Book contents
- Meteorology and Climate of the Southern Hemisphere
- Meteorology and Climate of the Southern Hemisphere
- Copyright page
- Contents
- Contributors
- Foreword
- Preface
- Acknowledgements
- Dedication to Harry van Loon
- 1 General Circulation and Climate Dynamics of the Southern Hemisphere Troposphere
- 2 The Stratosphere in the Southern Hemisphere
- 3 Stratosphere–Troposphere Coupling in the Southern Hemisphere
- 4 Role of the Oceans in Southern Hemisphere Climate
- 5 Meteorology and Climate of the Tropics in the Southern Hemisphere
- 6 Meteorology of the Southern Hemisphere Extratropics
- 7 Meteorology and Climate of Antarctica
- 8 Meteorology and Climate of South America and Surrounding Oceans
- 9 Meteorology and Climate of the African Region South of the Equator
- 10 Meteorology and Climate of Australasia and the Pacific Islands
- 11 Interannual and Intraseasonal Variability in the Southern Hemisphere
- 12 Climate Change and Low-Frequency Climate Variability
- 13 Climate Modelling Challenges and Prospects in the Southern Hemisphere
- Glossary
- Acronyms
- Index
- References
13 - Climate Modelling Challenges and Prospects in the Southern Hemisphere
Published online by Cambridge University Press: 10 December 2025
Book contents
- Meteorology and Climate of the Southern Hemisphere
- Meteorology and Climate of the Southern Hemisphere
- Copyright page
- Contents
- Contributors
- Foreword
- Preface
- Acknowledgements
- Dedication to Harry van Loon
- 1 General Circulation and Climate Dynamics of the Southern Hemisphere Troposphere
- 2 The Stratosphere in the Southern Hemisphere
- 3 Stratosphere–Troposphere Coupling in the Southern Hemisphere
- 4 Role of the Oceans in Southern Hemisphere Climate
- 5 Meteorology and Climate of the Tropics in the Southern Hemisphere
- 6 Meteorology of the Southern Hemisphere Extratropics
- 7 Meteorology and Climate of Antarctica
- 8 Meteorology and Climate of South America and Surrounding Oceans
- 9 Meteorology and Climate of the African Region South of the Equator
- 10 Meteorology and Climate of Australasia and the Pacific Islands
- 11 Interannual and Intraseasonal Variability in the Southern Hemisphere
- 12 Climate Change and Low-Frequency Climate Variability
- 13 Climate Modelling Challenges and Prospects in the Southern Hemisphere
- Glossary
- Acronyms
- Index
- References
Summary
Climate models remain an essential tool for studying the complexity of the climate system and for predicting the future evolution of the system. This chapter provides insight into the science of climate modelling in the Southern Hemisphere. It reviews the recent developments and improvements in climate modelling, including downscaling methods, parameterisation schemes, and model resolutions. It also discusses the usage of climate models, with a focus on their value in climate process studies, climate predictability, weather predictions, and seasonal forecasts, as well as climate change projections, management, detection, and attribution. This chapter reveals the challenges faced by the climate modelling community in the Southern Hemisphere and suggests directions for future research that could further unleash the potential of climate modelling in this hemisphere.
Keywords
Information
- Type
- Chapter
- Information
- Meteorology and Climate of the Southern Hemisphere , pp. 495 - 531Publisher: Cambridge University PressPrint publication year: 2025
References
Abba Omar, S. and Abiodun, B. J. (2021). Simulating the influence of topography on cut‐off lows over Southern Africa. International Journal of Climatology, 41(S1), E2231–E2243.CrossRefGoogle Scholar
Abiodun, B. J., Gutowski, W. J., Abatan, A. A. and Prusa, J. M. (2011). CAM-EULAG: a non-hydrostatic atmospheric climate model with grid stretching. Acta Geophysica, 59(6), 1158–1167.CrossRefGoogle Scholar
Abiodun, B. J., Odoulami, R. C., Sawadogo, W., Oloniyo, O. A., Abatan, A. A., New, M., Lennard, C., Izidine, P., Egbebiyi, . S. and MacMartin, D. G. (2021). Potential impacts of stratospheric aerosol injection on drought risk managements over major river basins in Africa. Climatic Change, 169(3–4).CrossRefGoogle Scholar
Abiodun, B. J., Prusa, J. M. and Gutowski, W. J. Jr. (2008). Implementation of a non-hydrostatic, adaptive-grid dynamics core in CAM3. Part I: comparison of dynamics cores in aqua-planet simulations. Climate Dynamics, 31(7–8), 795–810.Google Scholar
ACCSP. 2016. Australian Climate Change Science Programme: Climate Modelling. 2016. www.cawcr.gov.au/projects/Climatechange/impact/science/climate-modelling/.Google Scholar
Ambrizzi, T., Simões Reboita, M., Porfírio da Rocha, R. and Llopart, M. (2019). The state of the art and fundamental aspects of regional climate modeling in South America. Annals of the New York Academy of Sciences, 1436(1), 98–120.CrossRefGoogle ScholarPubMed
Andrian, L. G., Osman, M. and Vera, C. S. (2022). Climate predictability on seasonal timescales over South America from the NMME models. Climate Dynamics, 60, 3261–3276.Google Scholar
Angélil, O., Stone, D., Wehner, M., Paciorek, C. J., Krishnan, H. and Collins, W. (2017). An independent assessment of anthropogenic attribution statements for recent extreme temperature and rainfall events. Journal of Climate, 30(1), 5–16.CrossRefGoogle Scholar
Ardabili, S., Mosavi, A., Dehghani, M. and Várkonyi-Kóczy, A. R. (2020). Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review. In: Várkonyi-Kóczy, A. R. (ed.) Engineering for Sustainable Future. Cham, Switzerland, Springer International Publishing, pp. 52–62.Google Scholar
Arnhold, T. (2022). Clouds in the Southern Hemisphere More Precisely Understood. Phys.org. 27 January 2022. https://phys.org/news/2022-01-clouds-southern-hemisphere-precisely-understood.html.Google Scholar
Badger, A. M. and Dirmeyer, P. A. (2016). Remote tropical and sub-tropical responses to Amazon deforestation. Climate Dynamics, 46(9), 3057–3066.CrossRefGoogle Scholar
Balaji, V., Couvreux, F., Deshayes, J., Gautrais, J., Hourdin, F. and Rio, C. (2022). Are general circulation models obsolete? Proceedings of the National Academy of Sciences of the United States of America, 119(47), e2202075119.Google ScholarPubMed
Baldassare, D., Reichler, T., Plink-Björklund, P. and Slawson, J. (2023). Large Uncertainty in Observed Meridional Stream Function Tropical Expansion. EGUsphere, January, 1–24.CrossRefGoogle Scholar
Banerjee, A., Fyfe, J. C., Polvani, L. M., Waugh, D. and Chang, K. (2020). A pause in Southern Hemisphere circulation trends due to the Montreal Protocol. Nature, 579(7800), 544–548.CrossRefGoogle Scholar
Barimalala, R., Desbiolles, F., Blamey, R. C. and Reason, C. (2018). Madagascar influence on the South Indian Ocean convergence zone, the Mozambique Channel Trough and southern African Rainfall. Geophysical Research Letters, 45(20), 11,380–11,389.CrossRefGoogle Scholar
Barnett, T. P., Hasselmann, K., Chelliah, M., Delworth, T. L., Hegerl, G. C., Jones, P., Rasmusson, E. M., Roeckner, E., Ropelewski, C., Santer, B. D. and Tett, S. (1999). Detection and attribution of recent climate change: A status report. Bulletin of the American Meteorological Society, 80(12), 2631–2660.2.0.CO;2>CrossRefGoogle Scholar
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C. and Wedi, N. P. (2021). The digital revolution of earth-system science. Nature Computational Science, 1(2), 104–113.CrossRefGoogle ScholarPubMed
Bauer, P., Thorpe, A. and Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55.CrossRefGoogle ScholarPubMed
Beraki, A. F., DeWitt, D. G., Landman, W. A. and Olivier, C. (2014). Dynamical seasonal climate prediction using an ocean–atmosphere coupled climate model developed in partnership between South Africa and the IRI. Journal of Climate, 27(4), 1719–1741.CrossRefGoogle Scholar
Beraki, A. F., Morioka, Y., Engelbrecht, F. A., Nonaka, M., Thatcher, M., Kobo, N. and Behera, S. (2020). Examining the impact of multiple climate forcings on simulated Southern Hemisphere climate variability. Climate Dynamics, 54(11), 4775–4792.CrossRefGoogle Scholar
