Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-73-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-73-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
07 Jan 2021
Model experiment description paper |
| 07 Jan 2021
| 07 Jan 2021
A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales
Lawrence Livermore National Laboratory, Livermore, CA, USA
Chen Zhou
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Yunyan Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Stephen A. Klein
Lawrence Livermore National Laboratory, Livermore, CA, USA
Mark D. Zelinka
Lawrence Livermore National Laboratory, Livermore, CA, USA
Xue Zheng
Lawrence Livermore National Laboratory, Livermore, CA, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
Wei-Ting Chen
Department of Atmospheric Sciences, National Taiwan University,
Taipei, Taiwan
Chien-Ming Wu
Department of Atmospheric Sciences, National Taiwan University,
Taipei, Taiwan
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Aurore Voldoire, Romain Roehrig, Hervé Giordani, Robin Waldman, Yunyan Zhang, Shaocheng Xie, and Marie-Nöelle Bouin
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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.
Yu-Hung Chang, Wei-Ting Chen, Chien-Ming Wu, Christopher Moseley, and Chia-Chun Wu
Atmos. Chem. Phys., 21, 16709–16725, https://doi.org/10.5194/acp-21-16709-2021, https://doi.org/10.5194/acp-21-16709-2021, 2021
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The impacts of increasing cloud condensation nuclei on summertime diurnal precipitation in weak synoptic weather over complex topography in Taiwan were investigated by applying object-based tracking analyses to semi-realistic large-eddy simulations. In hotspots of orographic locking processes, rain initiation is delayed, which prolongs the development of local circulation and convection. For this organized regime, the occurrence of extreme diurnal precipitating systems is notably enhanced.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, https://doi.org/10.5194/gmd-14-1575-2021, 2021
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A stochastic deep convection parameterization is implemented into the US Department of Energy Energy Exascale Earth System Model Atmosphere Model version 1 (EAMv1). Compared to the default model, the well-known problem of
too much light rain and too little heavy rainis largely alleviated over the tropics with the stochastic scheme. Results from this study provide important insights into the model performance of EAMv1 when stochasticity is included in the deep convective parameterization.
Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, https://doi.org/10.5194/gmd-14-1219-2021, 2021
Peter A. Bogenschutz, Shuaiqi Tang, Peter M. Caldwell, Shaocheng Xie, Wuyin Lin, and Yao-Sheng Chen
Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, https://doi.org/10.5194/gmd-13-4443-2020, 2020
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This paper documents a tool that has been developed that can be used to accelerate the development and understanding of climate models. This version of the model, known as a the single-column model, is much faster to run than the full climate model, and we demonstrate that this tool can be used to quickly exploit model biases that arise due to physical processes. We show examples of how this single-column model can directly benefit the field.
Qi Tang, Stephen A. Klein, Shaocheng Xie, Wuyin Lin, Jean-Christophe Golaz, Erika L. Roesler, Mark A. Taylor, Philip J. Rasch, David C. Bader, Larry K. Berg, Peter Caldwell, Scott E. Giangrande, Richard B. Neale, Yun Qian, Laura D. Riihimaki, Charles S. Zender, Yuying Zhang, and Xue Zheng
Geosci. Model Dev., 12, 2679–2706, https://doi.org/10.5194/gmd-12-2679-2019, https://doi.org/10.5194/gmd-12-2679-2019, 2019
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
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The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Tao Zhang, Minghua Zhang, Wuyin Lin, Yanluan Lin, Wei Xue, Haiyang Yu, Juanxiong He, Xiaoge Xin, Hsi-Yen Ma, Shaocheng Xie, and Weimin Zheng
Geosci. Model Dev., 11, 5189–5201, https://doi.org/10.5194/gmd-11-5189-2018, https://doi.org/10.5194/gmd-11-5189-2018, 2018
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Tuning of uncertain parameters in global atmospheric general circulation models has extreme computational cost. In this study, we provide an automatic tuning method by combining an auto-optimization algorithm with hindcasts to improve climate simulations in CAM5. The tuning improved the overall performance of a well-calibrated model by about 10 %. The computational cost of the entire auto-tuning procedure is just equivalent to a single 20-year simulation of CAM5.
Kai Zhang, Philip J. Rasch, Mark A. Taylor, Hui Wan, Ruby Leung, Po-Lun Ma, Jean-Christophe Golaz, Jon Wolfe, Wuyin Lin, Balwinder Singh, Susannah Burrows, Jin-Ho Yoon, Hailong Wang, Yun Qian, Qi Tang, Peter Caldwell, and Shaocheng Xie
Geosci. Model Dev., 11, 1971–1988, https://doi.org/10.5194/gmd-11-1971-2018, https://doi.org/10.5194/gmd-11-1971-2018, 2018
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The conservation of total water is an important numerical feature for global Earth system models. Even small conservation problems in the water budget can lead to systematic errors in century-long simulations for sea level rise projection. This study quantifies and reduces various sources of water conservation error in the atmosphere component of the Energy Exascale Earth System Model.
