Articles | Volume 8, issue 2
https://doi.org/10.5194/gmd-8-409-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-409-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
24 Feb 2015
Development and technical paper |
| 24 Feb 2015
A new WRF-Chem treatment for studying regional-scale impacts of cloud processes on aerosol and trace gases in parameterized cumuli
Pacific Northwest National Laboratory, Richland, WA, USA
M. Shrivastava
Pacific Northwest National Laboratory, Richland, WA, USA
R. C. Easter
Pacific Northwest National Laboratory, Richland, WA, USA
J. D. Fast
Pacific Northwest National Laboratory, Richland, WA, USA
E. G. Chapman
Pacific Northwest National Laboratory, Richland, WA, USA
Y. Liu
Pacific Northwest National Laboratory, Richland, WA, USA
R. A. Ferrare
NASA Langley Research Center, Hampton, VA, USA
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Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
EGUsphere, https://doi.org/10.5194/egusphere-2024-112, https://doi.org/10.5194/egusphere-2024-112, 2024
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Various weather conditions influence different intense rains in southeastern Texas. MCSs contribute to both average and extreme rainfall year-round, while IDC play a role in heavy rain during summer and fall. In spring, fall, and winter, frontal weather triggers convection, while in summer, IDC is linked to both fronts and high-pressure systems, and MCSs are more associated with fronts. Front-associated convection starts around 1100 UTC, while high-pressure-related convection starts a bit later.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
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The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
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Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Lindsay M. Sheridan, Caleb Phillips, Alice C. Orrell, Larry K. Berg, Heidi Tinnesand, Raj K. Rai, Sagi Zisman, Dmitry Duplyakin, and Julia E. Flaherty
Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, https://doi.org/10.5194/wes-7-659-2022, 2022
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The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.
Ye Liu, Yun Qian, and Larry K. Berg
Wind Energ. Sci., 7, 37–51, https://doi.org/10.5194/wes-7-37-2022, https://doi.org/10.5194/wes-7-37-2022, 2022
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Uncertainties in initial conditions (ICs) decrease the accuracy of wind speed forecasts. We find that IC uncertainties can alter wind speed by modulating the weather system. IC uncertainties in local thermal gradient and large-scale circulation jointly contribute to wind speed forecast uncertainties. Wind forecast accuracy in the Columbia River Basin is confined by initial uncertainties in a few specific regions, providing useful information for more intense measurement and modeling studies.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
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Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
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Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
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Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Aditya Choukulkar, Larry K. Berg, Harindra J. S. Fernando, Eric P. Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, Mikhail Pekour, Yelena Pichugina, Mark T. Stoelinga, and David D. Turner
Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, https://doi.org/10.5194/gmd-12-4803-2019, 2019
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During the second Wind Forecast Improvement Project, improvements to the parameterizations were applied to the High Resolution Rapid Refresh model and its nested version. The impacts of the new parameterizations on the forecast of 80 m wind speeds and power are assessed, using sodars and profiling lidars observations for comparison. Improvements are evaluated as a function of the model’s initialization time, forecast horizon, time of the day, season, site elevation, and meteorological phenomena.
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
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Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
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This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Louis Marelle, Jean-Christophe Raut, Kathy S. Law, Larry K. Berg, Jerome D. Fast, Richard C. Easter, Manish Shrivastava, and Jennie L. Thomas
Geosci. Model Dev., 10, 3661–3677, https://doi.org/10.5194/gmd-10-3661-2017, https://doi.org/10.5194/gmd-10-3661-2017, 2017
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We develop the WRF-Chem 3.5.1 model to improve simulations of aerosols and ozone in the Arctic. Both species are important air pollutants and climate forcers, but models often struggle to reproduce observations in the Arctic. Our developments concern pollutant emissions, mixing, chemistry, and removal, including processes related to snow and sea ice. The effect of these changes are quantitatively validated against observations, showing significant improvements compared to the original model.
Jean-Christophe Raut, Louis Marelle, Jerome D. Fast, Jennie L. Thomas, Bernadett Weinzierl, Katharine S. Law, Larry K. Berg, Anke Roiger, Richard C. Easter, Katharina Heimerl, Tatsuo Onishi, Julien Delanoë, and Hans Schlager
Atmos. Chem. Phys., 17, 10969–10995, https://doi.org/10.5194/acp-17-10969-2017, https://doi.org/10.5194/acp-17-10969-2017, 2017
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We study the cross-polar transport of plumes from Siberian fires to the Arctic in summer, both in terms of transport pathways and efficiency of deposition processes. Those plumes containing soot may originate from anthropogenic and biomass burning sources in mid-latitude regions and may impact the Arctic climate by depositing on snow and ice surfaces. We evaluate the role of the respective source contributions, investigate the transport of plumes and treat pathway-dependent removal of particles.
Ivan Ortega, Sean Coburn, Larry K. Berg, Kathy Lantz, Joseph Michalsky, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, and Rainer Volkamer
Atmos. Meas. Tech., 9, 3893–3910, https://doi.org/10.5194/amt-9-3893-2016, https://doi.org/10.5194/amt-9-3893-2016, 2016
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We present an inherently calibrated retrieval to measure aerosol optical depth (AOD) and the aerosol phase function parameter, g, based on measurements of azimuth distributions of the Raman scattering probability (RSP), the near-absolute rotational Raman scattering (RRS) intensity by the University of Colorado two-dimensional (2-D) MAX-DOAS. The retrievals are maximally sensitive at low AOD and do not require absolute radiance calibration. We compare results with data from independent sensors.
Chun Zhao, Maoyi Huang, Jerome D. Fast, Larry K. Berg, Yun Qian, Alex Guenther, Dasa Gu, Manish Shrivastava, Ying Liu, Stacy Walters, Gabriele Pfister, Jiming Jin, John E. Shilling, and Carsten Warneke
Geosci. Model Dev., 9, 1959–1976, https://doi.org/10.5194/gmd-9-1959-2016, https://doi.org/10.5194/gmd-9-1959-2016, 2016
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In this study, the latest version of MEGAN is coupled within CLM4 in WRF-Chem. In this implementation, MEGAN shares a consistent vegetation map with CLM4. This improved modeling framework is used to investigate the impact of two land surface schemes on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models.
