Articles | Volume 12, issue 1
https://doi.org/10.5194/gmd-12-131-2019
© Author(s) 2019. 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-12-131-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Jan 2019
Development and technical paper |
| 07 Jan 2019
| 07 Jan 2019
The AFWA dust emission scheme for the GOCART aerosol model in WRF-Chem v3.8.1
Sandra L. LeGrand
CORRESPONDING AUTHOR
U.S. Army Engineer Research and Development Center, Hanover, NH, USA
Chris Polashenski
Alaska Projects Office, U.S. Army Cold Regions Research and Engineering Laboratory, Fairbanks, AK, USA
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Theodore W. Letcher
U.S. Army Engineer Research and Development Center, Hanover, NH, USA
Glenn A. Creighton
U.S. Air Force 557th Weather Wing, 16th Weather Squadron, Offutt Air Force Base, NE, USA
Steven E. Peckham
U.S. Army Engineer Research and Development Center, Hanover, NH, USA
Jeffrey D. Cetola
U.S. Air Force, Joint Base Langley–Eustis, VA, USA
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Sandra L. LeGrand, Theodore W. Letcher, Gregory S. Okin, Nicholas P. Webb, Alex R. Gallagher, Saroj Dhital, Taylor S. Hodgdon, Nancy P. Ziegler, and Michelle L. Michaels
Geosci. Model Dev., 16, 1009–1038, https://doi.org/10.5194/gmd-16-1009-2023, https://doi.org/10.5194/gmd-16-1009-2023, 2023
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Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Sandra L. LeGrand, Theodore W. Letcher, Gregory S. Okin, Nicholas P. Webb, Alex R. Gallagher, Saroj Dhital, Taylor S. Hodgdon, Nancy P. Ziegler, and Michelle L. Michaels
Geosci. Model Dev., 16, 1009–1038, https://doi.org/10.5194/gmd-16-1009-2023, https://doi.org/10.5194/gmd-16-1009-2023, 2023
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Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Theodore Letcher, Julie Parno, Zoe Courville, Lauren Farnsworth, and Jason Olivier
The Cryosphere, 16, 4343–4361, https://doi.org/10.5194/tc-16-4343-2022, https://doi.org/10.5194/tc-16-4343-2022, 2022
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We present a radiative transfer model that uses ray tracing to determine optical properties from computer-generated 3D renderings of snow resolved at the microscale and to simulate snow spectral reflection and transmission for visible and near-infrared light. We expand ray-tracing techniques applied to sub-1 cm3 snow samples to model an entire snowpack column. The model is able to reproduce known snow surface optical properties, and simulations compare well against field observations.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
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As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Nicholas C. Wright, Chris M. Polashenski, Scott T. McMichael, and Ross A. Beyer
The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020, https://doi.org/10.5194/tc-14-3523-2020, 2020
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This work presents a new dataset of sea ice surface fractions along NASA Operation IceBridge flight tracks created by processing hundreds of thousands of aerial images. These results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. We find preliminary evidence that first-year ice frequently has a lower melt pond fraction than adjacent multiyear ice, contrary to established knowledge in the sea ice community.
Aleksey Malinka, Eleonora Zege, Larysa Istomina, Georg Heygster, Gunnar Spreen, Donald Perovich, and Chris Polashenski
The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, https://doi.org/10.5194/tc-12-1921-2018, 2018
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Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. The melt pond reflectance is modeled in the framework of the radiative transfer theory and validated with field observations. It improves understanding of melting sea ice and enables better parameterization of the surface in Arctic atmospheric remote sensing (clouds, aerosols, trace gases) and re-evaluating Arctic climatic feedbacks at a new accuracy level.
Nicholas C. Wright and Chris M. Polashenski
The Cryosphere, 12, 1307–1329, https://doi.org/10.5194/tc-12-1307-2018, https://doi.org/10.5194/tc-12-1307-2018, 2018
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Satellites, planes, and drones capture thousands of images of the Arctic sea ice cover each year. However, few methods exist to reliably and automatically process these images for scientifically usable information. In this paper, we take the next step towards a community standard for analyzing these images by presenting an open-source platform able to accurately classify sea ice imagery into several important surface types.
Kimberly A. Casey, Chris M. Polashenski, Justin Chen, and Marco Tedesco
The Cryosphere, 11, 1781–1795, https://doi.org/10.5194/tc-11-1781-2017, https://doi.org/10.5194/tc-11-1781-2017, 2017
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We analyzed Greenland Ice Sheet (GrIS) average summer surface reflectance and albedo (2001–2016). MODIS Collection 6 data show a decreased magnitude of change over time due to sensor calibration corrections. Spectral band maps provide insight into GrIS surface processes likely occurring. Correctly measuring albedo and surface reflectance changes over time is crucial to monitoring atmosphere–ice interactions and ice mass balance. The results are applicable to many long-term MODIS studies.
