Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4443-2020
© Author(s) 2020. 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-13-4443-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
23 Sep 2020
Model description paper |
| 23 Sep 2020
| 23 Sep 2020
The E3SM version 1 single-column model
Peter A. Bogenschutz
CORRESPONDING AUTHOR
Lawrence Livermore National Laboratory, Livermore, CA, USA
Shuaiqi Tang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Peter M. Caldwell
Lawrence Livermore National Laboratory, Livermore, CA, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
Wuyin Lin
Brookhaven National Laboratory, Upton, NY, USA
Yao-Sheng Chen
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
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Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
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Maria J. Chinita, Mikael Witte, Marcin J. Kurowski, Joao Teixeira, Kay Suselj, Georgios Matheou, and Peter Bogenschutz
Geosci. Model Dev., 16, 1909–1924, https://doi.org/10.5194/gmd-16-1909-2023, https://doi.org/10.5194/gmd-16-1909-2023, 2023
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Peter A. Bogenschutz, Hsiang-He Lee, Qi Tang, and Takanobu Yamaguchi
Geosci. Model Dev., 16, 335–352, https://doi.org/10.5194/gmd-16-335-2023, https://doi.org/10.5194/gmd-16-335-2023, 2023
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Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
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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
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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.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
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We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Aurore Voldoire, Romain Roehrig, Hervé Giordani, Robin Waldman, Yunyan Zhang, Shaocheng Xie, and Marie-Nöelle Bouin
Geosci. Model Dev., 15, 3347–3370, https://doi.org/10.5194/gmd-15-3347-2022, https://doi.org/10.5194/gmd-15-3347-2022, 2022
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A single-column version of the global climate model CNRM-CM6-1 has been designed to ease development and validation of the model physics at the air–sea interface in a simplified environment. This model is then used to assess the ability to represent the sea surface temperature diurnal cycle. We conclude that the sea surface temperature diurnal variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m.
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.
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
Hsi-Yen Ma, Chen Zhou, Yunyan Zhang, Stephen A. Klein, Mark D. Zelinka, Xue Zheng, Shaocheng Xie, Wei-Ting Chen, and Chien-Ming Wu
Geosci. Model Dev., 14, 73–90, https://doi.org/10.5194/gmd-14-73-2021, https://doi.org/10.5194/gmd-14-73-2021, 2021
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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.
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
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.
Peter A. Bogenschutz, Andrew Gettelman, Cecile Hannay, Vincent E. Larson, Richard B. Neale, Cheryl Craig, and Chih-Chieh Chen
Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, https://doi.org/10.5194/gmd-11-235-2018, 2018
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This paper compares results of developmental versions of a widely used climate model. The simulations only differ in the choice of how to model the sub-grid-scale physics in the atmospheric model. This work is novel because it is the first time that a particular physics option has been tested in a fully coupled climate model. Here, we demonstrate that this physics option has the ability to produce credible coupled climate simulations, with improved metrics in certain fields.
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.
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.
K. Thayer-Calder, A. Gettelman, C. Craig, S. Goldhaber, P. A. Bogenschutz, C.-C. Chen, H. Morrison, J. Höft, E. Raut, B. M. Griffin, J. K. Weber, V. E. Larson, M. C. Wyant, M. Wang, Z. Guo, and S. J. Ghan
Geosci. Model Dev., 8, 3801–3821, https://doi.org/10.5194/gmd-8-3801-2015, https://doi.org/10.5194/gmd-8-3801-2015, 2015
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This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that is implemented in CAM v5.3. We show mean climate and tropical variability results from global simulations. The model has a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. We also show estimation of computational expense and sensitivity to number of subcolumns.
B. Lebassi-Habtezion and P. M. Caldwell
Geosci. Model Dev., 8, 817–828, https://doi.org/10.5194/gmd-8-817-2015, https://doi.org/10.5194/gmd-8-817-2015, 2015
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In this study we explore the problem in running default CAM5-SCM, which initializes the aerosol to zero, and test three potential fixes in four different cloud regimes: DYCOMSRF02, MPACE-B, RICO, and ARM95. Stratiform cloud cases (DYCOMS RF02 and MPACE-B) were found to have a strong dependence on aerosol concentration, while convective cases (RICO and ARM95) were relatively insensitive to aerosol specification.
