Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2949-2022
© Author(s) 2022. 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-15-2949-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
07 Apr 2022
Model evaluation paper |
| 07 Apr 2022
| 07 Apr 2022
Evaluation of a quasi-steady-state approximation of the cloud droplet growth equation (QDGE) scheme for aerosol activation in global models using multiple aircraft data over both continental and marine environments
Hengqi Wang
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing 100084, China
Yiran Peng
CORRESPONDING AUTHOR
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing 100084, China
Knut von Salzen
CORRESPONDING AUTHOR
Canadian Centre for Climate Modelling and Analysis, Environment and
Climate Change Canada, Victoria, British Columbia, Canada
Yan Yang
Beijing Weather Modification Office, Beijing, 100101, China
Wei Zhou
Beijing Weather Modification Office, Beijing, 100101, China
Delong Zhao
Beijing Weather Modification Office, Beijing, 100101, China
Related authors
No articles found.
Ruth A. R. Digby, Knut von Salzen, Adam H. Monahan, Nathan P. Gillett, and Jiangnan Li
Atmos. Chem. Phys., 25, 3109–3130, https://doi.org/10.5194/acp-25-3109-2025, https://doi.org/10.5194/acp-25-3109-2025, 2025
Short summary
Short summary
The refractive index of black carbon (BCRI), which determines how much energy black carbon absorbs and scatters, is difficult to measure, and different climate models use different values. We show that varying the BCRI across commonly used values can increase absorbing aerosol optical depth by 42 % and the warming effect from interactions between black carbon and radiation by 47 %, an appreciable fraction of the overall spread between models reported in recent literature assessments.
Yuanmou Du, Dantong Liu, Delong Zhao, Mengyu Huang, Ping Tian, Dian Wen, Wei Xiao, Wei Zhou, Hui He, Baiwan Pan, Dongfei Zuo, Xiange Liu, Yingying Jing, Rong Zhang, Jiujiang Sheng, Fei Wang, Yu Huang, Yunbo Chen, and Deping Ding
Atmos. Chem. Phys., 24, 13429–13444, https://doi.org/10.5194/acp-24-13429-2024, https://doi.org/10.5194/acp-24-13429-2024, 2024
Short summary
Short summary
By conducting in situ measurements, we investigated ice production processes in stratiform clouds with embedded convection over the North China Plain. The results show that the ice number concentration is strongly related to the distance to the cloud top, and the level with a larger distance to the cloud top has more graupel falling from upper levels, which promotes collision and coalescence between graupel and droplets and enhances secondary ice production.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-157, https://doi.org/10.5194/gmd-2024-157, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study combines Machine Learning with Concentration-Weighted Trajectory Analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Zizhan Hu, Yiran Peng, Mengke Zhu, and Jonathon S. Wright
EGUsphere, https://doi.org/10.5194/egusphere-2024-828, https://doi.org/10.5194/egusphere-2024-828, 2024
Preprint archived
Short summary
Short summary
Clouds and precipitation are among the most difficult features of the climate system to simulate. Water isotopes provide valuable information about how clouds and precipitation develop and evolve, but most models that simulate water isotopes cannot resolve individual clouds. Here we introduce a new isotope-enabled model, iPyCLES, that simulates liquid and ice clouds on scales of 10 to 100 meters. This model can help to translate isotopic observations into constraints for larger-scale models.
Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang
Atmos. Chem. Phys., 24, 2077–2097, https://doi.org/10.5194/acp-24-2077-2024, https://doi.org/10.5194/acp-24-2077-2024, 2024
Short summary
Short summary
The COVID-19 lockdowns reduced aerosol emissions. We ask whether these reductions affected regional aerosol optical depth (AOD) and compare the observed changes to predictions from Earth system models. Only India has an observed AOD reduction outside of typical variability. Models overestimate the response in some regions, but when key biases have been addressed, the agreement is improved. Our results suggest that current models can realistically predict the effects of future emission changes.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
Short summary
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
Short summary
Short summary
This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
Siyuan Li, Dantong Liu, Shaofei Kong, Yangzhou Wu, Kang Hu, Huang Zheng, Yi Cheng, Shurui Zheng, Xiaotong Jiang, Shuo Ding, Dawei Hu, Quan Liu, Ping Tian, Delong Zhao, and Jiujiang Sheng
Atmos. Chem. Phys., 22, 6937–6951, https://doi.org/10.5194/acp-22-6937-2022, https://doi.org/10.5194/acp-22-6937-2022, 2022
Short summary
Short summary
The understanding of secondary organic aerosols is hindered by the aerosol–gas evolution by different oxidation mechanisms. By concurrently measuring detailed mass spectra of aerosol and gas phases in a megacity online, we identified the primary and secondary source sectors and investigated the transformation between gas and aerosol phases influenced by photooxidation and moisture. The results will help us to understand the respective evolution of major sources in a typical urban environment.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
Short summary
Short summary
Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Ming Li, Husi Letu, Yiran Peng, Hiroshi Ishimoto, Yanluan Lin, Takashi Y. Nakajima, Anthony J. Baran, Zengyuan Guo, Yonghui Lei, and Jiancheng Shi
Atmos. Chem. Phys., 22, 4809–4825, https://doi.org/10.5194/acp-22-4809-2022, https://doi.org/10.5194/acp-22-4809-2022, 2022
Short summary
Short summary
To build on the previous investigations of the Voronoi model in the remote sensing retrievals of ice cloud products, this paper developed an ice cloud parameterization scheme based on the single-scattering properties of the Voronoi model and evaluate it through simulations with the Community Integrated Earth System Model (CIESM). Compared with four representative ice cloud schemes, results show that the Voronoi model has good capabilities of ice cloud modeling in the climate model.
