Articles | Volume 8, issue 11
https://doi.org/10.5194/gmd-8-3639-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-3639-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
06 Nov 2015
Model description paper |
| 06 Nov 2015
S4CAST v2.0: sea surface temperature based statistical seasonal forecast model
R. Suárez-Moreno
CORRESPONDING AUTHOR
Departamento de Geofísica y Meteorología, Facultad de Físicas, Universidad Complutense de Madrid, Plaza de las Ciencias 1, 28040 Madrid, Spain
Instituto de Geociencias (IGEO), Facultad de Ciencias Geológicas, Universidad Complutense de Madrid – CSIC, C/José Antonio Novais 12, 28040 Madrid, Spain
B. Rodríguez-Fonseca
Departamento de Geofísica y Meteorología, Facultad de Físicas, Universidad Complutense de Madrid, Plaza de las Ciencias 1, 28040 Madrid, Spain
Instituto de Geociencias (IGEO), Facultad de Ciencias Geológicas, Universidad Complutense de Madrid – CSIC, C/José Antonio Novais 12, 28040 Madrid, Spain
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This study evaluates machine learning (ML) methods to improve subseasonal-to-seasonal (S2S) rainfall forecasts in Senegal during the West African monsoon. Using high-resolution precipitation data and atmospheric-oceanic reanalysis, we show that ML models like ridge regression outperform traditional climate models. These methods enhance prediction accuracy and efficiency, offering valuable tools for climate risk management and water resource planning.
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This study evaluates machine learning (ML) methods to improve subseasonal-to-seasonal (S2S) rainfall forecasts in Senegal during the West African monsoon. Using high-resolution precipitation data and atmospheric-oceanic reanalysis, we show that ML models like ridge regression outperform traditional climate models. These methods enhance prediction accuracy and efficiency, offering valuable tools for climate risk management and water resource planning.
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This paper describes the first release of Spy4Cast, a python interface to run a maximum covariance analysis model to produce seasonal forecast. This API allows the user to increase automation and productivity, including determination of modes, crossvalidation hindcast and validation. It includes a visualisation module for the results as well as a preprocessing tool that can be also used for other climate variability studies.
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Dynamical models often struggle with complex interactions in remote regions, leading to reduced accuracy. To address this, statistical models that identify relationships between predictors and predictands are valuable. NN4CAST, our deep learning model, enhances seasonal predictions by capturing these dynamics effectively, especially in challenging regions like the North Atlantic. This advancement could benefit critical sectors including marine ecosystems, public health, and energy management.
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Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
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Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
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Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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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.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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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.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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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.
Cited articles
Adams, R. M., Chen, C. C., McCarl, B. A., and Weiher, R. F.: The economic consequences of ENSO events for agriculture, Clim. Res., 13, 165–172, 1999.
Ault, T. R., Cole, J. E., and St George, S.: The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models, Geophys. Res. Lett., 39, L21705, https://doi.org/10.1029/2012GL053424, 2012.
Baboo, S. S. and Shereef, I. K.: An efficient weather forecasting system using artificial neural network, International Journal of Environmental Science and Development, 1, 2010–0264, 2010.
Barnett, T. P.: Monte Carlo climate forecasting, J. Climate, 8, 1005–1022, 1995.
Barnett, T. P. and Preisendorfer, R.: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis, Mon. Weather Rev., 115, 1825–1850, 1987.
Barnett, T. P., Graham, N., Pazan, S., White, W., Latif, M., and Flügel, M.: ENSO and ENSO-related predictability. Part I: Prediction of equatorial Pacific sea surface temperature with a hybrid coupled ocean-atmosphere model, J. Climate, 6, 1545–1566, 1993.
Barnston, A. G.: Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score, Weather Forecast., 7, 699–709, 1992.
Barnston, A. G. and Ropelewski, C. F.: Prediction of ENSO episodes using canonical correlation analysis, J. Climate, 5, 1316–1345, 1992.