Bettolli, M. L., Solman, S. A., da Rocha, R. P., Llopart, M., Gutierrez, J. M., Fernández, J., Olmo, M. E., Lavin-Gullon, A., Chou, S. C., Rodrigues, D. C., Coppola, E., Balmaceda, H., Rocio, B., Marcelo, B., Josefina, D., Moira, F., Martín, H., Radan, M., Luiz, C., Santiago, V., (2021). The CORDEX Flagship Pilot Study in southeastern South America: a comparative study of statistical and dynamical downscaling models in simulating daily extreme precipitation events. Climate Dynamics, 56(5–6), 1589–1608.CrossRefGoogle Scholar
Biastoch, A., Rühs, S., Ivanciu, I., Schwarzkopf, F. U., Veitch, J., Reason, C., Zorita, E., Tim, N., Hünicke, B., Vafeidis, A. T., Santamaria-Aguilar, S., Kupfer, S. and Soltau, F. (2024). The Agulhas current system as an important driver for oceanic and terrestrial climate. In: von Maltitz, G. P., Midgley, G. F., Veitch, J., Brümmer, C., Rötter, R. P., Viehberg, F. A. and Veste, M. (eds.) Sustainability of Southern African Ecosystems under Global Change: Science for Management and Policy Interventions. Cham, Springer International Publishing, pp. 191–220.Google Scholar
Blázquez, J. and Solman, S. A. (2019). Relationship between projected changes in precipitation and fronts in the austral winter of the Southern Hemisphere from a suite of CMIP5 models. Climate Dynamics, 52(9), 5849–5860.CrossRefGoogle Scholar
Bonan, G. B., Lombardozzi, D. L., Wieder, W. R., Oleson, K. W., Lawrence, D. M., Hoffman, F. M. and Collier, N. (2019). Model structure and climate data uncertainty in historical simulations of the terrestrial carbon cycle (1850–2014). Global Biogeochemical Cycles, 33(10), 1310–1326.CrossRefGoogle Scholar
Bopape, M. M., Engelbrecht, F. A., Maisha, R., Chikoore, H., Ndarana, T., Lekoloane, L., Thatcher, M., Mulovhedzi, P. T., Rambuwani, G. T., Barnes, M., Mkhwanazi, M., Mphepya, J. (2022). Rainfall simulations of high-impact weather in South Africa with the conformal cubic atmospheric model (CCAM). Atmosphere, 13(12), 1987.CrossRefGoogle Scholar
Bopape, M. M., Marumo Sithole, H., Motshegwa, T., Rakate, E., Engelbrecht, F., Archer, E., Morgan, A., Ndimeni, L. and Botai, J. (2019). A regional project in support of the SADC cyber-infrastructure framework implementation: weather and climate. Data Science Journal, 18(July), 34.CrossRefGoogle Scholar
Bordoni, S., Kang, S. M., Shaw, T. A., Simpson, I. R. and Zanna, L. (2025). The futures of climate modeling. npj Clim Atmos Sci 8, 99.CrossRefGoogle Scholar
Brajard, J., Carrassi, A., Bocquet, M. and Bertino, L. (2019). Combining Data Assimilation and Machine Learning to Emulate a Dynamical Model from Sparse and Noisy Observations: A Case Study with the Lorenz 96 Model. Geoscientific Model Development Discussions, May, 1–21.CrossRefGoogle Scholar
Branstator, G. and Teng, H. (2010). Two limits of initial-value decadal predictability in a CGCM. Journal of Climate, 23(23), 6292–6311.CrossRefGoogle Scholar
Byrne, N. J., Shepherd, T. G. and Polichtchouk, I. (2019). Subseasonal‐to‐seasonal predictability of the Southern Hemisphere eddy‐driven jet during austral spring and early summer. Journal of Geophysical Research, 124(13), 2018JD030173.Google Scholar
Camilloni, I., Montroull, N., Gulizia, C. and Saurral, R. I. (2022). La Plata basin hydroclimate response to solar radiation modification with stratospheric aerosol injection. Frontiers in Climate, 4(763983), 10–3389.CrossRefGoogle Scholar
Camps-Valls, G., Tuia, D., Xiang Zhu, X. and Reichstein, M. (2021). Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. UK: John Wiley & Sons.CrossRefGoogle Scholar
Castellano, G. and Vessio, G. (2021). Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview. Neural Computing & Applications, 33(19), 12263–12282.CrossRefGoogle Scholar
Cavalcanti, I. F. A., Souza, D. C., Kubota, P. Y., Coelho, C. A. S., Figueroa, S. N. and Baker, J. C. A. (2022). The global monsoon system representation in BAM ‐v1.2 and HadGEM3 climate simulations. International Journal of Climatology, 42(15), 8089–8111.CrossRefGoogle Scholar
Chang, C., Deringer, V. L., Katti, K. S., Van Speybroeck, V. and Wolverton, C. M. (2023). Simulations in the Era of Exascale Computing. Nature Reviews Materials, March, 1–5.CrossRefGoogle Scholar
Chelliah, M. and Ropelewski, C. F. (2000). Reanalyses-based tropospheric temperature estimates: uncertainties in the context of global climate change detection. Journal of Climate, 13(17), 3187–3205.2.0.CO;2>CrossRefGoogle Scholar
CLIMACAP. (2012). CLIMACAP: Integrated CLImate Modeling And CAPacity Building in Latin America. https://leap.sei.org/documents/CLIMACAPFlyer.pdf.Google Scholar
Coelho, C. A. S., Baker, J. C. A., Spracklen, D. V., Kubota, P. Y., Souza, D. C., Guimarães, B. S., Figueroa, S. N., Bonatti, J. P., Sampaio, G., Klingaman, N. P., Chevuturi, A., Woolnough, S. J., Hart, N., Zilli, M. and Jones, C. D. (2022). A perspective for advancing climate prediction services in Brazil. Climate Resilience and Sustainability, 1(1), e29.CrossRefGoogle Scholar
Coelho, C. A. S., de Souza, D. C., Kubota, P. Y., Costa, S. M. S., Menezes, L., Guimarães, B. S., Figueroa, S. N., Bonatti, J. P., Cavalcanti, I. F. A., Sampaio, G., Klingaman, N. P. and Baker, J. C. A. (2021). Evaluation of climate simulations produced with the Brazilian global atmospheric model version 1.2. Climate Dynamics, 56(3), 873–898.CrossRefGoogle Scholar
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P. and Brönnimann, S. (2011). The twentieth century reanalysis project. Quarterly Journal of the Royal Meteorological Society, 137(654), 1–28.CrossRefGoogle Scholar
Corney, S., Grose, M., Bennett, J. C., White, C., Katzfey, J., McGregor, J., Holz, G. and Bindoff, N. L. (2013). Performance of downscaled regional climate simulations using a variable‐resolution regional climate model: Tasmania as a test case. Journal of Geophysical Research, 118(21), 11,936–11,950.Google Scholar
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Schmied, H. M., Hersbach, H. and Buontempo, C. (2020). WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth System Science Data, 12(3), 2097–2120.CrossRefGoogle Scholar
Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A., White, A. A. and Wood, N. (2005). A new dynamical core for the Met Office’s global and regional modelling of the atmosphere. Quarterly Journal of the Royal Meteorological Society, 131(608), 1759–1782.CrossRefGoogle Scholar
DelSole, T. and Tippett, M. K. (2018). Predictability in a changing climate. Climate Dynamics, 51(1), 531–545.CrossRefGoogle Scholar
de Abreu, R. C. de, Cunningham, C., Rudorff, C. M., Rudorff, N., Abatan, A. A., Tett, S. F. B., Dong, B., Lott, F. C. and Sparrow, S. N. (2019a). Contribution of anthropogenic climate change to April–May 2017 heavy precipitation over the Uruguay River Basin. Bulletin of the American Meteorological Society, 100(1), S37–S41.CrossRefGoogle Scholar
de Abreu, R. C., Tett, S. F. B., Schurer, A. and Rocha, H. R. (2019b). Attribution of detected temperature trends in Southeast Brazil. Geophysical Research Letters, 46(14), 8407–8414.CrossRefGoogle Scholar
de Souza, D. C. and Ramos da Silva, R. (2021). Ocean-Land Atmosphere Model (OLAM) performance for major extreme meteorological events near the coastal region of southern Brazil. Climimate Research, 84, 1–21.Google Scholar
de Souza, D. C., Ramos da Silva, R., Gomes da Silva, P., Fetter Filho, A. F. H., Mendez, F. J., Werth, D. (2022). A hybrid regional climate downscaling for the southern Brazil coastal region. International Journal of Climatology 42, 6753–6770.CrossRefGoogle Scholar
Deng, K., Azorin-Molina, C., Yang, S., Hu, C., Zhang, G., Minola, L. and Chen, D. (2022). Changes of Southern Hemisphere westerlies in the future warming climate. Atmospheric Research, 270(June), 106040.CrossRefGoogle Scholar
