Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4677-2023
© Author(s) 2023. 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-16-4677-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Aug 2023
Model description paper |
| 18 Aug 2023
| 18 Aug 2023
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
Bin Mu
School of Software Engineering, Tongji University, Shanghai 201804, China
Xiaodan Luo
School of Software Engineering, Tongji University, Shanghai 201804, China
Shijin Yuan
CORRESPONDING AUTHOR
School of Software Engineering, Tongji University, Shanghai 201804, China
Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing, China
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Bin Mu, Yuehan Cui, Shijin Yuan, and Bo Qin
Geosci. Model Dev., 15, 4105–4127, https://doi.org/10.5194/gmd-15-4105-2022, https://doi.org/10.5194/gmd-15-4105-2022, 2022
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An ENSO deep learning forecast model (ENSO-MC) is built to simulate the spatial evolution of sea surface temperature, analyse the precursor and identify the sensitive area. The results reveal the pronounced subsurface features before different types of events and indicate that oceanic thermal anomaly in the central and western Pacific provides a key long-term memory for predictions, demonstrating the potential usage of the ENSO-MC model in simulation, understanding and observations of ENSO.
Bin Mu, Bo Qin, and Shijin Yuan
Geosci. Model Dev., 14, 6977–6999, https://doi.org/10.5194/gmd-14-6977-2021, https://doi.org/10.5194/gmd-14-6977-2021, 2021
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Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air–sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
Bin Mu, Jing Li, Shijin Yuan, Xiaodan Luo, and Guokun Dai
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2020-27, https://doi.org/10.5194/npg-2020-27, 2020
Revised manuscript not accepted
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The North Atlantic Oscillation (NAO) phenomenon has a significant impact on the global climate. In this paper, we perform the identification of optimal precursors for NAO to investigate the predictability problem of NAO events. We select proper simulation duration and find out the structure of OPRs for two types of NAO events. Besides, the method applied in this work can be generalized to numerical models that do not have an adjoint model, and its efficiency has been further enhanced.
Bin Mu, Jing Li, Shijin Yuan, Xiaodan Luo, and Guokun Dai
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2019-25, https://doi.org/10.5194/npg-2019-25, 2019
Revised manuscript not accepted
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The North Atlantic Oscillation (NAO) phenomenon has a significant impact on the global climate. In this paper, we propose a hybrid algorithm to identify the perturbations that trigger NAO events. The result indicates that the perturbations solved by our method can trigger the NAO mode successfully. Moreover, using the parallel framework, the speedup ratio of the parallel algorithm achieves 40 compared to the serial version.
Linlin Zhang, Bin Mu, Shijin Yuan, and Feifan Zhou
Nonlin. Processes Geophys., 25, 693–712, https://doi.org/10.5194/npg-25-693-2018, https://doi.org/10.5194/npg-25-693-2018, 2018
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We propose a novel approach to solve conditional nonlinear optimal perturbation for identifying sensitive areas for tropical cyclone adaptive observations. This method is free of adjoint models and overcomes two obstacles, not having adjoint models and having dimensions higher than the problem space. All experimental results prove that it is a meaningful and effective method for solving CNOP and provides a new way for such research. This work aims to solve CNOP and identify sensitive areas.
Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-189, https://doi.org/10.5194/gmd-2024-189, 2025
Preprint under review for GMD
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In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.
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Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Bin Mu, Yuehan Cui, Shijin Yuan, and Bo Qin
Geosci. Model Dev., 15, 4105–4127, https://doi.org/10.5194/gmd-15-4105-2022, https://doi.org/10.5194/gmd-15-4105-2022, 2022
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An ENSO deep learning forecast model (ENSO-MC) is built to simulate the spatial evolution of sea surface temperature, analyse the precursor and identify the sensitive area. The results reveal the pronounced subsurface features before different types of events and indicate that oceanic thermal anomaly in the central and western Pacific provides a key long-term memory for predictions, demonstrating the potential usage of the ENSO-MC model in simulation, understanding and observations of ENSO.
