Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6381-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-6381-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2021
Research article |
| 16 Dec 2021
| 16 Dec 2021
Evaluation of Asian summer precipitation in different configurations of a high-resolution general circulation model in a range of decision-relevant spatial scales
Mark R. Muetzelfeldt
CORRESPONDING AUTHOR
National Centre for Atmospheric Science, University of Reading, Reading, UK
Reinhard Schiemann
National Centre for Atmospheric Science, University of Reading, Reading, UK
Andrew G. Turner
National Centre for Atmospheric Science, University of Reading, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Nicholas P. Klingaman
National Centre for Atmospheric Science, University of Reading, Reading, UK
Pier Luigi Vidale
National Centre for Atmospheric Science, University of Reading, Reading, UK
Malcolm J. Roberts
Met Office Hadley Centre, Exeter, UK
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Andrea Rivosecchi, Andrea Dittus, Ed Hawkins, Reinhard Schiemann, and Erich Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2026-1733, https://doi.org/10.5194/egusphere-2026-1733, 2026
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We analysed 250-year global temperature trends under zero CO2 emissions in the UK Earth System Model. Stopping emissions while global temperatures remain below +2 °C limits the post-net-zero mean surface warming. Annual hot extremes cool regionally after zero emissions in the midlatitudes, due to the expansion of evergreen vegetation. However, in this framework vegetation change is largely unconstrained by modern land-use changes, highlighting a potential limitation of idealised model protocols.
Bianca Mezzina, Christopher David Roberts, Matthias Aengenheyster, Rohit Ghosh, Malcolm John Roberts, and Marc Batlle Martin
EGUsphere, https://doi.org/10.5194/egusphere-2026-547, https://doi.org/10.5194/egusphere-2026-547, 2026
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El Nino-Southern Oscllation (ENSO) is a climate phenomenon that can influence climate worldwide, but current climate models still struggle to properly simulate its impacts. We investigate if a new set of models with unprecedented fine detail in the ocean and the atmosphere can simulate this better. By comparing several models and experiments, we find that higher detail does not always improve results and that natural climate variability strongly limits clear gains.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
Geosci. Model Dev., 18, 7735–7761, https://doi.org/10.5194/gmd-18-7735-2025, https://doi.org/10.5194/gmd-18-7735-2025, 2025
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The Next Generation of Earth Modeling Systems project (nextGEMS) developed two Earth system models that use horizontal grid spacing of 10 km and finer, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS simulated the Earth System climate over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Ting-Chen Chen, Hugues Goosse, Cécile Davrinche, Stephy Libera, Christopher Roberts, Matthias Aengenheyster, Kristian Strommen, Malcolm Roberts, Rohit Ghosh, and Jin-Song von Storch
Weather Clim. Dynam., 6, 1179–1193, https://doi.org/10.5194/wcd-6-1179-2025, https://doi.org/10.5194/wcd-6-1179-2025, 2025
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Southern Annular Mode (SAM) is a leading mode of Southern Hemisphere climate variability, but global models often overestimate its persistence in summer. Bias in SAM persistence is reduced and jet latitude improved using high-resolution sea surface temperature (SST)-prescribed models, pointing to the central role of SSTs. Control experiments explore the influence of model resolution and mesoscale ocean features on SAM persistence, highlighting the complex and subtle nature of air-sea coupling.
Rafaela Jane Delfino, Gerry Bagtasa, Pier Luigi Vidale, and Kevin Hodges
EGUsphere, https://doi.org/10.5194/egusphere-2025-4443, https://doi.org/10.5194/egusphere-2025-4443, 2025
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We investigate changes in tropical cyclone (TC) precipitation in the Philippines using high-resolution pseudo-global warming simulations. Analysis of Haiyan, Bopha, and Mangkhut shows robust increases in rainfall under future warming, with nonlinear deviations from Clausius–Clapeyron scaling driven by TC intensity and structural changes, underscoring evolving rainfall hazards in a warming climate.
Catherine A. Toolan, Joe Adabouk Amooli, Laura J. Wilcox, Bjørn H. Samset, Andrew G. Turner, and Daniel M. Westervelt
Atmos. Chem. Phys., 25, 10523–10557, https://doi.org/10.5194/acp-25-10523-2025, https://doi.org/10.5194/acp-25-10523-2025, 2025
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Our research explores how well air pollution and rainfall patterns in Africa are represented in current climate models by comparing model data to observations from 1981 to 2023. While most models capture seasonal air quality changes well, they struggle to replicate the distribution of non-dust pollutants and certain rainfall patterns, especially over east Africa. Improving these models is crucial for better climate predictions and preparing for future risks.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2685, https://doi.org/10.5194/egusphere-2025-2685, 2025
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As Earth system models become more complex, rapid and comprehensive evaluation through comparison with observational data is necessary. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require fast analysis. This paper describes a new Rapid Evaluation Framework (REF) that was developed for the Assessment Fast Track that will be run at the Earth System Grid Federation (ESGF) to inform the community about the performance of models.