D. Müller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid
Atmos. Meas. Tech., 7, 3487–3496, https://doi.org/10.5194/amt-7-3487-2014, https://doi.org/10.5194/amt-7-3487-2014, 2014
E. Kassianov, J. Barnard, M. Pekour, L. K. Berg, J. Shilling, C. Flynn, F. Mei, and A. Jefferson
Atmos. Meas. Tech., 7, 3247–3261, https://doi.org/10.5194/amt-7-3247-2014, https://doi.org/10.5194/amt-7-3247-2014, 2014
A. J. Scarino, M. D. Obland, J. D. Fast, S. P. Burton, R. A. Ferrare, C. A. Hostetler, L. K. Berg, B. Lefer, C. Haman, J. W. Hair, R. R. Rogers, C. Butler, A. L. Cook, and D. B. Harper
Atmos. Chem. Phys., 14, 5547–5560, https://doi.org/10.5194/acp-14-5547-2014, https://doi.org/10.5194/acp-14-5547-2014, 2014
Jiewen Shen, Bin Zhao, Shuxiao Wang, An Ning, Yuyang Li, Runlong Cai, Da Gao, Biwu Chu, Yang Gao, Manish Shrivastava, Jingkun Jiang, Xiuhui Zhang, and Hong He
EGUsphere, https://doi.org/10.5194/egusphere-2024-642, https://doi.org/10.5194/egusphere-2024-642, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We extensively compare various cluster-dynamics based parameterizations for sulfuric acid-dimethylamine nucleation and identify a newly developed parameterization derived from Atmospheric Cluster Dynamic Code (ACDC) simulations as the most reliable one. This study offers valuable reference for developing parameterizations of other nucleation system and is meaningful for the accurate quantification of the environmental and climate impacts of NPF.
Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
EGUsphere, https://doi.org/10.5194/egusphere-2024-112, https://doi.org/10.5194/egusphere-2024-112, 2024
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Various weather conditions influence different intense rains in southeastern Texas. MCSs contribute to both average and extreme rainfall year-round, while IDC play a role in heavy rain during summer and fall. In spring, fall, and winter, frontal weather triggers convection, while in summer, IDC is linked to both fronts and high-pressure systems, and MCSs are more associated with fronts. Front-associated convection starts around 1100 UTC, while high-pressure-related convection starts a bit later.
Chupeng Zhang, Shangfei Hai, Yang Gao, Yuhang Wang, Shaoqing Zhang, Lifang Sheng, Bin Zhao, Shuxiao Wang, Jingkun Jiang, Xin Huang, Xiaojing Shen, Junying Sun, Aura Lupascu, Manish Shrivastava, Jerome D. Fast, Wenxuan Cheng, Xiuwen Guo, Ming Chu, Nan Ma, Juan Hong, Qiaoqiao Wang, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 23, 10713–10730, https://doi.org/10.5194/acp-23-10713-2023, https://doi.org/10.5194/acp-23-10713-2023, 2023
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New particle formation is an important source of atmospheric particles, exerting critical influences on global climate. Numerical models are vital tools to understanding atmospheric particle evolution, which, however, suffer from large biases in simulating particle numbers. Here we improve the model chemical processes governing particle sizes and compositions. The improved model reveals substantial contributions of newly formed particles to climate through effects on cloud condensation nuclei.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
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The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Yuyang Li, Jiewen Shen, Bin Zhao, Runlong Cai, Shuxiao Wang, Yang Gao, Manish Shrivastava, Da Gao, Jun Zheng, Markku Kulmala, and Jingkun Jiang
Atmos. Chem. Phys., 23, 8789–8804, https://doi.org/10.5194/acp-23-8789-2023, https://doi.org/10.5194/acp-23-8789-2023, 2023
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We set up a new parameterization for 1.4 nm particle formation rates from sulfuric acid–dimethylamine (SA–DMA) nucleation, fully including the effects of coagulation scavenging and cluster stability. Incorporating the new parameterization into 3-D chemical transport models, we achieved better consistencies between simulation results and observation data. This new parameterization provides new insights into atmospheric nucleation simulations and its effects on atmospheric pollution or health.
Ernesto Reyes-Villegas, Douglas Lowe, Jill S. Johnson, Kenneth S. Carslaw, Eoghan Darbyshire, Michael Flynn, James D. Allan, Hugh Coe, Ying Chen, Oliver Wild, Scott Archer-Nicholls, Alex Archibald, Siddhartha Singh, Manish Shrivastava, Rahul A. Zaveri, Vikas Singh, Gufran Beig, Ranjeet Sokhi, and Gordon McFiggans
Atmos. Chem. Phys., 23, 5763–5782, https://doi.org/10.5194/acp-23-5763-2023, https://doi.org/10.5194/acp-23-5763-2023, 2023
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Organic aerosols (OAs), their sources and their processes remain poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem, can be a useful tool to describe primary OA (POA) production and aging. However, the main disadvantage is its complexity. We used a Gaussian process simulator to reproduce model results and to estimate the sources of model uncertainty. We do this by comparing the outputs with OA observations made at Delhi, India, in 2018.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
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Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Siegfried Schobesberger, Emma L. D'Ambro, Lejish Vettikkat, Ben H. Lee, Qiaoyun Peng, David M. Bell, John E. Shilling, Manish Shrivastava, Mikhail Pekour, Jerome Fast, and Joel A. Thornton
Atmos. Meas. Tech., 16, 247–271, https://doi.org/10.5194/amt-16-247-2023, https://doi.org/10.5194/amt-16-247-2023, 2023
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We present a new, highly sensitive technique for measuring atmospheric ammonia, an important trace gas that is emitted mainly by agriculture. We deployed the instrument on an aircraft during research flights over rural Oklahoma. Due to its fast response, we could analyze correlations with turbulent winds and calculate ammonia emissions from nearby areas at 1 to 2 km resolution. We observed high spatial variability and point sources that are not resolved in the US National Emissions Inventory.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
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Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
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How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Susannah M. Burrows, Richard C. Easter, Xiaohong Liu, Po-Lun Ma, Hailong Wang, Scott M. Elliott, Balwinder Singh, Kai Zhang, and Philip J. Rasch
Atmos. Chem. Phys., 22, 5223–5251, https://doi.org/10.5194/acp-22-5223-2022, https://doi.org/10.5194/acp-22-5223-2022, 2022
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Sea spray particles are composed of a mixture of salts and organic substances from oceanic microorganisms. In prior work, our team developed an approach connecting sea spray chemistry to ocean biology, called OCEANFILMS. Here we describe its implementation within an Earth system model, E3SM. We show that simulated sea spray chemistry is consistent with observed seasonal cycles and that sunlight reflected by simulated Southern Ocean clouds increases, consistent with analysis of satellite data.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Lindsay M. Sheridan, Caleb Phillips, Alice C. Orrell, Larry K. Berg, Heidi Tinnesand, Raj K. Rai, Sagi Zisman, Dmitry Duplyakin, and Julia E. Flaherty
Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, https://doi.org/10.5194/wes-7-659-2022, 2022
Short summary
Short summary
The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.