Barnston, A. G. and Smith, T. M.: Specification and prediction of global surface temperature and precipitation from global SST using CCA, J. Climate, 9, 2660–2697, 1996.
Barnston, A. G. and Tippett, M. K.: Climate information, outlooks, and understanding – where does the IRI stand?, Earth Perspectives, 1, 1–17, 2014.
Barnston, A. G. and van den Dool, H. M.: A degeneracy in cross-validated skill in regression-based forecasts, J. Climate, 6, 963–977, 1993.
Barnston, A. G., van den Dool, H. M., Rodenhuis, D. R., Ropelewski, C. R., Kousky, V. E., O'Lenic, E. A., and Leetmaa, A.: Long-lead seasonal forecasts-Where do we stand?, B. Am. Meteorol. Soc., 75, 2097–2114, 1994.
Barnston, A. G., He, Y., and Glantz, M. H.: Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset, B. Am. Meteorol. Soc., 80, 217–243, 1999.
Barnston, A. G., Tippet, M. K., van den Dool, H. M., and Unger, D. A.: Toward an Improved Multi-model ENSO Prediction, J. Appl. Meteorol. Clim., 54, 1579–1595, https://doi.org/10.1175/JAMC-D-14-0188.1, 2015.
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013.
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: from CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018, 2013.
Biasutti, M., Sobel, A. H., and Kushnir, Y.: AGCM precipitation biases in the tropical Atlantic, J. Climate, 19, 935–958, 2006.
Bjerknes, J.: Atmospheric teleconnections from the equatorial pacific 1, Mon. Weather Rev., 97, 163–172, 1969.
Bretherton, C. S., Smith, C., and Wallace, J. M.: An intercomparison of methods for finding coupled patterns in climate data, J. Climate, 5, 541–560, 1992.
Brown, J. N., Gupta, A. S., Brown, J. R., Muir, L. C., Risbey, J. S., Whetton, P., and Wijffels, S. E.: Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific, Climatic Change, 119, 147–161, 2013.
Bulić, I. H. and Kucharski, F.: Delayed ENSO impact on spring precipitation over North/Atlantic European region, Clim. Dynam., 382, 2593–2612, 2012.
Camberlin, P., Janicot, S., and Poccard, I.: Seasonality and atmospheric dynamics of the teleconnection between African rainfall and tropical sea-surface temperature: Atlantic vs. ENSO, Int. J. Climatol., 21, 973–1005, 2001.
Cane, M. A., Zebiak, S. E., and Dolan, S. C.: Experimental forecasts of EL Nino, Nature, 321, 827–832, 1986.
Chang, P., Fang, Y., Saravanan, R., Ji, L., and Seidel, H.: The cause of the fragile relationship between the Pacific El Nino and the Atlantic Nino, Nature, 443, 324–328, 2006.
Cherry, S.: Singular value decomposition analysis and canonical correlation analysis, J. Climate, 9, 2003–2009, 1996.
Cherry, S.: Some comments on singular value decomposition analysis, J. Climate, 10, 1759–1761, 1997.
Chung, C. E. and Ramanathan, V.: Weakening of North Indian SST gradients and the monsoon rainfall in India and the Sahel, J. Climate, 19, 2036–2045, 2006.
Coelho, C. A. S., Stephenson, D. B., Balmaseda, M., Doblas-Reyes, F. J., and van Oldenborgh, G. J.: Toward an integrated seasonal forecasting system for South America, J. Climate, 19, 3704–3721, 2006.
Dayan, H., Vialard, J., Izumo, T., and Lengaigne, M.: Does sea surface temperature outside the tropical Pacific contribute to enhanced ENSO predictability?, Clim. Dynam., 43, 1311–1325, 2014.
Deng, X., Huang, J., Qiao, F., Naylor, R. L., Falcon, W. P., Burke, M., and Battisti, D.: Impacts of El Nino-Southern Oscillation events on China's rice production, J. Geogr. Sci., 20, 3–16, 2010.
Diatta, S. and Fink, A. H.: Statistical relationship between remote climate indices and West African monsoon variability, Int. J. Climatol., 34, 3348–3367, https://doi.org/10.1002/joc.3912, 2014.
