Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5689-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-5689-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Embedding a one-column ocean model in the Community Atmosphere Model 5.3 to improve Madden–Julian Oscillation simulation in boreal winter
Yung-Yao Lan
Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
Huang-Hsiung Hsu
CORRESPONDING AUTHOR
Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
Wan-Ling Tseng
Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
International Degree Program in Climate Change and Sustainable Development, National Taiwan University, Taipei 10617, Taiwan
Li-Chiang Jiang
Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
Related authors
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-152, https://doi.org/10.5194/gmd-2023-152, 2023
Preprint under review for GMD
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We assess the scientific reproducibility and consistency of the findings across different SST feedback cycles in modeling science. The simulations demonstrate improved accuracy in capturing various aspects of the MJO. The impact of sub-seasonal SST feedback on the MJO by air-sea coupling is still incomplete. We overcame the limitations of the Flux Coupler (CPL) by incorporating asymmetric exchange frequencies for conducting various SST feedback experiments.
Wei-Liang Lee, Yi-Chi Wang, Chein-Jung Shiu, I-chun Tsai, Chia-Ying Tu, Yung-Yao Lan, Jen-Ping Chen, Hua-Lu Pan, and Huang-Hsiung Hsu
Geosci. Model Dev., 13, 3887–3904, https://doi.org/10.5194/gmd-13-3887-2020, https://doi.org/10.5194/gmd-13-3887-2020, 2020
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The Taiwan Earth System Model (TaiESM) is a new climate model developed in Taiwan. It includes several new features, and therefore it can better simulate the occurrence of convective rainfall, solar energy received by mountainous surfaces, and more detail chemical processes in aerosols. TaiESM can capture the trend of global warming after 1950 well, and its overall performance in most meteorological quantities is better than the average of global models used in IPCC AR5.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Veronique Bouchet, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Detlef Stammer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-376, https://doi.org/10.5194/essd-2023-376, 2023
Revised manuscript accepted for ESSD
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines are proposed as international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-152, https://doi.org/10.5194/gmd-2023-152, 2023
Preprint under review for GMD
Short summary
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We assess the scientific reproducibility and consistency of the findings across different SST feedback cycles in modeling science. The simulations demonstrate improved accuracy in capturing various aspects of the MJO. The impact of sub-seasonal SST feedback on the MJO by air-sea coupling is still incomplete. We overcame the limitations of the Flux Coupler (CPL) by incorporating asymmetric exchange frequencies for conducting various SST feedback experiments.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
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This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Wan-Ling Tseng, Huang-Hsiung Hsu, Yung-Yao Lan, Wei-Liang Lee, Chia-Ying Tu, Pei-Hsuan Kuo, Ben-Jei Tsuang, and Hsin-Chien Liang
Geosci. Model Dev., 15, 5529–5546, https://doi.org/10.5194/gmd-15-5529-2022, https://doi.org/10.5194/gmd-15-5529-2022, 2022
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We show that coupling a high-resolution one-column ocean model to three atmospheric general circulation models dramatically improves Madden–Julian oscillation (MJO) simulations. It suggests two major improvements to the coupling process in the preconditioning phase and strongest convection phase over the Maritime Continent. Our results demonstrate a simple but effective way to significantly improve MJO simulations and potentially seasonal to subseasonal prediction.
Chein-Jung Shiu, Yi-Chi Wang, Huang-Hsiung Hsu, Wei-Ting Chen, Hua-Lu Pan, Ruiyu Sun, Yi-Hsuan Chen, and Cheng-An Chen
Geosci. Model Dev., 14, 177–204, https://doi.org/10.5194/gmd-14-177-2021, https://doi.org/10.5194/gmd-14-177-2021, 2021
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A cloud macrophysics scheme utilizing grid-mean hydrometeor information is developed and evaluated for climate models. The GFS–TaiESM–Sundqvist (GTS) scheme can simulate variations of cloud fraction associated with relative humidity (RH) in a more consistent way than the default scheme of CAM5.3. Through better cloud–RH distributions, the GTS scheme helps to better represent cloud fraction, cloud radiative forcing, and thermodynamic-related climatic fields in climate simulations.