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The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
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A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Bin Mu, Bo Qin, and Shijin Yuan
Geosci. Model Dev., 14, 6977–6999, https://doi.org/10.5194/gmd-14-6977-2021, https://doi.org/10.5194/gmd-14-6977-2021, 2021
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Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air–sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
Shihe Ren, Xi Liang, Qizhen Sun, Hao Yu, L. Bruno Tremblay, Bo Lin, Xiaoping Mai, Fu Zhao, Ming Li, Na Liu, Zhikun Chen, and Yunfei Zhang
Geosci. Model Dev., 14, 1101–1124, https://doi.org/10.5194/gmd-14-1101-2021, https://doi.org/10.5194/gmd-14-1101-2021, 2021
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Sea ice plays a crucial role in global energy and water budgets. To get a better simulation of sea ice, we coupled a sea ice model with an atmospheric and ocean model to form a fully coupled system. The sea ice simulation results of this coupled system demonstrated that a two-way coupled model has better performance in terms of sea ice, especially in summer. This indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.
Bin Mu, Jing Li, Shijin Yuan, Xiaodan Luo, and Guokun Dai
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2020-27, https://doi.org/10.5194/npg-2020-27, 2020
Revised manuscript not accepted
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The North Atlantic Oscillation (NAO) phenomenon has a significant impact on the global climate. In this paper, we perform the identification of optimal precursors for NAO to investigate the predictability problem of NAO events. We select proper simulation duration and find out the structure of OPRs for two types of NAO events. Besides, the method applied in this work can be generalized to numerical models that do not have an adjoint model, and its efficiency has been further enhanced.
Bin Mu, Jing Li, Shijin Yuan, Xiaodan Luo, and Guokun Dai
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2019-25, https://doi.org/10.5194/npg-2019-25, 2019
Revised manuscript not accepted
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The North Atlantic Oscillation (NAO) phenomenon has a significant impact on the global climate. In this paper, we propose a hybrid algorithm to identify the perturbations that trigger NAO events. The result indicates that the perturbations solved by our method can trigger the NAO mode successfully. Moreover, using the parallel framework, the speedup ratio of the parallel algorithm achieves 40 compared to the serial version.
Bin Mu, Linlin Zhang, Shijin Yuan, and Wansuo Duan
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2019-24, https://doi.org/10.5194/npg-2019-24, 2019
Publication in NPG not foreseen
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In this paper, we rewrite the adaptive cooperation co-evolution of parallel particle swarm optimization and wolf search algorithm based on principal component analysis (ACPW) and applied it to solve conditional nonlinear optimal perturbation (CNOP) in the WRF-ARW for identifying sensitive areas of typhoon target observations. The experimental results show that the ACPW is meaningful, feasible and effective.
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The Cryosphere, 13, 1423–1439, https://doi.org/10.5194/tc-13-1423-2019, https://doi.org/10.5194/tc-13-1423-2019, 2019
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The Arctic sea ice extent is diminishing, which is deemed an immediate response to a warmer Earth. However, quantitative estimates about the contribution due to transport and melt to the sea ice loss are still vague. This study mainly utilizes satellite observations to quantify the dynamic and thermodynamic aspects of ice loss for nearly 40 years (1979–2016). In addition, the potential impacts on ice reduction due to different atmospheric circulation pattern are highlighted.
Linlin Zhang, Bin Mu, Shijin Yuan, and Feifan Zhou
Nonlin. Processes Geophys., 25, 693–712, https://doi.org/10.5194/npg-25-693-2018, https://doi.org/10.5194/npg-25-693-2018, 2018
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We propose a novel approach to solve conditional nonlinear optimal perturbation for identifying sensitive areas for tropical cyclone adaptive observations. This method is free of adjoint models and overcomes two obstacles, not having adjoint models and having dimensions higher than the problem space. All experimental results prove that it is a meaningful and effective method for solving CNOP and provides a new way for such research. This work aims to solve CNOP and identify sensitive areas.
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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.
