Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-1009-2023
© Author(s) 2023. 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-16-1009-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
09 Feb 2023
Development and technical paper |
| 09 Feb 2023
| 09 Feb 2023
Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1
Sandra L. LeGrand
CORRESPONDING AUTHOR
U.S. Army Engineer Research and Development Center, Geospatial Research Laboratory, Alexandria, Virginia, USA
Department of Geography, University of California, Los Angeles, California, USA
Theodore W. Letcher
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Gregory S. Okin
Department of Geography, University of California, Los Angeles, California, USA
Nicholas P. Webb
USDA-ARS Jornada Experimental Range, Las Cruces, New Mexico, USA
Alex R. Gallagher
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Saroj Dhital
USDA-ARS Jornada Experimental Range, Las Cruces, New Mexico, USA
Taylor S. Hodgdon
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Nancy P. Ziegler
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Michelle L. Michaels
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Related authors
Sandra L. LeGrand, Chris Polashenski, Theodore W. Letcher, Glenn A. Creighton, Steven E. Peckham, and Jeffrey D. Cetola
Geosci. Model Dev., 12, 131–166, https://doi.org/10.5194/gmd-12-131-2019, https://doi.org/10.5194/gmd-12-131-2019, 2019
Short summary
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.
Danny M. Leung, Jasper F. Kok, Longlei Li, Gregory S. Okin, Catherine Prigent, Martina Klose, Carlos Pérez García-Pando, Laurent Menut, Natalie M. Mahowald, David M. Lawrence, and Marcelo Chamecki
Atmos. Chem. Phys., 23, 6487–6523, https://doi.org/10.5194/acp-23-6487-2023, https://doi.org/10.5194/acp-23-6487-2023, 2023
Short summary
Short summary
Desert dust modeling is important for understanding climate change, as dust regulates the atmosphere's greenhouse effect and radiation. This study formulates and proposes a more physical and realistic desert dust emission scheme for global and regional climate models. By considering more aeolian processes in our emission scheme, our simulations match better against dust observations than existing schemes. We believe this work is vital in improving dust representation in climate models.
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
Short summary
Short summary
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.
Mark Hennen, Adrian Chappell, Nicholas Webb, Kerstin Schepanski, Matthew Baddock, Frank Eckardt, Tarek Kandakji, Jeff Lee, Mohamad Nobakht, and Johanna von Holdt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-423, https://doi.org/10.5194/gmd-2021-423, 2022
Revised manuscript not accepted
Short summary
Short summary
We use 90,000 dust point source observations (DPS), identified in satellite imagery across 9 global dryland environments to develop a novel dust emission model performance assessment. We evaluate the albedo-based dust emission model (AEM), which agrees with dust emission observations, or lack of emission 71 % of the time. Modelled dust occurs 27 % of the time with no observation, caused mostly by the incorrect assumption of infinite sediment supply and lack of dynamic dust entrainment thresholds.
Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin
Geosci. Model Dev., 14, 7639–7657, https://doi.org/10.5194/gmd-14-7639-2021, https://doi.org/10.5194/gmd-14-7639-2021, 2021
Short summary
Short summary
This study applies a fire-coupled dynamic vegetation model to quantify fire impact at monthly to annual scales. We find fire reduces grass cover by 4–8 % annually for widespread areas in south African savanna and reduces tree cover by 1 % at the periphery of tropical Congolese rainforest. The grass cover reduction peaks at the beginning of the rainy season, which quickly diminishes before the next fire season. In contrast, the reduction of tree cover is irreversible within one growing season.
Adrian Chappell, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-337, https://doi.org/10.5194/gmd-2021-337, 2021
Revised manuscript not accepted
Short summary
Short summary
Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Our results indicate that tuning dust emission models to dust in the atmosphere has hidden dust emission modelling weaknesses and its poor performance. Our new approach will reduce uncertainty and driven by prognostic albedo improve Earth System Models of aerosol effects on future environmental change.
Yan Yu, Olga V. Kalashnikova, Michael J. Garay, Huikyo Lee, Myungje Choi, Gregory S. Okin, John E. Yorks, James R. Campbell, and Jared Marquis
Atmos. Chem. Phys., 21, 1427–1447, https://doi.org/10.5194/acp-21-1427-2021, https://doi.org/10.5194/acp-21-1427-2021, 2021
Short summary
Short summary
Given the current uncertainties in the simulated diurnal variability of global dust mobilization and concentration, observational characterization of the variations in dust mobilization and concentration will provide a valuable benchmark for evaluating and constraining such model simulations. The current study investigates the diurnal cycle of dust loading across the global tropics, subtropics, and mid-latitudes by analyzing aerosol observations from the International Space Station.
