Articles | Volume 11, issue 10
https://doi.org/10.5194/gmd-11-4117-2018
© Author(s) 2018. 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-11-4117-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
12 Oct 2018
Model description paper |
| 12 Oct 2018
| 12 Oct 2018
An EC-Earth coupled atmosphere–ocean single-column model (AOSCM.v1_EC-Earth3) for studying coupled marine and polar processes
Kerstin Hartung
CORRESPONDING AUTHOR
Department of Meteorology, Stockholm University, Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Swedish e-Science Research Centre, Stockholm, Sweden
Department of Meteorology, Stockholm University, Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Swedish e-Science Research Centre, Stockholm, Sweden
Hamish Struthers
NSC, Linköping, Sweden
Linköping University, Linköping, Sweden
Anna-Lena Deppenmeier
Meteorology and Air Quality Department, Wageningen University, Wageningen, the Netherlands
R&D Weather and Climate Modeling, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Wilco Hazeleger
Meteorology and Air Quality Department, Wageningen University, Wageningen, the Netherlands
Netherlands eScience Center, Amsterdam, the Netherlands
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Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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Yang Liu, Jisk Attema, Ben Moat, and Wilco Hazeleger
Earth Syst. Dynam., 11, 77–96, https://doi.org/10.5194/esd-11-77-2020, https://doi.org/10.5194/esd-11-77-2020, 2020
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Poleward meridional energy transport (MET) has significant impact on the climate in the Arctic. In this study, we quantify and intercompare MET at subpolar latitudes from six reanalysis data sets. The results indicate that the spatial distribution and temporal variations of MET differ substantially among the reanalysis data sets. Our study suggests that the MET estimated from reanalyses is useful for the evaluation of energy transports but should be used with great care.
Xiang-Yu Li, Gunilla Svensson, Axel Brandenburg, and Nils E. L. Haugen
Atmos. Chem. Phys., 19, 639–648, https://doi.org/10.5194/acp-19-639-2019, https://doi.org/10.5194/acp-19-639-2019, 2019
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Lena Frey, Frida A.-M. Bender, and Gunilla Svensson
Atmos. Chem. Phys., 17, 9145–9162, https://doi.org/10.5194/acp-17-9145-2017, https://doi.org/10.5194/acp-17-9145-2017, 2017
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We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
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The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
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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 driven by either uniform erosion 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.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
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In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
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We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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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. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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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-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
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.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1, https://doi.org/10.5194/egusphere-2024-1, 2024
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This article gives an overview introduction of the IAP-CAS S2S (sub-seasonal to seasonal) ensemble forecasting system and MJO forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its advantages but also exhibits some biases, including underdispersive ensemble, overestimated amplitude and faster propagation speed when forecasting MJO. We also provide the explanation towards these biases and prospects for further improvement of this system in the future.
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.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
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.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
EGUsphere, https://doi.org/10.5194/egusphere-2024-335, https://doi.org/10.5194/egusphere-2024-335, 2024
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Large volcanic eruptions deposit material into the upper-atmosphere, which is capable of altering temperature and wind patterns of the Earth's atmosphere for years following. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a testbed for climate attribution studies.
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
Short summary
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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.
Cited articles
Baas, P., Bosveld, F., Lenderink, G., van Meijgaard, E., and Holtslag, A.
