Articles | Volume 8, issue 5
https://doi.org/10.5194/gmd-8-1509-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-1509-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
The Met Office Global Coupled model 2.0 (GC2) configuration
K. D. Williams
CORRESPONDING AUTHOR
Met Office, Exeter, UK
C. M. Harris
Met Office, Exeter, UK
A. Bodas-Salcedo
Met Office, Exeter, UK
J. Camp
Met Office, Exeter, UK
R. E. Comer
Met Office, Exeter, UK
D. Copsey
Met Office, Exeter, UK
D. Fereday
Met Office, Exeter, UK
T. Graham
Met Office, Exeter, UK
R. Hill
Met Office, Exeter, UK
T. Hinton
Met Office, Exeter, UK
P. Hyder
Met Office, Exeter, UK
S. Ineson
Met Office, Exeter, UK
G. Masato
University of Reading, Reading, UK
S. F. Milton
Met Office, Exeter, UK
M. J. Roberts
Met Office, Exeter, UK
D. P. Rowell
Met Office, Exeter, UK
C. Sanchez
Met Office, Exeter, UK
A. Shelly
Met Office, Exeter, UK
B. Sinha
National Oceanography Centre, Southampton, UK
D. N. Walters
Met Office, Exeter, UK
Met Office, Exeter, UK
T. Woollings
Atmospheric, Oceanic and Planetary Physics, Oxford, UK
P. K. Xavier
Met Office, Exeter, UK
Related authors
David Walters, Anthony J. Baran, Ian Boutle, Malcolm Brooks, Paul Earnshaw, John Edwards, Kalli Furtado, Peter Hill, Adrian Lock, James Manners, Cyril Morcrette, Jane Mulcahy, Claudio Sanchez, Chris Smith, Rachel Stratton, Warren Tennant, Lorenzo Tomassini, Kwinten Van Weverberg, Simon Vosper, Martin Willett, Jo Browse, Andrew Bushell, Kenneth Carslaw, Mohit Dalvi, Richard Essery, Nicola Gedney, Steven Hardiman, Ben Johnson, Colin Johnson, Andy Jones, Colin Jones, Graham Mann, Sean Milton, Heather Rumbold, Alistair Sellar, Masashi Ujiie, Michael Whitall, Keith Williams, and Mohamed Zerroukat
Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, https://doi.org/10.5194/gmd-12-1909-2019, 2019
Short summary
Short summary
Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application. We describe a recent iteration of these configurations, GA7/GL7, which includes new aerosol and snow schemes and addresses the four critical errors identified in GA6. GA7/GL7 will underpin the UK's contributions to CMIP6, and hence their documentation is important.
Yoko Tsushima, Florent Brient, Stephen A. Klein, Dimitra Konsta, Christine C. Nam, Xin Qu, Keith D. Williams, Steven C. Sherwood, Kentaroh Suzuki, and Mark D. Zelinka
Geosci. Model Dev., 10, 4285–4305, https://doi.org/10.5194/gmd-10-4285-2017, https://doi.org/10.5194/gmd-10-4285-2017, 2017
Short summary
Short summary
Cloud feedback is the largest uncertainty associated with estimates of climate sensitivity. Diagnostics have been developed to evaluate cloud processes in climate models. For this understanding to be reflected in better estimates of cloud feedbacks, it is vital to continue to develop such tools and to exploit them fully during the model development process. Code repositories have been created to store and document the programs which will allow climate modellers to compute these diagnostics.
Keith D. Williams and Alejandro Bodas-Salcedo
Geosci. Model Dev., 10, 2547–2566, https://doi.org/10.5194/gmd-10-2547-2017, https://doi.org/10.5194/gmd-10-2547-2017, 2017
Short summary
Short summary
The simulation of cloud is problematic for general circulation models. As clouds come in differing types, areal coverage, altitude and reflectivity, it is possible for a model to appear to perform well against a particular observational dataset through a compensation of errors. Here we evaluate a model's cloud simulation against a range of observational datasets, globally and across weather–climate timescales, in order to provide a comprehensive assessment.
David Walters, Ian Boutle, Malcolm Brooks, Thomas Melvin, Rachel Stratton, Simon Vosper, Helen Wells, Keith Williams, Nigel Wood, Thomas Allen, Andrew Bushell, Dan Copsey, Paul Earnshaw, John Edwards, Markus Gross, Steven Hardiman, Chris Harris, Julian Heming, Nicholas Klingaman, Richard Levine, James Manners, Gill Martin, Sean Milton, Marion Mittermaier, Cyril Morcrette, Thomas Riddick, Malcolm Roberts, Claudio Sanchez, Paul Selwood, Alison Stirling, Chris Smith, Dan Suri, Warren Tennant, Pier Luigi Vidale, Jonathan Wilkinson, Martin Willett, Steve Woolnough, and Prince Xavier
Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, https://doi.org/10.5194/gmd-10-1487-2017, 2017
Short summary
Short summary
Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application.
We describe a recent iteration of these configurations: GA6/GL6. This includes ENDGame: a new dynamical core designed to improve the model's accuracy, stability and scalability. GA6 is now operational in a variety of Met Office and UM collaborators applications and hence its documentation is important.
We describe a recent iteration of these configurations: GA6/GL6. This includes ENDGame: a new dynamical core designed to improve the model's accuracy, stability and scalability. GA6 is now operational in a variety of Met Office and UM collaborators applications and hence its documentation is important.
Veronika Eyring, Mattia Righi, Axel Lauer, Martin Evaldsson, Sabrina Wenzel, Colin Jones, Alessandro Anav, Oliver Andrews, Irene Cionni, Edouard L. Davin, Clara Deser, Carsten Ehbrecht, Pierre Friedlingstein, Peter Gleckler, Klaus-Dirk Gottschaldt, Stefan Hagemann, Martin Juckes, Stephan Kindermann, John Krasting, Dominik Kunert, Richard Levine, Alexander Loew, Jarmo Mäkelä, Gill Martin, Erik Mason, Adam S. Phillips, Simon Read, Catherine Rio, Romain Roehrig, Daniel Senftleben, Andreas Sterl, Lambertus H. van Ulft, Jeremy Walton, Shiyu Wang, and Keith D. Williams
Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, https://doi.org/10.5194/gmd-9-1747-2016, 2016
Short summary
Short summary
A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) in CMIP has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations.
D. N. Walters, K. D. Williams, I. A. Boutle, A. C. Bushell, J. M. Edwards, P. R. Field, A. P. Lock, C. J. Morcrette, R. A. Stratton, J. M. Wilkinson, M. R. Willett, N. Bellouin, A. Bodas-Salcedo, M. E. Brooks, D. Copsey, P. D. Earnshaw, S. C. Hardiman, C. M. Harris, R. C. Levine, C. MacLachlan, J. C. Manners, G. M. Martin, S. F. Milton, M. D. Palmer, M. J. Roberts, J. M. Rodríguez, W. J. Tennant, and P. L. Vidale
Geosci. Model Dev., 7, 361–386, https://doi.org/10.5194/gmd-7-361-2014, https://doi.org/10.5194/gmd-7-361-2014, 2014
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
EGUsphere, https://doi.org/10.5194/egusphere-2024-1414, https://doi.org/10.5194/egusphere-2024-1414, 2024
Short summary
Short summary
The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the HadGEM3 coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the sea floor exerts on ocean currents, and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Catherine Guiavarc'h, Dave Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene T. Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
EGUsphere, https://doi.org/10.5194/egusphere-2024-805, https://doi.org/10.5194/egusphere-2024-805, 2024
Short summary
Short summary
GOSI9 is the new UK’s hierarchy of global ocean and sea ice models. Developed as part of a collaboration between several UK research institutes it will be used for various applications such as weather forecast and climate prediction. The models, based on NEMO, are available at three resolutions 1°, ¼° and 1/12°. GOSI9 improves upon previous version by reducing global temperature and salinity biases and enhancing the representation of the Arctic sea ice and of the Antarctic Circumpolar Current.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
Short summary
Short summary
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Florian Pantillon, Silvio Davolio, Elenio Avolio, Carlos Calvo-Sancho, Diego S. Carrió, Stavros Dafis, Emmanouil Flaounas, Emanuele Silvio Gentile, Juan Jesus Gonzalez-Aleman, Suzanne Gray, Mario Marcello Miglietta, Platon Patlakas, Ioannis Pytharoulis, Didier Ricard, Antonio Ricchi, and Claudio Sanchez
EGUsphere, https://doi.org/10.5194/egusphere-2024-1105, https://doi.org/10.5194/egusphere-2024-1105, 2024
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Cyclone Ianos of September 2020 was a high impact but poorly predicted medicane ("Mediterranean hurricane"). A community effort of numerical modeling provides robust results to improve its prediction. It is found that the representation of local thunderstorms controls the interaction of Ianos with a jet stream at larger scales and its subsequent evolution. The results help understanding the peculiar dynamics of medicanes and provide guidance for the next generation of weather and climate models.