Donahue, A. S., Caldwell, P. M., Bertagna, L., Beydoun, H., Bogenschutz, P. A., Bradley, A. M., Clevenger, T. C., Foucar, J., Golaz, C., Guba, O., Hannah, W., Hillman, B. R., Johnson, J. N., Keen, N., Lin, W., Singh, B., Sreepathi, S., Taylor, M. A., Tian, J., Terai, C. R., Ullrich, P. A., Yuan, X. and Zhang, Y. (2024). To exascale and beyond – the simple cloud-resolving E3SM atmosphere model (SCREAM), a performance portable global atmosphere model for cloud-resolving scales. Journal of Advances in Modeling Earth Systems 16, e2024MS004314.CrossRefGoogle Scholar
Dong, S., Sprintall, J., Gille, S. T. and Talley, L. (2008). Southern Ocean mixed-layer depth from Argo float profiles. Journal of Geophysical Research, 113(C6).CrossRefGoogle Scholar
Dong, W., Krasting, J. P. and Guo, H. (2023). Analysis of precipitation diurnal cycle and variance in multiple observations, CMIP6 models, and a series of GFDL-AM4.0 simulations. Journal of Climate, 36(24), 8637–8655.CrossRefGoogle Scholar
Donkin, P. T. and Abiodun, B. J. (2022). Capability and sensitivity of MPAS-A in simulating tropical cyclones over the South-West Indian Ocean. Modeling Earth Systems and Environment, 9(1), 527–542.Google Scholar
Dubovik, O., Schuster, G. L., Xu, F., Hu, Y., Bösch, H., Landgraf, J. and Li, Z. (2021). Grand challenges in satellite remote sensing. Frontiers in Remote Sensing 2.CrossRefGoogle Scholar
ECA. (n.d). Climate Information Services. Accessed 22 June 2024. www.uneca.org/african-climate-policy-centre/climate-information-services.Google Scholar
Eisen, O. and Barbante, C. (2018). The European Project ‘Beyond EPICA – Oldest Ice’. EGU General Assembly Conference Abstracts, 19366.Google Scholar
Engelbrecht, F. A., Landman, W. A., Engelbrecht, C. J., Landman, S., Bopape, M. M., Roux, B., McGregor, J. L. and Thatcher, M. (2011). Multi-scale climate modelling over Southern Africa using a variable-resolution global model. Water SA, 37(5), 647–658.CrossRefGoogle Scholar
Engelbrecht, F. A., McGregor, J. L. and Engelbrecht, C. J. (2009). Dynamics of the Conformal-Cubic Atmospheric Model projected climate-change signal over southern Africa. International Journal of Climatology, 29(7), 1013–1033.CrossRefGoogle Scholar
Engelbrecht, F. and Ndarana, T. (2022). Predictability of Inter-Annual Variability in the Southern Hemisphere Subtropics. In, EGU22–12010. ui.adsabs.harvard.edu.CrossRefGoogle Scholar
Evans, J. P., Belmadani, A., Menkes, C., Stephenson, T., Thatcher, M., Gibson, P. B. and Peltier, A. (2024). Higher-resolution projections needed for small island climates. Nature Climate Change, 14(7), 668–670.CrossRefGoogle Scholar
Evans, J. P. and Boyer-Souchet, I. (2012). Local sea surface temperatures add to extreme precipitation in northeast Australia during La Niña. Geophysical Research Letters, 39(10).CrossRefGoogle Scholar
Evans, J. P., Virgilio, G. D., Hirsch, A. L., Hoffmann, P., Remedio, A. R., Ji, F., Rockel, B. and Coppola, E. (2021). The CORDEX-Australasia ensemble: evaluation and future projections. Climate Dynamics, 57(5), 1385–1401.CrossRefGoogle Scholar
Evans, J. P. (2011). CORDEX – an international climate downscaling initiative. In: 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16.Google Scholar
Fahad, A. A., Burls, N. J., Swenson, E. T. and Straus, D. M. (2021). The influence of South Pacific convergence zone heating on the South Pacific subtropical anticyclone. Journal of Climate, 34(10), 3787–3798.CrossRefGoogle Scholar
Feijoó, M. and Solman, S. (2022). Convection-permitting modeling strategies for simulating extreme rainfall events over Southeastern South America. Climate Dynamics, 59(9), 2549–2569.CrossRefGoogle Scholar
Figueroa, S. N., Bonatti, J. P., Kubota, P. Y., Grell, G. A., Morrison, H., Barros, S. R. M., Fernandez, J. P. R., Ramirez, E., Siqueira, L., Luzia, G., Silva, J., Silva, J. R., Pendharkar, J., Capistrano, V. B., Alvim, D. S., Enoré, D. P., Diniz, F. L. R., Satyamurti, P., Cavalcanti, I. F. A., Nobre, P., Barbosa, H. M. J., Mendes, C. L. and Panetta, J. (2016). The Brazilian global atmospheric model (BAM): performance for tropical rainfall forecasting and sensitivity to convective scheme and horizontal resolution. Weather and Forecasting, 31(5), 1547–1572.CrossRefGoogle Scholar
Fotso-Nguemo, T. C., Diallo, I., Diakhaté, M., Vondou, D. A., Mbaye, M. L., Haensler, A., Gaye, A. T. and Tchawoua, C. (2019). Projected changes in the seasonal cycle of extreme rainfall events from CORDEX simulations over Central Africa. Climatic Change, 155(3), 339–357.CrossRefGoogle Scholar
Fox-Rabinovitz, M., Cote, J., Dugas, B., Deque, M., McGregor, J. L. and Belochitski, A. (2008). Stretched-grid Model Intercomparison Project: decadal regional climate simulations with enhanced variable and uniform-resolution GCMs. Meteorology and Atmospheric Physics, 100(1–4), 159–178.CrossRefGoogle Scholar
Fox-Rabinovitz, M. S., Takacs, L. L., Govindaraju, R. C. and Suarez, M. J. (2001). A variable-resolution stretched-grid general circulation model: regional climate simulation. Monthly Weather Review, 129(3), 453–469.2.0.CO;2>CrossRefGoogle Scholar
Freitas, S. R., Grell, G. A. and Li, H. (2021). The Grell–Freitas (GF) convection parameterization: recent developments, extensions, and applications. Geoscientific Model Development, 14(9), 5393–5411.CrossRefGoogle Scholar
Fu, G., Charles, S. P., Chiew, F. H. S., Ekström, M. and Potter, N. J. (2018). Uncertainties of statistical downscaling from predictor selection: equifinality and transferability. Atmospheric Research, 203(May), 130–140.CrossRefGoogle Scholar
Fu, G., Charles, S. P. and Kirshner, S. (2013). Daily rainfall projections from general circulation models with a downscaling nonhomogeneous hidden markov model (NHMM) for south-eastern Australia. Hydrological Processes, 27(25), 3663–3673.CrossRefGoogle Scholar
Fuhrer, O., Chadha, T., Hoefler, T., Kwasniewski, G., Lapillonne, X., Leutwyler, D., Lüthi, D., Osuna, C., Schär, C., Schulthess, T. C. and Vogt, H. (2018). Near-global climate simulation at 1 Km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0. Geoscientific Model Development, 11(4), 1665–1681.Google Scholar
Garg, S. K., Toosi, A. N., Gopalaiyengar, S. K. and Buyya, R. (2014). SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. Journal of Network and Computer Applications, 45(October), 108–120.CrossRefGoogle Scholar
Gebrechorkos, S. H., Hülsmann, S. and Bernhofer, C. (2019). Statistically downscaled climate dataset for East Africa. Scientific Data, 6(1), 31.CrossRefGoogle ScholarPubMed
Ge, Y., Zhang, X., Atkinson, P. M., Stein, A. and Li, L. (2022). Geoscience-aware deep learning: a new paradigm for remote sensing. Egyptian Journal of Remote Sensing and Space Sciences, 5(June), 100047.Google Scholar
Gillett, N. P., Stone, D. A., Stott, P. A., Nozawa, T., Karpechko, A. Y., Hegerl, G. C., Wehner, M. F. and Jones, P. D. (2008). Attribution of polar warming to human influence. Nature Geoscience, 1(11), 750–754.CrossRefGoogle Scholar
Giorgi, F. (2019). Thirty years of regional climate modeling: Where are we and where are we going next? Journal of Geophysical Research: Atmospheres, 124, 5696–5723.Google Scholar
Giorgi, F. and Gutowski, W. J. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40(1), 467–490.CrossRefGoogle Scholar
Giorgi, F., Jones, C. and Asrar, G. (2009). Addressing climate information needs at the regional level: The CORDEX framework. WMO Bulletin, 58, 175–183.Google Scholar
Giorgi, F., and Coauthors (2022). The CORDEX-CORE EXP-I initiative: description and highlight results from the initial analysis. Bulletin of the American Meteorological Society, 103, E293–E310.CrossRefGoogle Scholar
Goddard, L., Mason, S. J., Zebiak, S. E., Ropelewski, C. F., Basher, R. and Cane, M. A. (2001). Current approaches to seasonal to interannual climate predictions. International Journal of Climatology, 21(9), 1111–1152.CrossRefGoogle Scholar
