Articles | Volume 18, issue 3
https://doi.org/10.5194/os-18-839-2022
© 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/os-18-839-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jun 2022
Research article |
| 08 Jun 2022
| 08 Jun 2022
Seasonal extrema of sea surface temperature in CMIP6 models
Yanxin Wang
Centre for Ocean and Atmospheric Sciences, University of East Anglia, Norwich, UK
Centre for Ocean and Atmospheric Sciences, University of East Anglia, Norwich, UK
David P. Stevens
Centre for Ocean and Atmospheric Sciences, University of East Anglia, Norwich, UK
Gillian M. Damerell
Centre for Ocean and Atmospheric Sciences, University of East Anglia, Norwich, UK
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Meredith G. Meyer, Esther Portela, Walker O. Smith Jr., and Karen J. Heywood
EGUsphere, https://doi.org/10.5194/egusphere-2024-3830, https://doi.org/10.5194/egusphere-2024-3830, 2024
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During the annual phytoplankton bloom, rates of primary production and carbon export in the Ross Sea, Antarctica are uncoupled from each other and from oxygen and carbon stocks. These biogeochemical rates support the high productivity, low export classification of the region and suggest that environmental factors influence these stocks and rates differently and make projections under future climate change scenarios difficult.
Ria Oelerich, Karen J. Heywood, Gillian M. Damerell, Marcel du Plessis, Louise C. Biddle, and Sebastiaan Swart
Ocean Sci., 19, 1465–1482, https://doi.org/10.5194/os-19-1465-2023, https://doi.org/10.5194/os-19-1465-2023, 2023
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At the southern boundary of the Antarctic Circumpolar Current, relatively warm waters encounter the colder waters surrounding Antarctica. Observations from underwater vehicles and altimetry show that medium-sized cold-core eddies influence the southern boundary's barrier properties by strengthening the slopes of constant density lines across it and amplifying its associated jet. As a result, the ability of exchanging properties, such as heat, across the southern boundary is reduced.
Pierre L'Hégaret, Florian Schütte, Sabrina Speich, Gilles Reverdin, Dariusz B. Baranowski, Rena Czeschel, Tim Fischer, Gregory R. Foltz, Karen J. Heywood, Gerd Krahmann, Rémi Laxenaire, Caroline Le Bihan, Philippe Le Bot, Stéphane Leizour, Callum Rollo, Michael Schlundt, Elizabeth Siddle, Corentin Subirade, Dongxiao Zhang, and Johannes Karstensen
Earth Syst. Sci. Data, 15, 1801–1830, https://doi.org/10.5194/essd-15-1801-2023, https://doi.org/10.5194/essd-15-1801-2023, 2023
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In early 2020, the EUREC4A-OA/ATOMIC experiment took place in the northwestern Tropical Atlantic Ocean, a dynamical region where different water masses interact. Four oceanographic vessels and a fleet of autonomous devices were deployed to study the processes at play and sample the upper ocean, each with its own observing capability. The article first describes the data calibration and validation and second their cross-validation, using a hierarchy of instruments and estimating the uncertainty.
Manoj Joshi, Robert A. Hall, David P. Stevens, and Ed Hawkins
Earth Syst. Dynam., 14, 443–455, https://doi.org/10.5194/esd-14-443-2023, https://doi.org/10.5194/esd-14-443-2023, 2023
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The 18.6-year lunar nodal cycle arises from variations in the angle of the Moon's orbital plane and affects ocean tides. In this work we use a climate model to examine the effect of this cycle on the ocean, surface, and atmosphere. The timing of anomalies is consistent with the so-called slowdown in global warming and has implications for when global temperatures will exceed 1.5 ℃ above pre-industrial levels. Regional anomalies have implications for seasonal climate areas such as Europe.
Peter M. F. Sheehan, Gillian M. Damerell, Philip J. Leadbitter, Karen J. Heywood, and Rob A. Hall
Ocean Sci., 19, 77–92, https://doi.org/10.5194/os-19-77-2023, https://doi.org/10.5194/os-19-77-2023, 2023
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We calculate the rate of turbulent kinetic energy dissipation, i.e. the mixing driven by small-scale ocean turbulence, in the western tropical Atlantic Ocean via two methods. We find good agreement between the results of both. A region of elevated mixing is found between 200 and 500 m, and we calculate the associated heat and salt fluxes. We find that double-diffusive mixing in salt fingers, a common feature of the tropical oceans, drives larger heat and salt fluxes than the turbulent mixing.
