contribution of local emissions and the influence of on-road vehicle control measures on air quality are evaluated by using IAQMS-street v2.0.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavčič, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 16, 5601–5626, https://doi.org/10.5194/gmd-16-5601-2023, https://doi.org/10.5194/gmd-16-5601-2023, 2023
Short summary
Short summary
Three-dimensional climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchař, Patrick Jöckel, Astrid Kerkweg, and Bastian Kern
Geosci. Model Dev., 16, 5561–5583, https://doi.org/10.5194/gmd-16-5561-2023, https://doi.org/10.5194/gmd-16-5561-2023, 2023
Short summary
Short summary
The columnar approach of gravity wave (GW) schemes results in dynamical model biases, but parallel decomposition makes horizontal GW propagation computationally unfeasible. In the global model EMAC, we approximate it by GW redistribution at one altitude using tailor-made redistribution maps generated with a ray tracer. More spread-out GW drag helps reconcile the model with observations and close the 60°S GW gap. Polar vortex dynamics are improved, enhancing climate model credibility.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
Geosci. Model Dev., 16, 5493–5514, https://doi.org/10.5194/gmd-16-5493-2023, https://doi.org/10.5194/gmd-16-5493-2023, 2023
Short summary
Short summary
With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023, https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary
Short summary
Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidentally) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011) and show that it improves the simulation of wet deposition.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023, https://doi.org/10.5194/gmd-16-5281-2023, 2023
Short summary
Short summary
A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023, https://doi.org/10.5194/gmd-16-5251-2023, 2023
Short summary
Short summary
In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
Daniel Yazgi and Tinja Olenius
Geosci. Model Dev., 16, 5237–5249, https://doi.org/10.5194/gmd-16-5237-2023, https://doi.org/10.5194/gmd-16-5237-2023, 2023
Short summary
Short summary
We present flexible tools to implement aerosol formation rate predictions in climate and chemical transport models. New-particle formation is a significant but uncertain factor affecting aerosol numbers and an active field within molecular modeling which provides data for assessing formation rates for different chemical species. We introduce tools to generate and interpolate formation rate lookup tables for user-defined data, thus enabling the easy inclusion and testing of formation schemes.
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023, https://doi.org/10.5194/gmd-16-5219-2023, 2023
Short summary
Short summary
Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.
Mingzhao Liu, Lars Hoffmann, Sabine Griessbach, Zhongyin Cai, Yi Heng, and Xue Wu
Geosci. Model Dev., 16, 5197–5217, https://doi.org/10.5194/gmd-16-5197-2023, https://doi.org/10.5194/gmd-16-5197-2023, 2023
Short summary
Short summary
We introduce new and revised chemistry and physics modules in the Massive-Parallel Trajectory Calculations (MPTRAC) Lagrangian transport model aiming to improve the representation of volcanic SO2 transport and depletion. We test these modules in a case study of the Ambae eruption in July 2018 in which the SO2 plume underwent wet removal and convection. The lifetime of SO2 shows highly variable and complex dependencies on the atmospheric conditions at different release heights.
Bernhard M. Enz, Jan P. Engelmann, and Ulrike Lohmann
Geosci. Model Dev., 16, 5093–5112, https://doi.org/10.5194/gmd-16-5093-2023, https://doi.org/10.5194/gmd-16-5093-2023, 2023
Short summary
Short summary
An algorithm to track tropical cyclones in model simulation data has been developed. The algorithm uses many combinations of varying parameter thresholds to detect weaker phases of tropical cyclones while still being resilient to false positives. It is shown that the algorithm performs well and adequately represents the tropical cyclone activity of the underlying simulation data. The impact of false positives on overall tropical cyclone activity is shown to be insignificant.