Gore, M., Abiodun, B. J. and Kucharski, F. (2020). Understanding the influence of ENSO patterns on drought over southern Africa using SPEEDY. Climate Dynamics, 54(1), 307–327.CrossRefGoogle Scholar
Goyal, R., Jucker, M., Gupta, A. S., Hendon, H. H. and England, M. H. (2021). Zonal wave 3 pattern in the Southern Hemisphere generated by tropical convection. Nature Geoscience, 14(10), 732–738.CrossRefGoogle Scholar
Grabowski, W. W. (2016). Towards global large eddy simulation: super-parameterization revisited. Journal of the Meteorological Society of Japan. Ser. II, 94(4), 327–344.CrossRefGoogle Scholar
Gutowski, W. J., Jr, Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O’Rourke, E., Rixen, M., Solman, S., Stephenson, T. and Tangang, F. (2016). WCRP coordinated regional downscaling experiment (CORDEX): a diagnostic MIP for CMIP6. Geoscientific Model Development, 9(11), 4087–4095.CrossRefGoogle Scholar
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. and Jones, A. D. (2020). The ongoing need for high-resolution regional climate models: process understanding and stakeholder information. Bulletin of the American Meteorological Society, 101(5), E664–E683.CrossRefGoogle Scholar
Han, J. and Bretherton, C. S. (2019). TKE-based moist eddy-diffusivity mass-flux (EDMF) parameterization for vertical turbulent mixing. Weather and Forecasting, 34(4), 869–886.CrossRefGoogle Scholar
Han, J., Witek, M. L., Teixeira, J., Sun, R., Pan, H-L., Fletcher, J. K. and Bretherton, C. S. (2016). Implementation in the NCEP GFS of a hybrid eddy-diffusivity mass-flux (EDMF) boundary layer parameterization with dissipative heating and modified stable boundary layer mixing. Weather and Forecasting, 31(1), 341–352.CrossRefGoogle Scholar
Hannah, W. M., Jones, C. R., Hillman, B. R., Norman, M. R., Bader, D. C., Taylor, M. A., Leung, L. R., Pritchard, M. S., Branson, M. D., Lin, G. and Pressel, K. G. (2020). Initial results from the super‐parameterized E3SM. Journal of Advances in Modeling Earth Systems, 12(1), e2019MS001863.CrossRefGoogle Scholar
Hartigan, J., MacNamara, S. and Leslie, L. M. (2020). Application of machine learning to attribution and prediction of seasonal precipitation and temperature trends in Canberra, Australia. Climate, 8(6), 76.Google Scholar
Hausfather, Z., Drake, H. F., Abbott, T. and Schmidt, G. A. (2020). Evaluating the performance of past climate model projections. Geophysical Research Letters, 47(1), e2019GL085378.CrossRefGoogle Scholar
Hirsch, A. L., Evans, J. P., Thomas, C., Conroy, B., Hart, M. A., Lipson, M. and Ertler, W. (2021). Resolving the influence of local flows on urban heat amplification during heatwaves. Environmental Research Letters, 16(6), 064066.CrossRefGoogle Scholar
Holtslag, A. A. M. and Boville, B. A. (1993). Local versus nonlocal boundary-layer diffusion in a global climate model. Journal of Climate, 6(10), 1825–1842.2.0.CO;2>CrossRefGoogle Scholar
Honnert, R., Couvreux, F., Masson, V. and Lancz, D. (2016). Sampling the structure of convective turbulence and implications for grey-zone parametrizations. Boundary-Layer Meteorology, 160(1), 133–156.CrossRefGoogle Scholar
Huh, O. K. (1991). Limitations and capabilities of the NOAA satellite advanced very high resolution radiometer (AVHRR) for remote sensing of the earth’s surface. Preventive Veterinary Medicine, 11(3), 167–183.CrossRefGoogle Scholar
Hung, M. and Wu, Y. H. (2018). Recent Advances and Applications in Remote Sensing.CrossRefGoogle Scholar
IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge and New York, NY, USA, p. 2391.Google Scholar
Jacobs, G., D’Addezio, J. M., Ngodock, H. and Souopgui, I. (2021). Observation and model resolution implications to ocean prediction. Ocean Modelling, 159, 101760.CrossRefGoogle Scholar
Jayne, S., Woods Hole Oceanographic Institution, Roemmich, D., Zilberman, N., Riser, S., Johnson, K., Johnson, G. and Piotrowicz, S. (2017). The Argo program: present and future. Oceanography, 30(2), 18–28.CrossRefGoogle Scholar
Jebeile, J. and Barberousse, A. (2021). Model spread and progress in climate modelling. European Journal for Philosophy of Science, 11(3), 66.CrossRefGoogle Scholar
Joyce, K. E., Belliss, S. E., Samsonov, S. V., McNeill, S. J. and Glassey, P. J. (2009). A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Progress in Physical Geography: Earth and Environment, 33(2), 183–207.Google Scholar
Judd, K. and Smith, L. (2001). Indistinguishable states. Physica D. Nonlinear Phenomena, 151(2–4), 125–141.CrossRefGoogle Scholar
Kageyama, A. and Sato, T. (2004). ‘Yin-Yang grid’: an overset grid in spherical geometry. Geochemistry, Geophysics, Geosystems, 5(9).CrossRefGoogle Scholar
Kain, J. S. and Michael Fritsch, J. (1998). Multiscale convective overturning in mesoscale convective systems: reconciling observations, simulations, and theory. Monthly Weather Review, 126(8), 2254–2573.2.0.CO;2>CrossRefGoogle Scholar
Kala, J. and Hirsch, A. L. (2020). Could crop albedo modification reduce regional warming over Australia? Weather and Climate Extremes, 30, 100282.CrossRefGoogle Scholar
Kalognomou, E-A., Lennard, C., Shongwe, M., Pinto, I., Favre, A., Kent, M., Hewitson, B., Dosio, A., Nikulin, G., Panitz, H-J. and Büchner, M. (2013). A diagnostic evaluation of precipitation in CORDEX models over Southern Africa. Journal of Climate, 26(23), 9477–9506.CrossRefGoogle Scholar
Karpatne, A. and Kumar, V. (2017). Big Data in Climate: Opportunities and Challenges for Machine Learning. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 21–22. KDD ’17. New York, NY, USA: Association for Computing Machinery.Google Scholar
Keat, W. J., Stein, T. H. M., Phaduli, E., Landman, S., Becker, E., Bopape, M-J. M., Hanley, K. E., Lean, H. W. and Webster, S. (2019). Convective initiation and storm life cycles in convection‐permitting simulations of the Met Office Unified Model over South Africa. Quarterly Journal of the Royal Meteorological Society, 145(721), 1323–1336.CrossRefGoogle Scholar
King, A. D., Hudson, D., Lim, E-P., Marshall, A. G., Hendon, H. H., Lane, T. P. and Alves, O. (2020). Sub‐seasonal to seasonal prediction of rainfall extremes in Australia. Quarterly Journal of the Royal Meteorological Society, 146(730), 2228–2249.CrossRefGoogle Scholar
Kinne, O. (2005). 25 years inter-research 1979–2004. Climate Research, 28(2), 95–107.CrossRefGoogle Scholar
Kuleshov, Y., Wang, Y., Apajee, J., Fawcett, R. and Jones, D. (2012). Prospects for improving the operational seasonal prediction of tropical cyclone activity in the Southern Hemisphere. Atmospheric and Climate Sciences, 2(3), 298–306.CrossRefGoogle Scholar
Landman, W. A. (2014). How the International Research Institute for Climate and Society has contributed towards seasonal climate forecast modelling and operations in South Africa. Earth Perspectives, 1(1), 1–13.CrossRefGoogle Scholar
Landman, W. A. and Beraki, A. (2012). Multi-model forecast skill for mid-summer rainfall over southern Africa. International Journal of Climatology, 32(2), 303–314.CrossRefGoogle Scholar
Larson, V. E. and Schanen, D. P. (2013). The subgrid importance latin hypercube sampler (SILHS): a multivariate subcolumn generator. Geoscientific Model Development, 6(5), 1813–1829.CrossRefGoogle Scholar
Larson, V. E. (2017). CLUBB-SILHS: A Parameterization of Subgrid Variability in the Atmosphere. arXiv [physics.ao-Ph].Google Scholar
Latif, M., Martin, T. and Park, W. (2013). Southern Ocean sector centennial climate variability and recent decadal trends. Journal of Climate, 26(19), 7767–7782.CrossRefGoogle Scholar
Latif, M., Martin, T., Reintges, A. and Park, W. (2017). Southern Ocean decadal variability and predictability. Current Climate Change Reports, 3(3), 163–173.CrossRefGoogle Scholar
Lawal, K. A., Abatan, A. A., Angélil, O., Olaniyan, E., Olusoji, V. H., Oguntunde, P. G., Lamptey, B., Abiodun, B. J., Shiogama, H., Wehner, M. F. and Stone, D. A. (2016). S13. The late onset of the 2015 wet season in Nigeria. Bulletin of the American Meteorological Society, 97(12), S24–S25.CrossRefGoogle Scholar