Callum Rollo, Karen J. Heywood, and Rob A. Hall
Geosci. Instrum. Method. Data Syst., 11, 359–373, https://doi.org/10.5194/gi-11-359-2022, https://doi.org/10.5194/gi-11-359-2022, 2022
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Using an underwater buoyancy-powered autonomous glider, we collected profiles of temperature and salinity from the ocean north-east of Barbados. Most of the temperature and salinity profiles contained staircase-like structures of alternating constant values and large gradients. We wrote an algorithm to identify these staircases. We hypothesise that these staircases are prevented from forming where background gradients in temperature and salinity are too great.
Michael P. Hemming, Jan Kaiser, Jacqueline Boutin, Liliane Merlivat, Karen J. Heywood, Dorothee C. E. Bakker, Gareth A. Lee, Marcos Cobas García, David Antoine, and Kiminori Shitashima
Ocean Sci., 18, 1245–1262, https://doi.org/10.5194/os-18-1245-2022, https://doi.org/10.5194/os-18-1245-2022, 2022
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An underwater glider mission was carried out in spring 2016 near a mooring in the northwestern Mediterranean Sea. The glider deployment served as a test of a prototype ion-sensitive field-effect transistor pH sensor. Mean net community production rates were estimated from glider and buoy measurements of dissolved oxygen and inorganic carbon concentrations before and during the spring bloom. Incorporating advection is important for accurate mass budgets. Unexpected metabolic quotients were found.
Yixi Zheng, David P. Stevens, Karen J. Heywood, Benjamin G. M. Webber, and Bastien Y. Queste
The Cryosphere, 16, 3005–3019, https://doi.org/10.5194/tc-16-3005-2022, https://doi.org/10.5194/tc-16-3005-2022, 2022
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New observations reveal the Thwaites gyre in a habitually ice-covered region in the Amundsen Sea for the first time. This gyre rotates anticlockwise, despite the wind here favouring clockwise gyres like the Pine Island Bay gyre – the only other ocean gyre reported in the Amundsen Sea. We use an ocean model to suggest that sea ice alters the wind stress felt by the ocean and hence determines the gyre direction and strength. These processes may also be applied to other gyres in polar oceans.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
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The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Jack Giddings, Karen J. Heywood, Adrian J. Matthews, Manoj M. Joshi, Benjamin G. M. Webber, Alejandra Sanchez-Franks, Brian A. King, and Puthenveettil N. Vinayachandran
Ocean Sci., 17, 871–890, https://doi.org/10.5194/os-17-871-2021, https://doi.org/10.5194/os-17-871-2021, 2021
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Little is known about the impact of chlorophyll on SST in the Bay of Bengal (BoB). Solar irradiance measured by an ocean glider and three Argo floats is used to determine the effect of chlorophyll on BoB SST during the 2016 summer monsoon. The Southwest Monsoon Current has high chlorophyll concentrations (∼0.5 mg m−3) and shallow solar penetration depths (∼14 m). Ocean mixed layer model simulations show that SST increases by 0.35°C per month, with the potential to influence monsoon rainfall.
Adam T. Blaker, Manoj Joshi, Bablu Sinha, David P. Stevens, Robin S. Smith, and Joël J.-M. Hirschi
Geosci. Model Dev., 14, 275–293, https://doi.org/10.5194/gmd-14-275-2021, https://doi.org/10.5194/gmd-14-275-2021, 2021
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FORTE 2.0 is a flexible coupled atmosphere–ocean general circulation model that can be run on modest hardware. We present two 2000-year simulations which show that FORTE 2.0 is capable of producing a stable climate. Earlier versions of FORTE were used for a wide range of studies, ranging from aquaplanet configurations to investigating the cold European winters of 2009–2010. This paper introduces the updated model for which the code and configuration are now publicly available.
