the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Jan 2024
Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach
Abstract. Heterogeneous radiative forcing in mid-latitudes, such as that exerted by aerosols, has been found to affect the Arctic climate, though the mechanisms remain debated. In this study, we leverage Deep Learning (DL) techniques to explore the complex response of the Arctic climate system to local radiative forcing over Europe. We conducted sensitivity experiments using the Max Planck Institute Earth System Model (MPI-ESM1.2) coupled with atmosphere-ocean–land surface components. Utilizing a DL-based clustering method, we classify atmospheric circulation patterns in a lower-dimensional space, focusing on Poleward Moist Static Energy Transport (PMSET) as our primary parameter. We developed a novel method to analyze the circulation patterns' contributions to various climatic parameter anomalies. Our findings indicate that the negative forcing over Europe alters existing circulation patterns and their occurrence frequency without introducing new ones. Specifically, we identify changes in a circulation pattern with a high-pressure system over Scandinavia as a key driver for reduced Sea Ice Concentration (SIC) in the Barents-Kara Sea during autumn. This circulation pattern also influences middle atmospheric dynamics, although its contribution is relatively minor compared to other circulation patterns that resemble the phases of the North Atlantic Oscillation (NAO). Our multidimensional approach combines DL algorithms and human expertise to offer a novel analytical tool that could have broader applications in climate science.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-3033', Anonymous Referee #1, 15 Feb 2024
This paper examines the European radiative forced responses to Arctic climate by using a combination method of two machine learning techniques, the k-mean clustering and convolution neural network. Specifically, the authors classified six patterns and discussed how these six patterns responds to the European radiative forcing. This paper is interesting, and the topic is crucial for the community; however, the manuscript is not well organized and the results are not well highlighted. Therefore, I do not suggest this paper to be published in Weather and Climate Dynamics before a major revision is made.
Major comments:
- The authors applied multiple encoding methods and clustered them into six groups of large-scale circulation patterns. However, the main six patterns in section 3.1 are not well-discussed in their physical meaning. Even though the main occurrence seasons are described, the dynamical interactions of each pattern are not investigated. Is there any existing large-scale circulation pattern that is similar with these group? If not, why and how different the patterns found are compared with the existing patterns. For example, by composing the same timing and location with surface temperature or other variables, can we obtain more meaning from these patterns? This will make the later discussions, such as section 3.2, easier since the physical meaning of each pattern is known.
Another suggestion is that the authors discuss Fig. 13 and 14 in details in section 3.1, and point out the most meaningful patterns that we need to focus on. Then in the section 3.2 and so on, the authors do not need to discuss all the patterns, which makes the comparison simpler. - The authors have too many figures and no clear storyline is provided. Also, the tittle is about Arctic climate responses to European Radiative forcing, which is too broad and no specific results. The authors should summarize the main responses with a more specific title and describe the corresponding physical process, rather than discuss all the results a little bit. In section 3.1, the authors should decide which patterns to focus and try to understand the physical meaning of the patterns. For instance, the patterns have stronger occurrence in summer are not important for poleward wave propagation. Or in Lines 685-694, the authors focus mainly on one pattern and discuss the possible dynamical process of it, which should be more emphasized or considered as the main conclusion of this manuscript.
- In section 2.4, the authors trained the AAE with only Control run data and lead to the conclusion in Lines 219-220, “This implies that the negative forcing over Europe does not introduce new discernible spatiotemporal patterns in the Northern Hemisphere extratropical large-scale circulation.” However, this conclusion seems to be overstated. The difference of reconstruction loss distribution (Fig.3) between the Control and Experiment runs are limited, indicating the same encoder can be used for both Control and Experiment runs. That is, if there are new discernible spatiotemporal patterns in the Experiment run data, they can also be captured by the encoder of the Control run. I suggest the authors also do another way round (train with Experiment) to confirm that the pattern found in both data can be inter-changeably used. Also, I would suggest to weaken the sentence in Lines 219-220.
- There is little discussion on the mean states between Control and Experiment runs. The authors should first discuss how different it is when the forcing is imposed through traditional methods, such as composite or even EOF/PCA. Since the authors directly start using the six patterns for explaining the forced signature, the results are hard to follow. The authors should give a basic state of how the forced signal look like. And why we need to use such complicated methods to study the forced signal?
Other comments are following:
- B1-04 is similar with Fig. B3-01; however, their occurrence seasons are so different. Why? Same for the Fig. B5-05. Are they corresponding to the C1 and C2 in Fig.6? Even though the methods are different, the results should match between each other in certain degree.
