Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4805-2022
© 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-15-4805-2022
© 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
| 
23 Jun 2022
Model evaluation paper |
| 23 Jun 2022
| 23 Jun 2022
An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation
Jingzhe Sun
Beijing Institute of Applied Meteorology, Beijing, China
Yingjing Jiang
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Shaoqing Zhang
CORRESPONDING AUTHOR
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
Qingdao Pilot National Laboratory for Marine Science and Technology
(QNLM), Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Weimin Zhang
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai,
China
Lv Lu
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Guangliang Liu
Shandong Provincial Key Laboratory of Computer Networks, Qilu
University of Technology (Shandong Academy of Sciences), Jinan, China
Yuhu Chen
Qingdao Pilot National Laboratory for Marine Science and Technology
(QNLM), Qingdao, China
Xiang Xing
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai,
China
Xiaopei Lin
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
Qingdao Pilot National Laboratory for Marine Science and Technology
(QNLM), Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Lixin Wu
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
Qingdao Pilot National Laboratory for Marine Science and Technology
(QNLM), Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Related authors
No articles found.
Wenbin Kou, Yang Gao, Dan Tong, Xiaojie Guo, Xiadong An, Wenyu Liu, Mengshi Cui, Xiuwen Guo, Shaoqing Zhang, Huiwang Gao, and Lixin Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-2500, https://doi.org/10.5194/egusphere-2024-2500, 2024
Short summary
Short summary
Unlike traditional numerical studies, we apply a high-resolution Earth system model, improving simulations of ozone and large-scale circulations such as atmospheric blocking. In addition to local heatwave effects, we quantify the impact of atmospheric blocking on downstream ozone concentrations, which is closely associated with the blocking position. We identify three major pathways of Rossby wave propagation, stressing the critical role of large-scale circulation play in regional air quality.
Lijing Cheng, Yuying Pan, Zhetao Tan, Huayi Zheng, Yujing Zhu, Wangxu Wei, Juan Du, Huifeng Yuan, Guancheng Li, Hanlin Ye, Viktor Gouretski, Yuanlong Li, Kevin E. Trenberth, John Abraham, Yuchun Jin, Franco Reseghetti, Xiaopei Lin, Bin Zhang, Gengxin Chen, Michael E. Mann, and Jiang Zhu
Earth Syst. Sci. Data, 16, 3517–3546, https://doi.org/10.5194/essd-16-3517-2024, https://doi.org/10.5194/essd-16-3517-2024, 2024
Short summary
Short summary
Observational gridded products are essential for understanding the ocean, the atmosphere, and climate change; they support policy decisions and socioeconomic developments. This study provides an update of an ocean subsurface temperature and ocean heat content gridded product, named the IAPv4 data product, which is available for the upper 6000 m (119 levels) since 1940 (more reliable after ~1955) for monthly and 1° × 1° temporal and spatial resolutions.
Feifan Yan, Hang Su, Yafang Cheng, Rujin Huang, Hong Liao, Ting Yang, Yuanyuan Zhu, Shaoqing Zhang, Lifang Sheng, Wenbin Kou, Xinran Zeng, Shengnan Xiang, Xiaohong Yao, Huiwang Gao, and Yang Gao
Atmos. Chem. Phys., 24, 2365–2376, https://doi.org/10.5194/acp-24-2365-2024, https://doi.org/10.5194/acp-24-2365-2024, 2024
Short summary
Short summary
PM2.5 pollution is a major air quality issue deteriorating human health, and previous studies mostly focus on regions like the North China Plain and Yangtze River Delta. However, the characteristics of PM2.5 concentrations between these two regions are studied less often. Focusing on the transport corridor region, we identify an interesting seesaw transport phenomenon with stagnant weather conditions, conducive to PM2.5 accumulation over this region, resulting in large health effects.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Dexun Chen, Yang Gao, Xiaopei Lin, Zhao Liu, and Xiaojing Lv
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-10, https://doi.org/10.5194/gmd-2024-10, 2024
Preprint withdrawn
Short summary
Short summary
The hardware-related perturbations caused by the heterogeneous many-core architectures can blend with software or human errors, which can affect the accuracy of the model consistency verification. We develop a deep learning-based consistency test tool for ESMs on the heterogeneous systems (ESM-DCT) and evaluate it in CESM on new Sunway system. The ESM-DCT can detect the existence of software or human errors when taking hardware-related perturbations into account.
Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, and Zhiwei Zhang
Geosci. Model Dev., 16, 6393–6412, https://doi.org/10.5194/gmd-16-6393-2023, https://doi.org/10.5194/gmd-16-6393-2023, 2023
Short summary
Short summary
Ocean surface waves play an important role in the air–sea interface but are rarely activated in high-resolution Earth system simulations due to their expensive computational costs. To alleviate this situation, this paper designs a new wave modeling framework with a multiscale grid system. Evaluations of a series of numerical experiments show that it has good feasibility and applicability in the WAVEWATCH III model, WW3, and can achieve the goals of efficient and high-precision wave simulation.
Chupeng Zhang, Shangfei Hai, Yang Gao, Yuhang Wang, Shaoqing Zhang, Lifang Sheng, Bin Zhao, Shuxiao Wang, Jingkun Jiang, Xin Huang, Xiaojing Shen, Junying Sun, Aura Lupascu, Manish Shrivastava, Jerome D. Fast, Wenxuan Cheng, Xiuwen Guo, Ming Chu, Nan Ma, Juan Hong, Qiaoqiao Wang, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 23, 10713–10730, https://doi.org/10.5194/acp-23-10713-2023, https://doi.org/10.5194/acp-23-10713-2023, 2023
Short summary
Short summary
New particle formation is an important source of atmospheric particles, exerting critical influences on global climate. Numerical models are vital tools to understanding atmospheric particle evolution, which, however, suffer from large biases in simulating particle numbers. Here we improve the model chemical processes governing particle sizes and compositions. The improved model reveals substantial contributions of newly formed particles to climate through effects on cloud condensation nuclei.
Zhenming Wang, Shaoqing Zhang, Yishuai Jin, Yinglai Jia, Yangyang Yu, Yang Gao, Xiaolin Yu, Mingkui Li, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 16, 705–717, https://doi.org/10.5194/gmd-16-705-2023, https://doi.org/10.5194/gmd-16-705-2023, 2023
Short summary
Short summary
To improve the numerical model predictability of monthly extended-range scales, we use the simplified slab ocean model (SOM) to restrict the complicated sea surface temperature (SST) bias from a 3-D dynamical ocean model. As for SST prediction, whether in space or time, the WRF-SOM is verified to have better performance than the WRF-ROMS, which has a significant impact on the atmosphere. For extreme weather events such as typhoons, the predictions of WRF-SOM are in good agreement with WRF-ROMS.
Huayang Cai, Hao Yang, Pascal Matte, Haidong Pan, Zhan Hu, Tongtiegang Zhao, and Guangliang Liu
Ocean Sci., 18, 1691–1702, https://doi.org/10.5194/os-18-1691-2022, https://doi.org/10.5194/os-18-1691-2022, 2022
Short summary
Short summary
Quantifying spatial–temporal water level dynamics is essential for water resources management in estuaries. In this study, we propose a simple yet powerful regression model to examine the influence of the world’s largest dam, the Three Gorges Dam (TGD), on the spatial–temporal water level dynamics within the Yangtze River estuary. The presented method is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled estuaries worldwide.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
Short summary
Short summary
To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Lu Yang, Hongli Fu, Xiaofan Luo, Shaoqing Zhang, and Xuefeng Zhang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-92, https://doi.org/10.5194/tc-2022-92, 2022
Revised manuscript not accepted
Short summary
Short summary
During the melting season in Arctic, sea ice thickness is difficult to detect directly by the satellite remote sensing. A bivariate regression model is put forward in this study to construct sea ice thickness. Comparisons with observations show that the new sea ice thickness data has some advantages over other data sets. The experiment shows that the model is expected to provide an available data for improving the forecast accuracy of sea ice variables in the Arctic sea ice melting season.
Zhao Liu, Shaoqing Zhang, Yang Shen, Yuping Guan, and Xiong Deng
Nonlin. Processes Geophys., 28, 481–500, https://doi.org/10.5194/npg-28-481-2021, https://doi.org/10.5194/npg-28-481-2021, 2021
Short summary
Short summary
A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for the AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.
