Articles | Volume 12, issue 6
https://doi.org/10.5194/gmd-12-2357-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-2357-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Jun 2019
Model description paper |
| 18 Jun 2019
| 18 Jun 2019
The UKC3 regional coupled environmental prediction system
Huw W. Lewis
CORRESPONDING AUTHOR
Met Office, Exeter, EX1 3PB, UK
Juan Manuel Castillo Sanchez
Met Office, Exeter, EX1 3PB, UK
Alex Arnold
Met Office, Exeter, EX1 3PB, UK
Joachim Fallmann
Met Office, Exeter, EX1 3PB, UK
now at: Institut für Physik der Atmosphäre, Johannes
Gutenberg-Universität Mainz, Mainz, Germany
Andrew Saulter
Met Office, Exeter, EX1 3PB, UK
Jennifer Graham
Met Office, Exeter, EX1 3PB, UK
now at: Centre for Environment, Fisheries and Aquaculture Science,
Pakefield Rd, Lowestoft, NR33 0HT, UK
Mike Bush
Met Office, Exeter, EX1 3PB, UK
John Siddorn
Met Office, Exeter, EX1 3PB, UK
Tamzin Palmer
Met Office, Exeter, EX1 3PB, UK
Adrian Lock
Met Office, Exeter, EX1 3PB, UK
John Edwards
Met Office, Exeter, EX1 3PB, UK
Lucy Bricheno
National Oceanography Centre, Liverpool, L3 5DA, UK
Alberto Martínez-de la Torre
Centre for Ecology & Hydrology, Wallingford, OX10 8BB, UK
James Clark
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
Related authors
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Preprint under review for GMD
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
Short summary
Short summary
Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
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.
Julia Rulent, Lucy M. Bricheno, J. A. Mattias Green, Ivan D. Haigh, and Huw Lewis
Nat. Hazards Earth Syst. Sci., 21, 3339–3351, https://doi.org/10.5194/nhess-21-3339-2021, https://doi.org/10.5194/nhess-21-3339-2021, 2021
Short summary
Short summary
High coastal total water levels (TWLs) can lead to flooding and hazardous conditions for coastal communities and environment. In this research we are using numerical models to study the interactions between the three main components of the TWL (waves, tides, and surges) on UK and Irish coasts during winter 2013/14. The main finding of this research is that extreme waves and surges can indeed happen together, even at high tide, but they often occurred simultaneously 2–3 h before high tide.
Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2021-46, https://doi.org/10.5194/wcd-2021-46, 2021
Preprint withdrawn
Short summary
Short summary
This study assesses the ability of the new Met Office IND1 numerical model to simulate tropical cyclones and their associated hazards, such as high winds and heavy rainfall. The new system consists of both atmospheric and oceanic models coupled together, allowing us to explore the sensitivity of cyclones to important air–sea feedbacks. We find that the model can accurately simulate tropical cyclone position, structure, and intensity, which are crucial for predicting and mitigating hazards.
Simon J. Dadson, Eleanor Blyth, Douglas Clark, Helen Davies, Richard Ellis, Huw Lewis, Toby Marthews, and Ponnambalan Rameshwaran
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-60, https://doi.org/10.5194/hess-2021-60, 2021
Manuscript not accepted for further review
Short summary
Short summary
Flood prediction helps national and regional planning and real-time flood response. In this study we apply and test a new way to make wide area predictions of flooding which can be combined with weather forecasting and climate models to give faster predictions of flooded areas. By simplifying the detailed floodplain topography we can keep track of the fraction of land flooded for hazard mapping purposes. When tested this approach accurately reproduces benchmark datasets for England.
Huw W. Lewis, John Siddorn, Juan Manuel Castillo Sanchez, Jon Petch, John M. Edwards, and Tim Smyth
Ocean Sci., 15, 761–778, https://doi.org/10.5194/os-15-761-2019, https://doi.org/10.5194/os-15-761-2019, 2019
Short summary
Short summary
Oceans are modified at the surface by winds and by the exchange of heat with the atmosphere. The effect of changing atmospheric information that is available to drive an ocean model of north-west Europe, which can simulate small-scale details of the ocean state, is tested. We show that simulated temperatures agree better with observations located near the coast around the south-west UK when using data from a high-resolution atmospheric model, and when atmosphere and ocean feedbacks are included.
Huw W. Lewis, Juan Manuel Castillo Sanchez, John Siddorn, Robert R. King, Marina Tonani, Andrew Saulter, Peter Sykes, Anne-Christine Pequignet, Graham P. Weedon, Tamzin Palmer, Joanna Staneva, and Lucy Bricheno
Ocean Sci., 15, 669–690, https://doi.org/10.5194/os-15-669-2019, https://doi.org/10.5194/os-15-669-2019, 2019
Short summary
Short summary
Forecasts of ocean temperature, salinity, currents, and sea height can be improved by linking state-of-the-art ocean and wave models, so that they can interact to better represent the real world. We test this approach in an ocean model of north-west Europe which can simulate small-scale details of the ocean state. The intention is to implement the system described in this study for operational use so that improved information can be provided to users of ocean forecast data.
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.
J. R. Siddorn, S. A. Good, C. M. Harris, H. W. Lewis, J. Maksymczuk, M. J. Martin, and A. Saulter
Ocean Sci., 12, 217–231, https://doi.org/10.5194/os-12-217-2016, https://doi.org/10.5194/os-12-217-2016, 2016
Short summary
Short summary
The Met Office provides a range of services in the marine environment. To support these services, and to ensure they evolve to meet the demands of users and are based on the best available science, a number of scientific challenges need to be addressed. The paper summarises the key challenges, and highlights some priorities for the ocean monitoring and forecasting research group at the Met Office.
I. D. Culverwell, H. W. Lewis, D. Offiler, C. Marquardt, and C. P. Burrows
Atmos. Meas. Tech., 8, 1887–1899, https://doi.org/10.5194/amt-8-1887-2015, https://doi.org/10.5194/amt-8-1887-2015, 2015
Short summary
Short summary
This paper describes the Radio Occultation Processing Package, ROPP, which is a suite of freely available programs provided by EUMETSAT for the processing of radio occultation data. Its capabilities are briefly reviewed, and examples of its use are given. Some current and prospective uses of ROPP are listed.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Preprint under review for GMD
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Lianne C. Harrison, Jennifer A. Graham, Piyali Chowdhury, Tiago A. M. Silva, Danja P. Hoehn, Alakes Samanta, Kunal Chakraborty, Sudheer Joseph, T. M. Balakrishnan Nair, and T. Srinivasa Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2024-3096, https://doi.org/10.5194/egusphere-2024-3096, 2024
Short summary
Short summary
Particle tracking models allow us to explore pathways of floating marine litter, source-to-sink, between countries. This study showed the influence of seasonality for dispersal in Bay of Bengal and how ocean current forcing impacts model performance. Most litter beached on the country of origin, but there was a greater spread shown between countries during the post-monsoon period (Oct–Jan). Results will inform future model developments as well as management of marine litter in the region.
Nieves G. Valiente, Andrew Saulter, Breogan Gomez, Christopher Bunney, Jian-Guo Li, Tamzin Palmer, and Christine Pequignet
Geosci. Model Dev., 16, 2515–2538, https://doi.org/10.5194/gmd-16-2515-2023, https://doi.org/10.5194/gmd-16-2515-2023, 2023
Short summary
Short summary
We document the Met Office operational global and regional wave models which provide wave forecasts up to 7 d ahead. Our models present coarser resolution offshore to higher resolution near the coastline. The increased resolution led to replication of the extremes but to some overestimation during modal conditions. If currents are included, wave directions and long period swells near the coast are significantly improved. New developments focus on the optimisation of the models with resolution.