- Line 231, why 40 indices in total? 4 latitudes with 5 PMSET values and two levels of troposphere? It is easy to miss the two levels of troposphere.
- Line 349, how does the authors consider the “statistically significant increases” in Fig. 10?
- Line 359-361, “between the daily mean PMSET” of upper and lower troposphere?
Citation: https://doi.org/10.5194/egusphere-2023-3033-RC1 - AC2: 'Reply on RC1', Sina Mehrdad, 28 Mar 2024
- The authors applied multiple encoding methods and clustered them into six groups of large-scale circulation patterns. However, the main six patterns in section 3.1 are not well-discussed in their physical meaning. Even though the main occurrence seasons are described, the dynamical interactions of each pattern are not investigated. Is there any existing large-scale circulation pattern that is similar with these group? If not, why and how different the patterns found are compared with the existing patterns. For example, by composing the same timing and location with surface temperature or other variables, can we obtain more meaning from these patterns? This will make the later discussions, such as section 3.2, easier since the physical meaning of each pattern is known.
-
RC2: 'Comment on egusphere-2023-3033', Anonymous Referee #2, 21 Feb 2024
Title: Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach
Authors: Sina Mehrdad, Dörthe Handorf, Ines Höschel, Khalil Karami, Johannes Quaas, Sudhakar Dipu, and Christoph Jacobi
Summary: This study utilizes deep learning techniques to investigate the Arctic atmospheric circulation responses to European radiative forcing, which is both intriguing and significant for advancing our understanding of applying convolutional auto-encoder frameworks in Arctic climate change research. Overall, the manuscript is well-written, and the authors extensively discuss the consistent dynamical responses, although interpreting causality remains challenging. Below, I provide a few major and minor comments for the authors' consideration.
Major comments:
- Baseline machine learning model comparison. This study presents an innovative application of convolutional autoencoders (AEs), which appears to be one of the first attempts in studying Arctic response to radiative forcing. However, I am still unclear about the motivation behind choosing convolutional AEs. For example, the authors mentioned that in Lines 538-539 that the FAE is a compelling method for generating a concise and informative representation, but did not elaborate on compare to what. Have the authors compared the performance of convolutional AEs with other machine learning or statistical methods? For example, similar clustering analyses could be conducted using self-organizing maps (SOMs), which are computationally less expensive than training convolutional AEs. SOMs have been used in studying atmospheric moisture transport in the Arctic or large-scale atmospheric circulations (e.g., Skific et al. 2009; Lee 2017). Additionally, principal component analysis (PCA) or empirical orthogonal function (EOF) analysis could be employed for clustering tests. Why not start with these simpler methods before diving into complex deep learning models? However, if the authors can demonstrate that convolutional AEs outperform SOMs or PCA/EOF, it would strengthen the justification for using convolutional AEs in this study. Perhaps the authors could consider quickly implementing these simpler methods and comparing the results with those obtained from convolutional AEs.
- What new physical or dynamical insight do we learn? I am curious about the new insights or knowledge gained from the new clustering method employed in this study. Lines 566-567 mention that well-established large-scale circulation patterns (e.g., NAO, AO, PNA) are identified, and consistent dynamical responses in the troposphere and stratosphere can be demonstrated. I assume that similar conclusions may be drawn from other clustering methods as well. Could we uncover new dynamical pathways in which the Arctic responds to European radiative forcing differently from previous understandings based on stratosphere-troposphere coupling? It would be helpful if the authors could create a table summarizing the dynamical responses associated with each cluster, indicating which dynamical responses are already known and which are new. Similarly, Lines 324-327 discuss the seasonality changes. What do these seasonality changes signify physically, and what can we learn from them?” In addition, what is the separation of WCVC and FSDC components brings us new insights?
- Linking the responses to European radiative forcing. I noticed that the discussion on the results seems to focus less on the direct response to radiative forcing and more on the subsequent atmospheric circulation responses and PMSET. For example, how does the European radiative forcing lead to increased upward EP flux for cluster 3 in SON (Figure 17)? Or how does the localized radiative forcing in Europe give rise to changes in 2m temperature across the entire Northern Hemisphere, as depicted in Figure 13? Some of the temperature increases appear contradictory to the cooling effect of aerosol negative forcing (or specifically here, the cloud forcing).
Minor comments:
Lines 58-60: perhaps the authors considering to cite two new studies on this topic: Xu et al. (2023) and Liang et al. (2024).
Line 123 and Figure 1: why there are statistically significant radiative forcing increase in eastern Siberia, Asia, and North Pacific?
