Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3815-2024
© Author(s) 2024. 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-17-3815-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
14 May 2024
Model evaluation paper |
| 14 May 2024
| 14 May 2024
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Emma Howard
CORRESPONDING AUTHOR
Bureau of Meteorology, Brisbane, Australia
Steven Woolnough
National Centre for Atmospheric Science, University of Reading, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Nicholas Klingaman
National Centre for Atmospheric Science, University of Reading, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Daniel Shipley
National Centre for Atmospheric Science, University of Reading, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Claudio Sanchez
Met Office, Exeter, UK
Simon C. Peatman
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
Cathryn E. Birch
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
Adrian J. Matthews
Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom
School of Mathematics, University of East Anglia, Norwich, United Kingdom
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Cited articles
Argueso, D., Romero, R., and Homar, V.: Precipitation Features of the Maritime Continent in Parameterized and Explicit Convection Models, J. Climate, 33, 2449–2466, https://doi.org/10.1175/JCLI-D-19-0416.1, 2020. a
Azaneu, M., Matthews, A., Lee, G., and Heywood, K.: Seaglider data from the Indian Ocean as a part of the Equatorial Line Observations (ELO), January–April 2019, Published Data Library (PDL), https://doi.org/10.5285/efcc09dc-ed05-775c-e053-6c86abc0bb49, 2022. a
Baranowski, D. B., Waliser, D. E., Jiang, X., Ridout, J. A., and Flatau, M. K.: Contemporary GCM Fidelity in Representing the Diurnal Cycle of Precipitation Over the Maritime Continent, J. Geophys. Res.-Atmos., 124, 747–769, https://doi.org/10.1029/2018JD029474, 2019. a
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019. a
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. a, b, c
Birch, C. E., Roberts, M. J., Garcia-Carreras, L., Ackerley, D., Reeder, M. J., Lock, A. P., and Schiemann, R.: Sea-breeze dynamics and convection initiation: The influence of convective parameterization in weather and climate model biases, J. Climate, 28, 8093–8108, https://doi.org/10.1175/JCLI-D-14-00850.1, 2015. a, b
Birch, C. E., Webster, S., Peatman, S. C., Parker, D. J., Matthews, A. J., Li, Y., and Hassim, M. E.: Scale interactions between the MJO and the western Maritime Continent, J. Climate, 29, 2471–2492, https://doi.org/10.1175/JCLI-D-15-0557.1, 2016. a
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. a
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. a
Bush, M., Boutle, I., Edwards, J., Finnenkoetter, A., Franklin, C., Hanley, K., Jayakumar, A., Lewis, H., Lock, A., Mittermaier, M., Mohandas, S., North, R., Porson, A., Roux, B., Webster, S., and Weeks, M.: The second Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL2, Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, 2023. a
Da Silva, N. A., Webber, B. G. M., Matthews, A. J., Feist, M. M., Stein, T. H. M., Holloway, C. E., and Abdullah, M. F. A. B.: Validation of GPM IMERG Extreme Precipitation in the Maritime Continent by Station and Radar Data, Earth Space Sci., 8, e2021EA001738, https://doi.org/10.1029/2021EA001738, e2021EA001738 2021EA001738, 2021. a
DeMott, C. A., Klingaman, N. P., and Woolnough, S. J.: Atmosphere-ocean coupled processes in the Madden-Julian oscillation, Rev. Geophys., 53, 1099–1154, https://doi.org/10.1002/2014RG000478, 2015. a
Dong, W., Krasting, J. P., and Guo, H.: Analysis of Precipitation Diurnal Cycle and Variance in Multiple Observations, CMIP6 Models, and a Series of GFDL-AM4.0 Simulations, J. Climate, 36, 8637–8655, https://doi.org/10.1175/JCLI-D-23-0268.1, 2023. a
