Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
DOI:
https://doi.org/10.11137/1982-3908_2022_45_40763Palavras-chave:
Aprendizado profundo, CNN-LSTM, TensorFlowResumo
O gelo marinho desempenha um papel fundamental na regulação térmica das regiões polares. Observações de satélites evidenciam que na Antártica o gelo apresentava, na série histórica, tendências positivas em cobertura e extensão. Em 2019 houve um padrão de inversões entre os valores da normal climatológica e dos dados de reanálise. Nesse contexto, este estudo teve como principal objetivo avaliar o potencial de previsibilidade de cobertura de gelo marinho com a aplicação de técnicas de RNAs em 3 mares que banham o continente Antártico, a saber: Weddell, Bellingshausen e Amundsen. Para tanto, foram utilizados como previsores a temperatura da superfície do mar, a temperatura do ar a 2 metros, a velocidade do vento a 10 metros, o albedo e os fluxos de calor latente e sensível, no período de 1979 a 2019. Os dados foram particionados em 70% para treinamento e 30% para testes. Modelos SARIMAX serviram como valores de referência para aferição da precisão das previsões com RNAs. Em todos os meses com anomalias absolutas superiores a 15% de concentração, o modelo de RNA CNN-LSTM superou os modelos MLP e SARIMAX.
Referências
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Ir-ving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Leven-berg, J., Mané, D., Monga, R., Moore S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, T., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y. & Zheng, X. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. https://arxiv.org/pdf/1603.04467.pdf
Akaike, H. 1974, ‘A new look at the statistical model identification’, IEEE Trans-actions on Automatic Control, vol. 19, no. 6, pp. 716-23. https://doi.org/10.1109/TAC.1974.1100705
Armour, K.C., Scott, J., Donohoe, A., Newsom, E.R. & Marshall J.C. 2016, ‘Southern Ocean warming delayed by circumpolar upwelling and equa-torward transport’, Nature Geoscience, vol. 9, no. 7, pp. 549-54. https://doi.org/10.1038/ngeo2731
Boetius, A., Anesio, A.M., Deming, J.W., Mikucki, J.A. & Rapp, J.Z. 2015, ‘Mi-crobial ecology of the cryosphere: sea ice and glacial habitats’, Nature Re-views Microbiology, vol. 13, no. 11, pp. 677-90. https://doi.org/10.1038/nrmicro3522
Box, G.E.P. & Jenkins, G.M. 1976, Time Series Analysis: Forecasting and Con-trol, Holden-Day, San Francisco, CA.
Breiman, L. 2001, ‘Random forests’, Machine learning, vol. 45, no. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324
Brownlee, J. 2016, Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow using Keras, Machine Learning Mastery.
Cavalieri, D.J., Gloersen P., Parkinson, C.L., Comiso, J.C. & Zwally, H.J. 1997, ‘Observed hemispheric asymmetry in global sea ice changes’, Science, vol. 278, no. 5340, pp. 1104-6. https://doi.org/10.1126/science.278.5340.1104
Cavalieri, D. & Parkinson, C.L. 2012, ‘Arctic sea ice variability and trends, 1979-2010’, The Cryosphere, vol. 6, no. 4, pp. 881-9. https://doi.org/10.5194/tc-6-881-2012
Chambault, P., Albertsen, C.M., Patterson, T.A., Hansen, R.G., Tervo, O., Laidre, K.L. & Heide-Jørgensen, M.P. 2018, ‘Sea surface temperature pre-dicts the movements of the Arctic cetacean: the bowhead whale’, Scientific reports, vol. 8, no. 1, pp. 1-12. https://doi.org/10.1038/s41598-018-27966-1
Chemke, R. & Polvani, L.M. 2020, ‘Using multiple large ensembles to elucidate the discrepancy between the 1979-2019 modeled and observed Antarctic Sea ice trends’, Geophysical Research Letters, vol. 47, no. 15, pp. e2020GL088339. https://doi.org/10.1029/2020GL088339
Chen, J., Li, M. & Wang, W. 2012, ‘Statistical uncertainty estimation using Ran-dom Forests and its application to drought forecast’, Mathematical Prob-lems in Engineering, vol. 2012, no. 915053. https://doi.org/10.1155/2012/915053
Evermann, J., Rehse, J.R. & Fettke, P. 2017, XES Tensorflow: Process predic-tion using the Tensorflow deep-learning framework, ArXv, vol. 1. https://arxiv.org/pdf/1705.01507.pdf
Gagné, M., Gillett, N. & Fyfe, P. 2015, ‘Observed and simulated changes in Antarctic sea ice extent over the past 50 years’, Geophysical Research Let-ters, vol. 42, no. 1, pp. 90-5. https://doi.org/10.1002/2014GL062231
Gloersen, P., Campbell, W.J., Cavalieri, D.J., Comiso, J.C., Parkinson, C.L. & Zwally, H.J. (eds) 1992, Arctic and antarctic sea ice: satellite passive-microwave observations and analysis, NASA, Washington D.C.