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Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, https://doi.org/10.5194/gmd-17-685-2024, 2024
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Global climate models with coarse grid sizes include uncertainties about the processes in aerosol–cloud–precipitation interactions. To reduce these uncertainties, here we performed numerical simulations using a new version of our global aerosol transport model with a finer grid size over a longer period than in our previous study. As a result, we found that the cloud microphysics module influences the aerosol distributions through both aerosol wet deposition and aerosol–cloud interactions.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
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In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Haiqin Li, Georg A. Grell, Ravan Ahmadov, Li Zhang, Shan Sun, Jordan Schnell, and Ning Wang
Geosci. Model Dev., 17, 607–619, https://doi.org/10.5194/gmd-17-607-2024, https://doi.org/10.5194/gmd-17-607-2024, 2024
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We developed a simple and realistic method to provide aerosol emissions for aerosol-aware microphysics in a numerical weather forecast model. The cloud-radiation differences between the experimental (EXP) and control (CTL) experiments responded to the aerosol differences. The strong positive precipitation biases over North America and Europe from the CTL run were significantly reduced in the EXP run. This study shows that a realistic representation of aerosol emissions should be considered.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
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The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Jiachen Liu, Eric Chen, and Shannon L. Capps
Geosci. Model Dev., 17, 567–585, https://doi.org/10.5194/gmd-17-567-2024, https://doi.org/10.5194/gmd-17-567-2024, 2024
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Air pollution harms human life and ecosystems, but its sources are complex. Scientists and policy makers use air pollution models to advance knowledge and inform control strategies. We implemented a recently developed numeral system to relate any set of model inputs, like pollutant emissions from a given activity, to all model outputs, like concentrations of pollutants harming human health. This approach will be straightforward to update when scientists discover new processes in the atmosphere.
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024, https://doi.org/10.5194/gmd-17-399-2024, 2024
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Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
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A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024, https://doi.org/10.5194/gmd-17-381-2024, 2024
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Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024, https://doi.org/10.5194/gmd-17-321-2024, 2024
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Turbulence-resolving flow models have strict performance requirements, as simulations often run for weeks using hundreds of processes. Many flow scenarios also require the flexibility to modify physical and numerical models for problem-specific requirements. With a new code written in Julia we hope to make such adaptations easier without compromising on performance. In this paper we discuss the modeling approach and present validation and performance results.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
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In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
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This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
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We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
David R. Shaw, Toby J. Carter, Helen L. Davies, Ellen Harding-Smith, Elliott C. Crocker, Georgia Beel, Zixu Wang, and Nicola Carslaw
Geosci. Model Dev., 16, 7411–7431, https://doi.org/10.5194/gmd-16-7411-2023, https://doi.org/10.5194/gmd-16-7411-2023, 2023
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Exposure to air pollution is one of the greatest risks to human health, and it is indoors, where we spend upwards of 90 % of our time, that our exposure is greatest. The INdoor CHEMical model in Python (INCHEM-Py) is a new, community-led box model that tracks the evolution and fate of atmospheric chemical pollutants indoors. We have shown the processes simulated by INCHEM-Py, its ability to model experimental data and how it may be used to develop further understanding of indoor air chemistry.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
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Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
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Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu
Geosci. Model Dev., 16, 7223–7235, https://doi.org/10.5194/gmd-16-7223-2023, https://doi.org/10.5194/gmd-16-7223-2023, 2023
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The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point 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.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
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We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Minjie Zheng, Hongyu Liu, Florian Adolphi, Raimund Muscheler, Zhengyao Lu, Mousong Wu, and Nønne L. Prisle
Geosci. Model Dev., 16, 7037–7057, https://doi.org/10.5194/gmd-16-7037-2023, https://doi.org/10.5194/gmd-16-7037-2023, 2023
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The radionuclides 7Be and 10Be are useful tracers for atmospheric transport studies. Here we use the GEOS-Chem to simulate 7Be and 10Be with different production rates: the default production rate in GEOS-Chem and two from the state-of-the-art beryllium production model. We demonstrate that reduced uncertainties in the production rates can enhance the utility of 7Be and 10Be as tracers for evaluating transport and scavenging processes in global models.
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, 6833–6856, https://doi.org/10.5194/gmd-16-6833-2023, https://doi.org/10.5194/gmd-16-6833-2023, 2023
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In addition to the dominant role of the PBL scheme on the results of the meteorological field, many factors in the model are influenced by large uncertainties. This study focuses on the uncertainties that influence numerical simulation results (including horizontal resolution, vertical resolution, near-surface scheme, initial and boundary conditions, underlying surface update, and update of model version), hoping to provide a reference for scholars conducting research on the model.
Owen K. Hughes and Christiane Jablonowski
Geosci. Model Dev., 16, 6805–6831, https://doi.org/10.5194/gmd-16-6805-2023, https://doi.org/10.5194/gmd-16-6805-2023, 2023
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Atmospheric models benefit from idealized tests that assess their accuracy in a simpler simulation. A new test with artificial mountains is developed for models on a spherical earth. The mountains trigger the development of both planetary-scale and small-scale waves. These can be analyzed in dry or moist environments, with a simple rainfall mechanism. Four atmospheric models are intercompared. This sheds light on the pros and cons of the model design and the impact of mountains on the flow.
Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng
Geosci. Model Dev., 16, 6757–6771, https://doi.org/10.5194/gmd-16-6757-2023, https://doi.org/10.5194/gmd-16-6757-2023, 2023
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This study explores how the variation in the source profiles adopted in chemical transport models (CTMs) impacts the simulated results of chemical components in PM2.5 based on sensitivity analysis. The impact on PM2.5 components cannot be ignored, and its influence can be transmitted and linked between components. The representativeness and timeliness of the source profile should be paid adequate attention in air quality simulation.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Sanam N. Vardag and Robert Maiwald
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-192, https://doi.org/10.5194/gmd-2023-192, 2023
Revised manuscript accepted for GMD
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We use the atmospheric transport model GRAMM/GRAL in an inversion to estimate urban CO2 emissions on neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation into the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
EGUsphere, https://doi.org/10.5194/egusphere-2023-1512, https://doi.org/10.5194/egusphere-2023-1512, 2023
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Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variables. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimizing simulations for better climate projections.