Isabel H. Smith, Paul D. Williams, and Reinhard Schiemann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2378, https://doi.org/10.5194/egusphere-2025-2378, 2025
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Mountain wave turbulence (MWT) has a dangerous and costly impact on the aviation sector. There's a lack of research into future projected MWT with global warming. Overall, MWT trends are seasonally and location dependent. Over several mountain ranges an increase arose particularly over Greenland and regions in Asia. A drop in MWT also developed over the Alps, the Rockys, Atlas and northern and central Andes. Southern Andes and the Himalayas had seasonal differences resulting in a mix of trends.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
Geosci. Model Dev., 18, 2725–2745, https://doi.org/10.5194/gmd-18-2725-2025, https://doi.org/10.5194/gmd-18-2725-2025, 2025
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The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the Met Office Hadley Centre coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the seafloor exerts on ocean currents and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Alex T. Archibald, Bablu Sinha, Maria R. Russo, Emily Matthews, Freya A. Squires, N. Luke Abraham, Stephane J.-B. Bauguitte, Thomas J. Bannan, Thomas G. Bell, David Berry, Lucy J. Carpenter, Hugh Coe, Andrew Coward, Peter Edwards, Daniel Feltham, Dwayne Heard, Jim Hopkins, James Keeble, Elizabeth C. Kent, Brian A. King, Isobel R. Lawrence, James Lee, Claire R. Macintosh, Alex Megann, Bengamin I. Moat, Katie Read, Chris Reed, Malcolm J. Roberts, Reinhard Schiemann, David Schroeder, Timothy J. Smyth, Loren Temple, Navaneeth Thamban, Lisa Whalley, Simon Williams, Huihui Wu, and Mingxi Yang
Earth Syst. Sci. Data, 17, 135–164, https://doi.org/10.5194/essd-17-135-2025, https://doi.org/10.5194/essd-17-135-2025, 2025
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Here, we present an overview of the data generated as part of the North Atlantic Climate System Integrated Study (ACSIS) programme that are available through dedicated repositories at the Centre for Environmental Data Analysis (CEDA; www.ceda.ac.uk) and the British Oceanographic Data Centre (BODC; bodc.ac.uk). The datasets described here cover the North Atlantic Ocean, the atmosphere above (it including its composition), and Arctic sea ice.
Kieran M. R. Hunt, Jean-Philippe Baudouin, Andrew G. Turner, A. P. Dimri, Ghulam Jeelani, Pooja, Rajib Chattopadhyay, Forest Cannon, T. Arulalan, M. S. Shekhar, T. P. Sabin, and Eliza Palazzi
Weather Clim. Dynam., 6, 43–112, https://doi.org/10.5194/wcd-6-43-2025, https://doi.org/10.5194/wcd-6-43-2025, 2025
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Western disturbances (WDs) are storms that predominantly affect north India and Pakistan during the winter months, where they play an important role in regional water security, but can also bring a range of natural hazards. In this review, we summarise recent literature across a range of topics: their structure and lifecycle, precipitation and impacts, interactions with large-scale weather patterns, representation in models, how well they are forecast, and their response to changes in climate.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
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We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Omar V. Müller, Patrick C. McGuire, Pier Luigi Vidale, and Ed Hawkins
Hydrol. Earth Syst. Sci., 28, 2179–2201, https://doi.org/10.5194/hess-28-2179-2024, https://doi.org/10.5194/hess-28-2179-2024, 2024
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This work evaluates how rivers are projected to change in the near future compared to the recent past in the context of a warming world. We show that important rivers of the world will notably change their flows, mainly during peaks, exceeding the variations that rivers used to exhibit. Such large changes may produce more frequent floods, alter hydropower generation, and potentially affect the ocean's circulation.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
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, 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, 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, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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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 is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
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.
Giorgia Di Capua, Dim Coumou, Bart van den Hurk, Antje Weisheimer, Andrew G. Turner, and Reik V. Donner
Weather Clim. Dynam., 4, 701–723, https://doi.org/10.5194/wcd-4-701-2023, https://doi.org/10.5194/wcd-4-701-2023, 2023
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Heavy rainfall in tropical regions interacts with mid-latitude circulation patterns, and this interaction can explain weather patterns in the Northern Hemisphere during summer. In this analysis we detect these tropical–extratropical interaction pattern both in observational datasets and data obtained by atmospheric models and assess how well atmospheric models can reproduce the observed patterns. We find a good agreement although these relationships are weaker in model data.
Charlie C. Suitters, Oscar Martínez-Alvarado, Kevin I. Hodges, Reinhard K. H. Schiemann, and Duncan Ackerley
Weather Clim. Dynam., 4, 683–700, https://doi.org/10.5194/wcd-4-683-2023, https://doi.org/10.5194/wcd-4-683-2023, 2023
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Atmospheric blocking describes large and persistent high surface pressure. In this study, the relationship between block persistence and smaller-scale systems is examined. Persistent blocks result from more interactions with small systems, but a block's persistence does not depend as strongly on the strength of these smaller features. This work is important because it provides more knowledge as to how blocks can be allowed to persist, which is something we still do not fully understand.