Yan Yu, Olga V. Kalashnikova, Michael J. Garay, Huikyo Lee, Myungje Choi, Gregory S. Okin, John E. Yorks, and James R. Campbell
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-975, https://doi.org/10.5194/acp-2019-975, 2019
Preprint withdrawn
Short summary
Short summary
Given the current uncertainties in the simulated diurnal variability of global dust mobilization and concentration, observational characterization of the variations in dust mobilization and concentration will provide a valuable benchmark for evaluating and constraining such model simulations. The current study investigates the diurnal cycle of dust loading across the global tropics, sub-tropics, and mid-latitudes by analyzing aerosol observations from the International Space Station.
Sandra L. LeGrand, Chris Polashenski, Theodore W. Letcher, Glenn A. Creighton, Steven E. Peckham, and Jeffrey D. Cetola
Geosci. Model Dev., 12, 131–166, https://doi.org/10.5194/gmd-12-131-2019, https://doi.org/10.5194/gmd-12-131-2019, 2019
Short summary
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.
Alex R. Baker, Maria Kanakidou, Katye E. Altieri, Nikos Daskalakis, Gregory S. Okin, Stelios Myriokefalitakis, Frank Dentener, Mitsuo Uematsu, Manmohan M. Sarin, Robert A. Duce, James N. Galloway, William C. Keene, Arvind Singh, Lauren Zamora, Jean-Francois Lamarque, Shih-Chieh Hsu, Shital S. Rohekar, and Joseph M. Prospero
Atmos. Chem. Phys., 17, 8189–8210, https://doi.org/10.5194/acp-17-8189-2017, https://doi.org/10.5194/acp-17-8189-2017, 2017
Short summary
Short summary
Man's activities have greatly increased the amount of nitrogen emitted into the atmosphere. Some of this nitrogen is transported to the world's oceans, where it may affect microscopic marine plants and cause ecological problems. The huge size of the oceans makes direct monitoring of nitrogen inputs impossible, so computer models must be used to assess this issue. We find that current models reproduce observed nitrogen deposition to the oceans reasonably well and recommend future improvements.
Related subject area
Climate and Earth system modeling
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0
Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1
A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
Coupling the regional climate model ICON-CLM v2.6.6 into the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical mixing parameterization on Arctic sea ice and upper ocean properties using the NEMO-SI3 model
Methane dynamics in the Baltic Sea: investigating concentration, flux and isotopic composition patterns using the coupled physical-biogeochemical model BALTSEM-CH4 v1.0
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
Short summary
Short summary
We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
Short summary
Short summary
Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
Short summary
Short summary
The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
Short summary
Short summary
We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
Short summary
Short summary
Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
Short summary
Short summary
Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
Short summary
Short summary
Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
Short summary
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
Short summary
Short summary
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
Short summary
Short summary
All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
Short summary
Short summary
We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
Short summary
Short summary
In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
Short summary
Short summary
Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
Short summary
Short summary
Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
Short summary
Short summary
In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
Short summary
Short summary
We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
EGUsphere, https://doi.org/10.5194/egusphere-2024-923, https://doi.org/10.5194/egusphere-2024-923, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI version 2.0 including the regional climate model ICON-CLM coupled with the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in the ICON-CLM model makes it more flexible to couple with an external ocean model and an external hydrological discharge model.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Short summary
We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-362, https://doi.org/10.5194/egusphere-2024-362, 2024
Short summary
Short summary
In this study, we improve an existing climate model to account for human water usage across domestic, industrial, and agriculture purposes. With the new capabilities, the model is now better equipped for studying questions related to water scarcity in both present and future conditions under climate change. Despite the advancements, there remains important limitations in our modelling framework which requires further work.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-36, https://doi.org/10.5194/gmd-2024-36, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-49, https://doi.org/10.5194/gmd-2024-49, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We study the parameters involved in the turbulent kinetic energy mixed layer penetration scheme of the NEMO model in Arctic sea ice-covered regions. This evaluation reveals the impact of these parameters on mixed layer depth, sea surface temperature and salinity, and ocean stratification. Our findings also demonstrate considerable impacts on sea ice thickness and sea ice concentration, emphasizing the importance of accurate ocean mixing representation in understanding Arctic climate dynamics.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-211, https://doi.org/10.5194/gmd-2023-211, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Methane (CH4) cycling in the Baltic Sea is studied through model simulations, allowing a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source, and two main sinks: CH4 oxidation in the water (87 % of the sinks) and outgassing to the atmosphere (13 % of the sinks). This study addresses CH4 emissions from coastal seas and is a first step towards understanding the relative importance of open water outgassing compared to local coastal hotspots.