Scott E. Giangrande, Zhe Feng, Michael P. Jensen, Jennifer M. Comstock, Karen L. Johnson, Tami Toto, Meng Wang, Casey Burleyson, Nitin Bharadwaj, Fan Mei, Luiz A. T. Machado, Antonio O. Manzi, Shaocheng Xie, Shuaiqi Tang, Maria Assuncao F. Silva Dias, Rodrigo A. F de Souza, Courtney Schumacher, and Scot T. Martin
Atmos. Chem. Phys., 17, 14519–14541, https://doi.org/10.5194/acp-17-14519-2017, https://doi.org/10.5194/acp-17-14519-2017, 2017
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The Amazon forest is the largest tropical rain forest on the planet, featuring
prolific and diverse cloud conditions. The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment was motivated by demands to gain a better understanding of aerosol and cloud interactions on climate and the global circulation. The routine DOE ARM observations from this 2-year campaign are summarized to help quantify controls on clouds and precipitation over this undersampled region.
Yoko Tsushima, Florent Brient, Stephen A. Klein, Dimitra Konsta, Christine C. Nam, Xin Qu, Keith D. Williams, Steven C. Sherwood, Kentaroh Suzuki, and Mark D. Zelinka
Geosci. Model Dev., 10, 4285–4305, https://doi.org/10.5194/gmd-10-4285-2017, https://doi.org/10.5194/gmd-10-4285-2017, 2017
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Cloud feedback is the largest uncertainty associated with estimates of climate sensitivity. Diagnostics have been developed to evaluate cloud processes in climate models. For this understanding to be reflected in better estimates of cloud feedbacks, it is vital to continue to develop such tools and to exploit them fully during the model development process. Code repositories have been created to store and document the programs which will allow climate modellers to compute these diagnostics.
Mark J. Webb, Timothy Andrews, Alejandro Bodas-Salcedo, Sandrine Bony, Christopher S. Bretherton, Robin Chadwick, Hélène Chepfer, Hervé Douville, Peter Good, Jennifer E. Kay, Stephen A. Klein, Roger Marchand, Brian Medeiros, A. Pier Siebesma, Christopher B. Skinner, Bjorn Stevens, George Tselioudis, Yoko Tsushima, and Masahiro Watanabe
Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, https://doi.org/10.5194/gmd-10-359-2017, 2017
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The Cloud Feedback Model Intercomparison Project (CFMIP) aims to improve understanding of cloud-climate feedback mechanisms and evaluation of cloud processes and cloud feedbacks in climate models. CFMIP also aims to improve understanding of circulation, regional-scale precipitation and non-linear changes. CFMIP is contributing to the 6th phase of the Coupled Model Intercomparison Project (CMIP6) by coordinating a hierarchy of targeted experiments with cloud-related model outputs.
Shuaiqi Tang, Shaocheng Xie, Yunyan Zhang, Minghua Zhang, Courtney Schumacher, Hannah Upton, Michael P. Jensen, Karen L. Johnson, Meng Wang, Maike Ahlgrimm, Zhe Feng, Patrick Minnis, and Mandana Thieman
Atmos. Chem. Phys., 16, 14249–14264, https://doi.org/10.5194/acp-16-14249-2016, https://doi.org/10.5194/acp-16-14249-2016, 2016
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Data observed during the Green Ocean Amazon (GoAmazon2014/5) experiment are used to derive the large-scale fields in this study. The morning propagating convective systems are active during the wet season but rare during the dry season. The afternoon convections are active in both seasons, with heating and moistening in the lower level corresponding to the vertical convergence of eddy fluxes. Case study shows distinguish large-scale environments for three types of convective systems in Amazonia.
M. P. Jensen, T. Toto, D. Troyan, P. E. Ciesielski, D. Holdridge, J. Kyrouac, J. Schatz, Y. Zhang, and S. Xie
Atmos. Meas. Tech., 8, 421–434, https://doi.org/10.5194/amt-8-421-2015, https://doi.org/10.5194/amt-8-421-2015, 2015
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A major component of the 2011 Midlatitude Continental Convective Clouds Experiment (MC3E) was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state. This manuscript describes the details of the MC3E radiosonde operations including the instrumentation, data processing and analysis of the impacts of bias correction and algorithm assumptions on the determination of forcing data sets.
G. de Boer, M. D. Shupe, P. M. Caldwell, S. E. Bauer, O. Persson, J. S. Boyle, M. Kelley, S. A. Klein, and M. Tjernström
Atmos. Chem. Phys., 14, 427–445, https://doi.org/10.5194/acp-14-427-2014, https://doi.org/10.5194/acp-14-427-2014, 2014
M. S. Johnston, S. Eliasson, P. Eriksson, R. M. Forbes, K. Wyser, and M. D. Zelinka
Atmos. Chem. Phys., 13, 12043–12058, https://doi.org/10.5194/acp-13-12043-2013, https://doi.org/10.5194/acp-13-12043-2013, 2013
Related subject area
Atmospheric sciences
tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics
Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1
WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model
Representing effects of surface heterogeneity in a multi-plume eddy diffusivity mass flux boundary layer parameterization
Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?