Chenjie Yu, Dantong Liu, Kang Hu, Ping Tian, Yangzhou Wu, Delong Zhao, Huihui Wu, Dawei Hu, Wenbo Guo, Qiang Li, Mengyu Huang, Deping Ding, and James D. Allan
Atmos. Chem. Phys., 22, 4375–4391, https://doi.org/10.5194/acp-22-4375-2022, https://doi.org/10.5194/acp-22-4375-2022, 2022
Short summary
Short summary
In this study, we applied a new technique to investigate the aerosol properties on both a mass and number basis and CCN abilities in Beijing suburban regions. The size-resolved aerosol chemical compositions and CCN activation measurement enable a detailed analysis of BC-containing particle hygroscopicity and its size-dependent contribution to the CCN activation. The results presented in this study will affect future models and human health studies.
Julia Schmale, Sangeeta Sharma, Stefano Decesari, Jakob Pernov, Andreas Massling, Hans-Christen Hansson, Knut von Salzen, Henrik Skov, Elisabeth Andrews, Patricia K. Quinn, Lucia M. Upchurch, Konstantinos Eleftheriadis, Rita Traversi, Stefania Gilardoni, Mauro Mazzola, James Laing, and Philip Hopke
Atmos. Chem. Phys., 22, 3067–3096, https://doi.org/10.5194/acp-22-3067-2022, https://doi.org/10.5194/acp-22-3067-2022, 2022
Short summary
Short summary
Long-term data sets of Arctic aerosol properties from 10 stations across the Arctic provide evidence that anthropogenic influence on the Arctic atmospheric chemical composition has declined in winter, a season which is typically dominated by mid-latitude emissions. The number of significant trends in summer is smaller than in winter, and overall the pattern is ambiguous with some significant positive and negative trends. This reflects the mixed influence of natural and anthropogenic emissions.
Donglin Chen, Hong Liao, Yang Yang, Lei Chen, Delong Zhao, and Deping Ding
Atmos. Chem. Phys., 22, 1825–1844, https://doi.org/10.5194/acp-22-1825-2022, https://doi.org/10.5194/acp-22-1825-2022, 2022
Short summary
Short summary
The black carbon (BC) vertical profile plays a critical role in BC–meteorology interaction, which also influences PM2.5 concentrations. More BC mass was assigned into high altitudes (above 1000 m) in the model, which resulted in a stronger cooling effect near the surface, a larger temperature inversion below 421 m, more reductions in PBLH, and a larger increase in near-surface PM2.5 in the daytime caused by the direct radiative effect of BC.
Quan Liu, Dantong Liu, Yangzhou Wu, Kai Bi, Wenkang Gao, Ping Tian, Delong Zhao, Siyuan Li, Chenjie Yu, Guiqian Tang, Yunfei Wu, Kang Hu, Shuo Ding, Qian Gao, Fei Wang, Shaofei Kong, Hui He, Mengyu Huang, and Deping Ding
Atmos. Chem. Phys., 21, 14749–14760, https://doi.org/10.5194/acp-21-14749-2021, https://doi.org/10.5194/acp-21-14749-2021, 2021
Short summary
Short summary
Through simultaneous online measurements of detailed aerosol compositions at both surface and surface-influenced mountain sites, the evolution of aerosol composition during daytime vertical transport was investigated. The results show that, from surface to the top of the planetary boundary layer, the oxidation state of organic aerosol had been significantly enhanced due to evaporation and further oxidation of these evaporated gases.
Dongfei Zuo, Deping Ding, Yichen Chen, Ling Yang, Delong Zhao, Mengyu Huang, Ping Tian, Wei Xiao, Wei Zhou, Yuanmou Du, and Dantong Liu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-221, https://doi.org/10.5194/amt-2021-221, 2021
Publication in AMT not foreseen
Short summary
Short summary
According to the echo attenuation analysis of mixed precipitation, the melting layer is found to be the key factor affecting the attenuation correction. This study hereby proposes an adaptive echo attenuation correction method based on the melting layer, and uses the ground-based S-band radar to extract the echo on the aircraft trajectory to verify the correction results. The results show that the echo attenuation correction value above the melting layer is related to the flight position.
Shuo Ding, Dantong Liu, Kang Hu, Delong Zhao, Ping Tian, Fei Wang, Ruijie Li, Yichen Chen, Hui He, Mengyu Huang, and Deping Ding
Atmos. Chem. Phys., 21, 681–694, https://doi.org/10.5194/acp-21-681-2021, https://doi.org/10.5194/acp-21-681-2021, 2021
Short summary
Short summary
In this study, we for the first time characterized the detailed black carbon (BC) microphysics at a mountain site located at the top of the planetary boundary layer (PBL) influenced by surface emission over the North China Plain. We investigated the optical and hygroscopic properties of BC at this level as influenced by microphysical properties. Such information will constrain the impacts of BC in influencing the PBL dynamics and low-level cloud formation over anthropogenically polluted regions.