Jacqueline Elizabeth Russell, Richard John Bantges, Helen Elizabeth Brindley, and Alejandro Bodas-Salcedo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-4, https://doi.org/10.5194/essd-2024-4, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
We present a dataset of top of atmosphere diurnally resolved reflected solar and emitted thermal energy for Earth-system model evaluation. The multi-year, monthly hourly dataset, derived from observations made by the Geostationary Earth Radiation Budget instrument, covers the range 60° N–60° S, 60° E–60° W at one degree resolution. Comparison with two versions of the Hadley Centre Global Environmental model highlight how the data can be used to assess updates to key model parameterisations.
Colin Gareth Jones, Fanny Adloff, Ben Booth, Peter Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene Hewitt, Hazel Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm Roberts, Benjamin 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 J. Doblas-Reyes, Markus G. Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Froelicher, Neven Fuckar, Matthew Gidden, Helge Goessling, Rune Grand Graversen, Silvio Gualdi, Jose Manuel Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason A. Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Melia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard Wood, Shuting Yang, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2024-453, https://doi.org/10.5194/egusphere-2024-453, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming ~6 years (i.e. to 2030). Advances in these areas will increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, as well as deliver significantly improved scientific support to international climate policy, such as the 7th IPCC Assessment Report and the UNFCCC Global Stocktake under the Paris Climate Agreement.
Alexander T. Archibald, Bablu Sinha, Maria Russo, Emily Matthews, Freya Squires, N. Luke Abraham, Stephane Bauguitte, Thomas Bannan, Thomas Bell, David Berry, Lucy Carpenter, Hugh Coe, Andrew Coward, Peter Edwards, Daniel Feltham, Dwayne Heard, Jim Hopkins, James Keeble, Elizabeth C. Kent, Brian King, Isobel R. Lawrence, James Lee, Claire R. Macintosh, Alex Megann, Ben I. Moat, Katie Read, Chris Reed, Malcolm Roberts, Reinhard Schiemann, David Schroeder, Tim Smyth, Loren Temple, Navaneeth Thamban, Lisa Whalley, Simon Williams, Huihui Wu, and Ming-Xi Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-405, https://doi.org/10.5194/essd-2023-405, 2024
Preprint under review for ESSD
Short summary
Short summary
Here we present an overview of the data generated as part of the North Atlantic Climate System Integrated Studies (ACSIS) programme which are available through dedicated repositories at the Centre for Environmental Data Analysis (CEDA, www.ceda.ac.uk) and the British Oceanographic Data Centre (BODC, bodc.ac.uk). ACSIS data cover the full North Atlantic System comprising: the North Atlantic Ocean, the atmosphere above it including its composition, Arctic Sea Ice and the Greenland Ice Sheet.
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.
Claudio Sanchez, Suzanne Gray, Ambrogio Volonte, Florian Pantillon, Segolene Berthou, and Silvio Davolio
EGUsphere, https://doi.org/10.5194/egusphere-2023-2431, https://doi.org/10.5194/egusphere-2023-2431, 2023
Short summary
Short summary
Medicane Ianos was a very intense cyclone which led to harmful impacts over Greece. We explore what processes are important for the forecasting of medicane Ianos, with the use of the MetOffice weather model. There is a preceding precipitation event before Ianos’s birth, whose energetics generate a bubble in the tropopause. This bubble creates the necessary conditions for Ianos to emerge and strengthen, the processes are enhanced in simulations with a warmer Mediterranean Sea.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2023-1731, https://doi.org/10.5194/egusphere-2023-1731, 2023
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used for UK contributions to CMIP7. Documentation of physical science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details of how SI3 was adapted to work with Met Office coupling methodology, along with thorough documentation of coupling processes in the model.
Emmanouil Flaounas, Leonardo Aragão, Lisa Bernini, Stavros Dafis, Benjamin Doiteau, Helena Flocas, Suzanne L. Gray, Alexia Karwat, John Kouroutzoglou, Piero Lionello, Mario Marcello Miglietta, Florian Pantillon, Claudia Pasquero, Platon Patlakas, María Ángeles Picornell, Federico Porcù, Matthew D. K. Priestley, Marco Reale, Malcolm J. Roberts, Hadas Saaroni, Dor Sandler, Enrico Scoccimarro, Michael Sprenger, and Baruch Ziv
Weather Clim. Dynam., 4, 639–661, https://doi.org/10.5194/wcd-4-639-2023, https://doi.org/10.5194/wcd-4-639-2023, 2023
Short summary
Short summary
Cyclone detection and tracking methods (CDTMs) have different approaches in defining and tracking cyclone centers. This leads to disagreements on extratropical cyclone climatologies. We present a new approach that combines tracks from individual CDTMs to produce new composite tracks. These new tracks are shown to correspond to physically meaningful systems with distinctive life stages.
Christian Ferrarin, Florian Pantillon, Silvio Davolio, Marco Bajo, Mario Marcello Miglietta, Elenio Avolio, Diego S. Carrió, Ioannis Pytharoulis, Claudio Sanchez, Platon Patlakas, Juan Jesús González-Alemán, and Emmanouil Flaounas
Nat. Hazards Earth Syst. Sci., 23, 2273–2287, https://doi.org/10.5194/nhess-23-2273-2023, https://doi.org/10.5194/nhess-23-2273-2023, 2023
Short summary
Short summary
The combined use of meteorological and ocean models enabled the analysis of extreme sea conditions driven by Medicane Ianos, which hit the western coast of Greece on 18 September 2020, flooding and damaging the coast. The large spread associated with the ensemble highlighted the high model uncertainty in simulating such an extreme weather event. The different simulations have been used for outlining hazard scenarios that represent a fundamental component of the coastal risk assessment.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
Short summary
Short summary
Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Julia F. Lockwood, Galina S. Guentchev, Alexander Alabaster, Simon J. Brown, Erika J. Palin, Malcolm J. Roberts, and Hazel E. Thornton
Nat. Hazards Earth Syst. Sci., 22, 3585–3606, https://doi.org/10.5194/nhess-22-3585-2022, https://doi.org/10.5194/nhess-22-3585-2022, 2022
Short summary
Short summary
We describe how we developed a set of 1300 years' worth of European winter windstorm footprints, using a multi-model ensemble of high-resolution global climate models, for use by the insurance industry to analyse windstorm risk. The large amount of data greatly reduces uncertainty on risk estimates compared to using shorter observational data sets and also allows the relationship between windstorm risk and predictable large-scale climate indices to be quantified.
Alex West, Edward Blockley, and Matthew Collins
The Cryosphere, 16, 4013–4032, https://doi.org/10.5194/tc-16-4013-2022, https://doi.org/10.5194/tc-16-4013-2022, 2022
Short summary
Short summary
In this study we explore a method of examining model differences in ice volume by looking at the seasonal ice growth and melt. We use simple physical relationships to judge how model differences in key variables affect ice growth and melt and apply these to three case study models with ice volume ranging from very thin to very thick. Results suggest that differences in snow and melt pond cover in early summer are most important in causing the sea ice differences for these models.
