Articles | Volume 22, issue 10
https://doi.org/10.5194/nhess-22-3285-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-3285-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Oct 2022
Research article |
| 12 Oct 2022
| 12 Oct 2022
Sensitivity of simulating Typhoon Haiyan (2013) using WRF: the role of cumulus convection, surface flux parameterizations, spectral nudging, and initial and boundary conditions
Rafaela Jane Delfino
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, United
Kingdom
Institute of Environmental Science and Meteorology, University of
the Philippines Diliman, Quezon City, Philippines
Gerry Bagtasa
Institute of Environmental Science and Meteorology, University of
the Philippines Diliman, Quezon City, Philippines
Kevin Hodges
Department of Meteorology, University of Reading, Reading, United
Kingdom
Pier Luigi Vidale
Department of Meteorology, University of Reading, Reading, United
Kingdom
Related authors
No articles found.
Reinhard K. H. Schiemann, Grenville Lister, Rosalyn Hatcher, Dan Hodson, Bryan Lawrence, Len Shaffrey, Jeff Cole, Andrea Dittus, Jenny Mecking, Jon Robson, Simon Wilson, Yevgeny Aksenov, Adam T. Blaker, Ben Harvey, Oscar Martínez-Alvarado, Annette Osprey, Stephanie Rynders, Sharar Ahmadi, Jake Aylmer, Laura Baker, David Case, Emanuele Silvio Gentile, Steve George, Kevin Hodges, Eliza Karlowska, Charlotte Lang, Hua Lu, Niamh O'Callaghan, Weronika Osmolska, Scott Osprey, Tony Phillips, David Schröder, Robin Smith, Andrew G. Turner, Steve Woolnough, Martin Andrews, Andrew Coward, Steven Hardiman, James Harle, John W. Rostron, David M. H. Sexton, Bablu Sinha, Jonny Williams, and Chris Wilson
EGUsphere, https://doi.org/10.5194/egusphere-2026-2281, https://doi.org/10.5194/egusphere-2026-2281, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This paper presents a new large ensemble of climate model simulations: The CANARI HadGEM3 Large Ensemble (CANARI LE). The CANARI LE has a comparatively high resolution, provides sub-daily output on atmospheric pressure levels, and boundary conditions for driving regional model simulations. The CANARI LE is thus a community resource that lends itself for the study of physical coupled climate processes, weather systems, and impacts in a changing climate.
Rafaela Jane Delfino, Gerry Bagtasa, Pier Luigi Vidale, and Kevin Hodges
Nat. Hazards Earth Syst. Sci., 26, 3579–3595, https://doi.org/10.5194/nhess-26-3579-2026, https://doi.org/10.5194/nhess-26-3579-2026, 2026
Short summary
Short summary
High-resolution simulations reveal that future warming amplifies tropical cyclone extreme rainfall in the Philippines, with inner-core increases exceeding 30–40 %, indicating a shift toward higher-intensity rainfall events. This response is primarily driven by thermodynamic moisture increases, while dynamical changes generally offset part of the increase. Tropical cyclone (TC) rainfall scaling is therefore largely moisture-driven, with TC-specific dynamics modulating the magnitude of rainfall enhancement.
Stella Bourdin, Kevin Hodges, Yushan Han, Leo Saffin, Alexander Baker, John Methven, and Pier Luigi Vidale
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-403, https://doi.org/10.5194/essd-2026-403, 2026
Preprint under review for ESSD
Short summary
Short summary
This paper describes a new dataset for the study of historical tropical cyclones. RABTracks is a global dataset that combines official tropical cyclone records with multiple weather reanalyses and tracking methods from 1940 to today. It extends cyclone tracks before formation and after dissipation, adding information on intensity, size, structure, and transition to extratropical storms. The dataset is designed to support improved cyclone risk analysis.
Wenyao Gan, Martin Best, Pier Luigi Vidale, I. Colin Prentice, and Sandy P. Harrison
EGUsphere, https://doi.org/10.5194/egusphere-2026-2804, https://doi.org/10.5194/egusphere-2026-2804, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Plants dynamically adjust their photosynthetic capacity as environmental conditions change, but most land models represent this with fixed parameters set separately for each plant type. We added a simple optimality-based photosynthesis scheme to the Joint UK Land Environment Simulator, allowing photosynthetic capacity to acclimate, and tested it against flux-tower observations together with the standard setups. The new scheme improves simulated carbon uptake without worsening energy fluxes.
Dorothy Heinrich, Erin Coughlan de Perez, Leanne Archer, Nadia Bloemendaal, Kevin I. Hodges, Helen Hooker, Theodore G. Shepherd, Nathan Sparks, Ralf Toumi, and Elisabeth Stephens
EGUsphere, https://doi.org/10.5194/egusphere-2026-2758, https://doi.org/10.5194/egusphere-2026-2758, 2026
Short summary
Short summary
An unprecedented tropical cyclone (TC) can turn into a disaster if it is unforeseen. In this work, we explore different TC datasets to identify simulated events that could be unprecedented. We look at whether these scenarios could be considered be plausible and how different these would be from past records, and illustrate with examples. We highlight that there is a wealth of scenarios that could be used by disaster managers to imagine and prepare for unprecedented events.