Meixin Zhang, Chun Zhao, Zhiyuan Cong, Qiuyan Du, Mingyue Xu, Yu Chen, Ming Chen, Rui Li, Yunfei Fu, Lei Zhong, Shichang Kang, Delong Zhao, and Yan Yang
Atmos. Chem. Phys., 20, 5923–5943, https://doi.org/10.5194/acp-20-5923-2020, https://doi.org/10.5194/acp-20-5923-2020, 2020
Short summary
Short summary
Analysis of multiple numerical experiments over the Himalayas and Tibetan Plateau (TP) shows that the complex topography results in 50 % stronger overall cross-Himalayan transport during the pre-monsoon season primarily due to the strengthened efficiency of near-surface meridional transport towards the TP, enhanced wind speed in some valleys and deeper valley channels associated with larger transported BC mass volume, which leads to 30–50 % stronger BC radiative heating over the TP.
Quan Liu, Dantong Liu, Qian Gao, Ping Tian, Fei Wang, Delong Zhao, Kai Bi, Yangzhou Wu, Shuo Ding, Kang Hu, Jiale Zhang, Deping Ding, and Chunsheng Zhao
Atmos. Chem. Phys., 20, 3931–3944, https://doi.org/10.5194/acp-20-3931-2020, https://doi.org/10.5194/acp-20-3931-2020, 2020
Short summary
Short summary
We present a series of aircraft-based in situ measurements of aerosol chemical components and size distributions over the North China Plain, and the hygroscopicity is derived from aerosol chemical composition. These results reveal the vertical characteristics of aerosol hygroscopicity, and we investigated their impacts on optical properties and activation under different moisture and pollution conditions over this polluted region.
Jing Wei, Zhanqing Li, Maureen Cribb, Wei Huang, Wenhao Xue, Lin Sun, Jianping Guo, Yiran Peng, Jing Li, Alexei Lyapustin, Lei Liu, Hao Wu, and Yimeng Song
Atmos. Chem. Phys., 20, 3273–3289, https://doi.org/10.5194/acp-20-3273-2020, https://doi.org/10.5194/acp-20-3273-2020, 2020
Short summary
Short summary
This study introduced an enhanced space–time extremely randomized trees (STET) approach to improve the 1 km resolution ground-level PM2.5 estimates across China using the remote sensing technology. The STET model shows high accuracy and strong predictive power and appears to outperform most models reported by previous studies. Thus, it is of great importance for future air pollution studies at medium- or small-scale areas and will be applied to generate the historical PM2.5 dataset across China.
Ping Tian, Dantong Liu, Delong Zhao, Chenjie Yu, Quan Liu, Mengyu Huang, Zhaoze Deng, Liang Ran, Yunfei Wu, Shuo Ding, Kang Hu, Gang Zhao, Chunsheng Zhao, and Deping Ding
Atmos. Chem. Phys., 20, 2603–2622, https://doi.org/10.5194/acp-20-2603-2020, https://doi.org/10.5194/acp-20-2603-2020, 2020
Short summary
Short summary
This study paints a full picture of the evolution of vertical characteristics of aerosol optical properties and shortwave heating impacts of carbonaceous aerosols during different stages of pollution events over the Beijing region and highlights the increased contribution of brown carbon absorption, especially at higher levels, during pollution.
Xiaoyan Liu, Yan-Lin Zhang, Yiran Peng, Lulu Xu, Chunmao Zhu, Fang Cao, Xiaoyao Zhai, M. Mozammel Haque, Chi Yang, Yunhua Chang, Tong Huang, Zufei Xu, Mengying Bao, Wenqi Zhang, Meiyi Fan, and Xuhui Lee
Atmos. Chem. Phys., 19, 11213–11233, https://doi.org/10.5194/acp-19-11213-2019, https://doi.org/10.5194/acp-19-11213-2019, 2019
Short summary
Short summary
Although a total ban on straw burning has been enforced in eastern China, the regionally transported biomass burning emissions remarkably impacted the chemical and optical properties of carbonaceous aerosols in Nanjing, which were quantified by a calculation based on measured data and a simulation based on a model. Results showed that regionally transported biomass burning emissions significantly contributed to the carbonaceous aerosols and impacted the solar radiation balance of the atmosphere.
Jing Wei, Yiran Peng, Rashed Mahmood, Lin Sun, and Jianping Guo
Atmos. Chem. Phys., 19, 7183–7207, https://doi.org/10.5194/acp-19-7183-2019, https://doi.org/10.5194/acp-19-7183-2019, 2019
Short summary
Short summary
This study evaluates the suitability of 11 satellite-derived aerosol products in describing the spatio-temporal variations over the world. Our results show similar global patterns among these products but noticeable spatial heterogeneity and numerical differences over land regions. In general, MODIS products perform best at reflecting the spatial distributions and capturing the temporal trends of aerosol. This study help readers select a suitable aerosol dataset for their studies.