A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
RoadSurf 1.1: open-source road weather model library
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
The ddeq Python library for point source quantification from remote sensing images (version 1.0)
Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)
A general comprehensive evaluation method for cross-scale precipitation forecasts
Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
An objective identification technique for potential vorticity structures associated with African easterly waves
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
FUME 2.0 – Flexible Universal processor for Modeling Emissions
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands
Balloon drift estimation and improved position estimates for radiosondes
Emission ensemble approach to improve the development of multi-scale emission inventories
What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Assessing acetone for the GISS ModelE2.1 Earth system model
Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations
A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0
Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
Mixed-Precision Computing in the GRIST Dynamical Core for Weather and Climate Modelling
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
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
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
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Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Nathan P. Arnold
Geosci. Model Dev., 17, 5041–5056, https://doi.org/10.5194/gmd-17-5041-2024, https://doi.org/10.5194/gmd-17-5041-2024, 2024
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Earth system models often represent the land surface at smaller scales than the atmosphere, but surface–atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
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Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
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Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
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Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
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RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
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The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
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We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
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The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
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By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
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We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
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This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
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Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
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The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
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The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
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There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and 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.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
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This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
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We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
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This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
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Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
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In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
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We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 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 for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
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HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
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This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
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An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
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This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
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Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
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Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
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Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
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This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
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Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
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Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
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Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
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Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
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An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
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Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-68, https://doi.org/10.5194/gmd-2024-68, 2024
Revised manuscript accepted for GMD
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This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The equation terms in the governing equations that are sensitive (insensitive) to the precision level have been identified. The performance of mixed-precision computing for weather and climate simulations was analyzed.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
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In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
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.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-195, https://doi.org/10.5194/gmd-2023-195, 2024
Revised manuscript accepted for GMD
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The satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator highlighting its added value through a case study and diagnostics.
Cited articles
Adames, Á, F. and Kim, D.: The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations, J. Atmos. Sci., 73, 913–941, https://doi.org/10.1175/JAS-D-15-0170.1, 2016.
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The version-2 Global Precipitation
Climatology Project (GPCP) monthly precipitation analysis (1979–present), J.
Hydrometeorol., 4, 1147–1167, 2003.
Ahn, M.-S., Kim, D., Sperber, K. R., Kang, I.-S., Maloney, E., Waliser, D.,
and Hendon, H.: MJO simulation in CMIP5 climate models: MJO skill metrics
and process-oriented diagnosis, Clim. Dynam., 49, 4023–4045,
https://doi.org/10.1007/s00382-017-3558-4, 2017.
Barton, N. P., Klein, S. A., Boyle, J. S., and Zhang, Y.: Arctic synoptic
regimes: Comparing domain-wide Arctic cloud observations with CAM4 and CAM5
during similar dynamics, J. Geophys. Res., 117, D15205,
https://doi.org/10.1029/2012JD017589, 2012.
Barton, N. P., Klein, S. A., and Boyle, J. S.: On the contribution of
longwave radiation to global climate model biases in Arctic lower
tropospheric stability, J. Climate, 27, 7250–7269, https://doi.org/10.1175/JCLI-D-14-00126.1, 2014.
Berg, L. K. and Kassianoy, E. I.: Temporal variability of fair- weather
cumulus statistics at the ACRF SGP site, J. Climate, 21, 3344–3358, 2008.
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus,
R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H.,
Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity,
Nat. Geosci., 8, 261–268, https://doi.org/10.1038/NGEO2398,
2015.
Chandra, A. S., Zhang, C., Klein, S. A., and Ma, H.-Y.: Low-cloud
characteristics over the tropical western Pacific from ARM observations and
CAM5 simulations, J. Geophys. Res.-Atmos., 120, 8953–8970, https://doi.org/10.1002/2015JD023369, 2015.
Chen, W.-T., Wu, C.-M., and Ma, H.-Y.: Evaluating the bias of South China
Sea summer monsoon precipitation associated with fast physical processes
using climate model hindcast approach, J. Climate, 32, 4491–4507,
https://doi.org/10.1175/JCLI-D-18-0660.1, 2019.
Ciesielski, P. E., Johnson, R. H., Jiang, X., Zhang, Y., and Xie, S.:
Relationships between radiation, clouds, and convection during DYNAMO, J.
Geophys. Res.-Atmos., 122, 2529–2548, https://doi.org/10.1002/2016JD025965, 2017.
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R.
T., Miller, M., and Martner, B. E.: Objective determination of cloud heights
and radar reflectivities using a combination of active remote sensors at the
ARM CART sites, J. Appl. Meteorol., 39, 645–665, 2000.
