Novel Deep Learning Method for Forecasting ENSO

Articles in Press

Document Type : Research Paper

Authors

1 Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Iran.

2 Department of Civil Engineering, Civil Engineering and Architecture Faculty, Shahid Chamran University of Ahvaz, Iran.

10.22055/jhs.2024.47965.1322

Abstract

Ability of predicting climate phenomena enables international organization and governments to manage natural disasters such as droughts. El Niño Sothern Oscillation (ENSO) is one the most influential and crucial phenomenon follows with large scale climatic events and can be used for predicting droughts and floods all around the world. Due to such a great importance, a new Convolutional Neural Network method based on augmented data (ACNN) for predicting ENSO on a relatively long period is developed in this research. The method is developed based on CNN to forecast ENSO six month earlier. Sea Surface Temperature (SST) anomaly maps are given to the model as the predictors and Niño 3.4 Index is the predictand. The method applies convolutional tensors to extract features from the maps, and delivers them to a fully connected neural network to discover connections between Niño Index and the features. A tricky augmentation process is used to increase the number of input data to compensate lack of observations. The model’s skill correlation is over 0.83 for January-February-March season, while, the original CNN method’ correlation is 0.71. The model can be executed on GPUs of a laptop without any need to super computers. The feature that makes it a great tool for predicting ENSO even for research institutions in low income countries.