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
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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).
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-169, https://doi.org/10.5194/gmd-2023-169, 2023
Revised manuscript accepted for GMD
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, a milestone that has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs, thus perfectly suited for future CO2 geo-stationery and imager satellites.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-171, https://doi.org/10.5194/gmd-2023-171, 2023
Revised manuscript accepted for GMD
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PyRTlib is an attractive educational software because it provides a flexible and user friendly tool to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols and hydrometeors). PyRTlib is a so called Radiative Transfer model which are commonly used to simulate and understand remote sensing observations from ground-based, airborne or satellite instruments.
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
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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
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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
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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.
Cited articles
Alfaro, S. C., Gaudichet, A., Gomes, L., and Maillé, M.: Mineral aerosol
production by wind erosion: aerosol particle sizes and binding energies,
Geophys. Res. Lett., 25, 991–994, https://doi.org/10.1029/98GL00502,
1998. a
Al-Hemoud, A., Al-Sudairawi, M., Neelamanai, S., Naseeb, A., and Behbehani,
W.: Socioeconomic effect of dust storms in Kuwait, Arab. J. Geosci., 10,
18, doi:10.1007/s12517-016-2816-9, 2017. a
Alizadeh Choobari, O., Zawar-Reza, P., and Sturman, A.: Low level jet
intensification by mineral dust aerosols, Ann. Geophys., 31, 625–632,
https://doi.org/10.5194/angeo-31-625-2013, 2013. a
Barnard, J. C., Fast, J. D., Paredes-Miranda, G., Arnott, W. P., and Laskin,
A.: Technical Note: Evaluation of the WRF-Chem “Aerosol Chemical to Aerosol
Optical Properties” Module using data from the MILAGRO campaign, Atmos. Chem.
Phys., 10, 7325–7340, https://doi.org/10.5194/acp-10-7325-2010, 2010. a
Barnum, B. H., Winstead, N. S., Wesely, J., Hakola, A., Colarco, P. R., Toon,
O. B., Ginoux, P., Brooks, G., Hasselbarth, L., and Toth, B.: Forecasting
dust storms using the CARMA-Dust Model and MM5 weather data, Environ. Model.
Softw., 19, 129–140, https://doi.org/10.1016/S1364-8152(03)00115-4, 2004. a, b
Beljaars, A. C. M.: The parameterization of surface fluxes in large-scale
models under free convection, Q. J. Roy. Meteor. Soc., 121, 255–270,
https://doi.org/10.1002/qj.49712152203, 1994. a
Bian, H., Tie, X., Cao, J., Ying, Z., Han, S., and Xue, Y.: Analysis of a
severe dust storm event over China: Application of the WRF-Dust model,
Aerosol Air Qual. Res., 11, 419–428,
https://doi.org/10.4209/aaqr.2011.04.0053, 2011. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V. M., Kondo, Y., Liao, H., Lohmann, U., and Rasch, P.: Clouds
and aerosols, in: Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia,
Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 571–657, 2013. a
Bullard, J. E., McTainsh, G. H., and Pudmenzky, C.: Factors affecting the
nature and rate of dust production from natural dune sands, Sedimentology,
54, 169–182, https://doi.org/10.1111/j.1365-3091.2006.00827.x, 2007. a
Chappell, A., Warren, A., O'Donoghue, A., Robinson, A., Thomas, A., and
Bristow, C.: The implications for dust emission modeling of spatial and
vertical variations in horizontal dust flux and particle size in the
Bodélé Depression, Northern Chad, J. Geophys. Res.-Atmos., 113,
D04214, https://doi.org/10.1029/2007JD009032, 2008. a
Chen, S., Zhao, C., Qian, Y., Leung, L. R., Huang, J., Huang, Z., Bi, J.,
Zhang, W., Shi, J., Yang, L., and Li, D.: Regional modeling of dust mass
balance and radiative forcing over East Asia using WRF-Chem, Aeolian Res.,
15, 15–30, https://doi.org/10.1016/j.aeolia.2014.02.001, 2014. a
Chepil, W. S.: Dynamics of wind erosion: I. Nature of movement of soil by
wind, Soil Sci., 60, 305–320, 1945. a
Chin, M., Savoie, D. L., Huebert, B. J., Bandy, A. R., Thornton, D. C.,
Bates, T. S., Quinn, P. K., Saltzman, E. S., and De Bruyn, W. J.: Atmospheric
sulfur cycle simulated in the global model GOCART: Comparison with field
observations and regional budgets, J. Geophys. Res.-Atmos., 105,
24689–24712, https://doi.org/10.1029/2000JD900385, 2000. a, b
Colarco, P., da Silva, A., Chin, M., and Diehl, T.: Online simulations of
global aerosol distributions in the NASA GEOS-4 model and comparisons to
satellite and ground-based aerosol optical depth, J. Geophys. Res.-Atmos.