Clothiaux, E. E., Miller, M. A., Perez, R. C., Turner, D. D., Moran, K. P.,
Martner, B. E., Ackerman, T. P., Mace, G. G., Marchand, R. T., Widener, K.
B., Rodriguez, D. J., Uttal, T., Mather, J. H., Flynn, C. J., Gaustad, K.
L., and Ermold, B.: The ARM millimeter wave cloud radars (MMCRs) and the
active remote sensing of clouds (ARSCL) value added product (VAP), U.S.
Department of Energy Tech. Memo. ARM VAP-002.1, 56 pp., 2001.
Covey, C., Gleckler, P. J., Doutriaux, C., Williams, D. N., Dai, A.,
Fasullo, J., Trenberth, K., and Berg, A.: Metrics for diurnal cycle of
precipitation: Toward routine benchmarks for climate models, J. Climate,
29, 4461–4471, 2016.
Dai, A.: Precipitation characteristics in eighteen coupled climate models,
J. Climate, 19, 4605–4630, 2006.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hoìlm, E. V., Isaksen, L., Kallberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Theìpaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.
Gates, W. L.: AMIP: The Atmospheric Model Intercomparison Project, B.
Am. Meteorol. Soc., 73, 1962–1970, 1992.
Gerrity, J. P. and McPherson, R. D.: Noise analysis of a limited-area
fine-mesh prediction model, ESSA Technical Memoranda, WBTM NMC 46,
PB-191-188, 81 pp., 1970.
Golaz, J., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H., Lin, W., Lipscomb, W. H., Ma, P., Mccoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019.
Guilyardi, E., Wittenberg, A., Fedorov, A., Collins, M., Wang, C.,
Capotondi, A., van Oldenborgh, G. J., and Stockdale, T.: Understanding El
Niño in ocean-atmosphere general circulation models, B. Am. Meteorol.
Soc., 90, 325–340, 2009.
Hagos, S. M., Zhang, C., Feng, Z., Burleyson, C. D., DeMott, C., Kerns, B.,
Benedict, J., and Martini, M.: The impact of the diurnal cycle on the
propagation of Madden-Julian Oscillation convection across the Maritime
Continent, J. Adv. Model. Earth Sy., 8, 1552–1564, https://doi.org/10.1002/2016MS000725, 2016.
Hannah, W. M. and Maloney, E. D.: The moist static energy budget in NCAR
CAM5 hindcasts during DYNAMO, J. Adv. Model. Earth Sy., 6, 420–440,
2014.
Jakob, C.: Cloud cover in the ECMWF reanalysis, J. Climate, 12, 947–959,
1999.
Jiang, X.: Key processes for the eastward propagation of the Madden-Julian
Oscillation based on multimodel simulations, J. Geophys. Res.-Atmos., 122, 755–770, https://doi.org/10.1002/2016JD025955, 2017.
Jiang, X., Lau, N.-C., and Klein, S. A.: Role of eastward propagating
convection systems in the diurnal cycle and seasonal mean of summertime
rainfall over the U.S. Great Plains, Geophys. Res. Lett., 33, L19809,
https://doi.org/10.1029/2006GL027022, 2006.
Jiang, X., Waliser, D. E., Olson, W. S., Tao, W.-K., L'Ecuyer, T. S., Shige,
S., Li, K.-F., Yung, Y. L., Lang, S., and Takayabu, Y. N.: Vertical diabatic
heating structure of the MJO: Intercomparison between recent reanalyses and
TRMM estimates, Mon. Weather Rev., 139, 3208–3223, 2011.
Jiang, X., Waliser, D. E., Xavier, P. K., Petch, J., Klingaman, N. P.,
Woolnough, S. J., Guan, B., Bellon, G., Crueger, T., DeMott, C., Hannay, C.,
Lin, H., Hu, W., Kim, D., Lappen, C.-L., Lu, M.-M., Ma, H.-Y., Miyakawa, T.,
Ridout, J. A., Schubert, S. D., Scinocca, J., Seo, K.-H., Shindo, E., Song,
X., Stan, C., Tseng, W.-L., Wang, W., Wu, T., Wu, X., Wyser, K., Zhang, G.
J., Zhu, H.: Vertical structure and physical processes of the Madden-Julian
oscillation: Exploring key model physics in climate simulations, J. Geophys.
Res.-Atmos., 120, 4718–4748, https://doi.org/10.1002/2014JD022375, 2015.
Johnson, R. H. and Ciesielski, P. E.: Structure and Properties of
Madden–Julian Oscillations Deduced from DYNAMO Sounding Arrays, J. Atmos.
Sci., 70, 3157–3179, https://doi.org/10.1175/JAS-D-13-065.1,
2013.
Kato, S., Loeb, N. G., Fred, F. G., Doelling, D. R., Rutan, D. A., Caldwell,
T. E., Yu, L., and Weller, R. A.: Surface irradiances consistent with
CERES-derived top-of-atmosphere shortwave and longwave irradiances, J.