Farrell Morgan, Ben Harvey, Kevin Hodges, and Oscar Martínez-Alvarado
EGUsphere, https://doi.org/10.5194/egusphere-2026-289, https://doi.org/10.5194/egusphere-2026-289, 2026
Short summary
Short summary
The socio-economic risk represented by mid-latitude cyclones in Europe is changing in a warming climate, but these changes are not uniform across all cyclones. Here we investigate climate change effects on contrasting historical cyclones that impacted the UK. We show that the changes in likelihood, intensity and causes do depend on each cyclone’s characteristics. This highlights that accounting for cyclone-specific responses is critical for assessing impacts and developing adaptation strategies.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Lorenzo Sangelantoni, Stefano Tibaldi, Leone Cavicchia, Enrico Scoccimarro, Pier Luigi Vidale, Kevin Hodges, Vivien Mavel, Mattia Almansi, Chiara Cagnazzo, and Samuel Almond
EGUsphere, https://doi.org/10.5194/egusphere-2024-4157, https://doi.org/10.5194/egusphere-2024-4157, 2025
Preprint archived
Short summary
Short summary
We introduce a new dataset of European windstorms linked to extratropical cyclones, spanning whole ERA5 reanalysis period (1940–present). Developed under Copernicus Climate Change Service, the dataset provides standardized, high-quality information on windstorm tracks and footprints for industries like insurance and risk management. Preliminary findings show an increase in cold-season windstorms and their impacts in parts of Europe. Tracking methods contribute to uncertainties in key statistics.
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
Short summary
Short summary
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.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Henri Rossi Pinheiro, Kevin Ivan Hodges, and Manoel Alonso Gan
Weather Clim. Dynam., 5, 881–894, https://doi.org/10.5194/wcd-5-881-2024, https://doi.org/10.5194/wcd-5-881-2024, 2024
Short summary
Short summary
Cut-off lows (COLs) are weather systems with varied structures and lifecycles, from upper atmospheric to deep vortices. Deep, strong COLs are common around Australia and the southwestern Pacific in autumn and spring, while shallow, weak COLs occur more in summer near the Equator. Jet streams play a crucial role in COL development, with different jets influencing its depth and strength. The study also emphasizes the need for better representation of diabatic processes in reanalysis data.
Alexander Frank Vessey, Kevin I. Hodges, Len C. Shaffrey, and Jonathan J. Day
Nat. Hazards Earth Syst. Sci., 24, 2115–2132, https://doi.org/10.5194/nhess-24-2115-2024, https://doi.org/10.5194/nhess-24-2115-2024, 2024
Short summary
Short summary
The risk posed to ships by Arctic cyclones has seldom been quantified due to the lack of publicly available historical Arctic ship track data. This study investigates historical Arctic ship tracks, cyclone tracks, and shipping incident reports to determine the number of shipping incidents caused by the passage of Arctic cyclones. Results suggest that Arctic cyclones have not been hazardous to ships and that ships are resilient to the rough sea conditions caused by Arctic cyclones.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Alexander F. Vessey, Kevin I. Hodges, Len C. Shaffrey, and Jonathan J. Day
Weather Clim. Dynam., 3, 1097–1112, https://doi.org/10.5194/wcd-3-1097-2022, https://doi.org/10.5194/wcd-3-1097-2022, 2022
Short summary
Short summary
Understanding the location and intensity of hazardous weather across the Arctic is important for assessing risks to infrastructure, shipping, and coastal communities. This study describes the typical lifetime and structure of intense winter and summer Arctic cyclones. Results show the composite development and structure of intense summer Arctic cyclones are different from intense winter Arctic and North Atlantic Ocean extra-tropical cyclones and from conceptual models.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Suzanne L. Gray, Kevin I. Hodges, Jonathan L. Vautrey, and John Methven
Weather Clim. Dynam., 2, 1303–1324, https://doi.org/10.5194/wcd-2-1303-2021, https://doi.org/10.5194/wcd-2-1303-2021, 2021
Short summary
Short summary
This research demonstrates, using feature identification and tracking, that anticlockwise rotating vortices at about 7 km altitude called tropopause polar vortices frequently interact with storms developing in the Arctic region, affecting their structure and where they occur. This interaction has implications for the predictability of Arctic weather, given the long lifetime but a relatively small spatial scale of these vortices compared with the density of the polar observation network.
Mark R. Muetzelfeldt, Reinhard Schiemann, Andrew G. Turner, Nicholas P. Klingaman, Pier Luigi Vidale, and Malcolm J. Roberts
Hydrol. Earth Syst. Sci., 25, 6381–6405, https://doi.org/10.5194/hess-25-6381-2021, https://doi.org/10.5194/hess-25-6381-2021, 2021
Short summary
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.
Cited articles
Adeniyi, M. O.: Sensitivities of the Tiedtke and Kain-Fritsch Convection
Schemes for RegCM4.5 over West Africa, Meteorology Hydrology and Water
Management, 7, 27–37, https://doi.org/10.26491/mhwm/103797, 2019.
Alaka Jr., G. J., Zhang, X., aand Gopalakrishnan, S. G.: High-Definition
Hurricanes: Improving Forecasts with Storm-Following Nests, B.
Am. Meteorol. Soc., 103, E680–E703, https://doi.org/10.1175/BAMS-D-20-0134.1, 2022.
Aragon, L. G. B. and Pura, A. G.: Analysis of the displacement error of the
WRF–ARW model in predicting tropical cyclone tracks over the Philippines,
Meteorol. Appl., 23, 401–408, https://doi.org/10.1002/met.1564, 2016.
Bagtasa, G.: Contribution of Tropical Cyclones to Rainfall in the
Philippines, J. Climate, 30, 3621–3633, https://doi.org/10.1175/JCLI-D-16-0150.1,
2017.
Bagtasa, G.: Influence of Madden–Julian oscillation on the intraseasonal
variability of summer and winter monsoon rainfall in the Philippines, J.