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A.,
Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J.,
Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y.,
Downie, R., and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with
probabilistic deep learning, Nat. Commun., 12, 5124, https://doi.org/10.1038/s41467-021-25257-4, 2021. a, b
Bintanja, R. and Selten, F. M.: Future increases in Arctic precipitation linked
to local evaporation and sea-ice retreat, Nature, 509, 479–482, 2014. a
Boisvert, L. N. and Stroeve, J. C.: The Arctic is becoming warmer and wetter as
revealed by the Atmospheric Infrared Sounder, Geophys. Res. Lett.,
42, 4439–4446, 2015. a
Boisvert, L., Wu, D., Vihma, T., and Susskind, J.: Verification of air/surface humidity differences from AIRS and ERA-Interim in support of turbulent flux estimation in the Arctic, J. Geophys. Res.-Atmoss., 120, 945–963,
https://doi.org/10.1002/2014JD021666, 2015. a
Boisvert, L. N., Webster, M. A., Petty, A. A., Markus, T., Bromwich, D. H., and Cullather, R. I.: Intercomparison of precipitation estimatesover the Arctic Ocean and its peripheral seas from reanalyses, J. Climate, 31, 8441–8462, https://doi.org/10.1175/JCLI-D-18-4850125.1, 2018. a
Bushuk, M. and Giannakis, D.: The Seasonality and Interannual
Variability of Arctic Sea Ice Reemergence, J. Climate, 30, 4657–4676, https://doi.org/10.1175/JCLI-D-16-0549.1, 2017. a
Chi, J. and Kim, H. C.: Prediction of Arctic Sea Ice Concentration Using a
Fully Data Driven Deep Neural Network, Remote Sens.-Basel, 9, 1305, https://doi.org/10.3390/rs9121305, 2017. a, b
Chi, J., Bae, J., and Kwon, Y.-J.: Two-Stream Convolutional Long- and
Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic
Sea Ice Prediction, Remote Sens.-Basel, 13, 3413, https://doi.org/10.3390/rs13173413, 2021. a, b
Choi, Y.-S., Ho, C.-H., Park, C.-E., Storelvmo, T., and Tan, I.: Influence of cloud phase composition on climate feedbacks, J. Geophys. Res.-Atmos., 119, 3687–3700, https://doi.org/10.1002/2013JD020582, 2014. a, b
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou,
D., Francis, J., Dethloff, K., Entekhabi, D., and Overland, J. A.: Recent
Arctic amplification and extreme mid-latitude weather, Nat. Geosci., 7,
627–637, 2014. a
Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., and Windnagel, A. K.: Sea Ice Index, Version 3, Boulder, Colorado USA. National Snow and Ice Data Center [data set], https://doi.org/10.7265/N5K072F8, 2017. a, b, c
Goosse, H., Kay, J. E., Armour, K. C., Bodas‐Salcedo, A., Chepfer, H.,
Docquier, D., Jonko, A. K., Kushner, P. J., Lecomte, O., Massonnet, F., Park,
H., Pithan, F., Svensson, G., and Vancoppenolle, M.: Quantifying climate
feedbacks in polar regions, Nat. Commun., 9, 1919, https://doi.org/10.1038/s41467-018-04173-0, 2018. a
He-Ping, L. I., You-Ming, X. U., and Rao, S. Q.: Analysis on Influence of Sea
Ice in North Pole Area on Runoff in the Upper Yellow River during Flood Seas
on, Adv. Water Sci., 11, 284–290, 2000. a
Holton, J. R. and Hakim, G. J.: An Introduction to Dynamic Meteorology, vol. Academic Press, 88, https://doi.org/10.1016/C2009-0-63394-8, 2013. a
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., and Zhang, H.-M.: Improvements of the daily optimum interpolation sea surface temperature (DOISST) version 2.1, J. Climate, 34, 2923–2939, https://doi.org/10.1175/JCLI-D-20-0166.1, 2021. a, b
Huang, T., Lühr, H., Wang, H., and Xiong, C.: The relationship of high-latitude thermospheric wind with ionospheric horizontal current,500 as observed by CHAMP satellite, J. Geophys. Res.-Space, 122, 12–378, https://doi.org/10.1002/2017JA024614, 2017. a
Huang, X., Chen, X., and Yue, Q.: Band-by-band contributions to the longwave cloud radiative feedbacks, Geophys. Res. Lett., 46, 6998–7006, https://doi.org/10.1029/2019GL083466, 2019. a
Huang, Y., Kleindessner, M., Munishkin, A., Varshney, D., Guo, P., and Wang,
J.: Benchmarking of Data-Driven Causality Discovery Approaches in the
Interactions of Arctic Sea Ice and Atmosphere, Front. Big Data, 4, 642,
https://doi.org/10.3389/fdata.2021.642182, 2021. a, b, c
Japan Meteorological Agency: JRA-55: Japanese 55-year Reanalysis,