A. M.: How to design single-column model experiments for comparison with
observed nocturnal low-level jets, Q. J. Roy. Meteor. Soc., 136, 671–684,
https://doi.org/10.1002/qj.592, 2010. a
Balmaseda, M. A., Mogensen, K., and Weaver, A. T.: Evaluation of the ECMWF
ocean reanalysis system ORAS4, Q. J. Roy. Meteor. Soc., 139, 1132–1161,
https://doi.org/10.1002/qj.2063, 2013. a
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B.,
Hirschi, M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model:
Verification from Field Site to Terrestrial Water Storage and Impact in the
Integrated Forecast System, J. Hydrometeor., 10, 623–643,
https://doi.org/10.1175/2008JHM1068.1, 2009. a
Basu, S., Holtslag, A. A. M., Van De Wiel, B. J. H., Moene, A. F., and
Steeneveld, G.-J.: An inconvenient “truth” about using sensible heat flux
as a surface boundary condition in models under stably stratified regimes,
Acta Geophys., 56, 88–99, https://doi.org/10.2478/s11600-007-0038-y, 2008. a
Beare, R. J., Macvean, M. K., Holtslag, A. A. M., Cuxart, J., Esau, I.,
Golaz, J.-C., Jimenez, M. A., Khairoutdinov, M., Kosovic, B., Lewellen, D.,
Lund, T. S., Lundquist, J. K., Mccabe, A., Moene, A. F., Noh, Y., Raasch, S.,
and Sullivan, P.: An Intercomparison of Large-Eddy Simulations of the Stable
Boundary Layer, Bound.-Lay. Meteorol., 118, 247–272,
https://doi.org/10.1007/s10546-004-2820-6, 2006. a
Bechtold, P., Redelsperger, J.-L., Beau, I., Blackburn, M., Brinkop, S.,
Grandper, J.-Y., Grant, A., Gregory, D., Guichard, F., How, C., and
Ioannidou, E.: A GCSS model intercomparison for a tropical squall line
observed during toga-coare. II: Intercomparison of single-column models and a
cloud-resolving model, Q. J. Roy. Meteor. Soc., 126, 865–888,
https://doi.org/10.1002/qj.49712656405, 2000. a, b
Betts, A. K. and Miller, M. J.: A new convective adjustment scheme. Part
II: Single column tests using GATE wave, BOMEX, ATEX and arctic
air-mass data sets, Q. J. Roy. Meteor. Soc., 112, 693–709,
https://doi.org/10.1002/qj.49711247308, 1986. a, b, c
Bosveld, F. C., Baas, P., Steeneveld, G.-J., Holtslag, A. A. M., Angevine,
W. M., Bazile, E., de Bruijn, E. I. F., Deacu, D., Edwards, J. M., Ek, M.,
Larson, V. E., Pleim, J. E., Raschendorfer, M., and Svensson, G.: The Third
GABLS Intercomparison Case for Evaluation Studies of Boundary-Layer Models,
Part B: Results and Process Understanding, Bound.-Lay. Meteorol.,
152, 157–187, https://doi.org/10.1007/s10546-014-9919-1, 2014. a
Bourassa, M. A., Gille, S. T., Bitz, C., Carlson, D., Cerovecki, I., Clayson,
C. A., Cronin, M. F., Drennan, W. M., Fairall, C. W., Hoffman, R. N.,
Magnusdottir, G., Pinker, R. T., Renfrew, I. A., Serreze, M., Speer, K.,
Talley, L. D., and Wick, G. A.: High-Latitude Ocean and Sea Ice Surface
Fluxes: Challenges for Climate Research, B. Am. Meteorol. Soc., 94, 403–423,
https://doi.org/10.1175/BAMS-D-11-00244.1, 2013. a
Bourlès, B., Lumpkin, R., McPhaden, M. J., Hernandez, F., Nobre, P.,
Campos, E., Yu, L., Planton, S., Busalacchi, A., Moura, A. D., Servain, J.,
and Trotte, J.: THE PIRATA PROGRAM, B. Am. Meteorol. Soc., 89, 1111–1126,
https://doi.org/10.1175/2008BAMS2462.1, 2008. a
Bretherton, C. S., Krueger, S. K., Wyant, M. C., Bechtold, P., Van Meijgaard,
E., Stevens, B., and Teixeira, J.: A GCSS Boundary-Layer Cloud Model
Intercomparison Study Of The First Astex Lagrangian Experiment, Bound.-Lay.