Juan Manuel Castillo, Huw W. Lewis, Akhilesh Mishra, Ashis Mitra, Jeff Polton, Ashley Brereton, Andrew Saulter, Alex Arnold, Segolene Berthou, Douglas Clark, Julia Crook, Ananda Das, John Edwards, Xiangbo Feng, Ankur Gupta, Sudheer Joseph, Nicholas Klingaman, Imranali Momin, Christine Pequignet, Claudio Sanchez, Jennifer Saxby, and Maria Valdivieso da Costa
Geosci. Model Dev., 15, 4193–4223, https://doi.org/10.5194/gmd-15-4193-2022, https://doi.org/10.5194/gmd-15-4193-2022, 2022
Short summary
Short summary
A new environmental modelling system has been developed to represent the effect of feedbacks between atmosphere, land, and ocean in the Indian region. Different approaches to simulating tropical cyclones Titli and Fani are demonstrated. It is shown that results are sensitive to the way in which the ocean response to cyclone evolution is captured in the system. Notably, we show how a more rigorous formulation for the near-surface energy budget can be included when air–sea coupling is included.
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.
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.
Bijoy Thompson, Claudio Sanchez, Boon Chong Peter Heng, Rajesh Kumar, Jianyu Liu, Xiang-Yu Huang, and Pavel Tkalich
Geosci. Model Dev., 14, 1081–1100, https://doi.org/10.5194/gmd-14-1081-2021, https://doi.org/10.5194/gmd-14-1081-2021, 2021
Short summary
Short summary
This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO, coupled using the OASIS3-MCT libraries. The model forecast deviation of selected fields relative to observations is within acceptable error limits of operational forecast models.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
Short summary
Short summary
Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Marie-Estelle Demory, Ségolène Berthou, Jesús Fernández, Silje L. Sørland, Roman Brogli, Malcolm J. Roberts, Urs Beyerle, Jon Seddon, Rein Haarsma, Christoph Schär, Erasmo Buonomo, Ole B. Christensen, James M. Ciarlo ̀, Rowan Fealy, Grigory Nikulin, Daniele Peano, Dian Putrasahan, Christopher D. Roberts, Retish Senan, Christian Steger, Claas Teichmann, and Robert Vautard
Geosci. Model Dev., 13, 5485–5506, https://doi.org/10.5194/gmd-13-5485-2020, https://doi.org/10.5194/gmd-13-5485-2020, 2020
Short summary
Short summary
Now that global climate models (GCMs) can run at similar resolutions to regional climate models (RCMs), one may wonder whether GCMs and RCMs provide similar regional climate information. We perform an evaluation for daily precipitation distribution in PRIMAVERA GCMs (25–50 km resolution) and CORDEX RCMs (12–50 km resolution) over Europe. We show that PRIMAVERA and CORDEX simulate similar distributions. Considering both datasets at such a resolution results in large benefits for impact studies.
Alex West, Mat Collins, and Ed Blockley
Geosci. Model Dev., 13, 4845–4868, https://doi.org/10.5194/gmd-13-4845-2020, https://doi.org/10.5194/gmd-13-4845-2020, 2020
Short summary
Short summary
This study calculates sea ice energy fluxes from data produced by ice mass balance buoys (devices measuring ice elevation and temperature). It is shown how the resulting dataset can be used to evaluate a coupled climate model (HadGEM2-ES), with biases in the energy fluxes seen to be consistent with biases in the sea ice state and surface radiation. This method has potential to improve sea ice model evaluation, so as to better understand spread in model simulations of sea ice state.
Vidya Varma, Olaf Morgenstern, Paul Field, Kalli Furtado, Jonny Williams, and Patrick Hyder
Atmos. Chem. Phys., 20, 7741–7751, https://doi.org/10.5194/acp-20-7741-2020, https://doi.org/10.5194/acp-20-7741-2020, 2020
Short summary
Short summary
The present generation of global climate models has an insufficiently reflected short-wave radiation, especially over the Southern Ocean. This leads to an excessive heating of the ocean surface in the model, creating sea surface temperature biases and subsequent problems with atmospheric dynamics. Misrepresentation of clouds could be attributed to this radiation bias; we try to address this issue by slowing the growth rate of ice crystals and improving the supercooled liquid clouds in the model.
Reinhard Schiemann, Panos Athanasiadis, David Barriopedro, Francisco Doblas-Reyes, Katja Lohmann, Malcolm J. Roberts, Dmitry V. Sein, Christopher D. Roberts, Laurent Terray, and Pier Luigi Vidale
Weather Clim. Dynam., 1, 277–292, https://doi.org/10.5194/wcd-1-277-2020, https://doi.org/10.5194/wcd-1-277-2020, 2020
Short summary
Short summary
In blocking situations the westerly atmospheric flow in the midlatitudes is blocked by near-stationary high-pressure systems. Blocking can be associated with extremes such as cold spells and heat waves. Climate models are known to underestimate blocking occurrence. Here, we assess the latest generation of models and find improvements in simulated blocking, partly due to increases in model resolution. These new models are therefore more suitable for studying climate extremes related to blocking.
Torben Koenigk, Ramon Fuentes-Franco, Virna Meccia, Oliver Gutjahr, Laura C. Jackson, Adrian L. New, Pablo Ortega, Christopher Roberts, Malcolm Roberts, Thomas Arsouze, Doroteaciro Iovino, Marie-Pierre Moine, and Dmitry V. Sein
Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-41, https://doi.org/10.5194/os-2020-41, 2020
Revised manuscript not accepted
Short summary
Short summary
The mixing of water masses into the deep ocean in the North Atlantic is important for the entire global ocean circulation. We use seven global climate models to investigate the effect of increasing the model resolution on this deep ocean mixing. The main result is that increased model resolution leads to a deeper mixing of water masses in the Labrador Sea but has less effect in the Greenland Sea. However, most of the models overestimate the deep ocean mixing compared to observations.
Mike Bush, Tom Allen, Caroline Bain, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Humphrey Lean, Adrian Lock, James Manners, Marion Mittermaier, Cyril Morcrette, Rachel North, Jon Petch, Chris Short, Simon Vosper, David Walters, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, https://doi.org/10.5194/gmd-13-1999-2020, 2020
Short summary
Short summary
In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against.
Alexander T. Archibald, Fiona M. O'Connor, Nathan Luke Abraham, Scott Archer-Nicholls, Martyn P. Chipperfield, Mohit Dalvi, Gerd A. Folberth, Fraser Dennison, Sandip S. Dhomse, Paul T. Griffiths, Catherine Hardacre, Alan J. Hewitt, Richard S. Hill, Colin E. Johnson, James Keeble, Marcus O. Köhler, Olaf Morgenstern, Jane P. Mulcahy, Carlos Ordóñez, Richard J. Pope, Steven T. Rumbold, Maria R. Russo, Nicholas H. Savage, Alistair Sellar, Marc Stringer, Steven T. Turnock, Oliver Wild, and Guang Zeng
Geosci. Model Dev., 13, 1223–1266, https://doi.org/10.5194/gmd-13-1223-2020, https://doi.org/10.5194/gmd-13-1223-2020, 2020
Short summary
Short summary
Here we present a description and evaluation of the UKCA stratosphere–troposphere chemistry scheme (StratTrop vn 1.0) implemented in the UK Earth System Model (UKESM1). UKCA StratTrop represents a substantial step forward compared to previous versions of UKCA. We show here that it is fully suited to the challenges of representing interactions in a coupled Earth system model and identify key areas and components for future development that will make it even better in the future.
Malcolm J. Roberts, Alex Baker, Ed W. Blockley, Daley Calvert, Andrew Coward, Helene T. Hewitt, Laura C. Jackson, Till Kuhlbrodt, Pierre Mathiot, Christopher D. Roberts, Reinhard Schiemann, Jon Seddon, Benoît Vannière, and Pier Luigi Vidale
Geosci. Model Dev., 12, 4999–5028, https://doi.org/10.5194/gmd-12-4999-2019, https://doi.org/10.5194/gmd-12-4999-2019, 2019
Short summary
Short summary
We investigate the role that horizontal grid spacing plays in global coupled climate model simulations, together with examining the efficacy of a new design of simulation experiments that is being used by the community for multi-model comparison. We found that finer grid spacing in both atmosphere and ocean–sea ice models leads to a general reduction in bias compared to observations, and that once eddies in the ocean are resolved, several key climate processes are greatly improved.