Monthly Means and Variances, Computational and Information Systems Laboratory [data set],
https://doi.org/10.5065/D60G3H5B, 2013. a, b
Johannessen, O. M., Bobylev, L. P., Shalina, E. V., and Sandven, S.: Sea ice in
the Arctic: past, present and future, Springer, https://doi.org/10.1007/978-3-030-21301-5, 2020. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteorol. Soc.,
77, 437–472,
https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a, b
Kapsch, M.-L., Graversen, R. G., Tjernström, M., and Bintanja, R.: The Effect
of Downwelling Longwave and Shortwave Radiation on Arctic Summer Sea Ice,
J. Climate, 29, 1143–1159, https://doi.org/10.1175/JCLI-D-15-0238.1, 2016. a, b, c, d
Kapsch, M.-L., Skific, N., Graversen, R. G., Tjernström, M., and Francis, J. A.: Summers with low Arctic sea ice linked to persistence of spring atmospheric circulation patterns, Clim. Dynam., 52, 2497–2512, https://doi.org/10.1007/s00382-018-4279-z, 2019. a
Kay, J. E. and Wood, R.: Timescale analysis of aerosol sensitivity during homogeneous freezing and implications for upper tropospheric water vapor budgets, Geophys. Res. Lett., 35, L10809, https://doi.org/10.1029/2007GL032628, 2008. a
Kim, Y. J., Kim, H.-C., Han, D., Lee, S., and Im, J.: Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks, The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020, 2020. a, b, c
Kwok, R. and Untersteiner, N.: The thinning of Arctic sea ice, Phys. Today, 64,
36–41, 2011. a
Liang, X., Losch, M., Nerger, L., Mu, L., Yang, Q., and Liu, C.: Using Sea
Surface Temperature Observations to Constrain Upper Ocean Properties in an
Arctic Sea Ice‐Ocean Data Assimilation System, J. Geophys.
Res.-Oceans, 124, 4727–4743,
https://doi.org/10.1029/2019JC015073, 2019. a
Liang, X., Li, X., Bi, H., Losch, M., Gao, Y., Zhao, F., Tian, Z., and Liu, C.:
A Comparison of Factors That Led to the Extreme Sea Ice Minima in the
Twenty-First Century in the Arctic Ocean, J. Climate, 35, 1249–1265, https://doi.org/10.1175/JCLI-D-21-0199.1, 2022. a
Liou, K.-N.: An introduction to atmospheric radiation, 2nd Edn., vol. 84, Elsevier, ISBN: 9780124514515, 2002. a
Liu, J., Song, M., Horton, R. M., and Hu, Y.: Reducing spread in climate model
projections of a September ice-free Arctic, P. Natl. Acad. Sci. USA, 110, 12571–12576,
2013. a
Liu, X. Y. and Liu, H. L.: Investigation of influence of atmospheric
variability on sea ice variation trend in recent years in the Arctic with
numerical sea ice-ocean coupled model, Chinese J. Geophys., 55,
2867–2875, 2012. a
Luo, B., Luo, D., Wu, L., Zhong, L., and Simmonds, I.: Atmospheric
circulation patterns which promote winter Arctic sea ice decline,
Environ. Res. Lett., 12, 054017, https://doi.org/10.1088/1748-9326/69d0,
2017. a
Luo, X.: The code source of IceTFT v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.7409157, 2022. a
Mu, B., Li, J., Yuan, S., Luo, X., and Dai, G.: NAO Index Prediction using LSTM
and ConvLSTM Networks Coupled with Discrete Wavelet Transform, in: 2019
International Joint Conference on Neural Networks (IJCNN), 1–8,
https://doi.org/10.1109/IJCNN.2019.8851968, 2019. a
Mu, B., Qin, B., Yuan, S., and Qin, X.: A Climate Downscaling Deep Learning
Model considering the Multiscale Spatial Correlations and Chaos of
Meteorological Events, Math. Probl. Eng., 2020, 1–17,
https://doi.org/10.1155/2020/7897824, 2020. a
Mu, B., Qin, B., and Yuan, S.: ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler, Geosci. Model Dev., 14, 6977–6999, https://doi.org/10.5194/gmd-14-6977-2021, 2021. a
Mu, B., Cui, Y., Yuan, S., and Qin, B.: Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0, Geosci. Model Dev., 15, 4105–4127, https://doi.org/10.5194/gmd-15-4105-2022, 2022. a
National Oceanic and Atmospheric Administration Physical Sciences Laboratory, Boulder Climate and Weather Information: Boulder-Monthly-Means-Snowfall: 1.0.0 (snowfall), Zenodo [data set], https://doi.org/10.5281/zenodo.7533097, 2023. a
Overland, J. E. and Wang, M.: Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice, Tellus A, 62, 1–9, https://doi.org/10.1111/j.1600-0870.2009.00421.x, 2010. a