Ye Liu, Yun Qian, and Larry K. Berg
Wind Energ. Sci., 7, 37–51, https://doi.org/10.5194/wes-7-37-2022, https://doi.org/10.5194/wes-7-37-2022, 2022
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Uncertainties in initial conditions (ICs) decrease the accuracy of wind speed forecasts. We find that IC uncertainties can alter wind speed by modulating the weather system. IC uncertainties in local thermal gradient and large-scale circulation jointly contribute to wind speed forecast uncertainties. Wind forecast accuracy in the Columbia River Basin is confined by initial uncertainties in a few specific regions, providing useful information for more intense measurement and modeling studies.
Ruqian Miao, Qi Chen, Manish Shrivastava, Youfan Chen, Lin Zhang, Jianlin Hu, Yan Zheng, and Keren Liao
Atmos. Chem. Phys., 21, 16183–16201, https://doi.org/10.5194/acp-21-16183-2021, https://doi.org/10.5194/acp-21-16183-2021, 2021
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We apply process-based and observation-constrained schemes to simulate organic aerosol in China and conduct comprehensive model–observation comparisons. The results show that anthropogenic semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) are the main sources of secondary organic aerosol (SOA) in polluted regions, for which the residential sector is perhaps the predominant contributor. The hydroxyl radical level is also important for SOA modeling in polluted regions.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
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Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Yaman Liu, Xinyi Dong, Minghuai Wang, Louisa K. Emmons, Yawen Liu, Yuan Liang, Xiao Li, and Manish Shrivastava
Atmos. Chem. Phys., 21, 8003–8021, https://doi.org/10.5194/acp-21-8003-2021, https://doi.org/10.5194/acp-21-8003-2021, 2021
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Secondary organic aerosol (SOA) is considered one of the most important uncertainties in climate modeling. We evaluate SOA performance in the Community Earth System Model version 2.1 (CESM2.1) configured with the Community Atmosphere Model version 6 with chemistry (CAM6-Chem) through a long-term simulation (1988–2019) with observations in the United States, which indicates monoterpene-formed SOA contributes most to the overestimation of SOA at the surface and underestimation in the upper air.
Duseong S. Jo, Alma Hodzic, Louisa K. Emmons, Simone Tilmes, Rebecca H. Schwantes, Michael J. Mills, Pedro Campuzano-Jost, Weiwei Hu, Rahul A. Zaveri, Richard C. Easter, Balwinder Singh, Zheng Lu, Christiane Schulz, Johannes Schneider, John E. Shilling, Armin Wisthaler, and Jose L. Jimenez
Atmos. Chem. Phys., 21, 3395–3425, https://doi.org/10.5194/acp-21-3395-2021, https://doi.org/10.5194/acp-21-3395-2021, 2021
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Secondary organic aerosol (SOA) is a major component of submicron particulate matter, but there are a lot of uncertainties in the future prediction of SOA. We used CESM 2.1 to investigate future IEPOX SOA concentration changes. The explicit chemistry predicted substantial changes in IEPOX SOA depending on the future scenario, but the parameterization predicted weak changes due to simplified chemistry, which shows the importance of correct physicochemical dependencies in future SOA prediction.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
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Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.
Lixia Liu, Yafang Cheng, Siwen Wang, Chao Wei, Mira L. Pöhlker, Christopher Pöhlker, Paulo Artaxo, Manish Shrivastava, Meinrat O. Andreae, Ulrich Pöschl, and Hang Su
Atmos. Chem. Phys., 20, 13283–13301, https://doi.org/10.5194/acp-20-13283-2020, https://doi.org/10.5194/acp-20-13283-2020, 2020
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This modeling paper reveals how aerosol–cloud interactions (ACIs) and aerosol–radiation interactions (ARIs) induced by biomass burning (BB) aerosols act oppositely on radiation, cloud, and precipitation in the Amazon during the dry season. The varying relative significance of ACIs and ARIs with BB aerosol concentration leads to a nonlinear dependence of the total climate response on BB aerosol loading and features the growing importance of ARIs at high aerosol loading.
Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang
Atmos. Chem. Phys., 20, 12265–12284, https://doi.org/10.5194/acp-20-12265-2020, https://doi.org/10.5194/acp-20-12265-2020, 2020
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In this study we evaluated the model performances for simulating secondary inorganic aerosol (SIA) and organic aerosol (OA) in PM2.5 in China against comprehensive datasets. The potential biases from factors related to meteorology, emission, chemistry, and atmospheric removal are systematically investigated. This study provides a comprehensive understanding of modeling PM2.5, which is important for studies on the effectiveness of emission control strategies.