Cited articles
Adams, D. K. and Comrie, A. C.: The North American Monsoon, B. Am. Meteorol. Soc., 78, 2197–2213, https://doi.org/10.1175/1520-0477(1997)078<2197:TNAM>2.0.CO;2, 1997. a
Aragnou, E., Watt, S., Duc, H. N., Cheeseman, C., Riley, M., Leys, J., White, S., Salter, D., Azzi, M., Chang, L. T.-C., Morgan, G., and Hannigan, I.: Dust transport from inland Australia and its impact on air quality and health on the eastern coast of Australia during the February 2019 dust storm, Atmosphere, 12, 141, https://doi.org/10.3390/atmos12020141, 2021. a
Asadov, Kh. G. and Kerimov, N. I.: On the necessity of correction of the methodology for calculating aerosol flux from the Earth’s surface to the atmosphere using the NDVI index, Fundamental and Applied Climatology, 3, 92–101, https://doi.org/10.21513/2410-8758-2019-3-92-101, 2019 (in Russian). a
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R., Kenyon, J. S., and Manikin, G. S.: A North American hourly assimilation and model forecast cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1, 2016. a
Bukowski, J. and van den Heever, S. C.: The impact of land surface properties on haboobs and dust lofting, J. Atmos. Sci., 79, 3195–3218, https://doi.org/10.1175/JAS-D-22-0001.1, 2022. a
Cakmur, R. V., Miller, R. L., Perlwitz, J., Geogdzhayev, I. V., Ginoux, P., Koch, D., Kohfeld, K. E., Tegen, I., and Zender, C. S.: Constraining the magnitude of the global dust cycle by minimizing the difference between a model and observations, J. Geophys. Res., 111, D06207, https://doi.org/10.1029/2005JD005791, 2006. a
Chappell, A., Van Pelt, S., Zobeck, T., and Dong, Z.: Estimating aerodynamic resistance of rough surfaces using angular reflectance, Remote Sens. Environ., 114, 1462–1470, https://doi.org/10.1016/j.rse.2010.01.025, 2010. a
Chin, M., Rood, R. B., Lin, S.-J., Müller, J.-F., and Thompson, A. M.: Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties, J. Geophys. Res.-Atmos., 105, 24671–24687, https://doi.org/10.1029/2000JD900384, 2000. a
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011. a
Cremades, P. G., Fernández, R. P., Allende, 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., 114, D14201, https://doi.org/10.1029/2008JD011236, 2009. a, b
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a
Evans, S., Ginoux, P., Malyshev, S., and Shevliakova, E.: Climate‐vegetation interaction and amplification of Australian dust variability, Geophys. Res. Lett., 43, 11823–11830, https://doi.org/10.1002/2016GL071016, 2016. a, b, c
Fast, J. D., Gustafson, 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 radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model, J. Geophys. Res., 111, D21305, https://doi.org/10.1029/2005JD006721, 2006. a, b
Fécan, F., Marticorena, B., and Bergametti, G.: Parametrization of the increase of the aeolian erosion threshold wind friction velocity due to soil moisture for arid and semi-arid areas, Ann. Geophys., 17, 149–157, https://doi.org/10.1007/s00585-999-0149-7, 1999. 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
Francis, D., Nelli, N., Fonseca, R., Weston, M., Flamant, C., and Cherif, C.: The dust load and radiative impact associated with the June 2020 historical Saharan dust storm, Atmos. Environ., 268, 118808, https://doi.org/10.1016/j.atmosenv.2021.118808, 2022. 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
Fu, L.-T.: Comparisons suggest more efforts are required to parameterize wind flow around shrub vegetation elements for predicting aeolian flux, Sci. Rep., 9, 3841, https://doi.org/10.1038/s41598-019-40491-z, 2019. a
Gallagher, A. R., LeGrand, S. L., Hodgdon, T. S., and Letcher, T. W.: Simulating environmental conditions for southwest United States convective dust storms using the Weather Research and Forecasting model v4.1, ERDC TR-22-11, U.S. Army Engineer Research and Development Center, Hanover, New Hampshire, USA, https://doi.org/10.21079/11681/44963, 2022. a, b, c, d, e, f, g, h, i
Gillette, D. A.: Fine particulate emissions due to wind erosion, Trans. Am. Soc. Agric. Eng., 20, 890–897, https://doi.org/10.13031/2013.35670, 1977. 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., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001. a, b, c, d
Gong, S. L.: A parameterization of sea-salt aerosol source function for sub- and super-micron particles, Global Biogeochem. Cy., 17, 1097, https://doi.org/10.1029/2003GB002079, 2003. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a, b
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500 m Grid, Version 6, NASA National Snow and Ice Data Center Distributed Active Archive Center Boulder, Colorado USA [data set], https://doi.org/10.5067/MODIS/MOD10A1.006, 2016. a, b
Hamzeh, N. H., Karami, S., Kaskaoutis, D. G., Tegen, I., Moradi, M., and Opp, C.: Atmospheric dynamics and numerical simulations of six frontal dust storms in the Middle East region, Atmosphere, 12, 125, https://doi.org/10.3390/atmos12010125, 2021. a
Han, J. and Pan, H.-L.: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System, Weather Forecast., 26, 520–533, https://doi.org/10.1175/WAF-D-10-05038.1, 2011. a
Hyde, P., Mahalov, A., and Li, J.: Simulating the meteorology and PM10 concentrations in Arizona dust storms using the Weather Research and Forecasting model with Chemistry (Wrf-Chem), J. Air Waste Manage., 68, 177–195, https://doi.org/10.1080/10962247.2017.1357662, 2018. a, b, c
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., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Ito, A. and Kok, J. F.: Do dust emissions from sparsely vegetated regions dominate atmospheric iron supply to the Southern Ocean?, J. Geophys. Res.-Atmos., 122, 3987–4002, https://doi.org/10.1002/2016JD025939, 2017. a, b, c
Iowa Environmental Mesonet: Documentation on IEM generated NEXRAD Mosaics, N0R (4bit) Reflectivity, https://mesonet.agron.iastate.edu/docs/nexrad_mosaic/, last access: 2 February 2023.