Delong Zhao, Mengyu Huang, Dantong Liu, Deping Ding, Ping Tian, Quan Liu, Wei Zhou, Jiujiang Sheng, Fei Wang, Kai Bi, Yan Yang, Xia Li, Yaqiong Hu, Xin Guo, Yang Gao, Hui He, Yunbo Chen, Shaofei Kong, and Jiayi Huang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-1118, https://doi.org/10.5194/acp-2017-1118, 2018
Preprint withdrawn
Short summary
Short summary
This study for the first time reports the 3D distributions of black carbon and detailed physical properties in the boundary layer over the North China Plain, using intensive aircraft measurements in both hot and cold seasons. The BC mass in the planetary boundary layer (PBL) was found to be largely influenced by meteorology which modulated the local emission and regional transport.
Xiaoning Xie, He Zhang, Xiaodong Liu, Yiran Peng, and Yangang Liu
Atmos. Chem. Phys., 17, 5877–5892, https://doi.org/10.5194/acp-17-5877-2017, https://doi.org/10.5194/acp-17-5877-2017, 2017
Short summary
Short summary
New complete cloud parameterizations of cloud droplet effective radius and the two-moment cloud-to-rain autoconversion process explicitly accounting for dispersion are implemented into CAM5.1. The results show that the consideration of dispersion effect can significantly reduce the changes induced by anthropogenic aerosols in the cloud-top effective radius and the liquid water path, which reduces the AIF substantially at a global scale, especially in the Northern Hemisphere.
Y. Peng, V. K. Arora, W. A. Kurz, R. A. Hember, B. J. Hawkins, J. C. Fyfe, and A. T. Werner
Biogeosciences, 11, 635–649, https://doi.org/10.5194/bg-11-635-2014, https://doi.org/10.5194/bg-11-635-2014, 2014
Related subject area
Climate and Earth system modeling
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation
2. Multiple aerosol types, J. Geophys. Res.-Atmos., 105, 6837–6844,
https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak, H., Ghan, S. J., and Rivera-Carpio, C.: A parameterization of
aerosol activation 1. Single aerosol type, J. Geophys. Res.-Atmos., 103,
6123–6131, https://doi.org/10.1029/97JD03735, 1998.
Albanese, D., Riccadonna, S., Donati, C., and Franceschi, P.: A practical
tool for maximal information coefficient analysis, Gigascience, 7, 1–8,
https://doi.org/10.1093/gigascience/giy032, 2018.
Albrecht, B.: 2011 CIRPAS Twin Otter Navigation and State Parameters. Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://doi.org/10.26023/36PD-HZKW-RJ03, 2008.
ARM (Atmospheric Radiation Measurement) user facility: Ambient Winds – AIMMS, updated hourly, 2014-02-15 to 2014-10-15, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, compiled by: Hobbe, J., ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/site_code::mao (last access: 26 October 2020), 2014a.
ARM (Atmospheric Radiation Measurement) user facility: Fast Cloud Droplet Probe (FCDP), updated hourly, 2014-02-15 to 2014-10-15, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, compiled by: Mei, F., ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/site_code::mao (last access: 26 October 2020), 2014b.
ARM (Atmospheric Radiation Measurement) user facility: Passive Cavity Aerosol Spectrometer, updated hourly, 2014-02-22 to 2014-3-23, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, compiled by: Tomlinson, J., ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/site_code::mao (last access: 26 October 2020), 2014c.
ARM (Atmospheric Radiation Measurement) user facility: Time of Flight Aerosol Mass Spectrometer, updated hourly, 2014-02-22 to 2014-03-23, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, compiled by: Shilling, J., ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/site_code::mao (last access: 26 October 2020), 2014d.
Barahona, D. and Nenes, A.: Parameterization of cloud droplet formation in
large-scale models: Including effects of entrainment, J. Geophys. Res.-Atmos., 112, D16206, https://doi.org/10.1029/2007JD008473, 2007.
Boucher, O. and Lohmann, U.: The sulfate-CCN-cloud albedo effect, Tellus B,
47, 281–300, https://doi.org/10.1034/j.1600-0889.47.issue3.1.x, 1995.
Brenguier, J. L.: Parameterization of the condensation process: a
theoretical approach, J. Atmos. Sci., 48, 264–282,
https://doi.org/10.1175/1520-0469(1991)048<0264:POTCPA>2.0.CO;2,
1991.
Chen, J., Liu, Y., Zhang, M., and Peng, Y.: New understanding and
quantification of the regime dependence of aerosol-cloud interaction for
studying aerosol indirect effects, Geophys. Res. Lett., 43, 1780–1787,
https://doi.org/10.1002/2016GL067683, 2016.
Cohard, J. M., Pinty, J. P., and Suhre, K.: On the parameterization of
activation spectra from cloud condensation nuclei microphysical properties,
J. Geophys. Res.-Atmos., 105, 11753–11766, https://doi.org/10.1029/1999JD901195,
2000.