Shaoqing Zhang, Haohuan Fu, Lixin Wu, Yuxuan Li, Hong Wang, Yunhui Zeng, Xiaohui Duan, Wubing Wan, Li Wang, Yuan Zhuang, Hongsong Meng, Kai Xu, Ping Xu, Lin Gan, Zhao Liu, Sihai Wu, Yuhu Chen, Haining Yu, Shupeng Shi, Lanning Wang, Shiming Xu, Wei Xue, Weiguo Liu, Qiang Guo, Jie Zhang, Guanghui Zhu, Yang Tu, Jim Edwards, Allison Baker, Jianlin Yong, Man Yuan, Yangyang Yu, Qiuying Zhang, Zedong Liu, Mingkui Li, Dongning Jia, Guangwen Yang, Zhiqiang Wei, Jingshan Pan, Ping Chang, Gokhan Danabasoglu, Stephen Yeager, Nan Rosenbloom, and Ying Guo
Geosci. Model Dev., 13, 4809–4829, https://doi.org/10.5194/gmd-13-4809-2020, https://doi.org/10.5194/gmd-13-4809-2020, 2020
Short summary
Short summary
Science advancement and societal needs require Earth system modelling with higher resolutions that demand tremendous computing power. We successfully scale the 10 km ocean and 25 km atmosphere high-resolution Earth system model to a new leading-edge heterogeneous supercomputer using state-of-the-art optimizing methods, promising the solution of high spatial resolution and time-varying frequency. Corresponding technical breakthroughs are of significance in modelling and HPC design communities.
Jiangyu Li and Shaoqing Zhang
Geosci. Model Dev., 13, 1035–1054, https://doi.org/10.5194/gmd-13-1035-2020, https://doi.org/10.5194/gmd-13-1035-2020, 2020
Short summary
Short summary
Two assimilation systems developed using two nearly independent wave models are used to study the influences of various error sources including mode bias on wave data assimilation; a statistical method is explored to make full use of the merits of individual assimilation systems for bias correction, thus improving wave analysis greatly. This study opens a door to further our understanding of physical processes in waves and associated air–sea interactions for improving wave modeling.
Mingchen Ma, Yang Gao, Yuhang Wang, Shaoqing Zhang, L. Ruby Leung, Cheng Liu, Shuxiao Wang, Bin Zhao, Xing Chang, Hang Su, Tianqi Zhang, Lifang Sheng, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 19, 12195–12207, https://doi.org/10.5194/acp-19-12195-2019, https://doi.org/10.5194/acp-19-12195-2019, 2019
Short summary
Short summary
Ozone pollution has become severe in China, and extremely high ozone episodes occurred in summer 2017 over the North China Plain. While meteorology impacts are clear, we find that enhanced biogenic emissions, previously ignored by the community, driven by high vapor pressure deficit, land cover change and urban landscape contribute substantially to ozone formation. This study has significant implications for ozone pollution control with more frequent heat waves and urbanization growth in future.
Yuxin Zhao, Xiong Deng, Shaoqing Zhang, Zhengyu Liu, Chang Liu, Gabriel Vecchi, Guijun Han, and Xinrong Wu
Nonlin. Processes Geophys., 24, 681–694, https://doi.org/10.5194/npg-24-681-2017, https://doi.org/10.5194/npg-24-681-2017, 2017
Short summary
Short summary
Here with a simple coupled model that simulates typical scale interactions in the climate system, we study the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA. Results show that an optimal OTW determined from the de-correlation timescale provides maximal observational information that best fits the characteristic variability of the coupled medium during the data blending process.
Xiaolin Yu, Shaoqing Zhang, Xiaopei Lin, and Mingkui Li
Nonlin. Processes Geophys., 24, 125–139, https://doi.org/10.5194/npg-24-125-2017, https://doi.org/10.5194/npg-24-125-2017, 2017
Short summary
Short summary
Parameter estimation (PE) with a global coupled data assimilation (CDA) system can improve the runs, but the improvement remains in a limited range. We have to come back to simple models to sort out the sources of noises. Incomplete observations and the chaotic nature of the atmosphere have much stronger influences on the PE through the state estimation (SE) process. Here, we propose the guidelines of how to enhance the signal-to-noise ratio under partial SE status.
G.-J. Han, X.-F. Zhang, S. Zhang, X.-R. Wu, and Z. Liu
Nonlin. Processes Geophys., 21, 357–366, https://doi.org/10.5194/npg-21-357-2014, https://doi.org/10.5194/npg-21-357-2014, 2014
Related subject area
Climate and Earth system modeling
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
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
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
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Using feature importance as exploratory data analysis tool on earth system models
CropSuite – 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)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
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
A non-intrusive, multi-scale, and flexible coupling interface in WRF
T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5): Model formulation and validation
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
The Earth Science Box Modeling Toolkit (ESBMTK)
High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Presentation, Calibration and Testing of the DCESS II Earth System Model of Intermediate Complexity (version 1.0)
Baseline Climate Variables for Earth System Modelling
The DOE E3SM Version 2.1: Overview and Assessment of the Impacts of Parameterized Ocean Submesoscales
Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework
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
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.