115, D14207, https://doi.org/10.1029/2009JD012820, 2010. a
Colarco, P. R., Toon, O. B., and Holben, B. N.: Saharan dust transport to the
Caribbean during PRIDE: 1. Influence of dust sources and removal mechanisms
on the timing and magnitude of downwind aerosol optical depth events from
simulations of in situ and remote sensing observations, J. Geophys.
Res.-Atmos., 108, 8589,
https://doi.org/10.1029/2002JD002658, 2003a. a, b, c, d, e
Colarco, P. R., Toon, O. B., Reid, J. S., Livingston, J. M., Russell, P. B.,
Redemann, J., Schmid, B., Maring, H. B., Savoie, D., Welton, E. J., and
Campbell, J. R.: Saharan dust transport to the Caribbean during PRIDE: 2.
Transport, vertical profiles, and deposition in simulations of in situ and
remote sensing observations, J. Geophys. Res.-Atmos., 108,
8590, https://doi.org/10.1029/2002JD002659, 2003b. a
Cremades, P. G., Fernández, R. P., Alllende, D. G., Mulena, G. C., and
Puliafito, S. E.: High resolution satellite derived erodibility factors for
WRF/Chem windblown dust simulations in Argentina, Atmósfera, 30, 11–25,
https://doi.org/10.20937/atm.2017.30.01.02, 2017. a
Darmenova, K., Sokolik, I. N., Shao, Y., Marticorena, B., and Bergametti, G.:
Development of a physically based dust emission module within the Weather
Research and Forecasting (WRF) model: Assessment of dust emission
parameterizations and input parameters for source regions in Central and East
Asia, J. Geophys. Res.-Atmos., 114, D14201, https://doi.org/10.1029/2008JD011236, 2009. a, b, c, d, e
Defries, R. S. and Townshend, J. R. G.: Global land cover characterization
from satellite data: from research to operational implementation?, Global
Ecol. Biogeogr., 8, 367–379,
https://doi.org/10.1046/j.1365-2699.1999.00139.x, 1999. a
De Longueville, F., Hountondji, Y.-C., Henry, S., and Ozer, P.: What do we
know about effects of desert dust on air quality and human health in West
Africa compared to other regions?, Sci. Total Environ., 409, 1–8,
https://doi.org/10.1016/j.scitotenv.2010.09.025, 2010. a
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D.,
Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting
global atmospheric ice nuclei distributions and their impacts on climate,
Proc. Natl. Acad. Sci. USA, 107, 11217–11222,
https://doi.org/10.1073/pnas.0910818107, 2010. a
Dipu, S., Prabha, T. V., Pandithurai, G., Dudhia, J., Pfister, G., Rajesh,
K., and Goswami, B. N.: Impact of elevated aerosol layer on the cloud
macrophysical properties prior to monsoon onset, Atmos. Environ., 70,
454–467, https://doi.org/10.1016/j.atmosenv.2012.12.036, 2013. a
Fast, J. D., Gustafson Jr., W. I., Easter, R. C., Zaveri, R. A., Barnard, J.
C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of ozone,
particulates, and aerosol direct forcing in an urban area using a new
fully-coupled meteorology, chemistry, and aerosol model, J. Geophys. Res.,
111, D21305, https://doi.org/10.1029/2005JD006721, 2006. a, b
Flaounas, E., Kotroni, V., Lagouvardos, K., Klose, M., Flamant, C., and
Giannaros, T. M.: Assessing atmospheric dust modelling performance of
WRF-Chem over the semi-arid and arid regions around the Mediterranean, Atmos.
Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-307, 2016. a
Fountoukis, C., Ackermann, L., Ayoub, M. A., Gladich, I., Hoehn, R. D., and
Skillern, A.: Impact of atmospheric dust emission schemes on dust production
and concentration over the Arabian Peninsula, Model. Earth Syst. Environ.,
2, 115, https://doi.org/10.1007/s40808-016-0181-z, 2016. a
Freitas, S. R., Longo, K. M., Alonso, M. F., Pirre, M., Marecal, V., Grell,
G., Stockler, R., Mello, R. F., and Sánchez Gácita, M.: PREP-CHEM-SRC
– 1.0: a preprocessor of trace gas and aerosol emission fields for
regional and global atmospheric chemistry models, Geosci. Model Dev., 4,
419–433, https://doi.org/10.5194/gmd-4-419-2011, 2011. a
Gillette, D. A.: Production of dust that may be carried great distances, in:
Desert Dust: Origin, Characteristics, and Effect on Man, edited by:
Pewe, T. L., Spec. Pap. Geol. Soc. Am., 86, 11–26, 1981. a
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O.,
and Lin, S. J.: Sources and distributions of dust aerosols simulated with the
GOCART model. J. Geophys. Res.-Atmos., 106, 20255–20273,
https://doi.org/10.1029/2000JD000053, 2001. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Ginoux, P., Prospero, J. M., Torres, O., and Chin, M.: Long-term simulation
of global dust distribution with the GOCART model: correlation with North
Atlantic Oscillation, Environ. Modell. Softw., 19, 113–128,
https://doi.org/10.1016/S1364-8152(03)00114-2, 2004. a
Gong, S. L., Zhang, X. Y., Zhao, T. L., McKendry, I. G., Jaffe, D. A., and
Lu, N. M.: Characterization of soil dust aerosol in China and its transport
and distribution during 2001 ACE-Asia: 2. Model simulation and validation, J.