Wei-Liang Lee, Yi-Chi Wang, Chein-Jung Shiu, I-chun Tsai, Chia-Ying Tu, Yung-Yao Lan, Jen-Ping Chen, Hua-Lu Pan, and Huang-Hsiung Hsu
Geosci. Model Dev., 13, 3887–3904, https://doi.org/10.5194/gmd-13-3887-2020, https://doi.org/10.5194/gmd-13-3887-2020, 2020
Short summary
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The Taiwan Earth System Model (TaiESM) is a new climate model developed in Taiwan. It includes several new features, and therefore it can better simulate the occurrence of convective rainfall, solar energy received by mountainous surfaces, and more detail chemical processes in aerosols. TaiESM can capture the trend of global warming after 1950 well, and its overall performance in most meteorological quantities is better than the average of global models used in IPCC AR5.
Chuan-Yao Lin, Chiung-Jui Su, Hiroyuki Kusaka, Yuko Akimoto, Yang-Fan Sheng, Jr-Chuan Huang, and Huang-Hsiung Hsu
Atmos. Chem. Phys., 16, 1809–1822, https://doi.org/10.5194/acp-16-1809-2016, https://doi.org/10.5194/acp-16-1809-2016, 2016
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This study evaluated the impact of urbanization over northern Taiwan using the Weather Research and Forecasting (WRF) Model coupled with the Noah land-surface model and a modified urban canopy model (WRF-UCM2D). WRF-UCM2D performed much better than the original UCM coupled with WRF (WRF-UCM) at non-urban stations with a low urban fraction during nighttime. The result of this study has crucial implications for assessing the impacts of urbanization on air quality and regional climate.
W.-L. Lee, Y. Gu, K. N. Liou, L. R. Leung, and H.-H. Hsu
Atmos. Chem. Phys., 15, 5405–5413, https://doi.org/10.5194/acp-15-5405-2015, https://doi.org/10.5194/acp-15-5405-2015, 2015
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This paper investigates 3-D mountain effects on solar flux distributions and their impact on surface hydrology over the western United States, specifically the Rocky Mountains and the Sierra Nevada, using the global CCSM4 (CAM4/CLM4) with a 0.23°×0.31° resolution for simulations over 6 years. We show that deviations in the net surface fluxes are not only affected by 3-D mountains but also influenced by feedbacks of cloud and snow in association with the long-term simulations.
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Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
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Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
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Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
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We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
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This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Jiachen Lu, Negin Nazarian, Melissa Hart, Scott Krayenhoff, and Alberto Martilli
EGUsphere, https://doi.org/10.5194/egusphere-2023-2811, https://doi.org/10.5194/egusphere-2023-2811, 2023
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based "mass flux" term. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
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We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
Preprint under review for GMD
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion by either uniform erosion processes where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea level history, material properties, and the relative influence of different erosional processes.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
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In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
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The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
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Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
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Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
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We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P. P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkini, E.: The Version 2.1 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
Ahn, M.-S., Kim, D., Sperber, K. R., Kang, I.-S., Maloney, E., Waliser, D., and Hendon, H.: MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis, Clim. Dynam., 49, 4023–4045, https://doi.org/10.1007/s00382-017-3558-4, 2017.
Ahn, M.-S., Kim, D., Kang, D., Lee, J., Sperber, K. R., Glecker, P. J., Jiang, X., Ham, Y.-G., and Kim, H.: MJO propagation across the Maritime Continent: Are CMIP6 models better than CMIP5 models?, Geophys. Res. Lett., 47, e2020GL087250, https://doi.org/10.1029/2020GL087250, 2020.
Alappattu, D. P., Wang, Q., Kalogiros, J., Guy, N., and Jorgensen, D. P.: Variability of upper ocean thermohaline structure during a MJO event from DYNAMO aircraft observations, J. Geophys. Res.-Oceans, 122, 1122–1140, https://doi.org/10.1002/2016JC012137, 2017.
