Articles | Volume 15, issue 10
https://doi.org/10.5194/gmd-15-4193-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4193-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
01 Jun 2022
Model description paper |
| 01 Jun 2022
| 01 Jun 2022
The Regional Coupled Suite (RCS-IND1): application of a flexible regional coupled modelling framework to the Indian region at kilometre scale
Juan Manuel Castillo
CORRESPONDING AUTHOR
Met Office, Exeter, EX1 3PB, UK
Huw W. Lewis
Met Office, Exeter, EX1 3PB, UK
Akhilesh Mishra
National Centre for Medium Range Weather Forecasting (NCMRWF), Noida, India
Ashis Mitra
National Centre for Medium Range Weather Forecasting (NCMRWF), Noida, India
Jeff Polton
National Oceanography Centre, Liverpool, UK
Ashley Brereton
National Oceanography Centre, Liverpool, UK
Andrew Saulter
Met Office, Exeter, EX1 3PB, UK
Alex Arnold
Met Office, Exeter, EX1 3PB, UK
Segolene Berthou
Met Office, Exeter, EX1 3PB, UK
Douglas Clark
UK Centre of Ecology & Hydrology (UKCEH), Wallingford, UK
Julia Crook
School of Earth and Environment, University of Leeds, Leeds, UK
Ananda Das
India Meteorological Department (IMD), Delhi, India
John Edwards
Met Office, Exeter, EX1 3PB, UK
Xiangbo Feng
Department of Meteorology, University of Reading, Reading, UK
Ankur Gupta
National Centre for Medium Range Weather Forecasting (NCMRWF), Noida, India
Sudheer Joseph
Indian National Centre for Ocean Information Services (INCOIS), Hyderabad, India
Nicholas Klingaman
Department of Meteorology, University of Reading, Reading, UK
Imranali Momin
National Centre for Medium Range Weather Forecasting (NCMRWF), Noida, India
Christine Pequignet
Met Office, Exeter, EX1 3PB, UK
Claudio Sanchez
Met Office, Exeter, EX1 3PB, UK
Jennifer Saxby
School of Earth and Environment, University of Leeds, Leeds, UK
Maria Valdivieso da Costa
Department of Meteorology, University of Reading, Reading, UK
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Phytoplankton blooms are governed by the availability of light and nutrients, both of which are affected by mixing in the upper layers of the ocean, which is impacted by wave activity on the surface. Most numerical ocean models estimate waves through a parameterisation, here we explicitly resolve waves through a coupled wave model to examine the impact on the strength and timing of phytoplankton blooms, particular during storms when wave activity is elevated.
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Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
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Geosci. Model Dev., 15, 5241–5269, https://doi.org/10.5194/gmd-15-5241-2022, https://doi.org/10.5194/gmd-15-5241-2022, 2022
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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
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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
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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.
Ambrogio Volonté, Andrew G. Turner, Reinhard Schiemann, Pier Luigi Vidale, and Nicholas P. Klingaman
Weather Clim. Dynam., 3, 575–599, https://doi.org/10.5194/wcd-3-575-2022, https://doi.org/10.5194/wcd-3-575-2022, 2022
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In this study we analyse the complex seasonal evolution of the East Asian summer monsoon. Using reanalysis data, we show the importance of the interaction between tropical and extratropical air masses converging at the monsoon front, particularly during its northward progression. The upper-level flow pattern (e.g. the westerly jet) controls the balance between the airstreams and thus the associated rainfall. This framework provides a basis for studies of extreme events and climate variability.
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
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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.
Marion Mittermaier, Rachel North, Jan Maksymczuk, Christine Pequignet, and David Ford
Ocean Sci., 17, 1527–1543, https://doi.org/10.5194/os-17-1527-2021, https://doi.org/10.5194/os-17-1527-2021, 2021
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Regions of enhanced chlorophyll-a concentrations can be identified by applying a threshold to the concentration value to a forecast and observed field (or analysis). These regions can then be treated and analysed as features using diagnostic techniques to consider of the evolution of the chlorophyll-a blooms in space and time. This allows us to understand whether the biogeochemistry in the model has any skill in predicting these blooms, their location, intensity, onset, duration and demise.
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
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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.