Li, C., Zwiers, F., Zhang, X., Li, G., Sun, Y. and Wehner, M. (2021). Changes in annual extremes of daily temperature and precipitation in CMIP6 models. Journal of Climate, 34(9), 3441–3460.CrossRefGoogle Scholar
Li, H., Li, J., Guan, X., Liang, B., Lai, Y. and Luo, X. (2019). Research on Overfitting of Deep Learning. In 2019 15th International Conference on Computational Intelligence and Security (CIS), pp. 78–81.CrossRefGoogle Scholar
Lima, D. C. A., Soares, P. M. M., Semedo, A., Cardoso, R. M., Cabos, W. and Sein, D. V. (2019). A climatological analysis of the Benguela coastal low‐level jet. Journal of Geophysical Research, 124(7), 3960–3978.Google Scholar
Lipzig, N. P. M. V., Walle, J. V. de, Belušić, D., Berthou, S., Coppola, E., Demuzere, M., Fink, A. H., Finney, D. L., Glazer, R., Ludwig, P., Marsham, J. H., Nikulin, G., Pinto, J. G., Rowell, D. P., Wu, M. and Thiery, W. (2022). Representation of precipitation and top-of-atmosphere radiation in a multi-model convection-permitting ensemble for the Lake Victoria Basin (East-Africa). Climate Dynamics, 60, 4033–4035.Google Scholar
Liu, C., Allan, R. P., Mayer, M., Hyder, P., Loeb, N. G., Roberts, C. D., Valdivieso, M., Edwards, J. M. and Vidale, P-L. (2017). Evaluation of satellite and reanalysis-based global net surface energy flux and uncertainty estimates. Journal of Geophysical Research, D: Atmospheres, 122(12), 6250–6272.Google ScholarPubMed
Long, X., Widlansky, M. J., Spillman, C. M., Kumar, A., Balmaseda, M., Thompson, P. R., Chikamoto, Y., Smith, G. A., Huang, B., Shin, C-S., Merrifield, M. A., Sweet, W. V., Leuliette, E., Annamalai, H. S., Marra, J. J. and Mitchum, G. (2021). Seasonal forecasting skill of sea‐level anomalies in a multi‐model prediction framework. Journal of Geophysical Research, C: Oceans, 126(6), e2020JC017060.Google Scholar
Lucas-Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P., Lemonsu, A., Kotlarski, S. and Caillaud, C. (2021). Convection ‐permitting modeling with regional climate models: latest developments and next steps. Wiley Interdisciplinary Reviews. Climate Change, 12(6), e731.CrossRefGoogle Scholar
Lu, D., Liu, S. and Ricciuto, D. (2019). An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science. In 2019 International Conference on Data Mining Workshops (ICDMW), 270–278.CrossRefGoogle Scholar
Lungo, A., Kim, S., Jiang, M., Cho, G. and Kim, Y. (2020). Sensitivity study of WRF simulations over tanzania for extreme events during wet and dry seasons. Atmosphere, 11(5), 459.CrossRefGoogle Scholar
Mächler, L., Baggenstos, D., Krauss, F., Schmitt, J., Bereiter, B., Walther, R., Reinhard, C., Tuzson, B., Emmenegger, L. and Fischer, H. (2023). Laser-induced sublimation extraction for centimeter-resolution multi-species greenhouse gas analysis on ice cores. Atmospheric, 16(2), 355–372.Google Scholar
Malardel, S., Wedi, N., Deconinck, W., Diamantakis, M., Kuehnlein, C., Mozdzynski, G., Hamrud, M. and Smolarkiewicz, P. (2016). A New Grid for the IFS. ECMWF.Google Scholar
Maoyi, M. L. and Abiodun, B. J. (2021). How well does MPAS-atmosphere simulate the characteristics of the Botswana High? Climate Dynamics, 57, 2109–2128.CrossRefGoogle Scholar
Maoyi, M. L. and Abiodun, B. J. (2022). Investigating the response of the Botswana High to El Niño Southern Oscillation using a variable resolution global climate model. Theoretical and Applied Climatology, 147(3), 1601–1615.CrossRefGoogle Scholar
Maraun, D., Widmann, M. and Gutiérrez, J. M. (2019). Statistical downscaling skill under present climate conditions: a synthesis of the VALUE perfect predictor experiment. International Journal of Climatology, 39(9), 3692–3703.CrossRefGoogle Scholar
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M. and Venema, V. K. (2010). Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48(3).CrossRefGoogle Scholar
Marchi, S., Fichefet, T., Goosse, H., Zunz, V., Tietsche, S., Day, J. J. and Hawkins, Ed. (2019). Reemergence of Antarctic sea ice predictability and its link to deep ocean mixing in global climate models. Climate Dynamics, 52(5), 2775–2797.CrossRefGoogle Scholar
Mason, P. J. (1989). Large-eddy simulation of the convective atmospheric boundary layer. Journal of the Atmospheric Sciences, 46(11), 1492–1516.2.0.CO;2>CrossRefGoogle Scholar
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., et al. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.Google Scholar
Maúre, G., Pinto, I., Ndebele-Murisa, M., Muthige, M., Lennard, C., Nikulin, G., Dosio, A. and Meque, A. (2018). The southern African climate under 1.5 °C and 2 °C of global warming as simulated by CORDEX regional climate models. Environmental Research Letters, 13(6), 065002.CrossRefGoogle Scholar
McGregor, J. L. and Dix, M. R. (2008). An updated description of the conformal-cubic atmospheric model. In: Hamilton, K. and Ohfuchi, W. (eds.) High Resolution Numerical Modelling of the Atmosphere and Ocean. New York, Springer New York, pp. 51–75.Google Scholar
Meehl, G. A., Richter, J. H., Teng, H., Capotondi, A., Cobb, K., Doblas-Reyes, F., Donat, M. G., England, M. H., Fyfe, J. C., Han, W., Kim, H., Kirtman, B. P., Kushnir, Y., Lovenduski, N. S., Mann, M. E., Merryfield, W. J., Nieves, V., Pegion, K., Rosenbloom, N., Sanchez, S. C., Scaife, A. A., Smith, D., Subramanian, A. C., Sun, L., Thompson, D., Ummenhofer, C. C. and Xie, S-P. (2021). Initialized Earth System prediction from subseasonal to decadal timescales. Nature Reviews Earth & Environment, 2(5), 340–357.CrossRefGoogle Scholar
Meehl, G. A., Yang, D., Arblaster, J. M., Bates, S. C., Rosenbloom, N., Neale, R., Bacmeister, J., Lauritzen, P. H., Bryan, F., Small, J., Truesdale, J., Hannay, C., Shields, C., Strand, W. G., Dennis, J., Danabasoglu, G. (2019). Effects of model resolution, physics and coupling on Southern Hemisphere storm tracks in CESM1.3. Geophysical Research Letters, 46(21), 12408–12416.CrossRefGoogle Scholar
Meque, A. and Abiodun, B. J. (2015). Simulating the link between ENSO and summer drought in Southern Africa using regional climate models. Climate Dynamics, 44(7), 1881–1900.CrossRefGoogle Scholar
Meque, A., Gamedze, S., Moitlhobogi, T., Booneeady, P., Samuel, S. and Mpalang, L. (2021). Numerical weather prediction and climate modelling: challenges and opportunities for improving climate services delivery in Southern Africa. Climate Services, 23, 100243.CrossRefGoogle Scholar
Meroni, A. N., Oundo, K. A., Muita, R., Bopapȩ, M-J., Maisha, T. R., Lagasio, M., Parodi, A. and Venuti, G. (2021). Sensitivity of some African heavy rainfall events to microphysics and planetary boundary layer schemes: impacts on localised storms. Quarterly Journal of the Royal Meteorological Society, 147(737), 2448–2468.CrossRefGoogle Scholar
Milbrandt, J. A. and Yau, M. K. (2005). A multimoment bulk microphysics parameterization. Part II: a proposed three-moment closure and scheme description. Journal of the Atmospheric Sciences, 62(9), 3065–3081.CrossRefGoogle Scholar
Miyamoto, A., Nakamura, H., Miyasaka, T., Kosaka, Y., Taguchi, B. and Nishii, K. (2022). Maintenance mechanisms of the wintertime subtropical high over the South Indian Ocean. Journal of Climate, 35(10), 2989–3005.CrossRefGoogle Scholar
Mladek, R. (2024). Models. Models – S2S – ECMWF Confluence Wiki. 2024. https://confluence.ecmwf.int/display/S2S/Models.Google Scholar
Molod, A., Takacs, L., Suarez, M. and Bacmeister, J. (2015). Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geoscientific Model Development, 8(5), 1339–1356.CrossRefGoogle Scholar
Morioka, Y., Doi, T. and Behera, S. K. (2018). Decadal climate predictability in the southern Indian Ocean captured by SINTEX-F using a simple SST-nudging scheme. Scientific Reports, 8(1), 1029.CrossRefGoogle ScholarPubMed