Emanuel, K. A.: Atmospheric convection, Oxford University Press, ISBN 0195066308, 1994.
Ferek, R. J., Hegg, D. A., Hobbs, P. V., Durkee, P., and Nielsen, K.: Measurements of ship-induced tracks in clouds off the Washington coast, J. Geophys. Res.-Atmos., 103, 23199–23206, https://doi.org/10.1029/98JD02121, 1998.
Flossmann, A. I., Hall, W. D., and Pruppacher, H. R.: A Theoretical Study of the Wet Removal of Atmospheric Pollutants. Part I: The Redistribution of Aerosol Particles Captured through Nucleation and Impaction Scavenging by Growing Cloud Drops, J. Atmos. Sci., 42, 583–606, https://doi.org/10.1175/1520-0469(1985)042<0583:ATSOTW>2.0.CO;2, 1985.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J. L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu R., and Zhou B., Cambridge University Press, in press, 2022.
Forster, P. M., Richardson, T., Maycock, A. C., Smith, C. J., Samset, B. H.,
Myhre, G., Andrews, T., Pincus, R., and Schulz, M.: Recommendations for
diagnosing effective radiative forcing from climate models for CMIP6, J.
Geophys. Res., 121, 12460–12475, https://doi.org/10.1002/2016JD025320, 2016.
Fountoukis, C. and Nenes, A.: Continued development of a cloud droplet
formation parameterization for global climate models, J. Geophys. Res. D
Atmos., 110, 1–10, https://doi.org/10.1029/2004JD005591, 2005.
Fountoukis, C., Nenes, A., Meskhidze, N., Bahreini, R., Conant, W. C.,
Jonsson, H., Murphy, S., Sorooshian, A., Varutbangkul, V., Brechtel, F.,
Flagan, R. C., and Seinfeld, J. H.: Aerosol-cloud drop concentration closure
for clouds sampled during the International Consortium for Atmospheric
Research on Transport and Transformation 2004 campaign, J. Geophys. Res.-Atmos., 112, D10, https://doi.org/10.1029/2006JD007272, 2007.
Gerber, H. E., Frick, G. M., Jensen, J. B., and Hudson, J. G.: Entrainment,
mixing, and microphysics in trade-wind cumulus, J. Meteorol. Soc. Japan,
86A, 87–106, https://doi.org/10.2151/jmsj.86a.87, 2008.
Ghan, S. J.: Technical Note: Estimating aerosol effects on cloud radiative forcing, Atmos. Chem. Phys., 13, 9971–9974, https://doi.org/10.5194/acp-13-9971-2013, 2013.
Ghan, S. J., Abdul-Razzak, H., Nenes, A., Ming, Y., Liu, X., Ovchinnikov, M., Shipway, B., Meskhidze, N., Xu, J., and Shi, X.: Droplet nucleation: Physically-based parameterizations and comparative evaluation, J. Adv. Model. Earth Sys., 3, M10001, https://doi.org/10.1029/2011MS000074, 2011.
Guibert, S., Snider, J. R., and Brenguier, J. L.: Aerosol activation in
marine stratocumulus clouds: 1. Measurement validation for a closure study,
J. Geophys. Res.-Atmos., 108, 8628, https://doi.org/10.1029/2002jd002678, 2003.
Hallberg, A., Wobrock, W., Flossmann, A. I., Bower, K. N., Noone, K. J.,
Wiedensohler, A., Hansson, H. C., Wendisch, M., Berner, A., Kruisz, C., Laj,
P., Facchini, M. C., Fuzzi, S., and Arends, B. G.: Microphysics of clouds:
Model vs measurements, Atmos. Environ., 31, 2453–2462,
https://doi.org/10.1016/S1352-2310(97)00041-1, 1997.
Herrington, A. R. and Reed, K. A.: On resolution sensitivity in the
Community Atmosphere Model, Q. J. R. Meteorol. Soc., 146, 3789–3807,
https://doi.org/10.1002/qj.3873, 2020.
Jarecka, D., Pawlowska, H., Grabowski, W. W., and Wyszogrodzki, A. A.: Modeling microphysical effects of entrainment in clouds observed during EUCAARI-IMPACT field campaign, Atmos. Chem. Phys., 13, 8489–8503, https://doi.org/10.5194/acp-13-8489-2013, 2013.
Jones, A. and Slingo, A.: Predicting cloud-droplet effective radius and
indirect sulphate aerosol forcing using a general circulation model, Q. J.
R. Meteorol. Soc., 122, 1573–1595, https://doi.org/10.1002/qj.49712253506, 1996.
Jones, A., Roberts, D. L., and Slingo, A.: A climate model study of indirect
radiative forcing by anthropogenic sulphate aerosols, Nature, 370,
450–453, https://doi.org/10.1038/370450a0, 1994.
Kang, I. S., Yang, Y. M., and Tao, W. K.: GCMs with implicit and explicit
representation of cloud microphysics for simulation of extreme precipitation
frequency, Clim. Dyn., 45, 325–335, https://doi.org/10.1007/s00382-014-2376-1,
2015.
