Dr Nicola Maher

Contacts

PhD (UNSW 2016)
Research/DECRA Fellow
Chief Investigator for the the ARC Centre of Excellence for Climate Extremes

Research interests

  • Climate Change Science
  • Physical Oceanography
  • Atmospheric Dynamics

My research uses global coupled climate models to investigate the dynamics, impacts and future changes modes of climate variability. My research interests lie in the following areas:

  1. Single Model Initial-Condition Large Ensemble (SMILE ) modelling -investigate the forced response to greenhouse gases and internal variability
  2. Develop/leverage new tools - machine learning/artificial intelligence (e.g. neural networks, long-term short-term memory network, ensemble classifiers)
  3. Understand future projections - particularly projections of internal variability and extreme events
  4. ENSO research - dynamics, teleconnections and ENSO itself in a warming world

Groups

Projects

Google Scholar Profile: scholar.google.com/citations

Journal Publications (24)

Submitted (1)

  1. Capotondi, A., McGregor, S., McPhaden, M.J., Cravatte, S., Holbrook, N.J., Imada, Y., Sanchez, S.C., Sprintall, J., Stuecker, M.F., Ummenhofer, C.C., Zeller, M., Farneti, R., Graffino G., Hu S., Karnauskas K.B., Kosaka Y., Kucharski F., Mayer M., Qiu B., Santoso A., Taschetto A.S., Wang F., Zhang X., Holmes R.M., Luo J-J, Maher, N., Martinez-Villalobos, C., Naha, R., Stevenson, S., Sullivan, A., van Rensch, P. Mechanisms of Tropical Pacific Decadal Variability. Submitted to Nature Reviews Earth & Environment

Published (23)