Climate, 33, 9581–9594, https://doi.org/10.1175/JCLI-D-20-0305.1, 2020.
Bagtasa, G.: Analog forecasting of tropical cyclone rainfall in the
Philippines, Weather and Climate Extremes, 32, 100323,
https://doi.org/10.1016/j.wace.2021.100323, 2021.
Biswas, M. K., Bernardet, L., and Dudhia, J.: Sensitivity of hurricane
forecasts to cumulus parameterizations in the HWRF model, Geophys. Res.
Lett., 41, 9113–9119, https://doi.org/10.1002/2014GL062071, 2014.
Bopape, M.-J. M., Cardoso, H., Plant, R. S., Phaduli, E., Chikoore, H.,
Ndarana, T., Khalau, L., and Rakate, E.: Sensitivity of Tropical Cyclone Idai
Simulations to Cumulus Parametrization Schemes, Atmosphere, 12, 932,
https://doi.org/10.3390/atmos12080932, 2021.
Brucal, A., Roezer, V., Dookie, D. S., Byrnes, R., Ravago, M. V., and Cruz,
F.: Disaster impacts and financing: local insights from the Philippines,
https://www.lse.ac.uk/granthaminstitute/publication/disaster-
impacts-and-financing-local-insights-from-the-philippines/
(last access: 28 June 2021), 9 June 2020.
Brutsaert, W.: A theory for local evaporation (or heat transfer) from rough
and smooth surfaces at ground level, Water Resour. Res., 11, 543–550,
https://doi.org/10.1029/WR011i004p00543, 1975a.
Brutsaert, W.: The Roughness Length for Water Vapor, Sensible Heat, and
other Scalars, J. Atmos. Sci., 32, 2028–2031,
https://doi.org/10.1175/1520-0469(1975)032<2029:TRLFWV>2.0.CO;2, 1975b.
Cha, D.-H., Jin, C.-S., Lee, D.-K., and Kuo, Y.-H.: Impact of intermittent
spectral nudging on regional climate simulation using Weather Research and
Forecasting model, J. Geophys. Res., 116, D10103, https://doi.org/10.1029/2010JD015069,
2011.
Charnock, H.: Wind stress on a water surface, Q. J. Roy. Meteor. Soc., 81,
639–640, https://doi.org/10.1002/qj.49708135027, 1955.
Chen, F. and Dudhia, J.: Coupling an advanced land-surface/ hydrology model
with the Penn State/NCAR MM5 modeling system. Part I: model description and
implementation, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129>0569:CAALSH>2.0.CO;2, 2001.
Chen, S., Campbell, T. J., Jin, H., Gaberšek, S., Hodur, R. M., and Martin,
P.: Effect of two-way air–sea coupling in high and low wind speed
regimes, Mon. Weather Rev., 138, 3579–3602, https://doi.org/10.1175/2009MWR3119.1,
2010.
Cinco, T. A., de Guzman, R. G., Ortiz, A. M. D., Delfino, R. J. P., Lasco,
R. D., Hilario, F. D., and Ares, E. D.: Observed trends and impacts of
tropical cyclones in the Philippines, Int. J. Climatol., 36, 4638–4650,
https://doi.org/10.1002/joc.4659, 2016.
Comiso, J. C., Perez, G. P., and Stock, L. V.: Enhanced Pacific Ocean Sea
Surface Temperature and Its Relation to Typhoon Haiyan, J. Environ. Sci.
Manag., 18, 1–10 https://doi.org/10.47125/jesam/2015_1/01, 2015.
Cruz, F. and Narisma, G.: WRF simulation of the heavy rainfall over
Metropolitan Manila, Philippines during tropical cyclone Ketsana: a
sensitivity study, Meteorol. Atmos. Phys., 128, 415–428,
https://doi.org/10.1007/s00703-015-0425-x, 2016.
Davis, C., Wang, W., Chen, S. S., Chen, Y., Corbosiero, K., DeMaria, M.,
Dudhia, J., Holland, G., Klemp, J., Michalakes, J., Reeves, H., Rotunno, R.,
Snyder, C., and Xiao, Q.: Prediction of Landfalling Hurricanes with the
Advanced Hurricane WRF Model, Mon. Weather Rev., 136, 1990–2005,
https://doi.org/10.1175/2007MWR2085.1, 2008.
Di, Z., Gong, W., Gan, Y., She, C., and Duan, Q.: Combinatorial Optimization
for WRF Physical Parameterization Schemes : A Case Study of Three-Day
Typhoon Simulations over the Northwest Pacific Ocean, Atmosphere, 10, 233,
https://doi.org/10.3390/atmos10050233, 2019.
Donelan, M. A., Haus, B. K., Reul, N., Plant, W. J., Sti- assnie, M.,
Graber, H. C., Brown, O. B., and Saltzman, E. S.: On the limiting
aerodynamic roughness of the ocean in very strong winds, Geophys. Res.
Lett., 31, L18306, https://doi.org/10.1029/2004GL019460, 2004.
Dudhia, J.: Numerical study of convection observed during the Winter Monsoon
Experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46,
3077–3107, https://doi.org/10.1175/1520-0469(1989)046>3077:NSOCOD>2.0.CO;2, 1989.
Du Duc, T., Hoang Duc, H., Hole, L. R., Hoang, L., Luong, H., Thanh, T., and
Khanh, H. M.: Impacts of Different Physical Parameterization Configurations
on Widespread Heavy Rain Forecast over the Northern Area of Vietnam in
WRF-ARW Model, Adv. Meteorol., 2019, 1010858, https://doi.org/10.1155/2019/1010858,
2019.