Camille Mouchel-Vallon, Julia Lee-Taylor, Alma Hodzic, Paulo Artaxo, Bernard Aumont, Marie Camredon, David Gurarie, Jose-Luis Jimenez, Donald H. Lenschow, Scot T. Martin, Janaina Nascimento, John J. Orlando, Brett B. Palm, John E. Shilling, Manish Shrivastava, and Sasha Madronich
Atmos. Chem. Phys., 20, 5995–6014, https://doi.org/10.5194/acp-20-5995-2020, https://doi.org/10.5194/acp-20-5995-2020, 2020
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The GoAmazon 2014/5 field campaign took place near the city of Manaus, Brazil, isolated in the Amazon rainforest, to study the impacts of urban pollution on natural air masses. We simulated this campaign with an extremely detailed organic chemistry model to understand how the city would affect the growth and composition of natural aerosol particles. Discrepancies between the model and the measurements indicate that the chemistry of naturally emitted organic compounds is still poorly understood.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
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Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Aditya Choukulkar, Larry K. Berg, Harindra J. S. Fernando, Eric P. Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, Mikhail Pekour, Yelena Pichugina, Mark T. Stoelinga, and David D. Turner
Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, https://doi.org/10.5194/gmd-12-4803-2019, 2019
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During the second Wind Forecast Improvement Project, improvements to the parameterizations were applied to the High Resolution Rapid Refresh model and its nested version. The impacts of the new parameterizations on the forecast of 80 m wind speeds and power are assessed, using sodars and profiling lidars observations for comparison. Improvements are evaluated as a function of the model’s initialization time, forecast horizon, time of the day, season, site elevation, and meteorological phenomena.
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, https://doi.org/10.5194/acp-19-4367-2019, 2019
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To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather prediction models, we have assessed its temporal and spatial variability at various scales in the Columbia River Gorge during the WFIP2 field experiment. The turbulence dissipation rate shows large spatial variability, even at the microscale, with larger values in sites located downwind of complex orographic structures or in wind farm wakes. Distinct diurnal and seasonal cycles in ε have also been found.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
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This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Maria Sand, Bjørn H. Samset, Yves Balkanski, Susanne Bauer, Nicolas Bellouin, Terje K. Berntsen, Huisheng Bian, Mian Chin, Thomas Diehl, Richard Easter, Steven J. Ghan, Trond Iversen, Alf Kirkevåg, Jean-François Lamarque, Guangxing Lin, Xiaohong Liu, Gan Luo, Gunnar Myhre, Twan van Noije, Joyce E. Penner, Michael Schulz, Øyvind Seland, Ragnhild B. Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Fangqun Yu, Kai Zhang, and Hua Zhang
Atmos. Chem. Phys., 17, 12197–12218, https://doi.org/10.5194/acp-17-12197-2017, https://doi.org/10.5194/acp-17-12197-2017, 2017
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The role of aerosols in the changing polar climate is not well understood and the aerosols are poorly constrained in the models. In this study we have compared output from 16 different aerosol models with available observations at both poles. We show that the model median is representative of the observations, but the model spread is large. The Arctic direct aerosol radiative effect over the industrial area is positive during spring due to black carbon and negative during summer due to sulfate.
Louis Marelle, Jean-Christophe Raut, Kathy S. Law, Larry K. Berg, Jerome D. Fast, Richard C. Easter, Manish Shrivastava, and Jennie L. Thomas
Geosci. Model Dev., 10, 3661–3677, https://doi.org/10.5194/gmd-10-3661-2017, https://doi.org/10.5194/gmd-10-3661-2017, 2017
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We develop the WRF-Chem 3.5.1 model to improve simulations of aerosols and ozone in the Arctic. Both species are important air pollutants and climate forcers, but models often struggle to reproduce observations in the Arctic. Our developments concern pollutant emissions, mixing, chemistry, and removal, including processes related to snow and sea ice. The effect of these changes are quantitatively validated against observations, showing significant improvements compared to the original model.
Jean-Christophe Raut, Louis Marelle, Jerome D. Fast, Jennie L. Thomas, Bernadett Weinzierl, Katharine S. Law, Larry K. Berg, Anke Roiger, Richard C. Easter, Katharina Heimerl, Tatsuo Onishi, Julien Delanoë, and Hans Schlager
Atmos. Chem. Phys., 17, 10969–10995, https://doi.org/10.5194/acp-17-10969-2017, https://doi.org/10.5194/acp-17-10969-2017, 2017
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We study the cross-polar transport of plumes from Siberian fires to the Arctic in summer, both in terms of transport pathways and efficiency of deposition processes. Those plumes containing soot may originate from anthropogenic and biomass burning sources in mid-latitude regions and may impact the Arctic climate by depositing on snow and ice surfaces. We evaluate the role of the respective source contributions, investigate the transport of plumes and treat pathway-dependent removal of particles.
Yang Yang, Hailong Wang, Steven J. Smith, Richard Easter, Po-Lun Ma, Yun Qian, Hongbin Yu, Can Li, and Philip J. Rasch
Atmos. Chem. Phys., 17, 8903–8922, https://doi.org/10.5194/acp-17-8903-2017, https://doi.org/10.5194/acp-17-8903-2017, 2017
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Sulfate has significant impacts on air quality and climate. Local sulfate pollution could result from remote influences, making domestic mitigation efforts inefficient. Using CESM with a sulfur source-tagging technique, we found that, over regions with relatively low emissions, sulfate concentrations are primarily attributed to non-local sources and sulfate indirect radiative forcing over the Southern Hemisphere is more sensitive to emission perturbation than the polluted Northern Hemisphere.