Emmanouil Flaounas, Leonardo Aragão, Lisa Bernini, Stavros Dafis, Benjamin Doiteau, Helena Flocas, Suzanne L. Gray, Alexia Karwat, John Kouroutzoglou, Piero Lionello, Mario Marcello Miglietta, Florian Pantillon, Claudia Pasquero, Platon Patlakas, María Ángeles Picornell, Federico Porcù, Matthew D. K. Priestley, Marco Reale, Malcolm J. Roberts, Hadas Saaroni, Dor Sandler, Enrico Scoccimarro, Michael Sprenger, and Baruch Ziv
Weather Clim. Dynam., 4, 639–661, https://doi.org/10.5194/wcd-4-639-2023, https://doi.org/10.5194/wcd-4-639-2023, 2023
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Cyclone detection and tracking methods (CDTMs) have different approaches in defining and tracking cyclone centers. This leads to disagreements on extratropical cyclone climatologies. We present a new approach that combines tracks from individual CDTMs to produce new composite tracks. These new tracks are shown to correspond to physically meaningful systems with distinctive life stages.
Elliott Michael Sainsbury, Reinhard K. H. Schiemann, Kevin I. Hodges, Alexander J. Baker, Len C. Shaffrey, Kieran T. Bhatia, and Stella Bourdin
Weather Clim. Dynam., 3, 1359–1379, https://doi.org/10.5194/wcd-3-1359-2022, https://doi.org/10.5194/wcd-3-1359-2022, 2022
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Post-tropical cyclones (PTCs) can bring severe weather to Europe. By tracking and identifying PTCs in five global climate models, we investigate how the frequency and intensity of PTCs may change across Europe by 2100. We find no robust change in the frequency or intensity of Europe-impacting PTCs in the future. This study indicates that large uncertainties surround future Europe-impacting PTCs and provides a framework for evaluating PTCs in future generations of climate models.
Kieran M. R. Hunt and Andrew G. Turner
Weather Clim. Dynam., 3, 1341–1358, https://doi.org/10.5194/wcd-3-1341-2022, https://doi.org/10.5194/wcd-3-1341-2022, 2022
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More than half of India's summer monsoon rainfall arises from low-pressure systems: storms originating over the Bay of Bengal. In observation-based data, we examine how the generation and pathway of these storms are changed by the
boreal summer intraseasonal oscillation– the chief means of large-scale control on the monsoon at timescales of a few weeks. Our study offers new insights for useful prediction of these storms, important for both water resources planning and disaster early warning.
Julia F. Lockwood, Galina S. Guentchev, Alexander Alabaster, Simon J. Brown, Erika J. Palin, Malcolm J. Roberts, and Hazel E. Thornton
Nat. Hazards Earth Syst. Sci., 22, 3585–3606, https://doi.org/10.5194/nhess-22-3585-2022, https://doi.org/10.5194/nhess-22-3585-2022, 2022
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We describe how we developed a set of 1300 years' worth of European winter windstorm footprints, using a multi-model ensemble of high-resolution global climate models, for use by the insurance industry to analyse windstorm risk. The large amount of data greatly reduces uncertainty on risk estimates compared to using shorter observational data sets and also allows the relationship between windstorm risk and predictable large-scale climate indices to be quantified.
Rafaela Jane Delfino, Gerry Bagtasa, Kevin Hodges, and Pier Luigi Vidale
Nat. Hazards Earth Syst. Sci., 22, 3285–3307, https://doi.org/10.5194/nhess-22-3285-2022, https://doi.org/10.5194/nhess-22-3285-2022, 2022
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We showed the effects of altering the choice of cumulus schemes, surface flux options, and spectral nudging with a high level of sensitivity to cumulus schemes in simulating an intense typhoon. We highlight the advantage of using an ensemble of cumulus parameterizations to take into account the uncertainty in simulating typhoons such as Haiyan in 2013. This study is useful in addressing the growing need to plan and prepare for as well as reduce the impacts of intense typhoons in the Philippines.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
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We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Ambrogio Volonté, Andrew G. Turner, Reinhard Schiemann, Pier Luigi Vidale, and Nicholas P. Klingaman
Weather Clim. Dynam., 3, 575–599, https://doi.org/10.5194/wcd-3-575-2022, https://doi.org/10.5194/wcd-3-575-2022, 2022
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In this study we analyse the complex seasonal evolution of the East Asian summer monsoon. Using reanalysis data, we show the importance of the interaction between tropical and extratropical air masses converging at the monsoon front, particularly during its northward progression. The upper-level flow pattern (e.g. the westerly jet) controls the balance between the airstreams and thus the associated rainfall. This framework provides a basis for studies of extreme events and climate variability.
Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale
Geosci. Model Dev., 15, 269–289, https://doi.org/10.5194/gmd-15-269-2022, https://doi.org/10.5194/gmd-15-269-2022, 2022
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Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.
Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2021-46, https://doi.org/10.5194/wcd-2021-46, 2021
Preprint withdrawn
Short summary
Short summary
This study assesses the ability of the new Met Office IND1 numerical model to simulate tropical cyclones and their associated hazards, such as high winds and heavy rainfall. The new system consists of both atmospheric and oceanic models coupled together, allowing us to explore the sensitivity of cyclones to important air–sea feedbacks. We find that the model can accurately simulate tropical cyclone position, structure, and intensity, which are crucial for predicting and mitigating hazards.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J. E., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 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. a
Ajayamohan, R. S., Rao, S. A., and Yamagata, T.: Influence of Indian Ocean
dipole on poleward propagation of boreal summer intraseasonal oscillations, J. Climate, 21, 5437–5454, https://doi.org/10.1175/2008JCLI1758.1, 2008. a, b
Arakawa, A. and Schubert, W. H.: Interaction of a cumulus cloud ensemble with
the large-scale environment, Part I, J. Atmos. Sci., 31, 674–701, https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2, 1974. a
Bador, M., Boé, J., Terray, L., Alexander, L. V., Baker, A., Bellucci, A., Haarsma, R., Koenigk, T., Moine, M.-P., Lohmann, K., Putrasahan, D. A., Roberts, C., Roberts, M., Scoccimarro, E., Schiemann, R., Seddon, J., Senan, R., Valcke, S., and Vanniere, B.: Impact of higher spatial atmospheric resolution on precipitation extremes over land in global climate models, J. Geophys. Res.-Atmos., 125, e2019JD032184, https://doi.org/10.1029/2019JD032184, 2019. a
Bechtold, P., Semane, N., Lopez, P., Chaboureau, J.-P., Beljaars, A., and
Bormann, N.: Representing equilibrium and nonequilibrium convection in
large-scale models, J. Atmos. Sci., 71, 734–753, https://doi.org/10.1175/JAS-D-13-0163.1, 2014. a
Becker, T., Stevens, B., and Hohenegger, C.: Imprint of the convective
parameterization and sea-surface temperature on large-scale convective
self-aggregation, J. Adv. Model. Earth Syst., 9, 1488–1505, https://doi.org/10.1002/2016MS000865, 2017. a
Bollasina, M. A. and Ming, Y.: The general circulation model precipitation bias over the southwestern equatorial Indian Ocean and its implications for
simulating the South Asian monsoon, Clim. Dynam., 40, 823–838,
https://doi.org/10.1007/s00382-012-1347-7, 2013. a
Bush, S. J., Turner, A. G., Woolnough, S. J., Martin, G. M., and Klingaman,
N. P.: The effect of increased convective entrainment on Asian monsoon biases
in the MetUM general circulation model, Q. J. Roy. Meteorol. Soc., 141, 311–326, https://doi.org/10.1002/qj.2371, 2015. a, b, c, d
Chen, H., Yu, R., Li, J., Yuan, W., and Zhou, T.: Why nocturnal long-duration
rainfall presents an eastward-delayed diurnal phase of rainfall down the
Yangtze River valley, J. Climate, 23, 905–917, https://doi.org/10.1175/2009JCLI3187.1, 2010. a
Covey, C., Gleckler, P. J., Doutriaux, C., Williams, D. N., Dai, A., Fasullo,
J., Trenberth, K. E., and Berg, A.: Metrics for the diurnal cycle of
precipitation: Toward routine benchmarks for climate models, J. Climate, 29, 4461–4471, https://doi.org/10.1175/JCLI-D-15-0664.1, 2016. a, b, c, d
Curio, J., Chen, Y., Schiemann, R., Turner, A. G., Wong, K. C., Hodges, K., and Li, Y.: Comparison of a manual and an automated tracking method for Tibetan Plateau vortices, Adv. Atmos. Sci., 35, 965–980,
https://doi.org/10.1007/s00376-018-7278-4, 2018. a
Dai, A. and Trenberth, K. E.: The diurnal cycle and its depiction in the
Community Climate System Model, J. Climate, 17, 930–951,
https://doi.org/10.1175/1520-0442(2004)017<0930:TDCAID>2.0.CO;2, 2004. a
Dai, A. and Wang, J.: Diurnal and semidiurnal tides in global surface pressure fields, J. Atmos. Sci., 56, 3874–3891,
https://doi.org/10.1175/1520-0469(1999)056<3874:DASTIG>2.0.CO;2, 1999. a
Dai, A., Lin, X., and Hsu, K.-L.: The frequency, intensity, and diurnal cycle
of precipitation in surface and satellite observations over low-and mid-latitudes, Clim. Dynam., 29, 727–744, https://doi.org/10.1007/s00382-007-0260-y, 2007. a, b
Ding, R., Ha, K.-J., and Li, J.: Interdecadal shift in the relationship between the East Asian summer monsoon and the tropical Indian Ocean, Clim.