Eckstein, D., Künzel, V., Schäfer, L., and Winges, M.: Global Climate
Risk Index 2020, Who Suffers Most from Extreme Weather Events?
Weather-Related Loss Events in 2018 and 1999 to 2018, Germanwatch,
ISBN 978-3-943704-77-8, https://www.germanwatch.org/en/17307, 2020.
Emanuel, K. A.: The finite-amplitude nature of tropical cyclogenesis, J.
Atmos. Sci., 46, 3431–3456, 1989.
Emanuel, K: Increasing destructiveness of tropical cyclones over the past
30 years, Nature, 436, 686–688, https://doi.org/10.1038/nature03906, 2005.
Emanuel, K. A.: An Air-Sea Interaction Theory for Tropical Cyclones. Part I:
Steady-State Maintenance, J. Atmos. Sci., 43, 585–605,
https://doi.org/10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2, 1986.
EM-DAT: The Emergency Events Database, Université Catholique de Louvain
(UCL), CRED, edited by: Guha-Sapir, D., Below, R., and Hoyois, Ph., Brussels,
Belgium, last database update: 30 June 2020, https://www.emdat.be/,
last access: 20 December 2020.
Floors, R., Batchvarova, E., Gryning, S.-E., Hahmann, A. N., Peña, A., and
Mikkelsen, T.: Atmospheric boundary layer wind profile at a flat coastal site
– wind speed lidar measurements and mesoscale modeling results, Adv. Sci.
Res., 6, 155–159, https://doi.org/10.5194/asr-6-155-2011, 2011.
Flores, R. A. A.: Geo-visual analytics on the verification of the PAGASA operational numerical weather prediction model rainfall forecast, Int. Arch.
Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 215–222,
https://doi.org/10.5194/isprs-archives-XLII-4-W19-215-2019, 2019.
Gallo, F., Daron, J., Macadam, I., Cinco, T., Villafuerte II, M., Buonomo,
E., Tucker, S., Hein-Griggs, D., and Jones, R. G.: High-resolution regional
climate model projections of future tropical cyclone activity in the
Philippines, Int. J. Climatol., 39, 1181–1194, https://doi.org/10.1002/joc.5870, 2019.
Gao, K., Harris, L., Chen, J.-H., Lin, S.-J., and Hazelton, A.: Improving
AGCM hurricane structure with two-way nesting, J. Adv. Model. Earth Sy., 11,
278–292, https://doi.org/10.1029/2018MS001359, 2019.
Glisan, J. M., Gutowski Jr., W. J., Cassano, J. J., and Higgins, M. E.:
Effects of Spectral Nudging in WRF on Arctic Temperature and Precipitation
Simulations, J. Climate, 26, 3985–3999, https://doi.org/10.1175/JCLI-D-12-00318.1,
2013.
Green, B. W. and Zhang F.: Impacts of Air–Sea Flux Parameterizations on
the Intensity and Structure of Tropical Cyclones, Mon. Weather Rev., 141,
2308–2324, https://doi.org/10.1175/MWR-D-12-00274.1, 2013.
Guo, X. and Zhong, W.: The Use of a Spectral Nudging Technique to Determine
the Impact of Environmental Factors on the Track of Typhoon Megi (2010),
Atmosphere, 8, 257, https://doi.org/10.3390/atmos8120257, 2017.
Gutmann, E. D., Rasmussen, R. M., Liu, C., Ikeda, K., Bruyere, C. L., Done,
J. M., Garrè, L., Friis-Hansen, P., and Veldore, V.: Changes in Hurricanes
from a 13-Yr Convection-Permitting Pseudo–Global Warming Simulation, J.
Climate, 31, 3643–3657, https://doi.org/10.1175/JCLI-D-17-0391.1, 2018.
Harris, L. M.: On the relative performance of one-way and two-way grid
nesting, PhD thesis, University of Washington ProQuest Dissertations
Publishing, UMI Number: 3406128, 2010.
Harris, L. M. and Durran, D. R.: An Idealized Comparison of One-Way and
Two-Way Grid Nesting, Mon. Weather Rev., 138, 2174–2187,
https://doi.org/10.1175/2010MWR3080.1, 2010.
Hashimoto, A., Done, J. M., Fowler, L. D., and Bruyère, C. L.: Tropical
cyclone activity in nested regional and global grid-refined simulations,
Clim. Dynam., 47, 497–508, https://doi.org/10.1007/s00382-015-2852-2, 2015.
Heming, J. T.: Tropical cyclone tracking and verification techniques for Met
Office numerical weather prediction models, Meteorol. Appl., 24, 1–8,
https://doi.org/10.1002/met.1599, 2017.
Hennermann, K.: ERA5: uncertainty estimation, European Centre for
Medium-Range Weather Forecasts,
https://confluence.ecmwf.int/display/CKB/ERA5%3A+uncertainty+estimation (last access: 7 June 2020), 2018.
Hersbach, H., Bell, B., Berrisford, P., et al.: The ERA5 global reanalysis,
Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hodges, K. I.: Feature tracking on the unit sphere, Mon. Weather Rev., 123,
3458–3465, https://doi.org/10.1175/1520-0493(1995)123<3458:FTOTUS>2.0.CO;2, 1995.
Hodges, K. I. and Klingaman, N. P.: Prediction errors of tropical cyclones
in the western north Pacific in the Met Office global forecast model,
Weather Forecast., 34, 1189–1209, https://doi.org/10.1175/WAF-D-19-0005.1, 2019.