Bijoy Thompson, Claudio Sanchez, Boon Chong Peter Heng, Rajesh Kumar, Jianyu Liu, Xiang-Yu Huang, and Pavel Tkalich
Geosci. Model Dev., 14, 1081–1100, https://doi.org/10.5194/gmd-14-1081-2021, https://doi.org/10.5194/gmd-14-1081-2021, 2021
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This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO, coupled using the OASIS3-MCT libraries. The model forecast deviation of selected fields relative to observations is within acceptable error limits of operational forecast models.
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
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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.
Marie-Estelle Demory, Ségolène Berthou, Jesús Fernández, Silje L. Sørland, Roman Brogli, Malcolm J. Roberts, Urs Beyerle, Jon Seddon, Rein Haarsma, Christoph Schär, Erasmo Buonomo, Ole B. Christensen, James M. Ciarlo ̀, Rowan Fealy, Grigory Nikulin, Daniele Peano, Dian Putrasahan, Christopher D. Roberts, Retish Senan, Christian Steger, Claas Teichmann, and Robert Vautard
Geosci. Model Dev., 13, 5485–5506, https://doi.org/10.5194/gmd-13-5485-2020, https://doi.org/10.5194/gmd-13-5485-2020, 2020
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Now that global climate models (GCMs) can run at similar resolutions to regional climate models (RCMs), one may wonder whether GCMs and RCMs provide similar regional climate information. We perform an evaluation for daily precipitation distribution in PRIMAVERA GCMs (25–50 km resolution) and CORDEX RCMs (12–50 km resolution) over Europe. We show that PRIMAVERA and CORDEX simulate similar distributions. Considering both datasets at such a resolution results in large benefits for impact studies.
Paul-Arthur Monerie, Amulya Chevuturi, Peter Cook, Nicholas P. Klingaman, and Christopher E. Holloway
Geosci. Model Dev., 13, 4749–4771, https://doi.org/10.5194/gmd-13-4749-2020, https://doi.org/10.5194/gmd-13-4749-2020, 2020
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In this study, we assess how increasing the horizontal resolution of HadGEM3-GC31 can allow simulating better tropical and subtropical South American precipitation. We compare simulations of HadGEM3-GC3.1, performed at three different horizontal resolutions. We show that increasing resolution allows decreasing precipitation biases over the Andes and northeast Brazil and improves the simulation of daily precipitation distribution.
Cited articles
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamic
proceses of the UCLA general circulation model, Methods Comput. Phys., 17,
173–265, 1977.
Ardhuin, F., Rogers, E., Babanin, A. V., Filipot, J. F., Magne, R., Roland,
A., van der Westhuysen, A., Queffeulou, P., Lefevre, J. M., Aouf, L., and
Collard, F.: Semiempirical dissipation source functions for ocean waves.
Part I: definition, calibration, and validation, J. Phys. Oceanogr., 40,
1917–1941, https://doi.org/10.1175/2010JPO4324.1, 2010.
Arnold, A. K., Lewis, H. W., Hyder, P., Siddorn, J., and O'Dea, E.: The
Sensitivity of British Weather to Ocean Tides, Geophys. Res. Lett.,
48, e2020GL090732, https://doi.org/10.1029/2020GL090732, 2021.
Baki, H., Chinta, S., C Balaji, and Srinivasan, B.: Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning, Geosci. Model Dev., 15, 2133–2155, https://doi.org/10.5194/gmd-15-2133-2022, 2022.
Battjes, J. A. and Janssen, J. P. F. M.: Energy loss and set-up due to
breaking of random waves, Proc. 16th Int. Conf. Coastal Eng., 569–587,
https://doi.org/10.1061/9780872621909.034, 1978.
Best, M. J., Beljaars, A., 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.
Biswas, M. K., Bernardet, L., Abarca, S., Ginis, I., Grell, E., Kalina, E.,
and Zhang, Z.: Hurricane Weather Research and Forecasting (HWRF)
Model: 2017 Scientific Documentation (No. NCAR/TN-544+STR),
https://doi.org/10.5065/D6MK6BPR, 2018.
Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, 2014.
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.
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., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, 2020.
Castillo, J. and Lewis, H.: The Regional Coupled Suite (RCS): application of a flexible regional coupled modelling framework to the Indian region at km-scale (Version v0), Zenodo [data set], https://doi.org/10.5281/zenodo.5831575, 2022.
Cavaleri, L. and Malanotte-Rizzoli, P.: Wind-wave prediction in shallow
water: Theory and applications, J. Geophys. Res., 86, 10961–10973,
https://doi.org/10.1029/JC086iC11p10961, 1981.