Dynam., 34, 1059–1071, https://doi.org/10.1007/s00382-009-0555-2, 2010. a
ESRI, E.: Shapefile technical description, An ESRI white paper, 39, available at: https://support.esri.com/en/white-paper/279 (last access: 15 December 2021), 1998. a
Field, P. R., Roberts, M. J., and Wilkinson, J. M.: Simulated lightning in a
convection permitting global model, J. Geophys. Res.-Atmos., 123, 9370–9377, https://doi.org/10.1029/2018JD029295, 2018. a, b, c
Golding, B. W.: Quantitative precipitation forecasting in the UK, J. Hydrol., 239, 286–305, https://doi.org/10.1016/S0022-1694(00)00354-1, 2000. a
Grams, C. M., Binder, H., Pfahl, S., Piaget, N., and Wernli, H.: Atmospheric
processes triggering the central European floods in June 2013, Nat. Hazards Earth Syst. Sci., 14, 1691–1702, https://doi.org/10.5194/nhess-14-1691-2014, 2014. a
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. a, b, c, d, e, f
He, B., Huang, X., Ma, M., Chang, Q., Tu, Y., Li, Q., Zhang, K., and Hong, Y.: Analysis of flash flood disaster characteristics in China from 2011 to 2015, Nat. Hazards, 90, 407–420, https://doi.org/10.1007/s11069-017-3052-7, 2018. a
Holloway, C. E., Woolnough, S. J., and Lister, G. M. S.: The effects of
explicit versus parameterized convection on the MJO in a large-domain
high-resolution tropical case study. Part I: Characterization of large-scale
organization and propagation, J. Atmos. Sci., 70, 1342–1369, https://doi.org/10.1175/JAS-D-12-0227.1, 2013. a
Houze Jr., R. A.: Mesoscale convective systems, Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004RG000150, 2004. a
Hsu, P.-C., Lee, J.-Y., and Ha, K.-J.: Influence of boreal summer intraseasonal oscillation on rainfall extremes in southern China, Int. J. Climatol., 36, 1403–1412, https://doi.org/10.1002/joc.4433, 2016. a
Hunt, K. M. and Menon, A.: The 2018 Kerala floods: a climate change perspective, Clim. Dynam., 54, 2433–2446, https://doi.org/10.1007/s00382-020-05123-7, 2020. a
Hurkmans, R., Terink, W., Uijlenhoet, R., Torfs, P., Jacob, D., and Troch, P. A.: Changes in streamflow dynamics in the Rhine basin under three high-resolution regional climate scenarios, J. Climate, 23, 679–699,
https://doi.org/10.1175/2009JCLI3066.1, 2010. a, b
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5,
487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004. a, b, c
Kendon, E. J., Prein, A. F., Senior, C. A., and Stirling, A.: Challenges and
outlook for convection-permitting climate modelling, Philos. T. Roy. Soc. A, 379, 20190547, https://doi.org/10.1098/rsta.2019.0547, 2021. a
Kennedy, J., Titchner, H., Rayner, N., and Roberts, M. J.:
input4MIPs.MOHC.SSTsAndSeaIce.HighResMIP.MOHC-HadISST-2-2-0-0-0, Earth System Grid Federation, UK Met Office, https://doi.org/10.22033/ESGF/input4MIPs.1221, 2017. a
Khairoutdinov, M., Randall, D., and DeMott, C.: Simulations of the atmospheric general circulation using a cloud-resolving model as a superparameterization of physical processes, J. Atmos. Sci., 62, 2136–2154,
https://doi.org/10.1175/JAS3453.1, 2005. a
Klingaman, N. P., Martin, G. M., and Moise, A. F.: ASoP (v1.0): A set of
methods for analyzing scales of precipitation in general circulation models,
Geosci. Model Dev., 10, 57–83, https://doi.org/10.5194/gmd-10-57-2017, 2017. a, b, c
Lehner, B.: HydroBASINS: Global watershed boundaries and sub-basin delineations derived from HydroSHEDS data at 15 second resolution, Technical Documentation Version 1.c (with and without inserted lakes), available at:
https://hydrosheds.org/images/inpages/HydroBASINS_TechDoc_v1c.pdf (last access: 15 December 2021), 2014. a
Lehner, B. and Grill, G.: Global river hydrography and network routing:
baseline data and new approaches to study the world's large river systems,
Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/hyp.9740, 2013. a, b
Levine, R. C., Turner, A. G., Marathayil, D., and Martin, G. M.: The role of
northern Arabian Sea surface temperature biases in CMIP5 model simulations
and future projections of Indian summer monsoon rainfall, Clim. Dynam., 41, 155–172, https://doi.org/10.1007/s00382-012-1656-x, 2013. a
Li, J., Li, Y., Zhao, T., Schiemann, R., Jiang, X., and Muetzelfeldt, M.:
Northeastward propagation of nocturnal precipitation over the Sichuan Basin,
Int. J. Climatol., 41, E2863–E2879, https://doi.org/10.1002/joc.6886, 2020. a, b
Li, P., Furtado, K., Zhou, T., Chen, H., Li, J., Guo, Z., and Xiao, C.: The