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.
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.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future 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 us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to 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 ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
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
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous 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 sub-sea CO2 injection.
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.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
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.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
Short summary
Short summary
CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used 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. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The Icosahedral Nonhydrostatic (ICON) Model 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.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged 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.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
EGUsphere, https://doi.org/10.5194/egusphere-2024-2072, https://doi.org/10.5194/egusphere-2024-2072, 2024
Short summary
Short summary
We outline and validate developments to the pre-existing process-based model T&C to better represent cropland processes. Foreseen applications of T&C-CROP include hydrological and carbon storage implications of land-use transitions involving crop, forest, and pasture conversion, as well as studies on optimal irrigation and fertilization under a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ulrich Georg Wortmann, Tina Tsan, Mahrukh Niazi, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-1864, https://doi.org/10.5194/egusphere-2024-1864, 2024
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library designed to separate model description from numerical implementation. This approach results in well-documented, easily readable, and maintainable model code, allowing students and researchers to concentrate on conceptual challenges rather than mathematical intricacies.
Malcolm John 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so 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.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Esteban Fernández and Gary Shaffer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-122, https://doi.org/10.5194/gmd-2024-122, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Here we describe, calibrate and test DCESS II, a new, broad, adaptable and fast Earth System Model. DCESS II has been designed for global simulations over time scales 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 should prove to be a useful, computational-friendly tool for simulations of past climates as well as for future Earth System projections.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2363, https://doi.org/10.5194/egusphere-2024-2363, 2024
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 132 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most heavily used variables from Earth System Models, based on an assessment of data publication and download records from the largest archive of global climate projects.
Katherine Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golez, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautum 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. Ordonez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-149, https://doi.org/10.5194/gmd-2024-149, 2024
Revised manuscript accepted for GMD
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 biases reduction in temperature, salinity, and sea-ice extent in the North Atlantic, a small strengthening of the Atlantic Meridional Overturning Circulation, and improvements in many atmospheric climatological variables.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. 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).
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Cited articles
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation,
Mon. Weather Rev., 129, 2884–2903, 2001.
Anderson, J. L.: A local least squares framework for ensemble filtering,
Mon. Weather Rev., 131, 634–642, 2003.
Anderson, J. L. and Anderson, S. L.: A monte carlo implementation of the
nonlinear filtering problem to produce ensemble assimilations and forecasts,
Mon. Weather Rev., 127, 2741–2758, 1999.
Anderson, J. L., Wyman, B., Zhang, S., and Hoar, T.: Assimilation of surface
pressure observations using an ensemble filter in an idealized global
atmospheric prediction system, J. Atmos. Sci., 62, 2925–2938, 2005.
Anderson, J. L. and Collins N.: Scalable Implementations of Ensemble Filter
Algorithms for Data Assimilation, J. Atmo. Ocean Tech., 24, 1452–1463, https://doi.org/10.1175/JTECH2049.1, 2007.
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and
Arellano, A.: The Data Assimilation Research Testbed: A community facility,
B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009.
Arblaster, J. M., Meehl, G. A., and Karoly, D. J.: Future climate change in
the Southern Hemisphere: Competing effects of ozone and greenhouse gases,
Geophys. Res. Lett., 38, L02701, https://doi.org/10.1029/2010GL045384, 2011.
Asefi-Najafabady, S., Vandekar, K., Seimon, A., Lawrence, P., and Lawrence,
D.: Climate change, population and poverty: vulnerability and exposure to
heat stress in East Africa, Climatic Change, 148, 561–573, https://doi.org/10.1007/s10584-018-2211-5,
2018.
Balmaseda, M. and Anderson, D.: Impact of initialization strategies and
observations on seasonal forecast skill, Geophys. Res. Lett., 36, L01701,
https://doi.org/10.1029/2008GL035561, 2009.
Bitz, C. M.: Some aspects of uncertainty in predicting sea ice retreat, in:
Arctic Sea Ice Decline: observations, projections, mechanisms, and
implications, edited by: deWeaver, E.,
Bitz, C. M., and Tremblay, B., AGU Geophysical Monograph Series, American Geophysical Union, 180, 63–76, 2008.
Bloom, S. C., Takacs, L. L., da Silva, A. M., and Ledvina, D.: Data
assimilation using incremental analysis updates, Mon. Weather Rev., 124,
1256, https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2, 1996.