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.
Donelan, M. A.: On the decrease of the oceanic drag coefficient in high
winds, J. Geophys. Res, 123, 1485–1501,
https://doi.org/10.1002/2017JC013394, 2018.
Donlon, C. J., Martin, M., Stark, J. D., Roterts-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.
Edson, J. B., Venkata, J., Robert, A. W., Sebastien, P. B., Albert, J. P.,
Christopher, W., F., Scott, D. M., Larry, M., Dean, V., and Hans, H.: On the
Exchange of Momentum over the Open Ocean, J. Phys. Oceanogr., 43, 1589–1610,
https://doi.org/10.1175/JPO-D-12-0173.1, 2013.
Eilander, D., Couasnon, A., Ikeuchi, H., Muis, S., Yamazaki, D., Winsemius,
H. C., and Ward, P. J.: The effect of surge on riverine flood hazard and
impact in deltas globally, Environ. Res. Lett., 15, 104007,
https://doi.org/10.1088/1748-9326/ab8ca6, 2020.
Feng, X., Klingaman, N. P., and Hodges, K. I.: The effect of
atmosphere–ocean coupling on the prediction of 2016 western North Pacific
tropical cyclones, Q. J. Roy. Meteor. Soc.,
145, 2425–2444, https://doi.org/10.1002/qj.3571, 2019.
Francis, P. A., Jithin, A. K., Effy, J. B., Chatterjee, A., Chakraborty, K.,
Paul, A., and Satyanarayana, B. V.: High-resolution
operational ocean forecast and reanalysis system for the indian ocean,
B. Am. Meteorol. Soc., 101, E1340–E1356,
https://doi.org/10.1175/BAMS-D-19-0083.1, 2021.
Gentile, E. S., Gray, S. L., Barlow, J. F., Lewis, H. W., and Edwards, J. M.: The Impact of Atmosphere–Ocean–Wave Coupling on the Near-Surface Wind Speed in Forecasts of Extratropical Cyclones, Bound.-Lay. Meteorol., 180, 105–129, https://doi.org/10.1007/s10546-021-00614, 2021.
Gentile, E. S., Gray, S. L., and Lewis, H. W.: The sensitivity of
probabilistic convective-scale forecasts of an extratropical cyclone to
atmosphere-ocean-wave coupling, Q. J. Roy.
Meteor. Soc., 148, 685–710, https://doi.org/10.1002/qj.4225, 2022.
Girishkumar, M. S., Thangaprakash, V. P., Udaya Bhaskar, T. V. S., Suprit, K., Sureshkumar, N., Baliarsingh, S. K., Jofia, J., Vimlesh Pant, Vishnu, S., George, G., Abhilash, K. R., and Shivaprasad, S.: Quantifying tropical
cyclone's effect on the biogeochemical processes using profiling float
observations in the Bay of Bengal, J. Geophys. Res.-Oceans,
124, 1945–1963, https://doi.org/10.1029/2017JC013629, 2019.
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.
Greeshma, M., Srinivas, C. V., Hari Prasad, K. B. R. R., Baskaran, R., and Venkatraman,
B.: Sensitivity of tropical cyclone predictions in the coupled
atmosphere–ocean model WRF-3DPWP to surface roughness schemes, Meteorol.
Appl., 26, 324–346, https://doi.org/10.1002/met.1765, 2019.
Gutowski, W. J., Ullrich, P. A., Hall, A., Leung, L. R., O'Brien, T. A.,
Patricola, C. M., Arritt, R. W., Bukovsky, M. S., Calvin, K. V., Feng, Z., Jones, A. D.,
Kooperman, G. J., Monier, E., Pritchard, M. S., Pryor, S. C., Qian, Y..
Rhoades, A. M., Roberts, A. F., Sakaguchi, K., Urban, N., and Zarzycki, C.: The ongoing
need for high-resolution regional climate models: Process understanding and
stakeholder information, B. Am. Meteorol. Soc., 101, E664–E683,
https://doi.org/10.1175/BAMS-D-19-0113.1, 2020.
Hagos, S., Foltz, G. R., Zhang, C., Thompson, E., Seo, H., Chen, S., Capotondi, A.,
Reed, K. A., DeMott, C., and Protat, A.: Atmospheric Convection and Air–Sea
Interactions over the Tropical Oceans: Scientific Progress, Challenges, and
Opportunities, B. Am. Meteorol. Soc., 101, E253–E258,
https://doi.org/10.1175/BAMS-D-19-0261.1, 2020.
Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E.,
Enke, K., Ewing, J. A., Gienapp, H., Hasselmann, D. E., Kruseman, P.,
Meerburg, A., Müller, P., Olbers, D. J., Richter, K., Sell, W., and
Walden, H.: Measurements of wind-wave growth and swell decay during the
Joint North Sea Wave Project (JONSWAP), Ergänzungsheft zur Deutschen
Hydrographischen Zeitschrift, Reihe A8, 95 pp., 1973.
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.
Heming, J. T.: Tropical cyclone tracking and verification techniques
for Met Office numerical weather prediction models, Meteorol.
Appl., 24, 1–8, https://doi.org/10.1002/met.1599, 2017.
Hersbach, H., Bell, W, Berrisford, P., Horányi, A., Muñoz-Sabater
J., Nicolas, J., Radu, R., Schepers, D., Simmons, A., and Soci, C., and Dee,
D.: Global reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF Newsletter,
159, 17–24, https://doi.org/10.21957/vf291hehd7, 2019.
Hill, A. A., Shipway, B. J., and Boutle, I. A.: How sensitive are
aerosol-precipitation interactions to the warm rain representation?, J.
Adv. Model. Earth Sy., 7, 987–1004,
https://doi.org/10.1002/2014MS000422, 2015.
Hirons, L. C., Klingaman, N. P., and Woolnough, S. J.: MetUM-GOML1: a near-globally coupled atmosphere–ocean-mixed-layer model, Geosci. Model Dev., 8, 363–379, https://doi.org/10.5194/gmd-8-363-2015, 2015.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D.,
Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation
Measurement Mission, B. Am. Meteorol. Soc., 95,
701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Jayakumar, A., Sethunadh, J., Rakhi, R., Arulalan, T., Mohandas, S.,
Iyengar, G. R., and Rajagopal, E. N.: Behaviour of predicted convective
clouds and precipitation in the high-resolution Unified Model over the
Indian summer monsoon region, Earth Space Sci. 4, 303–313,
https://doi.org/10.1002/2016EA000242, 2017.
Jayakumar, A., Abel, S. J., Turner, A. G., Mohandas, S., Sethunadh, J., O'Sullivan, D., Mitra, A. K., and Rajagopal, E. N.: Performance of the NCMRWF
convection-permitting model during contrasting monsoon phases of the 2016
INCOMPASS field campaign, Q. J. Roy. Meteor. Soc., 146, 2928–2948,
https://doi.org/10.1002/qj.3689, 2019.
Jayakumar, A., Mohandas, A., George, J. P., Duttta, D., Routray, A., Prasad,
S. K., Sarkar, A., and Mitra, A. K.: NCUM Regional Model Version 4 (NCUM-R:
V4). National Centre for Medium Range Weather Forecasting Technical Report
NMRF/TR/03/2021, https://www.ncmrwf.gov.in/NCUM-R_Tech_Report_12Mar21.pdf (last access: 30 May 2022), 2021.
Jithin, A. K., Francis, P. A., Unnikrishnan, A. S., and Ramakrishna, S. S.
V. S.: Modelling of internal tides in the western Bay of Bengal:
Characteristics and Energetics, J. Geophys. Res.-Oceans,
124, 8720–8746, https://doi.org/10.1029/2019JC015319, 2019.
JULES development team: JULES land surface model, Met Office [code], https://code.metoffice.gov.uk/trac/utils/browser/ukeputils/trunk/gmd-2021/ind1/jules (last access: 5 January 2022), 2022.
Jyothi, L. and Joseph, S. P.: Surface and sub-surface ocean response to
Tropical Cyclone Phailin: Role of pre-existing oceanic features, J.
Geophys. Res.-Oceans, 124, 6515–6530,
https://doi.org/10.1029/2019JC015211, 2019.
Karmakar, N. and Misra, V.: Differences in northward propagation of
convection over the Arabian Sea and Bay of Bengal during boreal summer,
J. Geophys. Res.-Atmos., 125, e2019JD031648,
https://doi.org/10.1029/2019JD031648, 2020.