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O.,
Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., Van Den
Heever, S. C., and Yano, J. I.: Representation of microphysical processes in
cloud-resolving models: Spectral (bin) microphysics versus bulk
parameterization, Rev. Geophys., 53, 247–322, https://doi.org/10.1002/2014RG000468,
2015.
Khvorostyanov, V. I. and Curry, J. A.: Parameterization of cloud drop
activation based on analytical asymptotic solutions to the supersaturation
equation, J. Atmos. Sci., 66, 1905–1925, https://doi.org/10.1175/2009JAS2811.1,
2009.
Kiehl, J. T., Schneider, T. L., Rasch, P. J., Barth, M. C., and Wong, J.:
Radiative forcing due to sulfate aerosols from simulations with the National
Center for Atmospheric Research Community Climate Model, Version 3, J.
Geophys. Res.-Atmos., 105, 1441–1457, https://doi.org/10.1029/1999JD900495, 2000.
Kivekäs, N., Kerminen, V. M., Anttila, T., Korhonen, H., Lihavainen, H.,
Komppula, M., and Kulmala, M.: Parameterization of cloud droplet activation
using a simplified treatment of the aerosol number size distribution, J.
Geophys. Res.-Atmos., 113, D15207, https://doi.org/10.1029/2007JD009485, 2008.
Kleinman, L. I., Daum, P. H., Lee, Y.-N., Lewis, E. R., Sedlacek III, A. J., Senum, G. I., Springston, S. R., Wang, J., Hubbe, J., Jayne, J., Min, Q., Yum, S. S., and Allen, G.: Aerosol concentration and size distribution measured below, in, and above cloud from the DOE G-1 during VOCALS-REx, Atmos. Chem. Phys., 12, 207–223, https://doi.org/10.5194/acp-12-207-2012, 2012.
Kreidenweis, S. M., Petters, M. D., and DeMott, P. J.: Single-parameter estimates of aerosol water content, Environ. Res. Lett., 3, 035002, https://doi.org/10.1088/1748-9326/3/3/035002, 2008.
Li, S., Zhang, F., Jin, X., Sun, Y., Wu, H., Xie, C., Chen, L., Liu, J., Wu,
T., Jiang, S., Cribb, M., and Li, Z.: Characterizing the ratio of nitrate to
sulfate in ambient fine particles of urban Beijing during 2018–2019, Atmos.
Environ., 237, 117662, https://doi.org/10.1016/j.atmosenv.2020.117662, 2020.
Li, S. M., Strawbridge, K. B., Leaitch, W. R., and Macdonald, A. M.: Aerosol
backscattering determined from chemical and physical properties and lidar
observations over the east coast of Canada, Geophys. Res. Lett., 25,
1653–1656, https://doi.org/10.1029/98GL00910, 1998.
Liu, Q., Liu, D., Gao, Q., Tian, P., Wang, F., Zhao, D., Bi, K., Wu, Y., Ding, S., Hu, K., Zhang, J., Ding, D., and Zhao, C.: Vertical characteristics of aerosol hygroscopicity and impacts on optical properties over the North China Plain during winter, Atmos. Chem. Phys., 20, 3931–3944, https://doi.org/10.5194/acp-20-3931-2020, 2020.
Lohmann, U.: Impact of sulfate aerosols on albedo and lifetime of clouds: A
sensitivity study with the ECHAM4 GCM, J. Geophys. Res.-Atmos., 102,
13685–13700, https://doi.org/10.1029/97JD00631, 1997.
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.
Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A. F.,
Schumacher, C., Wang, J., Biscaro, T., Brito, J., Calheiros, A., Jardine,
K., Medeiros, A., Portela, B., De Sá, S. S., Adachi, K., Aiken, A. C.,
Alblbrecht, R., Alexander, L., Andreae, M. O., Barbosa, H. M. J., Buseck,
P., Chand, D., Comstmstmstock, J. M., Day, D. A., Dubey, M., Fan, J.,
Fastst, J., Fisch, G., Fortner, E., Giangrande, S., Gilllles, M.,
Goldststein, A. H., Guenther, A., Hubbbbe, J., Jensen, M., Jimenez, J. L.,
Keutstsch, F. N., Kim, S., Kuang, C., Laskskin, A., McKinney, K., Mei, F.,
Millller, M., Nascimento, R., Pauliquevis, T., Pekour, M., Peres, J.,
Petäjä, T., Pöhlklker, C., Pöschl, U., Rizzo, L., Schmid,
B., Shilllling, J. E., Silva Dias, M. A., Smith, J. N., Tomlmlinson, J. M.,
Tóta, J., and Wendisch, M.: The green ocean amazon experiment
(GOAMAZON2014/5) observes pollution affecting gases, aerosols, clouds, and
rainfall over the rain forest, B. Am. Meteorol. Soc., 98, 981–997,
https://doi.org/10.1175/BAMS-D-15-00221.1, 2017.
Menon, S., Del Genio, A. D., Koch, D., and Tselioudis, G.: GCM simulations of
the aerosol indirect effect: Sensitivity to cloud parameterization and
aerosol Burden, J. Atmos. Sci., 59, 692–713,
https://doi.org/10.1175/1520-0469(2002)059<0692:gsotai>2.0.co;2,
2002.
