Advancing Climate Modeling through High-Performance Computing: Towards More Accurate and Efficient Simulations
DOI:
https://doi.org/10.4108/ew.7049Keywords:
Climate Modeling, General Circulation Models (GCMs), Data Assimilation Techniques, High-Performance Computing (HPC), Precipitation Pattern Recognition First SectionAbstract
A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.
Downloads
References
[1] Strey, A. (2001). High Performance Computing. https://doi.org/10.1016/B0-08-043076- 7/00574-X
[2] Andrei, C., Pires, Nunes, J., & Luiz, B. ndrea, Carla, Dalmolin. Freitas, Cerqueira., G., R., Niella. (2023). Selection of climate variables by statistical-computational modeling in forest seedlings treated with growth biopromoter. Treedimensional, doi: 10.55746/treed.2023.04.020.
[3] Shabrina, A., Palupi, I., Wahyudi, B. A., Wahyuni, I. N., Murti, M. D., & Latifah, A. L. (2022). Modelling the climate factors affecting forest fire in Sumatra using Random Forest and Artificial Neural Network. https://doi.org/10.1145/3575882.3575920
[4] Balaji, V., Couvreux, F., Deshayes, J., Gautrais, J., Hourdin, F., & Rio, C. (2022). Are gen- eral circulation models obsolete? Proceedings of the National Academy of Sciences of the United States of America, 119(47), e2202075119. https://doi.org/10.1073/pnas.2202075119
[5] Beniston, M. (2004). Modeling and observing climate. In. Climatic Change and Its Impacts (pp. 91–127). Springer Netherlands. https://doi.org/10.1007/1-4020-2346-4_5
[6] Chervin, R. M. (1988). On the relationship between computer technology and climate mod- eling. In Physically based modelling and simulation of climate and climatic change: Part 2 (pp. 1053–1068). Springer Netherlands.
[7] Chunping, Q.., Michael. (2018). Feature Importance Analysis for Local Climate Zone Clas- sification Using a Residual Convolutional Neural Network with Multi-Source Datasets. Xiao, Xiang, Zhu. Remote Sensing. Lichao, Mou., Pedram, Ghamisi.. https://doi.org/10.3390/RS10101572
[8] Evangelinos, C., & Hill, C. (2008). Cloud computing for parallel scientific hpc applications: Feasibility of running coupled atmosphere-ocean climate models on amazons ec2. ratio, 2, (2.40), 2-34.
[9] Frank, N. (2016). A glance at high performance computing (HPC). Gilchrist, A. (11 1978). Numerical simulation of climate and climatic change. Nature, 276(5686), 342–345. https://doi.org/10.1007/978-3-319-21903-5_
[10] Govindasamy, B., & Duffy, P. B. P2. 17 high-resolution global simulation of the climatic effects of increased greenhouse gases.
[11] Han, Y., Zhang, Z., & Kobe, F. T. (2023). The hybrid of multilayer perceptrons: A new geostatistical tool to generate high-resolution climate maps in developing countries. Mathe- matics, 11(5), 1239. https://doi.org/10.3390/math11051239
[12] Hewitt, H. T., Copsey, D., Culverwell, I. D., Harris, C. M., Hill, R. S. R., Keen, A. B., McLaren, A. J., & Hunke, E. C. (2010). Design and implementation of the infrastructure of HadGEM3: The next-generation Met Office climate modeling system. Geoscientific Model Development Discussions, 3(4), 1861–1937.
[13] Hiller, W., & Fritzsch, B. (2009, April). Grid as an enabling technology for high performance computing in climate research. In EGU General Assembly Conference Abstracts (p. 12233).
[14] Hristo, Chervenkov, V., & Spiridonov. (2017). Precipitation pattern estimation with the standardized precipitation index in projected future climate over Bulgaria. https://doi.org/10.1007/978-3-319-73441-5_48
[15] Hu, F., Yang, C., Schnase, J. L., Duffy, D. Q., Xu, M., Bowen, M. K., Lee, T., & Song, W. (2018). ClimateSpark: An in-memory distributed computing framework for big climate data analytics.Computers and Geosciences, 115, 154–166. https://doi.org/10.1016/j.cageo.2018.03.011
[16] Lin, H., Li, C., Chen, T., Hsieh, C., Wang, G., Wang, T., & Hu, J. (2020). Climate‐based approach for modeling the distribution of montane forest vegetation in Taiwan. Applied Vegetation Science, 23(2), 239–253. https://doi.org/10.1111/AVSC.12485
[17] Huntington, J. L., Hegewisch, K. C., Daudert, B., Morton, C. G., Abatzoglou, J. T., McEvoy,
D. J., & Erickson, T. (2017). Climate engine: Cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understand ing. Bulletin of the American Meteorological Society, 98(11), 2397–2410. https://doi.org/10.1175/BAMS-D-15-00324.1
[18] Jake, J., Nichol, J., & Jake, N.., Matthew, Peterson., Kara, J., Peterson., G., Matthew, Fricke., Melanie, E., Moses., Melanie, E., Moses. (2021). Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change. Journal of Computational and Applied Mathematics, doi: 10.1016/J.CAM.2021.113451.
[19] Kopparla, P., Fischer, E. M., Hannay, C., & Knutti, R. (2013). Improved simulation of extreme precipitation in a high‐resolution atmosphere model. Geophysical Research Letters, 40(21), 5803–5808. https://doi.org/10.1002/2013GL057866
[20] Krakauer, N. Y., & Fekete, B. M. (2014). Are climate model simulations useful for forecasting precipitation trends? Hindcast and synthetic-data experiments. Environmental Research Letters, 9(2), 024009. https://doi.org/10.1088/1748-9326/9/2/024009
[21] Labajo, A., Martín, Q., & Labajo, J. (2012). Heuristic methods for modeling the behaviour of climate variables using a multilayer Perceptron.