Sepehr Fathi, Mark Gordon, and Yongsheng Chen
Geosci. Model Dev., 16, 5069–5091, https://doi.org/10.5194/gmd-16-5069-2023, https://doi.org/10.5194/gmd-16-5069-2023, 2023
Short summary
Short summary
We have combined various capabilities within a WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The study objective was to resolve transport processes at the scale of measurements to assess and optimize aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological and within 1–2 standard deviations of observed wind fields. Mass was conserved in the model within 5 % of input emissions.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
Short summary
Short summary
The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
Short summary
Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Willem Elias van Caspel, David Simpson, Jan Eiof Jonson, Anna Maria Katarina Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah Walker, and Mathew Heal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-147, https://doi.org/10.5194/gmd-2023-147, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Radiation coming from the sun is essential to atmospheric chemistry, driving the break-up, or photo-dissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of these photo-dissociation effects is therefore essential in atmospheric chemistry modeling. One such models is the EMEP MSC-W model, for which in this paper a new way of calculating the photo-dissociation rates is tested and evaluated.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
Short summary
Short summary
A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Yosuke Yamazaki
Geosci. Model Dev., 16, 4749–4766, https://doi.org/10.5194/gmd-16-4749-2023, https://doi.org/10.5194/gmd-16-4749-2023, 2023
Short summary
Short summary
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
Short summary
Short summary
To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev., 16, 4617–4638, https://doi.org/10.5194/gmd-16-4617-2023, https://doi.org/10.5194/gmd-16-4617-2023, 2023
Short summary
Short summary
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-139, https://doi.org/10.5194/gmd-2023-139, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
The existing ZTD models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data for modeling. This model considers the daily-cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, https://doi.org/10.5194/gmd-16-4551-2023, 2023
Short summary
Short summary
The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev., 16, 4405–4425, https://doi.org/10.5194/gmd-16-4405-2023, https://doi.org/10.5194/gmd-16-4405-2023, 2023
Short summary
Short summary
Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, https://doi.org/10.5194/gmd-16-4427-2023, 2023
Short summary
Short summary
We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403, https://doi.org/10.5194/gmd-16-4385-2023, https://doi.org/10.5194/gmd-16-4385-2023, 2023
Short summary
Short summary
The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383, https://doi.org/10.5194/gmd-16-4367-2023, https://doi.org/10.5194/gmd-16-4367-2023, 2023
Short summary
Short summary
Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Laurent Menut
Geosci. Model Dev., 16, 4265–4281, https://doi.org/10.5194/gmd-16-4265-2023, https://doi.org/10.5194/gmd-16-4265-2023, 2023
Short summary
Short summary
This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211, https://doi.org/10.5194/gmd-16-4193-2023, https://doi.org/10.5194/gmd-16-4193-2023, 2023
Short summary
Short summary
In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
Short summary
Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062, https://doi.org/10.5194/gmd-16-4041-2023, https://doi.org/10.5194/gmd-16-4041-2023, 2023
Short summary
Short summary
We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
Short summary
Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
Short summary
Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, https://doi.org/10.5194/gmd-16-3873-2023, 2023
Short summary
Short summary
The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Cited articles
Amos, H. M., Jacob, D. J., Holmes, C. D., Fisher, J. A., Wang, Q., Yantosca, R. M., Corbitt, E. S., Galarneau, E., Rutter, A. P., Gustin, M. S., Steffen, A., Schauer, J. J., Graydon, J. A., Louis, V. L. St., Talbot, R. W., Edgerton, E. S., Zhang, Y., and Sunderland, E. M.: Gas-particle partitioning of atmospheric Hg(II) and its effect on global mercury deposition, Atmos. Chem. Phys., 12, 591–603, https://doi.org/10.5194/acp-12-591-2012, 2012.
AuYang, D., Chen, J., Zheng, W., Zhang, Y., Shi, G., Sonke, J. E.,
Cartigny, P., Cai, H., Yuan, W., Liu, L., Gai, P., and Liu, C.:
South-hemispheric marine aerosol Hg and S isotope compositions reveal
different oxidation pathways, National Science Open, 1, 20220014, https://doi.org/10.1360/nso/20220014,
2022.
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, 2014.
Ballabio, C., Jiskra, M., Osterwalder, S., Borrelli, P., Montanarella, L.,
and Panagos, P.: A spatial assessment of mercury content in the European
Union topsoil, Sci. Total Environ., 769, 144755,
https://doi.org/10.1016/j.scitotenv.2020.144755, 2021.
Bieser, J., Slemr, F., Ambrose, J., Brenninkmeijer, C., Brooks, S., Dastoor, A., DeSimone, F., Ebinghaus, R., Gencarelli, C. N., Geyer, B., Gratz, L. E., Hedgecock, I. M., Jaffe, D., Kelley, P., Lin, C.-J., Jaegle, L., Matthias, V., Ryjkov, A., Selin, N. E., Song, S., Travnikov, O., Weigelt, A., Luke, W., Ren, X., Zahn, A., Yang, X., Zhu, Y., and Pirrone, N.: Multi-model study of mercury dispersion in the atmosphere: vertical and interhemispheric distribution of mercury species, Atmos. Chem. Phys., 17, 6925–6955, https://doi.org/10.5194/acp-17-6925-2017, 2017.
Calvert, J. G. and Lindberg, S. E.: Mechanisms of mercury removal by O3 and
OH in the atmosphere, Atmos. Environ., 39, 3355–3367,
https://doi.org/10.1016/j.atmosenv.2005.01.055, 2005.
Crutzen, P. J. and Zimmermann, P. H.: The changing photochemistry of the
troposphere, Tellus A, 43,
136–151, https://doi.org/10.1034/j.1600-0870.1991.00012.x, 1991.