Dan Chen, Zhiquan Liu, Jerome Fast, and Junmei Ban
Atmos. Chem. Phys., 16, 10707–10724, https://doi.org/10.5194/acp-16-10707-2016, https://doi.org/10.5194/acp-16-10707-2016, 2016
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Extreme haze events occurred frequently over China recently, and adequately predicting peak PM2.5 concentrations is still challenging. In this study, the sulfate–nitrate–ammonium relevant heterogeneous reactions were parameterized for the first time in the WRF-Chem model. We evaluated the performance of WRF-Chem and used the model to investigate the sensitivity of heterogeneous reactions on simulated peak sulfate, nitrate, and ammonium concentrations in the vicinity of Beijing during October 2014.
Ivan Ortega, Sean Coburn, Larry K. Berg, Kathy Lantz, Joseph Michalsky, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, and Rainer Volkamer
Atmos. Meas. Tech., 9, 3893–3910, https://doi.org/10.5194/amt-9-3893-2016, https://doi.org/10.5194/amt-9-3893-2016, 2016
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We present an inherently calibrated retrieval to measure aerosol optical depth (AOD) and the aerosol phase function parameter, g, based on measurements of azimuth distributions of the Raman scattering probability (RSP), the near-absolute rotational Raman scattering (RRS) intensity by the University of Colorado two-dimensional (2-D) MAX-DOAS. The retrievals are maximally sensitive at low AOD and do not require absolute radiance calibration. We compare results with data from independent sensors.
Chun Zhao, Maoyi Huang, Jerome D. Fast, Larry K. Berg, Yun Qian, Alex Guenther, Dasa Gu, Manish Shrivastava, Ying Liu, Stacy Walters, Gabriele Pfister, Jiming Jin, John E. Shilling, and Carsten Warneke
Geosci. Model Dev., 9, 1959–1976, https://doi.org/10.5194/gmd-9-1959-2016, https://doi.org/10.5194/gmd-9-1959-2016, 2016
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In this study, the latest version of MEGAN is coupled within CLM4 in WRF-Chem. In this implementation, MEGAN shares a consistent vegetation map with CLM4. This improved modeling framework is used to investigate the impact of two land surface schemes on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models.
N. I. Kristiansen, A. Stohl, D. J. L. Olivié, B. Croft, O. A. Søvde, H. Klein, T. Christoudias, D. Kunkel, S. J. Leadbetter, Y. H. Lee, K. Zhang, K. Tsigaridis, T. Bergman, N. Evangeliou, H. Wang, P.-L. Ma, R. C. Easter, P. J. Rasch, X. Liu, G. Pitari, G. Di Genova, S. Y. Zhao, Y. Balkanski, S. E. Bauer, G. S. Faluvegi, H. Kokkola, R. V. Martin, J. R. Pierce, M. Schulz, D. Shindell, H. Tost, and H. Zhang
Atmos. Chem. Phys., 16, 3525–3561, https://doi.org/10.5194/acp-16-3525-2016, https://doi.org/10.5194/acp-16-3525-2016, 2016
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Processes affecting aerosol removal from the atmosphere are not fully understood. In this study we investigate to what extent atmospheric transport models can reproduce observed loss of aerosols. We compare measurements of radioactive isotopes, that attached to ambient sulfate aerosols during the 2011 Fukushima nuclear accident, to 19 models using identical emissions. Results indicate aerosol removal that is too fast in most models, and apply to aerosols that have undergone long-range transport.
L. Kleinman, C. Kuang, A. Sedlacek, G. Senum, S. Springston, J. Wang, Q. Zhang, J. Jayne, J. Fast, J. Hubbe, J. Shilling, and R. Zaveri
Atmos. Chem. Phys., 16, 1729–1746, https://doi.org/10.5194/acp-16-1729-2016, https://doi.org/10.5194/acp-16-1729-2016, 2016
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Atmospheric measurements of total organic aerosol (OA) and tracers of anthropogenic and biogenic emissions are used to quantify synergistic effects (A–B interactions) between two classes of precursors in the formation of OA. Regressions are consistent with the Sacramento plume composed mainly of modern carbon, and OA correlating best with an anthropogenic tracer. It is found that meteorological conditions during a pollution episode can mimic effects of A–B interactions.
Kai Zhang, Chun Zhao, Hui Wan, Yun Qian, Richard C. Easter, Steven J. Ghan, Koichi Sakaguchi, and Xiaohong Liu
Geosci. Model Dev., 9, 607–632, https://doi.org/10.5194/gmd-9-607-2016, https://doi.org/10.5194/gmd-9-607-2016, 2016
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A sub-grid treatment based on Weibull distribution is introduced to CAM5 to take into account the impact of unresolved variability of surface wind speed on sea salt and dust emissions. Simulations show that sub-grid wind variability has relatively small impacts on the global mean sea salt emissions, but considerable influence on dust emissions. Dry convective eddies and mesoscale flows associated with complex topography are the major causes of dust emission enhancement.
X. Liu, P.-L. Ma, H. Wang, S. Tilmes, B. Singh, R. C. Easter, S. J. Ghan, and P. J. Rasch
Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, https://doi.org/10.5194/gmd-9-505-2016, 2016
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In this study, we describe and evaluate a new four-mode version of the Modal Aerosol Module (MAM4) in the Community Atmosphere Model version 5 (CAM5). Compared to the current three-mode version of MAM in CAM5, MAM4 significantly improves the simulation of seasonal variation of BC concentrations in the polar regions, by increasing the BC concentrations in all seasons and particularly in cold seasons.
A. Lupascu, R. Easter, R. Zaveri, M. Shrivastava, M. Pekour, J. Tomlinson, Q. Yang, H. Matsui, A. Hodzic, Q. Zhang, and J. D. Fast
Atmos. Chem. Phys., 15, 12283–12313, https://doi.org/10.5194/acp-15-12283-2015, https://doi.org/10.5194/acp-15-12283-2015, 2015
R. Zhang, H. Wang, Y. Qian, P. J. Rasch, R. C. Easter, P.-L. Ma, B. Singh, J. Huang, and Q. Fu
Atmos. Chem. Phys., 15, 6205–6223, https://doi.org/10.5194/acp-15-6205-2015, https://doi.org/10.5194/acp-15-6205-2015, 2015
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We use the CAM5 model with a novel source-tagging technique to characterize the fate of BC particles emitted from various geographical regions and sectors and their transport pathways to the Himalayas and Tibetan Plateau (HTP). We show a comprehensive picture of the seasonal and regional dependence of BC source attributions, and find strong seasonal and spatial variations in BC-in-snow radiative forcing in the HTP that can be quantitatively attributed to the various regional/sectoral sources.