Amante, C. and Eakins, B. W.: ETOPO1 1 arc-minute globe relief model: Procedures, data sources and analysis, NOAA Tech. Memo. NESDIS NGDC-24, NOAA, Silver Spring, MD, 19 pp., https://doi.org/10.7289/V5C8276M, 2009.
Behringer, D. W. and Xue, Y.: Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean, Proc. Eighth Symp. on integrated observing and assimilation systems for atmosphere, oceans, and land surface, AMS 84th annual meeting in Washington State Convention and Trade Center, Seattle, Washington, 11–15 January 2004, Am. Meteorol. Soc., 2.3., http://origin.cpc.ncep.noaa.gov/products/people/yxue/pub/13.pdf (last access: 11 August 2020), 2004.
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013.
Bernie, D., Guilyardi, E., Madec, G., Slingo, J., Woolnough, S., and Cole, J.: Impact of resolving the diurnal cycle in an ocean–atmosphere GCM. Part 2: a diurnally coupled CGCM, Clim. Dynam., 31, 909–925, https://doi.org/10.1007/s00382-008-0429-z, 2008.
Boyle, J. S., Klein, S. A., Lucas, D. D., Ma, H.-Y., Tannahill, J., and Xie, S.: The parametric sensitivity of CAM5's MJO, J. Geophys. Res.-Atmos., 120, 1424–1444, https://doi.org/10.1002/2014JD022507, 2015.
Bui, H. X. and Maloney, E. D.: Changes in Madden-Julian Oscillation precipitation and wind variance under global warming, Geophys. Res. Lett., 45, 7148–7155, https://doi.org/10.1029/2018GL078504, 2018.
Chang, M.-Y., Li, T., Lin, P.-L., and Chang, T.-H.: Forecasts of MJO Events during DYNAMO with a Coupled Atmosphere-Ocean Model: Sensitivity to Cumulus Parameterization Scheme, J. Meteorol. Res.-PRC, 33, 1016–1030, https://doi.org/10.1007/s13351-019-9062-5, 2019.
Clivar Madden–Julian Oscillation Working Group: MJO simulation diagnostics, J. Climate, 22, 3006–3030, https://doi.org/10.1175/2008JCLI2731.1, 2009.
Crueger, T., Stevens, B., and Brokopf, R.: The Madden–Julian Oscillation in ECHAM6 and the introduction of an objective MJO metric, J. Climate, 26, 3241–3257, https://doi.org/10.1175/JCLI-D-12-00413.1, 2013.
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., Kampenhout, L. V., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
de Szoeke, S. P., Skyllingstad, E. D., Zuidema, P., and Chandra, A. S.: Cold pools and their influence on the tropical marine boundary layer, J. Atmos. Sci., 74, 1149–1168, https://doi.org/10.1175/JAS-D-16-0264.1, 2017.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137: 553–597, https://doi.org/10.1002/qj.828, 2011.
Delworth, T. L., Broccoli, A. J., Rosati, A., Stouffer, R. J., Balaji, V., Beesley, J. A., Cooke, W. F., Dixon, K. W., Dunne, J., Dunne, K. A., Durachta, J. W., Findell, K. L., Ginoux, P., Gnanadesikan, A., Gordon, C. T., Griggies, S. M., Gudgil, R., Harrison, M. J., Held, I. M., Hemler, R. S., Horowitz, L. W., Klein, S. A., Knutson, T. R., Kushner, P. J., Langenhorst, A. R., Lee, H. C., Lin, S. J., Lu, J., Malyshev, S. L., Milly, P. C. D., Ramaswamy, V., Russell, J., Schwarzkopf, M. D., Shevliakova, E., Sirutis, J. J., Spelman, M. J., Stern, W. F., Winton, M., Wittenberg, A. T., Wyman, B., Zeng, F., and Zhang, R.: GFDL's CM2 global coupled climate models. Part 1: Formulation and simulation characteristics, J. Climate, 19, 643–674, https://doi.org/10.1175/JCLI3629.1, 2006.