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S.,
Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C.,
Fischer, C., Fox Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J.,
Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E.,
Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System
Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916,
2020.
De Simone, F., Gencarelli, C. N., Hedgecock, I. M., and Pirrone, N.: Global
atmospheric cycle of mercury: a model study on the impact of oxidation
mechanisms, Environ. Sci. Pollut. R., 21, 4110–4123,
https://doi.org/10.1007/s11356-013-2451-x, 2014.
Dibble, T. S., Tetu, H. L., Jiao, Y., Thackray, C. P., and Jacob, D. J.:
Modeling the OH-Initiated Oxidation of Mercury in the Global Atmosphere
without Violating Physical Laws, J. Phys. Chem. A, 124,
444–453, https://doi.org/10.1021/acs.jpca.9b10121, 2020.
Driscoll, C. T., Mason, R. P., Chan, H. M., Jacob, D. J., and Pirrone, N.:
Mercury as a Global Pollutant: Sources, Pathways, and Effects, Environ. Sci.
Technol., 47, 4967–4983, https://doi.org/10.1021/es305071v, 2013.
Durnford, D., Dastoor, A., Ryzhkov, A., Poissant, L., Pilote, M., and Figueras-Nieto, D.: How relevant is the deposition of mercury onto snowpacks? – Part 2: A modeling study, Atmos. Chem. Phys., 12, 9251–9274, https://doi.org/10.5194/acp-12-9251-2012, 2012.
Ebinghaus, R., Jennings, S. G., Kock, H. H., Derwent, R. G., Manning, A. J.,
and Spain, T. G.: Decreasing trends in total gaseous mercury observations in
baseline air at Mace Head, Ireland from 1996 to 2009, Atmos. Environ., 45,
3475–3480, https://doi.org/10.1016/j.atmosenv.2011.01.033, 2011.
El-Harbawi, M.: Air quality modelling, simulation, and computational
methods: a review, Environ. Rev., 21, 149–179, https://doi.org/10.1139/er-2012-0056, 2013.
Emmons, L. K., Schwantes, R. H., Orlando, J. J., Tyndall, G., Kinnison, D.,
Lamarque, J. F., Marsh, D., Mills, M. J., Tilmes, S., Bardeen, C., Buchholz,
R. R., Conley, A., Gettelman, A., Garcia, R., Simpson, I., Blake, D. R.,
Meinardi, S., and Pétron, G.: The Chemistry Mechanism in the Community
Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001882,
https://doi.org/10.1029/2019MS001882, 2020.
Fernandez, R. P., Carmona Balea, A., Cuevas, C. A., Barrera, J. A.,
Kinnison, D. E., Lamarque, J. F., Blaszczak Boxe, C., Kim, K., Choi, W.,
Hay, T., Blechschmidt, A. M., Schönhardt, A., Burrows, J. P., and Saiz
Lopez, A.: Modeling the Sources and Chemistry of Polar Tropospheric Halogens
(Cl, Br, and I) Using the CAM-Chem Global Chemistry-Climate Model, J. Adv.
Model. Earth Sy., 11, 2259–2289, https://doi.org/10.1029/2019MS001655, 2019.
Fu, X., Yang, X., Lang, X., Zhou, J., Zhang, H., Yu, B., Yan, H., Lin, C.-J., and Feng, X.: Atmospheric wet and litterfall mercury deposition at urban and rural sites in China, Atmos. Chem. Phys., 16, 11547–11562, https://doi.org/10.5194/acp-16-11547-2016, 2016.
Fu, X., Liu, C., Zhang, H., Xu, Y., Zhang, H., Li, J., Lyu, X., Zhang, G., Guo, H., Wang, X., Zhang, L., and Feng, X.: Isotopic compositions of atmospheric total gaseous mercury in 10 Chinese cities and implications for land surface emissions, Atmos. Chem. Phys., 21, 6721–6734, https://doi.org/10.5194/acp-21-6721-2021, 2021.
Fu, X. W., Zhang, H., Yu, B., Wang, X., Lin, C.-J., and Feng, X. B.: Observations of atmospheric mercury in China: a critical review, Atmos. Chem. Phys., 15, 9455–9476, https://doi.org/10.5194/acp-15-9455-2015, 2015.
Gencarelli, C. N., Bieser, J., Carbone, F., De Simone, F., Hedgecock, I. M., Matthias, V., Travnikov, O., Yang, X., and Pirrone, N.: Sensitivity model study of regional mercury dispersion in the atmosphere, Atmos. Chem. Phys., 17, 627–643, https://doi.org/10.5194/acp-17-627-2017, 2017.
Gettelman, A., Mills, M. J., Kinnison, D. E., Garcia, R. R., Smith, A. K.,
Marsh, D. R., Tilmes, S., Vitt, F., Bardeen, C. G., McInerny, J., Liu, H.