L. Marelle, J.-C. Raut, J. L. Thomas, K. S. Law, B. Quennehen, G. Ancellet, J. Pelon, A. Schwarzenboeck, and J. D. Fast
Atmos. Chem. Phys., 15, 3831–3850, https://doi.org/10.5194/acp-15-3831-2015, https://doi.org/10.5194/acp-15-3831-2015, 2015
M. C. Wyant, C. S. Bretherton, R. Wood, G. R. Carmichael, A. Clarke, J. Fast, R. George, W. I. Gustafson Jr., C. Hannay, A. Lauer, Y. Lin, J.-J. Morcrette, J. Mulcahy, P. E. Saide, S. N. Spak, and Q. Yang
Atmos. Chem. Phys., 15, 153–172, https://doi.org/10.5194/acp-15-153-2015, https://doi.org/10.5194/acp-15-153-2015, 2015
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Simulations from a group of GCMs, forecast models, and regional models are compared with aircraft and ship observations of the marine boundary layer (MBL) in the southeast Pacific region during the VOCALS-REx field campaign of October-November 2008. Gradients of cloud, aerosol, and chemical properties in and above the MBL extending from the Peruvian coast westward along 20 degrees south are compared during the period.
B. H. Samset, G. Myhre, A. Herber, Y. Kondo, S.-M. Li, N. Moteki, M. Koike, N. Oshima, J. P. Schwarz, Y. Balkanski, S. E. Bauer, N. Bellouin, T. K. Berntsen, H. Bian, M. Chin, T. Diehl, R. C. Easter, S. J. Ghan, T. Iversen, A. Kirkevåg, J.-F. Lamarque, G. Lin, X. Liu, J. E. Penner, M. Schulz, Ø. Seland, R. B. Skeie, P. Stier, T. Takemura, K. Tsigaridis, and K. Zhang
Atmos. Chem. Phys., 14, 12465–12477, https://doi.org/10.5194/acp-14-12465-2014, https://doi.org/10.5194/acp-14-12465-2014, 2014
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Far from black carbon (BC) emission sources, present climate models are unable to reproduce flight measurements. By comparing recent models with data, we find that the atmospheric lifetime of BC may be overestimated in models. By adjusting modeled BC concentrations to measurements in remote regions - over oceans and at high altitudes - we arrive at a reduced estimate for BC radiative forcing over the industrial era.
S. Archer-Nicholls, D. Lowe, S. Utembe, J. Allan, R. A. Zaveri, J. D. Fast, Ø. Hodnebrog, H. Denier van der Gon, and G. McFiggans
Geosci. Model Dev., 7, 2557–2579, https://doi.org/10.5194/gmd-7-2557-2014, https://doi.org/10.5194/gmd-7-2557-2014, 2014
D. Müller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid
Atmos. Meas. Tech., 7, 3487–3496, https://doi.org/10.5194/amt-7-3487-2014, https://doi.org/10.5194/amt-7-3487-2014, 2014
E. Kassianov, J. Barnard, M. Pekour, L. K. Berg, J. Shilling, C. Flynn, F. Mei, and A. Jefferson
Atmos. Meas. Tech., 7, 3247–3261, https://doi.org/10.5194/amt-7-3247-2014, https://doi.org/10.5194/amt-7-3247-2014, 2014
H. Matsui, M. Koike, Y. Kondo, J. D. Fast, and M. Takigawa
Atmos. Chem. Phys., 14, 10315–10331, https://doi.org/10.5194/acp-14-10315-2014, https://doi.org/10.5194/acp-14-10315-2014, 2014
J. D. Fast, J. Allan, R. Bahreini, J. Craven, L. Emmons, R. Ferrare, P. L. Hayes, A. Hodzic, J. Holloway, C. Hostetler, J. L. Jimenez, H. Jonsson, S. Liu, Y. Liu, A. Metcalf, A. Middlebrook, J. Nowak, M. Pekour, A. Perring, L. Russell, A. Sedlacek, J. Seinfeld, A. Setyan, J. Shilling, M. Shrivastava, S. Springston, C. Song, R. Subramanian, J. W. Taylor, V. Vinoj, Q. Yang, R. A. Zaveri, and Q. Zhang
Atmos. Chem. Phys., 14, 10013–10060, https://doi.org/10.5194/acp-14-10013-2014, https://doi.org/10.5194/acp-14-10013-2014, 2014
V. H. Almanza, L. T. Molina, G. Li, J. Fast, and G. Sosa
Atmos. Chem. Phys., 14, 8483–8499, https://doi.org/10.5194/acp-14-8483-2014, https://doi.org/10.5194/acp-14-8483-2014, 2014
C. Knote, A. Hodzic, J. L. Jimenez, R. Volkamer, J. J. Orlando, S. Baidar, J. Brioude, J. Fast, D. R. Gentner, A. H. Goldstein, P. L. Hayes, W. B. Knighton, H. Oetjen, A. Setyan, H. Stark, R. Thalman, G. Tyndall, R. Washenfelder, E. Waxman, and Q. Zhang
Atmos. Chem. Phys., 14, 6213–6239, https://doi.org/10.5194/acp-14-6213-2014, https://doi.org/10.5194/acp-14-6213-2014, 2014
A. J. Scarino, M. D. Obland, J. D. Fast, S. P. Burton, R. A. Ferrare, C. A. Hostetler, L. K. Berg, B. Lefer, C. Haman, J. W. Hair, R. R. Rogers, C. Butler, A. L. Cook, and D. B. Harper
Atmos. Chem. Phys., 14, 5547–5560, https://doi.org/10.5194/acp-14-5547-2014, https://doi.org/10.5194/acp-14-5547-2014, 2014
R. A. Zaveri, R. C. Easter, J. E. Shilling, and J. H. Seinfeld
Atmos. Chem. Phys., 14, 5153–5181, https://doi.org/10.5194/acp-14-5153-2014, https://doi.org/10.5194/acp-14-5153-2014, 2014
T. K. Tesfa, H.-Y. Li, L. R. Leung, M. Huang, Y. Ke, Y. Sun, and Y. Liu
Geosci. Model Dev., 7, 947–963, https://doi.org/10.5194/gmd-7-947-2014, https://doi.org/10.5194/gmd-7-947-2014, 2014
G. W. Mann, K. S. Carslaw, C. L. Reddington, K. J. Pringle, M. Schulz, A. Asmi, D. V. Spracklen, D. A. Ridley, M. T. Woodhouse, L. A. Lee, K. Zhang, S. J. Ghan, R. C. Easter, X. Liu, P. Stier, Y. H. Lee, P. J. Adams, H. Tost, J. Lelieveld, S. E. Bauer, K. Tsigaridis, T. P. C. van Noije, A. Strunk, E. Vignati, N. Bellouin, M. Dalvi, C. E. Johnson, T. Bergman, H. Kokkola, K. von Salzen, F. Yu, G. Luo, A. Petzold, J. Heintzenberg, A. Clarke, J. A. Ogren, J. Gras, U. Baltensperger, U. Kaminski, S. G. Jennings, C. D. O'Dowd, R. M. Harrison, D. C. S. Beddows, M. Kulmala, Y. Viisanen, V. Ulevicius, N. Mihalopoulos, V. Zdimal, M. Fiebig, H.-C. Hansson, E. Swietlicki, and J. S. Henzing
Atmos. Chem. Phys., 14, 4679–4713, https://doi.org/10.5194/acp-14-4679-2014, https://doi.org/10.5194/acp-14-4679-2014, 2014
P.-L. Ma, P. J. Rasch, J. D. Fast, R. C. Easter, W. I. Gustafson Jr., X. Liu, S. J. Ghan, and B. Singh
Geosci. Model Dev., 7, 755–778, https://doi.org/10.5194/gmd-7-755-2014, https://doi.org/10.5194/gmd-7-755-2014, 2014
M. S. Long, W. C. Keene, R. C. Easter, R. Sander, X. Liu, A. Kerkweg, and D. Erickson
Atmos. Chem. Phys., 14, 3397–3425, https://doi.org/10.5194/acp-14-3397-2014, https://doi.org/10.5194/acp-14-3397-2014, 2014
N. Voisin, L. Liu, M. Hejazi, T. Tesfa, H. Li, M. Huang, Y. Liu, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 4555–4575, https://doi.org/10.5194/hess-17-4555-2013, https://doi.org/10.5194/hess-17-4555-2013, 2013