Lines 161-162: why 8 days? Does this indicate a certain physical process dominating?
Line 230: the maximum PMSET? Or both maximum and minimum PMSETs?
Lines 246-248: could the authors provide a figure to demonstrate this grid transformation?
Lines 298 and 301: Are there any reference papers for WCVC and FSDC?
Figure 8: it seems the signal-to-noise ration is quite small. How could the authors say these responses are important?
Figures 9 and Figure 10: combine these two figure into one figure?
Figure 12: the arrows are hardly seen. Perhaps the authors can try to enhance the visibility of the arrows.
Lines 458-460: but the seasonal distribution changes?
Figure 17: the EP flux divergence does not exactly match the pattern of zonal wind anomalies in some seasons and clusters. How could we relate the EP flux change to zonal wind change?
Lines 540-542: the authors mentioned that the external forcing can modify the circulation patterns. But in Lines 778-680, the authors conclude that the radiative forcing only alter the existing circulation patterns, and does not introduce new patterns. These two sentences seem contradictory somewhat. Could the authors clarify and reconcile these statements?
Line 689: upper troposphere lower stratosphere —> upper troposphere and lower stratosphere?
References:
Xu, M., Tian, W., Zhang, J., Screen, J.A., Zhang, C. and Wang, Z., 2023. Important role of stratosphere-troposphere coupling in the Arctic mid-to-upper tropospheric warming in response to sea-ice loss. npj Climate and Atmospheric Science, 6(1), p.9.
Lee, C.C., 2017. Reanalysing the impacts of atmospheric teleconnections on cold‐season weather using multivariate surface weather types and self‐organizing maps. International Journal of Climatology, 37(9), pp.3714-3730.
Liang, Y.C., Kwon, Y.O., Frankignoul, C., Gastineau, G., Smith, K.L., Polvani, L.M., Sun, L., Peings, Y., Deser, C., Zhang, R. and Screen, J., 2024. The Weakening of the Stratospheric Polar Vortex and the Subsequent Surface Impacts as Consequences to Arctic Sea Ice Loss. Journal of Climate, 37(1), pp.309-333.
Skific, N., Francis, J.A. and Cassano, J.J., 2009. Attribution of projected changes in atmospheric moisture transport in the Arctic: A self-organizing map perspective. Journal of Climate, 22(15), pp.4135-4153.
Citation: https://doi.org/10.5194/egusphere-2023-3033-RC2 - AC1: 'Reply on RC2', Sina Mehrdad, 28 Mar 2024
- Baseline machine learning model comparison. This study presents an innovative application of convolutional autoencoders (AEs), which appears to be one of the first attempts in studying Arctic response to radiative forcing. However, I am still unclear about the motivation behind choosing convolutional AEs. For example, the authors mentioned that in Lines 538-539 that the FAE is a compelling method for generating a concise and informative representation, but did not elaborate on compare to what. Have the authors compared the performance of convolutional AEs with other machine learning or statistical methods? For example, similar clustering analyses could be conducted using self-organizing maps (SOMs), which are computationally less expensive than training convolutional AEs. SOMs have been used in studying atmospheric moisture transport in the Arctic or large-scale atmospheric circulations (e.g., Skific et al. 2009; Lee 2017). Additionally, principal component analysis (PCA) or empirical orthogonal function (EOF) analysis could be employed for clustering tests. Why not start with these simpler methods before diving into complex deep learning models? However, if the authors can demonstrate that convolutional AEs outperform SOMs or PCA/EOF, it would strengthen the justification for using convolutional AEs in this study. Perhaps the authors could consider quickly implementing these simpler methods and comparing the results with those obtained from convolutional AEs.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-3033', Anonymous Referee #1, 15 Feb 2024
This paper examines the European radiative forced responses to Arctic climate by using a combination method of two machine learning techniques, the k-mean clustering and convolution neural network. Specifically, the authors classified six patterns and discussed how these six patterns responds to the European radiative forcing. This paper is interesting, and the topic is crucial for the community; however, the manuscript is not well organized and the results are not well highlighted. Therefore, I do not suggest this paper to be published in Weather and Climate Dynamics before a major revision is made.
Major comments:
- The authors applied multiple encoding methods and clustered them into six groups of large-scale circulation patterns. However, the main six patterns in section 3.1 are not well-discussed in their physical meaning. Even though the main occurrence seasons are described, the dynamical interactions of each pattern are not investigated. Is there any existing large-scale circulation pattern that is similar with these group? If not, why and how different the patterns found are compared with the existing patterns. For example, by composing the same timing and location with surface temperature or other variables, can we obtain more meaning from these patterns? This will make the later discussions, such as section 3.2, easier since the physical meaning of each pattern is known.