Climate, 26, 2719–2740, https://doi.org/10.1175/JCLI-D-12-00436.1, 2013.
Klein, S. A., Jiang, X., Boyle, J. S., Malyshev, S., and Xie, S.: Diagnosis
of the summertime warm and dry bias over the U.S. Southern Great Plains in
the GFDL climate model using a weather forecasting approach, Geophys. Res.
Lett., 33, L18805, https://doi.org/10.1029/2006GL027567, 2006.
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J. S., and
Gleckler, P. J: Are climate model simulations of clouds improving? An
evaluation using the ISCCP simulator, J. Geophys. Res., 118, 1329–1342, https://doi.org/10.1002/jgrd.50141, 2013.
Klingaman, N. P., Woolnough, S. J., Jiang, X., Waliser, D., Xavier, P. K.,
Petch, J., Caian, M., Hannay, C., Kim, D., Ma, H.-Y., Merryfield, W. J.,
Miyakawa, T., Pritchard, M., Ridout, J. A., Roehrig, R., Shindo, E., Vitart,
F., Wang, H., Cavanaugh, N. R., Mapes, B. E., Shelly, A., and Zhang, G. J.:
Vertical structure and physical processes of the Madden–Julian oscillation:
Linking hindcast fidelity to simulated diabatic heating and moistening, J.
Geophys. Res.-Atmos., 120, 4690–4717, https://doi.org/10.1002/2014JD022374, 2015.
Lin, Y., Donner, L. J., Petch, J., Bechtold, P., Boyle J. S., Klein, S. A.,
Komori, T., Wapler, K., Willett, M., Xie, X., Zhao, M., Xie, S., McFarlane,
S. A., and Schumacher, C.: TWP-ICE global atmospheric model intercomparison:
convection responsiveness and resolution impact, J. Geophys. Res., 117, D09111, https://doi.org/10.1029/2011JD017018, 2012.
Loeb, G. N., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F.,
Kato, S., Manalo-Smith, N., and Wong, T.: Toward optimal closure of the
Earth's top-of-atmosphere radiation budget, J. Climate, 22, 748–766,
https://doi.org/10.1175/2008JCLI2637.1, 2009.
Ma, H.-Y.: A multi-year short-range hindcast experiment with CESM1, Lawrence Livermore National Laboratory, HPSS archive, available at: https://portal.nersc.gov/archive/home/h/hyma/www/CAPT/CAPT_Long, last access: 17 December 2020a.
Ma, H.-Y.: Initial conditions for the multi-year short-range hindcast experiment with CESM1, Lawrence Livermore National Laboratory, HPSS archive, available at: https://portal.nersc.gov/archive/home/h/hyma/www/CAPT/CAPT_Long/IC/, last access: 17 December 2020b.
Ma, H.-Y., Xie, S., Boyle, J. S., Klein, S. A., and Zhang, Y.: Metrics and
diagnostics for precipitation-related processes in climate model short-range
hindcasts, J. Climate, 26, 1516–1534, 2013.
Ma, H. Y., Xie, S., Klein, S. A., Williams, K. D., Boyle, J. S., Bony, S.,
Douville, H., Fermepin, S., Medeiros, B., Tyteca, S., and Watanabe, M.: On
the correspondence between mean forecast errors and climate errors in CMIP5
models, J. Climate, 27, 1781–1798, 2014.
Ma, H.-Y., Chuang, C. C., Klein, S. A., Lo, M.-H., Zhang, Y., Xie, S.,
Zheng, X., Ma, P.-L., Zhang, Y., and Phillips, T. J.: An improved hindcast
approach for evaluation and diagnosis of physical processes in global
climate models, J. Adv. Model. Earth Sy., 7, 1810–1827, https://doi.org/10.1002/2015MS000490, 2015.
Ma, H.-Y., Klein, S. A., and Xie, S.: Documentation for Multi-year (1997–2012) CAPT Hindcast Output, Lawrence Livermore National Laboratory, available at: https://portal.nersc.gov/archive/home/h/hyma/www/CAPT/CAPT_Long/CAPT_Long_output_cesm1_0_5_v5.pdf (last access: 17 December 2020), 2016.
Ma, H. Y., Klein, S. A., Xie, S., Zhang, C., Tang, S., Tang, Q., Morcrette,
C. J., Van Weverberg, K., Petch, J., Ahlgrimm, M., Berg, L. K., Cheruy, F.,
Cole, J., Forbes, R., Gustafson Jr., W. I., Huang, M., Liu, Y., Merryfield,
W., Qian, Y., Roehrig, R., and Wang, Y.-C.: CAUSES: On the role of surface
energy budget errors to the warm surface air temperature error over the Cen-
tral United States, J. Geophys. Res.-Atmos., 123, 2888–2909, https://doi.org/10.1002/2017JD027194, 2018.