Overland, J. E. and Wang, M.: When will the summer Arctic be nearly sea ice
free?, Geophys. Res. Lett., 40, 2097–2101, 2013. a
Parkinson, C. L., Cavalieri, D. J., Gloersen, P., Zwally, H. J., and Comiso,
J. C.: Arctic sea ice extents, areas, and trends, 1978–1996, J.
Geophys. Res.-Oceans, 104, 20837–20856, 1999. a
Perovich, D., Grenfell, T., Light, B., and Hobbs, P.: Seasonal evolution of the albedo of multiyear Arctic sea
ice, J. Geophys. Res., 107, 8044, https://doi.org/10.1029/2000JC000438, 2002. a
Perovich, D. K., Light, B., Eicken, H., Jones, K. F., Runciman, K., and Nghiem,
S. V.: Increasing solar heating of the Arctic Ocean and adjacent seas,
1979–2005: Attribution and role in the ice‐albedo feedback, Geophys.
Res. Lett., 34, L19505, https://doi.org/10.1029/2007GL031480, 2007. a
Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and Precipitation, 18, 381–382, https://doi.org/10.1080/02786829808965531, 1978. a
Polyakova, E. I., Journel, A. G., Polyakov, I. V., and Bhatt, U. S.: Changing
relationship between the North Atlantic Oscillation and key North Atlantic
climate parameters, Geophys. Res. Lett., 33, 1–4, https://doi.org/10.1029/2005GL024573, 2006. a
Ramsayer, K.: 2020 Arctic Sea Ice Minimum at Second Lowest on Record, NASA
Global Climate Change, Vital Signs of the Planet, https://www.nasa.gov/feature/goddard/2020/2020-arctic-sea-ice-minimum-at-second-lowest-on-record (last access: 22 September 2020), 2020. a
Ren, Y., Li, X., and Zhang, W.: A data-driven deep learning model for weekly
sea ice concentration prediction of the Pan-Arctic during the melting season,
IEEE T. Geosci. Remote, 60, 4304819, https://doi.org/10.1109/TGRS.2022.3177600, 2022. a
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface Temperature, J. Climate, 20, 5473–5496, https://doi.org/10.1175/JCLI-D-14-00293.1, 2007. a, b
Rinke, A., Knudsen, E. M., Mewes, D., Dorn, W., Handorf, D., Dethloff, K., and Moore, J.: Arctic summer sea ice melt and related atmospheric conditions in coupled regional climate model simulations and observations, J. Geophys. Res.-Atmo., 124, 6027–6039, https://doi.org/10.1029/2018JD030207, 2019. a
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in
recent Arctic temperature amplification, Nature, 464, 1334–1337, 2010. a
Screen, J. A. and Simmonds, I.: Declining summer snowfall in the Arctic:
causes, impacts and feedbacks, Clim. Dynam., 38, 2243–2256, 2012. a
Sea Ice Outlook: 2019 June Report, https://www.arcus.org/sipn/sea-ice-outlook/2019/june, last access: 21 June 2019a. a
Sea Ice Outlook: 2019 July Report, https://www.arcus.org/sipn/sea-ice-outlook/2019/july, last access: 24 July 2019b. a
Sea Ice Outlook: 2019 August Report, https://www.arcus.org/sipn/sea-ice-outlook/2019/august, last access: 30 August 2019c. a
Sea Ice Outlook: 2020 June Report, https://www.arcus.org/sipn/sea-ice-outlook/2020/june, last access: 26 June 2020a. a
Sea Ice Outlook: 2020 July Report, https://www.arcus.org/sipn/sea-ice-outlook/2020/july, last access: 27 July 2020b. a
Sea Ice Outlook: 2020 August Report, https://www.arcus.org/sipn/sea-ice-outlook/2020/august, last access: 31 August 2020c. a
Sea Ice Outlook: 2021 June Report, https://www.arcus.org/sipn/sea-ice-outlook/2021/june, last access: 26 June 2021a. a