Tamzin E. Palmer, Carol F. McSweeney, Ben B. B. Booth, Matthew D. K. Priestley, Paolo Davini, Lukas Brunner, Leonard Borchert, and Matthew B. Menary
Earth Syst. Dynam., 14, 457–483, https://doi.org/10.5194/esd-14-457-2023, https://doi.org/10.5194/esd-14-457-2023, 2023
Short summary
Short summary
We carry out an assessment of an ensemble of general climate models (CMIP6) based on the ability of the models to represent the key physical processes that are important for representing European climate. Filtering the models with the assessment leads to more models with less global warming being removed, and this shifts the lower part of the projected temperature range towards greater warming. This is in contrast to the affect of weighting the ensemble using global temperature trends.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
Short summary
Short summary
Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Jeff Polton, James Harle, Jason Holt, Anna Katavouta, Dale Partridge, Jenny Jardine, Sarah Wakelin, Julia Rulent, Anthony Wise, Katherine Hutchinson, David Byrne, Diego Bruciaferri, Enda O'Dea, Michela De Dominicis, Pierre Mathiot, Andrew Coward, Andrew Yool, Julien Palmiéri, Gennadi Lessin, Claudia Gabriela Mayorga-Adame, Valérie Le Guennec, Alex Arnold, and Clément Rousset
Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, https://doi.org/10.5194/gmd-16-1481-2023, 2023
Short summary
Short summary
The aim is to increase the capacity of the modelling community to respond to societally important questions that require ocean modelling. The concept of reproducibility for regional ocean modelling is developed: advocating methods for reproducible workflows and standardised methods of assessment. Then, targeting the NEMO framework, we give practical advice and worked examples, highlighting key considerations that will the expedite development cycle and upskill the user community.
Diego Bruciaferri, Marina Tonani, Isabella Ascione, Fahad Al Senafi, Enda O'Dea, Helene T. Hewitt, and Andrew Saulter
Geosci. Model Dev., 15, 8705–8730, https://doi.org/10.5194/gmd-15-8705-2022, https://doi.org/10.5194/gmd-15-8705-2022, 2022
Short summary
Short summary
More accurate predictions of the Gulf's ocean dynamics are needed. We investigate the impact on the predictive skills of a numerical shelf sea model of the Gulf after changing a few key aspects. Increasing the lateral and vertical resolution and optimising the vertical coordinate system to best represent the leading physical processes at stake significantly improve the accuracy of the simulated dynamics. Additional work may be needed to get real benefit from using a more realistic bathymetry.
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
Short summary
Short summary
Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Patrick Le Moigne, Eric Bazile, Anning Cheng, Emanuel Dutra, John M. Edwards, William Maurel, Irina Sandu, Olivier Traullé, Etienne Vignon, Ayrton Zadra, and Weizhong Zheng
The Cryosphere, 16, 2183–2202, https://doi.org/10.5194/tc-16-2183-2022, https://doi.org/10.5194/tc-16-2183-2022, 2022
Short summary
Short summary
This paper describes an intercomparison of snow models, of varying complexity, used for numerical weather prediction or academic research. The results show that the simplest models are, under certain conditions, able to reproduce the surface temperature just as well as the most complex models. Moreover, the diversity of surface parameters of the models has a strong impact on the temporal variability of the components of the simulated surface energy balance.
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.
Julia Rulent, Lucy M. Bricheno, J. A. Mattias Green, Ivan D. Haigh, and Huw Lewis
Nat. Hazards Earth Syst. Sci., 21, 3339–3351, https://doi.org/10.5194/nhess-21-3339-2021, https://doi.org/10.5194/nhess-21-3339-2021, 2021
Short summary
Short summary
High coastal total water levels (TWLs) can lead to flooding and hazardous conditions for coastal communities and environment. In this research we are using numerical models to study the interactions between the three main components of the TWL (waves, tides, and surges) on UK and Irish coasts during winter 2013/14. The main finding of this research is that extreme waves and surges can indeed happen together, even at high tide, but they often occurred simultaneously 2–3 h before high tide.
Vinod Kumar, Julia Remmers, Steffen Beirle, Joachim Fallmann, Astrid Kerkweg, Jos Lelieveld, Mariano Mertens, Andrea Pozzer, Benedikt Steil, Marc Barra, Holger Tost, and Thomas Wagner
Atmos. Meas. Tech., 14, 5241–5269, https://doi.org/10.5194/amt-14-5241-2021, https://doi.org/10.5194/amt-14-5241-2021, 2021
Short summary
Short summary
We present high-resolution regional atmospheric chemistry model simulations focused around Germany. We highlight the importance of spatial resolution of the model itself as well as the input emissions inventory and short-scale temporal variability of emissions for simulations. We propose a consistent approach for evaluating the simulated vertical distribution of NO2 using MAX-DOAS measurements while also considering its spatial sensitivity volume and change in sensitivity within this volume.
Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2021-46, https://doi.org/10.5194/wcd-2021-46, 2021
Preprint withdrawn
Short summary
Short summary
This study assesses the ability of the new Met Office IND1 numerical model to simulate tropical cyclones and their associated hazards, such as high winds and heavy rainfall. The new system consists of both atmospheric and oceanic models coupled together, allowing us to explore the sensitivity of cyclones to important air–sea feedbacks. We find that the model can accurately simulate tropical cyclone position, structure, and intensity, which are crucial for predicting and mitigating hazards.
Simon J. Dadson, Eleanor Blyth, Douglas Clark, Helen Davies, Richard Ellis, Huw Lewis, Toby Marthews, and Ponnambalan Rameshwaran
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-60, https://doi.org/10.5194/hess-2021-60, 2021
Manuscript not accepted for further review
Short summary
Short summary
Flood prediction helps national and regional planning and real-time flood response. In this study we apply and test a new way to make wide area predictions of flooding which can be combined with weather forecasting and climate models to give faster predictions of flooded areas. By simplifying the detailed floodplain topography we can keep track of the fraction of land flooded for hazard mapping purposes. When tested this approach accurately reproduces benchmark datasets for England.
Svetlana Jevrejeva, Lucy Bricheno, Jennifer Brown, David Byrne, Michela De Dominicis, Andy Matthews, Stefanie Rynders, Hindumathi Palanisamy, and Judith Wolf
Nat. Hazards Earth Syst. Sci., 20, 2609–2626, https://doi.org/10.5194/nhess-20-2609-2020, https://doi.org/10.5194/nhess-20-2609-2020, 2020
Short summary
Short summary
We explore the role of waves, storm surges and sea level rise for the Caribbean region with a focus on the eastern Caribbean islands. We simulate past extreme events, suggesting a storm surge might reach 1.5 m and coastal wave heights up to 12 m offshore and up to 5 m near the coast of St Vincent. We provide sea level projections of up to 2.2 m by 2100. Our work provides quantitative evidence for policy-makers, scientists and local communities to actively protect against climate change.
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.
Sam Jones, Mark Inall, Marie Porter, Jennifer A. Graham, and Finlo Cottier
Ocean Sci., 16, 389–403, https://doi.org/10.5194/os-16-389-2020, https://doi.org/10.5194/os-16-389-2020, 2020
Short summary
Short summary
The ocean is an important source of nutrients and organisms to coastal waters, but it is not clear what controls current flow between the deep ocean and the coast. We contrasted ocean flow pathways and coastal water properties between summer 2013 and a series of intense storms in December 2013. Further, we assessed the likelihood of storms occurring over the North Atlantic during each winter. We found that local weather patterns exert a strong influence on coastal water properties and origins.
Andrew J. Wiltshire, Maria Carolina Duran Rojas, John M. Edwards, Nicola Gedney, Anna B. Harper, Andrew J. Hartley, Margaret A. Hendry, Eddy Robertson, and Kerry Smout-Day
Geosci. Model Dev., 13, 483–505, https://doi.org/10.5194/gmd-13-483-2020, https://doi.org/10.5194/gmd-13-483-2020, 2020
Short summary
Short summary
We present the Global Land (GL) configuration of the Joint UK Land Environment Simulator (JULES). JULES-GL7 can be used to simulate the exchange of heat, water and momentum over land and is therefore applicable for helping understand past and future changes, and forms the land component of the HadGEM3-GC3.1 climate model. The configuration is freely available subject to licence restrictions.
Toby R. Marthews, Eleanor M. Blyth, Alberto Martínez-de la Torre, and Ted I. E. Veldkamp
Hydrol. Earth Syst. Sci., 24, 75–92, https://doi.org/10.5194/hess-24-75-2020, https://doi.org/10.5194/hess-24-75-2020, 2020
Short summary
Short summary
Climate change impact modellers can only act on predictions of the occurrence of an extreme event in the Earth system if they know the uncertainty in that prediction and how uncertainty is attributable to different model components. Using eartH2Observe data, we quantify the balance between different sources of uncertainty in global evapotranspiration and runoff, making a crucial contribution to understanding the spatial distribution of water resources allocation deficiencies.