Keywords

Main Subjects

References

  1. Blöschl, G., Hall, J., Viglione, A. et al. Changing climate both increases and decreases European river floods. Nature 573, 108–111 (2019).
  2. McPhaden, M. J., Zebiak, S. E. & Glantz, M. H. ENSO as an integrating concept in Earth science. Science 314, 1740–1745 (2006)
  3. Mohammad Naisipour, Iraj Saeedpanah, Arash Adib, Multimodal ENSO Forecast, 20 March 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3474635/v1]
  4. Chen, D., Cane, M. A., Kaplan, A., Zebiak, S. E. & Huang, D. Predictability of ElNiño over the past 148 years. Nature 428, 733–736 (2004)
  5. Gao, C. & Zhang, R. H. The roles of atmospheric wind and entrained watertemperature (Te) in the second-year cooling of the 2010–12 La Niña event.Clim. Dyn. 48, 597–617 (2017)
  6. Gebbie, G. & Tziperman, E. Predictability of SST-modulated westerly windbursts. J. Clim.22, 3894–3909 (2009)
  7. Izumo, T. et al. T. Influence of the state of the Indian Ocean Dipole on thefollowing year’s El Niño. Nat. Geosci. 3, 168–172 (2010).
  8. Bindoff, N. L. et al. in Climate Change 2013: The Physical Science Basis. Contributionof Working Group I to the Fifth Assessment Report of the IntergovernmentalPanel on Climate Change (eds Stocker, T. F. et al.) Ch. 10 (Cambridge Univ. Press,(2013).
  9. Labibzadeh, S. A. Sadrnejad, M. Naisipour, An Assessment of Compressive Size Effect of Plane Concrete Using Combination of Micro-Plane Damage Based Model and 3D Finite Elements Approach, American Journal of Applied Sciences, 2008, 5 (2), 106-109.
  10. Masoud Aminzadeh, Seyed Mehdi Tavakkoli, Multiscale topology optimization of structures by using isogeometrical level set approach, Finite Elements in Analysis and Design, Volume 235, 2024, 104167, ISSN 0168-874X, https://doi.org/10.1016/j.finel.2024.104167.
  11. Afshar M. H., Naisipour M., Amani J., Node moving adaptive refinement strategy for planar elasticity problems using discrete least squares meshless method, Finite Elements in Analysis and Design, 2011, 47,12, 1315-1325.
  12. Afshar M. H., Amani J., Naisipour M., Mixed discrete least squares meshless method for planar elasticity problems using regular and irregular nodal distributions, Engineering analysis with boundary elements, 36,5, 894-902.
  13. Naisipour, MH Afshar, B Hassani, AR Firoozjaee, Collocation Discrete Least Squares (CDLS) method for elasticity problems and grid irregularity effect assessment, Am. J. Applied Sci, 2008, 5, 1595-1601
  14. Hawkins, E. &Sutton, R. The potential to narrow uncertainty in projections of regional precipitation change. Clim. Dyn. 37, 407–418 (2011).
  15. Terrer, C., Phillips, R.P., Hungate, B.A. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).
  16. Seungmok Paik, Seung-Ki Min, Carley E. Iles, Erich M. Fischer, Andrew P. Schurer, Volcanic-induced global monsoon drying modulated by diverse El Niño responses, Science Advances, 2020
  17. Jaemo Yang, Ju-Hye Kim, Pedro A. Jiménez, Manajit Sengupta, Jimy Dudhia, Yu Xie, Anastasios Golnas, Ralf Giering, An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis, Solar Energy, Volume 220, 2021
  18. Aengenheyster, M., Feng, Q. Y., van der Ploeg, F. & Dijkstra, H. A. The point of no return for climate action: effects of climate uncertainty and risk tolerance. Earth System Dynamics 9(3), 1085–1095 (2018).
  19. Naisipour, M. H. Afshar, B. HASANI, F. A. RAHMANI, Collocation discrete least square (CDLS) method for elasticity problems, International Journal of Civil Engineering, 2009, 7 (1), 9-18
  20. Labibzadeh, R. Modaresi, M. Naisipour, Efficiency Test of The Discrete Least Squares Meshless Method In Solving Heat Conduction Problems Using Error Estimation, SHARIF: CIVIL ENINEERING, 2015, 312 (32), 31-40
  21. Michel, C., Li, C., Simpson, I. R., Bethke, I., King, M. P., & Sobolowski, S. The Change in the ENSO Teleconnection under a Low Global Warming Scenario and the Uncertainty due to Internal Variability, Journal of Climate, 33(11), 4871-4889, 2020.
  22. Nicola Maher, Flavio Lehner, and Jochem Marotzke, Quantifying the role of internal variability in the temperature we expect to observe in the coming decades, Environmental Research Letters, 15-054014, 2020
  23. Collins, M. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Ch. 12 (Cambridge Univ. Press, 2013).