DeMott, C. A., Klingaman, N. P., and Woolnough, S. J.: Atmosphere-ocean coupled processes in the Madden-Julian oscillation, Rev. Geophys., 53, 1099–1154, https://doi.org/10.1002/2014RG000478, 2015.
DeMott, C. A., Klingaman, N. P., Tseng, W.-L., Burt, M. A., Gao, Y., and Randall, D. A.: The convection connection: How ocean feedbacks affect tropical mean moisture and MJO propagation, J. Geophys. Res.-Atmos., 124, 11910–11931, https://doi.org/10.1029/2019JD031015, 2019.
Gaspar, P., Gregoris, Y., and Lefevre, J.-M.: A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: tests at station papa and long-term upper ocean study site, J. Geophys. Res.-Oceans, 95, 16179–16193, https://doi.org/10.1029/JC095iC09p16179, 1990.
Grassl, H.: The dependence of the measured cool skin of the ocean on wind stress and total heat flux, Bound.-Lay. Meteorol., 10, 465–474, https://doi.org/10.1007/BF00225865, 1976.
Haertel, P.: Prospects for Erratic and Intensifying Madden-Julian Oscillations, Climate, 8, 24, https://doi.org/10.3390/cli8020024, 2020.
Hannah, W. M. and Maloney, E. D.: The moist static energy budget in NCAR CAM5 hindcasts during DYNAMO, J. Adv. Model. Earth Sy., 6, 420–440, https://doi.org/10.1002/2013MS000272, 2014.
Hagos, S. M., Zhang, C., Feng, Z., Burleyson, C. D., De Mott, C., Kerns, B., Benedict, J. J., and Martini, M. N.: The impact of the diurnal cycle on the propagation of Madden-Julian Oscillation convection across the Maritime Continent, J. Adv. Model. Earth Sy., 8, 1552–1564, https://doi.org/10.1002/2016MS000725, 2016.
Hasse, L.: The sea surface temperature deviation and the heat flow at the sea–air interface, Bound.-Lay. Meteorol., 1, 368–379, https://doi.org/10.1007/BF02186037, 1971.
He, S., Yang, S., and Li, Z.: Influence of Latent Heating over the Asian and Western Pacific Monsoon Region on Sahel Summer Rainfall, Sci. Rep. 7, 7680, https://doi.org/10.1038/s41598-017-07971-6, 2017.
Heath, A., Gonzalez, A. O., Gehne, M., and Jaramillo, A.: Interactions of large-scale dynamics and Madden-Julian Oscillation propagation in multi-model simulations, J. Geophys. Res.-Atmos., 126, e2020JD033988, https://doi.org/10.1029/2020JD033988, 2021.
Hong, X., Reynolds, C. A., Doyle, J. D., May, P., and O'Neill, L.: Assessment of upper-ocean variability and the Madden-Julian Oscillation in extended-range air–ocean coupled mesoscale simulations, Dynam. Atmos. Oceans, 78, 89–105, https://doi.org/10.1016/j.dynatmoce.2017.03.002, 2017.
Hung, M.-P., Lin, J.-L., Wang, W., Kim, D., Shinoda, T., and Weaver, S. J.: MJO and convectively coupled equatorial waves simulated by CMIP5 climate models, J. Climate, 26, 6185–6214, https://doi.org/10.1175/JCLI-D-12-00541.1, 2013.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The community Earth system model: A framework for collaborative research, B. Am. Meteorol. Soc., 94, 1319–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013.
Jiang, X., Waliser, D. E., Xavier, P. K., Petch, J., Klingaman, N. P., Woolnough, S. J., Guan, B., Bellon, G., Crueger, T., DeMott, C., Hannay, C., Lin, H., Hu, W., Kim, D., Lappen, C.-L., Lu, M.-M., Ma, H.-Y., Miyakawa, T., Ridout, J. A., Schubert, S. D., Scinocca, J., Seo, K.-H., Shindo, E., Song, X., Stan, C., Tseng, W.-L., Wang, W., Wu, T., Wu, X., Wyser, K., Zhang, G. J., and Zhu, H.