Ding, H., Keenlyside, N. S., and Latif, M.: Impact of the equatorial Atlantic on the El Nino southern oscillation, Clim. Dynam., 38, 1965–1972, 2012.
Doi, T., Vecchi, G. A., Rosati, A. J., and Delworth, T. L.: Biases in the Atlantic ITCZ in seasonal-interannual variations for a coarse-and a high-resolution coupled climate model, J. Climate, 25, 5494–5511, 2012.
Drosdowsky, W. and Chambers, L. E.: Near-global sea surface temperature anomalies as predictors of Australian seasonal rainfall, J. Climate, 14, 1677–1687, 2001.
Elsner, J. B. and Schmertmann, C. P.: Assessing forecast skill through cross validation, Weather Forecast., 9, 619–624, 1994.
Enfield, D. B. and Cid-Serrano, L.: Projecting the risk of future climate shifts, Int. J. Climatol., 26, 885–895, 2006.
Folland, C. K., Palmer, T. N., and Parker, D. E.: Sahel rainfall and worldwide sea temperatures, 1901–85, Nature, 320, 602–607, 1986.
Fontaine, B. and Janicot, S.: Sea surface temperature fields associated with West African rainfall anomaly types, J. Climate, 9, 2935–2940, 1996.
Fontaine, B., Trzaska, S., and Janicot, S.: Evolution of the relationship between near global and Atlantic SST modes and the rainy season in West Africa: statistical analyses and sensitivity experiments, Clim. Dynam., 14, 353–368, 1998.
Fontaine, B., Philippon, N., and Camberlin, P.: An improvement of June–September rainfall forecasting in the Sahel based upon region April–May moist static energy content (1968–1997), Geophys. Res. Lett., 26, 2041–2044, 1999.
Fontaine, B., Monerie, P. A., Gaetani, M., and Roucou, P.: Climate adjustments over the African-Indian monsoon regions accompanying Mediterranean Sea thermal variability, J. Geophys. Res.-Atmos., 116, D23122, https://doi.org/10.1029/2011JD016273, 2011.
Frankignoul, C. and Hasselmann, K.: Stochastic climate models, part II application to sea-surface temperature anomalies and thermocline variability, Tellus, 29, 289–305, 1977.
Gaetani, M., Fontaine, B., Roucou, P., and Baldi, M.: Influence of the Mediterranean Sea on the West African monsoon: Intraseasonal variability in numerical simulations, J. Geophys. Res.-Atmos., 115, D24115, https://doi.org/10.1029/2010JD014436, 2010.
Gardner, M. W. and Dorling, S. R.: Artificial neural networks (the multilayer perceptron)–a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627–2636, 1998.
Garric, G., Douville, H., and Déqué, M.: Prospects for improved seasonal predictions of monsoon precipitation over Sahel, Int. J. Climatol., 22, 331–345, 2002.
Giannini, A., Chiang, J. C., Cane, M. A., Kushnir, Y., and Seager, R.: The ENSO teleconnection to the tropical Atlantic Ocean: contributions of the remote and local SSTs to rainfall variability in the tropical Americas, J. Climate, 14, 4530–4544, 2001.
Giannini, A., Saravanan, R., and Chang, P.: Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales, Science, 302, 1027–1030, 2003.
Gill, A.: Some simple solutions for heat-induced tropical circulation, Q. J. Roy. Meteor. Soc., 106, 447–462, 1980.
Glahn, H. R. and Lowry, D. A.: The use of model output statistics (MOS) in objective weather forecasting, J. Appl. Meteorol., 11, 1203–1211, 1972.
Hansen, J. W., Hodges, A. W., and Jones, J. W.: ENSO Influences on Agriculture in the Southeastern United States, J. Climate, 11, 404–411, 1998.
Ham, Y. G., Kug, J. S., Park, J. Y., and Jin, F. F.: Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events, Nat. Geosci., 6, 112–116, 2013a.