Jack Giddings, Adrian J. Matthews, Nicholas P. Klingaman, Karen J. Heywood, Manoj Joshi, and Benjamin G. M. Webber
Weather Clim. Dynam., 1, 635–655, https://doi.org/10.5194/wcd-1-635-2020, https://doi.org/10.5194/wcd-1-635-2020, 2020
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The impact of chlorophyll on the southwest monsoon is unknown. Here, seasonally varying chlorophyll in the Bay of Bengal was imposed in a general circulation model coupled to an ocean mixed layer model. The SST increases by 0.5 °C in response to chlorophyll forcing and shallow mixed layer depths in coastal regions during the inter-monsoon. Precipitation increases significantly to 3 mm d-1 across Myanmar during June and over northeast India and Bangladesh during October, decreasing model bias.
Rob A. Hall, Barbara Berx, and Gillian M. Damerell
Ocean Sci., 15, 1439–1453, https://doi.org/10.5194/os-15-1439-2019, https://doi.org/10.5194/os-15-1439-2019, 2019
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Internal tides are subsurface waves generated by tidal flows over ocean ridges. When they break they create turbulence that drives an upward flux of nutrients from the deep ocean to the nutrient-poor photic zone. Measuring internal tides is problematic because oceanographic moorings are often
fished-outby commercial trawlers. We show that autonomous ocean gliders and acoustic Doppler current profilers can be used together to accurately measure the amount of energy carried by internal tides.
Reiner Onken, Heinz-Volker Fiekas, Laurent Beguery, Ines Borrione, Andreas Funk, Michael Hemming, Jaime Hernandez-Lasheras, Karen J. Heywood, Jan Kaiser, Michaela Knoll, Baptiste Mourre, Paolo Oddo, Pierre-Marie Poulain, Bastien Y. Queste, Aniello Russo, Kiminori Shitashima, Martin Siderius, and Elizabeth Thorp Küsel
Ocean Sci., 14, 321–335, https://doi.org/10.5194/os-14-321-2018, https://doi.org/10.5194/os-14-321-2018, 2018
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In June 2014, high-resolution oceanographic data were collected in the
western Mediterranean Sea by two research vessels, 11 gliders, moored
instruments, drifters, and one profiling float. The objective
of this article is to provide an overview of the data set which
is utilised by various ongoing studies, focusing on (i) water masses and circulation, (ii) operational forecasting, (iii) data assimilation, (iv) variability of the ocean, and (v) new payloads
for gliders.
Peter M. F. Sheehan, Barbara Berx, Alejandro Gallego, Rob A. Hall, Karen J. Heywood, Sarah L. Hughes, and Bastien Y. Queste
Ocean Sci., 14, 225–236, https://doi.org/10.5194/os-14-225-2018, https://doi.org/10.5194/os-14-225-2018, 2018
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We calculate tidal velocities using observations of ocean currents collected by an underwater glider. We use these velocities to investigate the location of sharp boundaries between water masses in shallow seas. Narrow currents along these boundaries are important transport pathways around shallow seas for pollutants and organisms. Tides are an important control on boundary location in summer, but seawater salt concentration can also influence boundary location, especially in winter.
Michael P. Hemming, Jan Kaiser, Karen J. Heywood, Dorothee C.E. Bakker, Jacqueline Boutin, Kiminori Shitashima, Gareth Lee, Oliver Legge, and Reiner Onken
Ocean Sci., 13, 427–442, https://doi.org/10.5194/os-13-427-2017, https://doi.org/10.5194/os-13-427-2017, 2017
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Underwater gliders are useful platforms for monitoring the world oceans at a high resolution. An experimental pH sensor was attached to an underwater glider in the Mediterranean Sea, which is an important carbon sink region. Comparing measurements from the glider with those obtained from a ship indicated that there were issues with the experimental pH sensor. Correcting for these issues enabled us to look at pH variability in the area related to biomass abundance and physical water properties.
Imke Grefe, Sophie Fielding, Karen J. Heywood, and Jan Kaiser
Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-73, https://doi.org/10.5194/bg-2017-73, 2017
Revised manuscript not accepted
Bastien Y. Queste, Liam Fernand, Timothy D. Jickells, Karen J. Heywood, and Andrew J. Hind
Biogeosciences, 13, 1209–1222, https://doi.org/10.5194/bg-13-1209-2016, https://doi.org/10.5194/bg-13-1209-2016, 2016
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In stratified shelf seas, physical and biological conditions can lead to seasonal oxygen depletion when consumption exceeds supply. An ocean glider obtained a high-resolution 3-day data set of biochemical and physical properties in the central North Sea. The data revealed very high oxygen consumption rates, far exceeding previously reported rates. A consumption–supply oxygen budget indicates a localized or short-lived resuspension event causing rapid remineralization of benthic organic matter.