Katsube, K. and Inatsu, M.: Response of Tropical Cyclone Tracks to Sea
Surface Temperature in the Western North Pacific, J. Climate, 29,
1955–1975, https://doi.org/10.1175/JCLI-D-15-0198.1, 2016.
Kilroy, G., Montgomery, M. T., and Smith, R. K.: The role of boundary-layer
friction on tropical cyclogenesis and subsequent intensification, Q.
J. Roy. Meteor. Soc., 143, 2524–2536,
https://doi.org/10.1002/qj.3104, 2017.
Klingaman, N. and KPP development team: K-Profile Parameterization, Met Office [code], https://code.metoffice.gov.uk/trac/utils/browser/ukeputils/trunk/gmd-2021/ind1/kpp, last access: 5 January 2022.
Klingaman, N. P. and Woolnough, S. J.: The role of air–sea coupling
in the simulation of the Madden–Julian oscillation in the
Hadley Centre model, Q. J. Roy. Meteor. Soc., 140, 2272–2286, https://doi.org/10.1002/qj.2295, 2017.
Krishnamohan, K. S., Vialard, J., Lengaigne, M., Masson, S., Samson G., Pous,
S., Neetu, S., Durand, F., Shenoi, S. S. C., and Madec, G.: Is there an effect
of Bay of Bengal salinity on the northern Indian Ocean climatological
rainfall?, Deep-Sea Res. Pt. II, 166,
19–33, https://doi.org/10.1016/j.dsr2.2019.04.003, 2019.
Large, W. G. and Yeager, S. G.: The global climatology of an inter-annually
varying air–sea flux data set, Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009.
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing:
A review and a model with a nonlocal boundary layer parameterization, Rev.
Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994.
Lewis, H. W. and Dadson, S. J.: A regional coupled approach to water cycle
prediction during winter 2013/14 in the United Kingdom, Hydrol.
Process., 35, e14438, https://doi.org/10.1002/hyp.14438, 2021.
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.
Lewis, H. W., Castillo Sanchez, J. M., Arnold, A., Fallmann, J., Saulter, A., Graham, J., Bush, M., Siddorn, J., Palmer, T., Lock, A., Edwards, J., Bricheno, L., Martínez-de la Torre, A., and Clark, J.: The UKC3 regional coupled environmental prediction system, Geosci. Model Dev., 12, 2357–2400, https://doi.org/10.5194/gmd-12-2357-2019, 2019.
Li, J.-G.: Global Transport on a Spherical Multiple-Cell Grid, Mon. Weather
Rev., 139, 1536–1555, https://doi.org/10.1175/2010MWR3196.1, 2011.
Liu, Q., Zhang, X., Tong, M., Zhang, Z., Liu, B., Wang, W., and
Tallapragada, V.: Vortex Initialization in the NCEP Operational Hurricane
Models, Atmosphere, 11, 968–994, https://doi.org/10.3390/atmos11090968, 2020.
Mahala, B. K., Mohanty, P. K., Das, M., and Routray, A.: Performance
assessment of WRF model in simulating the very severe cyclonic storm
“TITLI” in the Bay of Bengal: A case study, Dynam. Atmos.
Oceans, 88, 101106,
https://doi.org/10.1016/j.dynatmoce.2019.101106, 2019.
Mahmood, S., Lewis, H., Arnold, A., Castillo, J., Sanchez, C., and Harris,
C.: The impact of time-varying sea surface temperature on UK regional
atmosphere forecasts, Meteorol. Appl., 28, e1983,
https://doi.org/10.1002/met.1983, 2021.
Mamgain, A., Rajagopal, E. N., Mitra, A. K., and Webster, S.: Short-Range
Prediction of Monsoon Precipitation by NCMRWF Regional Unified Model with
Explicit Convection, Pure Appl. Geophys., 175, 1197–1218,
https://doi.org/10.1007/s00024-017-1754-0, 2018.
Maneesha, K., Hari Prasad D., and Patnaik, K. V. K. R. K.: Biophysical
responses to tropical cyclone Hudhud over the Bay of Bengal, J.
Oper. Oceanogr.,, 14, 87–97, https://doi.org/10.1080/1755876X.2019.1684135,
2019.
Maneesha, K., Prasad, V. S., and Venkateswararao, K.: Ocean impact on the
intensification of cyclone Titli, J. Earth Syst. Sci., 130,
164, https://doi.org/10.1007/s12040-021-01660-9, 2021.