Catherine Guiavarc'h, Jonah Roberts-Jones, Chris Harris, Daniel J. Lea, Andrew Ryan, and Isabella Ascione
Ocean Sci., 15, 1307–1326, https://doi.org/10.5194/os-15-1307-2019, https://doi.org/10.5194/os-15-1307-2019, 2019
Short summary
Short summary
Coupled atmosphere–ocean modelling systems allow changes in the ocean to directly and immediately feed back on the atmosphere and enable improved weather prediction and ocean forecasts. This is particularly true if the coupled feedbacks are also considered in the way real-time observations of the atmospheric and oceanic states are used to obtain the initial conditions for the forecasts. Here we demonstrate promising performance from such a coupled system when used for ocean prediction.
Alex West, Mat Collins, Ed Blockley, Jeff Ridley, and Alejandro Bodas-Salcedo
The Cryosphere, 13, 2001–2022, https://doi.org/10.5194/tc-13-2001-2019, https://doi.org/10.5194/tc-13-2001-2019, 2019
Short summary
Short summary
This study presents a framework for examining the causes of model errors in Arctic sea ice volume, using HadGEM2-ES as a case study. Simple models are used to estimate how much of the error in energy arriving at the ice surface is due to error in key Arctic climate variables. The method quantifies how each variable affects sea ice volume balance and shows that for HadGEM2-ES an annual mean low bias in ice thickness is likely due to errors in surface melt onset.
David Walters, Anthony J. Baran, Ian Boutle, Malcolm Brooks, Paul Earnshaw, John Edwards, Kalli Furtado, Peter Hill, Adrian Lock, James Manners, Cyril Morcrette, Jane Mulcahy, Claudio Sanchez, Chris Smith, Rachel Stratton, Warren Tennant, Lorenzo Tomassini, Kwinten Van Weverberg, Simon Vosper, Martin Willett, Jo Browse, Andrew Bushell, Kenneth Carslaw, Mohit Dalvi, Richard Essery, Nicola Gedney, Steven Hardiman, Ben Johnson, Colin Johnson, Andy Jones, Colin Jones, Graham Mann, Sean Milton, Heather Rumbold, Alistair Sellar, Masashi Ujiie, Michael Whitall, Keith Williams, and Mohamed Zerroukat
Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, https://doi.org/10.5194/gmd-12-1909-2019, 2019
Short summary
Short summary
Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application. We describe a recent iteration of these configurations, GA7/GL7, which includes new aerosol and snow schemes and addresses the four critical errors identified in GA6. GA7/GL7 will underpin the UK's contributions to CMIP6, and hence their documentation is important.
Manu Anna Thomas, Abhay Devasthale, Torben Koenigk, Klaus Wyser, Malcolm Roberts, Christopher Roberts, and Katja Lohmann
Geosci. Model Dev., 12, 1679–1702, https://doi.org/10.5194/gmd-12-1679-2019, https://doi.org/10.5194/gmd-12-1679-2019, 2019
Short summary
Short summary
Cloud processes occur at scales ranging from few micrometres to hundreds of kilometres. Their representation in global climate models and their fidelity are thus sensitive to the choice of spatial resolution. Here, cloud radiative effects simulated by models are evaluated using a satellite dataset, with a focus on investigating the sensitivity to spatial resolution. The evaluations are carried out using two approaches: the traditional statistical comparisons and the process-oriented evaluation.
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
Short summary
Short summary
The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
David Storkey, Adam T. Blaker, Pierre Mathiot, Alex Megann, Yevgeny Aksenov, Edward W. Blockley, Daley Calvert, Tim Graham, Helene T. Hewitt, Patrick Hyder, Till Kuhlbrodt, Jamie G. L. Rae, and Bablu Sinha
Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, https://doi.org/10.5194/gmd-11-3187-2018, 2018
Short summary
Short summary
We document the latest version of the shared UK global configuration of the
NEMO ocean model. This configuration will be used as part of the climate
models for the UK contribution to the IPCC 6th Assessment Report.
30-year integrations forced with atmospheric forcing show that the new
configurations have an improved simulation in the Southern Ocean with the
near-surface temperatures and salinities and the sea ice all matching the
observations more closely.
Reinhard Schiemann, Pier Luigi Vidale, Len C. Shaffrey, Stephanie J. Johnson, Malcolm J. Roberts, Marie-Estelle Demory, Matthew S. Mizielinski, and Jane Strachan
Hydrol. Earth Syst. Sci., 22, 3933–3950, https://doi.org/10.5194/hess-22-3933-2018, https://doi.org/10.5194/hess-22-3933-2018, 2018
Short summary
Short summary
A new generation of global climate models with resolutions between 50 and 10 km is becoming available. Here, we assess how well one such model simulates European precipitation. We find clear improvements in the mean precipitation pattern, and importantly also for extreme daily precipitation over 30 major European river basins. Despite remaining limitations, new high-resolution global models hold great promise for improved climate predictions of European precipitation at impact-relevant scales.
Jeff K. Ridley, Edward W. Blockley, Ann B. Keen, Jamie G. L. Rae, Alex E. West, and David Schroeder
Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, https://doi.org/10.5194/gmd-11-713-2018, 2018
Short summary
Short summary
The sea ice component of the Met Office coupled climate model, HadGEM3-GC3.1, is presented and evaluated. We determine that the mean state of the sea ice is well reproduced for the Arctic; however, a warm sea surface temperature bias over the Southern Ocean results in a low Antarctic sea ice cover.
Dustin J. Swales, Robert Pincus, and Alejandro Bodas-Salcedo
Geosci. Model Dev., 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, https://doi.org/10.5194/gmd-11-77-2018, 2018
Short summary
Short summary
This paper introduces a new version of diagnostic software (COSP2) intended to facilitate more straightforward comparisons between climate models and observational cloud datasets. This version allows users to more closely incorporate their own models assumptions within COSP, while also being computationally more efficient and straightforward for users to extend and build upon.
Huw W. Lewis, Juan Manuel Castillo Sanchez, Jennifer Graham, Andrew Saulter, Jorge Bornemann, Alex Arnold, Joachim Fallmann, Chris Harris, David Pearson, Steven Ramsdale, Alberto Martínez-de la Torre, Lucy Bricheno, Eleanor Blyth, Victoria A. Bell, Helen Davies, Toby R. Marthews, Clare O'Neill, Heather Rumbold, Enda O'Dea, Ashley Brereton, Karen Guihou, Adrian Hines, Momme Butenschon, Simon J. Dadson, Tamzin Palmer, Jason Holt, Nick Reynard, Martin Best, John Edwards, and John Siddorn
Geosci. Model Dev., 11, 1–42, https://doi.org/10.5194/gmd-11-1-2018, https://doi.org/10.5194/gmd-11-1-2018, 2018
Short summary
Short summary
In the real world the atmosphere, oceans and land surface are closely interconnected, and yet prediction systems tend to treat them in isolation. Those feedbacks are often illustrated in natural hazards, such as when strong winds lead to large waves and coastal damage, or when prolonged rainfall leads to saturated ground and high flowing rivers. For the first time, we have attempted to represent some of the feedbacks between sky, sea and land within a high-resolution forecast system for the UK.
Yoko Tsushima, Florent Brient, Stephen A. Klein, Dimitra Konsta, Christine C. Nam, Xin Qu, Keith D. Williams, Steven C. Sherwood, Kentaroh Suzuki, and Mark D. Zelinka
Geosci. Model Dev., 10, 4285–4305, https://doi.org/10.5194/gmd-10-4285-2017, https://doi.org/10.5194/gmd-10-4285-2017, 2017
Short summary
Short summary
Cloud feedback is the largest uncertainty associated with estimates of climate sensitivity. Diagnostics have been developed to evaluate cloud processes in climate models. For this understanding to be reflected in better estimates of cloud feedbacks, it is vital to continue to develop such tools and to exploit them fully during the model development process. Code repositories have been created to store and document the programs which will allow climate modellers to compute these diagnostics.