Alberto Martínez-de la Torre and Gonzalo Miguez-Macho
Hydrol. Earth Syst. Sci., 23, 4909–4932, https://doi.org/10.5194/hess-23-4909-2019, https://doi.org/10.5194/hess-23-4909-2019, 2019
Short summary
Short summary
Over semi-arid regions, it is essential to have a correct representation of the groundwater processes in climate modelling. We present a land surface and groundwater model that incorporates groundwater–soil interactions, groundwater–rivers flow and lateral transport at the subsurface. We study the groundwater influence on soil moisture distribution and memory, and on evapotranspiration in the Iberian Peninsula. Shallow water table regions persist and provide water to the surface during droughts.
Marina Tonani, Peter Sykes, Robert R. King, Niall McConnell, Anne-Christine Péquignet, Enda O'Dea, Jennifer A. Graham, Jeff Polton, and John Siddorn
Ocean Sci., 15, 1133–1158, https://doi.org/10.5194/os-15-1133-2019, https://doi.org/10.5194/os-15-1133-2019, 2019
Short summary
Short summary
A new high-resolution ocean model at 1.5 km has replaced the 7 km system for delivering short-term forecasts of the North-West European Shelf seas. The products (temperature, salinity, currents, and sea surface height) are available on the Copernicus Marine Service catalogue. This study focuses on the high-resolution impact on the quality of the products delivered to the users. Results show that the high-resolution model is better at resolving the variability of the physical variables.
Huw W. Lewis, John Siddorn, Juan Manuel Castillo Sanchez, Jon Petch, John M. Edwards, and Tim Smyth
Ocean Sci., 15, 761–778, https://doi.org/10.5194/os-15-761-2019, https://doi.org/10.5194/os-15-761-2019, 2019
Short summary
Short summary
Oceans are modified at the surface by winds and by the exchange of heat with the atmosphere. The effect of changing atmospheric information that is available to drive an ocean model of north-west Europe, which can simulate small-scale details of the ocean state, is tested. We show that simulated temperatures agree better with observations located near the coast around the south-west UK when using data from a high-resolution atmospheric model, and when atmosphere and ocean feedbacks are included.
Huw W. Lewis, Juan Manuel Castillo Sanchez, John Siddorn, Robert R. King, Marina Tonani, Andrew Saulter, Peter Sykes, Anne-Christine Pequignet, Graham P. Weedon, Tamzin Palmer, Joanna Staneva, and Lucy Bricheno
Ocean Sci., 15, 669–690, https://doi.org/10.5194/os-15-669-2019, https://doi.org/10.5194/os-15-669-2019, 2019
Short summary
Short summary
Forecasts of ocean temperature, salinity, currents, and sea height can be improved by linking state-of-the-art ocean and wave models, so that they can interact to better represent the real world. We test this approach in an ocean model of north-west Europe which can simulate small-scale details of the ocean state. The intention is to implement the system described in this study for operational use so that improved information can be provided to users of ocean forecast data.
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.
Jennifer K. Brooke, R. Chawn Harlow, Russell L. Scott, Martin J. Best, John M. Edwards, Jean-Claude Thelen, and Mark Weeks
Geosci. Model Dev., 12, 1703–1724, https://doi.org/10.5194/gmd-12-1703-2019, https://doi.org/10.5194/gmd-12-1703-2019, 2019
Short summary
Short summary
This paper evaluates a significant cold land surface temperature bias in semi-arid regions in the Met Office Unified Model when compared with satellite observations. Sparse vegetation canopies are not well represented in ancillary datasets, in particular regions of cold bias are correlated with low bare soil cover fractions. The study demonstrates the difficulties in modelling land surface temperatures that match state-of-the-art satellite retrievals required for operational data assimilation.
Md Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Jan Polcher, Clément Albergel, Emanuel Dutra, Gabriel Fink, Alberto Martínez-de la Torre, and Simon Munier
Hydrol. Earth Syst. Sci., 23, 1973–1994, https://doi.org/10.5194/hess-23-1973-2019, https://doi.org/10.5194/hess-23-1973-2019, 2019
Short summary
Short summary
This study investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period of 11 years based on six global hydrologic models and five precipitation datasets. Results show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure.
Alberto Martínez-de la Torre, Eleanor M. Blyth, and Graham P. Weedon
Geosci. Model Dev., 12, 765–784, https://doi.org/10.5194/gmd-12-765-2019, https://doi.org/10.5194/gmd-12-765-2019, 2019
Short summary
Short summary
Land–surface interactions with the atmosphere are key for weather and climate modelling studies, both in research and in the operational systems that provide scientific tools for decision makers. Regional assessments will be influenced by the characteristics of the land. We improved the representation of river flows in Great Britain by including a dependency on the terrain slope. This development will be reflected not only in river flows, but in the whole water cycle represented by the model.
Joanne Williams, Maialen Irazoqui Apecechea, Andrew Saulter, and Kevin J. Horsburgh
Ocean Sci., 14, 1057–1068, https://doi.org/10.5194/os-14-1057-2018, https://doi.org/10.5194/os-14-1057-2018, 2018
Short summary
Short summary
Tide predictions based on tide-gauge observations are not just astronomical tides; they also contain periodic sea level changes due to the weather. Forecasts of total water level during storm surges add the immediate effects of the weather to the astronomical tide prediction and thus risk double-counting these effects. We use a global model to see how much double-counting may affect these forecasts and also how much of the Highest Astronomical Tide may be due to recurrent weather patterns.
Eleanor M. Blyth, Alberto Martinez-de la Torre, and Emma L. Robinson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-153, https://doi.org/10.5194/hess-2018-153, 2018
Manuscript not accepted for further review
Short summary
Short summary
In a warming climate, the water budget of the land is subject to varying forces such as increasing evaporative demand, mainly through the increased temperature, and changes to the precipitation, which might go up or down. Using a verified, physically based model over with 55 years, an analysis of the water budget demonstrates that Great Britain is getting warmer and wetter. We demonstrated that amount of water captured on the trees has an impact on the overall trend.
Jennifer A. Graham, Enda O'Dea, Jason Holt, Jeff Polton, Helene T. Hewitt, Rachel Furner, Karen Guihou, Ashley Brereton, Alex Arnold, Sarah Wakelin, Juan Manuel Castillo Sanchez, and C. Gabriela Mayorga Adame
Geosci. Model Dev., 11, 681–696, https://doi.org/10.5194/gmd-11-681-2018, https://doi.org/10.5194/gmd-11-681-2018, 2018
Short summary
Short summary
This paper describes the next-generation ocean forecast model for the European NW shelf, AMM15 (Atlantic Margin Model, 1.5 km resolution). The current forecast system has a resolution of 7 km. While this is sufficient to represent large-scale circulation, many dynamical features (such as eddies, frontal jets, and internal tides) can only begin to be resolved at 0–1 km resolution. Here we introduce AMM15 and demonstrate its ability to represent the mean state and variability of the region.
Alberto Martínez-de la Torre, Eleanor M. Blyth, and Graham P. Weedon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-750, https://doi.org/10.5194/hess-2017-750, 2018
Manuscript not accepted for further review
Short summary
Short summary
Land surface interactions with the atmosphere are key for weather and climate modelling studies, both in research and in the operational systems that provide scientific tools for decision makers. Regional assessments will be influenced by the characteristics of the land. We improved the representation of Great Britain river flows by including a dependency on terrain slope. This development will be reflected not only in river flows, but in the whole water cycle represented by the model/system.
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.
Enda O'Dea, Rachel Furner, Sarah Wakelin, John Siddorn, James While, Peter Sykes, Robert King, Jason Holt, and Helene Hewitt
Geosci. Model Dev., 10, 2947–2969, https://doi.org/10.5194/gmd-10-2947-2017, https://doi.org/10.5194/gmd-10-2947-2017, 2017
Short summary
Short summary
An update to an ocean modelling configuration for the European North West Shelf is described. It is assessed against observations and climatologies for 1981–2012. Sensitivities in the model configuration updates are assessed to understand changes in the model system. The model improves upon an existing model of the region, although there remain some areas with significant biases. The paper highlights the dependence upon the quality of the river inputs.