Karumuri, R. K., Kunchala, R. K., Attada, R., Dasari, H. P., and Hoteit, I.: Seasonal simulations of summer aerosol optical depth over the Arabian Peninsula using WRF‐Chem: Validation, climatology, and variability, Int. J. Climatol., 42, 2901–2922, https://doi.org/10.1002/joc.7396, 2022. a
Kim, D., Chin, M., Bian, H., Tan, Q., Brown, M. E., Zheng, T., You, R., Diehl, T., Ginoux, P., and Kucsera, T.: The effect of the dynamic surface bareness on dust source function, emission, and distribution, J. Geophys. Res.-Atmos., 118, 871–886, https://doi.org/10.1029/2012JD017907, 2013. a, b
Kim, K., Kim, S., Choi, M., Kim, M., Kim, J., Shin, I., Kim, J., Chung, C., Yeo, H., Kim, S., Joo, S. J., McKeen, S. A., and Zhang, L.: Modeling Asian dust storms using WRF‐Chem during the DRAGON‐Asia Field Campaign in April 2012, J. Geophys. Res.-Atmos., 126, e2021JD034793, https://doi.org/10.1029/2021JD034793, 2021. a
King, J., Nickling, W. G., and Gillies, J. A.: Representation of vegetation and other nonerodible elements in aeolian shear stress partitioning models for predicting transport threshold, J. Geophys. Res.-Earth, 110, F04015, https://doi.org/10.1029/2004JF000281, 2005. a
Kok, J. F.: A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle, P. Natl. Acad. Sci. USA, 108, 1016–1021, https://doi.org/10.1073/pnas.1014798108, 2011. a
Kok, J. F., Parteli, E. J. R., 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, b
Kok, J. F., Albani, S., Mahowald, N. M., and Ward, D. S.: An improved dust emission model – Part 2: Evaluation in the Community Earth System Model, with implications for the use of dust source functions, Atmos. Chem. Phys., 14, 13043–13061, https://doi.org/10.5194/acp-14-13043-2014, 2014. a
Koven, C. D. and Fung, I.: Identifying global dust source areas using high-resolution land surface form, J. Geophys. Res., 113, D22204, https://doi.org/10.1029/2008JD010195, 2008. a
Kuchera, E. L., Rentschler, S. A., Creighton, G. A., and Rugg, S. A.: A review of operational ensemble forecasting efforts in the United States Air Force, Atmosphere, 12, 677, https://doi.org/10.3390/atmos12060677, 2021. a
Letcher, T. W. and LeGrand, S. L.: A comparison of simulated dust produced by three dust-emission schemes in WRF-Chem: Case study assessment, ERDC/CRREL TR-18-13, U.S. Army Engineer Research and Development Center, Hanover, New Hampshire, USA, https://doi.org/10.21079/11681/28868, 2018. a
Letcher, T. W., LeGrand, S. L., and Michaels, M. L.: WRF-Chem-v4.1-AFWA-Drag-Partition-Modifications: Full model code release (bug-fix release) (v1.1.1), Zenodo [code], https://doi.org/10.5281/zenodo.7447886, 2022. a, b
Li, J., Okin, G. S., Herrick, J. E., Belnap, J., Miller, M. E., Vest, K., and Draut, A. E.: Evaluation of a new model of aeolian transport in the presence of vegetation, J. Geophys. Res.-Earth, 118, 288–306, https://doi.org/10.1002/jgrf.20040, 2013. a
Ma, S., Zhang, X., Gao, C., Tong, D. Q., Xiu, A., Wu, G., Cao, X., Huang, L., Zhao, H., Zhang, S., Ibarra-Espinosa, S., Wang, X., Li, X., and Dan, M.: Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1, CAMx v6.50, CHIMERE v2017r4, and WRF-Chem v3.9.1), Geosci. Model Dev., 12, 4603–4625, https://doi.org/10.5194/gmd-12-4603-2019, 2019. a
Marshall, J. K.: Drag measurements in roughness arrays of varying density and distribution, Agric. Meteorol., 8, 269–292, https://doi.org/10.1016/0002-1571(71)90116-6, 1971. a, b, c
Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme, J. Geophys. Res., 100, 16415, https://doi.org/10.1029/95JD00690, 1995. a, b, c