Pierre Mathiot, Adrian Jenkins, Christopher Harris, and Gurvan Madec
Geosci. Model Dev., 10, 2849–2874, https://doi.org/10.5194/gmd-10-2849-2017, https://doi.org/10.5194/gmd-10-2849-2017, 2017
Keith D. Williams and Alejandro Bodas-Salcedo
Geosci. Model Dev., 10, 2547–2566, https://doi.org/10.5194/gmd-10-2547-2017, https://doi.org/10.5194/gmd-10-2547-2017, 2017
Short summary
Short summary
The simulation of cloud is problematic for general circulation models. As clouds come in differing types, areal coverage, altitude and reflectivity, it is possible for a model to appear to perform well against a particular observational dataset through a compensation of errors. Here we evaluate a model's cloud simulation against a range of observational datasets, globally and across weather–climate timescales, in order to provide a comprehensive assessment.
Rafael Abel, Claus W. Böning, Richard J. Greatbatch, Helene T. Hewitt, and Malcolm J. Roberts
Ocean Sci. Discuss., https://doi.org/10.5194/os-2017-24, https://doi.org/10.5194/os-2017-24, 2017
Revised manuscript not accepted
Short summary
Short summary
In coupled global atmosphere ocean models a feedback from ocean surface currents to atmospheric winds was found. Surface winds are energized by about 30 % of the ocean currents. We were able to implement this feedback in uncoupled ocean models which results in a realistic surface flux coupling. Due to changes in the dissipation the kinetic energy of the time-variable flow is increased up to 10 % when this feedback is implemented. Implementation in other models should be straightforward.
David Walters, Ian Boutle, Malcolm Brooks, Thomas Melvin, Rachel Stratton, Simon Vosper, Helen Wells, Keith Williams, Nigel Wood, Thomas Allen, Andrew Bushell, Dan Copsey, Paul Earnshaw, John Edwards, Markus Gross, Steven Hardiman, Chris Harris, Julian Heming, Nicholas Klingaman, Richard Levine, James Manners, Gill Martin, Sean Milton, Marion Mittermaier, Cyril Morcrette, Thomas Riddick, Malcolm Roberts, Claudio Sanchez, Paul Selwood, Alison Stirling, Chris Smith, Dan Suri, Warren Tennant, Pier Luigi Vidale, Jonathan Wilkinson, Martin Willett, Steve Woolnough, and Prince Xavier
Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, https://doi.org/10.5194/gmd-10-1487-2017, 2017
Short summary
Short summary
Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application.
We describe a recent iteration of these configurations: GA6/GL6. This includes ENDGame: a new dynamical core designed to improve the model's accuracy, stability and scalability. GA6 is now operational in a variety of Met Office and UM collaborators applications and hence its documentation is important.
We describe a recent iteration of these configurations: GA6/GL6. This includes ENDGame: a new dynamical core designed to improve the model's accuracy, stability and scalability. GA6 is now operational in a variety of Met Office and UM collaborators applications and hence its documentation is important.
Jason Holt, Patrick Hyder, Mike Ashworth, James Harle, Helene T. Hewitt, Hedong Liu, Adrian L. New, Stephen Pickles, Andrew Porter, Ekaterina Popova, J. Icarus Allen, John Siddorn, and Richard Wood
Geosci. Model Dev., 10, 499–523, https://doi.org/10.5194/gmd-10-499-2017, https://doi.org/10.5194/gmd-10-499-2017, 2017
Short summary
Short summary
Accurately representing coastal and shelf seas in global ocean models is one of the grand challenges of Earth system science. Here, we explore what the options are for improving this by exploring what the important physical processes are that need to be represented. We use a simple scale analysis to investigate how large the resulting models would need to be. We then compare this with how computer power is increasing to provide estimates of when this might be feasible in the future.
Mark J. Webb, Timothy Andrews, Alejandro Bodas-Salcedo, Sandrine Bony, Christopher S. Bretherton, Robin Chadwick, Hélène Chepfer, Hervé Douville, Peter Good, Jennifer E. Kay, Stephen A. Klein, Roger Marchand, Brian Medeiros, A. Pier Siebesma, Christopher B. Skinner, Bjorn Stevens, George Tselioudis, Yoko Tsushima, and Masahiro Watanabe
Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, https://doi.org/10.5194/gmd-10-359-2017, 2017
Short summary
Short summary
The Cloud Feedback Model Intercomparison Project (CFMIP) aims to improve understanding of cloud-climate feedback mechanisms and evaluation of cloud processes and cloud feedbacks in climate models. CFMIP also aims to improve understanding of circulation, regional-scale precipitation and non-linear changes. CFMIP is contributing to the 6th phase of the Coupled Model Intercomparison Project (CMIP6) by coordinating a hierarchy of targeted experiments with cloud-related model outputs.
Reindert J. Haarsma, Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, Susanna Corti, Neven S. Fučkar, Virginie Guemas, Jost von Hardenberg, Wilco Hazeleger, Chihiro Kodama, Torben Koenigk, L. Ruby Leung, Jian Lu, Jing-Jia Luo, Jiafu Mao, Matthew S. Mizielinski, Ryo Mizuta, Paulo Nobre, Masaki Satoh, Enrico Scoccimarro, Tido Semmler, Justin Small, and Jin-Song von Storch
Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, https://doi.org/10.5194/gmd-9-4185-2016, 2016
Short summary
Short summary
Recent progress in computing power has enabled climate models to simulate more processes in detail and on a smaller scale. Here we present a common protocol for these high-resolution runs that will foster the analysis and understanding of the impact of model resolution on the simulated climate. These runs will also serve as a more reliable source for assessing climate risks that are associated with small-scale weather phenomena such as tropical cyclones.
Helene T. Hewitt, Malcolm J. Roberts, Pat Hyder, Tim Graham, Jamie Rae, Stephen E. Belcher, Romain Bourdallé-Badie, Dan Copsey, Andrew Coward, Catherine Guiavarch, Chris Harris, Richard Hill, Joël J.-M. Hirschi, Gurvan Madec, Matthew S. Mizielinski, Erica Neininger, Adrian L. New, Jean-Christophe Rioual, Bablu Sinha, David Storkey, Ann Shelly, Livia Thorpe, and Richard A. Wood
Geosci. Model Dev., 9, 3655–3670, https://doi.org/10.5194/gmd-9-3655-2016, https://doi.org/10.5194/gmd-9-3655-2016, 2016
Short summary
Short summary
We examine the impact in a coupled model of increasing atmosphere and ocean horizontal resolution and the frequency of coupling between the atmosphere and ocean. We demonstrate that increasing the ocean resolution from 1/4 degree to 1/12 degree has a major impact on ocean circulation and global heat transports. The results add to the body of evidence suggesting that ocean resolution is an important consideration when developing coupled models for weather and climate applications.
Veronika Eyring, Mattia Righi, Axel Lauer, Martin Evaldsson, Sabrina Wenzel, Colin Jones, Alessandro Anav, Oliver Andrews, Irene Cionni, Edouard L. Davin, Clara Deser, Carsten Ehbrecht, Pierre Friedlingstein, Peter Gleckler, Klaus-Dirk Gottschaldt, Stefan Hagemann, Martin Juckes, Stephan Kindermann, John Krasting, Dominik Kunert, Richard Levine, Alexander Loew, Jarmo Mäkelä, Gill Martin, Erik Mason, Adam S. Phillips, Simon Read, Catherine Rio, Romain Roehrig, Daniel Senftleben, Andreas Sterl, Lambertus H. van Ulft, Jeremy Walton, Shiyu Wang, and Keith D. Williams
Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, https://doi.org/10.5194/gmd-9-1747-2016, 2016
Short summary
Short summary
A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) in CMIP has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations.
Alex E. West, Alison J. McLaren, Helene T. Hewitt, and Martin J. Best
Geosci. Model Dev., 9, 1125–1141, https://doi.org/10.5194/gmd-9-1125-2016, https://doi.org/10.5194/gmd-9-1125-2016, 2016
Short summary
Short summary
This study compares two methods of coupling a sea ice model to an atmospheric model in a series of idealized one-dimensional experiments. The JULES method calculates surface variables in the atmosphere; the CICE method calculates surface variables in the sea ice. It is found that simulations of all variables are more accurate in the JULES method, likely because of the shorter time step of the atmosphere.