  1. Malagón-Santos, V., Slangen, A. B. A., Hermans, T. H. J., Dangendorf, S., Marcos, M., and Maher, N.. Improving Statistical Projections of Ocean Dynamic Sea-level Change Using Pattern Recognition Techniques, EGUsphere Ocean Science 19, 499-515 https://doi.org/10.5194/os-19-499-2023
  2. Maher, N., Wills, R.C.J., DiNezio, P., Klavans, J., Milinski, S., Sanchez, S.C., Stevenson, S., Stuecker, M.F. and Wu, X. The future of the El Niño-Southern Oscillation: Using large ensembles to illuminate time-varying responses and inter-model differences. Earth System Dynamics 14, 413-431 https://doi.org/10.5194/esd-14-413-2023
  3. ​Maher, N., Kay, J.E.. and Capotondi, A. Modulation of ENSO Teleconnections over North America by the Pacific Decadal Oscillation, Environmental Research Letters 17 114005 https://doi.org/10.1088/1748-9326/ac9327
  4. Maher, N., Tabarin, T.P. and Milinski, S. (2022). Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events, Earth System Dynamics, https://doi.org/10.5194/esd-2021-105
  5. ​Ward, B., F.S.R. Pausata, and Maher, N. (2021). The sensitivity of the ENSO to volcanic aerosol spatial distribution in the MPI large ensemble. Earth System Dynamics. Earth System Dynamics, 12, 975–996, https://doi.org/10.5194/esd-12-975-2021
  6. Suarez-Gutierrez, L, Maher, N, and Milinski, S. (2021). Exploiting large ensembles for a better yet simpler climate model evaluation. Climate Dynamics.  https://doi.org/10.1007/s00382-021-05821-w 
  7. Maher, N., Power, S and Marotzke J. (2021). More accurate quantification of model-to-model agreement in externally forced climatic responses over the coming century. Nature Communications 12, 788 https://doi.org/10.1038/s41467-020-20635-w
  8. Milinski, S.,  Maher, N., and Olonscheck, D.  (2020). How large does a large ensemble need to be? Earth System Dynamics 11, 885-901 doi.org/10.5194/esd-11-885-2020
  9. Fiedler, S., Crueger, T., D'Agostino, R., Peters, P., Becker, T., Leutwyler, D., Paccini, L., Burdanowitz, J., Buehler, S.A., Cortes, A.U., Dauhut, T., Dommenget, D., Fraedrich, K., Jungandreas, L., Maher, N., Naumann, A.K., Rugenstein, M., Sakradzija, M., Schmidt, H., Sielmann, F., Stephan, C., Timmreck, C., Zhu, X. and Stevens, B. (2020). Simulated Tropical Precipitation Assessed Across Three Major Phases of the Coupled Model Intercomparison Project (CMIP). Monthly Weather Review, 148 (9): 3653–3680 https://doi.org/10.1175/MWR-D-19-0404.1
  10. Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E., Brunner, L., Knutti, R., and Hawkins, E.  (2020). Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6 Earth System Dynamics  https://doi.org/10.5194/esd-2019-93
  11. Maher, N., Lehner, F and Marotzke J. (2020). Quantifying the role of internal variability in the climate we will observe in the coming decades. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ab7d02
  12. Perry, S.J., McGregor, S., Sen Gupta, A., England, E. and Maher, N. (2020). Projected late 21st Century changes to the regional impacts of the El Nino-Southern Oscillation. Climate Dynamics. link.springer.com/article/10.1007/s00382-019-05006-6
  13. Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M. Dobrynin, M., Kornblueh, L., Kröger, J., Takano, Y.,  Ghosh, R., Hedemann, C., Li, C., Li, H., Manzini, E., Notz, D., Putrasahan, D., Boysen, L., Claussen, M., Ilyina, T., Olonscheck, D., Raddatz, T., Stevens, B. and Marotzke, J. (2019). The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability. JAMES https://doi.org/10.1029/2019MS001639 
  14. Maher, N., Matei, D., Milinski, S., and Marotzke, J. (2018). ENSO change in climate projections: Forced response or internal variability? Geophys. Res. Lett., 45. doi.org/10.1029/2018GL079764 
  15. Maher, N. England, M. H., Sen Gupta, A. and Spence, P. (2018), Role of Pacific trade winds in driving ocean temperature during the recent slowdown and projections under a wind trend reversal, Clim Dyn. doi.org/10.1007/s00382-017-3923-3 
  16. Donat M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A. and Maher, N. (2016), More extreme rain in the driest and wettest regions of the globe. Nature Climate Change, doi:10.1038/nclimate2941 
  17. Maher W., Maher, N., Taylor, A., Krikowa, F., and Mikac, K. M. (2016). The use of the marine gastropod, Cellana tramoserica as a biomonitor of metal contamination in near shore environments, Environ. Monit. Assess, doi: 10.1007/s10661-016-5380-6 
  18. Maher, N., McGregor, S., England, M. H., and Sen Gupta, A. (2015), Effects of volcanism on tropical variability, Geophys. Res. Lett., 42 ,6024–6033 
  19. Meehl, G. A., Teng, H., Maher, N. and England, M. H. (2015), Effects of Mt Pinatubo eruption on decadal climate prediction skill, Geophys. Res. Lett., 42, 10,840–10,846, doi:10.1002/ 2015GL066608. 
  20. England, M. H., Kajtar, J. N., Maher ,N. (2015), Robust warming projections despite the recent hiatus, Nature Climate Change, 5, 394-396 
  21. Griffin, J., Latief, H., Kongko, W., Harig, S., Horspool, N., Hanung, R., Rojali, A., Maher, N., Fuchs, A., Hossen, J., Upi, S., Dewanto, S. E., Rakowsky, N. and Cummins, P. (2015), An evaluation of onshore digital elevation models for modeling tsunami inundation zones. Frontiers in Earth Science, 3, 32 
  22. Maher, N., Sen Gupta, A., and England, M. E. (2014), Drivers of decadal hiatus periods in the 20th and 21st centuries, Geophys. Res. Lett., 41, 5978–5986 
  23. Griffiths, R.W, Maher, N and Hughes, G.O. (2011) ,Ocean stratification under oscillatory surface buoyancy forcing, J. Mar. Res., 69, 523-543 

Book Chapters (1)

  1. McGregor, S., Khodri, M., Maher, N., Ohba, M., Pausata, F. and Stevenson, S. (2020) The effect of strong volcanic eruptions on ENSO. McPhaden, M.J., Santoso, S. and Cai, W. (Eds.) El Nino Southern Oscillation in a Changing Climate American Geophysical Union

Special Issue Preface/Perspective (1)

  1. Maher, N, Milinski, S and Ludwig, R (2021). Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble, Earth System Dynamics, 12, 401–418, https://doi.org/10.5194/esd-12-401-2021

Conference Papers (1)

  1. Vietinghoff, D, Heine, C., Böttinger, M., Maher, N., Jungclaus, J.H., Scheuermann, G. (2021). Visual Analysis of Spatio-Temporal Trends in Time-Dependent Ensemble Data Sets on the Example of the North Atlantic Oscillation. PacificVis 

White Papers (1)

  1. Maher, N., DiNezio, P., Capotondi, A. and Kay, J. (2021). Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles. AI4ESP: Artificial Intelligence for Earth System Predictability. Department of Energy. https://www.ai4esp.org/files/AI4ESP1087_Maher_Nicola.pdf

Other Publications (2)

  1. Suarez-Gutierrez, L, Maher, N, and Milinski, S.  (2020). Evaluating the internal variability and forced response in Large Ensembles. US CLIVAR Variations, 18, 2.
  2. Maher, N. (2018). Natural drivers of interannual to decadal variations in surface climate. BAMOS, 31(2), 9-12

Supervised students