Meteorol., 93, 341–380, https://doi.org/10.1023/A:1002005429969, 1999. a, b
Breugem, W.-P., Chang, P., Jang, C., Mignot, J., and Hazeleger, W.: Barrier
layers and tropical Atlantic SST biases in coupled GCMs, Tellus A, 60,
885–897, https://doi.org/10.1111/j.1600-0870.2008.00343.x, 2008. a, b
Clayson, C. A. and Chen, A.: Sensitivity of a Coupled Single-Column Model in
the Tropics to Treatment of the Interfacial Parameterizations, J. Climate,
15, 1805–1831, https://doi.org/10.1175/1520-0442(2002)015<1805:SOACSC>2.0.CO;2, 2002. a
Cuxart, J., Holtslag, A. A. M., Beare, R. J., Bazile, E., Beljaars, A.,
Cheng, A., Conangla, L., Ek, M., Freedman, F., Hamdi, R., Kerstein, A.,
Kitagawa, H., Lenderink, G., Lewellen, D., Mailhot, J., Mauritsen, T., Perov,
V., Schayes, G., Steeneveld, G.-J., Svensson, G., Taylor, P., Weng, W.,
Wunsch, S., and Xu, K.-M.: Single-Column Model Intercomparison for a Stably
Stratified Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 118, 273–303,
https://doi.org/10.1007/s10546-005-3780-1, 2006. a
Day, J. J., Svensson, G., Brooks, I. M., Bitz, C., Broman, L., Carver, G.,
Chevallier, M., Goessling, H., Hartung, K., Jung, T., Kay, J. E., Kolstad,
E. W., Perovich, D., Screen, J., Siemen, S., and Váňa, F.: The
Abisko Polar Prediction School, B. Am. Meteorol. Soc., 98, 445–447,
https://doi.org/10.1175/BAMS-D-16-0119.1, 2017. a
de Roode, S. R., Sandu, I., van der Dussen, J. J., Ackerman, A. S., Blossey,
P., Jarecka, D., Lock, A., Siebesma, A. P., and Stevens, B.: Large-Eddy
Simulations of EUCLIPSE–GASS Lagrangian Stratocumulus-to-Cumulus
Transitions: Mean State, Turbulence, and Decoupling, J. Atmos. Sci., 73,
2485–2508, https://doi.org/10.1175/JAS-D-15-0215.1, 2016. a, b
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. a
Deppenmeier, A.-L., Haarsma, R. J., and Hazeleger, W.: Ocean mixing and cloud
feedbacks linked to tropical Atlantic SST Variability, in preparation,
2018. a
Egger, J. and Schmid, S.: Elimination of spurious inertial oscillations in
boundary-layer models with time-dependent geostrophic winds, Bound.-Lay.
Meteorol., 43, 393–402, https://doi.org/10.1007/BF00121715, 1988. a
Fofonoff, N. and Millard, R. C.: Algorithms for Computation of Fundamental
Properties of Seawater, Unesco Technical Papers in Marine Science, 1983. a
Gaspar, P., Grégoris, 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. a, b
Ghan, S., Randall, D., Xu, K.-M., Cederwall, R., Cripe, D., Hack, J.,
Iacobellis, S., Klein, S., Krueger, S., Lohmann, U., Pedretti, J., Robock,
A., Rotstayn, L., Somerville, R., Stenchikov, G., Sud, Y., Walker, G., Xie,
S., Yio, J., and Zhang, M.: A comparison of single column model simulations
of summertime midlatitude continental convection, J. Geophys. Res.-Atmos.,
105, 2091–2124, https://doi.org/10.1029/1999JD900971, 2000. a, b
Giordani, H., Caniaux, G., and Voldoire, A.: Intraseasonal mixed-layer heat
budget in the equatorial Atlantic during the cold tongue development in 2006,
J. Geophys. Res.-Oceans, 118, 650–671, 2013. a
Goyette, S. and Perroud, M.: Interfacing a one-dimensional lake model with a
single-column atmospheric model: Application to the deep Lake Geneva,
Switzerland, Water Resour. Res., 48, W04507, https://doi.org/10.1029/2011WR011223,
2012. a
Guichard, F., Petch, J. C., Redelsperger, J.-L., Bechtold, P., Chaboureau,
J.-P., Cheinet, S., Grabowski, W., Grenier, H., Jones, C. G., Köhler, M.,
Piriou, J.-M., Tailleux, R., and Tomasini, M.: Modelling the diurnal cycle of