L., Solomon, S. C., Polvani, L. M., Emmons, L. K., Lamarque, J. F., Richter,
J. H., Glanville, A. S., Bacmeister, J. T., Phillips, A. S., Neale, R. B.,
Simpson, I. R., DuVivier, A. K., Hodzic, A., and Randel, W. J.: The Whole
Atmosphere Community Climate Model Version 6 (WACCM6), J.
Geophys. Res.-Atmos., 124, 12380–12403, https://doi.org/10.1029/2019JD030943,
2019.
Goodsite, M. E., Plane, J. M., and Skov, H.: A theoretical study of the
oxidation of Hg0 to HgBr2 in the troposphere, Environ. Sci. Technol., 38,
1772–1776, https://doi.org/10.1021/es034680s, 2004.
Grell, G. and Baklanov, A.: Integrated modeling for forecasting weather and
air quality: A call for fully coupled approaches, Atmos. Environ., 45,
6845–6851, https://doi.org/10.1016/j.atmosenv.2011.01.017, 2011.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.
Holmes, C. D., Jacob, D. J., Mason, R. P., and Jaffe, D. A.: Sources and
deposition of reactive gaseous mercury in the marine atmosphere, Atmos.
Environ., 43, 2278–2285, https://doi.org/10.1016/j.atmosenv.2009.01.051, 2009.
Holmes, C. D., Jacob, D. J., Corbitt, E. S., Mao, J., Yang, X., Talbot, R., and Slemr, F.: Global atmospheric model for mercury including oxidation by bromine atoms, Atmos. Chem. Phys., 10, 12037–12057, https://doi.org/10.5194/acp-10-12037-2010, 2010.
Horowitz, H. M., Jacob, D. J., Zhang, Y., Dibble, T. S., Slemr, F., Amos, H. M., Schmidt, J. A., Corbitt, E. S., Marais, E. A., and Sunderland, E. M.: A new mechanism for atmospheric mercury redox chemistry: implications for the global mercury budget, Atmos. Chem. Phys., 17, 6353–6371, https://doi.org/10.5194/acp-17-6353-2017, 2017.
Jiskra, M., Sonke, J. E., Obrist, D., Bieser, J., Ebinghaus, R., Myhre, C.
L., Pfaffhuber, K. A., Wängberg, I., Kyllönen, K., Worthy, D.,
Martin, L. G., Labuschagne, C., Mkololo, T., Ramonet, M., Magand, O., and
Dommergue, A.: A vegetation control on seasonal variations in global
atmospheric mercury concentrations, Nat. Geosci., 11, 244–250,
https://doi.org/10.1038/s41561-018-0078-8, 2018.
Lamarque, J.-F., Emmons, L. K., Hess, P. G., Kinnison, D. E., Tilmes, S., Vitt, F., Heald, C. L., Holland, E. A., Lauritzen, P. H., Neu, J., Orlando, J. J., Rasch, P. J., and Tyndall, G. K.: CAM-chem: description and evaluation of interactive atmospheric chemistry in the Community Earth System Model, Geosci. Model Dev., 5, 369–411, https://doi.org/10.5194/gmd-5-369-2012, 2012.
Lamborg, C. H., Fitzgerald, W. F., O'Donnell, J., and Torgersen, T.: A
non-steady-state compartmental model of global-scale mercury biogeochemistry
with interhemispheric atmospheric gradients, Geochim. Cosmochim. Ac., 66,
1105–1118, https://doi.org/10.1016/S0016-7037(01)00841-9, 2002.
Lei, H., Liang, X.-Z., Wuebbles, D. J., and Tao, Z.: Model analyses of atmospheric mercury: present air quality and effects of transpacific transport on the United States, Atmos. Chem. Phys., 13, 10807–10825, https://doi.org/10.5194/acp-13-10807-2013, 2013.
Lelieveld, J., Gromov, S., Pozzer, A., and Taraborrelli, D.: Global tropospheric hydroxyl distribution, budget and reactivity, Atmos. Chem. Phys., 16, 12477–12493, https://doi.org/10.5194/acp-16-12477-2016, 2016.
Lin, S. J., and Rood, R. B.: An explicit flux-form semi-Lagrangian
shallow-water model on the sphere, Q. J. Roy. Meteor. Soc., 123, 2477–2498,
https://doi.org/10.1256/smsqj.54415, 1997.
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016.
Lyman, S. N. and Jaffe, D. A.: Formation and fate of oxidized mercury in
the upper troposphere and lower stratosphere, Nat. Geosci., 5, 114–117,
https://doi.org/10.1038/ngeo1353, 2012.