Browne, P. A. and Leeuwen, P. J. van: Twin experiments with the equivalent
weights particle filter and HadCM3, Q. J. Roy. Meteor. Soc., 141,
3399–3414, https://doi.org/10.1002/qj.2621, 2015.
Browne, P. A. and Wilson, S.: A simple method for integrating a complex model
into an ensemble data assimilation system using MPI, Environ. Modell. Softw.,
68, 122–128, 2015.
Browne, P. A., Rosnay, P., Zuo H., Bennett, A., and Dawson, A.: Weakly
Coupled Ocean–Atmosphere Data Assimilation in the ECMWF NWP System, Remote
Sens., 11, 234, https://doi.org/10.3390/rs11030234, 2019.
Carton, J. A., Chepurin, G. A., and Chen, L.: SODA3: A New Ocean Climate
Reanalysis, J. Climate, 31, 6967–6983,
https://doi.org/10.1175/JCLI-D-18-0149.1, 2018.
Chandan, D. and Peltier, W. R.: On the mechanisms of warming the mid-Pliocene and the inference of a hierarchy of climate sensitivities with relevance to the understanding of climate futures, Clim. Past, 14, 825–856, https://doi.org/10.5194/cp-14-825-2018, 2018.
Chang, P., Zhang, S., Danabasoglu, G., Yeager, S. G., Fu, H., Wang, H., et
al.: An unprecedented set of high-resolution earth system simulations for
understanding multiscale interactions in climate variability and change, J.
Adv. Model. Earth Sy., 12, e2020MS002298,
https://doi.org/10.1029/2020MS002298, 2020.
Cheng, W., Curchitser, E., Ladd, C., Stabeno, P., and Wang, M.: Influences
of sea ice on the Eastern Bering Sea: NCAR CESM simulations and comparison
with observations, Deep-Sea Res. Pt. II, 109, 27–38, https://doi.org/10.1016/j.dsr2.2014.03.002, 2014.
Chiodo, G., Garcia-Herrera, R., Calvo, N., Vaquero, J. A., Añel, and
Matthes, K.: The impact of a future solar minimum on climate change
projections in the Northern Hemisphere, Environ. Res. Lett., 11, 034015
https://doi.org/10.1088/1748-9326/11/3/034015, 2016.
Coelho, C. A. S. and Goddard, L.: El Niño–induced tropical droughts in
climate change projections, J. Climate, 22, 6456–6476,
https://doi.org/10.1175/2009JCLI3185.1, 2009.
Collins, M.: Climate predictability on interannual to decadal time scales:
The initial value problem, Clim. Dynam., 19, 671–692, 2002.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., et al.: The Twentieth
Century Reanalysis Project, Q. J. Roy. Meteor. Soc., 137, 1–28,
https://doi.org/10.1002/qj.776, 2011.
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., et al.: The Community
Earth System Model version 2 (CESM2), J. Adv. Model. Earth Sy., 12,
e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
Dee, D., Uppala, S. M., Simmons, A. J., et al.: The ERA-Interim reanalysis: Configuration and
performance of the data assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dennis, J. M., Edwards, J., Evans, K. J., Guba, O., Lauritzen, P. H., Mirin, A. A., St-Cyr, A., Taylor, M. 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.
Derber, J. and Rosati, A.: A global oceanic data assimilation system, J.
Phys. Oceanogr., 19, 1333–1347, 1989.
Evans, K. J., Lauritzen, P. H., Mishra, S. K., Neale, R. B., Taylor, M. A., and Tribbia, J. J.: AMIP Simulation with
the CAM4 Spectral Element Dynamical Core, J. Climate, 26, 689–709, https://doi.org/10.1175/JCLI-D-11-00448.1, 2013.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J. Geophys.
Res., 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994.
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and
practical implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003.
Fasullo, J. T. and Nerem, R. S.: Interannual variability in global mean sea
level estimated from the CESM large and last millennium ensembles, Water, 8, 491, https://doi.org/10.3390/w8110491, 2016.
Gantt, B., He, J., Zhang, X., Zhang, Y., and Nenes, A.: Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects, Atmos. Chem. Phys., 14, 7485–7497, https://doi.org/10.5194/acp-14-7485-2014, 2014.
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two
and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999.
Glotfelty, T., He, J., and Zhang, Y.: The impact of future climate policy
scenarios on air quality and aerosol/cloud interactions using an advanced
version of CESM/CAM5. Part II: Future trend analysis and impacts of
projected anthropogenic emissions, Atmos. Environ., 152, 531–552, 2017.