Meskhidze, N., Nenes, A., Conant, W. C., and Seinfeld, J. H.: Evaluation of a
new cloud droplet activation parameterization with in situ data from
CRYSTAL-FACE and CSTRIPE, J. Geophys. Res. D Atmos., 110, 1–10,
https://doi.org/10.1029/2004JD005703, 2005.
Ming, Y., Ramaswamy, V., Donner, L. J., and Phillips, V. T. J.: A new
parameterization of cloud droplet activation applicable to general
circulation models, J. Atmos. Sci., 63, 1348–1356,
https://doi.org/10.1175/JAS3686.1, 2006.
Morrison, H. and Pinto, J. O.: Mesoscale modeling of springtime arctic
mixed-phase stratiform clouds using a new two-moment bulk microphysics
scheme, J. Atmos. Sci., 62, 3683–3704, https://doi.org/10.1175/JAS3564.1, 2005.
Murray, F. W.: On the Computation of Saturation Vapor Pressure, J. Appl.
Meteorol., 6, 203–204, https://doi.org/10.1175/1520-0450(1967)006<0203:otcosv>2.0.co;2, 1967.
Nakao, S., Tang, P., Tang, X., Clark, C. H., Qi, L., Seo, E., Asa-Awuku, A.,
and Cocker, D.: Density and elemental ratios of secondary organic aerosol:
Application of a density prediction method, Atmos. Environ., 68, 273–277,
https://doi.org/10.1016/j.atmosenv.2012.11.006, 2013.
Nenes, A. and Seinfeld, J. H.: Parameterization of cloud droplet formation
in global climate models, J. Geophys. Res.-Atmos., 108, 4415,
https://doi.org/10.1029/2002jd002911, 2003.
Nenes, A., Ghan, S., Abdul-Razzak, H., Chuang, P. Y., and Seinfeld, J. H.: Kinetic limitations on cloud droplet formation and impact on cloud albedo, Tellus B, 53, 133–149, https://doi.org/10.3402/tellusb.v53i2.16569, 2001.
Pandis, S. N., Seinfeld, J. H., and Pilinis, C.: Chemical composition
differences in fog and cloud droplets of different sizes, Atmos. Environ.
Part A, Gen. Top., 24, 1957–1969, https://doi.org/10.1016/0960-1686(90)90529-V,
1990.
Peng, Y., Lohmann, U., Leaitch, R., Banic, C., and Couture, M.: The cloud
albedo-cloud droplet effective radius relationship for clean and polluted
clouds from RACE and FIRE.ACE, J. Geophys. Res.-Atmos., 107, AAC
1-1–AAC 1-6, https://doi.org/10.1029/2000JD000281, 2002.
Peng, Y., Lohmann, U., and Leaitch, W. R.: Importance of vertical velocity
variations in the cloud droplet nucleation process of marine stratus clouds,
J. Geophys. Res.-Atmos., 110, 1–13, https://doi.org/10.1029/2004JD004922, 2005.
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007.
Pruppacher, H. R., Klett, J. D., and Wang, P. K.: Microphysics of Clouds and
Precipitation, Aerosol Sci. Technol., 28, 381–382,
https://doi.org/10.1080/02786829808965531, 1998.
Ramaswamy, V., Collins, W., Haywood, J., Lean, J., Mahowald, N., Myhre, G., Naik, V., Shine, K. P., Soden, B., Stenchikov, G., and Storelvmo, T.: Radiative Forcing of Climate: The Historical Evolution of the Radiative Forcing Concept, the Forcing Agents and their Quantification, and Applications, Meteorol. Monogr., 59, 14.1–14.101, https://doi.org/10.1175/amsmonographs-d-19-0001.1, 2019.
Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., McVean, G.,
Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., and Sabeti, P. C.:
Detecting novel associations in large data sets, Science,
334, 1518–1524, https://doi.org/10.1126/science.1205438, 2011.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From
Air Pollution to Climate Change, 3rd edn., John Wiley and Sons Inc., New York, USA, ISBN 978-1-118-94740-1, 2016.
Shilling, J. E., Pekour, M. S., Fortner, E. C., Artaxo, P., de Sá, S., Hubbe, J. M., Longo, K. M., Machado, L. A. T., Martin, S. T., Springston, S. R., Tomlinson, J., and Wang, J.: Aircraft observations of the chemical composition and aging of aerosol in the Manaus urban plume during GoAmazon 2014/5, Atmos. Chem. Phys., 18, 10773–10797, https://doi.org/10.5194/acp-18-10773-2018, 2018.
Shipway, B. J. and Abel, S. J.: Analytical estimation of cloud droplet
nucleation based on an underlying aerosol population, Atmos. Res., 96,
344–355, https://doi.org/10.1016/j.atmosres.2009.10.005, 2010.
Slawinska, J., Grabowski, W. W., Pawlowska, H., and Morrison, H.: Droplet
activation and mixing in large-eddy simulation of a shallow cumulus field,
J. Atmos. Sci., 69, 444–462, https://doi.org/10.1175/JAS-D-11-054.1, 2012.
Snider, J. R. and Brenguier, J. L.: Cloud condensation nuclei and cloud
droplet measurements during ACE-2, Tellus B,
52, 828–842, https://doi.org/10.1034/j.1600-0889.2000.00044.x, 2000.