Jaap Schellekens, Emanuel Dutra, Alberto Martínez-de la Torre, Gianpaolo Balsamo, Albert van Dijk, Frederiek Sperna Weiland, Marie Minvielle, Jean-Christophe Calvet, Bertrand Decharme, Stephanie Eisner, Gabriel Fink, Martina Flörke, Stefanie Peßenteiner, Rens van Beek, Jan Polcher, Hylke Beck, René Orth, Ben Calton, Sophia Burke, Wouter Dorigo, and Graham P. Weedon
Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, https://doi.org/10.5194/essd-9-389-2017, 2017
Short summary
Short summary
The dataset combines the results of 10 global models that describe the global continental water cycle. The data can be used as input for water resources studies, flood frequency studies etc. at different scales from continental to medium-scale catchments. We compared the results with earth observation data and conclude that most uncertainties are found in snow-dominated regions and tropical rainforest and monsoon regions.
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.
Jun She, Icarus Allen, Erik Buch, Alessandro Crise, Johnny A. Johannessen, Pierre-Yves Le Traon, Urmas Lips, Glenn Nolan, Nadia Pinardi, Jan H. Reißmann, John Siddorn, Emil Stanev, and Henning Wehde
Ocean Sci., 12, 953–976, https://doi.org/10.5194/os-12-953-2016, https://doi.org/10.5194/os-12-953-2016, 2016
Short summary
Short summary
This white paper addresses key scientific challenges and research priorities for the development of operational oceanography in Europe for the next 5–10 years. Knowledge gaps and deficiencies are identified in relation to common scientific challenges in four EuroGOOS knowledge areas: European ocean observations, modelling and forecasting technology, coastal operational oceanography, and operational ecology.
Heather Cannaby, Matthew D. Palmer, Tom Howard, Lucy Bricheno, Daley Calvert, Justin Krijnen, Richard Wood, Jonathan Tinker, Chris Bunney, James Harle, Andrew Saulter, Clare O'Neill, Clare Bellingham, and Jason Lowe
Ocean Sci., 12, 613–632, https://doi.org/10.5194/os-12-613-2016, https://doi.org/10.5194/os-12-613-2016, 2016
Short summary
Short summary
The Singapore government commissioned a modelling study of regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events. We find that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century, these being 0.52 m(0.74 m) under the RCP 4.5(8.5) scenario.
Momme Butenschön, James Clark, John N. Aldridge, Julian Icarus Allen, Yuri Artioli, Jeremy Blackford, Jorn Bruggeman, Pierre Cazenave, Stefano Ciavatta, Susan Kay, Gennadi Lessin, Sonja van Leeuwen, Johan van der Molen, Lee de Mora, Luca Polimene, Sevrine Sailley, Nicholas Stephens, and Ricardo Torres
Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016, https://doi.org/10.5194/gmd-9-1293-2016, 2016
Short summary
Short summary
ERSEM 15.06 is a model for marine biogeochemistry and the lower trophic levels of the marine food web. It comprises a pelagic and benthic sub-model including the microbial food web and the major biogeochemical cycles of carbon, nitrogen, phosphorus, silicate, and iron using dynamic stochiometry. Further features include modules for the carbonate system and calcification. We present full mathematical descriptions of all elements along with examples at various scales up to 3-D applications.
J. R. Siddorn, S. A. Good, C. M. Harris, H. W. Lewis, J. Maksymczuk, M. J. Martin, and A. Saulter
Ocean Sci., 12, 217–231, https://doi.org/10.5194/os-12-217-2016, https://doi.org/10.5194/os-12-217-2016, 2016
Short summary
Short summary
The Met Office provides a range of services in the marine environment. To support these services, and to ensure they evolve to meet the demands of users and are based on the best available science, a number of scientific challenges need to be addressed. The paper summarises the key challenges, and highlights some priorities for the ocean monitoring and forecasting research group at the Met Office.
M. Haller, F. Janssen, J. Siddorn, W. Petersen, and S. Dick
Ocean Sci., 11, 879–896, https://doi.org/10.5194/os-11-879-2015, https://doi.org/10.5194/os-11-879-2015, 2015
Short summary
Short summary
Automated measurement systems called FerryBox are installed on cargo ships in the North Sea. Operational model forecasts have been compared to FerryBox data of water temperature and salinity. We wanted to know how well the simulations agree with the observations. We found out that water temperature simulation gives satisfying results, while salinity simulation still could be improved. It turned out that assimilation of observational data into operational models gives strong benefits.
S. R. Kolusu, J. H. Marsham, J. Mulcahy, B. Johnson, C. Dunning, M. Bush, and D. V. Spracklen
Atmos. Chem. Phys., 15, 12251–12266, https://doi.org/10.5194/acp-15-12251-2015, https://doi.org/10.5194/acp-15-12251-2015, 2015
I. D. Culverwell, H. W. Lewis, D. Offiler, C. Marquardt, and C. P. Burrows
Atmos. Meas. Tech., 8, 1887–1899, https://doi.org/10.5194/amt-8-1887-2015, https://doi.org/10.5194/amt-8-1887-2015, 2015
Short summary
Short summary
This paper describes the Radio Occultation Processing Package, ROPP, which is a suite of freely available programs provided by EUMETSAT for the processing of radio occultation data. Its capabilities are briefly reviewed, and examples of its use are given. Some current and prospective uses of ROPP are listed.
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
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
Related subject area
Climate and Earth system modeling
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Using feature importance as exploratory data analysis tool on earth system models
CropSuite – A comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – The ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
A non-intrusive, multi-scale, and flexible coupling interface in WRF
T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5): Model formulation and validation
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
The Earth Science Box Modeling Toolkit (ESBMTK)
High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Baseline Climate Variables for Earth System Modelling
The DOE E3SM Version 2.1: Overview and Assessment of the Impacts of Parameterized Ocean Submesoscales
Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
Short summary
Short summary
CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used for climate impact assessments, adaptation and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
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
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of 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 on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
EGUsphere, https://doi.org/10.5194/egusphere-2024-2072, https://doi.org/10.5194/egusphere-2024-2072, 2024
Short summary
Short summary
We outline and validate developments to the pre-existing process-based model T&C to better represent cropland processes. Foreseen applications of T&C-CROP include hydrological and carbon storage implications of land-use transitions involving crop, forest, and pasture conversion, as well as studies on optimal irrigation and fertilization under a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ulrich Georg Wortmann, Tina Tsan, Mahrukh Niazi, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-1864, https://doi.org/10.5194/egusphere-2024-1864, 2024
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library designed to separate model description from numerical implementation. This approach results in well-documented, easily readable, and maintainable model code, allowing students and researchers to concentrate on conceptual challenges rather than mathematical intricacies.
Malcolm John Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present day and future projections, and to build links with other communities to provide more robust climate information.
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
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O’Rourke, and Beth Dingley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2363, https://doi.org/10.5194/egusphere-2024-2363, 2024
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 132 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most heavily used variables from Earth System Models, based on an assessment of data publication and download records from the largest archive of global climate projects.
Katherine Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golez, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautum Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordonez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-149, https://doi.org/10.5194/gmd-2024-149, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer biases reduction in temperature, salinity, and sea-ice extent in the North Atlantic, a small strengthening of the Atlantic Meridional Overturning Circulation, and improvements in many atmospheric climatological variables.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the north and south Atlantic (south Pacific and Indian Ocean).
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. 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 test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
Short summary
Short summary
We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
Short summary
Short summary
Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Cited articles
Akan, Ç., Moghimi, S., Özkan-Haller, H. T., Osborne, J., and Kurapov,
A.: On the dynamics of the Mouth of the Columbia River: Results from a
three-dimensional fully coupled wave-current interaction model, J. Geophys.
Res.-Oceans, 122, 5218–5236, https://doi.org/10.1002/2016JC012307, 2017
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamic
processes of the UCLA general circulation model, Methods Comput. Phys., 17,
173–265, 1977.
Aranami, K., Davies, T., and Wood, N.: A mass restoration scheme for
limited-area models with semi-Lagrangian advection, Q. J. Roy. Meteor. Soc.,
141, 1795–1803, https://doi.org/10.1002/qj.2482, 2015.