Feng, X., Klingaman, N. P., Hodges, K. I., and Guo, Y.-P.: Western North Pacific Tropical Cyclones in the Met Office Global Seasonal Forecast System: Performance and ENSO Teleconnections, J. Climate, 33, 10489–10504, https://doi.org/10.1175/JCLI-D-20-0255.1, 2020. a
Good, S., Fiedler, E., Mao, C., Martin, M., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses, Remote Sens., 12, 720, https://doi.org/10.3390/rs12040720, 2020. a, b
Gregory, D. and Rowntree, P. R.: A Mass Flux Convection Scheme with Representation of Cloud Ensemble Characteristics and Stability-Dependent Closure, Mon. Weather Rev., 118, 1483–1506, 1990. a
Hagos, S. M., Zhang, C., Feng, Z., Burleyson, C. D., Mott, C. D., Kerns, B., Benedict, J. J., and Martini, M. N.: The impact of the diurnal cycle on the propagation of Madden – Julian Oscillation convection across the Maritime Continent, J. Adv. Model. Earth Sy., 8, 1552–1564, https://doi.org/10.1002/2016MS000725, 2016. a
Hassim, M. E. E., Lane, T. P., and Grabowski, W. W.: The diurnal cycle of rainfall over New Guinea in convection-permitting WRF simulations, Atmos. Chem. Phys., 16, 161–175, https://doi.org/10.5194/acp-16-161-2016, 2016. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Holloway, C. E., Woolnough, S. J., and Lister, G. M.: The effects of explicit versus parameterized convection on the MJO in a large-domain high-resolution tropical case study. Part I: Characterization of large-scale organization and propagation, J. Atmos. Sci., 70, 1342–1369, https://doi.org/10.1175/JAS-D-12-0227.1, 2013. a
Howard, E., Woolnough, S., Klingaman, N., Shipley, D., and Sanchez, C.: Evaluation of multi-season convection permitting atmosphere – mixed layer ocean simulations of the Maritime Continent, Zenodo [code and data set], https://doi.org/10.5281/zenodo.8344912, 2023. a
Huffman, G., Bolvin, D., Braithwaite, D., Hsu, K., Joyce, R., and Xie, P.: Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) L3 Half Hourly 0.1 degree × 0.1 degree v5., Tech. rep., National Aeronautics and Space Administration, https://catalogue.ceda.ac.uk/uuid/ff725747de574f7dbb8236a9c31984e5 (last access: 2 March 2024), 2018. a
Inness, P. M. and Slingo, J. M.: Simulation of the Madden–Julian Oscillation in a Coupled General Circulation Model. Part I: Comparison with Observations and an Atmosphere-Only GCM, J. Climate, 16, 345–364, https://doi.org/10.1175/1520-0442(2003)016<0345:SOTMJO>2.0.CO;2, 2003. a
Jerlov, N. G.: Marine Optics, Elsevier, ISBN 978-0-444-41490-8, 1976. a
Jones, R. W., Sanchez, C., Lewis, H., Warner, J., Webster, S., and Macholl, J.: Impact of Domain Size on Tropical Precipitation Within Explicit Convection Simulations, Geophys. Res. Lett., 50, e2023GL104672, https://doi.org/10.1029/2023GL104672, 2023. a
Kim, H. M., Webster, P. J., Hoyos, C. D., and Kang, I. S.: Ocean–atmosphere coupling and the boreal winter MJO, Clim. Dynam., 35, 771–784, https://doi.org/10.1007/s00382-009-0612-x, 2010. a
King, A. D. and Vincent, C. L.: Using Global and Regional Model Simulations to Understand Maritime Continent Wet-Season Rainfall Variability, Geophys. Res. Lett., 45, 12534–12543, https://doi.org/10.1029/2018GL080201, 2018. a
Kumar, A., Zhang, L., and Wang, W.: Sea Surface Temperature–Precipitation Relationship in Different Reanalyses, Mon. Weather Rev., 141, 1118–1123, https://doi.org/10.1175/MWR-D-12-00214.1, 2013. a, b
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. a, b
Lee, R. W., Woolnough, S. J., Charlton‐Perez, A. J., and Vitart, F.: ENSO Modulation of MJO Teleconnections to the North Atlantic and Europe, Geophys. Res. Lett., 46, 13535–13545, https://doi.org/10.1029/2019GL084683, 2019. a