Lyman, S. N., Cheng, I., Gratz, L. E., Weiss-Penzias, P., and Zhang, L.: An
updated review of atmospheric mercury, Sci. Total Environ., 707, 135575,
https://doi.org/10.1016/j.scitotenv.2019.135575, 2020.
Mahaffey, K. R., Sunderland, E. M., Chan, H. M., Choi, A. L., Grandjean, P.,
Mariën, K., Oken, E., Sakamoto, M., Schoeny, R., Weihe, P., Yan, C., and
Yasutake, A.: Balancing the benefits of n-3 polyunsaturated fatty acids and
the risks of methylmercury exposure from fish consumption, Nutr. Rev., 69,
493–508, https://doi.org/10.1111/j.1753-4887.2011.00415.x, 2011.
Malcolm, E. G., Ford, A. C., Redding, T. A., Richardson, M. C., Strain, B.
M., and Tetzner, S. W.: Experimental investigation of the scavenging of
gaseous mercury by sea salt aerosol, J. Atmos. Chem., 63, 221–234,
https://doi.org/10.1007/s10874-010-9165-y, 2009.
Neu, J. L. and Prather, M. J.: Toward a more physical representation of precipitation scavenging in global chemistry models: cloud overlap and ice physics and their impact on tropospheric ozone, Atmos. Chem. Phys., 12, 3289–3310, https://doi.org/10.5194/acp-12-3289-2012, 2012.
Obrist, D., Roy, E. M., Harrison, J. L., Kwong, C. F., Munger, J. W.,
Moosmüller, H., Romero, C. D., Sun, S., Zhou, J., and Commane, R.:
Previously unaccounted atmospheric mercury deposition in a midlatitude
deciduous forest, P. Natl. Acad. Sci. USA, 118,
e2105477118, https://doi.org/10.1073/pnas.2105477118, 2021.
Pacyna, E. G., Pacyna, J. M., and Pirrone, N.: European emissions of
atmospheric mercury from anthropogenic sources in 1995, Atmos.
Environ., 35, 2987–2996, https://doi.org/10.1016/S1352-2310(01)00102-9, 2001.
Pacyna, E. G., Pacyna, J. M., Fudala, J., Strzelecka-Jastrzab, E.,
Hlawiczka, S., and Panasiuk, D.: Mercury emissions to the atmosphere from
anthropogenic sources in Europe in 2000 and their scenarios until 2020, Sci.
Total Environ., 370, 147–156, https://doi.org/10.1016/j.scitotenv.2006.06.023, 2006.
Pacyna, J. M., Travnikov, O., De Simone, F., Hedgecock, I. M., Sundseth, K., Pacyna, E. G., Steenhuisen, F., Pirrone, N., Munthe, J., and Kindbom, K.: Current and future levels of mercury atmospheric pollution on a global scale, Atmos. Chem. Phys., 16, 12495–12511, https://doi.org/10.5194/acp-16-12495-2016, 2016.
Schmidt, J. A., Jacob, D. J., Horowitz, H. M., Hu, L., Sherwen, T., Evans,
M. J., Liang, Q., Suleiman, R. M., Oram, D. E., Le Breton, M., Percival, C.
J., Wang, S., Dix, B., and Volkamer, R.: Modeling the observed tropospheric
BrO background: Importance of multiphase chemistry and implications for
ozone, OH, and mercury, J. Geophys. Res.-Atmos., 121,
811–819, https://doi.org/10.1002/2015JD024229, 2016.
Seigneur, C., Karamchandani, P., Lohman, K., Vijayaraghavan, K., and Shia,
R. L.: Multiscale modeling of the atmospheric fate and transport of mercury,
J. Geophys. Res.-Atmos., 106, 27795–27809,
https://doi.org/10.1029/2000JD000273, 2001.
Selin, N. E.: Global Biogeochemical Cycling of Mercury: A Review, Annu. Rev.
Env. Resour., 34, 43–63, https://doi.org/10.1146/annurev.environ.051308.084314, 2009.
Selin, N. E., Jacob, D. J., Park, R. J., Yantosca, R. M., Strode, S.,
Jaeglé, L., and Jaffe, D.: Chemical cycling and deposition of
atmospheric mercury: Global constraints from observations, J.
Geophys. Res., 112, D02308, https://doi.org/10.1029/2006JD007450, 2007.
Selin, N. E., Jacob, D. J., Yantosca, R. M., Strode, S., Jaeglé, L., and
Sunderland, E. M.: Global 3-D land-ocean-atmosphere model for mercury:
Present-day versus preindustrial cycles and anthropogenic enrichment factors
for deposition, Global Biogeochem. Cy., 22, GB2011, https://doi.org/10.1029/2007GB003040,
2008.