Arnold, A.: The sensitivity of AMM15 ocean model sea surface temperature to
shortwave radiation forcing, Met Office Forecasting Research Technical Report
No: 627, available at:
https://www.metoffice.gov.uk/binaries/content/assets/mohippo/pdf/library/weather-science-technical-reports/frtr_627_2018p.pdf
(last access: 18 February 2019), 2018.
Barron, C. N., Kara, A. B., Martin, P. J., Rhodes, R. C., and Smedstad, L.
F.: Formulation, implementation and examination of vertical coordinate
choices in the global Navy Coastal Ocean Model (NCOM), Ocean Model., 11,
347–375, https://doi.org/10.1016/j.ocemod.2005.01.004, 2006.
Bayler, G. and Lewit, H.: The Navy Operational Global and Regional
Atmospheric Prediction Systems at the Fleet Numerical Oceanography Center,
Weather Forecast., 7, 273–279,
https://doi.org/10.1175/1520-0434(1992)007<0273:TNOGAR>2.0.CO;2, 1992.
Best, M. J.: Representing urban areas within operational numerical weather
prediction models, Bound.-Lay. Meteorol., 114, 91–109,
https://doi.org/10.1007/s10546-004-4834-5, 2005.
Best, M. J., Beljaars, A. C. M., Polcher, J., and Viterbo, P.: A proposed
structure for coupling tiled surfaces with the planetary boundary layer, J.
Hydrometeorol., 5, 1271–1278, https://doi.org/10.1175/JHM-382.1, 2004.
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.
Bidlot, J. R.: Present status of wave forecasting at ECMWF, ECMWF Workshop on
Ocean Waves, Shinfield Park, Reading, UK, 25–27 June 2012, 1–15, 2012.
Bierkens, M. F. P., Bell, V. A., Burek, P., Chaney, N., Condon, L., David, C.
H., de Roo, A., Döll, P., Drost, N., Famiglietti, J. S., Flörke, M.,
Gochis, D. J., Houser, P., Hut, R., Keune, J., Kollet, S., Maxwell, R.,
Reager, J. T., Samaniego, L., Sudicky, E., Sutanudjaja, E. H., van de Giesen,
N., Winsemius, H., and Wood, E. F.: Hyper-resolution global hydrological
modelling: what is next?, Hydrol. Process., 29, 310–320,
https://doi.org/10.1002/hyp.10391, 2015.
Bohnenstengel, S. I., Evans, S., Clark, P. A., and Belcher, S.: Simulations
of the London urban heat island, Q. J. Roy. Meteor. Soc., 137, 1625–1640,
https://doi.org/10.1002/qj.855, 2011.
Bolaños, R., Brown, J. M., and Souza, A. J.: Wave–current interactions
in a tide dominated estuary, Cont. Shelf Res., 87, 109–123,
https://doi.org/10.1016/j.csr.2014.05.009, 2014.
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model
for coastal regions: 1. Model description and validation, J. Geophys.
Res.-Ocean, 104, 7649–7666, https://doi.org/10.1029/98JC02622, 1999.
Boutle, I., Price, J., Kudzotsa, I., Kokkola, H., and Romakkaniemi, S.:
Aerosol–fog interaction and the transition to well-mixed radiation fog,
Atmos. Chem. Phys., 18, 7827–7840, https://doi.org/10.5194/acp-18-7827-2018,
2018.
Breivik, Ø., Mogensen, K., Bidlot, J. R., Balmaseda, M. A., and Janssen,
P. A. E. M.: Surface wave effects in the NEMO ocean model: Forced and coupled
experiments, J. Geophys. Res.-Oceans, 120, 2973–2992,
https://doi.org/10.1002/2014JC010565, 2015.
Breivik, Ø., Bidlot J.-R., and Janssen P. A.: A stokes drift approximation
based on the Phillips spectrum, Ocean Model., 100, 49–56,
https://doi.org/10.1016/j.ocemod.2016.01.005, 2016.
Brown, A. R., Beare, R. J., Edwards, J. M., Lock, A. P., Keogh, S. J.,
Milton, S. F., and Walters, D. N.: Upgrades to the boundary-layer scheme in
the Met Office numerical weather prediction model, Bound.-Lay. Meteorol.,
128, 117–132, https://doi.org/10.1007/s10546-008-9275-0, 2008.
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.:
Unified Modeling 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.
Brown, J. M., Bolaños, R., and Wolf, J.: Impact assessment of advanced
coupling features in a tide–surge–wave model, POLCOMS-WAM, in a shallow
water application, J. Marine Syst., 87, 13–24,
https://doi.org/10.1016/j.jmarsys.2011.02.006, 2011.
Bush, M., Allen, T., Bain, C., Boutle, I., Edwards, J., Finnenkoetter, A., Franklin, C., Hanley, K., Lean, H., Lock, A., Manners, J., Mittermaier, M., Morcrette, C., North, R., Petch, J., Short, C., Vosper, S., Walters, D., Webster, S., Weeks, M., Wilkinson, J., Wood, N., and Zerroukat, M.: The first Met Office Unified Model/JULES Regional Atmosphere and Land configuration, RAL1, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-130, in review, 2019.
Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y.,
Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin,
G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley,
S., Stephens, N., and Torres, R.: ERSEM 15.06: a generic model for marine
biogeochemistry and the ecosystem dynamics of the lower trophic levels,
Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016,
2016.
Byrne, D., Papritz, L., Frenger, I., Münnich, M., and Gruber, N.:
Atmospheric Response to Mesoscale Sea Surface Temperature Anomalies:
Assessment of Mechanisms and Coupling Strength in a High-Resolution Coupled
Model over the South Atlantic, J. Atmos. Sci., 72, 1872–1890,
https://doi.org/10.1175/JAS-D-14-0195.1, 2015.
Castillo, J. M. C. and Lewis, H. W.: Evaluating and optimising the cost of
coupling in the UKC2 regional system, Met Office Forecasting Research
Technical Report No: 621, available at:
https://www.metoffice.gov.uk/binaries/content/assets/mohippo/pdf/library/frtr_621_2017p.pdf
(last access: 18 February 2019), 2017.
Cavaleri, L., Abdalla, S., Benetazzo, A., Bertotti, L., Bidlot, J.-R.,
Breivik, Ø., Carniel, S., Jensen, R. E., Portilla-Yandun, J., Rogers, W.
E., Roland, A., Sanchez-Arcilla, A., Smith, J. M., Staneva, J., Toledo, Y.,
van Vledder, G. Ph., and van der Westhuysen, A. J.: Wave modelling in coastal
and inner seas, Prog. Oceanogr., 167, 164–233,
https://doi.org/10.1016/j.pocean.2018.03.010, 2018.
Climate change initative (CCI): https://www.esa-landcover-cci.org/
(last access: 18 February 2019), 2018.
CEH: Land Cover Map, Centre for Ecology & Hydrology, available at:
https://eip.ceh.ac.uk/lcm/lcmdata (last access: 18 February 2019),
2007.
Charney, J. G. and Phillips, N. A.: Numerical integration of the
quasi-geostrophic equations for barotropic and simple baroclinic flows, J.
Meteor., 10, 71–99, https://doi.org/10.1175/1520-0469(1953)0102.0.CO;2, 1953.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M.
J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O.,
Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land
Environment Simulator (JULES), model description – Part 2: Carbon fluxes and
vegetation dynamics, Geosci. Model Dev., 4, 701–722,
https://doi.org/10.5194/gmd-4-701-2011, 2011.
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.:
Convection-permitting models: a step-change in rainfall forecasting,
Meteorol. Appl., 23, 165–181, https://doi.org/10.1002/met.1538, 2016.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A Statistical
Exploration of the Relationships of Soil Moisture Characteristics to the
Physical Properties of Soils, Water Resour. Res., 20, 682–690,
https://doi.org/10.1029/WR020i006p00682, 1984.
Craig, P. D. and Banner, M. L.: Modeling wave-enhanced turbulence in the
ocean surface layer, J. Phys. Oceanogr., 24, 2546–2559,
https://doi.org/10.1029/2007JC004246, 1994.
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, I., Biblot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
Donelan, M. A.: On the decrease of the oceanic drag coefficient in high
winds, J. Geophys. Res.-Oceans, 123, 1485–1501, https://doi.org/10.1002/2017JC013394,
2018.