C. Heuzé, J. K. Ridley, D. Calvert, D. P. Stevens, and K. J. Heywood
Geosci. Model Dev., 8, 3119–3130, https://doi.org/10.5194/gmd-8-3119-2015, https://doi.org/10.5194/gmd-8-3119-2015, 2015
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Most ocean models, including NEMO, have unrealistic Southern Ocean deep convection. That is, through extensive areas of the Southern Ocean, they exhibit convection from the surface of the ocean to the sea floor. We find this convection to be an issue as it impacts the whole ocean circulation, notably strengthening the Antarctic Circumpolar Current. Using sensitivity experiments, we show that counter-intuitively the vertical mixing needs to be enhanced to reduce this spurious convection.
Cited articles
Andrews, M. B., Ridley, J. K., Wood, R. A., Andrews, T., Blockley, E. W., Booth, B., Burke, E., Dittus, A. J., Florek, P., and Gray, L. J.:
Historical simulations with HadGEM3-GC3. 1 for CMIP6, J. Adv. Model. Earth Sy., 12, e2019MS001995, https://doi.org/10.1029/2019MS001995, 2020. a, b
Bayr, T., Latif, M., Dommenget, D., Wengel, C., Harlaß, J., and Park, W.:
Mean-state dependence of ENSO atmospheric feedbacks in climate models, Clim. Dynam., 50, 3171–3194, https://doi.org/10.1007/s00382-017-3799-2, 2018. a
Bayr, T., Domeisen, D. I., and Wengel, C.:
The effect of the equatorial Pacific cold SST bias on simulated ENSO teleconnections to the North Pacific and California, Clim. Dynam., 53, 3771–3789, https://doi.org/10.1007/s00382-019-04746-9, 2019. a
Beadling, R., Russell, J., Stouffer, R., Mazloff, M., Talley, L., Goodman, P., Sallée, J., Hewitt, H., Hyder, P., and Pandde, A.:
Representation of Southern Ocean Properties across Coupled Model Intercomparison Project Generations: CMIP3 to CMIP6, J. Climate, 33, 6555–6581, https://doi.org/10.1175/JCLI-D-19-0970.1, 2020. a, b
Beaumet, J., Krinner, G., Déqué, M., Haarsma, R., and Li, L.:
Assessing bias corrections of oceanic surface conditions for atmospheric models, Geosci. Model Dev., 12, 321–342, https://doi.org/10.5194/gmd-12-321-2019, 2019. a
Bi, D., Dix, M., Marsland, S., O'Farrell, S., Sullivan, A., Bodman, R., Law, R., Harman, I., Srbinovsky, J., Rashid, H. A., et al.:
Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model, Journal of Southern Hemisphere Earth Systems Science, 70, 225–251, https://doi.org/10.1071/ES19040, 2020. a
Bleck, R.:
An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, Ocean Model., 4, 55–88, https://doi.org/10.1016/S1463-5003(01)00012-9, 2002. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., and Bopp, L.:
Presentation and evaluation of the IPSL-CM6A-LR climate model, J. Adv. Model. Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019ms002010, 2020. a
Boyer, T. P., Garcia, H. E., Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Reagan, J. R., Weathers, K. A., Baranova, O. K., Seidov, D., and Smolyar, I. V.: World Ocean Atlas 2018. Temperature, NOAA National Centers for Environmental Information [data set], https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18 (last access: 31 May 2022), 2018. a
Burls, N. J., Muir, L., Vincent, E. M., and Fedorov, A.:
Extra-tropical origin of equatorial Pacific cold bias in climate models with links to cloud albedo, Clim. Dynam., 49, 2093–2113, https://doi.org/10.1007/s00382-016-3435-6, 2017. a
Chassignet, E. P., Yeager, S. G., Fox-Kemper, B., Bozec, A., Castruccio, F., Danabasoglu, G., Horvat, C., Kim, W. M., Koldunov, N., Li, Y., Lin, P., Liu, H., Sein, D. V., Sidorenko, D., Wang, Q., and Xu, X.:
Impact of horizontal resolution on global ocean–sea ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, 2020. a
Cheung, W. W. and Frölicher, T. L.:
Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific, Sci. Rep.-UK, 10, 1–10, https://doi.org/10.1038/s41598-020-63650-z, 2020. a