Another suggestion is that the authors discuss Fig. 13 and 14 in details in section 3.1, and point out the most meaningful patterns that we need to focus on. Then in the section 3.2 and so on, the authors do not need to discuss all the patterns, which makes the comparison simpler. - The authors have too many figures and no clear storyline is provided. Also, the tittle is about Arctic climate responses to European Radiative forcing, which is too broad and no specific results. The authors should summarize the main responses with a more specific title and describe the corresponding physical process, rather than discuss all the results a little bit. In section 3.1, the authors should decide which patterns to focus and try to understand the physical meaning of the patterns. For instance, the patterns have stronger occurrence in summer are not important for poleward wave propagation. Or in Lines 685-694, the authors focus mainly on one pattern and discuss the possible dynamical process of it, which should be more emphasized or considered as the main conclusion of this manuscript.
- In section 2.4, the authors trained the AAE with only Control run data and lead to the conclusion in Lines 219-220, “This implies that the negative forcing over Europe does not introduce new discernible spatiotemporal patterns in the Northern Hemisphere extratropical large-scale circulation.” However, this conclusion seems to be overstated. The difference of reconstruction loss distribution (Fig.3) between the Control and Experiment runs are limited, indicating the same encoder can be used for both Control and Experiment runs. That is, if there are new discernible spatiotemporal patterns in the Experiment run data, they can also be captured by the encoder of the Control run. I suggest the authors also do another way round (train with Experiment) to confirm that the pattern found in both data can be inter-changeably used. Also, I would suggest to weaken the sentence in Lines 219-220.
- There is little discussion on the mean states between Control and Experiment runs. The authors should first discuss how different it is when the forcing is imposed through traditional methods, such as composite or even EOF/PCA. Since the authors directly start using the six patterns for explaining the forced signature, the results are hard to follow. The authors should give a basic state of how the forced signal look like. And why we need to use such complicated methods to study the forced signal?
Other comments are following:
- B1-04 is similar with Fig. B3-01; however, their occurrence seasons are so different. Why? Same for the Fig. B5-05. Are they corresponding to the C1 and C2 in Fig.6? Even though the methods are different, the results should match between each other in certain degree.
- Line 231, why 40 indices in total? 4 latitudes with 5 PMSET values and two levels of troposphere? It is easy to miss the two levels of troposphere.
- Line 349, how does the authors consider the “statistically significant increases” in Fig. 10?
- Line 359-361, “between the daily mean PMSET” of upper and lower troposphere?
Citation: https://doi.org/10.5194/egusphere-2023-3033-RC1 - AC2: 'Reply on RC1', Sina Mehrdad, 28 Mar 2024
- The authors applied multiple encoding methods and clustered them into six groups of large-scale circulation patterns. However, the main six patterns in section 3.1 are not well-discussed in their physical meaning. Even though the main occurrence seasons are described, the dynamical interactions of each pattern are not investigated. Is there any existing large-scale circulation pattern that is similar with these group? If not, why and how different the patterns found are compared with the existing patterns. For example, by composing the same timing and location with surface temperature or other variables, can we obtain more meaning from these patterns? This will make the later discussions, such as section 3.2, easier since the physical meaning of each pattern is known.
-
RC2: 'Comment on egusphere-2023-3033', Anonymous Referee #2, 21 Feb 2024
Title: Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach
Authors: Sina Mehrdad, Dörthe Handorf, Ines Höschel, Khalil Karami, Johannes Quaas, Sudhakar Dipu, and Christoph Jacobi
Summary: This study utilizes deep learning techniques to investigate the Arctic atmospheric circulation responses to European radiative forcing, which is both intriguing and significant for advancing our understanding of applying convolutional auto-encoder frameworks in Arctic climate change research. Overall, the manuscript is well-written, and the authors extensively discuss the consistent dynamical responses, although interpreting causality remains challenging. Below, I provide a few major and minor comments for the authors' consideration.