Donlon, C. J., Martin, M., Stark, J. D., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA), Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017,
2012.
Durnford, D., Fortin, V., Smith, G. C., Archambault, B., Deacu, D., Dupont,
F., Dyck, S., Martinez, Y., Klyszejko, E., MacKay, M., Liu, L., Pellerin, P.,
Pietroniro, A., Roy, F., Vu, V., Winter, B., Yu, W., Spence, C., Bruxer, J.,
and Dickhout, J.: Toward an Operational Water Cycle Prediction System for the
Great Lakes and St. Lawrence River, B. Am. Meteorol. Soc., 99, 521–546,
https://doi.org/10.1175/BAMS-D-16-0155.1, 2018.
Dzwonkowski, B., Greer, A. T., Briseño-Avena, C., Krause, J. W., Soto, I.
M., Hernandez, F. J., Deary, A. L., Wiggert, J. D., Joung, D., Fitzpatrick,
P. J., O'Brien, S. J., Dykstra, S. L., Lau, Y., Cambazoglu, M. K., Lockridge,
G., Howden, S. D., Shiller, A. M., and Graham, W. M.: Estuarine influence on
biogeochemical properties of the Alabama shelf during the fall season, Cont.
Shelf Res., 140, 96–109, https://doi.org/10.1016/j.csr.2017.05.001, 2017.
Emerton, R. E., Stephens, E. M., Pappenberger, F., Pagano, T. C., Weerts, A.
H., Wood, A. W., Salamon, P., Brown, J. D., Hjerdt, N., Donnelly, C., Baugh,
C. A., and Cloke, H. L.: Continental and global scale flood forecasting
systems, WIRES Water, 3, 391–418, https://doi.org/10.1002/wat2.1137, 2016.
Fallmann, J., Lewis, H., Castillo, J., Arnold, A., and Ramsdale, S.: Impact
of sea surface temperature on stratiform cloud formation over the North Sea,
Geophys. Res. Lett., 44, 4296–4303, https://doi.org/10.1002/2017GL073105, 2017.
Fallmann, J., Lewis, H., Castillo, J., and Lock, A.: Impact of
high-resolution ocean-atmosphere coupling on fog formation over the North
Sea, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.3488, online first, 2019.
Flores, R. P., Rijnsburger, S., Horner-Devine, A. R., Souza, A. J., and
Pietrzak, J. D.: The impact of storms and stratification on sediment
transport in the Rhine region of freshwater influence, J. Geophys.
Res.-Oceans, 122, 4456–4477, https://doi.org/10.1002/2016JC012362, 2017.
Forzieri, G., Feyen, L., Russo, S., Vousdoukas, M., Alfieri, L., Stephen, O.,
Migliavacca, M., Bianchi, A., Rojas, R., and Cid, A.: Multi-hazard assessment
in Europe under climate change, Clim. Change, 137, 105–119,
https://doi.org/10.1007/s10584-016-1661-x, 2016.
Frenger, I., Gruber, N., Knutti, R., and Münnich, M.: Imprint of Southern
Ocean eddies on winds, clouds and rainfall, Nat. Geosci., 6, 608–612,
https://doi.org/10.1038/ngeo1863, 2013.
Gemmrich, J. and Monahan, A.: Covariability of Near-Surface Wind Speed
Statistics and Mesoscale Sea Surface Temperature Fluctuations, J. Phys.
Oceanog., 48, 465–478, https://doi.org/10.1175/JPO-D-17-0177.1, 2018.
Graham, J. A., O'Dea, E., Holt, J., Polton, J., Hewitt, H. T., Furner, R.,
Guihou, K., Brereton, A., Arnold, A., Wakelin, S., Castillo Sanchez, J. M.,
and Mayorga Adame, C. G.: AMM15: a new high-resolution NEMO configuration for
operational simulation of the European north-west shelf, Geosci. Model Dev.,
11, 681–696, https://doi.org/10.5194/gmd-11-681-2018, 2018.
Gräwe, U., Holtermann, P., Klingbeil, K., and Burchard, H.: Advantages of
vertically adaptive coordinates in numerical models of stratified shelf seas,
Ocean Model., 92, 56–68, https://doi.org/10.1016/j.ocemod.2015.05.008, 2015.
Gronholz, A., Gräwe, U., Paul, A., and Schulz, M.: Investigating the
effects of a summer storm on the North Sea stratification using a regional
coupled ocean-atmosphere model, Ocean Dynam., 67, 211–235,
https://doi.org/10.1007/s10236-016-1023-2, 2017.
Hackerott, J. A., Pezzi, L. P., Bakhoday Paskyabi, M., Oliveira, A. P.,
Reuder, J., de Souza, R. B., and de Camargo, R.: The role of roughness and
stability on the momentum flux in the Marine Atmospheric Surface Layer: a
study on the Southwestern Atlantic Ocean, J. Geophys. Res., 123, 3914–3932,
https://doi.org/10.1002/2017JD027994, 2018.
Hasselmann, S., Hasselmann, K., Allender, J. H., and Barnett, P.:
Computations and parameterisations of the nonlinear energy transfer in a
gravity wave spectrum – Part 2: Parameterisations of the nonlinear energy
transfer for application in wave models, J. Phys. Oceanogr., 15, 1378–1391,
https://doi.org/10.1175/1520-0485(1985)0152.0.CO;2, 1985.
Hewitt, H. T., Bell, M. J., Chassignet, E. P., Czaja, A., Ferreira, D.,
Griffies, S. M., Hyder, P., McClean, J. L., New, A. L., and Roberts, M. J.:
Will high-resolution global ocean models benefit coupled predictions on
short-range to climate timescales?, Ocean Model., 120, 120–136,
https://doi.org/10.1016/j.ocemod.2017.11.002, 2017.
Hirons, L. C., Klingaman, N. P., and Woolnough, S. J.: The impact of air-sea
interactions on the representation of tropical precipitation extremes, J.
Adv. Model. Earth Syst., 10, 550–559, https://doi.org/10.1002/2017MS001252, 2018.
Holt, J. T. and James, I. D.: An s coordinate density evolving model of the
northwest European continental shelf: 1. Model description and density
structure, J. Geophys. Res., 106, 14015–14034, https://doi.org/10.1029/2000JC000304,
2001.
Holt, J., Hyder, P., Ashworth, M., Harle, J., Hewitt, H. T., Liu, H., New, A.
L., Pickles, S., Porter, A., Popova, E., Allen, J. I., Siddorn, J., and Wood,
R.: Prospects for improving the representation of coastal and shelf seas in
global ocean models, Geosci. Model Dev., 10, 499–523,
https://doi.org/10.5194/gmd-10-499-2017, 2017.
Jiménez, P. A. and Dudhia, J.: On the Need to Modify the Sea Surface
Roughness Formulation over Shallow Waters, J. Appl. Meteor. Climatol., 57,
1101–1110, https://doi.org/10.1175/JAMC-D-17-0137.1, 2018.
Jones, P.: ESMF_RegridWeightGen, available at:
https://www.earthsystemcog.org/projects/regridweightgen/ (last access:
18 February 2019), 2015.
Jung, T., Miller, M. J., Palmer, T. N., Towers, P., Wedi, N., Achuthavarier,
D., Adams, J. M., Altshuler, E. L., Cash, B. A., Kinter, J. L., Marx, L.,
Stan, C., and Hodges, K. I.: High-Resolution Global Climate Simulations with
the ECMWF Model in Project Athena: Experimental Design, Model Climate, and
Seasonal Forecast Skill, J. Climate, 25, 3155–3172,
https://doi.org/10.1175/JCLI-D-11-00265.1, 2012.
Kay, A. L., Rudd, A. C., Davies, H. N., Kendon, E. J., and Jones, R. G.: Use
of very high resolution climate model data for hydrological modelling:
Baseline performance and future flood changes, Clim. Change, 133, 193–208,
https://doi.org/10.1007/s10584-015-1455-6, 2015.
Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. F., Roberts, M. J., Chan,
S. C., Evans, J. P., Fosser, G., and Wilkinson, J. M.: Do
Convection-Permitting Regional Climate Models Improve Projections of Future
Precipitation Change?, B. Am. Meteorol. Soc., 98, 79–93,
https://doi.org/10.1175/BAMS-D-15-0004.1, 2017.