Lellouche, J.-M., Greiner, E., Bourdallé-Badie, R., Garric, G., Melet, A., Drévillon, M., Bricaud, C., Hamon, M., Le Galloudec, O., Regnier, C., Candela, T., Testut, C.-E., Gasparin, F., Ruggiero, G., Benkiran, M., Drillet, Y., and Le Traon, P.-Y.: The Copernicus Global
Lewis, E., Fowler, H., Alexander, L., Dunn, R., McClean, F., Barbero, R., Guerreiro, S., Li, X.-F., and Blenkinsop, S.: GSDR: A Global Sub-Daily Rainfall Dataset, J. Climate, 32, 4715–4729, https://doi.org/10.1175/JCLI-D-18-0143.1, 2019. a
Ling, J., Zhang, C., Joyce, R., Xie, P.-P., and Chen, G.: Possible Role of the Diurnal Cycle in Land Convection in the Barrier Effect on the MJO by the Maritime Continent, Geophys. Res. Lett., 46, 3001–3011, https://doi.org/10.1029/2019GL081962, 2019. a
Liu, S., Raghavan, S. V., Ona, B. J., and Nguyen, N. S.: Bias evaluation in rainfall over Southeast Asia in CMIP6 models, J. Hydrol., 621, 129593, https://doi.org/10.1016/j.jhydrol.2023.129593, 2023. a, b
Love, B. S., Matthews, A. J., and Lister, G. M.: The diurnal cycle of precipitation over the Maritime Continent in a high-resolution atmospheric model, Q. J. Roy. Meteor. Soc., 137, 934–947, https://doi.org/10.1002/qj.809, 2011. a
MacLachlan, C., Arribas, A., Peterson, K. A., 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. a
Muetzelfeldt, M. R., Schiemann, R., Turner, A. G., Klingaman, N. P., Vidale, P. L., and Roberts, M. J.: Evaluation of Asian summer precipitation in different configurations of a high-resolution general circulation model in a range of decision-relevant spatial scales, Hydrol. Earth Syst. Sci., 25, 6381–6405, https://doi.org/10.5194/hess-25-6381-2021, 2021. a
Neale, R. and Slingo, J.: The Maritime Continent and its role in the global climate: A GCM study, J. Climate, 16, 834–848, https://doi.org/10.1175/1520-0442(2003)016<0834:TMCAIR>2.0.CO;2, 2003. a, b
Pearson, K. J., Lister, G. M., Birch, C. E., Allan, R. P., Hogan, R. J., and Woolnough, S. J.: Modelling the diurnal cycle of tropical convection across the “grey zone”, Q. J. Roy. Meteor. Soc., 140, 491–499, https://doi.org/10.1002/qj.2145, 2014. a
Peatman, S. C., Matthews, A. J., and Stevens, D. P.: Propagation of the Madden-Julian Oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation, Q. J. Roy. Meteor. Soc., 140, 814–825, https://doi.org/10.1002/qj.2161, 2014. a, b
Potvin, C. K. and Flora, M. L.: Sensitivity of Idealized Supercell Simulations to Horizontal Grid Spacing: Implications for Warn-on-Forecast, Mon. Weather Rev., 143, 2998–3024, https://doi.org/10.1175/MWR-D-14-00416.1, 2015. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
Roberts, M. J., Baker, A., Blockley, E. W., Calvert, D., Coward, A., Hewitt, H. T., Jackson, L. C., Kuhlbrodt, T., Mathiot, P., Roberts, C. D., Schiemann, R., Seddon, J., Vannière, B., and Vidale, P. L.: Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments, Geosci. Model Dev., 12, 4999–5028, https://doi.org/10.5194/gmd-12-4999-2019, 2019. a
Shelly, A., Xavier, P., Copsey, D., Johns, T., Rodríguez, J. M., Milton, S., and Klingaman, N.: Coupled versus uncoupled hindcast simulations of the Madden-Julian Oscillation in the Year of Tropical Convection, Geophys. Res. Lett., 41, 5670–5677, https://doi.org/10.1002/2013GL059062, 2014. a
Slingo, J., Inness, P., Neale, R., Woolnough, S., and Yang, G.: Scale interactions on diurnal to seasonal timescales and their relevance to model systematic errors, Ann. Geophys., 46, 139–155, https://doi.org/10.4401/ag-3383, 2003. a, b
Stein, T. H. M., Hogan, R. J., Clark, P. A., Halliwell, C. E., Hanley, K. E., Lean, H. W., Nicol, J. C., and Plant, R. S.: The DYMECS Project: A Statistical Approach for the Evaluation of Convective Storms in High-Resolution NWP Models, B. Am. Meteorol. Soc., 96, 939–951, https://doi.org/10.1175/BAMS-D-13-00279.1, 2015. a