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
Related subject area
Climate and Earth system modeling
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0
Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Introducing the MESMER-M-TPv0.1.0 module: Spatially Explicit Earth System Model Emulation for Monthly Precipitation and Temperature
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
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cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
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A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
Coupling the regional climate model ICON-CLM v2.6.6 into the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
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We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
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A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
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Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
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Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
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We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
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Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
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Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
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The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
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We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
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Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
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Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
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Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
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The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
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Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
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We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
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The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleußner
EGUsphere, https://doi.org/10.5194/egusphere-2024-278, https://doi.org/10.5194/egusphere-2024-278, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Their joint distribution largely determines the division into climate regimes. Yet, projecting precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows to generate monthly means of local precipitation and temperature at low computational costs.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
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In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
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We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
EGUsphere, https://doi.org/10.5194/egusphere-2024-923, https://doi.org/10.5194/egusphere-2024-923, 2024
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The regional Earth system model GCOAST-AHOI version 2.0 including the regional climate model ICON-CLM coupled with the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in the ICON-CLM model makes it more flexible to couple with an external ocean model and an external hydrological discharge model.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-362, https://doi.org/10.5194/egusphere-2024-362, 2024
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In this study, we improve an existing climate model to account for human water usage across domestic, industrial, and agriculture purposes. With the new capabilities, the model is now better equipped for studying questions related to water scarcity in both present and future conditions under climate change. Despite the advancements, there remains important limitations in our modelling framework which requires further work.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Cited articles
Ackerman, A. S., VanZanten, M. C., Stevens, B., Savic-Jovcic, V., Bretherton, C. S., Chlond, A., Golaz, J., Jiang, H., Khairoutdinov, M., Krueger, S. K., Lewellen D. C., Lock A., Moeng C., Nakamura, K., Petters, M. D., Snider, J. R., Weinbrecht, S., and Zulauf, M.: Large-Eddy Simulations of a Drizzling, Stratocumulus-Topped Marine Boundary Layer, Mon. Weather Rev., 137, 1083–1110, https://doi.org/10.1175/2008MWR2582.1, 2009.
Betts, A. K. and Miller, M. J.: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets, Q. J. Roy. Meteor. Soc., 112, 693–709, https://doi.org/10.1002/qj.49711247308, 1986.
Bogenschutz, P. A.: The E3SM version 1 Single Column Model code, Zenodo, https://doi.org/10.5281/zenodo.3742207, 2020a.
Bogenschutz, P. A.: E3SM single column case library, available at: https://github.com/E3SM-Project/scmlib/wiki/E3SM-Single-Column-Model-Case-Library, last access: 3 March 2020b.
Bogenschutz, P. A. and Krueger, S. K.: A simplified PDF parameterization of subgrid-scale clouds and turbulence for cloud-resolving models, J. Adv. Model. Earth Sy., 5, 195–211, https://doi.org/10.1002/jame.20018, 2013.
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Schanen, D. P., Meyer, N. R., and Craig, C.: Unified parameterization of the planetary boundary layer and shallow convection with a higher-order turbulence closure in the Community Atmosphere Model: single-column experiments, Geosci. Model Dev., 5, 1407–1423, https://doi.org/10.5194/gmd-5-1407-2012, 2012.
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., and Schanen, D. P.: Higher-Order Turbulence Closure and Its Impact on Climate Simulations in the Community Atmosphere Model, J. Climate, 26, 9655–9676, https://doi.org/10.1175/JCLI-D-13-00075.1, 2013.
Brown, A. R., Cederwall, R. T., Chlond, A., Duynkerke, P. G., Golaz, J. C., Khairoutdinov, M., Lewellen, D. C., Lock, A. P., MacVean, M. K., Moeng, C.-H., Neggers, R. A. J., Siebesma, A. P., and Stevens, B.: Large-eddy simulation of the diurnal cycle of shallow cumulus convection over land, Q. J. Roy. Meteor. Soc., 128, 1075–1093, https://doi.org/10.1256/003590002320373210, 2002.
Caldwell, P. M., Mametjanov, A., Tang, Q., Van Roekel, L. P., Golaz, J.‐C., Lin, W.,
Bader, D. C., Keen, N. D., Feng, Y., Jacob, R., Maltrud, M. E., Roberts, A. F., Tayler, M. A.,
Veneziani, M., Wang, H., Wolfe, J. D., Balaguru, K., Cameron-Smith, P., Dong, L., Klein, S. A.,
Leung, L., Li, H. Y., Li, Q. L., Liu, X., Neale, R. B., Pinheiro, M., Qian, Y., Ullrich, P. A., Xie,
S., Yang, Y., Zhang, Y., Zhang, K., and Zhou, T.: The DOE E3SM coupled model version 1:
Description and results at high resolution, J. Adv. Model. Earth Syst., 11,
4095–4146, https://doi.org/10.1029/2019MS001870, 2019.