Ham, Y. G., Sung, M. K., An, S. I., Schubert, S. D., and Kug, J. S.: Role of tropical Atlantic SST variability as a modulator of El Niño teleconnections, Asia-Pac. J. Atmos. Sci., 1–15, 2013b.
Harrison, D. E. and Larkin, N. K.: El Niño-Southern Oscillation sea surface temperature and wind anomalies, 1946–1993, Rev. Geophys., 36, 353–399, 1998.
Hasselmann, K.: Stochastic climate models part I. Theory, Tellus, 28, 473–485, 1976.
Haylock, M. R., Peterson, T. C., Alves, L. M., Ambrizzi, T., Anunciação, Y. M. T., Baez, J., and Vincent, L. A.: Trends in total and extreme South American rainfall in 1960–2000 and links with sea surface temperature, J. Climate, 19, 1490–1512, 2006.
Hsieh, W. W.: Nonlinear canonical correlation analysis of the tropical Pacific climate variability using a neural network approach, J. Climate, 14, 2528–2539, 2001.
Hsieh, W. W. and Tang, B.: Applying neural network models to prediction and data analysis in meteorology and oceanography, B. Am. Meteorol. Soc., 79, 1855–1870, 1998.
Janicot, S.: Spatiotemporal variability of West African rainfall. Part I: Regionalizations and typings, J. Climate, 5, 489–497, 1992.
Janicot, S., Moron, V., and Fontaine, B.: Sahel droughts and ENSO dynamics, Geophys. Res. Lett., 23, 515–518, 1996.
Janicot, S., Harzallah, A., Fontaine, B., and Moron, V.: West African monsoon dynamics and eastern equatorial Atlantic and Pacific SST anomalies (1970–88), J. Climate, 11, 1874–1882, 1998.
Janicot, S., Trzaska, S., and Poccard, I.: Summer Sahel-ENSO teleconnection and decadal time scale SST variations, Clim. Dynam., 18, 303–320, 2001.
Janowiak, J. E.: An investigation of interannual rainfall variability in Africa, J. Climate, 1, 240–255, 1988.
Ji, M., Kumar, A., and Leetmaa, A.: A multiseason climate forecast system at the National Meteorological Center, B. Am. Meteorol. Soc., 75, 569–577, 1994a.
Ji, M., Kumar, A., and Leetmaa, A.: An experimental coupled forecast system at the National Meteorological Center, Tellus A, 46, 398–418, 1994b.
Joly, M. and Voldoire, A.: Influence of ENSO on the West African monsoon: temporal aspects and atmospheric processes, J. Climate, 22, 3193–3210, 2009.
Keenlyside, N. S., Ding, H., and Latif, M.: Potential of equatorial Atlantic variability to enhance El Niño prediction, Geophys. Res. Lett., 40, 2278–2283, 2013.
Klein, S. A., Soden, B. J., and Lau, N. C.: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge, J. Climate, 12, 917–932, 1999.
Klein, W. H. and Glahn, H. R.: Forecasting local weather by means of model output statistics, B. Am. Meteorol. Soc., 55, 1217–1227, 1974.
Knutti, R., Stocker, T. F., Joos, F., and Plattner, G. K.: Probabilistic climate change projections using neural networks, Clim. Dynam., 21, 257–272, 2003.
Korecha, D. and Barnston, A. G.: Predictability of June–September rainfall in Ethiopia, Mon. Weather Rev., 135, 628–650, 2007.
Kovats, R. S.: El Niño and human health, B. World Health Organ., 78, 1127–1135, 2000.
Kovats, R. S., Bouma, M. J., Hajat, S., Worrall, E., and Haines, A.: El Niño and health, The Lancet, 362, 1481–1489, 2003.
Latif, M. and Barnett, T. P.: Interactions of the tropical oceans, J. Climate, 8, 952–964, 1995.
Legates, D. R. and Willmott, C. J.: Mean seasonal and spatial variability in gauge-corrected, global precipitation, Int. J. Climatol., 10, 111–127, 1990.