Jiang, X., Waliser, D. E., Xavier, P. K., Petch, J., Klingaman, N. P., Woolnough, S. J., Guan, B., Bellon, G., Crueger, T., DeMott, C., Hannay, C., Lin, H., Hu, W., Kim, D., Lappen, C.-L., Lu, M.-M., Ma, H.-Y., Miyakawa, T., Ridout, J. A., Schubert, S. D., Scinocca, J., Seo, K.-H., Shindo, E., Song, X., Stan, C., Tseng, W.-L., Wang, W., Wu, T., Wu, X., Wyser, K., Zhang, G. J., and Zhu, H.: Vertical structure and physical processes of the MaddenJulian oscillation: Exploring key model physics in climate simulations, J. Geophys. Res.-Atmos., 120, 4718–4748, https://doi.org/10.1002/2014JD022375, 2015.
Jiang, X., Adames, Á. F., Kim, D., Maloney, E. D., Lin, H., Kim, H., Zhang, C., DeMott, C. A., and Klingaman, N. P.: Fifty years of research on the Madden-Julian Oscillation: Recent progress, challenges, and perspectives, J. Geophys. Res.-Atmos., 125, e2019JD030911, https://doi.org/10.1029/2019JD030911, 2020.
Johnson, R. H. and Ciesielski, P. E.: Multiscale variability of the atmospheric boundary layer during DYNAMO, J. Atmos. Sci., 74, 4003–4021, https://doi.org/10.1175/JAS-D-17-0182.1, 2017.
Kim, D., Sobel, A. H., Maloney, E. D., Frierson, D. M., and Kang, I.-S.: A systematic relationship between intraseasonal variability and mean state bias in AGCM simulations, J. Climate, 24, 5506–5520, https://doi.org/10.1175/2011JCLI4177.1, 2011.
Kim, D., Kim H., and Lee, M.-I.: Why does the MJO detour the Maritime Continent during austral summer?, Geophys. Res. Lett., 44, 2579–2587, https://doi.org/10.1002/2017GL072643, 2017.
Kim, H.-M., Hoyos, C. D., Webster, P. J., and Kang, I.-S.: Ocean–atmosphere coupling and the boreal winter MJO, Clim. Dynam., 35, 771–784, https://doi.org/10.1007/s00382-009-0612-x, 2010.
Kiranmayi, L. and Maloney, E. D.: Intraseasonal moist static energy budget in reanalysis data, J. Geophys. Res., 116, D21117, https://doi.org/10.1029/2011JD016031, 2011.
Lan, Y.-Y., Tsuang, B.-J., Tu, C.-Y., Wu, T.-Y., Chen, Y.-L., and Hsieh, C.-I.: Observation and Simulation of Meteorology and Surface Energy Components over the South China Sea in Summers of 2004 and 2006, Terr. Atmos. Ocean. Sci., 21, 325–342, https://doi.org/10.3319/TAO.2009.04.07.01(A), 2010.
Lan, Y.-Y., Hsu, H.-H., Tsuang, B.-J., and Tu, C.-Y.: rceclccr/CAM5_SIT_v1.0 (v1.0.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.5510795, 2021.
Lee, H.-T. and NOAA CDR Program: NOAA Climate Data Record (CDR) of Daily Outgoing Longwave Radiation (OLR), Version 1.2, NOAA National Climatic Data Center, https://doi.org/10.7289/V5SJ1HH2, 2011.
Li, T., Tam, F., Fu, X., Zhou, T., and Zhu, W.: Causes of the intraseasonal SST variability in the tropical Indian Ocean, Atmos. Ocean. Sci. Lett., 1, 18–23, https://doi.org/10.1080/16742834.2008.11446758, 2008.
Li, T., Ling, J., and Hsu, P.-C.: Madden–Julian Oscillation: Its discovery, dynamics, and impact on East Asia, J. Meteorol. Res.-PRC, 34, 20–42, https://doi.org/10.1007/s13351-020-9153-3, 2020.