CMIP: Coupled Model Intercomparison Project Phase 6 (CMIP6) data, Working Group on Coupled Modeling of the World Climate Research Programme, Earth System Grid Federation [data set], https://esgf-index1.ceda.ac.uk/, last access: 1 June 2022. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D., DuVivier, A., Edwards, J., Emmons, L., Fasullo, J., Garcia, R., and Gettelman, A.:
The Community Earth System Model version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Dare, R. A. and McBride, J. L.:
The threshold sea surface temperature condition for tropical cyclogenesis, J. Climate, 24, 4570–4576, https://doi.org/10.1175/JCLI-D-10-05006.1, 2011. a
Fathrio, I., Iizuka, S., Manda, A., Kodama, Y.-M., Ishida, S., Moteki, Q., Yamada, H., and Tachibana, Y.:
Assessment of western Indian Ocean SST bias of CMIP5 models, J. Geophys. Res.-Oceans, 122, 3123–3140, https://doi.org/10.1002/2016JC012443, 2017. a
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of Climate Models, Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–866, https://doi.org/10.1017/CBO9781107415324, 2013. a, b
Frölicher, T. L. and Laufkötter, C.:
Emerging risks from marine heat waves, Nat. Commun., 9, 1–4, https://doi.org/10.1038/s41467-018-03163-6, 2018. a
Ge, X., Wang, W., Kumar, A., and Zhang, Y.:
Importance of the vertical resolution in simulating SST diurnal and intraseasonal variability in an oceanic general circulation model, J. Climate, 30, 3963–3978, https://doi.org/10.1175/JCLI-D-16-0689.1, 2017. a
Gilford, D. M., Solomon, S., and Emanuel, K. A.:
On the seasonal cycles of tropical cyclone potential intensity, J. Climate, 30, 6085–6096, https://doi.org/10.1175/JCLI-D-16-0827.1, 2017. a
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., and Bader, D. C.:
The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a, b
Gouretski, V.:
World Ocean Circulation Experiment – Argo Global Hydrographic Climatology, Ocean Sci., 14, 1127–1146, https://doi.org/10.5194/os-14-1127-2018, 2018a. a
Gouretski, V.: WOCE-Argo Global Hydrographic Climatology (WAGHC Version 1.0), World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.1594/WDCC/WAGHC_V1.0, 2018b. a
Held, I., Guo, H., Adcroft, A., Dunne, J., Horowitz, L., Krasting, J., Shevliakova, E., Winton, M., Zhao, M., and Bushuk, M.:
Structure and performance of GFDL's CM4. 0 climate model, J. Adv. Model. Earth Sy., 11, 3691–3727, https://doi.org/10.1029/2019MS001829, 2019. a
Holland, G. J.:
The maximum potential intensity of tropical cyclones, J. Atmos. Sci., 54, 2519–2541, https://doi.org/10.1175/1520-0469(1997)054%3C2519:TMPIOT%3E2.0.CO;2, 1997. a
Hughes, T. P., Anderson, K. D., Connolly, S. R., Heron, S. F., Kerry, J. T., Lough, J. M., Baird, A. H., Baum, J. K., Berumen, M. L., Bridge, T. C., et al.:
Spatial and temporal patterns of mass bleaching of corals in the Anthropocene, Science, 359, 80–83, https://doi.org/10.1126/science.aan8048, 2018. a
Hyder, P., Edwards, J. M., Allan, R. P., Hewitt, H. T., Bracegirdle, T. J., Gregory, J. M., Wood, R. A., Meijers, A. J., Mulcahy, J., and Field, P.:
Critical Southern Ocean climate model biases traced to atmospheric model cloud errors, Nat. Commun., 9, 1–17, https://doi.org/10.1038/s41467-018-05634-2, 2018. a
Jiang, X. and Li, J.:
Influence of the annual cycle of sea surface temperature on the monsoon onset, J. Geophys. Res-Atmos., 116, D10105, https://doi.org/10.1029/2010JD015236, 2011. a
Jones, T., Parrish, J. K., Peterson, W. T., Bjorkstedt, E. P., Bond, N. A., Ballance, L. T., Bowes, V., Hipfner, J. M., Burgess, H. K., Dolliver, J. E., et al.:
Massive mortality of a planktivorous seabird in response to a marine heatwave, Geophys. Res. Lett., 45, 3193–3202, https://doi.org/10.1002/2017GL076164, 2018. a
Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., and Bleck, R.:
GISS-E2. 1: Configurations and Climatology, J. Adv. Model. Earth Sy., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020. a, b, c, d
Law, R. M., Ziehn, T., Matear, R. J., Lenton, A., Chamberlain, M. A., Stevens, L. E., Wang, Y.-P., Srbinovsky, J., Bi, D., Yan, H., and Vohralik, P. F.:
The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 1: Model description and pre-industrial simulation, Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10-2567-2017, 2017. a
Letelier, J., Pizarro, O., and Nuñez, S.:
Seasonal variability of coastal upwelling and the upwelling front off central Chile, J. Geophys. Res.-Oceans, 114, C12009, https://doi.org/10.1029/2008JC005171, 2009. a
Levine, R. C. and Turner, A. G.:
Dependence of Indian monsoon rainfall on moisture fluxes across the Arabian Sea and the impact of coupled model sea surface temperature biases, Clim. Dynam., 38, 2167–2190, https://doi.org/10.1007/s00382-011-1096-z, 2012. a
Li, G. and Xie, S.-P.:
Origins of tropical-wide SST biases in CMIP multi-model ensembles, Geophys. Res. Lett., 39, L22703, https://doi.org/10.1029/2012gl053777, 2012. a
Li, J.-L., Xu, K.-M., Jiang, J., Lee, W.-L., Wang, L.-C., Yu, J.-Y., Stephens, G., Fetzer, E., and Wang, Y.-H.:
An overview of CMIP5 and CMIP6 simulated cloud ice, radiation fields, surface wind stress, sea surface temperatures, and precipitation over tropical and subtropical oceans, J. Geophys. Res-Atmos., 125, e2020JD032848, https://doi.org/10.1029/2020JD032848, 2020. a, b, c, d
Liu, F., Lu, J., Luo, Y., Huang, Y., and Song, F.:
On the oceanic origin for the enhanced seasonal cycle of SST in the midlatitudes under global warming, J. Climate, 33, 8401–8413, https://doi.org/10.1175/JCLI-D-20-0114.1, 2020. a
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M., Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K., Paver, C. R., and Smolyar, I.:
World Ocean Atlas 2018, Volume 1: Temperature, US government Printing Office, Washington, DC, 2018. a
Lyu, K., Zhang, X., and Church, J. A.:
Regional dynamic sea level simulated in the CMIP5 and CMIP6 models: mean biases, future projections, and their linkages, J. Climate, 33, 6377–6398, https://doi.org/10.1175/JCLI-D-19-1029.1, 2020. a, b, c
McKenna, S., Santoso, A., Gupta, A. S., Taschetto, A. S., and Cai, W.:
Indian Ocean Dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO, Sci. Rep.-UK, 10, 1–13, https://doi.org/10.1038/s41598-020-68268-9, 2020. a
Misra, V., Marx, L., Brunke, M., and Zeng, X.:
The equatorial Pacific cold tongue bias in a coupled climate model, J. Climate, 21, 5852–5869, https://doi.org/10.1175/2008JCLI2205.1, 2008. a
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., and Haak, H.:
A Higher-resolution Version of the Max Planck Institute Earth System Model (MPI-ESM1. 2-HR), J. Adv. Model. Earth Sy., 10, 1383–1413, https://doi.org/10.1029/2017MS001217, 2018. a
Myers, T. A., Scott, R. C., Zelinka, M. D., Klein, S. A., Norris, J. R., and Caldwell, P. M.:
Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity, Nat. Clim. Change, 11, 501–507, https://doi.org/10.1038/s41558-021-01039-0, 2021. a
Palmen, E.:
On the formation and structure of tropical hurricanes, Geophysica, 3, 26–38, 1948. a
Park, S., Shin, J., Kim, S., Oh, E., and Kim, Y.:
Global climate simulated by the Seoul National University atmosphere model version 0 with a unified convection scheme (SAM0-UNICON), J. Climate, 32, 2917–2949, https://doi.org/10.1175/JCLI-D-18-0796.1, 2019. a
Prodhomme, C., Terray, P., Masson, S., Izumo, T., Tozuka, T., and Yamagata, T.:
Impacts of Indian Ocean SST biases on the Indian Monsoon: as simulated in a global coupled model, Clim. Dynam., 42, 271–290, https://doi.org/10.1007/s00382-013-1671-6, 2014. a
Prodhomme, C., Voldoire, A., Exarchou, E., Deppenmeier, A.-L., García-Serrano, J., and Guemas, V.:
How does the seasonal cycle control equatorial Atlantic interannual variability?, Geophys. Res. Lett., 46, 916–922, https://doi.org/10.1029/2018GL080837, 2019. a
Rayner, N., Parker, D. E., Horton, E., Folland, C. K., Alexander, L. V., Rowell, D., Kent, E., and Kaplan, A.:
Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res-Atmos., 108, https://doi.org/10.1029/2002jd002670, 2003 (data available at: http://www.metoffice.gov.uk/hadobs/hadisst/, last access: 1 June 2022). a, b
Richter, I.:
Climate model biases in the eastern tropical oceans: causes, impacts and ways forward, WIREs Clim. Change, 6, 345–358, https://doi.org/10.1002/wcc.338, 2015. a
Richter, I. and Tokinaga, H.:
An overview of the performance of CMIP6 models in the tropical Atlantic: mean state, variability, and remote impacts, Clim. Dynam., 55, 2579–2601, https://doi.org/10.1007/s00382-020-05409-w, 2020. a, b, c, d
Richter, I., Xie, S.-P., Wittenberg, A. T., and Masumoto, Y.:
Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation, Clim. Dynam., 38, 985–1001, https://doi.org/10.1007/s00382-011-1038-9, 2012. a, b
Richter, I., Xie, S.-P., Behera, S. K., Doi, T., and Masumoto, Y.:
Equatorial Atlantic variability and its relation to mean state biases in CMIP5, Clim. Dynam., 42, 171–188, https://doi.org/10.1007/s00382-012-1624-5, 2014. a
Rouault, M., Florenchie, P., Fauchereau, N., and Reason, C. J.:
South East tropical Atlantic warm events and southern African rainfall, Geophys. Res. Lett., 30, GL014840, https://doi.org/10.1029/2002GL014840, 2003. a
Roxy, M.:
Sensitivity of precipitation to sea surface temperature over the tropical summer monsoon region–and its quantification, Clim. Dynam., 43, 1159–1169, https://doi.org/10.1007/s00382-013-1881-y, 2014. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.:
Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020. a
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., and Palmieri, J.:
UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019. a
Semmler, T., Danilov, S., Gierz, P., Goessling, H., Hegewald, J., Hinrichs, C., Koldunov, N. V., Khosravi, N., Mu, L., and Rackow, T.:
Simulations for CMIP6 with the AWI climate model AWI-CM-1-1, J. Adv. Model. Earth Sy., 12, e2019MS002009, https://doi.org/10.1029/2019MS002009, 2020. a
Shchepetkin, A. F. and McWilliams, J. C.:
The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model, Ocean Model., 9, 347–404, https://doi.org/10.1016/j.ocemod.2004.08.002, 2005. a
Shu, Q., Wang, Q., Song, Z., Qiao, F., Zhao, J., Chu, M., and Li, X.:
Assessment of sea ice extent in CMIP6 with comparison to observations and CMIP5, Geophys. Res. Lett., 47, e2020GL087965, https://doi.org/10.1029/2020GL087965, 2020. a
Sijikumar, S. and Rajeev, K.:
Role of the Arabian Sea warm pool on the precipitation characteristics during the monsoon onset period, J. Climate, 25, 1890–1899, https://doi.org/10.1175/JCLI-D-11-00286.1, 2012. a
Song, F. and Zhang, G. J.:
The impacts of horizontal resolution on the seasonally dependent biases of the Northeastern Pacific ITCZ in coupled climate models, J. Climate, 33, 941–957, https://doi.org/10.1175/JCLI-D-19-0399.1, 2020. a, b, c
Sun, Y., Zhong, Z., Li, T., Yi, L., Hu, Y., Wan, H., Chen, H., Liao, Q., Ma, C., and Li, Q.:
Impact of ocean warming on tropical cyclone size and its destructiveness, Sci. Rep.-UK, 7, 1–10, https://doi.org/10.1038/s41598-017-08533-6, 2017. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.:
The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019. a
Talley, L. D.:
Some aspects of ocean heat transport by the shallow, intermediate and deep overturning circulations, Geophysical Monograph-American Geophysical Union, Washington, DC, 112, 1–22, 1999. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.:
Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019.