Goldenson, N., Doherty, S. J., Bitz, C. M., Holland, M. M., Light, B., and Conley, A. J.: Arctic climate response to forcing from light-absorbing particles in snow and sea ice in CESM, Atmos. Chem. Phys., 12, 7903–7920, https://doi.org/10.5194/acp-12-7903-2012, 2012.
Goosse, H. and Holland, M. M.: Mechanisms of interdecadal Arctic climate
variability in the Community Climate System Model CCSM2, J. Climate, 18,
3552–3570, 2005.
Gropp, W., Lusk, E., Doss, N., and Skjellum, A.: A high-performance,
portable implementation of the mpi message passing interface standard,
Parallel Comput., 22, 789–828, 1996.
Gropp, W., Lusk, E., and Thakur, R.: Using MPI-2: Advanced Features of the
Message-Passing Interface, MIT Press, 1999.
Han, G., Wu, X., Zhang, S., Liu, Z., and Li, W.: Error covariance estimation
for coupled data assimilation using a Lorenz atmosphere and a simple
pycnocline ocean model, J. Climate, 26, 10218–10231, 2013.
Hunke, E. C. and Lipscomb, W. H.: CICE: The Los Alamos sea ice model user's
manual, version 4, Los. Alamos. National Laboratory Tech. Rep.,
LA-CC-06-012, 2008.
Jiang, Y., Zhang, S., and Sun, J.: Program codes and main data for the paper “An Online Ensemble Coupled Data Assimilation Capability for the Community Earth System Model: System Design and Evaluation”, Zenodo [code and data set], https://doi.org/10.5281/zenodo.5733849, 2021.
Karspeck, A. R., Yeager, S., Danabasoglu, G., Hoar, Y., Collins, N., Raeder, K., Anderson, J., and Tribbia, J.: An Ensemble
Adjustment Kalman filter for the CCSM4 ocean component, J. Climate, 26,
7392–7413, 2013.
Karspeck, A. R., Danabasoglu, G., Anderson, J., Karol, S., Karol, S., Collins, N., Vertenstein, M., Raeder, K., Hoar, T., Neale, R., Edwards, J., and Craig, A.: A global coupled ensemble data assimilation system using the community earth system model and the data assimilation research testbed, Q. J. Roy Meteor. Soc., 144, 2304–2430, https://doi.org/10.1002/qj.3308, 2018.
Kleist, D. T., Parrish, D. F., Derber, J. C., Treadon, R., Wu, W.-S., and
Lord, S.: Introduction of the GSI into the NCEP Global Data Assimilation
System, Mon. Weather Rev., 24, 1691–1705, 2009.
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K., and Janssen P.: A
coupled data assimilation system for climate reanalysis, Q. J. Roy.
Meteor. Soc., 142, 65–78, https://doi.org/10.1002/qj.2629, 2016.
Lawrence, D. M., Oleson, K. W., Flanner, M. G., et al.: Parameterization
improvements and functional and structural advances in version 4 of the
Community Land Model, J. Adv. Model. Earth Sy., 3, MS000045,
https://doi.org/10.1029/2011MS00045, 2011.
Levitus, S., Antonov, J. I., Wang, J., Delworth, T. L., Dixon, K. W., and
Broccoli, A. J.: Anthropogenic warming of Earth's climate system, Science,
292, 267–270, 2001.
Levitus, S., Antonov, J. I., and Boyer, T. P.:
Warming of the world ocean, 1955–2003, Geophys. Res. Lett., 32, L02604, https://doi.org/10.1029/2004GL021592,
2005.
Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., and Zweng, M. M.: World ocean heat content and thermosteric sea level
change (0–2000 m), 1955–2010, Geophys. Res. Lett., 39, L10603, https://doi.org/10.1029/2012GL051106, 2012.
Lu, F., Liu, Z., Zhang, S., and Liu, Y.: Strongly coupled data assimilation
using leading averaged coupled covariance (LACC). Part II: CGCM
applications, Mon. Weather Rev., 143, 4645–4659, 2015.
Lu, L., Zhang, S., Yeager, S. G., Danabasoglu, G., Chang, P., Wu, L., Lin, X., Rosati, A., and Lu, F.: Impact of coherent ocean
stratification on AMOC reconstruction by coupled data assimilation with a
biased model, J. Climate, 33, 7319–7334,
https://doi.org/10.1175/JCLI-D-19-0735.1, 2020.