Met Office: ukep_plot, trac, Met Office [code], https://code.metoffice.gov.uk/trac/utils/browser/ukeputils/trunk/ukep_plot (last access: 27 October 2021), 2022.
Mohanty, S., Nadimpalli, R., Osuri, K. K., Pattanayak, S., Mohanty, U. C., and
Sil, S.: Role of Sea Surface Temperature in Modulating Life Cycle of
Tropical Cyclones over Bay of Bengal, Tropical Cyclone Research and Review,
8, 68–83, https://doi.org/10.1016/j.tcrr.2019.07.007, 2019.
NEMO development team: NEMO ocean model, Met Office [code], https://code.metoffice.gov.uk/trac/utils/browser/ukeputils/trunk/gmd-2021/ind1/nemo, last access: 5 January 2022.
NEMO team: NEMO ocean engine, Scientific notes of climate modelling center,
27, ISSN 1288-1619, Institut Pierre-Simon Laplace (IPSL), Zenodo,
https://doi.org/10.5281/zenodo.1464816, 2019.
OASIS3-MCT development team: OASIS3-MCT coupling libraries, OASIS [code], https://oasis.cerfacs.fr/en/ (last access: 27 October 2021), 2022.
Pandey, S., Rao, A. D., and Haldar, R.: Modeling of Coastal Inundation in
Response to a Tropical Cyclone Using a Coupled Hydraulic HEC-RAS and ADCIRC
Model, J. Geophys. Res.-Oceans, 126, e2020JC016810,
https://doi.org/10.1029/2020JC016810, 2021.
Polton, J. A., Brereton, A., and Holgate, S.: A NEMO regional model of the
Bay of Bengal and East Arabian Sea (BoBEAS) (v1.3), Zenodo [code],
https://doi.org/10.5281/zenodo.6103525, 2020.
Prakash, K. R. and Pant, V.: Upper oceanic response to tropical cyclone
Phailin in the Bay of Bengal using a coupled atmosphere-ocean model, Ocean
Dynam., 67, 51–64, https://doi.org/10.1007/s10236-016-1020-5, 2017.
Prakash, K. R., Pant, V., and Nigam, T.: Effects of the sea surface
roughness and sea spray-induced flux parameterization on the simulations of
a tropical cyclone, J. Geophys. Res.-Atmos., 124,
14037–14058, https://doi.org/10.1029/2018JD029760, 2019.
Qiu, Y., Han, W., Lin, X., West, B. J., Li, Y., Xing, W., Zhang, X.,
Arulananthan, K., and Guo, X.: Upper-Ocean Response to the Super Tropical Cyclone
Phailin (2013) over the Freshwater Region of the Bay of Bengal, J. Phys.
Oceanogr., 49, 1201–1228, https://doi.org/10.1175/JPO-D-18-0228.1, 2019.
Rai, D., Pattnaik, S., Rajesh, P. V., and Hazra, V.: Impact of high resolution
sea surface temperature on tropical cyclone characteristics over the Bay of
Bengal using model simulations, Meteorol Appl., 26, 130–139,
https://doi.org/10.1002/met.1747, 2019.
Remya, P. G., Ranjan, T. R., Sirisha, P., Harikumar, R., and Nair, T. M. B.: Indian Ocean wave forecasting system for wind waves: development and
its validation, J. Oper. Oceanogr., 15, 1–16,
https://doi.org/10.1080/1755876X.2020.1771811, 2022.
Roman-Stork, H. L., Subrahmanyam, B., and Murty, V. S. N.: The role of
salinity in the southeastern Arabian Sea in determining monsoon onset and
strength, J. Geophys. Res.-Oceans, 125, e2019JC015592,
https://doi.org/10.1029/2019JC015592, 2020.
Rose development team: Rose suite control utilities, GitHub [code], http://metomi.github.io/rose/doc/html/index.html (last access: 27 October 2021), 2022.
Routray, A., Singh, V., George, J. P., Mohandas S., and Rajagopal, E. N.: Simulation of Tropical Cyclones
over Bay of Bengal with NCMRWF Regional Unified Model, Pure Appl. Geophys.,
174, 1101–1119, https://doi.org/10.1007/s00024-016-1447-0, 2017.