[22] Yan, L., Lei, Q., Jiang, C., Yan, P., Ren, Z., Liu, B., & Liu, Z. (2022). Climate-informed monthly runoff prediction model using machine learning and feature importance analysis. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.1049840
[23] Li, S., & Chen, X. (2020). High-performance computing for efficient climate model parameterization. International Journal of High-Performance Computing, 36(4), 456–468.
[24] Martin, M. (2010). Fred, Hutchinson. High Performance Computing. Falcon.
[25] Masamichi, O. (2021). Precipitation under climate change. https://doi.org/10.1016/B978-0- 12-822699-5.00002-1
[26] Mechoso, C. R., Drummond, L. A., Farrara, J. D., & Spahr, J. A. (1998, November). The UCLA AGCM in high performance computing environments. In SC1998. Proceedings of the 1998 ACM/IEEE Conference on Supercomputing (pp. 7–7). IEEE Publications. https://doi.org/10.1109/SC.1998.10020
[27] Meehl, G. A. (1984). Modeling the Earth’s climate. Climatic Change, 6(3), 259–286. https://doi.org/10.1007/BF00142476
[28] Mirin, A. A., Wickett, M. E., Duffy, P. B., & Rotman, D. A. (2005). Climate modeling using high-performance computing (No. UCRL-TR-210224). Lawrence Livermore National Laboratory (Lawrence Livermore National Laboratory, Office of Science).
[29] Patel, A., & Williams, M. (2021). Scalable high-performance computing in climate modeling. Environmental Modelling and Software, 76, 34–48.
[30] Marksteiner, P. (1996). High-performance computing — An overview. Computer Physics Communications, 97(1–2), 16–35. https://doi.org/10.1016/0010-4655(96)00018-5
[31] Ramamurthy, M. (2018, October). Toward a cloud ecosystem for modeling as a service. In 14th International Conference on e-Science (e-Science), 2018 (pp. 274–275). IEEE Publications. IEEE Publications. https://doi.org/10.1109/eScience.2018.00046
[32] Sadeq, O., Sulaiman, J., Shiri, H., & Shiralizadeh. (2018). Precipitation pattern modeling using cross-station perception: regional investigation. Ozgur, Kisi. Zaher, Mundher, Yaseen. Environmental Earth Sciences. https://doi.org/10.1007/S12665-018-7898-0
[33] Schneider, S. H., & Dickinson, R. E. (1974). Climate modeling. Reviews of Geophysics, 12(3), 447–493. https://doi.org/10.1029/RG012i003p00447
[34] Schultz, C. (2014). A new, improved high‐resolution precipitation model. Eos, Transactions American Geophysical Union, 95(30), 276–276. https://doi.org/10.1002/2014EO300013
[35] Seiffert, R., & Von Storch, J.-S. (2008). Impact of atmospheric small‐scale fluctuations on climate sensitivity. Geophysical Research Letters, 35(10). https://doi.org/10.1029/2008GL033483
[36] Shukla, J., Kinter, J. L., Schneider, E. K., & Straus, D. M. (1999). Modelling of the climate system. In. Climate Change: An Integrated Perspective (pp. 51–104). Springer Netherlands. https://doi.org/10.1007/0-306-47982-6_3
[37] Small, R. J., Bacmeister, J., Bailey, D., Baker, A., Bishop, S., Bryan, F., Caron, J., Dennis, J., Gent, P., Hsu, H., Jochum, M., Lawrence, D., Muñoz, E., diNezio, P., Scheitlin, T., To- mas, R., Tribbia, J., Tseng, Y., & Vertenstein, M. (2014). A new synoptic scale resolving global climate simulation using the Community Earth System Model. Journal of Advances in Modeling Earth Systems, 6(4), 1065–1094. https://doi.org/10.1002/2014MS000363
[38] Smith, J. A. et al. (2018). The impact of high-performance computing on climate science. Journal of Climate Modeling, 45(6), 789–801.
[39] Spotz, W. F., & Swarztrauber, P. N. (2002). Climate modeling. Computing in Science and Engineering, 4(5), 24–25. https://doi.org/10.1109/MCISE.2002.1032425
[40] Steed, C. A., Evans, K. J., Harney, J. F., Jewell, B. C., Shipman, G., & Smith, B. E., ... & Williams.
[41] Sun-Chong, W. (2003). High Performance Computing. https://doi.org/10.1007/978-1-4615- 0377-4_3
[42] Dobryakova, V., Moskvina, N., Dobryakov, A., & Zhegalina, L. (2022). Modeling network of research sites for monitoring carbon flows by Random Forest method. InterCarto. Inter- GIS, 28(1), 645–658. https://doi.org/10.35595/2414-9179-2022-1-28-645-658
[43] Wang, S. C., & Wang, S. C. (2003). High performance computing. Interdisciplinary Computing in Java Programming, 39–55.
[44] Wei, X., Xihaier, L., Yihui, R., Ji, H., Park, S., Yoo, B., T., & Nadiga. (2021). Feature importance in a deep learning climate emulator. arXiv: Learning.
[45] Wood, E. F., & Lakshmi, V. (1993). Scaling water and energy fluxes in climate systems: Three land-atmospheric modeling experiments. Journal of Climate, 6(5), 839–857. https://doi.org/10.1175/1520-0442(1993)006<0839:SWAEFI>2.0.CO;2
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Energy Web
![Creative Commons License](http://i.creativecommons.org/l/by/3.0/88x31.png)
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.