Covey, C., Gleckler, P. J., Doutriaux, C., Williams, D. N., Dai, A., Fasullo, J., Trenberth, K., and Berg, A.: Metrics for the Diurnal Cycle of Precipitation: Toward Routine Benchmarks for Climate Models, J. Climate, 29, 4461–4471, https://doi.org/10.1175/JCLI-D-15-0664.1, 2016.
Dai, A. and Trenberth K. E.: The Diurnal Cycle and Its Depiction in the Community Climate System Model, J. Climate, 17, 930–951, https://doi.org/10.1175/1520-0442(2004)017<0930:TDCAID>2.0.CO;2, 2004.
Dennis, J. M., Edwards, J., Evans, K. J., Guba, O., Lauritzen, P. H., Mirin, A. A., and Worley, P. H.: CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model, Int. J. High Perform. C., 26, 74–89, https://doi.org/10.1177/1094342011428142, 2012.
Gettelman, A., Liu, X., Ghan, S. J., Morrison, H., Park, S., Conley, A. J., Klein, S. A., Boyle, J., Mitchell, D. L., and Li, J.-L. F.:, Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model, J. Geophys. Res., 115, D18216, https://doi.org/10.1029/2009JD013797, 2010.
Gettelman, A., Truesdale, J. E., Bacmeister, J. T., Caldwell, P. M., Neale, R. B., Bogenschutz, P. A., and Simpson, I. R.: The Single Column Atmosphere Model version 6 (SCAM6): Not a scam but a tool for model evaluation and development, J. Adv. Model. Earth Sy., 11, 1381–1401, https://doi.org/10.1029/2018MS001578, 2019.
Ghan, S., Randall, D., Xu, K.-M., Cederwall, R., Cripe, D., Hack, J., Iacobellis, S., Klein,
S., Krueger, S., Lohman, U., Pedretti, J., Robock, A., Rotstayn, L., Somerville, R., Stenchikov,
G., Sud, Y., Walker, G., Xie, S., Yio, J., and Zhang, M.: A comparison of single column model simulations of summertime midlatitude continental convection, J. Geophys. Res.-Atmos., 105, 2091–2124, https://doi.org/10.1029/1999JD900971, 2000.
Golaz, J., Larson, V. E., and Cotton, W. R.: A PDF-Based Model for Boundary Layer Clouds. Part I: Method and Model Description, J. Atmos. Sci., 59, 3540–3551, https://doi.org/10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2, 2002.
Golaz, J.-C., Caldwell, P. M., Roekel, L. P., Peterson, M. P., Tang, Q., Wolfe, J., Abeshu,
G., Anatharaj, V., Asay-Davis, X., Bader, D., Baldwin, S., Bisht, G., Bogenschutz, P.,
Branstetter, M., Brunke, M., Brus, S., Burrows, S., Cameron-Smith, P., Donahue, A., Deakin,
M., Easter, R., Evans, K., Yeng, F., Flanner, M., Foucar, J., Fyke, J., Griffin, B., Hannay, C.,
Harrop, B., Hoffman, M., Hunke, E., Jacob, R., Jacobsen, D., Jeffery, N., Jones, P., Keen, N.,
Klein, S., Larson, V., Leung, L., Li, H., Lin, W., Lipscomb, W., Ma, P., Mahajan, S., Maltrud,
M., Mametjanov, A., McClean, J., McCoy, R., Neale, R., Price, S., Qian, S., Rasch, P., Eyre, J.
E., Riley, W., Ringler, T., Roberts, A., Roesler, E., Salinger, A., Shaheen, Z., Shi, X., Singh, B.,
Tang, J., Taylor, M., Thorton, P., Turner, A., Veneziani, M., Wan, H., Wang, H., Wang, S.,
Williams, D., Wolfram, P., Worley, P., Xie, S., Yang, Y., Yoon, J., Zelinka, M., Zender, C.,
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.