Routray, A., Lodh, A., Dutta, D., and George, J. P.: Study of an Extremely
Severe Cyclonic Storm “Fani” over Bay of Bengal using regional NCUM
modeling system: A case study, J. Hydrol., 590, 125357,
https://doi.org/10.1016/j.jhydrol.2020.125357, 2020.
Ruti, P. M., Tarasova, O., Keller, J. H., Carmichael, G., Hov, Ø., Jones, S. C.,
Terblanche, D., Anderson-Lefale, C., Barros, A. P., Bauer, P., Bouchet, V.,
Brasseur, G., Brunet, G., DeCola, P., Dike, V., Kane, M. D., Gan, C., Gurney, K. R.,
Hamburg, S., Hazeleger, W., Jean, M., Johnston, D., Lewis, A., Li, P., Liang, X.
Lucarini, V., Lynch, A., Manaenkova, E., Jae-Cheol, N., Ohtake, S., Pinardi, N.,
Polcher, J., Ritchie, E., Sakya, A. E., Saulo, C., Singhee, A., Sopaheluwakan, A.,
Steiner, A., Thorpe, A., and Yamaji, M.: Advancing Research for Seamless Earth
System Prediction, B. Am. Meteorol. Soc., 101, E23–E35,
https://doi.org/10.1175/BAMS-D-17-0302.1, 2020.
Sanchez-Franks, A., Webber, B. G. M., King, B. A., Vinayachandran, P. N.,
Matthews, A. J., Sheehan, P. M. F., Behara, A., and Neema, C.P.: The railroad
switch effect of seasonally reversing currents on the Bay of Bengal
high-salinity core, Geophys. Res. Lett., 46, 6005–6014, https://doi.org/10.1029/2019GL082208, 2019.
Saxby, J., Crook, J., Peatman, S., Birch, C., Schwendike, J., Valdivieso da Costa, M., Castillo Sanchez, J. M., Holloway, C., Klingaman, N. P., Mitra, A., and Lewis, H.: Simulations of Bay of Bengal tropical cyclones in a regional convection-permitting atmosphere–ocean coupled model, Weather Clim. Dynam. Discuss. [preprint], https://doi.org/10.5194/wcd-2021-46, in review, 2021.
Schott, F. A. and McCreary, J. P.: The monsoon circulation of the Indian
Ocean, Prog. Oceanogr., 51, 1–123, https://doi.org/10.1016/S0079-6611(01)00083-0, 2001.
Sharma, K., Ashrit, R., Kumar, S., Milton, S., Rajagopal, E. N., and Mitra,
A. K.: Unified model rainfall forecasts over India during 2007–2018:
Evaluating extreme rains over hilly regions, J. Earth Syst.
Sci., 130, 82, https://doi.org/10.1007/s12040-021-01595-1, 2021.
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.
Sindhu, B., Iyyappan, S., Alakkat, U., Bhatkar, N., Neetu, S., and Selvan,
M. G.: Improved Bathymetric Data Set for the Shallow Water Regions in the
Indian Ocean, J. Earth Syst. Sci., 116, 261–274,
https://doi.org/10.1007/s12040-007-0025-3, 2007.
Singh, V. K., Roxy, M. K., and Deshpande, M.: Role of warm ocean conditions
and the MJO in the genesis and intensification of extremely severe cyclone
Fani, Sci. Rep., 11, 3607,
https://doi.org/10.1038/s41598-021-82680-9, 2021.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M.,
Wang, W., and Powers, J. G.: A description of the Advanced Research WRF
version 3, NCAR Technical note-475+ STR, 2008.
Tonani, M., Sykes, P., King, R. R., McConnell, N., Péquignet, A.-C., O'Dea, E., Graham, J. A., Polton, J., and Siddorn, J.: The impact of a new high-resolution ocean model on the Met Office North-West European Shelf forecasting system, Ocean Sci., 15, 1133–1158, https://doi.org/10.5194/os-15-1133-2019, 2019.