Snider, J. R., Guibert, S., Brenguier, J. L., and Putaud, J. P.: Aerosol
activation in marine stratocumulus clouds: 2. Köhler and parcel theory
closure studies, J. Geophys. Res.-Atmos., 108, 8629, https://doi.org/10.1029/2002jd002692,
2003.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974.
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977.
UCAR/NCAR – Earth Observing Laboratory: 1970. DOE Gulf Stream 1 (G-1) CAS Data, Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://data.eol.ucar.edu/dataset/89.262 (last access: 30 January 2018), 2008a.
UCAR/NCAR – Earth Observing Laboratory: 1970. DOE Gulf Stream 1 (G-1) LWC (Gerber Probe) Data, Version 1.0. UCAR/NCAR – Earth Observing Laboratory [data set], https://data.eol.ucar.edu/dataset/89.288 (last access: 30 January 2018), 2008b.
UCAR/NCAR – Earth Observing Laboratory: 1970. DOE Gulf Stream 1 (G-1) Navigation and State Parameters, Version 1.0. UCAR/NCAR – Earth Observing Laboratory [data set], https://data.eol.ucar.edu/dataset/89.217 (last access: 30 January 2018), 2008c.
UCAR/NCAR – Earth Observing Laboratory: 1970. DOE Gulf Stream 1 (G-1) PCASP Data, Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://data.eol.ucar.edu/dataset/89.289 (last access: 30 January 2018), 2008d.
Wang, H., Peng, Y., Salzen, K. von, Yang, Y., Zhou, W., and Zhao, D.:
Evaluation of a numerically efficient aerosol activation scheme by using
cloud data from multiple aircraft campaigns in continental and marine
regions, Zenodo [data set and code], https://doi.org/10.5281/ZENODO.4841035, 2021.
Wang, M., Peng, Y., Liu, Y., Liu, Y., Xie, X., and Guo, Z.: Understanding
Cloud Droplet Spectral Dispersion Effect Using Empirical and Semi-Analytical
Parameterizations in NCAR CAM5.3, Earth Sp. Sci., 7, e2020EA001276,
https://doi.org/10.1029/2020EA001276, 2020.
Wexler, A. S. and Clegg, S. L.: Atmospheric aerosol models for systems
including the ions H+, NH4+, Na+, so42−, NO3−, Cl−, Br−, and H2O, J.
Geophys. Res.-Atmos., 107, 4207, https://doi.org/10.1029/2001JD000451, 2002.
Whitby, K. T.: The physical characteristics of sulfur aerosols, Atmos.
Environ., 12, 135–159, https://doi.org/10.1016/0004-6981(78)90196-8, 1978.
Wood, R., Mechoso, C. R., Bretherton, C. S., Weller, R. A., Huebert, B., Straneo, F., Albrecht, B. A., Coe, H., Allen, G., Vaughan, G., Daum, P., Fairall, C., Chand, D., Gallardo Klenner, L., Garreaud, R., Grados, C., Covert, D. S., Bates, T. S., Krejci, R., Russell, L. M., de Szoeke, S., Brewer, A., Yuter, S. E., Springston, S. R., Chaigneau, A., Toniazzo, T., Minnis, P., Palikonda, R., Abel, S. J., Brown, W. O. J., Williams, S., Fochesatto, J., Brioude, J., and Bower, K. N.: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx): goals, platforms, and field operations, Atmos. Chem. Phys., 11, 627–654, https://doi.org/10.5194/acp-11-627-2011, 2011.
Zamora, M. L., Peng, J., Hu, M., Guo, S., Marrero-Ortiz, W., Shang, D., Zheng, J., Du, Z., Wu, Z., and Zhang, R.: Wintertime aerosol properties in Beijing, Atmos. Chem. Phys., 19, 14329–14338, https://doi.org/10.5194/acp-19-14329-2019, 2019.
Zhang, Y., Zhang, X., Wang, K., He, J., Leung, L. R., Fan, J., and Nenes, A.:
Incorporating an advanced aerosol activation parameterization into WRF-CAM5:
Model evaluation and parameterization intercomparison, J. Geophys. Res.,
120, 6952–6979, https://doi.org/10.1002/2014JD023051, 2015.
Zhou, W., Gao, M., He, Y., Wang, Q., Xie, C., Xu, W., Zhao, J., Du, W., Qiu, Y., Lei, L., Fu, P., Wang, Z., Worsnop, D. R., Zhang, Q., and Sun, Y.: Response of aerosol chemistry to clean air action in Beijing, China: Insights from two-year ACSM measurements and model simulations, Environ. Pollut., 255, 113345, https://doi.org/10.1016/j.envpol.2019.113345, 2019.
Short summary
The aerosol activation scheme is an important part of the general circulation model, but evaluations using observed data are mostly regional. This research introduced a numerically efficient aerosol activation scheme and evaluated it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The decent performance indicates that the scheme is suitable for simulations of cloud droplet number concentrations over wide conditions.
The aerosol activation scheme is an important part of the general circulation model, but...