Li, X., Tang, Y., Zhou, L., Chen, D., and Yao, Z.: Assessment of Madden–Julian oscillation simulations with various configurations of CESM, Clim. Dynam., 47, 2667–2690, https://doi.org/10.1007/s00382-016-2991-0, 2016.
Ling, J., Zhao, Y., and Chen, G.: Barrier effect on MJO propagation by the Maritime Continent in the MJO Task Force/GEWEX atmospheric system study models, J. Climate, 32, 5529–5547, https://doi.org/10.1175/JCLI-D-18-0870.1, 2019.
Madden, R. A. and Julian, P. R.: Description of global-scale circulation cells in the tropics with a 40–50 d period, J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2, 1972.
Madden, R. A. and Julian, P. R.: Observations of the 40–50 d tropical oscillation – A review, Mon. Weather Rev., 122, 814–837, https://doi.org/10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2, 1994.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys., 20, 851–875, https://doi.org/10.1029/RG020i004p00851, 1982.
Neale, R. B., Chen, C.-C., Gettelman, A., Lauritzen, P. H., Park, S., Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J.-F., Marsh, D., Mills, M., Smith, A. K., Tilmes, S., Vitt, F., Cameron-Smith, P., Collins, W. D., Iacono, M. J., Easter, R. C., Ghan, S. J., Liu, X., Rasch, P. J., and Taylor, M. A.: Description of the NCAR Community Atmosphere Model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+STR, Natl. Cent. for Atmos. Res., Boulder, CO, 289 pp., https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf (last access: 8 October 2022), 2012.
Newman, M., Sardeshmukh, P. D., and Penland, C.: How important is air–sea coupling in ENSO and MJO evolution?, J. Climate, 22, 2958–2977, https://doi.org/10.1175/2008JCLI2659.1, 2009.
Oh, J.-H, Kim, B.-M., Kim, K.-Y., Song, H.-J., and Lim, G.-H.: The impact of the diurnal cycle on the MJO over the Maritime Continent: a modeling study assimilating TRMM rain rate into global analysis, Clim. Dynam., 40, 893–911, https://doi.org/10.1007/s00382-012-1419-8, 2013.
Paulson, C. A. and Simpson, J. J.: The temperature difference across the cool skin of the ocean, J. Geophys. Res., 86, 11044–11054, https://doi.org/10.1029/JC086iC11p11044, 1981.
Pei, S., Shinoda, T., Soloviev, A., and Lien, R.-C.: Upper ocean response to the atmospheric cold pools associated with the Madden-Julian Oscillation, Geophys. Res. Lett., 45, 5020–5029, https://doi.org/10.1029/2018GL077825, 2018.
Pujiana, K., Moum, J. N., and Smyth, W. D.: The role of subsurface turbulence in redistributing upper-ocean heat, freshwater, and momentum in response to the MJO in the equatorial Indian Ocean, J. Phys. Oceanogr., 48, 197–220, https://doi.org/10.1175/JPO-D-17-0146.1, 2018.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
Ren, P. F., Gao, L., Ren, H.-L., Rong, X., and Li, J.: Representation of the Madden–Julian Oscillation in CAMSCSM, J. Meteorol. Res.-PRC, 33, 627–650, https://doi.org/10.1007/s13351-019-8118-x, 2019.
Reynolds, R. W. and Smith, T. M.: A high-resolution global sea surface temperature climatology, J. Climate, 8, 1571–1583, https://doi.org/10.1175/1520-0442(1995)008<1571:AHRGSS>2.0.CO;2, 1995.
Schreck, C. J., Lee, H.-T., and Knapp, K. R.: HIRS outgoing longwave radiation – Daily climate data record: Application toward identifying tropical subseasonal variability, Remote Sens.-Basel, 10, 1325, https://doi.org/10.3390/rs10091325, 2018.
Sobel, A. H., Maloney, E. D., Bellon, G., and Dargan, M. F.: The role of surface heat fluxes in tropical intraseasonal oscillations, Nat. Geosci., 1, 653–657, https://doi.org/10.1038/ngeo312, 2008.