Sea Ice Outlook: 2021 July Report, https://www.arcus.org/sipn/sea-ice-outlook/2021/july, last access: 27 July 2021b. a
Sea Ice Outlook: 2021 August Report, https://www.arcus.org/sipn/sea-ice-outlook/2021/august, last access: 31 August 2021c. a
Sea Ice Outlook: 2021 September Report, https://www.arcus.org/sipn/sea-ice-outlook/2021/september, last accessed: 21 September 2021d. a
Sea Ice Outlook: 2022 June Report, https://www.arcus.org/sipn/sea-ice-outlook/2022/june, last access: 27 June 2022a. a
Sea Ice Outlook: 2022 July Report, https://www.arcus.org/sipn/sea-ice-outlook/2022/july, last access: 26 July 2022b. a
Sea Ice Outlook: 2022 August Report, https://www.arcus.org/sipn/sea-ice-outlook/2022/august, last access: 25 August 2022c. a
Sea Ice Outlook: 2022 September Report, https://www.arcus.org/sipn/sea-ice-outlook/2022/september, last accessed: 22 September 2022d. a
Stroeve, J., Hamilton, L. C., Bitz, C. M., and Blanchard-Wrigglesworth, E.:
Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook
2008–2013, Geophys. Res. Lett., 41, 2411–2418, 2014. a
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland,
M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3 and
observations, Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676, 2012. a
Sturm, M., Holmgren, J., and Perovich, D. K.: Winter snow cover on the sea ice of the Arctic Ocean at the
Surface Heat Budget of the Arctic Ocean (SHEBA): Temporal evolution and
spatial variability, J. Geophys. Res., 107, 8047, https://doi.org/10.1029/2000JC000400, 2002. a
Tong, J., Chen, M., Qiu, Y., Yanping, L. I., Cao, J., Sciences, O. E.,
University, X., and of Marine Environmental Science, S. K. L.: Contrasting
patterns of river runoff and sea-ice melted water in the Canada Basin, Acta
Oceanol. Sin., 33, 46–52, https://doi.org/10.1007/s13131-014-0488-4, 2014. a
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,
Kaiser, Ł., and Polosukhin, I.: Attention is all you need, ArXiv [preprint], https://doi.org/10.48550/arXiv.1706.03762, 2017. a
Voosen, P.: New feedbacks speed up the demise of Arctic sea ice, Science, 369,
1043–1044,
https://doi.org/10.1126/science.369.6507.1043, 2020. a
Wallace, J. M. and Hobbs, P. V.: Atmospheric Science: An Introductory Survey, 2nd Edn., Academic Press, https://doi.org/10.1016/C2009-0-00034-8, 2006. a
Watanabe, E., Wang, J., Sumi, A., and Hasumi, H.: Arctic dipole anomaly and its contribution to sea ice export from the Arctic Ocean in the 20th century, Geophys. Res. Lett., 33, 160–176, https://doi.org/10.1029/2006GL028112, 2006. a
Weatherly, J. W. and Walsh, J. E.: The effects of precipitation and river
runoff in a coupled ice-ocean model of the Arctic, Clim. Dynam., 12,
785–798, 1996. a
Wei, K., Liu, J., Bao, Q., He, B., Ma, J., Li, M., Song, M., and Zhu, Z.:
Subseasonal to seasonal Arctic sea-ice prediction: A grand challenge of
climate science, Atmos. Ocean. Sci. Lett., 14, 100052, https://doi.org/10.1016/J.AOSL.2021.100052, 2021.
a
Zheng, F., Sun, Y., Yang, Q., and Longjiang, M. U.: Evaluation of Arctic
Sea-ice Cover and Thickness Simulated by MITgcm, Adv. Atmos.
Sci., 38, 29–48, https://doi.org/10.1007/s00376-020-9223-6, 2021. a
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which...