  24. Deser, C., Hurrell, J. W. & Phillips, A. S. The role of the North Atlantic Oscillation inEuropean climate projections. Clim. Dyn. 49, 3141–3157 (2017).
  25. Marotzke, J. Quantifying the irreducible uncertainty in near term climate projections.Wiley Interdiscip. Rev. Clim. Change 10, e563 (2019).
  26. Scaife, A. A. & Smith, D. A signal-to-noise paradox in climate science. npj Clim. Atmos.Sci. 1, 28 (2018).
  27. Smith, D.M., Scaife, A.A., Eade, R. et al. North Atlantic climate far more predictable than models imply. Nature 583, 796–800 (2020).
  28. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experimentdesign. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
  29. Boer, G. J. et al. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6.Geosci. Model Dev. 9, 3751–3777 (2016).
  30. Cai, W. et al. More extreme swings of the South Pacific convergence zone due to greenhouse warming. Nature 488, 365–369 (2012).
  31. Philander, S. G. H. El Niño southern oscillation phenomena. Nature 302, 295–301 (1983).
  32. Siegert, F., Ruecker, G., Hinrichs, A. & Hoffmann, A. Increased damage from fires in logged forests during droughts caused by El Niño. Nature 414, 437–440 (2001).
  33. Chen, D., Cane, M. A., Kaplan, A., Zebiak, S. E. & Huang, D. Predictability of El Niٌo over the past 148 years. Nature 428, 733–736(2004).
  34. Chen, D., Zebiak, S. E., Busalacchi, A. J. & Cane, M. A. An improved procedure for El Niño forecasting: Implications forpredictability. Science 269, 1699 (1995).
  35. Fedorov, A., Harper, S., Philander, S., Winter, B. & Wittenberg, A. How predictable is El Niٌo? Bulletin of the American MeteorologicalSociety 84, 911 (2003).
  36. Chen, D. et al. Strong influence of westerly wind bursts on El Niño diversity. Nature Geoscience 8, 339–345, https://doi.org/10.1038/ngeo2399 (2015).
  37. Chen, N. & Majda, A. J. Simple stochastic dynamical models capturing the statistical diversity of El Niٌo Southern Oscillation.Proceedings of the National Academy of Sciences, 201620766 (2017).
  38. Meinen, C. S. & McPhaden, M. J. Observations of warm water volume changes in the equatorial Pacific and their relationship to El Niño and La Niٌ Journal of Climate 13, 3551–3559 (2000).
  39. Suarez, M. J. & Schopf, P. S. A delayed action oscillator for ENSO. Journal of the Atmospheric Sciences 45, 3283–3287 (1988).
  40. l. Jin, F.-F. An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. Journal of the Atmospheric Sciences 54, 811–829 (1997).
  41. Li, T. Phase transition of the El Niño–Southern Oscillation: A stationary SST mode. Journal of the Atmospheric Sciences 54, 2872–2887 (1997).
  42. McPhaden, M. J. Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophysical Research Letters 30 (2003).
  43. Ham, Y.-G., Kug, J.-S., Park, J.-Y. & Jin, F.-F. Sea surface temperature in the north tropical Atlantic as a trigger for El Nino/Southern Oscillation events. Nature Geoscience 6, 112–116 (2013).
  44. -J LUO, S. MASSON, S. K. BEHERA, T. YAMAGATA, Extended ENSO Predictions Using a Fully Coupled Ocean–Atmosphere Model, J. Climate, 2008
  45. Luo, J.-J., S. Masson, S. Behera, S. Shingu, and T. Yamagata (2005), Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts, J. Clim., 18, 4474–4494
  46. Doi, T., A. Storto, S. K. Behera, A. Navarra, and T. Yamagata, 2017: Improved prediction of the Indian Ocean Dipole Mode by use of subsurface ocean observations. J. Climate, 30, 7953-7970.
  47. Doi, T., S. K. Behera, and T. Yamagata (2019), Merits of a 108-Member Ensemble System in ENSO and IOD Predictions. J. Climate, 32, 957–972, https://doi.org/10.1175/JCLI-D-18-0193.1
  48. Doi, T., S. K. Behera, and T. Yamagata, 2020: Predictability of the Super IOD Event in 2019 and Its Link With El Niño Modoki.Geophysical Research Letters, 47, e2019GL086713.
  49. Ham, Y., Kim, J. & Luo, J. Deep learning for multi-year ENSO forecasts. Nature 573, 568–572 (2019).
  50. Mobaraki, H. Arzani, M. Torabi, M. Naisipour, Application of DLSM Method in 2D Elastostatic Crack Problems, 9th International Congruous of Civil Engineering, 2014
  51. Naisipour, M. H. Afshar, B. Hassani, M Zeinali, , An error indicator for two-dimensional elasticity problems in the discrete least squares meshless method,8th International Congress on Civil Engineering, Shiraz, 1388. https://civilica.com/doc/62892