a, b, c
Trenberth, K. E.:
What are the seasons?, B. Am. Meteorol. Soc., 64, 1276–1282, https://doi.org/10.1175/1520-0477(1983)064<1276:WATS>2.0.CO;2, 1983. a
Volodin, E., Mortikov, E., Kostrykin, S., Galin, V. Y., Lykossov, V., Gritsun, A., Diansky, N., Gusev, A., and Iakovlev, N.:
Simulation of the present-day climate with the climate model INMCM5, Clim. Dynam., 49, 3715–3734, https://doi.org/10.1007/s00382-017-3539-7, 2017. a, b
Wang, C., Zhang, L., Lee, S.-K., Wu, L., and Mechoso, C. R.:
A global perspective on CMIP5 climate model biases, Nat. Clim. Change, 4, 201–205, https://doi.org/10.1038/nclimate2118, 2014. a, b, c, d
Wang, C., Zou, L., and Zhou, T.:
SST biases over the Northwest Pacific and possible causes in CMIP5 models, Science China Earth Sciences, 61, 792–803, https://doi.org/10.1007/s11430-017-9171-8, 2018. a
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M., Huang, A., Zhang, Y., and Liu, X.:
The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6 , Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019. a, b
Wu, T., Zhang, F., Zhang, J., Jie, W., Zhang, Y., Wu, F., Li, L., Yan, J., Liu, X., Lu, X., Tan, H., Zhang, L., Wang, J., and Hu, A.:
Beijing Climate Center Earth System Model version 1 (BCC-ESM1): model description and evaluation of aerosol simulations, Geosci. Model Dev., 13, 977–1005, https://doi.org/10.5194/gmd-13-977-2020, 2020. a
Xavier, P. K., Duvel, J.-P., and Doblas-Reyes, F. J.:
Boreal summer intraseasonal variability in coupled seasonal hindcasts, J. Climate, 21, 4477–4497, https://doi.org/10.1175/2008JCLI2216.1, 2008. a
Yashayaev, I. M. and Zveryaev, I. I.:
Climate of the seasonal cycle in the North Pacific and the North Atlantic oceans, Int. J. Climatol., 21, 401–417, https://doi.org/10.1002/joc.585, 2001. a
Yu, L.:
Global variations in oceanic evaporation (1958–2005): The role of the changing wind speed, J. Climate, 20, 5376–5390, https://doi.org/10.1175/2007JCLI1714.1, 2007. a
Zhang, L. and Zhao, C.:
Processes and mechanisms for the model SST biases in the North Atlantic and North Pacific: a link with the Atlantic meridional overturning circulation, J. Adv. Model. Earth Sy., 7, 739–758, https://doi.org/10.1002/2014MS000415, 2015. a
Zhu, Y., Zhang, R.-H., and Sun, J.:
North Pacific upper-ocean cold temperature biases in CMIP6 simulations and the role of regional vertical mixing, J. Climate, 33, 7523–7538, https://doi.org/10.1175/jcli-d-19-0654.1, 2020. a, b
Short summary
It is important that climate models give accurate projections of future extremes in summer and winter sea surface temperature because these affect many features of the global climate system. Our results demonstrate that some models would give large errors if used for future projections of these features, and models with more detailed representation of vertical structure in the ocean tend to have a better representation of sea surface temperature, particularly in summer.
It is important that climate models give accurate projections of future extremes in summer and...