J. Brioude, D. Arnold, A. Stohl, M. Cassiani, D. Morton, P. Seibert, W. Angevine, S. Evan, A. Dingwell, J. D. Fast, R. C. Easter, I. Pisso, J. Burkhart, and G. Wotawa
Geosci. Model Dev., 6, 1889–1904, https://doi.org/10.5194/gmd-6-1889-2013, https://doi.org/10.5194/gmd-6-1889-2013, 2013
R. C. Moffet, T. C. Rödel, S. T. Kelly, X. Y. Yu, G. T. Carroll, J. Fast, R. A. Zaveri, A. Laskin, and M. K. Gilles
Atmos. Chem. Phys., 13, 10445–10459, https://doi.org/10.5194/acp-13-10445-2013, https://doi.org/10.5194/acp-13-10445-2013, 2013
H. Wang, R. C. Easter, P. J. Rasch, M. Wang, X. Liu, S. J. Ghan, Y. Qian, J.-H. Yoon, P.-L. Ma, and V. Vinoj
Geosci. Model Dev., 6, 765–782, https://doi.org/10.5194/gmd-6-765-2013, https://doi.org/10.5194/gmd-6-765-2013, 2013
J. L. Thomas, J.-C. Raut, K. S. Law, L. Marelle, G. Ancellet, F. Ravetta, J. D. Fast, G. Pfister, L. K. Emmons, G. S. Diskin, A. Weinheimer, A. Roiger, and H. Schlager
Atmos. Chem. Phys., 13, 3825–3848, https://doi.org/10.5194/acp-13-3825-2013, https://doi.org/10.5194/acp-13-3825-2013, 2013
D. T. Shindell, J.-F. Lamarque, M. Schulz, M. Flanner, C. Jiao, M. Chin, P. J. Young, Y. H. Lee, L. Rotstayn, N. Mahowald, G. Milly, G. Faluvegi, Y. Balkanski, W. J. Collins, A. J. Conley, S. Dalsoren, R. Easter, S. Ghan, L. Horowitz, X. Liu, G. Myhre, T. Nagashima, V. Naik, S. T. Rumbold, R. Skeie, K. Sudo, S. Szopa, T. Takemura, A. Voulgarakis, J.-H. Yoon, and F. Lo
Atmos. Chem. Phys., 13, 2939–2974, https://doi.org/10.5194/acp-13-2939-2013, https://doi.org/10.5194/acp-13-2939-2013, 2013
B. H. Samset, G. Myhre, M. Schulz, Y. Balkanski, S. Bauer, T. K. Berntsen, H. Bian, N. Bellouin, T. Diehl, R. C. Easter, S. J. Ghan, T. Iversen, S. Kinne, A. Kirkevåg, J.-F. Lamarque, G. Lin, X. Liu, J. E. Penner, Ø. Seland, R. B. Skeie, P. Stier, T. Takemura, K. Tsigaridis, and K. Zhang
Atmos. Chem. Phys., 13, 2423–2434, https://doi.org/10.5194/acp-13-2423-2013, https://doi.org/10.5194/acp-13-2423-2013, 2013
M. S. Long, W. C. Keene, R. Easter, R. Sander, A. Kerkweg, D. Erickson, X. Liu, and S. Ghan
Geosci. Model Dev., 6, 255–262, https://doi.org/10.5194/gmd-6-255-2013, https://doi.org/10.5194/gmd-6-255-2013, 2013
J. E. Shilling, R. A. Zaveri, J. D. Fast, L. Kleinman, M. L. Alexander, M. R. Canagaratna, E. Fortner, J. M. Hubbe, J. T. Jayne, A. Sedlacek, A. Setyan, S. Springston, D. R. Worsnop, and Q. Zhang
Atmos. Chem. Phys., 13, 2091–2113, https://doi.org/10.5194/acp-13-2091-2013, https://doi.org/10.5194/acp-13-2091-2013, 2013
Related subject area
Atmospheric sciences
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry
Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model
Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm
The implementation of dust mineralogy in COSMO5.05-MUSCAT
Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity
Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME
GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system
Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1
Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1
Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution
Sensitivity of air quality model responses to emission changes: comparison of results based on four EU inventories through FAIRMODE benchmarking methodology
A simple and realistic aerosol emission approach for use in the Thompson–Eidhammer microphysics scheme in the NOAA UFS Weather Model (version GSL global-24Feb2022)
On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest
The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2
GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement
Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model
A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3
BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations
Investigating Ground-Level Ozone Pollution in Semi-Arid and Arid Regions of Arizona Using WRF-Chem v4.4 Modeling
The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1
FUME 2.0 – Flexible Universal processor for Modeling Emissions
Application of regional meteorology and air quality models based on MIPS CPU Platform
Spherical air mass factors in one and two dimensions with SASKTRAN 1.6.0
An improved version of the piecewise parabolic method advection scheme: description and performance assessment in a bidimensional test case with stiff chemistry in toyCTM v1.0.1
INCHEM-Py v1.2: a community box model for indoor air chemistry
Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemistry-transport model using Cloud-J v7.3e
Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2
How the meteorological spectral nudging impacts on aerosol radiation clouds interactions?
Assimilation of GNSS Tropospheric Gradients into the Weather Research and Forecasting Model Version 4.4.1
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations
Simulations of 7Be and 10Be with the GEOS-Chem global model v14.0.2 using state-of-the-art production rates
Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 2: Influence of uncertainty factors
A mountain-induced moist baroclinic wave test case for the dynamical cores of atmospheric general circulation models
The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
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The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
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In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
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The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
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In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
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Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
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This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
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PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way 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; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
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Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