Geophys. Res.-Atmos., 108, 4262,
https://doi.org/10.1029/2002JD002633, 2003.
Goudie, A. S. and Middleton, N. J.: Desert Dust in the Global System,
Springer, Berlin, 2006. a
Huang, J., Wang, T., Wang, W., Li, Z., and Yan, H.: Climate effects of dust
aerosols over East Asian arid and semiarid regions, J. Geophys. Res.-Atmos.,
119, 11398–11416, https://doi.org/10.1002/2014JD021796, 2014. a
Hunt, E., Adams-Selin, R. Jones, S. L., and Creighton G.: Using a modified
Fécan soil moisture calculation to predict dust emissions over semi-arid
and arid regions, paper presented at the 15th Annual WRF Users Workshop,
National Center for Atmospheric Research, Boulder, CO, June 2014,
available at: http://www2.mmm.ucar.edu/wrf/users/workshops/WS2014/ppts/5B.4.pdf
(last access: May 2018), 2014. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos.,
113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
In, H. J. and Park, S. U.: A simulation of long-range transport of Yellow
Sand observed in April 1998 in Korea, Atmos. Environ., 36, 4173–4187,
https://doi.org/10.1016/S1352-2310(02)00361-8, 2002. a
Iversen, J. D. and White, B. R.: Saltation threshold on Earth, Mars and
Venus, Sedimentology, 29, 111–119,
https://doi.org/10.1111/j.1365-3091.1982.tb01713.x, 1982. a
Jish Prakash, P., Stenchikov, G., Kalenderski, S., Osipov, S., and Bangalath,
H.: The impact of dust storms on the Arabian Peninsula and the Red Sea,
Atmos. Chem. Phys., 15, 199–222, https://doi.org/10.5194/acp-15-199-2015,
2015. a
Kain, J. S.: The Kain-Fritsch convective parameterization: an update, J.
Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004. a
Kalenderski, S., Stenchikov, G. L., and Zhao, C.: Modeling a typical
winter-time dust event over the Arabian Peninsula and the Red Sea, Atmos.
Chem. Phys., 13, 1999–2013, https://doi.org/10.5194/acp-13-1999-2013,
2013. a
Kalenderski, S. and Stenchikov, G.: High-resolution regional modeling of
summertime transport and impact of African dust over the Red Sea and Arabian
Peninsula, J. Geophys. Res.-Atmos., 121, 6435–6458,
https://doi.org/10.1002/2015JD024480, 2016. a
Klose, M. and Shao, Y.: Stochastic parameterization of dust emission and
application to convective atmospheric conditions, Atmos. Chem. Phys., 12,
7309–7320, https://doi.org/10.5194/acp-12-7309-2012, 2012. a
Klose, M. and Shao, Y.: Large-eddy simulation of turbulent dust emission,
Aeolian Res., 8, 49–58, https://doi.org/10.1016/j.aeolia.2012.10.010, 2013. a
Klose, M., Shao, Y., Li, X., Zhang, H., Ishizuka, M., Mikami, M., and Leys,
J. F.: Further development of a parameterization for convective turbulent dust
emission and evaluation based on field observations, J. Geophys. Res.-Atmos.,
119, 10441–10457, https://doi.org/10.1002/2014JD021688, 2014. a
Kok, J. F., Parteli, E. J., Michaels, T. I., and Karam, D. B.: The physics of
wind-blown sand and dust, Rep. Prog. Phys., 75, 106901,
https://doi.org/10.1088/0034-4885/75/10/106901, 2012. a
Kumar, R., Barth, M. C., Pfister, G. G., Naja, M., and Brasseur, G. P.:
WRF-Chem simulations of a typical pre-monsoon dust storm in northern India:
influences on aerosol optical properties and radiation budget, Atmos. Chem.
Phys., 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, 2014. a
Letcher, T. W. and LeGrand, S. L.: A comparison of simulated dust produced by
three dust-emission schemes in WRF-Chem, ERDC/CRREL TR-18-13, U.S. Army
Engineer Research and Development Center, Hanover, New Hampshire, USA, 2018. a
Liu, M., Westphal, D. L., Wang, S., Shimizu, A., Sugimoto, N., Zhou, J., and
Chen, Y.: A high-resolution numerical study of the Asian dust storms of April
2001, J. Geophys. Res.-Atmos., 108, 8653,
https://doi.org/10.1029/2002JD003178, 2003. a
Liu, M., Westphal, D. L., Walker, A. L., Holt, T. R., Richardson, K. A., and
Miller, S. D.: COAMPS real-time dust storm forecasting during Operation Iraqi
Freedom, Weather Forecast., 22, 192–206, https://doi.org/10.1175/WAF971.1,
2007. a
Liu, Z., Liu, Q., Lin, H.-C., Schwartz, C. S., Lee, Y.-H., and Wang, T.:
Three?dimensional variational assimilation of MODIS aerosol optical depth:
Implementation and application to a dust storm over East Asia, J. Geophys.