Shah, V., Jacob, D. J., Thackray, C. P., Wang, X., Sunderland, E. M.,
Dibble, T. S., Saiz-Lopez, A., Černušák, I., Kellö, V.,
Castro, P. J., Wu, R., and Wang, C.: Improved Mechanistic Model of the
Atmospheric Redox Chemistry of Mercury, Environ. Sci. Technol., 55, 14445–14456,
https://doi.org/10.1021/acs.est.1c03160, 2021.
Si, L. and Ariya, P.: Recent Advances in Atmospheric Chemistry of Mercury,
Atmosphere, 9, 76, https://doi.org/10.3390/atmos9020076, 2018.
Slemr, F., Weigelt, A., Ebinghaus, R., Bieser, J., Brenninkmeijer, C. A. M., Rauthe-Schöch, A., Hermann, M., Martinsson, B. G., van Velthoven, P., Bönisch, H., Neumaier, M., Zahn, A., and Ziereis, H.: Mercury distribution in the upper troposphere and lowermost stratosphere according to measurements by the IAGOS-CARIBIC observatory: 2014–2016, Atmos. Chem. Phys., 18, 12329–12343, https://doi.org/10.5194/acp-18-12329-2018, 2018.
Soerensen, A. L., Skov, H., Jacob, D. J., Soerensen, B. T., and Johnson, M.
S.: Global Concentrations of Gaseous Elemental Mercury and Reactive Gaseous
Mercury in the Marine Boundary Layer, Environ. Sci. Technol., 44, 7425–7430,
https://doi.org/10.1021/es903839n, 2010a.
Soerensen, A. L., Sunderland, E. M., Holmes, C. D., Jacob, D. J., Yantosca,
R. M., Skov, H., Christensen, J. H., Strode, S. A., and Mason, R. P.: An
Improved Global Model for Air-Sea Exchange of Mercury: High Concentrations
over the North Atlantic, Environ. Sci. Technol., 44, 8574–8580,
https://doi.org/10.1021/es102032g, 2010b.
Spivakovsky, C. M., Logan, J. A., Montzka, S. A., Balkanski, Y. J., Foreman
Fowler, M., Jones, D. B. A., Horowitz, L. W., Fusco, A. C., Brenninkmeijer,
C. A. M., Prather, M. J., Wofsy, S. C., and McElroy, M. B.:
Three-dimensional climatological distribution of tropospheric OH: Update and
evaluation, J. Geophys. Res.-Atmos., 105, 8931–8980,
https://doi.org/10.1029/1999JD901006, 2000.
Sprovieri, F., Pirrone, N., Ebinghaus, R., Kock, H., and Dommergue, A.: A review of worldwide atmospheric mercury measurements, Atmos. Chem. Phys., 10, 8245–8265, https://doi.org/10.5194/acp-10-8245-2010, 2010.
Sprovieri, F., Pirrone, N., Bencardino, M., D'Amore, F., Carbone, F., Cinnirella, S., Mannarino, V., Landis, M., Ebinghaus, R., Weigelt, A., Brunke, E.-G., Labuschagne, C., Martin, L., Munthe, J., Wängberg, I., Artaxo, P., Morais, F., Barbosa, H. D. M. J., Brito, J., Cairns, W., Barbante, C., Diéguez, M. D. C., Garcia, P. E., Dommergue, A., Angot, H., Magand, O., Skov, H., Horvat, M., Kotnik, J., Read, K. A., Neves, L. M., Gawlik, B. M., Sena, F., Mashyanov, N., Obolkin, V., Wip, D., Feng, X. B., Zhang, H., Fu, X., Ramachandran, R., Cossa, D., Knoery, J., Marusczak, N., Nerentorp, M., and Norstrom, C.: Atmospheric mercury concentrations observed at ground-based monitoring sites globally distributed in the framework of the GMOS network, Atmos. Chem. Phys., 16, 11915–11935, https://doi.org/10.5194/acp-16-11915-2016, 2016.
Sprovieri, F., Pirrone, N., Bencardino, M., D'Amore, F., Angot, H., Barbante, C., Brunke, E.-G., Arcega-Cabrera, F., Cairns, W., Comero, S., Diéguez, M. D. C., Dommergue, A., Ebinghaus, R., Feng, X. B., Fu, X., Garcia, P. E., Gawlik, B. M., Hageström, U., Hansson, K., Horvat, M., Kotnik, J., Labuschagne, C., Magand, O., Martin, L., Mashyanov, N., Mkololo, T., Munthe, J., Obolkin, V., Ramirez Islas, M., Sena, F., Somerset, V., Spandow, P., Vardè, M., Walters, C., Wängberg, I., Weigelt, A., Yang, X., and Zhang, H.: Five-year records of mercury wet deposition flux at GMOS sites in the Northern and Southern hemispheres, Atmos. Chem. Phys., 17, 2689–2708, https://doi.org/10.5194/acp-17-2689-2017, 2017.