Komen, G., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, H., and
Janssen, P. A. E. M.: Dynamics and Modelling of Ocean Waves, Cambridge
University Press, 532 pp., 1994.
Kunii, M., Ito, K., and Wada, A.: Preliminary Test of a Data Assimilation
System with a Regional High-Resolution Atmosphere–Ocean Coupled Model Based
on an Ensemble Kalman Filter, Mon. Weather Rev., 145, 565–581,
https://doi.org/10.1175/MWR-D-16-0068.1, 2017.
Larson, J., Jacob, R., and Ong, E.: The Model Coupling Toolkit: A New
Fortran90 Toolkit for Building Multiphysics Parallel Coupled Models, Int. J.
High Perform. C., 19, 277–292, https://doi.org/10.1177/1094342005056115, 2005.
Law Chune, S. and Aouf, L.: Wave effects in global ocean modeling:
parametrizations vs. forcing from a wave model, Ocean Dynam.,
https://doi.org/10.1007/s10236-018-1220-2, 2018.
Lea, D. J., Mirouze, I., Martin, M. J., King, R. R., Hines, A., Walters, D.,
and Thurlow, M.: Assessing a New Coupled Data Assimilation System Based on
the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model, Mon. Weather
Rev., 143, 4678–4694, https://doi.org/10.1175/MWR-D-15-0174.1, 2015.
Lewis, H., Mittermaier, M., Mylne, K., Norman, K., Scaife, A., Neal, R.,
Pierce, C., Harrison, D., Jewell, S., Kendon, M., Saunders, R., Brunet, G.,
Golding, B., Kitchen, M., Davies, P., and Pilling, C.: From months to minutes
– exploring the value of high-resolution rainfall observation and prediction
during the UK winter storms of 2013/2014, Meteorol. Appl., 22, 90–104,
https://doi.org/10.1002/met.1493, 2015.
Lewis, H. W., Castillo Sanchez, J. M., Graham, J., Saulter, A., Bornemann,
J., Arnold, A., Fallmann, J., Harris, C., Pearson, D., Ramsdale, S.,
Martínez-de la Torre, A., Bricheno, L., Blyth, E., Bell, V. A., Davies,
H., Marthews, T. R., O'Neill, C., Rumbold, H., O'Dea, E., Brereton, A.,
Guihou, K., Hines, A., Butenschon, M., Dadson, S. J., Palmer, T., Holt, J.,
Reynard, N., Best, M., Edwards, J., and Siddorn, J.: The UKC2 regional
coupled environmental prediction system, Geosci. Model Dev., 11, 1–42,
https://doi.org/10.5194/gmd-11-1-2018, 2018.
Li, J.-G.: Upstream non-oscillatory advection schemes, Mon. Weather Rev.,
136, 4709–4729, https://doi.org/10.1175/2008MWR2451.1, 2008.
Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B.: A
new boundary layer mixing scheme. Part I: Scheme description and SCM tests,
Mon. Weather Rev., 128, 3187–3199,
https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2, 2000.
Luiz do Vale Silva, T., Veleda, D., Araujo, M., and Tyaquiçã, P.:
Ocean–Atmosphere Feedback during Extreme Rainfall Events in Eastern
Northeast Brazil, J. Appl. Meteor. Climatol., 57, 1211–1229,
https://doi.org/10.1175/JAMC-D-17-0232.1, 2018.
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., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2015.
Madec, G. and the NEMO team: NEMO reference manual 3_6_STABLE:NEMO ocean
engine, Note du Pôle de modélisation, Institut Pierre-Simon Laplace
(IPSL), France, No. 27, ISSN 1288-1619, 2016.
Marsooli, R. and Lin, N.: Numerical modeling of historical storm tides and
waves and their interactions along the U.S. east and Gulf Coasts, J. Geophys.
Res.-Oceans, 123, 3844–3874, https://doi.org/10.1029/ 2017JC013434, 2018.
Martínez-de la Torre, A., Blyth, E. M., and Weedon, G. P.: Using
observed river flow data to improve the hydrological functioning of the JULES
land surface model (vn4.3) used for regional coupled modelling in Great
Britain (UKC2), Geosci. Model Dev., 12, 765–784,
https://doi.org/10.5194/gmd-12-765-2019, 2019.
Miller, A. J., Collins, M., Gualdi, S., Jensen, T. G., Misra, V., Pezzi, L. P.,
Pierce, D. W., Putrasahan, D., Seo, H., and Tseng, Y. H.: Coupled
ocean-atmosphere modeling and predictions, J. Mar. Res., 75,
361–402, https://doi.org/10.1357/002224017821836770, 2017.
Mittermaier, M. P.: A Strategy for Verifying Near-Convection-Resolving Model
Forecasts at Observing Sites, Weather Forecast., 29, 185–204,
https://doi.org/10.1175/WAF-D-12-00075.1, 2014.
Mogensen, K. S., Magnusson, L., and Bidlot, J.-R.: Tropical cyclone
sensitivity to ocean coupling in the ECMWF coupled model, J. Geophys.
Res.-Oceans, 122, 4392–4412, https://doi.org/10.1002/2017JC012753, 2017.
Oerder, V., Colas, F., Echevin, V., Masson, S., and Lemarié, F.: Impacts
of the mesoscale ocean-atmosphere coupling on the Peru-Chile ocean dynamics:
The current-induced wind stress modulation, J. Geophys. Res.-Oceans, 123,
812–833, https://doi.org/10.1002/2017JC013294, 2018.
Palmer, T. N.: Towards the probabilistic Earth-system simulator: a vision for
the future of climate and weather prediction, Q. J. Roy. Meteor. Soc., 138,
841–861, https://doi.org/10.1002/qj.1923, 2012.
Palmer, T. and Saulter, A.: Evaluating the effects of ocean current fields on
a UK regional wave model, Met Office Forecasting Research, Technical Report
No: 612, available at:
https://www.metoffice.gov.uk/binaries/content/assets/mohippo/pdf/j/i/frtr_612_2016p.pdf
(last access: 18 February 2019), 2016.
Pullen, J., Allard, R., Seo, H., Miller, A. J., Chen, S., Pezzi, L. P.,
Smith, T., Chu, P., Alves, J., and Caldeira, R.: Coupled ocean-atmosphere
forecasting at short and medium time scales, J. Mar. Res., 75,
877–921, https://doi.org/10.1357/002224017823523991, 2017a.
Pullen, J., Caldeira, R., Doyle, J. D., May, P., and Tomé, R.: Modeling
the air-sea feedback system of Madeira Island, J. Adv. Model. Earth Syst., 9,
1641–1664, https://doi.org/10.1002/2016MS000861, 2017b.
Rainaud, R., Brossier, C. L., Ducrocq, V., and Giordani, H.: High-resolution
air–sea coupling impact on two heavy precipitation events in the Western
Mediterranean, Q. J. Roy. Meteor. Soc., 143, 2448–2462, https://doi.org/10.1002/qj.3098,
2017.
Rascle, N., Ardhuin, F., Queffeulou, P., and Croizé-Fillon, D.: A global
wave parameter database for geophysical applications, Part 1:
Wave-current–turbulence interaction parameters for the open ocean based on
traditional parameterizations, Ocean Model., 25, 154–171,
https://doi.org/10.1016/j.ocemod.2008.07.006, 2008.
Reza Hashemi, M., Neill, S. P., and Davies, A. G.: A coupled tide-wave model
for the NW European shelf seas, Geophys. Astro. Fluid, 109, 234–253,
https://doi.org/10.1080/03091929.2014.944909, 2015.
Ricchi, A., Miglietta, M. M., Barbariol, F., Benetazzo, A., Bergamasco, A.,
Bonaldo, D., Cassardo, C., Falcieri, F. M., Modugno, G., Russo, A., Sclavo,
M., and Carniel, S.: Sensitivity of a Mediterranean Tropical-Like Cyclone to
Different Model Configurations and Coupling Strategies, Atmosphere, 8, 92,
https://doi.org/10.3390/atmos8050092, 2017.
Rockel, B., Will, A., and Hense, A.: The regional climate model COSMO-CLM
(CCLM), Meteorol. Z., 17, 347–348, https://doi.org/10.1127/0941-2948/2008/0309, 2008.