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Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
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We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a 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 in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
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The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
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We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
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A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which 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 and is thus perfectly suited for future CO2 geostationery and imager satellites.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
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Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
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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 variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
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Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
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Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024, https://doi.org/10.5194/gmd-17-1111-2024, 2024
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This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024, https://doi.org/10.5194/gmd-17-1091-2024, 2024
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With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024, https://doi.org/10.5194/gmd-17-815-2024, 2024
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GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
Piotr Zmijewski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 17, 759–780, https://doi.org/10.5194/gmd-17-759-2024, https://doi.org/10.5194/gmd-17-759-2024, 2024
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In computer simulations of clouds it is necessary to model the myriad of droplets that constitute a cloud. A popular method for this is to use so-called super-droplets (SDs), each representing many real droplets. It has remained a challenge to model collisions of SDs. We study how precipitation in a cumulus cloud depends on the number of SDs. Surprisingly, we do not find convergence in mean precipitation even for numbers of SDs much larger than typically used in simulations.
Roya Ghahreman, Wanmin Gong, Paul A. Makar, Alexandru Lupu, Amanda Cole, Kulbir Banwait, Colin Lee, and Ayodeji Akingunola
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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The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura
Geosci. Model Dev., 17, 651–684, https://doi.org/10.5194/gmd-17-651-2024, https://doi.org/10.5194/gmd-17-651-2024, 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.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-234, https://doi.org/10.5194/gmd-2023-234, 2024
Revised manuscript accepted for GMD
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air quality-challenged arid/semi-arid region in the US. The unique characteristics of semi-arid/arid regions, e.g., intense heat, minimal moisture, persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
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.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
EGUsphere, https://doi.org/10.5194/egusphere-2023-2740, https://doi.org/10.5194/egusphere-2023-2740, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure facilitating further processing to allow emission processing from continental to street scale.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2023-2962, https://doi.org/10.5194/egusphere-2023-2962, 2024
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There are relatively limited researches on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPU, have distinct advantages in energy efficiency and scalability. In this study, the air quality modeling system can run stably on MIPS CPU platform, and the experiment results verify the stability of scientific computing on the platform. The work provides a technical foundation for the scientific application based on MIPS CPU platforms.
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.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-209, https://doi.org/10.5194/gmd-2023-209, 2023
Revised manuscript accepted for GMD
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This study is about the modelling of the atmospheric composition in Europe and during the summer 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impact of two modelling processes able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Rohith Muraleedharan Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-202, https://doi.org/10.5194/gmd-2023-202, 2023
Revised manuscript accepted for GMD
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Global Navigation Satellite Systems provide moisture observations through its densely distributed ground station network. In this research, we assimilated a new type of observation called tropospheric gradient observations, which was never incorporated into a weather model. Here, we have developed a forward operator for gradient observations and performed impact studies. Promising improvements were observed in the humidity fields of the model in the assimilation study.
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.
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Short summary
This work presents a new methodology for representing regional-scale impacts of cloud processing on both aerosol and trace gases in sub-grid-scale convective clouds. Using the new methodology, we can better simulate the aerosol lifecycle over large areas. The results presented in this work highlight the potential change in column-integrated amounts of black carbon, organic aerosol, and sulfate aerosol, which were found to range from -50% for black carbon to +40% for sulfate.
This work presents a new methodology for representing regional-scale impacts of cloud processing...