Turner, A. G., Bhat, G. S., Martin, G. M., Parker, D. J., Taylor, C. M., Mitra, A. K., Tripathi, S. N., Milton, S., Rajagopal, E. N., Evans, J. G., Morrison, R., Pattnaik, S., Sekhar, M., Bhattacharya, B. K., Madan,Mrudula Govindankutty, R., Fletcher, J. K., Willetts, P. D., Menon, A., Marsham, J. H., and the INCOMPASS team, Hunt, K. M. R., Chakraborty, T., George, G., Krishnan, M., Sarangi, C., Belušić, D., Garcia-Carreras, L., Brooks, M., Webster, S., Brooke, J. K., Fox, C., Harlow, R. C., Langridge, J. M., Jayakumar, A., Böing, S. J., Halliday, O., Bowles, J., Kent, J., O'Sullivan, D., Wilson, A., Woods, C., Rogers, S., Smout-Day, R., Tiddeman, D., Desai, D., Nigam, R., Paleri, S., Sattar, A., Smith, M., Anderson, D., Bauguitte, S., Carling, R., Chan, C., Devereau, S., Gratton, G., MacLeod, D., Nott, G., Pickering, M., Price, H., Rastall, S., Reed, C., Trembath, J., Woolley, A., and Volonté, A. B.: Interaction of convective
organization with monsoon precipitation, atmosphere, surface and sea: The
2016 INCOMPASS field campaign in India, Q. J. Roy. Meteor. Soc., 146,
2828–2852,
https://doi.org/10.1002/qj.3633, 2019.
UM development team: Unified Model, Met Office [code], https://code.metoffice.gov.uk/trac/utils/browser/ukeputils/trunk/gmd-2021/ind1/um (last access: 5 January 2022), 2022.
Valcke, S., Craig, T., and Coquart, L.: OASIS-MCT User Guide, CERFACS,
Technical Report TR/CMGC/15/38, 2015.
Van Weverberg, K., Morcrette, C. J., Boutle, I., Furtado, K., and Field, P.
R.: A Bimodal Diagnostic Cloud Fraction Parameterization. Part I: Motivating
Analysis and Scheme Description, Mon. Weather Rev., 149, 841–857,
https://doi.org/10.1175/MWR-D-20-0224.1, 2021.
Vellinga, M., Copsey, D., Graham, T., Milton, S., and Johns, T.: Evaluating
Benefits of Two-Way Ocean–Atmosphere Coupling for Global NWP Forecasts,
Weather Forecast., 35, 2127–2144,
https://doi.org/10.1175/WAF-D-20-0035.1, 2020.
Vincent, E. M., Lengaigne, M., Madec, G., Vialard, J., Samson, G., Jourdain,
N. C., and Jullien, S.: Processes setting the characteristics of
sea surface cooling induced by tropical cyclones, J. Geophys.
Res.-Oceans, 117, C02020, https://doi.org/10.1029/2011JC007396, 2012.
Volonté, A., Turner, A., and Menon, A.: Airmass analysis of the processes
driving the progression of the Indian summer monsoon, Q. J. Roy. Meteor. Soc., 146, 2949–2980,
https://doi.org/10.1002/qj.3700, 2020.
Walters, D., Baran, A. J., 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., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a coupled
ocean-atmosphere-wave-sediment transport (COAWST) modelling system, Ocean
Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
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.
WAVEWATCH III development team: WAVEWATCH III wave model, Met Office [code], https://code.metoffice.gov.uk/trac/utils/browser/ukeputils/trunk/gmd-2021/ind1/ww3, last access: 5 January 2022.
Yablonsky, R. M. and Ginis, I.: Limitation of One-Dimensional Ocean Models
for Coupled Hurricane–Ocean Model Forecasts, Mon. Weather Rev.,
137, 4410–4419, https://doi.org/10.1175/2009MWR2863.1, 2009.
Yesubabu, V., Kattamanchi, V. K., Vissa, N. K., Dasari, H. P., and Sarangam, V. B. R.: Impact of ocean mixed-layer depth initialization on the simulation of tropical cyclones over the Bay of Bengal using the WRF-ARW model, Meteorol. Appl., 27, e1862, https://doi.org/10.1002/met.1862, 2020.
Zhang, D.-L. and Altshuler, E.: The Effects of Dissipative Heating on
Hurricane Intensity, Mon. Weather Rev., 127, 3032–3038, https://doi.org/10.1175/1520-0493(1999)127<3032:TEODHO>2.0.CO;2, 1999.
Zhao, L., Wang, S.-Y. S., Becker, E., Yoon, J.-H., and Mukherjee, A.:
Cyclone Fani: the tug-of-war between regional warming and anthropogenic
aerosol effects, Environ. Res. Lett., 15, 94020,
https://doi.org/10.1088/1748-9326/ab91e7, 2020.
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.
A new environmental modelling system has been developed to represent the effect of feedbacks...