Haykin, S. 2007, Redes Neurais: Princípios e Prática, 2nd edn, Bookman, Porto Alegre, RS.
Ho, T.K. 1995, ‘Random decision forests’, 3rd international conference on doc-ument analysis and recognition 1995, IEEE, Murray Hill, NJ, pp. 278-82. https://doi.org/10.1109/ICDAR.1995.598994
Hochreiter, S. & Schmidhuber, J. 1997, ‘Long Short-Term Memory’, Neural computation, vol. 9, no. 8, pp. 1735-80. https://doi.org/10.1162/neco.1997.9.8.1735
Hutchinson, D.K., England, M.H., Santoso, A. & Hogg, A.M. 2013, ‘Interhe-mispheric asymmetry in transient global warming: The role of Drake Pas-sage’, Geophysical Research Letters, vol. 40, no. 8, pp. 1587-93. https://doi.org/10.1002/grl.50341
Hu, M.Y., Zhang, G., Jiang, C.X. & Patuwo, B.E. 1999, ‘A Cross-Validation Analysis of Neural Network Out-of-Sample Performance in Exchange Rate Forecasting’, Decision Sciences, vol. 30, no. 1, pp. 197-216. https://doi.org/10.1111/j.1540-5915.1999.tb01606.x
Kirchmeier-Young, M.C., Zwiers, F.W. & Gillett, N.P. 2017, ‘Attribution of ex-treme events in Arctic sea ice extent’, Journal of Climate, vol. 30, no. 2, pp. 553-71. https://doi.org/10.1175/JCLI-D-16-0412.1
Lawrence, S., Giles, C.L., Tsoi, A.C. & Back, A.D. 1997, ‘Face recognition: A convolutional neural-network approach’, IEEE transactions on neural net-works, vol. 8, no. 1, pp. 98-113. https://doi.org/10.1109/72.554195
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. 1998, ‘Gradient-based learning applied to document recognition’, Proceedings of the IEEE, vol. 86, no. 21, pp. 2278-324. https://doi.org/10.1109/5.726791
Meehl, G.A, Arblaster, J.M., Chung, C.T.Y., Holland, M.M., DuVivier, A., Thompson, L., Yang, D. & Bitz, C.M. 2019, ‘Sustained ocean changes con-tributed to sudden Antarctic ice retreat in late 2016’, Nature Communica-tions, vol. 10, no. 1, pp. 1-9.
