the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An improved method of the Globally Resolved Energy Balance Model by the Bayes network
- 1School of Geography, Nanjing Normal University, Nanjing, 210023, China
- 2Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China
- 3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
- 4Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, China
- 5Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
- 1School of Geography, Nanjing Normal University, Nanjing, 210023, China
- 2Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China
- 3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
- 4Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, China
- 5Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
Abstract. This study introduces an improved method of the Globally Resolved Energy Balance Model(GREB) by the Bayes network. Starting from the climate elements relationship included in the GREB model, we reconstruct the model by the Bayes network to solve the problem of low model accuracy due to over-reliance on boundary conditions and initial conditions and the inability to use observed data for dynamic correction of model parameters. The improved model is applied to the simulation of surface average temperature and atmospheric average temperature based on the 3.75°×3.75° global data sets by Environmental Prediction (NCEP)/ National Center for Atmospheric Research(NCAR) from 1985 to 2014. The results illustrate that the improved model has higher average accuracy and lower spatial differentiation than the original GREB model. And the improved method provides a strong support for other dynamic model improvements.
Zhenxia Liu et al.
Status: open (until 27 Sep 2022)
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CC1: 'Comment on egusphere-2022-138', Richard Rosen, 08 Aug 2022
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It seems to me there is a major problem with this paper starting with equation #1, namely how was this derived. If we look at the equation carefully right away it there is no definition of the "surfact specific heat capacity". The concept of a heat capacity applies only to some well defined amount of mass that can be heated and, therefore, holds a certain amount of heat. This idea does not seem to apply to the surface of the earth. If it does the authors need to explain how, otherwise equation. Is it the heat capacity of the air, water, or land that is being considered. If equation #1 is not grounded in proper physics, then the entire paper appears useless.
Secondly, for starters, Baysian techniques are typically based on professional guesses. But how could any amount of guessing be more accurate than running models based on climate science?
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AC1: 'Reply on CC1', Wen Luo, 11 Aug 2022
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Thank you very much for your interest in our work and for your helpful and sound comments.
To the first question, this manuscript determines the correlation between different climate elements by referring to the GREB model. The dynamical equations in the article are cited from Ref. 1, where the physical meaning of the whole GREB model is clearly defined, so the conceptual definition of the GREB model is not what this manuscript will explore.
To the second question, as reviewer said, running models based on climate science is a more direct way to perform climate simulations, but the number of factors affecting climate processes and our current lack of comprehensive knowledge of them make existing models either difficult to effectively simulate their evolution or too complex, and sensitive to initial values and boundary conditions (Ref. 2, Ref. 3, Ref. 4). The idea of this manuscript is to use the existing climate models to construct a Bayes network (this means that the amount of guessing in the manuscript is constructed based on the existing climate science model, in section 2.1 of the manuscript). Then, the network is trained with real observation data (In section 3.2 of the manuscript), and finally the simulation of climate processes is realized. Although this manuscript is based on the statistical idea of Bayes network, it is not a complete gray box model. The network is constructed by considering the basic laws in climate science and applying the real observation data. Therefore, the research of this paper can be considered as an improvement to the climate models. And at the same time, the research of this manuscript also proposes a basic idea of combining dynamical and statistical models. From this perspective, it is reasonable and predictable to make better results than climate models by using the thesis method.
I hope to contact you further if you have any suggestions!
Ref. 1 Dommenget, D. and Flöter, J.: Conceptual understanding of climate change with a globally resolved energy balance model, Climate Dynamics, 37, 2143-2165, 10.1007/s00382-011-1026-0, 2011.
Ref. 2 Alley, R. B., Emanuel, K. A., and Zhang, F.: Advances in weather prediction, SCIENCE, 363, 342-344, 10.1126/science.aav7274, 2019.
Ref. 3 Fan, J., Meng, J., Ludescher, J., Chen, X., Ashkenazy, Y., Kurths, J., Havlin, S., and Schellnhuber, H. J.: Statistical physics approaches to the complex Earth system, PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 896, 1-84, 10.1016/j.physrep.2020.09.005, 2021.
Ref. 4 Zou, Y., Donner, R. V., Marwan, N., Donges, J. F., and Kurths, J.: Complex network approaches to nonlinear time series analysis, PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 787, 1-97, 10.1016/j.physrep.2018.10.005, 2019.
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CC2: 'Reply on AC1', Richard Rosen, 11 Aug 2022
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If you really believe that your proposed methodology can IMPROVE existing climate models, then why don't you call your paper something like "How to improve exisint climate models using a Bayes network". Then in the text you could list all the steps that need to be taken as implied by your methodology, and then you could illustrate how an actual climate forecast was improved using your methodology, if you have a numerical example that could do so. clearly, if you can succeed at doinig this the climate modelers will have to take notice and respond to your claims.
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AC2: 'Reply on CC2', Wen Luo, 15 Aug 2022
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Thank you very much for your further advice.
As you said, in section 2 of the manuscript, the method of "How to improve exisint climate models using a Bayes network" has been illustrated, and the main approach is to construct a Bayes network by using the interaction between different climate elements in existing climate models. This idea is verified by simulating two elements(surface average temperature and atmospheric average temperature ). The main reason for choosing the GREB model in the article is that the model is relatively simple, and the interaction relationship between the internal elements is relatively single, so the constructed Bayes network is also relatively simple. As for the suggestion about the title of the manuscript, I think it is only an attempt for now and there are still some problems to be solved, so I will carefully consider your suggestions and discuss these further in the discussion.
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AC2: 'Reply on CC2', Wen Luo, 15 Aug 2022
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CC2: 'Reply on AC1', Richard Rosen, 11 Aug 2022
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AC1: 'Reply on CC1', Wen Luo, 11 Aug 2022
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Zhenxia Liu et al.
Zhenxia Liu et al.
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