deep precipitating convection over land with cloud-resolving models and
single-column models, Q. J. Roy. Meteor. Soc., 130, 3139–3172,
https://doi.org/10.1256/qj.03.145, 2004. a
Hack, J. J. and Pedretti, J. A.: Assessment of Solution Uncertainties in
Single-Column Modeling Frameworks, J. Climate, 13, 352–365,
https://doi.org/10.1175/1520-0442(2000)013<0352:AOSUIS>2.0.CO;2, 2000. a
Hazeleger, W. and Haarsma, R. J.: Sensitivity of tropical Atlantic climate to
mixing in a coupled ocean–atmosphere model, Clim. Dynam., 25, 387–399,
2005. a
Hazeleger, W., Severijns, C., Semmler, T., Ştefănescu, S., Yang,
S., Wang, X., Wyser, K., Dutra, E., Baldasano, J. M., Bintanja, R.,
Bougeault, P., Caballero, R., Ekman, A. M. L., Christensen, J. H., van den
Hurk, B., Jimenez, P., Jones, C., Kållberg, P., Koenigk, T., McGrath, R.,
Miranda, P., Noije, T. V., Palmer, T., Parodi, J. A., Schmith, T., Selten,
F., Storelvmo, T., Sterl, A., Tapamo, H., Vancoppenolle, M., Viterbo, P., and
Willén, U.: EC-Earth: A Seamless Earth-System Prediction Approach
in Action, B. Am. Meteorol. Soc., 91, 1357–1363,
https://doi.org/10.1175/2010BAMS2877.1, 2010. a
Hazeleger, W., Wang, X., Severijns, C., Ştefănescu, S., Bintanja,
R., Sterl, A., Wyser, K., Semmler, T., Yang, S., van den Hurk, B., van Noije,
T., van der Linden, E., and van der Wiel, K.: EC-Earth V2.2:
description and validation of a new seamless earth system prediction model,
Clim. Dynam., 39, 2611–2629, https://doi.org/10.1007/s00382-011-1228-5, 2012. a
Holtslag, B.: Preface: GEWEX atmospheric boundary-layer study (GABLS) on
stable boundary layers, Bound.-Lay. Meteorol., 118, 243–246, 2006. a
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan,
Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini,
L., Watanabe, M., and Williamson, D.: The Art and Science of Climate Model
Tuning, B. Am. Meteorol. Soc., 98, 589–602, https://doi.org/10.1175/BAMS-D-15-00135.1,
2017. a
Hummels, R., Dengler, M., and Bourlès, B.: Seasonal and regional
variability of upper ocean diapycnal heat flux in the Atlantic cold tongue,
Prog. Oceanogr., 111, 52–74, https://doi.org/10.1016/j.pocean.2012.11.001, 2013. a
IOC and IAPSO: The international thermodynamic equation of seawater - 2010:
Calculation and use of thermodynamic properties, Tech. rep.,
Intergovernmental Oceanographic Commission, available at:
http://www.teos-10.org/pubs/TEOS-10_Manual.pdf (last access: 25 September 2018), 2010. a
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A., Boer,
G. D., Chen, M., Cole, J. N. S., Del Genio, A. D., Falk, M., Foster, M. J.,
Fridlind, A., Golaz, J.-C., Hashino, T., Harrington, J. Y., Hoose, C.,
Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M.,
Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M.,
Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C.,
Turner, D. D., Veron, D. E., Salzen, K. v., Walker, G. K., Wang, Z., Wolf,
A. B., Xie, S., Xu, K.-M., Yang, F., and Zhang, G.: Intercomparison of model
simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic
Cloud Experiment. I: single-layer cloud, Q. J. Roy. Meteor. Soc., 135,
979–1002, https://doi.org/10.1002/qj.416, 2009. a
Kraus, E. B. and Turner, J. S.: A one-dimensional model of the seasonal
thermocline II. The general theory and its consequences, Tellus, 19, 98–106,
https://doi.org/10.3402/tellusa.v19i1.9753, 1967. a, b
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A
review and a model with a nonlocal boundary layer parameterization, Rev.
Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994. a
Lecomte, O., Fichefet, T., Massonnet, F., and Vancoppenolle, M.: Benefits
from representing snow properties and related processes in coupled ocean–sea
ice models, Ocean Modell., 87, 81–85, https://doi.org/10.1016/j.ocemod.2014.11.005,
2015. a
Lemarié, F., Blayo, E., and Debreu, L.: Analysis of Ocean-atmosphere
Coupling Algorithms: Consistency and Stability, Procedia Computer Science,
51, 2066–2075, https://doi.org/10.1016/j.procs.2015.05.473, 2015. a
Lenderink, G., Siebesma, A. P., Cheinet, S., Irons, S., Jones, C. G.,
Marquet, P., üLLER, F. M., Olmeda, D., Calvo, J., Sánchez, E., and
Soares, P. M. M.: The diurnal cycle of shallow cumulus clouds over land: A
single-column model intercomparison study, Q. J. Roy. Meteor. Soc., 130,
3339–3364, https://doi.org/10.1256/qj.03.122, 2004. a, b
Ling, T., Xu, M., Liang, X.-Z., Wang, J. X. L., and Noh, Y.: A multilevel
ocean mixed layer model resolving the diurnal cycle: Development and
validation, J. Adv. Model. Earth Syst., 7, 1680–1692,
https://doi.org/10.1002/2015MS000476, 2015. a
Lohmann, U., McFarlane, N., Levkov, L., Abdella, K., and Albers, F.:
Comparing Different Cloud Schemes of a Single Column Model by Using Mesoscale
Forcing and Nudging Technique, J. Climate, 12, 438–461,
https://doi.org/10.1175/1520-0442(1999)012<0438:CDCSOA>2.0.CO;2, 1999. a
Lübbecke, J. F., Böning, C. W., Keenlyside, N. S., and Xie, S.-P.: On
the connection between Benguela and equatorial Atlantic Niños and the
role of the South Atlantic Anticyclone, J. Geophys. Res.-Oceans, 115, C09015,
https://doi.org/10.1029/2009JC005964, 2010. a, b
Madec, G.: NEMO ocean engine. Note du Pole de modélisation, Version
3.6 27, ISSN No 1288-1619, Institut Pierre-Simon Laplace (IPSL), France,
2016. a
NASA Goddard Space Flight Center and Ocean Ecology Laboratory: Sea-viewing
Wide Field-of-view Sensor (SeaWiFS) Ocean Color Data, NASA OB.DAAC,
Greenbelt, MD, USA, 2014. a
Niiler, P. and Kraus, E. B.: One-dimensional models of the upper ocean, in:
Modelling and Prediction of the Upper Layers of the Ocean, edited by: Kraus,
E. B., Pergamon, Tarrytown, NY, 143–172, 1977. a
Pithan, F., Ackerman, A., Angevine, W. M., Hartung, K., Ickes, L., Kelley,
M., Medeiros, B., Sandu, I., Steeneveld, G.-J., Sterk, H. A. M., Svensson,
G., Vaillancourt, P. A., and Zadra, A.: Select strengths and biases of models
in representing the Arctic winter boundary layer over sea ice: the Larcform 1
single column model intercomparison, J. Adv. Model. Earth Syst., 8,
1345–1357, https://doi.org/10.1002/2016MS000630, 2016. a, b, c
Price, J. F., Weller, R. A., and Pinkel, R.: Diurnal cycling: Observations
and models of the upper ocean response to diurnal heating, cooling, and wind
mixing, J. Geophys. Res.-Oceans, 91, 8411–8427,
https://doi.org/10.1029/JC091iC07p08411, 1986. a, b
Randall, D. A. and Cripe, D. G.: Alternative methods for specification of
observed forcing in single-column models and cloud system models, J. Geophys.
Res.-Atmos., 104, 24527–24545, https://doi.org/10.1029/1999JD900765, 1999. a, b
Randall, D. A., Xu, K.-M., Somerville, R. J. C., and Iacobellis, S.:
Single-Column Models and Cloud Ensemble Models as Links between Observations
and Climate Models, J. Climate, 9, 1683–1697,
https://doi.org/10.1175/1520-0442(1996)009<1683:SCMACE>2.0.CO;2, 1996. a, b
Rousset, C., Vancoppenolle, M., Madec, G., Fichefet, T., Flavoni, S.,
Barthélemy, A., Benshila, R., Chanut, J., Levy, C., Masson, S., and
Vivier, F.: The Louvain-La-Neuve sea ice model LIM3.6: global and regional
capabilities, Geosci. Model Dev., 8, 2991–3005,
https://doi.org/10.5194/gmd-8-2991-2015, 2015. a
Savre, J., Ekman, A. M. L., and Svensson, G.: Technical note: Introduction to
MIMICA, a large-eddy simulation solver for cloudy planetary boundary
layers, J. Adv. Model. Earth Syst., 6, 630–649, https://doi.org/10.1002/2013MS000292,
2014. a
Servain, J., Busalacchi, A. J., McPhaden, M. J., Moura, A. D., Reverdin, G.,
Vianna, M., and Zebiak, S. E.: A pilot research moored array in the tropical
Atlantic (PIRATA), B. Am. Meteorol. Soc., 79, 2019–2031, 1998. a
Sigg, R. and Svensson, G.: Three-dimensional simulation of the ASTEX
Lagrangian 1 field experiment with a regional numerical weather prediction
model, Q. J. Roy. Meteor. Soc., 130, 707–724, https://doi.org/10.1256/qj.02.209, 2004. a
Spengler, T., Renfrew, I. A., Terpstra, A., Tjernström, M., Screen, J.,
Brooks, I. M., Carleton, A., Chechin, D., Chen, L., Doyle, J., Esau, I.,
Hezel, P. J., Jung, T., Kohyama, T., Lüpkes, C., McCusker, K. E.,
Nygård, T., Sergeev, D., Shupe, M. D., Sodemann, H., and Vihma, T.:
High-Latitude Dynamics of Atmosphere–Ice–Ocean Interactions, B. Am.