Guo, Z., Wang, M., Qian, Y., Larson, V. E., Ghan, S., Ovchinnikov, M., Bogenschutz, P. A., Gettelman, A., and Zhou, T.: Parametric behaviors of CLUBB in simulations of low clouds in the Community Atmosphere Model (CAM), J. Adv. Model. Earth Sy., 7, 1005–1025, https://doi.org/10.1002/2014MS000405, 2015.
Hartung, K., Svensson, G., Struthers, H., Deppenmeier, A.-L., and Hazeleger, W.: An EC-Earth coupled atmosphere–ocean single-column model (AOSCM.v1_EC-Earth3) for studying coupled marine and polar processes, Geosci. Model Dev., 11, 4117–4137, https://doi.org/10.5194/gmd-11-4117-2018, 2018.
Holland, J. Z. and Rasmusson, E. M.: Measurements of the atmospheric mass, energy, and momentum budgets over a 500-kilometer square of tropical ocean, Mon. Weather Rev, 101, 44–55, https://doi.org/10.1175/1520-0493(1973)101<0044:MOTAME>2.3.CO;2, 1973.
Houze, R. A. and Betts, A. K.: Convection in GATE, Rev. Geophys., 19, 541–576, https://doi.org/10.1029/RG019i004p00541, 1981.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013.
Jensen, M. P., Petersen, W. A., Bansemer, A., Bharadwaj, N., Carey, L. D., Cecil, D. J., Collis, S. M., Del Genio, A. D., Dolan, B., Gerlach, J., Giangrande, S. E., Heymsfield, A., Heymsfield, G., Kollias, P., Lang, T. J., Nesbitt, S. W., Neumann, A., Poellot, M., Rutledge, S. A., Schwaller, M., Tokay, A., Williams, C. R., Wolff, D. B., Xie, S., and Zipser, E. J.: The Midlatitude Continental Convective Clouds Experiment (MC3E), B. Am. Meteorol. Soc., 97, 1667–1686, https://doi.org/10.1175/BAMS-D-14-00228.1, 2016.
Kato, S., Sun-Mack, S., Miller, W. F., Rose, F. G., Chen, Y., Minnis, P., and Wielicki, B. A.: Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles, J. Geophys. Res., 115, D00H28, https://doi.org/10.1029/2009JD012277, 2010.
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A., Boer, G. D., Chen, M., Cole, J. N. S., Del Genio, A. D., Falk, M., Foster, M. J., Fridlind, A., Golaz, J. C., Hashino, T., Harrington, J. Y., Hoose, C., Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M., Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M., Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C., Turner, D. D., Veron, D.E., Salzen, K. V., Walker, G. K., Wang, Z., Wolf, A. B., Xie, S., Xu, K. M., Yang, F., and Zhang, G.: Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. I: single-layer cloud, Q. J. Roy. Meteor. Soc., 135, 979–1002, https://doi.org/10.1002/qj.416, 2009.
Lebassi-Habtezion, B. and Caldwell, P. M.: Aerosol specification in single-column Community Atmosphere Model version 5, Geosci. Model Dev., 8, 817–828, https://doi.org/10.5194/gmd-8-817-2015, 2015.
Lee, M.-I., Schubert, S. D., Suarez, M. J., Bell, T. L., and Kim, K.-M.: Diurnal cycle of precipitation in the NASA Seasonal to Interannual Prediction Project atmospheric general circulation model, J. Geophys. Res., 112, D16111, https://doi.org/10.1029/2006JD008346, 2007.
Lee, M.-I., Schubert, S. D., Suarez, M. J., Schemm, J.-K. E., Pan, H.-L., Han, J., and Yoo, S.-H.: Role of convection triggers in the simulation of the diurnal cycle of precipitation over the United States Great Plains in a general circulation model, J. Geophys. Res., 113, D02111, https://doi.org/10.1029/2007JD008984, 2008.
Lin, S.-J. and Rood, R. B.: An explicit flux-form semi-lagrangian shallowwater model on the sphere, Q. J. Roy. Meteor. Soc.,123, 2531–2533, 1997.
Liu, J., Zheng, J., Li, Z., and Cribb, M.: Analysis of cloud condensation nuclei properties at a polluted site in southeastern China during the AMF-China Campaign, J. Geophys. Res.-Atmos., 116, D00K35, https://doi.org/10.1029/2011jd016395, 2011.
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016.
Ma, H.-Y., Chuang, C. C., Klein, S. A., Lo, M.-H., Zhang, Y., Xie, S., Zhang, 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.