Legler, D. M., Bryant, K. J., and O'Brien, J. J.: Impact of ENSO-related climate anomalies on crop yields in the US, Climatic Change, 42, 351–375, 1999.
Li, G. and Xie, S. P.: Origins of tropical-wide SST biases in CMIP multi-model ensembles, Geophys. Res. Lett., 39, L22703, https://doi.org/10.1029/2012GL053777, 2012.
Li, G. and Xie, S. P.: Tropical Biases in CMIP5 Multimodel Ensemble: The Excessive Equatorial Pacific Cold Tongue and Double ITCZ Problems, J. Climate, 27, 1765–1780, 2014.
Li, Z. and Kafatos, M.: Interannual variability of vegetation in the United States and its relation to El Nino/Southern Oscillation, Remote Sens. Environ., 71, 239–247, 2000.
Lin, J. L.: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean-atmosphere feedback analysis, J. Climate, 20, 4497–4525, 2007.
Linthicum, K. J., Anyamba, A., Chretien, J. P., Small, J., Tucker, C. J., and Britch, S. C.: The role of global climate patterns in the spatial and temporal distribution of vector-borne disease, in: Vector Biology, Ecology and Control, 3–13, Springer, the Netherlands, 2010.
Livezey, R. E. and Chen, W. Y.: Statistical field significance and its determination by Monte Carlo techniques, Mon. Weather Rev., 111, 46–59, 1983.
López-Parages, J. and Rodríguez-Fonseca, B.: Multidecadal modulation of El Niño influence on the Euro-Mediterranean rainfall, Geophys. Res. Lett., 39, L02704, https://doi.org/10.1029/2011GL050049, 2012.
López-Parages, J., Rodrígez-Fonseca, B., and Terray, L.: A mechanism for the multidecadal modulation of ENSO teleconnections with Europe, Clim. Dynam., 45, 867–880, 2014.
Losada, T., Rodríguez-Fonseca, B., Polo, I., Janicot, S., Gervois, S., Chauvin, F., and Ruti, P.: Tropical response to the Atlantic Equatorial mode: AGCM multimodel approach, Clim. Dynam., 35, 45–52, 2010a.
Losada, T., Rodríguez-Fonseca, B., Janicot, S., Gervois, S., Chauvin, F., and Ruti, P.: A multi-model approach to the Atlantic Equatorial mode: impact on the West African monsoon, Clim. Dynam., 35, 29–43, 2010b.
Losada, T., Rodríguez-Fonseca, B., Mohino, E., Bader, J., Janicot, S., and Mechoso, C. R.: Tropical SST and Sahel rainfall: A non-stationary relationship, Geophys. Res. Lett., 39, L12705, https://doi.org/10.1029/2012GL052423, 2012.
Lu, J.: The dynamics of the Indian Ocean sea surface temperature forcing of Sahel drought, Clim. Dynam., 33, 445–460, 2009.
Maia, A. H., Meinke, H., Lennox, S., and Stone, R.: Inferential, nonparametric statistics to assess the quality of probabilistic forecast systems, Mon. Weather Rev., 135, 351–362, 2007.
Majda, A. J., Timofeyev, I., and Eijnden, E. V.: Models for stochastic climate prediction, P. Natl. Acad. Sci., 96, 14687–14691, 1999.
Martín-Rey, M., Polo, I., Rodríguez-Fonseca, B., and Kucharski, F.: Changes in the interannual variability of the tropical Pacific as a response to an equatorial Atlantic forcing, Sci. Mar., 76, 105–116, 2012.
Martín-Rey, M., Rodríguez-Fonseca, B., Polo, I., and Kucharski, F.: On the Atlantic–Pacific Niños connection: a multidecadal modulated mode, Clim. Dynam., 43, 3163–3178, 2014.
Martín-Rey, M., Rodríguez-Fonseca, B., and Polo, I.: Atlantic opportunities for ENSO prediction, Geophys. Res. Lett., 42, 6802–6810, https://doi.org/10.1002/2015GL065062, 2015.