diurnal cycle of East Asian summer monsoon precipitation simulated by the Met
Office Unified Model at convection-permitting scales, Clim. Dynam., 55, 131–151, https://doi.org/10.1007/s00382-018-4368-z, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z
Marsham, J. H., Dixon, N. S., Garcia-Carreras, L., Lister, G. M. S., Parker,
D. J., Knippertz, P., and Birch, C. E.: The role of moist convection in the
West African monsoon system: Insights from continental-scale convection-permitting simulations, Geophys. Res. Lett., 40, 1843–1849, https://doi.org/10.1002/grl.50347, 2013. a, b, c
Martin, G. M., Milton, S. F., Senior, C. A., Brooks, M. E., Ineson, S.,
Reichler, T., and Kim, J.: Analysis and reduction of systematic errors through a seamless approach to modeling weather and climate, J. Climate, 23, 5933–5957, https://doi.org/10.1175/2010JCLI3541.1, 2010. a
Martin, G. M., Klingaman, N. P., and Moise, A. F.: Connecting spatial and
temporal scales of tropical precipitation in observations and the MetUM-GA6,
Geosci. Model Dev., 10, 105–126, https://doi.org/10.5194/gmd-10-105-2017, 2017. a, b, c, d
Met Office: Unified Model Partnership, available at: https://www.metoffice.gov.uk/research/approach/collaboration/unified-model/partnership, last access: 15 December 2021. a
Mitra, A. K., Momin, I. M., Rajagopal, E. N., Basu, S., Rajeevan, M. N., and
Krishnamurti, T. N.: Gridded daily Indian monsoon rainfall for 14 seasons:
Merged TRMM and IMD gauge analyzed values, J. Earth Syst. Sci., 122, 1173–1182, https://doi.org/10.1007/s12040-013-0338-3, 2013. a
Muetzelfeldt, M. R.: basmati, figshare [code], https://doi.org/10.6084/m9.figshare.17049440.v1, 2021a. a
Muetzelfeldt, M. R.: Muetzelfeldt, M. R.: cosmic, figshare [code], https://doi.org/10.6084/m9.figshare.17049428.v1, 2021b. a
Muetzelfeldt, M. R.: remake_v0.4.1, figshare [code], https://doi.org/10.6084/m9.figshare.17206988.v1, 2021c. a
Mulcahy, J. P., Jones, C., Sellar, A., Johnson, B., Boutle, I. A., Jones, A., Andrews, T., Rumbold, S. T., Mollard, J., Bellouin, N., Johnson, C. E., Williams, K. D., Grosvenor, D. P., and McCoy, D. T.: Improved aerosol processes and effective radiative forcing in HadGEM3 and UKESM1, J. Adv. Model. Earth Syst., 10, 2786–2805, https://doi.org/10.1029/2018MS001464, 2018. a
NOAA: Index of /precip/CMORPH_V1.0/CRT/8km-30min, NOAA [data set], https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/CRT/8km-30min/, last access: 15 December 2021. a
Ploshay, J. J. and Lau, N.-C.: Simulation of the diurnal cycle in tropical
rainfall and circulation during boreal summer with a high-resolution GCM, Mon. Weather Rev., 138, 3434–3453, https://doi.org/10.1175/2010MWR3291.1, 2010. a
PRIMAVERA: Climate model simulations, PRIMAVERA [code], https://www.primavera-h2020.eu/modelling/, last access: 15 December 2021. a
Reichle, R. H., Koster, R. D., De Lannoy, G. J. M., Forman, B. A., Liu, Q.,
Mahanama, S. P. P., and Touré, A.: Assessment and enhancement of MERRA
land surface hydrology estimates, J. Climate, 24, 6322–6338,
https://doi.org/10.1175/JCLI-D-10-05033.1, 2011. a
Roberts, M. J.: MOHC HadGEM3-GC31-HM model output prepared for CMIP6 HighResMIP, Earth System Grid Federation, Met Office, https://doi.org/10.22033/ESGF/CMIP6.446, 2017a. a
Roberts, M. J.: MOHC HadGEM3-GC31-LM model output prepared for CMIP6 HighResMIP, Earth System Grid Federation, Met Office, https://doi.org/10.22033/ESGF/CMIP6.1321, 2017b. a
Roberts, M. J.: MOHC HadGEM3-GC31-MM model output prepared for CMIP6
HighResMIP, Earth System Grid Federation, Met Office, https://doi.org/10.22033/ESGF/CMIP6.1902, 2017c. a
Roberts, M. J., Vidale, P. L., Senior, C., Hewitt, H. T., Bates, C., Berthou, S., Chang, P., Christensen, H. M., Danilov, S., Demory, M.-E., Griffies, S. M., Haarsma, R., Jung, T., Martin, G., Minobe, S., Ringler, T., Satoh, M., Schiemann, R., Scoccimarro, E., Stephens, G., and Wehner, M. F.: The benefits of global high resolution for climate simulation: process understanding and the enabling of stakeholder decisions at the regional scale, B. Am. Meteorol. Soc., 99, 2341–2359, https://doi.org/10.1175/BAMS-D-15-00320.1, 2018. a
Roberts, M. J., Baker, A., Blockley, E. W., Calvert, D., Coward, A., Hewitt, H. T., Jackson, L. C., Kuhlbrodt, T., Mathiot, P., Roberts, C. D., Schiemann, R., Seddon, J., Vannière, B., and Vidale, P. L.: Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments, Geosci. Model Dev., 12, 4999–5028, https://doi.org/10.5194/gmd-12-4999-2019, 2019. a, b, c, d