Mace, J., Jenson, E., McFarquhar, G., Comstock, J. Ackerman, T., Mitchell, D., Liu, X., and Garrett, T.: SPARTICUS: Small particles in cirrus science and operations plans, 33, U.S. Dep. of Energy, Washington, D. C., available at: https://www.arm.gov/publications/programdocs/doe-sc-arm-10-003.pdf (last access: October 2019), 2009.
Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch, G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785–4797, https://doi.org/10.5194/acp-16-4785-2016, 2016.
May, P. T., Mather, J. H., Vaughan, G., Jakob, C., McFarquhar, G. M., Bower, K. N., and Mace, G. G.: The Tropical Warm Pool International Cloud Experiment, B. Am. Meteorol. Soc., 89, 629–646, https://doi.org/10.1175/BAMS-89-5-629, 2008.
McFarquhar, G. M., Ghan, S., Verlinde, J., Korolev, A., Strapp, J. W., Schmid, B., Tomlinson, J. M., Wolde, M., Brooks, S. D., Cziczo, D., Dubey, M. K., Fan, J. W., Flynn, C., Gultepe, I., Hubbe, J., Gilles, M. K., Laskin, A., Lawson, P., Leaitch, W. R., Liu P., Liu, X. H., Lubin, D., Mazzoleni, C., Macdonald, A. M., Moffet, R. C., Morrison, H., Ovchinnikov, M., Shupe, M. D., Turner, D. D., Xie, S., Zelenyuk, A., Bae, K., Freer, A., and Glen, A.: Indirect and Semi-direct Aerosol Campaign: The Impact of Arctic Aerosols on Clouds, B. Am. Meteorol. Soc., 92, 183–201, https://doi.org/10.1175/2010bams2935.1, 2011.
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., Marsh, D., Mills, M., Smith, A. K.,
Tilmes, S., Vitt F., Morrison, H., Cameron-Smith, P., Collins, W., Iacono, M. J., Easter, R. C.,
Ghan, S. J., Liu, X., Rasch, P. J., and Taylor, M. A.: Description of the NCAR Community Atmosphere Model (CAM 5.0), NCAR Technical Note, 289 pp., available at: https://www.cesm.ucar.edu/models/atm-cam/docs/description/description.pdf, last access: November 2012.
Park, S.: A Unified Convection Scheme (UNICON) Part I: Formulation, J. Atmos. Sci., 71, 3902–3930, https://doi.org/10.1175/JAS-D-13-0233.1, 2014.
Randall, D. A. and Cripe, D. G.: Alternative methods for specification of observed forcing in single-column models and cloud system models, J. Geophys. Res.-Atmos., 104, 24527–24545, https://doi.org/10.1029/1999JD900765, 1999.
Randall, D. A., Xu, K., Somerville, R. J., and Iacobellis, S.: Single-Column Models and Cloud Ensemble Models as Links between Observations and Climate Models, J. Climate, 9, 1683–1697, https://doi.org/10.1175/1520-0442(1996)009<1683:SCMACE>2.0.CO;2, 1996.
Rasch, P. J., 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, L., Zhang, Y., Brent, L.,
Branstetter, M., Hannay, C., Mahajan S., Mametjanov, A., Neale, R., Richter, J., Yoon, J-H.,
Zender, C., Bader, B., Flanner, 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 Sy., 11, 2377–2411, https://doi.org/10.1029/2019MS001629, 2019.
Rauber, R. M., Stevens, B., Ochs, H. T., Knight, C., Albrecht, B. A., Blyth, A. M., Fairall, C. W., Jensen, J. B., Lasher-Trapp, S. G., Mayol-Bracero, O. L., Vali, G., Anderson, J. R., Baker, B. A., Bandy, A. R., Burnet, E., Brenguier, J., Brewer, W. A., Brown, P. R., Chuang, R., Cotton, W. R., Di Girolamo, L., Geerts, B., Gerber, H., Goke, S., Gomes, L., Heikes, B. G., Hudson, J. G., Kollias, P., Lawson, R. R., Krueger, S. K., Lenschow, D. H., Nuijens, L., O'Sullivan, D. W., Rilling, R. A., Rogers, D. C., Siebesma, A. P., Snodgrass, E., Stith, J. L., Thornton, D. C., Tucker, S., Twohy, C. H., and Zuidema, P.: Rain in Shallow Cumulus Over the Ocean: The RICO Campaign, B. Am. Meteorol. Soc., 88, 1912–1928, https://doi.org/10.1175/BAMS-88-12-1912, 2007.