Hodges, K., Cobb, A., and Vidale, P. L.: How well are Tropical Cyclones
represented in reanalysis data sets?, J. Climate, 30,
5243–5264,https://doi.org/10.1175/JCLI-D-16-0557.1, 2017.
Hong, S.-Y. and Lim, J.-O.: The WRF single-moment 6-class microphysics scheme
(WSM6), ournal of the Korean Meteorological Society, 42, 129–151, 2006.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2341, 2006.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.:
GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06,
Goddard Earth Sciences Data and Information Services Center
(GES DISC) [data set], Greenbelt, MD, https://doi.org/10.5067/GPM/IMERG/3B-HH/06, 2019.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental Panel
on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A.,
Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L.,
Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,
Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp.,
https://doi.org/10.1017/9781009157896, 2021.
Isaksen, L., Bonavita, M., Buizza, R., Fisher, M., Haseler, J., Leutbecher,
M., and Raynaud, L.: Ensemble of Data Assimilations at ECMWF, Technical
Memorandum No. 636, ECMWF, Reading, UK, https://doi.org/10.21957/obke4k60, 2010.
Islam, T., Srivastava, P. K., Rico-Ramirez, M. A., Dai, Q., Gupta, M., and
Singh, S. K.: Tracking a tropical cyclone through WRF–ARW simulation and
sensitivity of model physics, Nat. Hazards, 76, 1473–1495,
https://doi.org/10.1007/s11069-014-1494-8, 2015.
Jin, C. S., Cha, D. H., Lee, D. K., Suh, M.-S., Hong, S. Y., Kang, H. S., and
Ho, C. H.: Evaluation of climatological tropical cyclone activity over the
western North Pacific in the CORDEX-East Asia multi-RCM simulations, Clim.
Dynam., 47, 765–778, https://doi.org/10.1007/s00382-015-2869-6, 2015.
JMA (Japan Meteorological Agency): Annual Report 2013, JMA,
https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/AnnualReport/2013/Text/Text2013.pdf
(last access: 6 August 2020), 2013.
Judt, F., Klocke, D., Rios-Berrios, R., Vanniere, B., Ziemen, F., Auger, L.,
Biercamp, J., Bretherton, C., Chen, X., Düben, P., Hohenegger, C.,
Khairoutdinov, M., Kodama, C., Kornblueh, L., Lin, S.-J., Nakano, M.,
Neumann, P., Putman, W., Röber, N., Roberts, M., Satoh, M., Shibuya, R.,
Stevens, B., Vidale, P. L., and Wedi, N.: Tropical Cyclones in Global
Storm-Resolving Models, J. Meteorol. Soc. Jpn., 99, 579–602,
https://doi.org/10.2151/jmsj.2021-029, 2021.
Kain, J. S.: The Kain–Fritsch convective parameterization: an update, J.
Appl. Meteorol. Clim., 43, 170–181,
https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C.
J.: The International Best Track Archive for Climate Stewardship (IBTrACS),
B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010.
Kueh, M.-T., Chen, W.-M., Sheng, Y.-F., Lin, S. C., Wu, T.-R., Yen, E., Tsai,
Y.-L., and Lin, C.-Y.: Effects of horizontal resolution and air–sea flux
parameterization on the intensity and structure of simulated Typhoon Haiyan
(2013), Nat. Hazards Earth Syst. Sci., 19, 1509–1539,
https://doi.org/10.5194/nhess-19-1509-2019, 2019.
Lackmann, G. M.: Hurricane Sandy before 1900 and after 2100, B. Am. Meteorol.
Soc., 96, 547–560, https://doi.org/10.1175/BAMS-D-14-00123.1, 2015.
Lagmay, A. M. F., Agaton, R. P., Bahala, M. A. C., Briones, J. B. L. T.,
Cabacaba, K. M. C., Caro, C. V. C., Dasallas, L. L., Gonzalo, L. A. L.,
Ladiero, C. N., Lapidez, J. P., Mungcal, M. T. F., Puno, J. V. R., Ramos, M.
M. A. C., Santiago, J., Suarez, J. K., and Tablazon, J. P.: Devastating storm
surges of Typhoon Haiyan, Int. J. Disast. Risk Re., 11, 1–12,
https://doi.org/10.1016/j.ijdrr.2014.10.006, 2015.
Lander, M., Guard, C., and Camargo, S.: Super Typhoon Haiyan, in: State of
the Climate in 2013, B. Am. Meteorol. Soc., 95, S112– S114, 2014.
Lauwaet, D., Viaene, P., Brisson, E., Van Noije, T., Strunk, A., Van Looy,
Maiheu, B., Veldeman, N., Blyth, L., De Ridder, K. S., and Janssen, S.:
Impact of nesting resolution jump on dynamical downscaling ozone
concentrations over Belgium, Atmos. Environ., 67, 46–52,
https://doi.org/10.1016/j.atmosenv.2012.10.034, 2013.
Lee, J. and Wu, C.: The Role of Polygonal Eyewalls in Rapid Intensification
of Typhoon Megi (2010), 75, 4175–4199, https://doi.org/10.1175/JAS-D-18-0100.1, 2018.
Li, F., Song, J., and Li, X.: A preliminary evaluation of the necessity of
using a cumulus parameterization scheme in high-resolution simulations of
Typhoon Haiyan (2013), Nat. Hazards, 92, 647–671,
https://doi.org/10.1007/s11069-018-3218-y, 2018.