Roberts, N.: Assessing the spatial and temporal variation in the skill of
precipitation forecasts from an NWP model, Meteorol. Appl., 15, 163–169, https://doi.org/10.1002/met.57, 2008. a
Sato, T., Miura, H., Satoh, M., Takayabu, Y. N., and Wang, Y.: Diurnal cycle of precipitation in the tropics simulated in a global cloud-resolving model, J. Climate, 22, 4809–4826, https://doi.org/10.1175/2009JCLI2890.1, 2009. a
Satoh, M., Tomita, H., Yashiro, H., Miura, H., Kodama, C., Seiki, T., Noda, A. T., Yamada, Y., Goto, D., Sawada, M., Miyoshi, T., Niwa, Y., Hara, M., Ohno, T., Iga, S.-I., Arakawa, T., Inoue, T., and Kubokawa, H.: The non-hydrostatic icosahedral atmospheric model: Description and development, Progr. Earth Planet. Sci., 1, 18, https://doi.org/10.1186/s40645-014-0018-1, 2014. a
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.: Kilometer-scale climate models: Prospects and challenges, B. Am. Meteorol. Soc., 101, E567–E587,
https://doi.org/10.1175/BAMS-D-18-0167.1, 2020. a
Schiemann, R., Vidale, P. L., Shaffrey, L. C., Johnson, S. J., Roberts, M. J., Demory, M.-E., Mizielinski, M. S., and Strachan, J.: Mean and extreme
precipitation over European river basins better simulated in a 25 km AGCM,
Hydrol. Earth Syst. Sci., 22, 3933–3950, https://doi.org/10.5194/hess-22-3933-2018, 2018. a, b, c, d, e, f
Shashikanth, K., Madhusoodhanan, C. G., Ghosh, S., Eldho, T. I., Rajendran, K., and Murtugudde, R.: Comparing statistically downscaled simulations of Indian monsoon at different spatial resolutions, J. Hydrol., 519, 3163–3177, 2014. a
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-year
high-resolution global dataset of meteorological forcings for land surface
modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006. a
Smagorinsky, J.: Some aspects of the general circulation, Q. J. Roy. Meteorol. Soc., 90, 1–14, https://doi.org/10.1002/qj.49709038302, 1964. a
Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J.,
Smith, S. J., and Mauritsen, T.: MACv2-SP: A parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for
use in CMIP6, Geosci. Model Dev., 10, 433–452, https://doi.org/10.5194/gmd-10-433-2017, 2017. a, b
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X.,
Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYnamics of the Atmospheric General Circulation Modeled On Non-hydrostatic Domains, Progr. Earth Planet. Sci., 6, 1–17, 2019. a
Stirling, A. J. and Stratton, R. A.: Entrainment processes in the diurnal cycle of deep convection over land, Q. J. Roy. Meteorol. Soc., 138, 1135–1149, https://doi.org/10.1002/qj.1868, 2012. a, b
Titchner, H. A. and Rayner, N. A.: The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2: 1. Sea ice concentrations, J. Geophys. Res.-Atmos., 119, 2864–2889, https://doi.org/10.1002/2013JD020316, 2014. a
Tomassini, L.: Mesoscale circulations and organized convection in African
easterly waves, J. Atmos. Sci., 75, 4357–4381, https://doi.org/10.1175/JAS-D-18-0183.1, 2018. a
Vannière, B., Demory, M.-E., Vidale, P. L., Schiemann, R., Roberts, M. J., Roberts, C. D., Matsueda, M., Terray, L., Koenigk, T., and Senan, R.:
Multi-model evaluation of the sensitivity of the global energy budget and
hydrological cycle to resolution, Clim. Dynam., 52, 6817–6846,
https://doi.org/10.1007/s00382-018-4547-y, 2019. a, b, c, d
Verdin, K. L. and Verdin, J. P.: A topological system for delineation and
codification of the Earth's river basins, J. Hydrol., 218, 1–12,
https://doi.org/10.1016/S0022-1694(99)00011-6, 1999. a, b
Vergara-Temprado, J., Ban, N., Panosetti, D., Schlemmer, L., and Schär, C.: Climate Models Permit Convection at Much Coarser Resolutions Than Previously Considered, J. Climate, 33, 1915–1933, https://doi.org/10.1175/JCLI-D-19-0286.1, 2020. a
Virts, K. S. and Houze Jr., R. A.: Seasonal and intraseasonal variability of
mesoscale convective systems over the South Asian monsoon region, J. Atmos. Sci., 73, 4753–4774, https://doi.org/10.1175/JAS-D-16-0022.1, 2016. a
Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017. a
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J.,
Furtado, K., Hill, P., Lock, A. P., Manners, J., Morcrette, C., Mulcahy, J.,
Sanchez, C., Smith, C., Stratton, R. A., Tennant, W. J., Tomassini, L.,
Van Weverberg, K., Vosper, S., Willett, M. R., Browse, J., Bushell, A.,
Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B.,
Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar,
A., Ujiie, M., Whitall, M. A., Williams, K., and Zerroukat, M.: The Met
Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0
configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a, b
Webb, M. J., Lock, A. P., Bretherton, C. S., Bony, S., Cole, J. N. S., Idelkadi, A., Kang, S. M., Koshiro, T., Kawai, H., Ogura, T., Roehrig, R., Shin, Y., Mauritsen, T., Sherwood, S. C., Vial, J., Watanabe, M., Woelfle, M. D., and Zhao, M.: The impact of parametrized convection on cloud feedback, Philos. T. Roy. Soc. A, 373, 20140414,
https://doi.org/10.1098/rsta.2014.0414, 2015. a
Willetts, P. D., Marsham, J. H., Birch, C. E., Parker, D. J., Webster, S., and Petch, J.: Moist convection and its upscale effects in simulations of the
Indian monsoon with explicit and parametrized convection, Q. J. Roy. Meteorol. Soc., 143, 1073–1085, https://doi.org/10.1002/qj.2991, 2017. a, b, c
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and Xavier, P. K.: The Met Office global coupled model 3.0 and 3.1 (GC3.0 and GC3.1) configurations, J. Adv. Model. Earth Syst., 10, 357–380, https://doi.org/10.1002/2017MS001115, 2018. a
Wu, R. and Wang, B.: A contrast of the East Asian summer monsoon–ENSO
relationship between 1962–77 and 1978–93, J. Climate, 15, 3266–3279, https://doi.org/10.1175/1520-0442(2002)015<3266:ACOTEA>2.0.CO;2, 2002. a
Xavier, P. K., Marzin, C., and Goswami, B. N.: An objective definition of the
Indian summer monsoon season and a new perspective on the ENSO–monsoon
relationship, Q. J. Roy. Meteorol. Soc., 133, 749–764, https://doi.org/10.1002/qj.45, 2007. a
Yang, G.-Y. and Slingo, J.: The diurnal cycle in the tropics, Mon. Weather Rev., 129, 784–801, https://doi.org/10.1175/1520-0493(2001)129<0784:TDCITT>2.0.CO;2,
2001. a, b, c
Yang, X., Fei, J., Huang, X., Cheng, X., Carvalho, L. M. V., and He, H.:
Characteristics of mesoscale convective systems over China and its vicinity
using geostationary satellite FY2, J. Climate, 28, 4890–4907,
https://doi.org/10.1175/JCLI-D-14-00491.1, 2015. a
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh,
A.: APHRODITE: Constructing a long-term daily gridded precipitation dataset
for Asia based on a dense network of rain gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1, 2012. a
Yu, R., Zhou, T., Xiong, A., Zhu, Y., and Li, J.: Diurnal variations of summer precipitation over contiguous China, Geophys. Res. Lett., 34, L01704,
https://doi.org/10.1029/2006GL028129, 2007. a
Zabalza-Martínez, J., Vicente-Serrano, S. M., López-Moreno, J. I.,
Borràs Calvo, G., Savé, R., Pascual, D., Plá, E.,
Morán-Tejeda, E., Domínguez-Castro, F., and Tague, C.: The Influence
of Climate and Land-Cover Scenarios on Dam Management Strategies in a High
Water Pressure Catchment in Northeast Spain, Water, 10, 1668,
https://doi.org/10.3390/w10111668, 2018.
a
Zhang, L., Wu, P., Zhou, T., Roberts, M. J., and Schiemann, R.: Added value of high resolution models in simulating global precipitation characteristics,
Atmos. Sci. Lett., 17, 646–657, https://doi.org/10.1002/asl.715, 2016. a
Zhang, Q., Gu, X., Li, J., Shi, P., and Singh, V. P.: The impact of tropical
cyclones on extreme precipitation over coastal and inland areas of China and
its association to ENSO, J. Climate, 31, 1865–1880, https://doi.org/10.1175/JCLI-D-17-0474.1, 2018. a
Zhong, Z. and Hu, Y.: Impacts of tropical cyclones on the regional climate: An East Asian summer monsoon case, Atmos. Sci. Lett., 8, 93–99,
https://doi.org/10.1002/asl.158, 2007. a
Zhou, T., Yu, R., Chen, H., Dai, A., and Pan, Y.: Summer precipitation
frequency, intensity, and diurnal cycle over China: A comparison of satellite
data with rain gauge observations, J. Climate, 21, 3997–4010,
https://doi.org/10.1175/2008JCLI2028.1, 2008. a, b
Short summary
Simulating East Asian Summer Monsoon (EASM) rainfall poses many challenges because of its multi-scale nature. We evaluate three setups of a 14 km global climate model against observations to see if they improve simulated rainfall. We do this over catchment basins of different sizes to estimate how model performance depends on spatial scale. Using explicit convection improves rainfall diurnal cycle, yet more model tuning is needed to improve mean and intensity biases in simulated summer rainfall.
Simulating East Asian Summer Monsoon (EASM) rainfall poses many challenges because of its...