Stratton, R. A., Senior, C. A., Vosper, S. B., Folwell, S. S., Boutle, I. A., Earnshaw, P. D., Kendon, E., Lock, A. P., Malcolm, A., Manners, J., Morcrette, C. J., Short, C., Stirling, A. J., Taylor, C. M., Tucker, S., Webster, S., and Wilkinson, J. M.: A Pan-African Convection-Permitting Regional Climate Simulation with the Met Office Unified Model: CP4-Africa, J. Climate, 31, 3485–3508, https://doi.org/10.1175/JCLI-D-17-0503.1, 2018. a, b, c, d
Thompson, B., Sanchez, C., Sun, X., Song, G., Liu, J., Huang, X.-Y., and Tkalich, P.: A high-resolution atmosphere–ocean coupled model for the western Maritime Continent: development and preliminary assessment, Clim. Dynam., 52, 3951–3981, https://doi.org/10.1007/s00382-018-4367-0, 2019. a
Thompson, B., Sanchez, C., Heng, B. C. P., Kumar, R., Liu, J., Huang, X.-Y., and Tkalich, P.: Development of a MetUM (v 11.1) and NEMO (v 3.6) coupled operational forecast model for the Maritime Continent – Part 1: Evaluation of ocean forecasts, Geosci. Model Dev., 14, 1081–1100, https://doi.org/10.5194/gmd-14-1081-2021, 2021. a
Toh, Y. Y., Turner, A. G., Johnson, S. J., and Holloway, C. E.: Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble, Clim. Dynam., 50, 777–800, https://doi.org/10.1007/s00382-017-3641-x, 2018. a
Townshend, J. R. G.: Improved Global Data for Land Applications, Tech. rep., International Geosphere–Biosphere Programme, vol. 20, ISSN 0284-8015, Bib ID 366312, 1992. a
Valcke, S.: The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373–388, https://doi.org/10.5194/gmd-6-373-2013, 2013. a
Vincent, C. L. and Lane, T. P.: A 10-Year Austral Summer Climatology of Observed and Modeled Intraseasonal, Mesoscale, and Diurnal Variations over the Maritime Continent, J. Climate, 30, 3807–3828, https://doi.org/10.1175/JCLI-D-16-0688.1, 2017. a, b
Vincent, C. L. and Lane, T. P.: Mesoscale variation in diabatic heating around Sumatra, and its modulation with the Madden-Julian oscillation, Mon. Weather Rev., 146, 2599–2614, https://doi.org/10.1175/MWR-D-17-0392.1, 2018. a, b, c
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. a, b
Wang, Y., Heywood, K. J., Stevens, D. P., and Damerell, G. M.: Seasonal extrema of sea surface temperature in CMIP6 models, Ocean Sci., 18, 839–855, https://doi.org/10.5194/os-18-839-2022, 2022. a, b
Wei, Y., Pu, Z., and Zhang, C.: Diurnal Cycle of Precipitation Over the Maritime Continent Under Modulation of MJO: Perspectives From Cloud-Permitting Scale Simulations, J. Geophys. Res.-Atmos., 125, e2020JD032529, https://doi.org/10.1029/2020JD032529, 2020. a
Wheeler, M. and Hendon, H.: An All-Season Realtime Multi-Variate MJO Index: Development of an Index for Monitoring and Prediction., Mon. Weather Rev., 132, 1917–1932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2, 2004. a
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., Morcrette, C. J., and Bodas-Salcedo, A.: PC2: A prognostic cloud fraction and condensation scheme. II: Climate model simulations, Q. J. Roy. Meteor. Soc., 134, 2109–2125, https://doi.org/10.1002/qj.332, 2008. a, b
Xue, P., Malanotte-Rizzoli, P., Wei, J., and Eltahir, E. A. B.: Coupled Ocean-Atmosphere Modeling Over the Maritime Continent: A Review, J. Geophys. Res.-Oceans, 125, e2019JC014978, https://doi.org/10.1029/2019JC014978, 2020. a, b, c, d
Yokoi, S., Mori, S., Katsumata, M., Geng, B., Yasunaga, K., Syamsudin, F., Nurhayati, and Yoneyama, K.: Diurnal Cycle of Precipitation Observed in the Western Coastal Area of Sumatra Island: Offshore Preconditioning by Gravity Waves, Mon. Weather Rev., 145, 3745–3761, https://doi.org/10.1175/MWR-D-16-0468.1, 2017. a
Yoneyama, K. and Zhang, C.: Years of the Maritime Continent, Geophys. Res. Lett., 47, e2020GL087182, https://doi.org/10.1029/2020GL087182, 2020. a
Short summary
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used...