Res.-Atmos., 116, D23206,
https://doi.org/10.1029/2011JD016159, 2011. a
Lu, H. and Shao, Y.: A new model for dust emission by saltation bombardment,
J. Geophys. Res.-Atmos., 104, 16827–16842,
https://doi.org/10.1029/1999JD900169, 1999. a
Lu, S., da Silva, A. M., Chin, M., Wang, J., Moorthi, S., Juang, H., Chuang,
H.-Y., Tang, Y., Jones, L., Iredell, M., and McQueen, J. T.: The NEMS GFS
aerosol component; NCEP's global aerosol forecast system, NCEP Office Note
472, 2013. a
Lyapustin, A. and Wang, Y.: MCD19A2 MODIS/Terra+Aqua Land Aerosol Optical
Depth Daily L2G Global 1km SIN Grid V006, distributed by NASA EOSDIS Land
Processes DAAC, https://doi.org/10.5067/MODIS/MCD19A2.006, 2018. a
Mahowald, N., Albani, S., Kok, J. F., Engelstaeder, S., Scanza, R., Ward, D.
S., and Flanner M. G.: The size distribution of desert dust aerosols and its
impact on the Earth system, Aeolian Res., 15, 53–71,
https://doi.org/10.1016/j.aeolia.2013.09.002, 2014. a
Mahowald, N. M., Baker, A. R., Bergametti, G., Brooks, N., Duce, R. A.,
Jickells, T. D., Kubilay, N., Prospero, J. M., and Tegen, I.: Atmospheric
global dust cycle and iron inputs to the ocean, Global Biogeochem. Cy.,
19, GB4025, https://doi.org/10.1029/2004GB002402, 2005. a
Mahowald, N. M., Kloster, S., Engelstaedter, S., Moore, J. K., Mukhopadhyay,
S., McConnell, J. R., Albani, S., Doney, S. C., Bhattacharya, A., Curran, M.
A. J., Flanner, M. G., Hoffman, F. M., Lawrence, D. M., Lindsay, K.,
Mayewski, P. A., Neff, J., Rothenberg, D., Thomas, E., Thornton, P. E., and
Zender, C. S.: Observed 20th century desert dust variability: impact on
climate and biogeochemistry, Atmos. Chem. Phys., 10, 10875–10893,
https://doi.org/10.5194/acp-10-10875-2010, 2010. a
Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust cycle: 1.
Design of a soil-derived dust emission scheme, J. Geophys. Res.-Atmos.,
100, 16415–16430, https://doi.org/10.1029/95JD00690, 1995. a
McDonald, E. V. and Caldwell, T. G.: Geochemical characteristics of Iraqi dust
and soil samples and related impacts to weapon malfunctions, in: Military
Geography and Geology: History and Technology, edited by: Nathanail, C. P.,
Abrahart, R. J., and Bradshaw, R. P., Land Quality Press, Nottingham, 258–265,
2008. a
Middleton, N. J.: Desert dust hazards: A global review, Aeolian Res., 24,
53–63, https://doi.org/10.1016/j.aeolia.2016.12.001, 2017. a
Mitchell, K.: The community Noah land surface model (LSM), User's Guide,
available at:
ftp://ftp.emc.ncep.noaa.gov/mmb/gcp/ldas/noahlsm/ver_2.7.1
(last access: May 2018), 2005. a
Nabavi, S. O., Haimberger, L., and Samimi, C.: Sensitivity of WRF-chem
predictions to dust source function specification in West Asia, Aeolian Res.,
24, 115–131, https://doi.org/10.1016/j.aeolia.2016.12.005, 2017. a
Nakanishi, M. and Niino, H.: An improved Mellor-Yamada level-3 model: Its
numerical stability and application to a regional prediction of advection
fog, Bound.-Layer Meteor., 119, 397–407,
https://doi.org/10.1007/s10546-005-9030-8, 2006. a
Nickovic, S., Kallos, G., Papadopoulos, A., and Kakaliagou, O.: A model for
prediction of desert dust cycle in the atmosphere, J. Geophys. Res.-Atmos.,
106, 18113–18129, https://doi.org/10.1029/2000JD900794, 2001. a
NOAA/NCEP (National Centers for Environmental Prediction/National Weather
Service/NOAA/U.S. Department of Commerce): NCEP FNL Operational Model Global
Tropospheric Analyses, continuing from July 1999, Research data archive at
the National Center for Atmospheric Research, Computational and Information
Systems Laboratory, Boulder, CO,
https://doi.org/10.5065/D6M043C6 (last access: July 2017), 2000. a
Okin, G. S., Bullard, J. E., Reynolds, R. L., Ballantine, J. A. C.,
Schepanski, K., Todd, M. C., Belnap, J., Baddock, M. C., Gill, T. E., and
Miller, M. E.: Dust: Small-scale processes with global consequences. Eos,
Trans. Amer. Geophys. Union, 92, 241–242,
https://doi.org/10.1029/2011EO290001, 2011. a
Owen, P. R.: Saltation of uniform grains in air, J. Fluid Mech., 20,
225–242, 1964. a
Park, S. H., Gong, S. L., Zhao, T. L., Vet, R. J., Bouchet, V. S., Gong, W.,
Makar, P. A., Moran, M. D., Stroud, C., and Zhang, J.: Simulation of
entrainment and transport of dust particles within North America in April
2001 (“Red Dust Episode”), J. Geophys. Res., 112, D20209,
https://doi.org/10.1029/2007JD008443, 2007. a
Peters-Lidard, C. D., Kemp, E. M., Matsui, T., Santanello Jr., J. A., Kumar,
S. V., Jacob, J. P., Clune, T., Tao, W.-K., Chin, M., Hou, A., Case, J. L.,
Kim, D., Kim, K.-M., Lau, W., Liu, Y., Shi, J., Starr, D., Tan, Q., Tao, Z.,
Zaitchik, B. F., Zavodsky, B., Zhang, S. Q., and Zupanski, M.: Integrated
modeling of aerosol, cloud, precipitation and land processes at
satellite-resolved scales, Environ. Model. Softw., 67, 149–159,
https://doi.org/10.1016/j.envsoft.2015.01.007, 2015. a
Raupach, M.: Drag and drag partition on rough surfaces, Bound.-Lay. Meteor.,
60, 375–395, https://doi.org/10.1007/BF00155203, 1992. a
Ravi, S., D'odorico, P., Breshears, D. D., Field, J. P., Goudie, A. S.,
Huxman, T. E., Li, J., Okin, G. S., Swap, R. J., Thomas, A. D., Van Pelt, S.,
Whicker, J. J., and Zobeck, T.: Aeolian processes and the biosphere, Rev.