Li, X. and Pu, Z.: Sensitivity of Numerical Simulations of the Early Rapid
Intensification of Hurricane Emily to Cumulus Parameterization Schemes in
Different Model Horizontal Resolutions, J. Meteorol. Soc. Jpn. Ser. II, 87,
403–421, https://doi.org/10.2151/jmsj.87.403, 2009.
Liu, L., Wang, G., Zhang, Z., and Wang, H.: Effects of Drag Coefficients on
Surface Heat Flux during Typhoon Kalmaegi (2014), Adv. Atmos. Sci., 39,
1501–1518, https://doi.org/10.1007/s00376-022-1285-1, 2022.
Liu, P., Tsimpidi, A. P., Hu, Y., Stone, B., Russell, A. G., and Nenes, A.:
Differences between downscaling with spectral and grid nudging using WRF,
Atmos. Chem. Phys., 12, 3601–3610, https://doi.org/10.5194/acp-12-3601-2012, 2012.
Lucas-Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P.,
Lemonsu, A., Kotlarski, S., & Caillaud, C.: Convection-permitting
modeling with regional climate models: Latest developments and next steps.
WIREs Clim. Change, 12, e731, https://doi.org/10.1002/wcc.731, 2021.
Lyon, B, and Camargo, S. J.: The seasonally-varying influence of ENSO on
rainfall and tropical cyclone activity in the Philippines, Clim. Dynam.,
32, 125–141, https://doi.org/10.1007/s00382-008-0380-z, 2009.
Manganello, J. V., Hodges, K. I., Kinter III, J. L., Cash , B. A., Marx, L.,
Jung, T., Achuthavarier, D., Adams, J. M., Altshuler, E. L., Huang, B., Jin,
E. K., Stan, C., Towers, P., and Wedi, N.: Tropical cyclone climatology in a
10-km global atmospheric GCM: toward weather-resolving climate modeling, J.
Climate, 25, 3867–3893, https://doi.org/10.1175/JCLI-D-11-00346.1, 2012.
Matte, D., Laprise, R., and Thériault, J. M.: Comparison between
high-resolution climate simulations using single- and double-nesting
approaches within the Big-Brother experimental protocol, Clim. Dynam., 47,
3613–3626, https://doi.org/doi.org/10.1007/s00382-016-3031-9, 2016.
McSweeney, C., Jones, R., Lee, R., and Rowell, D.: Selecting CMIP5 GCMs for
downscaling over multiple regions, Clim. Dynam., 44, 3237–3260,
https://doi.org/10.1007/s00382-014-2418-8, 2015.
Mehra, A., Tallapragada, V., Zhang, Z., Liu, B., Zhu, L., Wang, W., and Kim,
H.-S.: Advancing the State of the Art in Operational Tropical Cyclone
Forecasting at Ncep, Tropical Cyclone Research and Review, 7, 51–56,
2019.
Mittal, R., Tewari, M., Radhakrishnan, C., Ray, P., and Singh, T.: Response
of tropical cyclone Phailin (2013) in the Bay of Bengal to climate
perturbations, Clim. Dynam., 53, 2013–2030,
https://doi.org/10.1007/s00382-019-04761-w, 2019.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682,
https://doi.org/10.1029/97JD00237, 1997.
MMML-NCAR (Mesoscale and Microscale Meteorology Laboratory – National Center
for Atmospheric Research): Weather Research & Forecasting Model (WRF), ARW
Version 4 Modeling System User's Guide, MMML-NCAR,
https://www.mmm.ucar.edu/weather-research-and-forecasting-model (last
access: 9 August 2021), 2019.
Mohandas, S. and Ashrit, R.: Sensitivity of different convective
parameterization schemes on tropical cyclone prediction using a mesoscale
model, Nat. Hazards, 73, 213–235, https://doi.org/10.1007/s11069-013-0824-6, 2014.
Mohanty, U. C., Osuri, K. K., Routray, A., Mohapatra, M., and Pattanayak,
S.: Simulation of bay of bengal tropical cyclones with wrf model: Impact of
initial and boundary conditions, Mar. Geod., 33, 294–314,
https://doi.org/10.1080/01490419.2010.518061, 2010.
Monin A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the
surface layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR,
151, 163–187,
https://gibbs.science/efd/handouts/monin_obukhov_1954.pdf (last access:
9 August 2021), 1954.
Montgomery, M. T., Smith, R. K., and Nguyen, S. V.: Sensitivity of
tropical-cyclone models to the surface drag coefficient, Q. J. Roy. Meteor.
Soc., 136, 1945–1953, https://doi.org/10.1002/qj.702, 2010.
Moon, J., Cha, D.-H., Lee, M., and Kim, J.: Impact of spectral nudging on
real-time tropical cyclone forecast, J. Geophys. Res.-Atmos., 123,
12647–12660, https://doi.org/10.1029/2018JD028550, 2018.
Mori, N., Kato, M., Kim, S., Mase, H., Shibutani, Y., Takemi, T., Tsuboki,
K., and Yasuda, T.: Local amplification of storm surge by Super Typhoon
Haiyan in Leyte Gulf, Geophys. Res. Lett., 41, 5106–5113,
https://doi.org/10.1002/2014GL060689, 2014.
Mure-Ravaud, M., Kavvas, M. L., and Dib, A.: Investigation of Intense
Precipitation from Tropical Cyclones during the 21st Century by Dynamical
Downscaling of CCSM4 RCP 4.5, Int. J. Environ. Res. Pu., 16, 687,
https://doi.org/10.3390/ijerph16050687, 2019.