Madden, R. A. and Julian, P. R.: Detection of a 40–50 day oscillation in
the zonal wind in the tropical Pacific, J. Atmos. Sci., 28, 702–708, 1971.
Madden, R. A. and Julian, P. R.: Description of global-scale circulation
cells in the tropics with a 40–50 day period, J. Atmos. Sci., 29,
1109–1123, 1972.
Medeiros, B., Williamson, D. L., Hannay, C., and Olson, J. G.: Southeast Pacific
stratocumulus in the Community Atmosphere Model, J. Climate, 25, 6175–6192,
2012.
Moncrieff,
M. W., Liu, C., and Bogenschutz, P.: Simulation, modeling, and
dynamically based parameterization of organized tropical convection for
global climate models, J. Atmos. Sci., 74, 1363–1380, 2017.
Morcrette, C. J., Van Weverberg, K., Ma, H. Y., Ahlgrimm, M., Bazile, E.,
Berg, L. K., Cheng, A., Cheruy, F., Cole, J., Forbes, R., and Gustafson Jr.,
W. I.: Introduction to CAUSES: Description of weather and climate models and
their near-surface temperature errors in 5 day hindcasts near the Southern Great Plains, J. Geophys. Res.-Atmos., 123, 2655–2683, https://doi.org/10.1002/2017JD027199, 2018.
National Center for Atmospheric Research (NCAR): CESM Models, UCAR/NCAR, Boulder, CO, available at: http://www.cesm.ucar.edu/models/cesm1.0/, last access: 17 December 2020a.
National Center for Atmospheric Research (NCAR): CESM Subversion input data repository, UCAR/NCAR, Boulder, CO, available at: https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/, last access: 17 December 2020b.
Neale, R. B., Chen, C. C., Gettelman, A., Lauritzen, P. H., Park, S.,
Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J. F.,
and Marsh, D.: Description of the NCAR community atmosphere model (CAM 5.0),
NCAR Tech. Note NCAR/TN-486+ STR, 2010.
NOAA Physical Sciences Laboratory: NOAA Optimum Interpolation (OI) Sea Surface Temperature (SST) V2, NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, available at: https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html, last access: 17 December 2020.
O'Brien, T. A., Collins, W. D., Kashinath, K., Rübel, O., Byna, S., Gu,
J., Krishnan, H., and Ullrich, P. A.: Resolution dependence of precipitation
statistical fidelity in hindcast simulations, J. Adv. Model. Earth Sy., 8,
976–990, https://doi.org/10.1002/2016MS000671, 2016.
Phillips, T. J., Potter, G. L., Williamson, D. L., Cederwall, R. T., Boyle,
J. S., Fiorino, M., Hnilo, J. J., Olson, J. G., Xie, S., and Yio, J. J.:
Evaluating parameterizations in general circulation models: Climate
simulation meets weather prediction, B. Am. Meteorol. Soc., 85,
1903–1915, 2004.
Phillips, T. J., Klein, S. A., Ma, H.-Y., Tang, Q., Xie, S., Williams, I.
N., Santanello, J. A., Cook, D. R., and Torn, M. S.: Using ARM Observations
to Evaluate Climate Model Simulations of Land-Atmosphere Coupling on the
U.S. Southern Great Plains, J. Geophys. Res.-Atmos., 122, 11524–11548.
https://doi.org/10.1002/2017JD027141, 2017.
Powell, S. W. and Houze, R. A.: The cloud population and onset of the
Madden-Julian Oscillation over the Indian Ocean during DYNAMO-AMIE, J.
Geophys. Res.-Atmos., 118, 11979–11995, https://doi.org/10.1002/2013JD020421, 2013.
Qin, Y., Lin, Y., Xu, S., Ma, H.-Y., and Xie, S.: A diagnostic PDF cloud scheme to improve subtropical low clouds in NCAR Community Atmosphere Model (CAM5), J. Adv. Model. Earth Sys., 10, 320–341, 2018.
Rasch, P., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S., Caldwell, P., Zhang, K., Easter, R., Cameron-Smith, P., Singh, B., Wan, H., Golaz, J.-C., Harrop, B., Roesler, E., Bacmeister, J., Larson, V., Evans, K., Qian, Y., Taylor, M., Leung, R., Zhang, Y., Brent, L., Branstettor, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J., Yoon, J., Zender, C., Bader, D., Flanner, M., Foucar, J., Jacob, R., Keen, N., Klein, S., Liu, X., Salinger, A., Shrivastava, M., and Yang, Y.: An Overview of the Atmospheric Component of the Energy Exascale Earth System Model, J. Adv. Model. Earth Syst., 11, 2377–2411, https://doi.org/10.1029/2019MS001629, 2019.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.:
An improved in situ and satellite SST analysis for climate, J. Climate, 15,
1609–1625, 2002.
Rossow, W. B. and Schiffer, R. A.: Advances in Understanding Clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2288, 1999.