Geophys., 49, RG3001, https://doi.org/10.1029/2010RG000328, 2011. a
Reynolds, C. A., Jackson, T. J., and Rawls, W. J.: Estimating soil
water-holding capacities by linking the Food and Agriculture Organization
soil map of the world with global pedon databases and continuous pedotransfer
functions, Water Resour. Res., 36, 3653–3662,
https://doi.org/10.1029/2000WR900130, 2000. a, b, c
Rizza, U., Anabor, V., Mangia, C., Miglietta, M. M., Degrazia, G. A., and
Passerini, G.: WRF-Chem simulation of a saharan dust outbreak over the
mediterranean regions, Ciência e Natura, 38, 330–336,
https://doi.org/10.5902/2179460X20249, 2016. a
Rushing, J. F., Harrison, J. A., and Tingle, J., S.: Evaluation of
application methods and products for mitigating dust for
lines-of-communication and base camp operations, ERDC/GSL TR-05-9, U.S. Army
Engineer Research and Development Center, Waterways Experiment Station,
Vicksburg, Mississippi, USA, 2005. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst., P., Keyser, D., Derber, J.,
Ek., M., Meng, J., Wei., H., Yang, R., Lord, S., van den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z.,
Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds., R. W., Rutledge, G., and
Goldberg, M.: The NCEP climate forecast system reanalysis, B. Am.
Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010. a
Shao, Y. P.: Physics and Modelling of Wind Erosion, Springer, Heidelberg,
2008. a
Shepherd, G., Terradellas, E., Baklanov, A., Kang, U., Sprigg, W., Nickovic,
S., Boloorani, A. D., Al-Dousari, A., Basart, S., Benedetti, A., and Sealy,
A.: Global assessment of sand and dust storms, United Nations Environment
Programme, Nairobi, 2016. a
Shinn, E. A., Smith, G. W., Prospero, J. M., Betzer, P., Hayes, M. L.,
Garrison, V., and Barber, R. T.: African dust and the demise of Caribbean
coral reefs, Geophys. Res. Lett., 27, 3029–3032,
https://doi.org/10.1029/2000GL011599, 2000. a
Sinclair, S. N. and Jones, S. L.: Subjective mapping of dust emission sources
by using MODIS imagery: Reproducibility assessment, ERDC/CRREL TR-17-8, U.S.
Army Engineer Research and Development Center, Hanover, New Hampshire, USA,
2017. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the
Advanced Research WRF Version 3, NCAR technical note, Mesoscale and
Microscale Meteorology Division, National Center for Atmospheric Research,
Boulder, Colorado, USA, 2008. a
Skiles, S. M., Painter, T. H., Belnap, J., Holland, L., Reynolds, R. L.,
Goldstein, H. L., and Lin, J.: Regional variability in dust-on-snow processes
and impacts in the Upper Colorado River Basin, Hydrol. Process., 29,
5397–5413, https://doi.org/10.1002/hyp.10569, 2015. a
Sprigg, W. A., Nickovic, S., Galgiani, J. N., Pejanovic, G., Petkovic, S.,
Vujadinovic, M., Vukovic, A., Dacic, M., DiBiase, S., Prasad, A., and
El-Askary, H.: Regional dust storm modeling for health services: the case of
valley fever, Aeolian Res., 14, 53–73,
https://doi.org/10.1016/j.aeolia.2014.03.001, 2014. a, b
Su, L. and Fung, J. C. H.: Sensitivities of WRF-Chem to dust emission schemes
and land surface properties in simulating dust cycles during springtime over
East Asia, J. Geophys. Res.-Atmos., 120, 11215–11230,
https://doi.org/10.1002/2015JD023446, 2015. a
Tanaka, T. Y. and Chiba, M.: Global simulation of dust aerosol with a
chemical transport model, MASINGAR, J. Meteor. Soc. Japan, 83, 255–278,
https://doi.org/10.2151/jmsj.83A.255, 2005. a
Teixeira, J. C., Carvalho, A. C., Tuccella, P., Curci, G., and Rocha, A.:
WRF-chem sensitivity to vertical resolution during a saharan dust event,
Phys. Chem. Earth, Parts A/B/C, 94, 188–195,
https://doi.org/10.1016/j.pce.2015.04.002, 2016. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek,
M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification
of the unified NOAH land surface model, 20th Conference on Weather Analysis
and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, WA,
American Meteorological Society, 10–15 January, 2004. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
forecasts of winter precipitation using an improved bulk microphysics scheme.