Mayaud, J. and Webb, N.: Vegetation in drylands: Effects on wind flow and aeolian sediment transport, Land, 6, 64, https://doi.org/10.3390/land6030064, 2017. a
Mesbahzadeh, T., Salajeghe, A., Sardoo, F. S., Zehtabian, G., Ranjbar, A., Marcello Miglietta, M., Karami, S., and Krakauer, N. Y.: Spatial-temporal variation characteristics of vertical dust flux simulated by WRF-Chem model with GOCART and AFWA dust emission schemes (Case study: Central Plateau of Iran), Appl. Sci., 10, 4536, https://doi.org/10.3390/app10134536, 2020. a
Michaels, M. L., Letcher, T. W., LeGrand, S. L., Webb, N. P., and Putnam, J. B.: Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module, ERDC/CRREL TR-22-2, U.S. Army Engineer Research and Development Center, Hanover, New Hampshire, USA, https://doi.org/10.21079/11681/42782, 2022. a, b, c
Miller, P. W., Williams, M., and Mote, T.: Modeled atmospheric optical and thermodynamic responses to an exceptional Trans‐Atlantic dust outbreak, J. Geophys. Res.-Atmos., 126, e2020JD032909, https://doi.org/10.1029/2020JD032909, 2021. a
Mohebbi, A., Green, G. T., Akbariyeh, S., Yu, F., Russo, B. J., and Smaglik, E. J.: Development of dust storm modeling for use in freeway safety and operations management: An Arizona case study, Transp. Res. Rec. J. Transp. Res. Board, 2673, 175–187, https://doi.org/10.1177/0361198119839978, 2019. a
Mohebbi, A., Yu, F., Cai, S., Akbariyeh, S., and Smaglik, E. J.: Spatial study of particulate matter distribution, based on climatic indicators during major dust storms in the State of Arizona, Front. Earth Sci., 15, 133–150, https://doi.org/10.1007/s11707-020-0814-4, 2020. 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 with condensation physics: Its design and verification, Boundary Layer Meteorol., 112, 1–31, https://doi.org/10.1023/B:BOUN.0000020164.04146.98, 2004. a, b
National Center for Atmospheric Research (NCAR): WRF Version 4.1, Github [code],
https://github.com/wrf-model/WRF/releases/tag/v4.1 (last access: 1 June 2020), 2019. a
Nguyen, H. D., Riley, M., Leys, J., and Salter, D.: Dust storm event of February 2019 in Central and East Coast of Australia and evidence of long-range transport to New Zealand and Antarctica, Atmosphere, 10, 653, https://doi.org/10.3390/atmos10110653, 2019. a
Nikfal, A., Raadatabadi, A., and Sehatkashani, S.: Investigation of dust schemes in the model WRF/Chem, J. Air Pollut. Health, 3, 1–8, 2018. a
NOAA National Centers for Environmental Information: Global Surface Hourly [ASOS Hourly Data: 2014] [data set], NOAA National Centers for Environmental Information, https://www.ncei.noaa.gov/data/global-hourly/archive/csv/2014.tar.gz (last access: 2 February 2023), 2001.
NOAA National Centers for Environmental Information: Rapid Refresh/Rapid Update Cycle, https://www.ncei.noaa.gov/products/weather-climate-models/rapid-refresh-update, last access: 1 February 2023a.
NOAA National Centers for Environmental Information: North American Mesoscale Forecast System, https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale, last access: 1 February 2023b.
NOAA National Weather Service U.S. Federal Aviation Administration, U.S. Department of Defense, and NOAA National Centers for Environmental Information: 1-Minute Page 1 Surface Weather Observations from the Automated Surface Observing Systems (ASOS) Network, [July 2014] [data set], NOAA National Centers for Environmental Information, NCEI DSI 6405_02, https://www.ncei.noaa.gov/data/automated-surface-observing-system-one-minute-pg1/access/2014/07/ (last access: 12 November 2021), 2005.