Morrison, H. and Gettelman, A. (2008). A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: description and numerical tests. Journal of Climate, 21(15), 3642–3659.CrossRefGoogle Scholar
Morris, T., Scanderbeg, M., West-Mack, D., Gourcuff, C., Poffa, N., Udaya Bhaskar, T. V. S., Hanstein, C., Diggs, S., Talley, L., Turpin, V., Liu, Z., Owens, B. (2024). Best practices for Core Argo floats – Part 1: getting started and data considerations. Frontiers in Marine Science, 11, 1358042.Google Scholar
Motshegwa, T., Wright, C., Sithole, H., Ngolwe, C. and Morgan, A. (2018). Developing a Cyber-Infrastructure for Enhancing Regional Collaboration on Education, Research, Science, Technology and Innovation. In 2018 IST-Africa Week Conference (IST-Africa), 1–9.Google Scholar
Murray, D., Hoell, A., Hoerling, M., Perlwitz, J., Quan, X-W., Allured, D., Zhang, T., Eischeid, J., Smith, C. A., Barsugli, J., McWhirter, J., Kreutzer, C. and Webb, R. S. (2020). Facility for weather and climate assessments (FACTS): a community resource for assessing weather and climate variability. Bulletin of the American Meteorological Society, 101(7), E1214–E1224.CrossRefGoogle Scholar
Naud, C. M., Posselt, D. J. and van den Heever, S. C. (2012). Observational analysis of cloud and precipitation in midlatitude cyclones: Northern versus Southern Hemisphere warm fronts. Journal of Climate, 25(14), 5135–5151.CrossRefGoogle Scholar
Nield, D. (2022). We May Finally Understand Why Clouds Are Different between Earth’s Hemispheres. ScienceAlert. 27 January 2022.Google Scholar
Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Büchner, M., Cerezo-Mota, R., Christensen, O. B., Déqué, M., Fernandez, J., Hänsler, A., van Meijgaard, E., Samuelsson, P., Sylla, M. B., and Sushama, L. (2012). Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. Journal of Climate, 25(18), 6057–6078.CrossRefGoogle Scholar
Nishant, N., Evans, J. P., Virgilio, G., Downes, S. M., Ji, F., Cheung, K. K. W., Tam, E., Miller, J., Beyer, K. and Riley, M. L. (2021). Introducing NARCliM1.5: evaluating the performance of regional climate projections for southeast Australia for 1950–2100. Earth’s Future, 9(7), e2020EF001833.CrossRefGoogle Scholar
Nooni, I. K., Fiifi Tawia Hagan, D., Ullah, W., Lu, J., Li, S., Prempeh, N. A., Gnitou, G. T. and Lim Kam Sian, K. T. C. (2022). Projections of Drought characteristics based on the CNRM-CM6 model over Africa. Collection FAO: Agriculture, 12(4), 495.Google Scholar
Odoulami, R. C., New, M., Wolski, P., Guillemet, G., Pinto, I., Lennard, C., Muri, H. and Tilmes, S. (2020). Stratospheric Aerosol Geoengineering could lower future risk of ‘Day Zero’ level droughts in Cape Town. Environmental Research Letters, 15(12), 124007.CrossRefGoogle Scholar
Odoulami, R. C., Wolski, P. and New, M. (2023). Attributing the driving mechanisms of the 2015–2017 drought in the Western Cape (South Africa) using self-organising maps. Environmental Research Letters, 18(7), 074043.CrossRefGoogle Scholar
O’Gorman, P. A. and Dwyer, J. G. (2018). Using machine learning to parameterize moist convection: potential for modeling of climate, climate change, and extreme events. Journal of Advances in Modeling Earth Systems, 10(10), 2548–2563.Google Scholar
Olmo, M. E., Balmaceda-Huarte, R. and Bettolli, M. L. (2022). Multi-model ensemble of statistically downscaled GCMs over southeastern South America: historical evaluation and future projections of daily precipitation with focus on extremes. Climate Dynamics, 59(9), 3051–3068.Google Scholar
Onishi, R. and Takahashi, K. (2012). A warm-bin–cold-bulk hybrid cloud microphysical model. Journal of the Atmospheric Sciences, 69(5), 1474–1497.CrossRefGoogle Scholar
Onyutha, C., Tabari, H., Rutkowska, A., Nyeko-Ogiramoi, P. and Willems, P. (2016). Comparison of different statistical downscaling methods for climate change rainfall projections over the Lake Victoria basin considering CMIP3 and CMIP5. Journal of Hydro-Environment Research, 12, 31–45.CrossRefGoogle Scholar
Osima, S., Indasi, V. S., Zaroug, M., Endris, H. S., Gudoshava, M., Misiani, H. O., Nimusiima, A., Anyah, R. O., Otieno, G., Ogwang, B. A., Jain, S., Kondowe, A. L., Mwangi, E., Lennard, C., Nikulin, G. and Dosio, A. (2018). Projected climate over the Greater Horn of Africa under 1.5 °C and 2 °C global warming. Environmental Research Letters, 13(6), 065004.CrossRefGoogle Scholar
Osman, M. and Vera, C. S. (2020). Predictability of extratropical upper-tropospheric circulation in the Southern Hemisphere by its main modes of variability. Journal of Climate, 33(4), 1405–1421.CrossRefGoogle Scholar
Osman, M., Vera, C. S. and Doblas-Reyes, F. J. (2016). Predictability of the tropospheric circulation in the Southern Hemisphere from CHFP models. Climate Dynamics, 46(7), 2423–2434.CrossRefGoogle Scholar
Otto, F. E. L. (2017). Attribution of weather and climate events. Annual Review of Environment and Resources, 42(1), 627–646.CrossRefGoogle Scholar
Palomino-Lemus, R., Córdoba-Machado, S., Gámiz-Fortis, S. R., Castro-Díez, Y. and Jesús Esteban-Parra, M. (2017). Climate change projections of boreal summer precipitation over tropical America by using statistical downscaling from CMIP5 models. Environmental Research Letters, 12(12), 124011.CrossRefGoogle Scholar
Park, S. (2014a). A unified convection scheme (UNICON). Part I: formulation. Journal of the Atmospheric Sciences, 71(11), 3902–3930.Google Scholar
Park, S. (2014b). A unified convection scheme (UNICON). Part II: simulation. Journal of the Atmospheric Sciences, 71(11), 3931–3973.Google Scholar
Philip, S., Kew, S., Oldenborgh, G. J. V., Otto, F., Vautard, R., Wiel, K. v. d., King, A., Lott, F., Arrighi, J., Singh, R. and van Aalst, M. (2020). A protocol for probabilistic extreme event attribution analyses. Advances in Statistical Climatology Meteorology and Oceanography, 6(2), 177–203.CrossRefGoogle Scholar
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P., Alias, A., Belušić, D., Berthou, S., Caillaud, C., Cardoso, R. M., Chan, S., Christensen, O. B., Dobler, A., Vries, H. d., Goergen, K., Kendon, E. J., Keuler, K., Lenderink, G., Lorenz, T., Mishra, A. N., Panitz, H-J., Schär, C., Soares, P. M. M., Truhetz, H. and Vergara-Temprado, J. (2021). The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation. Climate Dynamics, 56(11), 3581–3602.CrossRefGoogle Scholar
Pidcock, R. and McSweeney, R. (2022). Mapped: How Climate Change Affects Extreme Weather around the World. Carbon Brief. 4 August 2022. www.carbonbrief.org/mapped-how-climate-change-affects-extreme-weather-around-the-world/.Google Scholar
Prein, A. F. and Gobiet, A. (2017). Impacts of uncertainties in European gridded precipitation observations on regional climate analysis. International Journal of Climatology, 37(1), 305–327.CrossRefGoogle ScholarPubMed
Rampal, N., Gibson, P. B., Sood, A., Stuart, S., Fauchereau, N. C., Brandolino, C., Noll, B. and Meyers, T. (2022). High-resolution downscaling with interpretable deep learning: rainfall extremes over New Zealand. Weather and Climate Extremes, 38, 100525.CrossRefGoogle Scholar
Ranasinghe, R., Ruane, A. C., Vautard, R., Arnell, N., Coppola, E., Cruz, F. A., Dessai, S., Saiful Islam, A. K. M., Rahimi, M., Carrascal, D. R. and Sillmann, J. (2023). Climate Change Information for Regional Impact and for Risk Assessment. In: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., et al., (eds.) Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge and New York, Cambridge University Press, pp. 1767–1926.Google Scholar
Randall, D., Khairoutdinov, M., Arakawa, A. and Grabowski, W. (2003). Breaking
the cloud parameterization deadlock. Bulletin of the American Meteorological Society, 84(11), 1547–1564.CrossRefGoogle Scholar
Rao, J., Garfinkel, C. I., White, I. P. and Schwartz, C. (2020). The Southern Hemisphere minor sudden stratospheric warming in September 2019 and Its predictions in S2S models. Journal of Geophysical Research, 125(14), e2020JD032723.Google Scholar