J. G. L. Rae, H. T. Hewitt, A. B. Keen, J. K. Ridley, A. E. West, C. M. Harris, E. C. Hunke, and D. N. Walters
Geosci. Model Dev., 8, 2221–2230, https://doi.org/10.5194/gmd-8-2221-2015, https://doi.org/10.5194/gmd-8-2221-2015, 2015
Short summary
Short summary
The paper presents a new sea ice configuration, GSI6.0, in the Met Office coupled atmosphere-ocean-ice model. Differences in the sea ice from a previous configuration (GSI4.0) are explained in the context of a previously published sensitivity study. In summer, Arctic sea ice is thicker and more extensive than in GSI4.0, bringing it closer to the observationally derived data sets. In winter, the Arctic ice is thicker but less extensive than in GSI4.0.
M. S. Mizielinski, M. J. Roberts, P. L. Vidale, R. Schiemann, M.-E. Demory, J. Strachan, T. Edwards, A. Stephens, B. N. Lawrence, M. Pritchard, P. Chiu, A. Iwi, J. Churchill, C. del Cano Novales, J. Kettleborough, W. Roseblade, P. Selwood, M. Foster, M. Glover, and A. Malcolm
Geosci. Model Dev., 7, 1629–1640, https://doi.org/10.5194/gmd-7-1629-2014, https://doi.org/10.5194/gmd-7-1629-2014, 2014
A. Megann, D. Storkey, Y. Aksenov, S. Alderson, D. Calvert, T. Graham, P. Hyder, J. Siddorn, and B. Sinha
Geosci. Model Dev., 7, 1069–1092, https://doi.org/10.5194/gmd-7-1069-2014, https://doi.org/10.5194/gmd-7-1069-2014, 2014
J. P. Mulcahy, D. N. Walters, N. Bellouin, and S. F. Milton
Atmos. Chem. Phys., 14, 4749–4778, https://doi.org/10.5194/acp-14-4749-2014, https://doi.org/10.5194/acp-14-4749-2014, 2014
D. N. Walters, K. D. Williams, I. A. Boutle, A. C. Bushell, J. M. Edwards, P. R. Field, A. P. Lock, C. J. Morcrette, R. A. Stratton, J. M. Wilkinson, M. R. Willett, N. Bellouin, A. Bodas-Salcedo, M. E. Brooks, D. Copsey, P. D. Earnshaw, S. C. Hardiman, C. M. Harris, R. C. Levine, C. MacLachlan, J. C. Manners, G. M. Martin, S. F. Milton, M. D. Palmer, M. J. Roberts, J. M. Rodríguez, W. J. Tennant, and P. L. Vidale
Geosci. Model Dev., 7, 361–386, https://doi.org/10.5194/gmd-7-361-2014, https://doi.org/10.5194/gmd-7-361-2014, 2014
A. E. West, A. B. Keen, and H. T. Hewitt
The Cryosphere, 7, 555–567, https://doi.org/10.5194/tc-7-555-2013, https://doi.org/10.5194/tc-7-555-2013, 2013
Related subject area
Climate and Earth system modeling
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1
A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
Dynamic MJO forecasts using an ensemble subseasonal-to-seasonal forecast system of IAP-CAS model
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
Short summary
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
Short summary
Short summary
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
Short summary
Short summary
All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
Short summary
Short summary
We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
Short summary
Short summary
In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
Short summary
Short summary
Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
Short summary
Short summary
Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
Short summary
Short summary
In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
Short summary
Short summary
We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Short summary
We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1, https://doi.org/10.5194/egusphere-2024-1, 2024
Short summary
Short summary
This article gives an overview introduction of the IAP-CAS S2S (sub-seasonal to seasonal) ensemble forecasting system and MJO forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its advantages but also exhibits some biases, including underdispersive ensemble, overestimated amplitude and faster propagation speed when forecasting MJO. We also provide the explanation towards these biases and prospects for further improvement of this system in the future.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
Short summary
Short summary
Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
Short summary
Short summary
Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
EGUsphere, https://doi.org/10.5194/egusphere-2024-335, https://doi.org/10.5194/egusphere-2024-335, 2024
Short summary
Short summary
Large volcanic eruptions deposit material into the upper-atmosphere, which is capable of altering temperature and wind patterns of the Earth's atmosphere for years following. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a testbed for climate attribution studies.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
Short summary
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
Short summary
Short summary
A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
Short summary
Short summary
We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
Short summary
Short summary
This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., and Arkin, P.: The Version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003.
Allan, R. P., Liu, C., Loeb, N. G., Palmer, M. D., Roberts, M., Smith, D., and Vidale, P.-L.: Changes in global net radiative imbalance 1985–2012, Geophys. Res. Lett., 41, 5588–5597, https://doi.org/10.1002/2014GL060962, 2014.
Anstey, J. A. and Shepherd, T. G.: High-latitude influence of the quasi-biennial oscillation, Q. J. Roy. Meteor. Soc., 140, 1–21, 2014.
Anstey, J. A., Davini, P., Gray, L. J., Woollings, T. J., Butchart, N., Cagnazzo, C., Christiansen, B., Hardiman, S. C., Osprey, S. M., and Yang, S. T.: Multi-model analysis of Northern Hemisphere winter blocking: model biases and the role of resolution, J. Geophys. Res., 118, 3956–3971, https://doi.org/10.1002/jgrd.50231, 2012.
Bellenger, H., Guilyardi, E., Leloup, E., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: F}rom {CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018, https://doi.org/10.1007/s00382-013-1783-z, 2014.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R .L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J. L., Klein, S., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011.
Bodas-Salcedo, A., Williams, K. D., Field, P. R., and Lock, A. P.: The surface downwelling solar radiation surplus over the Southern Ocean in the Met Office model: the role of midlatitude cyclone clouds, J. Climate, 25, 7467–7486, https://doi.org/10.1175/JCLI-D-11-00702.1, 2012.
Bodas-Salcedo, A., Williams, K. D., Ringer, M. A., Beau, I., Cole, J. N. S., Dufresne, J.-L., Koshiro, T., Stevens, B., Wang, Z., and Yokohata, T.: Origins of the solar radiation biases over the S}outhern Ocean in {CFMIP2 models, J. Climate, 27, 41–56, https://doi.org/10.1175/JCLI-D-13-00169.1, 2014.
Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S., Dufresne, J. L., Hall, A., Hallegate, S., Holland, M. M., Ingram, W. J., Randall, D. A., Soden, B. J., Tselioudis, G., and Webb, M. J.: How well do we understand and evaluate climate change feedback processes?, J. Climate, 19, 3445–3482, https://doi.org/10.1175/JCLI3819.1, 2006.
Brewer, A. M.: Evidence for a world circulation provided by the measurements of helium and water vapor distribution in the stratosphere, Q. J. Roy. Meteor. Soc., 75, 351–363, 1949.
Brohan, P., Kennedy, J., Harris, I., Tett, S. F. B., and Jones, P. D.: Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850, J. Geophys. Res., 111, D12106, https://doi.org/10.1029/2005JD006548, 2006.
Brown, A. R., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.: Unified modelling and prediction of weather and climate: a 25 year journey, B. Am. Meteorol. Soc., 93, 1865–1877, https://doi.org/10.1175/BAMS-D-12-00018.1, 2012.
Cassou, C., Terray, L., Hurrel, J., and Deser, C.: North Atlantic winter climate regimes: spatial asymmetry, stationarity with time, and ocean forcing, J. Climate, 17, 1055–1067, 2004.
Chepfer, H., Bony, S., Winker, D., Chiriaco, M., Dufresne, J.-L., and Sèze, G.: Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model., Geophys. Res. Lett., 35, L15704, https://doi.org/10.1029/2008GL034207, 2008.
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J.-L., Minnis, P., Stubenrauch, C. J., and Zeng, S.: The GCM Oriented Calipso Cloud Product (CALIPSO-GOCCP), J. Geophys. Res., 115, D00H16, https://doi.org/10.1029/2009JD012251, 2010.
Chu, J.-H., Sampson, C. R., Levine, A. S., and Fukada, E.: The Joint Typhoon Warning Center tropical cyclone best-tracks, 1945–2000, Tech. Rep. NRL/MR/7540-02-16, Naval Research Laboratory, 112 pp., 2002.
