Overview Aotearoa Research Science
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Research · Climate Science

Bibliography & References

All sources cited across the Climate Science & AI branch, formatted in APA 7th edition style. DOIs and stable URLs are provided where available.

Bibliography

Foundations & Overview

  1. IPCC. (2023). Climate change 2023: Synthesis report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Core Writing Team, H. Lee & J. Romero, Eds.). IPCC. https://doi.org/10.59327/IPCC/AR6-9789291691647
  2. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
  3. Ali, H. E., Hemdan, B. A., El-Naggar, M. E., et al. (2025). Harnessing the power of microbial fuel cells as pioneering green technology: Advancing sustainable energy and wastewater treatment through innovative nanotechnology. Bioprocess and Biosystems Engineering. Retrieved from https://pubmed.ncbi.nlm.nih.gov/39754690/
  4. Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C., Ng, A. Y., Hassabis, D., Platt, J. C., … Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), Article 42. https://doi.org/10.1145/3485128
  5. Schneider, T., Lan, S., Stuart, A., & Teixeira, J. (2017). Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations. Geophysical Research Letters, 44(24), 12396–12417. https://doi.org/10.1002/2017GL076101

Historical Context

  1. Karunaratne, A. S., Chaogejilatu, Iizumi, T., et al. (2025). A climate impact attribution of historical rice yields in Sri Lanka using three crop models. Scientific Reports.
  2. Baghel, T., Babel, M. S., Shrestha, S., et al. (2022). A generalized methodology for ranking climate models based on climate indices for sector-specific studies: An application to the Mekong sub-basin. Science of the Total Environment.
  3. Charney, J. G., Arakawa, A., Baker, D. J., Bolin, B., Dickinson, R. E., Goody, R. M., Leith, C. E., Stommel, H. M., & Wunsch, C. I. (1979). Carbon dioxide and climate: A scientific assessment. Report of an ad hoc study group on carbon dioxide and climate. National Academy of Sciences. https://nap.nationalacademies.org/catalog/12181/carbon-dioxide-and-climate-a-scientific-assessment

Methodologies

  1. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
  2. Willard, J., Jia, X., Xu, S., Steinbach, M., & Kumar, V. (2022). Integrating scientific knowledge with machine learning for engineering and environmental systems. ACM Computing Surveys, 55(4), Article 66. https://doi.org/10.1145/3514228

Applications

  1. Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., & Battaglia, P. (2023). Learning skillful medium-range global weather forecasting. Science, 382(6677), 1416–1421. https://doi.org/10.1126/science.adi2336
  2. O’Gorman, P. A. (2015). Precipitation extremes under climate change. Current Climate Change Reports, 1(2), 49–59. https://doi.org/10.1007/s40641-015-0009-3 Preprint: https://arxiv.org/abs/1503.07557
  3. Miner, K. R., Meyerson, L. A., Biesecker, M., et al. (2020). Invasive species, extreme fire risk, and toxin release under a changing climate [Preprint]. arXiv. https://arxiv.org/abs/2008.01035
  4. Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., & Tian, Q. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619(7970), 533–538. https://doi.org/10.1038/s41586-023-06185-3
  5. Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., Hassanzadeh, P., Kashinath, K., & Anandkumar, A. (2022). FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators [Preprint]. arXiv. https://arxiv.org/abs/2202.11214
  6. Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., Hatfield, S., Battaglia, P., Sanchez-Gonzalez, A., Willson, M., Brenner, M. P., & Hoyer, S. (2024). Neural general circulation models for weather and climate. Nature, 632(8027), 1060–1066. https://doi.org/10.1038/s41586-024-07744-y

Challenges & Limitations

  1. Mehryar, S., Yazdanpanah, V., & Tong, J. (2024). AI and climate resilience governance. iScience, 27(6), 109905. https://doi.org/10.1016/j.isci.2024.109905
  2. NOAA Global Monitoring Laboratory. (2025). Trends in atmospheric carbon dioxide: Global monthly mean CO₂. National Oceanic and Atmospheric Administration. https://gml.noaa.gov/ccgg/trends/gl_trend.html

Future Directions

  1. Hoffmann, A. A., Montgomery, B. L., Popovici, J., et al. (2011). Successful establishment of Wolbachia in Aedes populations to suppress dengue transmission. Nature, 476(7361), 454–457. https://doi.org/10.1038/nature10356
  2. Velásquez, A. C., Castroverde, C. D. M., & He, S. Y. (2018). Plant–pathogen warfare under changing climate conditions. Current Biology, 28(10), R619–R634. https://doi.org/10.1016/j.cub.2018.03.054
  3. Leach, N. (2022). Architecture in the age of artificial intelligence: An introduction to AI for architects. Bloomsbury Visual Arts. ISBN 978-1-350-16551-6.
  4. Sanjay, J., Krishnan, R., Ramarao, M. V. S., et al. (2020). Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models. Advances in Climate Change Research, 11(2), 145–156.
  5. World Meteorological Organization. (2025, January 10). WMO confirms 2024 as warmest year on record at about 1.55 °C above pre-industrial level [Press release]. https://wmo.int/news/media-centre/wmo-confirms-2024-warmest-year-record-about-155degc-above-pre-industrial-level

Further Reading

  1. Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55. https://doi.org/10.1038/nature14956
  2. Hersbach, H., Bell, B., Berrisford, P., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
  3. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045