https://doi.org/10.1038/s41467-018-07865-9
Olah, C. 2015, Understanding LSTM Networks, Colah’s Blog, weblog, viewed 13 July 2021, <https://colah.github.io/posts/2015-08-Understanding-LSTMs/>
Oliva, M., Navarro, F., Hrbáček, F., Hernández, A., Nývlt, D., Pereira, P., Ruiz-Fernández, J. & Trigo, R. 2017, ‘Recent regional climate cooling on the Antarctic Peninsula and associated impacts on the cryosphere’, Science of The Total Environment, vol. 580, pp. 210-23. https://doi.org/10.1016/j.scitotenv.2016.12.030
Parkinson, C.L. 2002, ‘Trends in the length of the Southern Ocean sea-ice sea-son’, Annals of Glaciology, vol. 34, no. 1, pp. 435-40. https://doi.org/10.3189/172756402781817482
Parkinson, C.L. 2019, ’A 40-y record reveals gradual Antarctic Sea ice increas-es followed by decreases at rates far exceeding the rates seen in the Arc-tic’, Proceedings of the National Academy of Sciences, vol. 116, no. 29, pp. 14414-23. https://doi.org/10.1073/pnas.1906556116
Raphael, M.N., Marshall, G.J., Turner, J., Fogt, R.L., Schneider, D., Dixon, D.A., Hosking, J.S., Jones, J.M. & Hobbs, W.R. 2016, ‘The Amundsen Sea Low: Variability, Change and Impact on Antarctic climate’, Bulletin of the Ameri-can Meteorological Society, vol. 97, no. 1, pp. 111-21. https://doi.org/10.1175/BAMS-D-14-00018.1
Rosenblatt, F. 1958, ‘The Perceptron: A probabilistic model for information stor-age and organization in the brain’, Psychological Review, vol. 65, no. 6, pp. 386-408. https://doi.apa.org/doi/10.1037/h0042519
Rouch, L.A., Dörr, J., Holmes, C.R., Massonnet, F., Blockley, E.W., Notz, D., Rackwow, T., Raphael, M.N., O’Farrell, S.P., Bailey, D.A. & Bitz, C.M. 2020, ‘Antarctic Sea ice area in CMIP6’, Geophysical Research Letters, vol. 47, no. 9, pp. e2019GL086729. https://doi.org/10.1029/2019GL086729
Serreze, M.C., Holland, M.M. & Stroeve, J. 2007, ‘Perspectives on the Artic’s Shrinking sea-ice cover’, Science, vol. 315, no. 5818, pp. 1533-36. https://doi.org/10.1126/science.1139426
Strobl, C., Boulesteix, A.L., Zeileis, A., & Hothorn, T. 2007, ‘Bias in random for-est variable importance measures: Illustrations, sources and a solution’, BMC bioinformatics, vol. 8, no. 1, pp. 1-21. https://doi.org/10.1186/1471-2105-8-25
Stroeve, J.C., Kattsov, V., Barret A., Serreze, M., Pavlova, T., Holland, M. & Meier, W. 2012, ‘Trends in Arctic Sea ice extent from CMIP5, CMIP3 and observations’, Geophysical Research Letters, vol. 39, no. 16, pp. L16502. https://doi.org/10.1029/2012GL052676
Turner, J., Lu, H., White, I., King, J.C., Phillips, T., Scott H.J., Bracegirdle, T.J., Marshall, G.J., Mulvaney, R. & Deb, P. 2016, ‘Absence of 21st century warming on Antarctic Peninsula consistent with natural variability’, Nature, vol. 535, no. 7612, pp. 411-5. https://doi.org/10.1038/nature18645
Turner, J., Guarino, M.V., Arnatt, J., Jena, B., Marshall, G.J., Phillips, T., Bajish, C.C., Clem, K., Wang, Z., Andersson, T., Murphy, E.J. & Cavanagh, R. 2020, ‘Recent Decrease of Summer Sea Ice in the Weddell Sea, Antarcti-ca’, Geophysical Research Letters, vol. 47, no. 11, pp. e2020GL087127. https://doi.org/10.1029/2020GL087127
Yegnanarayana, B. 2005, Artificial Neural Networks, Prentice-Hall of India, No-va Deli.
Young, T., Hazarika, D., Poria, S. & Cambria, E. 2018, ‘Recent trends in deep learning based natural language processing’, IEEE Computational intelli-gence magazine, vol. 13, no. 3, pp. 55-75. https://arxiv.org/abs/1708.02709
Vaughan, D.G., Marshall, G.J., Connolley, W.M., Parkinson, C., Mulvaney, R., Hodgson, D.A., King, J.C., Pudsey, C.J. & Turner, J. 2003, ‘Recent rapid regional climate warming on the Antarctic Peninsula’, Climate Change, vol. 60, no. 3, pp. 243-74. https://doi.org/10.1023/A:1026021217991
Wilks, D.S. 2006, Statistical Methods in the Atmospheric Sciences, 2nd edn, Academic Press, London.
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