Mason, S. J., Goddard, L., Graham, N. E., Yulaeva, E., Sun, L., and Arkin, P. A.: The IRI seasonal climate prediction system and the 1997/98 El Niño event, B. Am. Meteorol. Soc., 80, 1853–1873, 1999.
McMichael, A. J., Woodruff, R. E., and Hales, S.: Climate change and human health: present and future risks, The Lancet, 367, 859–869, 2006.
Michaelsen, J.: Cross-validation in statistical climate forecast models, J. Clim. Appl. Meteorol., 26, 1589–1600, 1987.
Mohino, E., Janicot, S., and Bader, J.: Sahel rainfall and decadal to multi-decadal sea surface temperature variability, Clim. Dynam., 37, 419–440, 2011.
Mokhov, I. I. and Smirnov, D. A.: El Niño–Southern Oscillation drives North Atlantic Oscillation as revealed with nonlinear techniques from climatic indices, Geophys. Res. Lett., 33, L03708, https://doi.org/10.1029/2005GL024557, 2006.
Naylor, R. L., Falcon, W. P., Rochberg, D., and Wada, N.: Using El Nino/Southern Oscillation climate data to predict rice production in Indonesia, Climatic Change, 50, 255–265, 2001.
Newman, M. and Sardeshmukh, P. D.: A caveat concerning singular value decomposition, J. Climate, 8, 352–360, 1995.
Nnamchi, H. C. and Li, J.: Influence of the South Atlantic Ocean dipole on West African summer precipitation, J. Climate, 24, 1184–1197, 2011.
Nnamchi, H. C., Li, J., and Anyadike, R. N.: Does a dipole mode really exist in the South Atlantic Ocean?, J. Geophys. Res.-Atmos., 116, 2011.
Palmer, T. N.: Influence of the Atlantic, Pacific and Indian oceans on Sahel rainfall, Nature, 322, 251–253, https://doi.org/10.1038/322251a0, 1986.
Patz, J. A.: A human disease indicator for the effects of recent global climate change, P. Natl. Acad. Sci., 99, 12506–12508, 2002.
Patz, J. A., Campbell-Lendrum, D., Holloway, T., and Foley, J. A.: Impact of regional climate change on human health, Nature, 438, 310–317, 2005.
Penland, C. and Matrosova, L.: Prediction of tropical Atlantic sea surface temperatures using linear inverse modeling, J. Climate, 11, 483–496, 1998.
Penland, C. and Sardeshmukh, P. D.: The optimal growth of tropical sea surface temperature anomalies, J. Climate, 8, 1999–2024, 1995.
Phillips, J. G., Cane, M. A., and Rosenzweig, C.: ENSO, seasonal rainfall patterns and simulated maize yield variability in Zimbabwe, Agr. Forest Meteorol., 90, 39–50, 1998.
Podestá, G. P., Messina, C. D., Grondona, M. O., and Magrin, G. O.: Associations between grain crop yields in central-eastern Argentina and El Niño-Southern Oscillation, J. Appl. Meteorol., 38, 1488–1498, 1999.
Polo, I., Rodríguez-Fonseca, B., Losada, T., and García-Serrano, J.: Tropical Atlantic Variability modes (1979–2002). Part I: time-evolving SST modes related to West African rainfall, J. Climate, 21, 6457–6475, 2008.
Polo , I., Martin-Rey, M., Rodriguez-Fonseca, B., Kucharski, F., and Mechoso, C. R.: Processes in the Pacific La Niña onset triggered by the Atlantic Niño, Clim. Dynam., 44, 115–131, 2015.
Rasmusson, E. M. and Carpenter, T. H.: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño, Mon. Weather Rev., 110, 354–384, 1982.
Recalde-Coronel, G. C., Barnston, A. G., and Muñoz, Á. G.: Predictability of December-April Rainfall in Coastal and Andean Ecuador, J. Appl. Meteorol. Clim., 53, 1471–1493, https://doi.org/10.1175/JAMC-D-13-0133.1, 2014.