Raphael, M. N. and Hobbs, W. (2014). The influence of the large-scale atmospheric circulation on Antarctic sea ice during ice advance and retreat seasons. Geophysical Research Letters, 41(14), 5037–5045.CrossRefGoogle Scholar
Reboita, M. S., da Rocha, R. P., André de Souza, C., Chile Baldoni, T., da Silveira Silva, P. L. L. and Ferreira, G. W. S. (2022). Future projections of extreme precipitation climate indices over South America based on CORDEX-CORE multimodel ensemble. Atmosphere, 13(9), 1463.CrossRefGoogle Scholar
Reboita, M. S., Fernandez, J. P. R., Pereira Llopart, M., Porfirio da Rocha, R., Albertani Pampuch, L. and Cruz, F. T. (2014). Assessment of RegCM4.3 over the CORDEX South America domain: sensitivity analysis for physical parameterization schemes. Climate Research, 60(3), 215–234.CrossRefGoogle Scholar
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N. and Prabhat, F. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204.CrossRefGoogle ScholarPubMed
Revell, L. E., Kremser, S., Hartery, S., Harvey, M., Mulcahy, J. P., Williams, J., Morgenstern, O., McDonald, A. J., Varma, V., Bird, L. and Schuddeboom, A. (2019). The sensitivity of Southern Ocean aerosols and cloud microphysics to sea spray and sulfate aerosol production in the HadGEM3-GA7. 1 chemistry – climate model. Atmospheric Chemistry and Physics, 19(24), 15447–15466.Google Scholar
Ribeiro, G. G., Anderson, N. L. O., Barretos, N. J. C., Abreu, R., Alves, L., Dong, B., Lotţ, F. C. and Tett, S. F. B. (2022). Attributing the 2015/2016 Amazon Basin drought to anthropogenic influence. Climate Resilience and Sustainability, 1(1), e25.CrossRefGoogle Scholar
Richman, M. B. and Leslie, L. M. (2012). Adaptive machine learning approaches to seasonal prediction of tropical cyclones. Procedia Computer Science, 12, 276–281.CrossRefGoogle Scholar
Richter, J. H., Glanville, A. A., Edwards, J., Kauffman, B., Davis, N. A., Jaye, A., Kim, H., Pedatella, N. M., Sun, L., Berner, J., Kim, W. M., Yeager, S. G., Danabasoglu, G., Caron, J. M. and Oleson, K. W. (2022). Subseasonal Earth system prediction with CESM2. Weather and Forecasting, 37(6), 797–815.CrossRefGoogle Scholar
Rocha, R. P. da, Reboita, M. S., Dutra, L. M. M., Llopart, M. P. and Coppola, E. (2014). Interannual variability associated with ENSO: present and future climate projections of RegCM4 for South America-CORDEX domain. Climatic Change, 125(1), 95–109.CrossRefGoogle Scholar
Roemmich, D., Alford, M. H., Claustre, H., Johnson, K., King, B., Moum, J., Oke, P., Owens, W. B., Pouliquen, S., Purkey, S. and Scanderbeg, M. (2019). On the future of Argo: a global, full-depth, multi-disciplinary array. Frontiers in Marine Science, 6, 439.CrossRefGoogle Scholar
Rudorff, C., Sparrow, S., Guedes, M. R. G., Tett, S. F. B., Brêda, J. P. L. F., Cunningham, C., Ribeiro, F. N. D., Palharini, R. S. A. and Lott, F. C. (2022). Event attribution of Parnaíba River floods in Northeastern Brazil. Climate Resilience and Sustainability, 1(1), e16.CrossRefGoogle Scholar
Scaife, A. A. and Smith, D. (2018). A signal-to-noise paradox in climate science. npj Climate and Atmospheric Science, 1(1), 1–8.CrossRefGoogle Scholar
Schnase, J. L., Lee, T. J., Mattmann, C. A., Lynnes, C. S., Cinquini, L., Ramirez, P. M., Hart, A. F., Williams, D. N., Waliser, D., Rinsland, P. and Webster, W. P. (2016). Big data challenges in climate science. IEEE Geoscience and Remote Sensing Magazine, 4(3), 10–22.CrossRefGoogle Scholar
Seager, R., Cane, M., Henderson, N., Lee, D-E., Abernathey, R. and Zhang, H. (2019). Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases. Nature Climate Change, 9(7), 517–522.CrossRefGoogle Scholar
Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Luca, A. D., Vicente-Serrano, S. M., Wehner, M. and Zhou, B. (2021). 11 Chapter 11: Weather and Climate Extreme Events in a Changing Climate. www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FOD_Chapter11.pdf.Google Scholar
Senior, C. A., Marsham, J. H., Berthou, S., Burgin, L. E., Folwell, S. S., Kendon, E. J., Klein, C. M., Jones, R. G., Mittal, N., Rowell, D. P., Tomassini, L., Vischel, T., Becker, B., Birch, C. E., Crook, J., Dougill, A. J., Finney, D. L., Graham, R. J., Hart, N. C. G., Jack, C. D., Jackson, L. S., James, R., Koelle, B., Misiani, H., Mwalukanga, B., Parker, D. J., Stratton, R. A., Taylor, C. M., Tucker, S. O., Wainwright, C. M., Washington, R. and Willet, M. R. (2021). Convection-permitting regional climate change simulations for understanding future climate and informing decision-making in Africa. Bulletin of the American Meteorological Society, 102(6), E1206– E1223.CrossRefGoogle Scholar
Shillington, F. A., Reason, C. J. C., Duncombe Rae, C. M., Florenchie, P. and Penven, P. (2006). 4 Large scale physical variability of the benguela current large marine ecosystem (BCLME). In: Shannon, V., Hempel, G., Malanotte-Rizzoli, P., Moloney, C. and Woods, J. (eds.) Large Marine Ecosystems, volume 14. Amsterdam, the Netherlands, Elsevier, pp. 49–70.Google Scholar
Shima, S-I., Kusano, K., Kawano, A., Sugiyama, T. and Kawahara, S. (2009). Super-droplet method for the numerical simulation of clouds and precipitation: a particle-based microphysics model coupled with non-hydrostatic model. Quarterly Journal of the Royal Meteorological Society, 135(642), 1307–1320. arXiv [physics.ao-Ph].CrossRefGoogle Scholar
Siabi, E. K., Kabobah, A. T., Akpoti, K., Anornu, G. K., Amo-Boateng, M. and Nyantakyi, E. K. (2021). Statistical downscaling of global circulation models to assess future climate changes in the Black Volta Basin of Ghana. Environmental Challenges, 5, 100299.CrossRefGoogle Scholar
Singhroy, V. (2009). Satellite remote sensing applications for landslide detection and monitoring. In: Sassa, K. and Canuti, P. (eds.) Landslides – Disaster Risk Reduction. Berlin, Heidelberg, Springer, pp. 143–158.Google Scholar
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S-H. and Ringler, T. D. (2012). A multiscale nonhydrostatic atmospheric model using centroidal voronoi tesselations and C-grid staggering. Monthly Weather Review, 140(9), 3090–3105.CrossRefGoogle Scholar
Sniderman, J. M. K., Brown, J. R., Woodhead, J. D., King, A. D., Gillett, N. P., Tokarska, K. B., Lorbacher, K., Hellstrom, J., Drysdale, R. N. and Meinshausen, M. (2019). Southern Hemisphere subtropical drying as a transient response to warming. Nature Climate Change, 9(3), 232–236.Google Scholar
Somses, S., Bopape, M-J. M., Ndarana, T., Fridlind, A., Matsui, T., Phaduli, E., Limbo, A., Maikhudumu, S., Maisha, R. and Rakate, E. (2020). Convection parametrization and multi-nesting dependence of a heavy rainfall event over Namibia with Weather Research and Forecasting (WRF) model. Climate, 8(10), 112.CrossRefGoogle Scholar
Spinoni, J., Barbosa, P., Bucchignani, E., Cassano, J., Cavazos, T., Christensen, J. H., Christensen, O. B., Coppola, E., Evans, J., Geyer, B. and Giorgi, F. (2020). Future global meteorological drought hot spots: a study based on CORDEX data. Journal of Climate, 33(9), 3635–3661.CrossRefGoogle Scholar
Stan, C. and Xu, L. (2014). Climate simulations and projections with a super-parameterized climate model. Environmental Modelling & Software, 60, 134–152.CrossRefGoogle Scholar
Stockdale, T. N., Alves, O., Boer, G., Deque, M., Ding, Y., Kumar, A., Kumar, K., Landman, W., Mason, S., Nobre, P. and Scaife, A. (2010). Understanding and predicting seasonal-to-interannual climate variability – the producer perspective. Procedia Environmental Sciences, 1, 55–80.CrossRefGoogle Scholar
Stone, D. A., Christidis, N., Folland, C., Perkins-Kirkpatrick, S., Perlwitz, J., Shiogama, H., Wehner, M. F., Wolski, P., Cholia, S., Krishnan, H., Murray, D., Angélil, O., Beyerle, U., Ciavarella, A., Dittus, A., Quan, X-W., Tadross, M. (2019). Experiment design of the International CLIVAR C20C+ Detection and Attribution Project. Weather and Climate Extremes, 24, 100206.CrossRefGoogle Scholar