Cullen, M. J. P.: The unified forecast/climate model, Meteorol. Mag., 122, 81–94, 1993.
Davini, P., Cagnazzo, C., Fogli, P. G., Manzini, E., Gualdi, S., and Navarra, A.: European blocking and Atlantic jet stream variability in the NCEP/NCAR reanalysis and the CMCC-CMS climate model, Clim. Dynam., 43, 71–85, https://doi.org/10.1007/s00382-013-1873-y, 2014.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hölm, E. V., Isaksen, L., Kallberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, P. K., Peubey, C., de Rosnay, P., Tavolato, C., Thêpaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., 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.: Evalutation of climate models, in: IPCC WGI Fifth Assessment Report, chap. 9, Cambridge University Press, Cambridge, UK, New York, NY, USA, 2013.
Froude, L. S. R.: TIGGE: Comparison of the prediction of Northern Hemisphere extratropical cyclones by different ensemble prediction systems, Weather Forecast., 25, 819–836, 2010.
The HadGEM2 Development Team: G. M. Martin, Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., Hinton, T. J., Jones, C. D., McDonald, R. E., McLaren, A. J., O'Connor, F. M., Roberts, M. J., Rodriguez, J. M., Woodward, S., Best, M. J., Brooks, M. E., Brown, A. R., Butchart, N., Dearden, C., Derbyshire, S. H., Dharssi, I., Doutriaux-Boucher, M., Edwards, J. M., Falloon, P. D., Gedney, N., Gray, L. J., Hewitt, H. T., Hobson, M., Huddleston, M. R., Hughes, J., Ineson, S., Ingram, W. J., James, P. M., Johns, T. C., Johnson, C. E., Jones, A., Jones, C. P., Joshi, M. M., Keen, A. B., Liddicoat, S., Lock, A. P., Maidens, A. V., Manners, J. C., Milton, S. F., Rae, J. G. L., Ridley, J. K., Sellar, A., Senior, C. A., Totterdell, I. J., Verhoef, A., Vidale, P. L., and Wiltshire, A.: The HadGEM2 family of Met Office Unified Model climate configurations, Geosci. Model Dev., 4, 723–757, https://doi.org/10.5194/gmd-4-723-2011, 2011.
Hall, A. and Qu, X.: Using the current seasonal cycle to constrain snow albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502, https://doi.org/10.1029/2005GL025127, 2006.
Hannachi, A., Barnes, E. A., and Woollings, T.: Behaviour of the winter North Atlantic eddy-driven jet stream in the CMIP3 integrations, Clim. Dynam., 41, 995–1007, https://doi.org/10.1007/s00382-012-1560-4, 2013.
Hewitt, H. T., Copsey, D., Culverwell, I. D., Harris, C. M., Hill, R. S. R., Keen, A. B., McLaren, A. J., and Hunke, E. C.: Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system, Geosci. Model Dev., 4, 223–253, https://doi.org/10.5194/gmd-4-223-2011, 2011.
Hodges, K.: Feature tracking on a unit sphere, Mon. Weather Rev., 123, 3458–3465, 1995.
Holton, J. R. and Tan, H. C.: The influence of the equatorial quasi-biennial oscillation on the global circulation at 50 mb, J. Atmos. Sci., 37, 2200–2208, 1980.
Hunke, E. C. and Lipscomb, W. H.: CICE: The Los Alamos sea ice model, documentation and software. Version 3.1, Tech. Rep. LA-CC-98-16, Los Alamos National Laboratory, Los Alamos, NM, 2004.
Ingleby, B. and Huddleston, M.: Quality control of ocean temperature and salinity profiles – historical and real-time data, J. Marine Syst., 65, 158–175, https://doi.org/10.1016/j.jmarsys.2005.11.019, 2007.
Jiang, J. H., Su, H., Zhai, C., Perun, V. S., Genio, A. D., Nazarenko, L. S., Donner, L. J., Horowitz, L., Seman, C., Cole, J., Gettelman, A., Ringer, M. A., Rotstayn, L., Jeffrey, S., Wu, T., Brient, F., Dufresne, J.-L., Kawai, H., Koshiro, T., Watanabe, M., Lécuyer, T. S., Volodin, E. M., Iversen, T., Drange, H., Mesquita, M. D. S., Read, W. G., Waters, J. W., Tian, B., Teixeira, J., and Stephens, G. L.: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA A-Train satellite observations, J. Geophys. Res., 117, D14105, https://doi.org/10.1029/2011JD017237, 2012.
Jones, P. W.: First and second-order conservative remapping schemes for grids in spherical coordinates, Mon. Weather Rev., 127, 2204–2210, 1999.
Josey, S. A., Kent, E. C., and Taylor, P. K.: The S}outhampton Oceanography Centre (SOC) ocean – atmosphere heat, momentum and freshwater flux atlas, Report 6, Southampton Oceanography Centre, European Way, Southamton, {SO14 3ZH, UK, 55 pp., 1998.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, 1996.
Kang, S. M., Held, I. M., Frierson, D. M. W., and Zhao, M.: The response of the ITCZ to extratropical thermal forcing: i}dealized slab-ocean experiments with a {GCM, J. Climate, 21, 3521–3532, https://doi.org/10.1175/2007JCLI2146.1, 2008.
Kim, D., Sperber, K., Stern, W., Waliser, D., Kang, I.-S., Maloney, E., Wang, W., Weickmann, K., Benedict, J., and Khairoutdinov, M.: Application of MJO simulation diagnostics to climate models, J. Climate, 22, 6413–6436, 2009.
Kim, D., Xavier, P., Maloney, E., Wheeler, M., Waliser, D., Sperber, K., Hendon, H., Zhang, C., Neale, R., and Hwang, Y.-T.: Process-oriented MJO simulation diagnostic: moisture sensitivity of simulated convection, J. Climate, 27, 5379–5395, 2014.
Klein, S. A., Jiang, X., Boyle, J., Malyshev, S., and Xie, S.: Diagnosis of the summertime warm and dry bias over the U.S. S}outhern Great Plains in the {GFDL climate model using a weather forecasting approach, Geophys. Res. Lett., 33, L18805, https://doi.org/10.1029/2006GL027567, 2006.
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and Gleckler, P. J.: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator, J. Geophys. Res., 118, 1329–1342, https://doi.org/10.1002/jgrd.50141, 2013.
Klingaman, N. and Woolnough, S.: The role of air–sea coupling in the simulation of the Madden–Julian Oscillation in the Hadley Centre model, Q. J. Roy. Meteor. Soc., 140, 2272–2286, https://doi.org/10.1002/qj.2295, 2014.
Landsea, C. W. and Franklin, J. L.: Atlantic hurricane database uncertainty and presentation of a new database format, Mon. Weather Rev., 141, 3576–3592, https://doi.org/10.1175/MWR-D-12-00254.1, 2013.
Lengaigne, M., Guilyardi, E., Boulanger, J.-P., Menkes, C., Delecluse, P., Inness, P., Cole, J., and Slingo, J.: Triggering of El Nino by westerly wind events in a coupled general circulation model, Clim. Dynam., 23, 601–620, 2004.
Liu, C., Allan, R. P., Berrisford, P., Mayer, M., Hyder, P., Loeb, N., Smith, D., Vidale, P.-L., and Edwards, J. M.: Combining satellite observations and reanalysis energy transports to estimate global net surface energy fluxes 1985–2012, J. Geophys. Res., submitted, 2015.
Ma, H.-Y., Xie, S., Klein, S. A., Williams, K. D., Boyle, J. S., Bony, S., Douville, H., Fermepin, S., Medeiros, B., Tyteca, S., Watanabe, M., and Williamson, D.: On the correspondence between mean forecast errors and climate errors in CMIP5 models, J. Climate, 17, 1781–1798, https://doi.org/10.1175/JCLI-D-13-00474.1, 2014.
MacLachlan, C., Arribas, A., Peterson, D., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.2396, in press, 2015.
Madec, G.: NEMO ocean engine, Tech. Rep. 27, Note du Pole de modélisation, I}nstitut Pierre-Simon Laplace {(IPSL), 2008.