  52. Fazelpour, A. Patel, P. Shankar, J. D. Summers, A user study on exploring the sequencing of unit cell design guidelines, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2017.
  53. Karamy Moghadam, A., Mahdavi Adeli, M. Application of Artificial Neural Networks for Seismic Analysis and Design of Buried Pipelines in Heterogeneous Soils. Journal of Hydraulic Structures, 2020; 6(4): 60-74. doi: 10.22055/jhs.2021.35453.1153
  54. M. Kaleybar, H. Saadat and H. Khaloo, Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer, 13th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of, 2023, pp. 030-035, doi: 10.1109/ICCKE60553.2023.10326274.
  55. Oquab, M., Bottou, L., Laptev, I. & Sivic, J. Learning and transferring mid-levelimage representations using convolutional neural networks. In Proc. IEEE Conference.
  56. Ahmadabadi, O. N. Manzari and A. Ayatollahi, Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition, 13th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of, 2023, pp. 180-184, doi: 10.1109/ICCKE60553.2023.10326272.
  57. G. Ladani and H. B. Kashani, "Exploring 3D Transfer Learning CNN Models for Alzheimer's Disease Diagnosis from MRI Images," 2023, 13th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of, 2023, pp. 174-179, doi: 10.1109/ICCKE60553.2023.10326311.
  58. Beijing Climate Center (BCC). bcc-csm1-1 model output prepared for CMIP5, served by ESGF. World Data Center for Climate (WDCC) at DKRZ, 2014
  59. Yoo-Geun Ham, Jeong-Hwan Kim, Eun-Sol Kim, Kyoung-Woon On., Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data, Science Bulletin, Volume 66, Issue 13, 2021
  60. Giese, B. S. & Ray, S. El Niño variability in simple ocean data assimilation (SODA), 1871–2008. J. Geophys. Res. Oceans 116, 2011.
  61. Michael Blaivas, Laura N Blaivas, Kendra Campbell, Joseph Thomas, Sonia Shah, Kabir Yadav, Yiju Teresa Liu, Making Artificial Intelligence Lemonade Out of Data Lemons, Journal of Ultrasound in Medicine, 10.1002/jum.15889, 2021.
  62. Farhadipour and P. Taghipour, "Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique," 2023 13th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of, 2023, pp. 001-006, doi: 10.1109/ICCKE60553.2023.10326241.
  63. Vashagh, A. Akhoondkazemi, S. J. Zahabi and D. Shafie, "Enhanced Atrial Fibrillation (AF) Detection via Data Augmentation with Diffusion Model," 2023 13th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of, 2023, pp. 457-462, doi: 10.1109/ICCKE60553.2023.10326310.
  64. Dehghan, M. Naderan and S. E. Alavi, "Detection of Parkinso’s disease using Convolutional Neural Networks and Data Augmentation with SPECT images," 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of, 2022, pp. 001-006, doi: 10.1109/ICCKE57176.2022.9960085.
  65. Bidokhti and S. Ghaemmaghami, "Dual Memory Structure for Memory Augmented Neural Networks for Question-Answering Tasks," 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of, 2022, pp. 142-147, doi: 10.1109/ICCKE57176.2022.9959999.
  66. Ha, S., Liu, D. & Mu, L. Prediction of Yangtze River streamflow based on deep learning neural network with El Niño–Southern Oscillation. Sci Rep 11, 11738, 2021.
  67. R. Peebles Jr., “Central Limit Theorem” in “Probability, Random Variables and Random Signal Principles”, 4th ed., 2001.
  68. Pham TA, Tran VQ, Vu H-LT, Ly H-B, Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity. PLoS ONE 15(12): e0243030, 2020.
  69. Konstantin Eckle, Johannes Schmidt-Hieber, A comparison of deep networks with ReLU activation function and linear spline-type methods, Neural Networks, Volume 110, 2019.
  70. Behringer DW, Xue Y. Evaluation of the global ocean data assimilation system at NCEP: the Pacific Ocean. Proceedings of eighth symposium on integrated observing and assimilation systems for atmosphere, oceans, and land surface (AMS 84th Annual Meeting), 2004. and Land Surface (AMS 84th Annual Meeting).
  71. Chen, HC., Tseng, YH., Hu, ZZ. et al. Enhancing the ENSO Predictability beyond the Spring Barrier. Sci Rep 10, 984, 2020.
  72. Gupta, H. Kodamana and S. Sandeep, "Prediction of ENSO Beyond Spring Predictability Barrier Using Deep Convolutional LSTM Networks," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022 (3), 93-105.
Journal of Hydraulic Structures

Articles in Press, Accepted Manuscript
Available Online from 23 November 2024

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