Schubert, W. H., Wakefield, J. S., Steiner, E. J., and Cox, S. K.: Marine Stratocumulus Convection. Part I: Governing Equations and Horizontally Homogeneous Solutions, J. Atmos. Sci., 36, 1286–1307, https://doi.org/10.1175/1520-0469(1979)036<1286:MSCPIG>2.0.CO;2, 1979.
Siebesma, A. P., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J., Duynkerke, P. G., Jiang, H., Khairoutdinov, M., Lewellen, D., Moeng, C.-H., Sanchez E., Stevens, B., and Stevens, D. E.: A Large Eddy Simulation Intercomparison Study of Shallow Cumulus Convection, J. Atmos. Sci., 60, 1201–1219, https://doi.org/10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2, 2003.
Smalley, M., Suselj, K., Lebsock, M., and Teixeira, J.: A novel framework for evaluating and improving parameterized subtropical marine boundary layer clouds, Mon. Weather Rev., 147, 3241–3260, https://doi.org/10.1175/MWR-D-18-0394.1, 2019.
Stevens, B., Ackerman, A. S., Albrecht, B. A., Brown, A. R., Chlond, A., Cuxart, J., Duynkerke, P. G., Lewellen, D. C., Macvean, M. K., Neggers, R. A., Sanchez, E., Siebesma, A. P., and Stevens, D. E.: Simulations of Trade Wind Cumuli under a Strong Inversion, J. Atmos. Sci., 58, 1870–1891, https://doi.org/10.1175/1520-0469(2001)058<1870:SOTWCU>2.0.CO;2, 2001.
Stevens, B., Moeng, C.-H., Ackerman, A. S., Bretherton, C. S., Chlond, A., De Roode, S., Edwards, J., Golaz, J.-C., Jiang, H., Khairoutdinov, M., Kirkpatrick, M. P., Lewellen, D. C., Lock, A., Muller, F., Stevens, D. E., Whelan, E., and Zhu, P.: Evaluation of Large-Eddy Simulations via Observations of Nocturnal Marine Stratocumulus, Mon. Weather Rev., 133, 1443–1462, https://doi.org/10.1175/MWR2930.1, 2005.
Suselj, K., Teixeira, J., and Chung, D.: A Unified Model for Moist Convective Boundary Layers Based on a Stochastic Eddy-Diffusivity/Mass-Flux Parameterization, J. Atmos. Sci., 70, 1929?1953, https://doi.org/10.1175/JAS-D-12-0106.1, 2013.
Tang, S.: E3SM single column model case output, available at: https://portal.nersc.gov/project/mp193/sqtang/E3SM_SCM_runs/, last access: 17 October 2019.
Tang, S., Xie, S., Zhang, Y., Zhang, M., Schumacher, C., Upton, H., Jensen, M. P., Johnson, K. L., Wang, M., Ahlgrimm, M., Feng, Z., Minnis, P., and Thieman, M.: Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment, Atmos. Chem. Phys., 16, 14249–14264, https://doi.org/10.5194/acp-16-14249-2016, 2016.
Tang, S., Xie, S., Zhang, M., Tang, Q., Zhang, Y., Klein, S. A., Cook, D., and Sullivan, R.
C.: Differences in Eddy-Correlation and Energy-Balance Surface Turbulent Heat Flux Measurements and Their Impacts on the Large-Scale Forcing Fields at the ARM SGP Site, J. Geophys. Res.-Atmos., 124, 3301–3318, https://doi.org/10.1029/2018JD029689, 2019.
Verlinde, J., Harrington, J. Y., McFarquhar, G. M., Yannuzzi, V. T., Avramov, A., Greenberg, S., Johnson, N., Zhang, G., Poellot, M. R., Mather, J. H., Turner, D. D., Eloranta, E. W., Zak, B. D., Prenni, A. J., Daniel, J. S., Kok, G. L., Tobin, D. C., Holz, R., Sassen, K., Spangenberg, D., Minnis, P., Tooman, T. P., Ivey, M. D., Richardson, S. J., Bahrmann, C. P., Shupe, M., DeMott, P. J., Heymsfield, A. J., and Schofield, R.: The Mixed-Phase Arctic Cloud Experiment, B. Am. Meteorol. Soc., 88, 205–222, https://doi.org/10.1175/BAMS-88-2-205, 2007.