Sobel, A. and Maloney, E.: An Idealized Semi-Empirical Framework for Modeling the Madden–Julian Oscillation, J. Atmos. Sci., 69, 1691–1705, https://doi.org/10.1175/jas-d-11-0118.1, 2012.
Sobel, A. and Maloney, E.: Moisture Modes and the Eastward Propagation of the MJO, J. Atmos. Sci., 70, 187–192, https://doi.org/10.1175/Jas-D-12-0189.1, 2013.
Sun, D.-Z., Thang, T., Covey, C., Klein, S. A., Collins, W. D., Hack, J. J.,
Kiehl, J. T., Meehl, G. A., Held, I. M., and Suarez, M.: Radiative and
dynamical feedbacks over the equatorial cold tongue: Results from nine
atmospheric GCMs, J. Climate, 19, 4059–4074, 2006.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, 2001.
Van Weverberg, K., Morcrette, C. J., Ma, H.-Y., Klein, S. A., and Petch, J.
C.: Using regime analysis to identify the contribution of clouds to surface
temperature errors in weather and climate models, Q. J. Roy. Meteor. Soc., 141, 3190–3206, https://doi.org/10.1002/qj.2603, 2015.
Van Weverberg, K., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H. Y.,
Zhang, C., Xie, S., Tang, Q., Gustafson Jr., W. I., Qian, Y., and Berg, L.
K.: CAUSES: Attribution of surface radia- tion biases in NWP and climate
models near the U.S. South- ern Great Plains, J. Geophys. Res.-Atmos., 123,
3612–3644, https://doi.org/10.1002/2017JD027188, 2018.
Wan, H., Rasch, P. J., Zhang, K., Qian, Y., Yan, H., and Zhao, C.: Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models, Geosci. Model Dev., 7, 1961–1977, https://doi.org/10.5194/gmd-7-1961-2014, 2014.
Wheeler, M. C. and Hendon, H. H.: An all-season real-time multivariate MJO
index: Development of an index for monitoring and prediction, Mon. Weather
Rev., 132, 1917–1932, 2004.
Williams, K. D., Bodas-Salcedo, A., Déqué, M., Fermepin, S.,
Medeiros, B., Watanabe, M., Jakob, C., Klein, S. A., Senior, C. A., and
Williamson, D. L.: The Transpose-AMIP II Experiment and Its Application to
the Understanding of Southern Ocean Cloud Biases in Climate Models, J.
Climate, 26, 3258–3274, 2013.
Xavier, P. K., Petch, J. C., Klingaman, N. P., Woolnough, S. J., Jiang, X.,
Waliser, D. E., Caian, M., Cole, J., Hagos, S. M., Hannay, C., and Kim,
D.: Vertical structure and physical processes of the Madden-Julian
Oscillation: Biases and uncertainties at short range, J. Geophys.
Res.-Atmos., 120, 4749–4763, https://doi.org/10.1002/2014JD022718, 2015.
Xie, S., Zhang, M. H., Boyle, J. S., Cederwall, R. T., Potter, G. L., and
Lin, W. Y.: Impact of a revised convective triggering mechanism on CAM2
model simulations: results from short-range weather forecasts, J. Geophys.
Res., 109, D14102, https://doi.org/10.1029/2004JD004692, 2004.
Xie, S., Boyle, J. S., Klein, S. A., Liu, X., and Ghan, S.: Simulations of
Arctic mixed-phase clouds in forecasts with CAM3 and AM2 for M-PACE, J.
Geophys. Res., 113, D04211, https://doi.org/10.1029/2007JD009225, 2008.
Xie, S., Ma, H.-Y., Boyle, J. S., Klein, S. A., and Zhang, Y.: On the
correspondence between short- and long- timescale systematic errors in
CAM4/CAM5 for the Years of Tropical Convection, J. Climate, 25, 7937–7955,
2012.
Xie, S., Lin, W., Rasch, P. J., Ma, P.-L., Neale, R., Larson, V. E., Qian, Y., Bogenschutz, P. A., Caldwell, P., Cameron-Smith, P., Golaz, J.-C., Mahajan, S., Singh, B., Tang, Q., Wang, H., Yoon, J.-H., Zhang, K., and Zhang, Y.: Understanding cloud and convective characteristics in version 1 of the E3SM atmosphere model, J. Adv. Model. Earth Sy., 10, 2618–2644, https://doi.org/10.1029/2018MS001350, 2018.
Xie, S., Wang, Y.-C., Lin, W., Ma, H.-Y., Tang, Q., Tang, S., Zheng, X., Golaz, J.-C., Zhang, G., and Zhang, M.:
Improved Diurnal Cycle of Precipitation in E3SM with a Revised Convective
Triggering Function, J. Adv. Model. Earth Sy., 11, 2290–2310,
https://doi.org/10.1029/2019MS001702, 2019.