Meteorol. Soc., 97, ES179–ES182, https://doi.org/10.1175/BAMS-D-15-00302.1, 2016. a
Sterk, H. A. M., Steeneveld, G. J., and Holtslag, A. A. M.: The role of
snow-surface coupling, radiation, and turbulent mixing in modeling a stable
boundary layer over Arctic sea ice, J. Geophys. Res.-Atmos., 118, 1199–1217,
https://doi.org/10.1002/jgrd.50158, 2013. a
Svensson, G., Holtslag, A. A. M., Kumar, V., Mauritsen, T., Steeneveld,
G. J., Angevine, W. M., Bazile, E., Beljaars, A., de Bruijn, E. I. F., Cheng,
A., Conangla, L., Cuxart, J., Ek, M., Falk, M. J., Freedman, F., Kitagawa,
H., Larson, V. E., Lock, A., Mailhot, J., Masson, V., Park, S., Pleim, J.,
Söderberg, S., Weng, W., and Zampieri, M.: Evaluation of the Diurnal
Cycle in the Atmospheric Boundary Layer Over Land as Represented by a Variety
of Single-Column Models: The Second GABLS Experiment, Bound.-Lay.
Meteorol., 140, 177–206, https://doi.org/10.1007/s10546-011-9611-7, 2011. a, b
Tjernström, M., Shupe, M. D., Brooks, I. M., Persson, P. O. G.,
Prytherch, J., Salisbury, D. J., Sedlar, J., Achtert, P., Brooks, B. J.,
Johnston, P. E., Sotiropoulou, G., and Wolfe, D.: Warm-air advection, air
mass transformation and fog causes rapid ice melt, Geophys. Res. Lett., 42,
5594–5602, https://doi.org/10.1002/2015GL064373,2015. a, b
Valcke, S.: OASIS3 User Guide, Tech. rep., CERFACS, Toulouse, France,
2006. a
Voldoire, A., Demissie, T., Deppenmeier, A.-L., Exarchou, E., Frauen, C.,
Goubanova, K., Keenlyside, N., Koseki, S., Prodhomme, C., Sanchez-Gomez, E.,
Shen, M.-L., Shonk, J., Toniazzo, T., and Taore, A.-K.: Development of warm
SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts, Clim. Dynam., in
review, 2018. a
West, A. E., McLaren, A. J., Hewitt, H. T., and Best, M. J.: The location of
the thermodynamic atmosphere–ice interface in fully coupled models – a case
study using JULES and CICE, Geosci. Model Dev., 9, 1125–1141,
https://doi.org/10.5194/gmd-9-1125-2016, 2016. a
Williams, P. D.: Climatic impacts of stochastic fluctuations in air–sea
fluxes, Geophys. Res. Lett., 39, L10705, https://doi.org/10.1029/2012GL051813, 2012. a
Xie, S.-P. and Carton, J. A.: Tropical Atlantic Variability: Patterns,
Mechanisms, and Impacts, in: Earth's Climate, American Geophysical Union,
121–142, https://doi.org/10.1029/147GM07, 2004. a, b
Short summary
Single-column models have been used to develop weather and climate models for several decades. They decouple small-scale processes from large-scale forcing and allow us to test models in a controlled environment with reduced computational cost. Here, we present a fully coupled atmosphere–ocean single-column model, including sea ice. We demonstrate that it is a valuable tool to advance our understanding in marine and polar boundary layer processes and interactions of their coupled components.
Single-column models have been used to develop weather and climate models for several decades....
