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 (extended)
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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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CEC1: 'Comment on egusphere-2022-138', Juan Antonio Añel, 23 Aug 2022
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Dear authors,
First, many thanks for trying to comply with our Code and Data Policy. I want to bring to your attention an issue in your code: comments are not in English. When I open the files with the code, instead of comments, I get several lines of characters that do not mean anything. I guess you could have commented on your code in Chinese, which messed up. Ideally, the comments should be in English, as it is the language used for all the other parts of your submission and the language of our journal.
Please, consider fixing it.
Regards,
Juan A. Añel
Geoesci. Model Dev. Executive Editor
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AC3: 'Reply on CEC1', Wen Luo, 01 Sep 2022
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Dear editor
I'm terribly sorry for my oversight. I have made changes to the comments to comply with the Code and Data Policy. The new code of the paper is archived on Zenodo(https://doi.org/10.5281/zenodo.7039740).
I hope to contact you further if you have any suggestions!
Regards,
Wen Luo
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AC3: 'Reply on CEC1', Wen Luo, 01 Sep 2022
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RC1: 'Comment on egusphere-2022-138', Anonymous Referee #1, 27 Oct 2022
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The manuscript "An improved method of the Globally Resolved Energy Balance Model by the Bayes network" by Liu et al. discusses a Bayes network approach to simulate global temperatures given some climate forcing boundary conditions such as solar radiation, cloud cover or water vapor. While the subject matter is interesting and should be considered for publication, the manuscript is largely unintelligible and should not get published. There are far too many aspects in this manuscript that would need substantial revisions. I recommend to reject this manuscript with an opportunity to resubmit a substantially revised analysis. Details below.
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Major points:(*) Clarity: The manuscript lags clarity on the methods, results and aims on many different levels. It is essentially unintelligible for the reader to understand what has been done, what the results are and how we did get there. Below are a few more points that may help the authors to improve on this.
(*) GREB model: The authors seem use the term GREB model as a general concept of how to simulate the global temperatues based on some boundary conditions (e.g. solar radiation). At the same time the term GREB model also refers to a model published by Dommenget et al.. This is confusing. It is unclear what the GREB model by Dommenget et al. has to do with the Bayes network approach the authors use. It seems they are essentially unrelated models.
(*) Language: The authors use terms that are not commonly used and are therefor hard to understand. Examples:
"the change law of climate system"
"the average accuracy variation trend chart"
(*) IMPM model: What is the IMPM model? It says it is "improved". Improved based on what? It seems to imply that it is based on the GREB model by Dommenget et al., but I do not see how. From the text it seems to have nothing to do with the GREB model by Dommenget et al..
(*) "classification of climate elements is 5, 7, and 9": It is unclear what climate element classifications are. It is in particular unclear what a GREB model 5,7, and 9 would be in contrast to IMPM 5,7, and 9.
(*) Accuracy: How is accuracy defined?
(*) "Nodes" vs. "classification of climate elements is 5, 7, and 9". The authors define several nodes in the GRBE model (.e.g. water vapor, winds, etc.) and they later discuss "classification of climate elements is 5, 7, and 9". Are these entirely unrelated concepts? It needs substantial revisions to explain this better.
(*) Fig.2,3 : What is shown in this figure? The figure is largely unclear. What are the colors? What are the units? What is shown on z-axis labeled "Seasons" 0 to 80? Does this figure has something to do with surface temperature [Celsius]?(*) Fig.7: What is "accuracy variation trend"? It is largely unclear what "trend" could refer to.
(*) GREB model surface temperature: The GRBE model by Dommenget et al. is a flux corrected model that by construction has no biases (errors) in the simulation of the surface temperature. Then, how can the IMPM model be better at simulating surface temperature than the GRBE model, when the GREB model is by construction perfect?
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Other major points (as they appear in the text):
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line 55 "... these two average state variables includes most of the climate processes of the GREB ..": Why? Why not water vapour? It is not obvious why water vapour would not be important.
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line 189-191 "The tropospheric height of the poles is lower and the tropospheric height of the equator is higher, and which phenomenon leads to the result that temperature of the troposphere at the same height is higher in the poles.":This statement appears to have nothing to do with the analysis presented. It certainly has nothing to do with the GREB model by Dommenget et al., as there is no such thing a tropospheric height, as it is a one-layer model.
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line 202 "The all average accuracy of different situations from 1985 to 1994 ...": Does this imply the model is simulated for each year? Assuming they are different each year?How would this work in the GREB model by Dommenget et al.? This model is not simulating internal variability, but only the response to changes in external boundary conditions.
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Other minor points: not listed, as there are too many major points that need to be addressed first.
Zhenxia Liu et al.
Zhenxia Liu et al.
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