Seo, H.: Distinct influence of air-sea interactions mediated by mesoscale sea
surface temperature and surface current in the Arabian Sea, J. Climate, 30,
8061–8079, https://doi.org/10.1175/JCLI-D-16-0834.1, 2017.
Seo, H., Miller, A. J., and Roads, J. O.: The Scripps Coupled
Ocean-Atmosphere Regional (SCOAR) model, with applications in the eastern
Pacific sector, J. Climate, 20, 381–402, https://doi.org/10.1175/JCLI4016, 2007.
Shapiro, M., Shukla, J., Brunet, G., Nobre, C., Beland, M., Dole, R.,
Tremberth, K., Anthes, R., Asrar, G., Barrie, L., Bougeault, P., Brasseur,
G., Burridge, D., Busalacchi, A., Caughey, J., Chen, D., Church, B., Enomoto,
T., Hoskins, B., Hov, O., Laing, A., Le Treut, H., Marotzke, J., McBean, G.,
Meehl, G., Miller, M., Mills, B., Mitchell, J., Moncrieff, M., Nakazawa, T.,
Olafsson, H., Palmer, T., Parson, D., Rogers, D., Simmons, A., Troccoli, A.,
Toth, Z., Uccellini, L., Velden, C., and Wallace, J. M.: An Earth-System
prediction initiative for the 21st Century, B. Am. Meteorol. Soc., 91,
1377–1388, https://doi.org/10.1175/2010BAMS2944.1, 2010.
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling
system (ROMS): A split-explicit, free-surface,
topography-following-coordinate oceanic model, Ocean Model., 9, 347–404,
https://doi.org/10.1016/j.ocemod.2004.08.002, 2005.
Shimura, T., Mori, N., Takemi, T., and Mizuta, R.: Long-term impacts of ocean
wave-dependent roughness on global climate systems, J. Geophys. Res.-Oceans,
122, 1995–2011, https://doi.org/10.1002/2016JC012621, 2017.
Siddorn, J. R. and Furner, R.: An analytical stretching function that
combines the best attributes of geopotential and terrain-following vertical
coordinates, Ocean Model., 66, 1–3, https://doi.org/10.1016/j.ocemod.2013.02.001, 2013.
Siddorn, J. R., Good, S. A., Harris, C. M., Lewis, H. W., Maksymczuk, J.,
Martin, M. J., and Saulter, A.: Research priorities in support of ocean
monitoring and forecasting at the Met Office, Ocean Sci., 12, 217–231,
https://doi.org/10.5194/os-12-217-2016, 2016.
Simpson, J. H.: Physical processes in the ROFI regime, J. Marine Syst., 12,
3–15, https://doi.org/10.1016/S0924-7963(96)00085-1, 1997.
Small, R. J., de Szoeke, S. P., Xie, S. P., O'Neill, L., Seo, H., Song, Q.,
Cornillon, P., Spall, M., and Minobe, S.: Air–sea interaction over ocean
fronts and eddies, Dynam. Atmos. Oceans, 45, 274–319,
https://doi.org/10.1016/j.dynatmoce.2008.01.001, 2008.
Smith, S. D. and Banke, E. G.: Variation of the sea surface drag coefficient
with wind speed, Q. J. Roy. Meteor. Soc., 101, 665–673,
https://doi.org/10.1002/qj.49710142920, 1975.
Staneva, J., Alari, V., Breivik, Ø., Bidlot, J-.R., and Mogensen, K.:
Effects of wave-induced forcing on a circulation model of the North Sea,
Ocean Dynam., 67, 81–101, https://doi.org/10.1007/s10236-016-1009-0, 2017.
Staneva, J., Wahle, K., Koch, W., Behrens, A., Fenoglio-Marc, L., and Stanev,
E. V.: Coastal flooding: impact of waves on storm surge during extremes – a
case study for the German Bight, Nat. Hazards Earth Syst. Sci., 16,
2373–2389, https://doi.org/10.5194/nhess-16-2373-2016, 2016a.
Staneva, J., Wahle, K., Günther, H., and Stanev, E.: Coupling of wave and
circulation models in coastal–ocean predicting systems: a case study for the
German Bight, Ocean Sci., 12, 797–806,
https://doi.org/10.5194/os-12-797-2016, 2016b.
Tennant, W. and Beare, S.: New schemes to perturb sea-surface temperature and
soil moisture content in MOGREPS, Q. J. Roy. Meteor. Soc., 140, 1150–1160,
https://doi.org/10.1002/qj.2202, 2014.
WAVEWATCH III® Development Group (WW3DG):
User manual and system documentation of WAVEWATCH
III® version 5.16, Tech. Note 329,
NOAA/NWS/NCEP/MMAB, College Park, MD, USA, 326 pp. + Appendices, 2016.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical
turbulence models, J. Marine Res., 61, 235–265,
https://doi.org/10.1357/002224003322005087, 2003.
Valcke, S., Craig, T., and Coquart, L.: OASIS3-MCT User Guide, CERFACS,
Technical Report TR/CMGC/15/38, 2015.
Varlas, G., Katsafados, P., Papadopoulos, A., and Korres, G.: Implementation
of a two-way coupled atmosphere-ocean wave modeling system for assessing
air-sea interaction over the Mediterranean Sea, Atmos. Res., 208, 201–217,
https://doi.org/10.1016/j.atmosres.2017.08.019, 2017.
Vinayachandran, P. N., Matthews, A. J., Vijay Kumar, K., Sanchez-Franks, A.,
Thushara, V., George, J., Vijith, V., Webber, B. G., Queste, B. Y., Roy, R.,
Sarkar, A., Baranowski, D. B., Bhat, G. S., Klingaman, N. P., Peatman, S. C.,
Parida, C., Heywood, K. J., Hall, R., King, B., Kent, E. C., Nayak, A. A.,
Neema, C. P., Amol, P., Lotliker, A., Kankonkar, A., Gracias, D. G.,
Vernekar, S., D'Souza, A. C., Valluvan, G., Pargaonkar, S. M., Dinesh, K.,
Giddings, J., and Joshi, M.: BoBBLE (Bay of Bengal Boundary Layer
Experiment): Ocean–atmosphere interaction and its impact on the South Asian
monsoon, B. Am. Meteorol. Soc., 99, 1569–1587, https://doi.org/10.1175/BAMS-D-16-0230.1,
2018.
Wada, A. and Kunii, M.: The role of ocean-atmosphere interaction in Typhoon
Sinlaku (2008) using a regional coupled data assimilation system, J. Geophys.
Res.-Oceans, 122, 3675–3695, https://doi.org/10.1002/2017JC012750, 2017.
Wahle, K., Staneva, J., Koch, W., Fenoglio-Marc, L., Ho-Hagemann, H. T. M.,
and Stanev, E. V.: An atmosphere–wave regional coupled model: improving
predictions of wave heights and surface winds in the southern North Sea,
Ocean Sci., 13, 289–301, https://doi.org/10.5194/os-13-289-2017, 2017.
Walters, D., Baran, A., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J.,
Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J.,
Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van
Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Dalvi, M.,
Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A.,
Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M.,
Williams, K., and Zerroukat, M.: The Met Office Unified Model Global
Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model
Dev. Discuss., https://doi.org/10.5194/gmd-2017-291, in review, 2017.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a
Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system,
Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
Wood, N., Staniforth, 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 discretization of
the deep-atmosphere global non-hydrostatic equations, Q. J. Roy. Meteor.
Soc., 140, 1505–1520, https://doi.org/10.1002/qj.2235, 2014.
Zerroukat, M. and Shipway, B. J.: ZLF (Zero Lateral Flux): a simple mass
conservation method for semi-Lagrangian-based limited-area models, Q. J. Roy.
Meteor. Soc., 143, 2578–2584, https://doi.org/10.1002/qj.3108, 2017.
Short summary
In the real world the atmosphere, oceans and land surface are closely interconnected, and yet the prediction systems used for weather and ocean forecasting tend to treat them in isolation. This paper describes the third version of a regional modelling system which aims to represent the feedback processes between sky, sea and land. The main innovation introduced in this version enables waves to affect the underlying ocean. Coupled results from four different month-long simulations are analysed.
In the real world the atmosphere, oceans and land surface are closely interconnected, and yet...