Mu, L., Nerger, L., Tang, Q., Loza, S. N., Sidorenko, D., Wang, Q., Semmler, T., Zampieri, L., Losch, M., and Goessling, H. F.: Toward a Data Assimilation System
for Seamless Sea Ice Prediction Based on the AWI Climate Model, J. Adv.
Model. Earth Sy., 12, e2019MS001937, https://doi.org/10.1029/2019MS001937,
2020.
Neale, R. B., Gettelman, A., Park, S., Conley, A. J., Kinnison, D., Dan, M., Smith, A. K., Vitt, F., Morrison, H., and Cameronsmith P.: Description of the NCAR
Community Atmosphere Model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+STR,
1–289, Boulder, CO, National Center for Atmospheric Research, 2012.
Nerger, L. and Hiller, W.: Software for Ensemble-based Data Assimilation
Systems – Implementation Strategies and Scalability, Comput. Geosci., 55,
110–118, 2013.
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0), Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, 2020.
Penny, S. G., Akella, S., Buehner, M., Chevallier, M., Counillon, F., Draper, C., Frolov, S., Fujii, Y., Karspeck, A., Kumar, A., et al.: “Coupled Data Assimilation
for Integrated Earth System Analysis and Prediction: Goals, Challenges and
Recommendations”, WMO, WWRP 2017-3, 2017.
Raeder, K., Anderson, J. L., Collins, N., Hoar, T. J., and Pincus, R.:
DART/CAM: an ensemble data assimilation system for cesm atmospheric models,
J. Climate, 25, 6304–6317, 2012.
Rayner, N., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., and Rowell, D. P.: Global analyses of sea surface temperature, sea ice,
and night marine air temperature since the late nineteenth century, J.
Geophys. Res.-Atmos., 108, 4407,
https://doi.org/10.1029/2002JD002670, 2003.
Roberts, M. J., Jackson, L. C., Roberts, C. D., et al.: Sensitivity of
the Atlantic meridional overturning circulation to model resolution in CMIP6
HighResMIP simulations and implications for future changes, J. Adv. Model.
Earth Sy., 12, e2019MS002014, https://doi.org/10.1029/2019MS002014, 2020.
Rosati, A., Miyakoda, K., and Gudgel, R.: The impact of ocean initial conditions
on ENSO forecasting with a coupled model, Mon. Weather Rev., 125, 754–772,
1997.
Saha, S., Nadiga, S., Thiaw, C., and Wang, J.: The NCEP climate forecast
system, J. Climate, 27, 2185–2208, https ://doi.org/10.1175/JCLI3812.1,
2006.
Saha, S., Moorthi, S., Pan H.-L., et al.: The NCEP Climate Forecast System Reanalysis, B.
Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1,
2010.
Skachko, S., Buehner, M., Laroche, S., Lapalme, E., Smith, G., Roy, F., Surcel-Colan, D., Bélanger, J.-M., and Garand, L.: Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1), Geosci. Model Dev., 12, 5097–5112, https://doi.org/10.5194/gmd-12-5097-2019, 2019.
Slivinski, L. C., Compo, G. P., Whitaker, J. S., et al.: Towards a more
reliable historical reanalysis: Improvements for version 3 of the Twentieth
Century Reanalysis system, Q. J. Roy. Meteor. Soc., 145, 2876–2908,
https://doi.org/10.1002/qj.3598, 2019.
Small, R. J., Bacmeister, J., Bailey, D., et al.: A new synoptic scale
resolving global climate simulation using the community earth system model,
J. Adv. Model. Earth Sy., 6, 1065–1094,
https://doi.org/10.1002/2014MS000363, 2014.
Smith, R. D., Jones, P., Briegleb, B., et al.: The parallel ocean
program (POP) reference manual, Los Alamos National Laboratory tech. Rep.
LAUR-10-01853, 2010.
Poli, P., Hersbach, H., Tan, D. G. H., et al.: The data assimilation system and initial performance
evaluation of the ECMWF pilot reanalysis of the 20th-century assimilating
surface observations only (ERA-20C), ECMWF ERA Rep. 14, 59 pp., 2013.
Tang, Q., Mu, L., Sidorenko, D., Goessling, H., Semmler, T., and Nerger, L.:
Improving the ocean and atmosphere in a coupled ocean–atmosphere model by
assimilating satellite sea-surface temperature and subsurface profile data,
Q. J. Roy. Meteor. Soc., 146, 4014–4029, 2020.
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker,
J. S.: Ensemble square root filters, Mon. Weather Rev., 131, 1485–1490,
2003.