Subramanian, A. C., Jochum, M., Miller, A. J., Murtugudde, R., Neale, R. B., and Waliser, D. E.: The Madden–Julian oscillation in CCSM4, J. Climate, 24, 6261–6282, https://doi.org/10.1175/JCLI-D-11-00031.1, 2011.
Tseng, W.-L., Tsuang, B.-J., Keenlyside, N. S., Hsu, H.-H., and Tu, C.-Y.: Resolving the upper-ocean warm layer improves the simulation of the Madden-Julian oscillation, Clim. Dynam., 44, 1487–1503, https://doi.org/10.1007/s00382-014-2315-1, 2014.
Tseng, W.-L., Hsu, H.-H., Keenlyside, N., Chang, C.-W. J., Tsuang, B.-J., Tu, C.-Y., and Jiang, L.-C.: Effects of Orography and Land–Sea Contrast on the Madden–Julian Oscillation in the Maritime Continent: A Numerical Study Using ECHAM-SIT, J. Climate, 30, 9725–9741, https://doi.org/10.1175/JCLI-D-17-0051.1, 2017.
Tu, C.-Y. and Tsuang, B.-J.: Cool-skin simulation by a one-column ocean model, Geophys. Res. Lett., 32, L22602, https://doi.org/10.1029/2005GL024252, 2005.
Wang, W., Saha, S., Pan, H.-L., Nadiga, S., and White, G.: Simulation of ENSO in the new NCEP Coupled Forecast System Model (CFS03), Mon. Weather Rev., 133, 1574–1593, https://doi.org/10.1175/MWR2936.1, 2005.
Wang, W., Hung, M.-P., Weaver, S. J., Kumar, A., and Fu, X.: MJO prediction in the NCEP Climate Forecast System version 2, Clim. Dynam., 42, 2509–2520, https://doi.org/10.1007/s00382-013-1806-9, 2014.
Wheeler, M. and Kiladis, G. N.: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber-frequency domain, J. Atmos. Sci., 56, 374– 399, https://doi.org/10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2, 1999.
Wheeler, M. C. and Hendon, H. H.: An all-season real-time multivariate MJO index: development of an index for monitoring and prediction, Mon. Weather Rev., 132, 1917–1932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2, 2004.
Woolnough, S. J., Vitard, F., and Balmaseda, M. A.: The role of the ocean in the Madden–Julian oscillation: Implications for MJO prediction, Q. J. Roy. Meteor. Soc., 133, 117–128, https://doi.org/10.1002/qj.4, 2007.
Wu, C.-H. and Hsu, H.-H.: Potential Influence of Topography on the MJO in the Maritime Continent, J. Climate, 22, 5433–5448, https://doi.org/10.1175/2009JCLI2825.1, 2009.
Wu, J.: On the cool skin of the ocean, Bound.-Lay. Meteorol., 31, 203–207, https://doi.org/10.1007/BF00121179, 1985.
Yoneyama, K., Zhang, C., and Long, C.: Tracking pulses of the Madden–Julian oscillation, B. Am. Meteorol. Soc., 94, 1871–1891, https://doi.org/10.1175/BAMS-D-12-00157.1, 2013.
Zhang, C.: Madden-Julian oscillation, Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158, 2005.
Zhang, C. and Yoneyama, K.: CINDY/DYNAMO field campaign: Advancing our understanding of MJO initiation, in: World Scientific Series on Asia-Pacific Weather and Climate, Vol. 9, World Scientific Publishing Co. Pte Ltd., 339–348, https://doi.org/10.1142/9789813200913_0027, 2017.
Short summary
This study has shown that coupling a high-resolution 1-D ocean model (SIT 1.06) with the Community Atmosphere Model 5.3 (CAM5.3) significantly improves the simulation of the Madden–Julian Oscillation (MJO) over the standalone CAM5.3. Systematic sensitivity experiments resulted in more realistic simulations of the tropical MJO because they had better upper-ocean resolution, adequate upper-ocean thickness, coupling regions including the eastern Pacific and southern tropics, and a diurnal cycle.
This study has shown that coupling a high-resolution 1-D ocean model (SIT 1.06) with the...