Stone, D. A. and Hansen, G. (2016). Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change. Climate Dynamics, 47(5), 1399–1415.CrossRefGoogle Scholar
Storto, A., Alvera-Azcárate, A., Balmaseda, M. A., Barth, A., Chevallier, M., Counillon, F., Domingues, C. M., Drevillon, M., Drillet, Y., Forget, G. and Garric, G. (2019). Ocean reanalyses: recent advances and unsolved challenges. Frontiers in Marine Science, 6.CrossRefGoogle Scholar
Swart, N. C., Gille, S. T., Fyfe, J. C. and Gillett, N. P. (2018). Recent Southern Ocean warming and freshening driven by greenhouse gas emissions and ozone depletion. Nature Geoscience, 11(11), 836–841.CrossRefGoogle Scholar
Tang, S., Xie, S., Guo, Z., Hong, S-Y., Khouider, B., Klocke, D., Köhler, M., Koo, M-S., Krishna, P. M., Larson, V. E., Park, S., Vaillancourt, P. A., Wang, Y-C., Yang, J., Daleu, C. L., Homeyer, C. R., Jones, T. R., Malap, N., Neggers, R., Prabhakaran, T., Ramirez, E., Schumacher, C., Tao, C., Bechtold, P., Ma, H-Y., David Neelin, J., Zeng, X. (2022). Long‐term single‐column model intercomparison of diurnal cycle of precipitation over midlatitude and tropical land. Quarterly Journal of the Royal Meteorological Society, 148(743), 641–669.CrossRefGoogle Scholar
Tao, C., Xie, S., Tang, S., Lee, J., Ma, H-Y., Zhang, C. and Lin, W. (2023). Diurnal cycle of precipitation over global monsoon systems in CMIP6 simulations. Climate Dynamics, 60(11), 3947–3968.CrossRefGoogle Scholar
Tetzner, D., Thomas, E. R., Allen, C. S. and Wolff, E. W. (2021). A refined method to analyze insoluble particulate matter in ice cores, and its application to diatom sampling in the Antarctic Peninsula. Frontiers of Earth Science in China 9.Google Scholar
Thayer-Calder, K., Gettelman, A., Craig, C., Goldhaber, S., Bogenschutz, P. A., Chen, C-C, Morrison, H., Höft, J., Raut, E., Griffin, B. M. and Weber, J. K. (2015). A Unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model. Geoscientific Model Development Discussions, 8(6), 5041–5088.Google Scholar
Thompson, G. and Eidhammer, T. (2014). A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. Journal of the Atmospheric Sciences, 71(10), 3636–3658.CrossRefGoogle Scholar
Timbal, B., Fernandez, E. and Li, Z. (2009). Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environmental Modelling & Software, 24(3), 341–358.CrossRefGoogle Scholar
Trancoso, R., Syktus, J., Toombs, N., Ahrens, D., Koon-Ho Wong, K. and Dalla Pozza, R. (2020). Heatwaves intensification in Australia: a consistent trajectory across past, present and future. The Science of the Total Environment, 742, 140521.CrossRefGoogle Scholar
Trenberth, K. E. (ed.) (2022). The climate system. In: The Changing Flow of Energy Through the Climate System. Cambridge Cambridge University Press, pp. 60–78.CrossRefGoogle Scholar
USDOC. (2021). NOAA Upgrades Climate Website Amid Growing Demand for Climate Information. U.S. Department of Commerce. 12 October 2021. www.commerce.gov/news/press-releases/2021/10/noaa-upgrades-climate-website-amid-growing-demand-climate-information.Google Scholar
USNRC, National Research Council. (2012). A National Strategy for Advancing Climate Modeling. Washington, DC, National Academies Press.Google Scholar
Varma, V., Morgenstern, O., Field, P., Furtado, K., Williams, J. and Hyder, P. (2020). Improving the Southern Ocean cloud albedo biases in a general circulation model. Atmospheric Chemistry and Physics, 20(13), 7741–7751.Google Scholar
Veiga, S. F., Nobre, P., Giarolla, E., Capistrano, V., Baptista, M. Jr, Marquez, A. L., Figueroa, S. N., Bonatti, J. P., Kubota, P. and Nobre, C. A. (2019). The Brazilian Earth System Model ocean–atmosphere (BESM-OA) version 2.5: evaluation of Its CMIP5 historical simulation. Geoscientific Model Development, 12(4), 1613–1642.CrossRefGoogle Scholar
Vera, C., Baez, J., Douglas, M., Emmanuel, C. B., Marengo, J., Meitin, J., Nicolini, M., Nogues-Paegle, J., Paegle, J., Penalba, O., Salio, P., Saulo, C., Dias, M. A. S., Dias, P. S. and Zipser, E. (2006). The South American low-level jet experiment. Bulletin of the American Meteorological Society 87, 63–78.CrossRefGoogle Scholar
Wang, D., Post, W. M. and Wilson, B. E. (2010). Several computational opportunities and challenges associated with climate change modeling. Computing in Science & Engineering, 14.Google Scholar
Wang, F. and Tian, D. (2022). On deep learning-based bias correction and downscaling of multiple climate models simulations. Climate Dynamics, 59(11), 3451–3468.CrossRefGoogle Scholar
Wimmers, A., Velden, C. and Cossuth, J. H. (2019). Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery. Monthly Weather Review, 147(6), 2261–2282.CrossRefGoogle Scholar
Wu, Y., Miao, C., Fan, X., Gou, J., Zhang, Q. and Zheng, H. (2022). Quantifying the uncertainty sources of future climate projections and narrowing uncertainties with bias correction techniques. Earth’s Future, 10(11), e2022EF002963.CrossRefGoogle Scholar
Wyngaard, J. C. (2004). Toward numerical modeling in the ‘Terra Incognita’. Journal of the Atmospheric Sciences, 61(14), 1816–1826.2.0.CO;2>CrossRefGoogle Scholar
Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics. Conference Series, 1168(2), 022022.CrossRefGoogle Scholar
Yuan, X. and Li, C., (2008). Climate modes in southern high latitudes and their impacts on Antarctic sea ice. Journal of Geophysical Research: Oceans, 113, C06S91.CrossRefGoogle Scholar
Yuval, J. and O’Gorman, P. A. (2020). Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions. Nature Communications, 11(1), 3295.CrossRefGoogle Scholar
Zhang, J. and Haghighat, F. (2010). Development of Artificial neural network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers. Energy and Buildings, 42(4), 435–440.CrossRefGoogle Scholar
Zhang, L., Gan, B., Wang, H., Wu, L. and Cai, W. (2020). Essential role of the midlatitude South Atlantic variability in altering the Southern Hemisphere summer storm tracks. Geophysical Research Letters, 47(17), e2020GL087910.Google Scholar
Zhang, L., Delworth, T. L. and Jia, L. (2017). Diagnosis of decadal predictability of Southern Ocean sea surface temperature in the GFDL CM2.1 model. Journal of Climate, 30(16), 6309–6328.Google Scholar
Zhang, L., Delworth, T. L., Yang, X., Gudgel, R. G., Jia, L., Vecchi, G. A. and Zeng, F. (2017). Estimating decadal predictability for the Southern Ocean using the GFDL CM2.1 model. Journal of Climate, 30(14), 5187–5203.Google Scholar
Zhang, W., Kirtman, B., Siqueira, L., Clement, A. and Xia, J. (2021). Understanding the signal-to-noise paradox in decadal climate predictability from CMIP5 and an eddying global coupled model. Climate Dynamics, 56(9), 2895–2913.CrossRefGoogle Scholar
Zhao, M., Golaz, J-C, Held, I. M., Guo, H., Balaji, V., Benson, R., Chen, J-H, Chen, X., Donner, L. J., Dunne, J. P. and Dunne, K. (2018). The GFDL global atmosphere and land model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTs. Journal of Advances in Modeling Earth Systems, 10(3), 691–734.Google Scholar
Zhou, T., Zhang, W., Chen, D., Zhang, X., Li, C., Zuo, M. and Chen, X. (2022). Understanding and building upon pioneering work of Nobel Prize in Physics 2021 laureates Syukuro Manabe and Klaus Hasselmann: from greenhouse effect to earth system Science and beyond. SCIENCE CHINA Earth Sciences, 65(4), 589–600.Google Scholar
Zhu, Y., Zhang, R-H., Moum, J. N., Wang, F., Li, X. and Li, D. (2022). Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations. National Science Review, 9(8), nwac044.CrossRefGoogle ScholarPubMed
Zilli, M. T., Hart, N. C. G., Coelho, C. A. S., Chadwick, R., de Souza, D. C., Kubota, P. Y., Figueroa, S. N. and Cavalcanti, I. F. A. (2023). Characteristics of tropical–extratropical cloud bands over tropical and subtropical South America Simulated by BAM-1.2 and HadGEM3-GC3.1. Quarterly Journal of the Royal Meteorological Society, 149(753), 1498–1519.CrossRefGoogle Scholar
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