Whitaker, J. S., Compo, G. P., Wei, X., and Hamill, T. M.: Reanalysis without
radiosondes using ensemble data assimilation, Mon. Weather Rev., 132,
1190–1200, 2004.
Yang, X., Delworth, T. L., Zeng, F., Zhang, L., Cooke, W. F., Harrison, M.
J., Rosati, A., Underwood, S., Compo, G. P., and McColl, C.: On the Development
of GFDL's decadal prediction system: initialization approaches and
retrospective forecast assessment, J. Adv. Model. Earth Sy., 13,
e2021MS002529, https://doi.org/10.1029/2021MS002529, 2021.
Yu, X., Zhang, S., Li, J., Lu, L., Liu, Z., Li, M., Yu, H., Han, G., Lin, X., Wu, L., and Chang, P.: A multi-timescale EnOI-Like
high-efficiency approximate filter for coupled model data assimilation, J.
Adv. Model. Earth Sy., 11, 45–63, https://doi.org/10.1029/2018MS001504,
2019.
Zhang, F. Q., Snyder, C., and Sun, J.: Impacts of initial estimate and
observation availability on convective-scale data assimilation with an
ensemble kalman filter, Mon. Weather Rev., 132, 1–16, 2004.
Zhang, S.: A study of impacts of coupled model initial shocks and
state-parameter optimization on climate prediction using a simple pycnocline
prediction model, J. Climate, 24, 6210–6226, 2011.
Zhang, S. and Anderson, J. L.: Impact of spatially and temporally varying
estimates of error covariance on assimilation in a simple atmospheric model,
Tellus A, 55, 126–147, 2003.
Zhang, S. and Rosati, A.: An Inflated Ensemble Filter for Ocean Data
Assimilation with a Biased Coupled GCM, Mon. Weather Rev., 138, 3905–3931,
https://doi.org/10.1175/2010MWR3326.1, 2010.
Zhang, S., Harrison, M. J., Wittenberg, A. T., Rosati, A., Anderson, J. L., and
Balaji, V.: Initialization of an ENSO forecast system using a parallelized
ensemble filter, Mon. Weather Rev., 133, 3176–3201, 2005.
Zhang, S., Harrison, M. J., Rosati, A., and Wittenberg, A.: System design
and evaluation of coupled ensemble data assimilation for global oceanic
climate studies, Mon. Weather Rev., 135, 3541–3564,
https://doi.org/10.1175/MWR3466.1, 2007.
Zhang, S., Rosati, A., and Delworth, T.: The Adequacy of Observing Systems
in Monitoring the Atlantic Meridional Overturning Circulation and North
Atlantic Climate, J. Climate, 23, 5311–5324, 2010.
Zhang, S., Fu, H., Wu, L., Li, Y., Wang, H., Zeng, Y., Duan, X., Wan, W., Wang, L., Zhuang, Y., Meng, H., Xu, K., Xu, P., Gan, L., Liu, Z., Wu, S., Chen, Y., Yu, H., Shi, S., Wang, L., Xu, S., Xue, W., Liu, W., Guo, Q., Zhang, J., Zhu, G., Tu, Y., Edwards, J., Baker, A., Yong, J., Yuan, M., Yu, Y., Zhang, Q., Liu, Z., Li, M., Jia, D., Yang, G., Wei, Z., Pan, J., Chang, P., Danabasoglu, G., Yeager, S., Rosenbloom, N., and Guo, Y.: Optimizing high-resolution Community Earth System Model on a heterogeneous many-core supercomputing platform, Geosci. Model Dev., 13, 4809–4829, https://doi.org/10.5194/gmd-13-4809-2020, 2020a.
Zhang, S., Liu, Z., Zhang, X., et al.: Coupled data assimilation and
parameter estimation in coupled ocean–atmosphere models: a review, Clim.
Dynam., 54, 5127–5144, https://doi.org/10.1007/s00382-020-05275-6, 2020b.
Short summary
An online ensemble coupled data assimilation system with the Community Earth System Model is designed and evaluated. This system uses the memory-based information transfer approach which avoids frequent I/O operations. The observations of surface pressure, sea surface temperature, and in situ temperature and salinity profiles can be effectively assimilated into the coupled model. That will facilitate a long-term high-resolution climate reanalysis once the algorithm efficiency is much improved.
An online ensemble coupled data assimilation system with the Community Earth System Model is...