Nakamura, R., Shibayama, T., Esteban, M., and Iwamoto, T.: Future typhoon and
storm surges under different global warming scenarios: case study of typhoon
Haiyan (2013), Nat. Hazards, 82, 1645–1681, https://doi.org/10.1007/s11069-016-2259-3,
2016.
NCAS: cf python package, NCAS [code], https://ncas-cms.github.io/cf-python/, last access: 15 March 2022.
NDRRMC (National Disaster Risk Reduction and Management Council): NDRRMC
situational reports, NDRRMC, http://www.ndrrmc.gov.ph/ (last access:
2 February 2020), 2014.
Parker, C. L., Lynch, A. H., and Mooney, P. A.: Factors affecting the
simulated track and intensification of Tropical Cyclone Yasi (2011), Atmos.
Res., 194, 27–42, https://doi.org/10.1016/j.atmosres.2017.04.002, 2017.
Parker, C. L., Bruyère, C. L., Mooney, P. A., and Lynch, A. H.: The response
of land-falling tropical cyclone characteristics to projected climate change
in northeast Australia, Clim. Dynam., 51, 3467–3485,
https://doi.org/10.1007/s00382-018-4091-9, 2018.
Patricola, C. M. and Wehner, M. F: Article Anthropogenic influences on
major tropical cyclone events, Nature, 563, 339–346,
https://doi.org/10.1038/s41586-018-0673-2, 2018.
Peng, X., Fei, J., Huang, X., and Cheng, X.: Evaluation and Error Analysis
of Official Forecasts of Tropical Cyclones during 2005–14 over the Western
North Pacific. Part I: Storm Tracks, Weather Forecast., 32, 689–712,
https://doi.org/10.1175/WAF-D-16-0043.1, 2017.
Powell, M. D., Vickery, P. J., and Reinhold, T. A.: Reduced drag coefficient
for high wind speeds in tropical cyclones, Nature, 24, 395–419,
https://doi.org/10.1038/nature01481, 2003.
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J.,
Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell,
G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego,
G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen,
F., Barlage, M. J., Yu, W., and Duda, M. G.: The Weather Research and
Forecasting Model: Overview, System Efforts, and Future Directions, B. Am.
Meteorol. Soc., 98, 1717–1737, https://doi.org/10.1175/BAMS-D-15-00308.1, 2017.
Prater, B. and Evans, J.: Sensitivity of modeled tropical cyclone track and
structure of hurricane Irene (1999) to the convective parameterization
scheme, Meteorol. Atmos. Phys., 80, 103–115, https://doi.org/10.1007/s007030200018,
2002.
Raffa, M., Reder, A., Adinolfi, M., and Mercogliano, P.: A comparison between
one-step and two-step nesting strategy in the dynamical downscaling of
regional climate model COSMO-CLM at 2.2 km driven by ERA5 reanalysis,
Atmosphere, 12, 260, https://doi.org/10.3390/atmos12020260, 2021
Reddy, P., Sriram, D., Gunthe, S. S., and Balaji, C.: Impact of climate
change on intense Bay of Bengal tropical cyclones of the post-monsoom season:
a pseudo global warming approach, Clim. Dynam., 56, 2855–2879,
https://doi.org/10.1007/s00382-020-05618-3, 2021.
Satya, O. C., Mandailing, P. M., and Kaban, H.: Application of advanced
research WRF model using tropical (New TK) scheme and KF scheme in predicting
short-term weather in Palembang and its surrounding areas Application of
advanced research WRF model using tropical (New TK) scheme and KF scheme in
predicting short-term weather in Palembang and its surrounding areas, J.
Phys. Conf. Ser., 1282, 012025, https://doi.org/10.1088/1742-6596/1282/1/012025, 2019.
Shen, W., Tang, J., Wang, Y., Wang, S., and Niu, X.: Evaluation of WRF model
simulations of tropical cyclones in the western North Pacific over the
CORDEX East Asia domain, Clim. Dynam., 48, 2419–2435,
https://doi.org/10.1007/s00382-016-3213-5, 2017.
Shen, W., Song, J., Liu, G., Zhuang, Y., Wang, Y., and Tang, J.: The effect
of convection scheme on tropical cyclones simulations over the CORDEX East
Asia domain, Clim. Dynam., 52, 4695–4713, https://doi.org/10.1007/s00382-018-4405-y,
2019.
Shepherd, T. J. and Walsh, K. J.: Sensitivity of hurricane track to cumulus
parameterization schemes in the WRF model for three intense tropical
cyclones: impact of convective asymmetry, Meteorol. Atmos. Phys., 129,
345–374, https://doi.org/10.1007/s00703-016-0472-y, 2017.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the advanced
research WRF version 3, NCAR Technical Note, NCAR/TN-475+STR, National
Center for Atmospheric Research, Boulder, Colorado, 2008.
Smith, R. K., Montgomery, M. T., and Thomsen, G. L.: Sensitivity of
tropical-cyclone models to the surface drag coefficient in different
boundary-layer schemes, Q. J. Roy. Meteor. Soc., 140, 792–804,
https://doi.org/10.1002/qj.2057, 2014.
Smith, S. D.: Coefficients for Sea Surface Wind Stress, Heat Flux, and Wind
Profiles as a Function of Wind Speed and Temperature, J. Geophys. Res., 93,
15467–15472, https://doi.org/10.1029/JC093iC12p15467, 1988.
Soria, J. L. A., Switzer, A. D., Villanoy, C. L., Fritz, H. M., Bilgera, P.