Okin, G. S.: Dependence of wind erosion and dust emission on surface heterogeneity: Stochastic modeling, J. Geophys. Res., 110, D11208, https://doi.org/10.1029/2004JD005288, 2005. a, b
Okin, G. S.: A new model of wind erosion in the presence of vegetation, J. Geophys. Res., 113, F02S10, https://doi.org/10.1029/2007JF000758, 2008. a, b
Okin, G. S.: The contribution of brown vegetation to vegetation dynamics, 91, 743–755, https://doi.org/10.1890/09-0302.1, 2010. a
Parajuli, S. P. and Zender, C. S.: Projected changes in dust emissions and regional air quality due to the shrinking Salton Sea, Aeolian Res., 33, 82–92, https://doi.org/10.1016/j.aeolia.2018.05.004, 2018. a, b
Parajuli, S. P., Stenchikov, G. L., Ukhov, A., and Kim, H.: Dust emission modeling using a new high‐resolution dust source function in WRF‐Chem with implications for air quality, J. Geophys. Res.-Atmos., 124, 10109–10133, https://doi.org/10.1029/2019JD030248, 2019. a
Parajuli, S. P., Stenchikov, G. L., Ukhov, A., Shevchenko, I., Dubovik, O., and Lopatin, A.: Aerosol vertical distribution and interactions with land/sea breezes over the eastern coast of the Red Sea from lidar data and high-resolution WRF-Chem simulations, Atmos. Chem. Phys., 20, 16089–16116, https://doi.org/10.5194/acp-20-16089-2020, 2020. a
Peckham, S. E., Grell, G., McKeen, S. A., Ahmadov, R., Wong, K. Y., Barth, M., Pfister, G., Wiedinmyer, C., Fast, J. D., Gustafson, W. I., Ghan, S. J., Zaveri, R., Easter, R. C., Barnard, J., Chapman, E., Hewson, M., Schmitz, R., Salzmann, M., Beck, V., and Freitas, S. R.: WRF-Chem version 3.8.1 user’s guide, NOAA Technical Memorandum OAR GSD-48, 83 pp., https://doi.org/10.7289/V5/TM-OAR-GSD-48, 2017. a
Péré, J.-C., Rivellini, L., Crumeyrolle, S., Chiapello, I., Minvielle, F., Thieuleux, F., Choël, M., and Popovici, I.: Simulation of African dust properties and radiative effects during the 2015 SHADOW campaign in Senegal, Atmos. Res., 199, 14–28, https://doi.org/10.1016/j.atmosres.2017.07.027, 2018. a
Pierre, C., Bergametti, G., Marticorena, B., Kergoat, L., Mougin, E., and Hiernaux, P.: Comparing drag partition schemes over a herbaceous Sahelian rangeland: Drag partitions comparison in Sahel, J. Geophys. Res.-Earth, 119, 2291–2313, https://doi.org/10.1002/2014JF003177, 2014. a
Raupach, M. and Lu, H.: Representation of land-surface processes in aeolian transport models, Environ. Modell. Softw., 19, 93–112, https://doi.org/10.1016/S1364-8152(03)00113-0, 2004. a
Raupach, M. R.: Drag and drag partition on rough surfaces, Bound.-Lay. Meteorol., 60, 375–395, https://doi.org/10.1007/BF00155203, 1992. a
Raupach, M. R., Gillette, D. A., and Leys, J. F.: The effect of roughness elements on wind erosion threshold, J. Geophys. Res.-Atmos., 98, 3023–3029, https://doi.org/10.1029/92JD01922, 1993. 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 outbreakover the Mediterranean regions, Ciência e Natura, 38, 330–336, https://doi.org/10.5902/2179460X20249, 2016. a
Rizza, U., Kandler, K., Eknayan, M., Passerini, G., Mancinelli, E., Virgili, S., Morichetti, M., Nolle, M., Eleftheriadis, K., Vasilatou, V., and Ielpo, P.: Investigation of an intense dust outbreak in the Mediterranean using XMed-Dry Network, multiplatform observations, and numerical modeling, Appl. Sci., 11, 1566, https://doi.org/10.3390/app11041566, 2021. a
Saidou Chaibou, A. A., Ma, X., Kumar, K. R., Jia, H., Tang, Y., and Sha, T.: Evaluation of dust extinction and vertical profiles simulated by WRF-Chem with CALIPSO and AERONET over North Africa, J. Atmos. Sol.-Terr. Phy., 199, 105213, https://doi.org/10.1016/j.jastp.2020.105213, 2020. a
Saleeby, S. M., van den Heever, S. C., Bukowski, J., Walker, A. L., Solbrig, J. E., Atwood, S. A., Bian, Q., Kreidenweis, S. M., Wang, Y., Wang, J., and Miller, S. D.: The influence of simulated surface dust lofting and atmospheric loading on radiative forcing, Atmos. Chem. Phys., 19, 10279–10301, https://doi.org/10.5194/acp-19-10279-2019, 2019. a