Part II: Implementation of a new snow parameterization, Mon. Weather Rev.,
136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Uzan, L., Egert, S., and Alpert, P.: Ceilometer evaluation of the eastern
Mediterranean summer boundary layer height – first study of two Israeli
sites, Atmos. Meas. Tech., 9, 4387–4398,
https://doi.org/10.5194/amt-9-4387-2016, 2016. a
Wang, F., Zhao, X., Gerlein-Safdi, C., Mu, Y., Wang, D., and Lu, Q.: Global
sources, emissions, transport and deposition of dust and sand and their
effects on the climate and environment: a review, Front. Environ. Sci. Eng.,
11, 13,
https://doi.org/10.1007/s11783-017-0904-z, 2017. a
Wang, K., Zhang, Y., Yahya, K., Wu, S. Y., and Grell, G.: Implementation and
initial application of new chemistry-aerosol options in WRF/Chem for
simulating secondary organic aerosols and aerosol indirect effects for
regional air quality, Atmos. Environ., 115, 716–732,
https://doi.org/10.1016/j.atmosenv.2014.12.007, 2015. a
Wang, W., Bruyère, C., Duda, M., Dudhia, J., Gill, D., Michael, K., Keene,
K., Chen, M., Lin, H.-C., Michalakes, J., Rizvi, S., Zhang, X., Berner, J.,
Soyoung, H., and Fossell, K.: Guide for the Advanced Research WRF (ARW)
Modeling System Version 3.9, NCAR technical note, Mesoscale and Microscale
Meteorology Division, National Center for Atmospheric Research, Boulder,
Colorado, USA, 2017. a
Wang, Z., Ueda, H., and Huang, M., Y.: A deflation module for use in modeling
long-range transport of yellow sand over East Asia, J. Geophys. Res., 105,
26947–26959, https://doi.org/10.1029/2000JD900370, 2000. a
Webb, N. P., Chappell, A., Strong, C. L., Marx, S. K., and McTainsh, G. H.:
The significance of carbon-enriched dust for global carbon accounting, Global
Change Biol., 18, 3275–3278,
https://doi.org/10.1111/j.1365-2486.2012.02780.x, 2012. a
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z.,
Hunt, W. H., and Young, S. A.: Overview of the CALIPSO mission and CALIOP
data processing algorithms, J. Atmos. Ocean. Technol., 26, 2310–2323,
https://doi.org/10.1175/2009JTECHA1281.1, 2009. a
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of
mineral dust in the Hadley Centre climate model, J. Geophys. Res., 106,
18155–18166, https://doi.org/10.1029/2000JD900795, 2001. a
Young, S. A. and Vaughan, M. A.: The retrieval of profiles of particulate
extinction from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite
Observations (CALIPSO) data: Algorithm Description. J. Atmos. Ocean.
Technol., 26, 1105–1119, https://doi.org/10.1175/2008JTECHA1221.1, 2009. a
Zender, C. S.: Mineral Dust Entrainment and Deposition (DEAD) Model:
Description and 1990s dust climatology, J. Geophys. Res., 108, 4416,
https://doi.org/10.1029/2002JD002775, 2003. a
Zhang, Y., Liu, Y., Kucera, P. A., Alharbi, B. H., Pan, L., and Ghulam, A.:
Dust modeling over Saudi Arabia using WRF-Chem: March 2009 severe dust case,
Atmos. Environ., 119, 118–130,
https://doi.org/10.1016/j.atmosenv.2015.08.032, 2015. a
Zhao, C., Liu, X., Leung, L. R., Johnson, B., McFarlane, S. A., Gustafson
Jr., W. I., Fast, J. D., and Easter, R.: The spatial distribution of mineral
dust and its shortwave radiative forcing over North Africa: modeling
sensitivities to dust emissions and aerosol size treatments, Atmos. Chem.
Phys., 10, 8821–8838, https://doi.org/10.5194/acp-10-8821-2010, 2010. a
Zhao, C., Liu, X., Ruby Leung, L., and Hagos, S.: Radiative impact of mineral
dust on monsoon precipitation variability over West Africa, Atmos. Chem.
Phys., 11, 1879-1893, https://doi.org/10.5194/acp-11-1879-2011, 2011. a
Zhao, C., Chen, S., Leung, L. R., Qian, Y., Kok, J. F., Zaveri, R. A., and
Huang, J.: Uncertainty in modeling dust mass balance and radiative forcing
from size parameterization, Atmos. Chem. Phys., 13, 10733–10753,
https://doi.org/10.5194/acp-13-10733-2013, 2013. a
Zhao, T. L., Gong, S. L., Zhang, X. Y., Abdel-Mawgoud, A., and Shao, Y. P.:
An assessment of dust emission schemes in modeling east Asian dust storms, J.
Geophys. Res., 111, D05S90, https://doi.org/10.1029/2004JD005746, 2006. a
Short summary
This paper reviews the history, code, and performance of the three dust emission schemes embedded in the WRF-Chem model, including the GOCART, AFWA, and UoC dust emission schemes, and provides the first full documentation of the AFWA scheme. A simulation case study is provided to explore differences in model output. Results highlight the relative strengths of each scheme, indicate reasons for disagreement, and demonstrate the need for improved terrain characterization in dust emission models.
This paper reviews the history, code, and performance of the three dust emission schemes...