Martin, G. M., Milton, S. F., Senior, C. A., Brooks, M. E., Ineson, S., Reichler, T., and Kim, J.: Analysis and reduction of systematic errors through a seamless approach to modelling weather and climate, J. Climate, 23, 5933–5957, https://doi.org/10.1175/2010JCLI3541.1, 2010.
Megann, A., Storkey, D., Aksenov, Y., Alderson, S., Calvert, D., Graham, T., Hyder, P., Siddorn, J., and Sinha, B.: GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications, Geosci. Model Dev., 7, 1069–1092, https://doi.org/10.5194/gmd-7-1069-2014, 2014.
Nam, C., Bony, S., Dufresne, J.-L., and Chepfer, H.: The "too few, too bright" tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421, 2012.
Rae, J. G. L., Hewitt, H. T., Keen, A. J., Ridley, J. K., West, A. E., Harris, C. M., Hunke, E. C., and Walters, D. N.: Development of G}lobal Sea Ice 5.0 and 6.0 CICE configurations for the {Met Office Global Coupled Model, Geosci. Model Dev. Discuss., 8, 2529–2554, https://doi.org/10.5194/gmdd-8-2529-2015, 2015.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of SST, sea ice and night marine air temperature since the late nineteenth century, J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
Roberts, M. J., Vidale, P. L., Mizielinski, M., Demory, M.-E., Schiemann, R., Strachan, J., Hodges, K., Camp, J., and Bell, R.: Tropical cyclones in the UPSCALE ensemble of high resolution global climate models, J. Climate, 28, 574-596 https://doi.org/10.1175/JCLI-D-14-00131.1, 2015.
Rowell, D. P.: Simulating SST teleconnections to Africa: what is the state of the art?, J. Climate, 26, 5397–5417, https://doi.org/10.1175/JCLI-D-12-00761.1, 2013.
Scaife, A. A., Copsey, D., Gordon, C., Harris, C., Hinton, T., Keeley, S., O'Neill, A., Roberts, M., and Williams, K.: Improved Atlantic winter blocking in a climate model, Geophys. Res. Lett., 38, L23703, https://doi.org/10.1029/2011GL049573, 2011.
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T., Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., Hermanson, L., Knight, J. R., Lea, D. J., MacLachlan, C., Maidens, A., Martin, M., Peterson, A. K., Smith, D., Vellinga, M., Wallace, E., Waters, J., and Williams, A.: Skillful long-range prediction of European and North American winters, Geophys. Res. Lett., 41, 2514–2519, https://doi.org/10.1002/2014GL059637, 2014.
Schade, L. R. and Emanuel, K. A.: The ocean's effect on the intensity of tropical cyclones: results from a simple coupled atmosphere–ocean model, J. Atmos. Sci., 56, 642–651, 1999.
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.: Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011.
Senior, C. A., Andrews, T., Burton, C., Chadwick, R., Copsey, D., Graham, T., Hyder, P., Jackson, L., McDonald, R., Ridley, J., and Ringer, M.: Idealised climate change simulations with a high resolution physical model, HadGEM3-GC2, in preparation, 2015.
Shelly, A., Xavier, P., Copsey, D., Johns, T., Rodriguez, J. M., Milton, S., and Klingaman, N.: Coupled versus uncoupled hindcast simulations of the Madden–Julian Oscillation in the Year of Tropical Convection, Geophys. Res. Lett., 41, 5670–5677, https://doi.org/10.1002/2013GL059062, 2014.
Simpson, J., Adler, R., and North, G.: A proposed Tropical Rainfall Measuring Mission (TRMM) satellite, B. Am. Meteorol. Soc., 69, 278–295, 1988.
Smeed, D. A., McCarthy, G. D., Cunningham, S. A., Frajka-Williams, E., Rayner, D., Johns, W. E., Meinen, C. S., Baringer, M. O., Moat, B. I., Duchez, A., and Bryden, H. L.: Observed decline of the Atlantic meridional overturning circulation 2004–2012, Ocean Sci., 10, 29–38, https://doi.org/10.5194/os-10-29-2014, 2014.
Smith, R. N. B.: A scheme for predicting layer clouds and their water content in a general circulation model, Q. J. Roy. Meteor. Soc., 116, 435–460, 1990.
Sperber, K. R. and Kim, D.: Simplified metrics for the identification of the Madden–Julian Oscillation in models, Atmos. Sci. Lett., 13, 187–193, 2012.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Tibaldi, S. and Molteni, F.: On the operational predictability of blocking, Tellus A, 42, 343–365, 1990.
Trenberth, K. E. and Fasullo, J. T.: Simulation of present-day and Twenty-First-Century energy budgets of the Southern Oceans, J. Climate, 23, 440–454, https://doi.org/10.1175/2009JCLI3152.1, 2010.
Valcke, S.: The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373–388, https://doi.org/10.5194/gmd-6-373-2013, 2013.
Vosper, S. B., Wells, H., and Brown, A. R.: Accounting for non-uniform static stability in orographic drag parametrization, Q. J. Roy. Meteor. Soc., 135, 815–822, https://doi.org/10.1002/qj.407, 2009.
Walters, D. N., Best, M. J., Bushell, A. C., Copsey, D., Edwards, J. M., Falloon, P. D., Harris, C. M., Lock, A. P., Manners, J. C., Morcrette, C. J., Roberts, M. J., Stratton, R. A., Webster, S., Wilkinson, J. M., Willett, M. R., Boutle, I. A., Earnshaw, P. D., Hill, P. G., MacLachlan, C., Martin, G. M., Moufouma-Okia, W., Palmer, M. D., Petch, J. C., Rooney, G. G., Scaife, A. A., and Williams, K. D.: The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations, Geosci. Model Dev., 4, 919–941, https://doi.org/10.5194/gmd-4-919-2011, 2011.
Walters, D. N., Brooks, M. E., Boutle, I. A., Melvin, T. R. O., Stratton, R. A., Bushell, A. C., Copsey, D., Earnshaw, P. E., Gross, M. S., Hardiman, S. C., Harris, C. M., Heming, J. T., Klingaman, N. P., Levine, R. C., Manners, J., Martin, G. M., Milton, S. F., Mittermaier, M. P., Morcrette, C. J., Riddick, T. C., Roberts, M. J., Selwood, P. M., Tennant, W.J., Vidale, P.-L., Wilkinson, J. M., Wood, N., Woolnough, S. J., and Xavier, P. K.: The M}et Office Unified Model Global Atmosphere 6.0 and JULES {Global Land 6.0 configurations, in preparation, 2015.
Williams, K. D., Bodas-Salcedo, A., Deque, M., Fermepin, S., Medeiros, B., Watanabe, M., Jakob, C., Klein, S. A., Senior, C. A., and Williamson, D. L.: The T}ranspose-AMIP {II experiment and its application to the understanding of Southern Ocean cloud biases in climate models, J. Climate, 26, 3258–3274, https://doi.org/10.1175/JCLI-D-12-00429.1, 2013.
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., and Morcrette, C. J.: PC2}: a prognostic cloud fraction and condensation scheme. {I: Scheme description, Q. J. Roy. Meteor. Soc., 134, 2093–2107, https://doi.org/10.1002/qj.333, 2008.
Winker, D. M., Pelon, J., Coakley Jr, J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L., Treut, H. L., McCormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO mission: a global 3D view of aerosols and clouds, B. Am. Meteorol. Soc., 91, 1211–1229, https://doi.org/10.1175/2010BAMS3009.1, 2010.
Wood, N., Stainforth, A., White, A., Allen, T., Diamantakis, M., Gross, M., Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.: An inherently mass-conserving semi-implicit semi-Lagrangian discretisation of the deep-atmosphere global nonhydrostatic equations, Q. J. Roy. Meteor. Soc., 140, 1505–1520, https://doi.org/10.1002/qj.2235, 2014.
Woollings, T., Hannachi, A., and Hoskins, B.: Variability of the North Atlantic eddy-driven jet stream, Q. J. Roy. Meteor. Soc., 136, 856–868, 2010.
Zappa, G., Shaffrey, L. C., and Hodges, K. I.: The ability of CMIP5 models to simulate North Atlantic extratropical cyclones, J. Climate, 26, 5379–5396, 2013.