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global – 500 m V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A1.061, 2021a. a, b
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global – 500 m V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A3.061, 2021b. a, b
Shao, Y., Nickling, W., Bergametti, G., Butler, H., Chappell, A., Findlater, P., Gillies, J., Ishizuka, M., Klose, M., Kok, J. F., Leys, J., Lu, H., Marticorena, B., McTainsh, G., McKenna-Neuman, C., Okin, G. S., Strong, C., and Webb, N.: A tribute to Michael R. Raupach for contributions to aeolian fluid dynamics, Aeolian Res., 19, 37–54, https://doi.org/10.1016/j.aeolia.2015.09.004, 2015. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A description of the Advanced Research WRF Model version 4, NCAR Technical Notes; NCAR/TN-556+STR, UCAR/NCAR, https://doi.org/10.5065/1DFH-6P97, 2019. a, b
Solomos, S., Kalivitis, N., Mihalopoulos, N., Amiridis, V., Kouvarakis, G., Gkikas, A., Binietoglou, I., Tsekeri, A., Kazadzis, S., Kottas, M., Pradhan, Y., Proestakis, E., Nastos, P., and Marenco, F.: From tropospheric folding to Khamsin and Foehn winds: How atmospheric dynamics advanced a record-breaking dust episode in Crete, Atmosphere, 9, 240, https://doi.org/10.3390/atmos9070240, 2018. a
Solomos, S., Abuelgasim, A., Spyrou, C., Binietoglou, I., and Nickovic, S.: Development of a dynamic dust source map for NMME-DREAM v1.0 model based on MODIS Normalized Difference Vegetation Index (NDVI) over the Arabian Peninsula, Geosci. Model Dev., 12, 979–988, https://doi.org/10.5194/gmd-12-979-2019, 2019. a
Spyrou, C., Solomos, S., Bartsotas, N. S., Douvis, K. C., and Nickovic, S.: Development of a dust source map for WRF-Chem model based on MODIS NDVI, Atmosphere, 13, 868, https://doi.org/10.3390/atmos13060868, 2022. a, b
Stull, R. B. (Ed.): An Introduction to Boundary Layer Meteorology, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-009-3027-8, 1988. a
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
Tegen, I., Harrison, S. P., Kohfeld, K., Prentice, I. C., Coe, M., and Heimann, M.: Impact of vegetation and preferential source areas on global dust aerosol: Results from a model study, J. Geophys. Res.-Atmos., 107, 4576, https://doi.org/10.1029/2001JD000963, 2002. a, b, c, d
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 ABC, 94, 188–195, https://doi.org/10.1016/j.pce.2015.04.002, 2016. 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
Tong, D., Lee, P., Tang, Y., Baker, B., Campbell, P. C., Saylor, R., Chai, T., Lamsal, L. N., Krotkov, N. A., Li, C., and Kondragunta, S.: Advancing National Air Quality Forecasts through Emission Data Assimilation, 100th American Meteorological Society Annual Meeting, Boston, Massachusetts, USA, 15 January 2020, AMS, https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/366256 (last access: 1 March 2022), 2020. a
Tsarpalis, K., Papadopoulos, A., Mihalopoulos, N., Spyrou, C., Michaelides, S., and Katsafados, P.: The implementation of a mineral dust wet deposition scheme in the GOCART-AFWA module of the WRF model, Remote Sens., 10, 1595, https://doi.org/10.3390/rs10101595, 2018. a
Tsarpalis, K., Katsafados, P., Papadopoulos, A., and Mihalopoulos, N.: Assessing desert dust indirect effects on cloud microphysics through a cloud nucleation scheme: A case study over the Western Mediterranean, Remote Sens., 12, 3473, https://doi.org/10.3390/rs12213473, 2020. a
US Environmental Protection Agency: Pre-Generated Data File: Particulates, PM10 Mass (81102), 2014 [data set] https://aqs.epa.gov/aqsweb/airdata/hourly_81102_2014.zip (last access: 30 January 2023), 2022.