Richter, I. and Xie, S. P.: On the origin of equatorial Atlantic biases in coupled general circulation models, Clim. Dynam., 31, 587–598, 2008.
Richter, I., Xie, S. P., Wittenberg, A. T., and Masumoto, Y.: Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation, Clim. Dynam., 38, 985–1001, 2012.
Rimbu, N., Lohmann, G., Felis, T., and Pätzold, J.: Shift in ENSO teleconnections recorded by a northern Red Sea coral, J. Climate, 16, 1414–1422, 2003.
Rodríguez-Fonseca, B., Polo, I., García-Serrano, J., Losada, T., Mohino, E., Mechoso, C. R., and Kucharski, F.: Are Atlantic Niños enhancing Pacific ENSO events in recent decades?, Geophys. Res. Lett., 36, L20705, https://doi.org/10.1029/2009GL040048, 2009.
Rodríguez-Fonseca, B., Janicot, S., Mohino, E., Losada, T., Bader, J., Caminade, C., and Voldoire, A.: Interannual and decadal SST-forced responses of the West African monsoon, Atmos. Sci. Lett., 12, 67–74, 2011.
Rodríguez-Fonseca, B., Mohino, E., Mechoso, C. R., Caminade, C., Biasutti, M., Gaetani, M., García-Serrano, J., Vizy, E. K., Cook, K., Xue, Y., Polo, I., Losada, L., Druyan, L., Fontaine, B., Bader, J., Doblas-Reyes, F. J., Goddard, L., Janicot, S., Arribas, A., Lau, W., Colman, A., Vellinga, M., Rowell, D. P., Kucharski, F., and Voldoire, A.: Variability and Predictability of West African Droughts. A review on the role of Sea Surface Temperature Anomalies, J. Climate, 8, 4034–4060, https://doi.org/10.1175/JCLI-D-14-00130.1, 2015.
Roe, G. H. and Steig, E. J.: Characterization of millennial-scale climate variability, J. Climate, 17, 1929–1944, 2004.
Rowell, D. P.: Teleconnections between the tropical Pacific and the Sahel, Q. J. Roy. Meteor. Soc., 127, 1683–1706, 2001.
Rowell, D. P.: The impact of Mediterranean SSTs on the Sahelian rainfall season, J. Climate, 16, 849–862, 2003.
Rudolf, B., Becker, A., Schneider, U., Meyer-Christoffer, A., and Ziese, M.: The new "GPCC Full Data Reanalysis Version 5" providing high-quality gridded monthly precipitation data for the global land-surface is public available since December 2010, GPCC status report December, 2010.
Saravanan, R. and Chang, P.: Interaction between tropical Atlantic variability and El Nino-southern oscillation, J. Climate, 13, 2177–2194, 2000.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., and Rudolf, B.: GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle, Theor. Appl. Climatol., 115, 15–40, 2014.
Schurer, A. P., Hegerl, G. C., Mann, M. E., Tett, S. F., and Phipps, S. J.: Separating forced from chaotic climate variability over the past millennium, J. Climate, 26, 6954–6973, 2013.
Shin, S. I., Sardeshmukh, P. D., and Webb, R. S.: Optimal tropical sea surface temperature forcing of North American drought, J. Climate, 23, 3907–3917, 2010.
Smith, T. M. and Reynolds, R. W.: Extended reconstruction of global sea surface temperatures based on COADS data (1854–1997), J. Climate, 16, 1495–1510, 2003.
Smith, T. M. and Reynolds, R. W.: Improved extended reconstruction of SST (1854–1997), J. Climate, 17, 2466–2477, 2004.
Smith, T. M., Reynolds, R. W., Peterson, T. C., and Lawrimore, J.: Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880–2006), J. Climate, 21, 2283–2296, 2008.
Shukla, R. P., Tripathi, K. C., Pandey, A. C., and Das, I. M. L.: Prediction of Indian summer monsoon rainfall using Niño indices: a neural network approach, Atmos. Res., 102, 99–109, 2011.