Talbot, R., Mao, H., Scheuer, E., Dibb, J., Avery, M., Browell, E., Sachse, G., Vay, S., Blake, D., Huey, G., and Fuelberg, H.: Factors influencing the large-scale distribution of Hg0 in the Mexico City area and over the North Pacific, Atmos. Chem. Phys., 8, 2103–2114, https://doi.org/10.5194/acp-8-2103-2008, 2008.
Temme, C., Blanchard, P., Steffen, A., Banic, C., Beauchamp, S., Poissant,
L., Tordon, R., and Wiens, B.: Trend, seasonal and multivariate analysis
study of total gaseous mercury data from the Canadian atmospheric mercury
measurement network (CAMNet), Atmos. Environ., 41, 5423–5441,
https://doi.org/10.1016/j.atmosenv.2007.02.021, 2007.
Tilmes, S., Hodzic, A., Emmons, L. K., Mills, M. J., Gettelman, A.,
Kinnison, D. E., Park, M., Lamarque, J. F., Vitt, F., Shrivastava, M.,
Campuzano Jost, P., Jimenez, J. L., and Liu, X.: Climate Forcing and Trends
of Organic Aerosols in the Community Earth System Model (CESM2), J. Adv.
Model. Earth Sy., 11, 4323–4351, https://doi.org/10.1029/2019MS001827, 2019.
Travnikov, O. and Ilyin, I.: The EMEP/MSC-E mercury modeling system, in:
Mercury Fate and Transport in the Global Atmosphere: Emissions,
Measurements, and Models, edited by: Pirrone, N. and Mason, R. P., Springer,
US, 571–587, 2009.
Travnikov, O., Angot, H., Artaxo, P., Bencardino, M., Bieser, J., D'Amore, F., Dastoor, A., De Simone, F., Diéguez, M. D. C., Dommergue, A., Ebinghaus, R., Feng, X. B., Gencarelli, C. N., Hedgecock, I. M., Magand, O., Martin, L., Matthias, V., Mashyanov, N., Pirrone, N., Ramachandran, R., Read, K. A., Ryjkov, A., Selin, N. E., Sena, F., Song, S., Sprovieri, F., Wip, D., Wängberg, I., and Yang, X.: Multi-model study of mercury dispersion in the atmosphere: atmospheric processes and model evaluation, Atmos. Chem. Phys., 17, 5271–5295, https://doi.org/10.5194/acp-17-5271-2017, 2017.
van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., and van der Werf, G. R.: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015), Geosci. Model Dev., 10, 3329–3357, https://doi.org/10.5194/gmd-10-3329-2017, 2017.
Wang, F., Saiz-Lopez, A., Mahajan, A. S., Gómez Martín, J. C., Armstrong, D., Lemes, M., Hay, T., and Prados-Roman, C.: Enhanced production of oxidised mercury over the tropical Pacific Ocean: a key missing oxidation pathway, Atmos. Chem. Phys., 14, 1323–1335, https://doi.org/10.5194/acp-14-1323-2014, 2014.
Wang, S., Apel, E. C., Schwantes, R. H., Bates, K. H., Jacob, D. J.,
Fischer, E. V., Hornbrook, R. S., Hills, A. J., Emmons, L. K., Pan, L. L.,
Honomichl, S., Tilmes, S., Lamarque, J. F., Yang, M., Marandino, C. A.,
Saltzman, E. S., Bruyn, W., Kameyama, S., Tanimoto, H., Omori, Y., Hall, S.
R., Ullmann, K., Ryerson, T. B., Thompson, C. R., Peischl, J., Daube, B. C.,
Commane, R., McKain, K., Sweeney, C., Thames, A. B., Miller, D. O., Brune,
W. H., Diskin, G. S., DiGangi, J. P., and Wofsy, S. C.: Global Atmospheric
Budget of Acetone: Air-Sea Exchange and the Contribution to Hydroxyl
Radicals, J. Geophys. Res.-Atmos., 125, e2020JD032553,
https://doi.org/10.1029/2020JD032553, 2020.
Wang, X., Jacob, D. J., Downs, W., Zhai, S., Zhu, L., Shah, V., Holmes, C. D., Sherwen, T., Alexander, B., Evans, M. J., Eastham, S. D., Neuman, J. A., Veres, P. R., Koenig, T. K., Volkamer, R., Huey, L. G., Bannan, T. J., Percival, C. J., Lee, B. H., and Thornton, J. A.: Global tropospheric halogen (Cl, Br, I) chemistry and its impact on oxidants, Atmos. Chem. Phys., 21, 13973–13996, https://doi.org/10.5194/acp-21-13973-2021, 2021.