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
Vukovic, A., Vujadinovic, M., Pejanovic, G., Andric, J., Kumjian, M. R., Djurdjevic, V., Dacic, M., Prasad, A. K., El-Askary, H. M., Paris, B. C., Petkovic, S., Nickovic, S., and Sprigg, W. A.: Numerical simulation of “an American haboob”, Atmos. Chem. Phys., 14, 3211–3230, https://doi.org/10.5194/acp-14-3211-2014, 2014. a, b, c
Walker, A. L., Liu, M., Miller, S. D., Richardson, K. A., and Westphal, D. L.: Development of a dust source database for mesoscale forecasting in southwest Asia, J. Geophys. Res., 114, D18207, https://doi.org/10.1029/2008JD011541, 2009. a
Walter, B., Gromke, C., and Lehning, M.: Shear-stress partitioning in live plant canopies and modifications to Raupach’s model, Bound.-Lay. Meteorol., 144, 217–241, https://doi.org/10.1007/s10546-012-9719-4, 2012. a
Webb, N. P. and Pierre, C.: Quantifying anthropogenic dust emissions, Earth's Future, 6, 286–295, https://doi.org/10.1002/2017EF000766, 2018. a
Webb, N. P., Herrick, J. E., and Duniway, M. C.: Ecological site-based assessments of wind and water erosion: informing accelerated soil erosion management in rangelands, Ecol. Appl, 24, 1405–1420, https://doi.org/10.1890/13-1175.1, 2014a. a
Webb, N. P., Okin, G. S., and Brown, S.: The effect of roughness elements on wind erosion: The importance of surface shear stress distribution, J. Geophys. Res.-Atmos., 119, 6066–6084, https://doi.org/10.1002/2014JD021491, 2014b. a, b
Webb, N. P., Chappell, A., LeGrand, S. L., Ziegler, N. P., and Edwards, B. L.: A note on the use of drag partition in aeolian transport models, Aeolian Res., 42, 100560, https://doi.org/10.1016/j.aeolia.2019.100560, 2020. a, b
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model, J. Geophys. Res.-Atmos., 106, 18155–18166, https://doi.org/10.1029/2000JD900795, 2001. a
World Meteorological Organization (WMO): Manual on Codes Volume I.1 Annex II to the WMO Technical Regulations Part A – Alphanumeric Codes, (WMO No.306, Part I.1 Part A), 2011 edition, updated in 2019, 480 pp., ISBN 978-92-63-10306-2, Geneva, Switzerland, https://library.wmo.int/doc_num.php?explnum_id=10235 (last access: 25 January 2021), 2019. a
Xu, X., Wang, J., Wang, Y., Henze, D. K., Zhang, L., Grell, G. A., McKeen, S. A., and Wielicki, B. A.: Sense size-dependent dust loading and emission from space using reflected solar and infrared spectral measurements: An observation system simulation experiment, J. Geophys. Res.-Atmos., 122, 8233–8254, https://doi.org/10.1002/2017JD026677, 2017. a
Yu, M. and Yang, C.: Improving the non-hydrostatic numerical dust model by integrating soil moisture and greenness vegetation fraction data with different spatiotemporal resolutions, PLOS One, 11, e0165616, https://doi.org/10.1371/journal.pone.0165616, 2016. a
Yu, M., Wu, B., Yan, N., Xing, Q., and Zhu, W.: A method for estimating the aerodynamic roughness length with NDVI and BRDF signatures using multi-temporal Proba-V data, Remote Sens., 9, 6, https://doi.org/10.3390/rs9010006, 2016. a
Yuan, T., Chen, S., Huang, J., Zhang, X., Luo, Y., Ma, X., and Zhang, G.: Sensitivity of simulating a dust storm over Central Asia to different dust schemes using the WRF-Chem model, Atmos. Environ., 207, 16–29, https://doi.org/10.1016/j.atmosenv.2019.03.014, 2019.
a
Zhang, L., Grell, G. A., McKeen, S. A., Ahmadov, R., Froyd, K. D., and Murphy, D.: Inline coupling of simple and complex chemistry modules within the global weather forecast model FIM (FIM-Chem v1), Geosci. Model Dev., 15, 467–491, https://doi.org/10.5194/gmd-15-467-2022, 2022. a
Zhao, J., Ma, X., Wu, S., and Sha, T.: Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations, Atmos. Res., 241, 104978, https://doi.org/10.1016/j.atmosres.2020.104978, 2020. a
Zhou, M., Zhang, L., Chen, D., Gu, Y., Fu, T.-M., Gao, M., Zhao, Y., Lu, X., and Zhao, B.: The impact of aerosol–radiation interactions on the effectiveness of emission control measures, Environ. Res. Lett., 14, 024002, https://doi.org/10.1088/1748-9326/aaf27d, 2019. a
Ziegler, N. P., Webb, N. P., Chappell, A., and LeGrand, S. L.: Scale invariance of albedo‐based wind friction velocity, J. Geophys. Res.-Atmos., 125, e2019JD031978, https://doi.org/10.1029/2019JD031978, 2020. a, b, c
Zobeck, T. M., Sterk, G., Funk, R., Rajot, J. L., Stout, J. E., and Van Pelt, R. S.: Measurement and data analysis methods for field-scale wind erosion studies and model validation, Earth Surf. Proc. Land., 28, 1163–1188, https://doi.org/10.1002/esp.1033, 2003. a
Short summary
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.
Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This...