H. T., Cabrera, O. C., Siringan, F. P., Maria, Y. Y., Ramos, R. D., and
Fernandez, I. Q.: Repeat Storm Surge Disasters of Typhoon Haiyan and Its
1897 Predecessor in the Philippines, B. Am. Meteorol. Soc., 97, 31–48,
https://doi.org/10.1175/BAMS-D-14-00245.1, 2016.
Spencer, P., Shaw, B., and Pajuelas, B.: Sensitivity of typhoon Parma to
various WRF model configurations, in: Technical Report on 92nd American
Meteorological Society Annual Meeting, 26 January 2012, New Orleans, LA,
American Meteorological Society, Boston, MA, 608–619, 2012.
Sun, Y., Zhong, Z., Lu, W., and Hu, Y.: Why are tropical cyclone tracks over
the western North Pacific sensitive to the cumulus parameterization scheme in
regional climate modeling? A case study for Megi (2010), Mon. Weather Rev.,
142, 1240–1249, https://doi.org/10.1175/MWR-D-13-00232.1, 2015.
Takayabu, I., Hibino, K., Sasaki, H., Shiogama, H., Mori, N., Shibutani, Y.,
and Takemi, T.: Climate change effects on the worst-case storm surge: a case
study of Typhoon Haiyan, Environ. Res. Lett. 10, 064011,
https://doi.org/10.1088/1748-9326/10/6/064011, 2015.
Tang, J., Wang, S., Niu, X., Hui, P., and Zong, P.: Impact of spectral
nudging on regional climate simulation over CORDEX East Asia using WRF,
Clim. Dynam., 48, 2339–2357, https://doi.org/10.1007/s00382-016-3208-2, 2017.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek,
M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification
of the unified NOAH land surface model in the WRF model, in: 20th Conference
on Weather Analysis and Forecasting/16th Conference on Numerical Weather
Prediction, 14 January 2004, 11–15,
https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm (last
access: 18 August 2021), 2004.
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization
in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989.
Torn, R. D. and Davis, D. A.: The influence of shallow convection on tropical
cyclone track forecasts, Mon. Weather Rev., 140, 2188–2197,
https://doi.org/10.1175/MWR-D-11-00246.1, 2012.
University of Reading GitLab: TRACK, GitLab,
https://gitlab.act.reading.ac.uk/track/track (last access: 14 December 2021), 2022.
Villafuerte, M. Q., John, I. I., Lambrento, C. R., Hodges, K. I., Cruz, F.
T., Cinco, T. A., and Narisma, G. T.: Sensitivity of tropical cyclones to
convective parameterization schemes in RegCM4, Clim. Dynam., 56, 1625–1642,
https://doi.org/10.1007/s00382-020-05553-3, 2021.
WRF: WRF Source Codes and Graphics Software Downloads, WRF,
https://www2.mmm.ucar.edu/wrf/users/download/get_source.html (last access: 27 September 2021), 2022a.
WRF: WPS V4 Geographical Static Data Downloads Page, WRF,
https://www2.mmm.ucar.edu/wrf/users/download/get_sources_wps_geog.html#mandatory (last access: 27 September 2021), 2022b.
Wu, Z., Jiang, C., Deng, B., Chen, J., and Liu, X.: Sensitivity of WRF
simulated typhoon track and intensity over the South China Sea to horizontal
and vertical resolutions, Acta Oceanol. Sin., 38, 74–83,
https://doi.org/10.1007/s13131-019-1459-z, 2019.
Xu, Z., and Z.-L. Yang, 2015: A new dynamical downscaling approach with GCM
bias corrections and spectral nudging. J. Geophys. Res. Atmos., 120,
3063–3084, https://doi.org/10.1002/2014JD022958.
Xue, F., and Fan, F.: Anomalous western Pacific subtropical high during
late summer in weak La Niña years: Contrast between 1981 and 2013, Adv.
Atmos. Sci., 33, 1351–1360, https://doi.org/10.1007/s00376-016-5281-1, 2016.
Yonson, R., Gaillard, J. C., and Noy, I.: The measurement of disaster risk:
An example from tropical cyclones in the Philippines, Rev. Dev. Econ., 2,
736–765, 2016.
Zhang, C., Wang, Y., and Hamilton, K.: Improved representation of boundary
layer clouds over the Southeast Pacific in ARW-WRF using a modified TK
cumulus parameterization scheme, Mon. Weather Rev., 139, 3489–3513,
https://doi.org/10.1175/MWR-D-10-05091.1, 2011.
Zhang, D.-L. and Chen, H.: Importance of the upper-level warm core in the
rapid intensification of a tropical cyclone, Geophys. Res. Lett., 39,
L02806, https://doi.org/10.1029/2011GL050578, 2012.
Zhang, J. A. and Marks, F. D.: Effects of Horizontal Diffusion on Tropical
Cyclone Intensity Change and Structure in Idealized Three-Dimensional
Numerical Simulations, Mon. Weather Rev., 143, 3981–3995,
https://doi.org/10.1175/MWR-D-14-00341.1, 2015.
Zhang, X., Gopalakrishnan, S. G., Trahan, S., Quirino, T. S., Liu, Q., Zhang,
Z., Alaka, G., and Tallapragada, V.: Representing multiscale interactions in
the Hurricane Weather Research and Forecasting modeling system: Design of
multiple sets of movable multi-level nesting and the basin-scale HWRF
forecast application, Weather Forecast., 31, 2019–2034,
https://doi.org/10.1175/WAF-D-16-0087.1, 2016.
Short summary
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.
We showed the effects of altering the choice of cumulus schemes, surface flux options, and...
Altmetrics
Final-revised paper
Preprint