Weigelt, A., Ebinghaus, R., Manning, A. J., Derwent, R. G., Simmonds, P. G.,
Spain, T. G., Jennings, S. G., and Slemr, F.: Analysis and interpretation of
18 years of mercury observations since 1996 at Mace Head, Ireland, Atmos.
Environ., 100, 85–93, https://doi.org/10.1016/j.atmosenv.2014.10.050, 2015.
Weiss-Penzias, P., Amos, H. M., Selin, N. E., Gustin, M. S., Jaffe, D. A., Obrist, D., Sheu, G.-R., and Giang, A.: Use of a global model to understand speciated atmospheric mercury observations at five high-elevation sites, Atmos. Chem. Phys., 15, 1161–1173, https://doi.org/10.5194/acp-15-1161-2015, 2015.
Wesely, M. L.: Parameterization of surface resistances to gaseous dry
deposition in regional-scale numerical models, Atmos. Environ., 41, 52–63,
https://doi.org/10.1016/j.atmosenv.2007.10.058, 1989.
Wright, L. P., Zhang, L., and Marsik, F. J.: Overview of mercury dry deposition, litterfall, and throughfall studies, Atmos. Chem. Phys., 16, 13399–13416, https://doi.org/10.5194/acp-16-13399-2016, 2016.
Xu, H., Sonke, J. E., Guinot, B., Fu, X., Sun, R., Lanzanova, A., Candaudap,
F., Shen, Z., and Cao, J.: Seasonal and Annual Variations in Atmospheric Hg
and Pb Isotopes in Xi'an, China, Environ. Sci. Technol., 51, 3759–3766,
https://doi.org/10.1021/acs.est.6b06145, 2017.
Ye, Z., Mao, H., Lin, C.-J., and Kim, S. Y.: Investigation of processes controlling summertime gaseous elemental mercury oxidation at midlatitudinal marine, coastal, and inland sites, Atmos. Chem. Phys., 16, 8461–8478, https://doi.org/10.5194/acp-16-8461-2016, 2016.
Zhang, H., Feng, X., Larssen, T., Qiu, G., and Vogt, R. D.: In Inland China,
Rice, Rather than Fish, Is the Major Pathway for Methylmercury Exposure,
Environ. Health Persp., 118, 1183–1188, https://doi.org/10.1289/ehp.1001915, 2010.
Zhang, P. and Zhang, Y.: Jampo26/CAM6-CHEM-Hg: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5877214, 2022.
Zhang, Y., Jaeglé, L., van Donkelaar, A., Martin, R. V., Holmes, C. D., Amos, H. M., Wang, Q., Talbot, R., Artz, R., Brooks, S., Luke, W., Holsen, T. M., Felton, D., Miller, E. K., Perry, K. D., Schmeltz, D., Steffen, A., Tordon, R., Weiss-Penzias, P., and Zsolway, R.: Nested-grid simulation of mercury over North America, Atmos. Chem. Phys., 12, 6095–6111, https://doi.org/10.5194/acp-12-6095-2012, 2012.
Zhang, Y., Jacob, D. J., Horowitz, H. M., Chen, L., Amos, H. M.,
Krabbenhoft, D. P., Slemr, F., St. Louis, V. L., and Sunderland, E. M.:
Observed decrease in atmospheric mercury explained by global decline in
anthropogenic emissions, P. Natl. Acad. Sci. USA,
113, 526–531, https://doi.org/10.1073/pnas.1516312113, 2016.
Zhang, Y., Horowitz, H., Wang, J., Xie, Z., Kuss, J., and Soerensen, A. L.:
A Coupled Global Atmosphere-Ocean Model for Air-Sea Exchange of Mercury:
Insights into Wet Deposition and Atmospheric Redox Chemistry, Environ. Sci.
Technol., 53, 5052–5061, https://doi.org/10.1021/acs.est.8b06205, 2019.
Zhang, Y., Song, Z., Huang, S., Zhang, P., Peng, Y., Wu, P., Gu, J.,
Dutkiewicz, S., Zhang, H., Wu, S., Wang, F., Chen, L., Wang, S., and Li, P.:
Global health effects of future atmospheric mercury emissions, Nat. Commun.,
12, 3035, https://doi.org/10.1038/s41467-021-23391-7, 2021.
Short summary
Mercury is a global pollutant that can be transported over long distance through the atmosphere. We develop a new online global model for atmospheric mercury. The model reproduces the observed global atmospheric mercury concentrations and deposition distributions by simulating the emissions, transport, and physicochemical processes of atmospheric mercury. And we find that the seasonal variations of atmospheric Hg are the result of multiple processes and have obvious regional characteristics.
Mercury is a global pollutant that can be transported over long distance through the atmosphere....