Vogelmann, A. M., McFarquhar, G. M., Ogren, J. A., Turner, D. D., Comstock, J. M., Feingold, G., Long, C. N., Jonsson, H. H., Bucholtz, A., Collins, D. R., Diskin, G. S., Gerber, H., Lawson, R. P., Woods, R. K., Andrews, E., Yang, H., Chiu, J. C., Hartsock, D., Hubbe, J. M., Lo, C., Marshak, A., Monroe, J. W., McFarlane, S. A., Schmid, B., Tomlinson, J. M., and Toto, T.: RACORO Extended-Term Aircraft Observations of Boundary Layer Clouds, B. Am. Meteorol. Soc., 93, 861–878, https://doi.org/10.1175/BAMS-D-11-00189.1, 2012.
Webster, P. J. and Lukas, R.: TOGA COARE: The Coupled Ocean-Atmosphere Response Experiment, B. Am. Meteorol. Soc., 73, 1377–1416, https://doi.org/10.1175/1520-0477(1992)073<1377:TCTCOR>2.0.CO;2, 1992.
Xie, S., Xu, K.-M., Cederwall, R. T., Bechtold, P., Genio, A. D., Klein, S. A., Cripe, D., Ghan, S. J., David, G., Iacobellis, S. F., Krueger, S. K., Lohmann, U., Petch, J. C., Randall, D. A., Rotstayn, L. D., Sommerville, C. J., Yogesh, C., Salzen, K., Walker, G. K., Wolf, A., Yio, J. J., Zhang, G., and Zhang, M.: Intercomparison and Evaluation of Cumulus Parameterization under Summertime Midlatitude Continental Conditions, Q. J. Roy. Meteor. Soc., 128, 1095–1136, 2002.
Xie, S., Cederwall, R. T., and Zhang, M.: Developing long-term single-column model/cloud system-resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations, J. Geophys. Res., 109, D01104, https://doi.org/10.1029/2003JD004045, 2004.
Xie, S., Klein, S., Zhang, M., Yio, J., Cederall, R., and McCoy, R.: Developing large-scale forcing data for single-column and cloud-resolving models from the Mixed-Phase Arctic Cloud Experiment, J. Geophys. Res., 111, D19104, https://doi.org/10.1029/2005JD006950, 2006.
Xie, S., Hume, T., Jakob, C., Klein, S., McCoy, R., and Zhang, M.: Observed large-scale structures and diabatic heating and drying profiles during TWP-ICE, J. Climate, 23, 57–79, https://doi.org/10.1175/2009JCLI3071.1, 2010.
Xie, S., Zhang, Y., Giangrande, S. E., Jensen, M. P., McCoy, R., and Zhang, M.: Interactions between cumulus convection and its environment as revealed by the MC3E sounding array, J. Geophys. Res.-Atmos., 119, 11784–11808, https://doi.org/10.1002/2014JD022011, 2014.
Xie, S., Lin, W., Rasch, P. J., Ma, P.-L., Neale, R., Larson, V. E., et al.: Understanding cloud and convective characteristics in version 1 of the E3SM atmosphere model, J. Adv. Model. Earth Syst., 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. J., 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.
Yoneyama, K., Zhang, C., and Long, C. N.: Tracking Pulses of the Madden-Julian Oscillation, B. Am. Meteorol. Soc., 94, 1871–1891, https://doi.org/10.1175/BAMS-D-12-00157.1, 2013.
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. and Bretherton, C.: Mechanisms of Low Cloud-Climate Feedback in Idealized Single-Column Simulations with the Community Atmospheric Model, Version 3 (CAM3), J. Climate, 21, 4859–4878, https://doi.org/10.1175/2008JCLI2237.1, 2008.
Zhang, M. H. and Lin, J. N.: Constrained Variational Analysis of Sounding Data Based on Column-Integrated Budgets of Mass, Heat, Moisture, and Momentum: Approach and Application to ARM Measurements, J. Atmos. Sci., 54, 1503–1524, https://doi.org/10.1175/1520-0469(1997)054<1503:CVAOSD>2.0.CO;2, 1997.
Zhang, Y., Xie, S., Lin, W., Klein, S. A., Zelinka, M., Ma, P.‐L., Rasch, P. J., Qian, Y.,
Tang, Qi., and Ma, H.-Y: Evaluation of clouds in version 1 of the 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., Caldwell, P., 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
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.
This paper documents a tool that has been developed that can be used to accelerate the...