Xie, S. C., McCoy, R. B., Klein, S. A., Cederwall, R. T., Wiscombe, W. J.,
Clothiaux, E. E., Gaustad, K. L., Golaz, J. C., Hall, S. D., Jensen, M. P.,
Johnson, K. L., Lin, Y. L., Long, C. N., Mather, J. H., McCord, R. A.,
McFarlane, S. A., Palanisamy, G., Shi, Y., and Turner, D. D. D.: Arm Climate
Modeling Best Estimate Data a New Data Product for Climate Studies, B. Am.
Meteorol. Soc., 91, 13–20, https://doi.org/10.1175/2009bams2891.1, 2010.
Xu, W. and Rutledge, S. A.: Convective characteristics of the
Madden–Julian Oscillation over the Central Indian Ocean observed by
shipborne radar during DYNAMO, J. Atmos. Sci., 71, 2859–2877, 2014.
Yanai, M., Esbensen, S., and Chu, J.-H.: Determination of bulk properties
of tropical cloud clusters from large-scale heat and moisture budgets, J.
Atmos. Sci., 30, 611–627, 1973.
Yang, F., Pan, H., Krueger, S. K., Moorthi, S., and Lord, S. J.: Evaluation
of the NCEP Global Forecast System at the ARM SGP site, Mon. Weather Rev., 134, 3668–3690, 2006.
Zhang, C.: Madden–Julian Oscillation: Bridging weather and climate, B.
Am. Meteorol. Soc., 94, 1849–1870, https://doi.org/10.1175/bams-d-12-00026.1, 2013.
Zhang, C. and Ling, J.: Barrier effect of the Indo-Pacific Maritime
Continent on the MJO: Perspectives from tracking MJO precipitation, J.
Climate, 30, 3439–3459, 2017.
Zhang, K., Wan, H., Liu, X., Ghan, S. J., Kooperman, G. J., Ma, P.-L., Rasch, P. J., Neubauer, D., and Lohmann, U.: Technical Note: On the use of nudging for aerosol–climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631–8645, https://doi.org/10.5194/acp-14-8631-2014, 2014.
Zhang, M., Xie, S., Liu, X., Lin, W., Zhang K., Ma, H.-Y., Zheng, X., and Zhang, Y.: Toward Understanding the Simulated Phase Partitioning of Arctic
Single-Layer Mixed-Phase Clouds in E3SM, Earth Space Sci., 7, e2020EA001125,
https://doi.org/10.1002/essoar.10502164.1, 2020.
Zhang, Y. and Klein, S. A.: Mechanisms affecting the transition from
shallow to deep convection over land: Inferences from observations of the
diurnal cycle collected at the ARM Southern Great Plains Site, J. Atmos.
Sci., 67, 2943–2959, 2010.
Zhang, Y., Xie, S., Klein, S. A., Marchand, R., Kollias, P., Clothiaux, E.
E., Lin, W., Johnson, K., Swales, D., Bodas-Salcedo, A., Tang, S., Haynes,
J. M., Collis, S., Jensen, M., Bharadwaj, N., Hardin, J., and Isom, B.: The
ARM cloud radar simulator for global climate models: Bridging field data and
climate models, B. Am. Meteorol. Soc., 99, 21–26, https://doi.org/10.1175/BAMS-D-16-0258.1, 2018.
Zhang, Y., Xie, S., Lin, W., Klein, S. A., Zelinka, M. D., Ma, P.-L., Rasch,
P. J., Qian, Y., Tang, Q., and Ma, H.-Y.: Evaluation of clouds in version 1
of E3SM Atmosphere Model with satellite simulators, J. Adv. Model. Earth
Sy., 11, 1253–1268, https://doi.org/10.1029/2018MS001562, 2019.
Zheng, X., Klein, S. A., Ma, H.-Y., Bogenschutz, P., Gettelman, A., and
Larson, V. E.: Assessment of marine boundary layer cloud simulations in the
Community Atmosphere Model with Cloud Layers Unified By Binormals and
updated microphysics scheme based on ARM observations from the Azores, J.
Geophys. Res.-Atmos., 121, 8472–8492, https://doi.org/10.1002/2016JD025274, 2016.
Zheng, X., Klein, S. A., Ma, H.-Y., Caldwell, P. M., Larson, V. E.,
Gettelman, A., and Bogenschutz, P.: A cloudy planetary boundary layer
oscillation arising from the coupling of turbulence with precipitation in
climate simulations, J. Adv. Model. Earth Sy., 9, 1973–1993, https://doi.org/10.1002/2017MS000993, 2017.
Short summary
We propose an experimental design of a suite of multi-year, short-term hindcasts and compare them with corresponding observations or measurements for periods based on different weather and climate phenomena. This atypical way of evaluating model performance is particularly useful and beneficial, as these hindcasts can give scientists a robust picture of modeled precipitation, and cloud and radiation processes from their diurnal variation to year-to-year variability.
We propose an experimental design of a suite of multi-year, short-term hindcasts and compare...