Tang, B., Hsieh, W. W., Monahan, A. H., Tangang, F. T.: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures, J. Climate, 13, 287–293, 2000.
Tao, F., Yokozawa, M., Zhang, Z., Hayashi, Y., Grassl, H., and Fu, C.: Variability in climatology and agricultural production in China in association with the East Asian summer monsoon and El Niño Southern Oscillation, Clim. Res., 28, 23–30, 2004.
Toniazzo, T. and Woolnough, S.: Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts, Clim. Dynam., 43, 2889–2913, 2013.
Travasso, M. I., Magrin, G. O., Grondona, M. O., and Rodríguez, G. R.: The use of SST and SOI anomalies as indicators of crop yield variability, Int. J Climatol., 29, 23–29, 2009.
Trenberth, K. E., Caron, J. M., Stepaniak, D. P., and Worley, S.: Evolution of El Niño–Southern Oscillation and global atmospheric surface temperatures, J. Geophys. Res.-Atmos., 107, AAC5.1–AAC5.17, https://doi.org/10.1029/2000JD000298, 2002.
Van den Dool, H. M.: Searching for analogues, how long must we wait?, Tellus A, 46, 314–324, 1994.
Van Oldenborgh, G. J. and Burgers, G.: Searching for decadal variations in ENSO precipitation teleconnections, Geophys. Res. Lett., 32, L15701, https://doi.org/10.1029/2005GL023110, 2005.
Vannière, B., Guilyardi, E., Madec, G., Doblas-Reyes, F. J., and Woolnough, S.: Using seasonal hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on ENSO, Clim. Dynam., 40, 963–981, 2013.
Verdin, J., Funk, C., Klaver, R., and Roberts, D.: Exploring the correlation between Southern Africa NDVI and Pacific sea surface temperatures: results for the 1998 maize growing season, Int. J. Remote Sens., 20, 2117–2124, 1999.
Vimont, D. J.: Analysis of the Atlantic meridional mode using linear inverse modeling: Seasonality and regional influences, J. Climate, 25, 1194–1212, 2012.
Vislocky, R. L. and Fritsch, J. M.: Improved model output statistics forecasts through model consensus, B. Am. Meteorol. Soc., 76, 1157–1164, 1995.
Wahl, S., Latif, M., Park, W., and Keenlyside, N.: On the tropical Atlantic SST warm bias in the Kiel Climate Model, Clim. Dynam., 36, 891–906, 2011.
Wallace, J. M., Smith, C., and Bretherton, C. S.: Singular value decomposition of wintertime sea surface temperature and 500-mb height anomalies, J. Climate, 5, 561–576, 1992.
Wang, S. Y., L'Heureux, M., and Chia, H. H.: ENSO prediction one year in advance using western North Pacific sea surface temperatures, Geophys. Res. Lett., 39, L05702, https://doi.org/10.1029/2012GL050909, 2012.
Ward, M. N.: Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa at interannual and multidecadal timescales, J. Climate, 11, 3167–3191, 1998.
Widmann, M.: One-dimensional CCA and SVD, and their relationship to regression maps, J. Climate, 18, 2785–2792, 2005.
Xue, Y., Chen, M., Kumar, A., Hu, Z. Z., and Wang, W.: Prediction skill and bias of tropical Pacific sea surface temperatures in the NCEP Climate Forecast System version 2, J. Climate, 26, 5358–5378, 2013.
Zebiak, S. E. and Cane, M. A.: A Model El Niño-Southern Oscillation, Mon. Weather Rev., 115, 2262–2278, 1987.
Short summary
The non-stationary links between sea surface temperature and global atmospheric circulation have served to create the S⁴CAST model. Here we describe the model, based on a statistical tool to be focused on the study of teleconnections and predictability of any climate-related variable that keeps a link with sea surface temperature. Due to its intuitive operation and free availability of the code, the model can be used both to supplement general circulation models and in a purely academic context.
The non-stationary links between sea surface temperature and global atmospheric circulation have...