Major comments:
- Baseline machine learning model comparison. This study presents an innovative application of convolutional autoencoders (AEs), which appears to be one of the first attempts in studying Arctic response to radiative forcing. However, I am still unclear about the motivation behind choosing convolutional AEs. For example, the authors mentioned that in Lines 538-539 that the FAE is a compelling method for generating a concise and informative representation, but did not elaborate on compare to what. Have the authors compared the performance of convolutional AEs with other machine learning or statistical methods? For example, similar clustering analyses could be conducted using self-organizing maps (SOMs), which are computationally less expensive than training convolutional AEs. SOMs have been used in studying atmospheric moisture transport in the Arctic or large-scale atmospheric circulations (e.g., Skific et al. 2009; Lee 2017). Additionally, principal component analysis (PCA) or empirical orthogonal function (EOF) analysis could be employed for clustering tests. Why not start with these simpler methods before diving into complex deep learning models? However, if the authors can demonstrate that convolutional AEs outperform SOMs or PCA/EOF, it would strengthen the justification for using convolutional AEs in this study. Perhaps the authors could consider quickly implementing these simpler methods and comparing the results with those obtained from convolutional AEs.
- What new physical or dynamical insight do we learn? I am curious about the new insights or knowledge gained from the new clustering method employed in this study. Lines 566-567 mention that well-established large-scale circulation patterns (e.g., NAO, AO, PNA) are identified, and consistent dynamical responses in the troposphere and stratosphere can be demonstrated. I assume that similar conclusions may be drawn from other clustering methods as well. Could we uncover new dynamical pathways in which the Arctic responds to European radiative forcing differently from previous understandings based on stratosphere-troposphere coupling? It would be helpful if the authors could create a table summarizing the dynamical responses associated with each cluster, indicating which dynamical responses are already known and which are new. Similarly, Lines 324-327 discuss the seasonality changes. What do these seasonality changes signify physically, and what can we learn from them?” In addition, what is the separation of WCVC and FSDC components brings us new insights?
- Linking the responses to European radiative forcing. I noticed that the discussion on the results seems to focus less on the direct response to radiative forcing and more on the subsequent atmospheric circulation responses and PMSET. For example, how does the European radiative forcing lead to increased upward EP flux for cluster 3 in SON (Figure 17)? Or how does the localized radiative forcing in Europe give rise to changes in 2m temperature across the entire Northern Hemisphere, as depicted in Figure 13? Some of the temperature increases appear contradictory to the cooling effect of aerosol negative forcing (or specifically here, the cloud forcing).
Minor comments:
Lines 58-60: perhaps the authors considering to cite two new studies on this topic: Xu et al. (2023) and Liang et al. (2024).
Line 123 and Figure 1: why there are statistically significant radiative forcing increase in eastern Siberia, Asia, and North Pacific?
Lines 161-162: why 8 days? Does this indicate a certain physical process dominating?
Line 230: the maximum PMSET? Or both maximum and minimum PMSETs?
Lines 246-248: could the authors provide a figure to demonstrate this grid transformation?
Lines 298 and 301: Are there any reference papers for WCVC and FSDC?
Figure 8: it seems the signal-to-noise ration is quite small. How could the authors say these responses are important?
Figures 9 and Figure 10: combine these two figure into one figure?
Figure 12: the arrows are hardly seen. Perhaps the authors can try to enhance the visibility of the arrows.
Lines 458-460: but the seasonal distribution changes?
Figure 17: the EP flux divergence does not exactly match the pattern of zonal wind anomalies in some seasons and clusters. How could we relate the EP flux change to zonal wind change?
Lines 540-542: the authors mentioned that the external forcing can modify the circulation patterns. But in Lines 778-680, the authors conclude that the radiative forcing only alter the existing circulation patterns, and does not introduce new patterns. These two sentences seem contradictory somewhat. Could the authors clarify and reconcile these statements?
Line 689: upper troposphere lower stratosphere —> upper troposphere and lower stratosphere?
References:
Xu, M., Tian, W., Zhang, J., Screen, J.A., Zhang, C. and Wang, Z., 2023. Important role of stratosphere-troposphere coupling in the Arctic mid-to-upper tropospheric warming in response to sea-ice loss. npj Climate and Atmospheric Science, 6(1), p.9.
Lee, C.C., 2017. Reanalysing the impacts of atmospheric teleconnections on cold‐season weather using multivariate surface weather types and self‐organizing maps. International Journal of Climatology, 37(9), pp.3714-3730.
Liang, Y.C., Kwon, Y.O., Frankignoul, C., Gastineau, G., Smith, K.L., Polvani, L.M., Sun, L., Peings, Y., Deser, C., Zhang, R. and Screen, J., 2024. The Weakening of the Stratospheric Polar Vortex and the Subsequent Surface Impacts as Consequences to Arctic Sea Ice Loss. Journal of Climate, 37(1), pp.309-333.
Skific, N., Francis, J.A. and Cassano, J.J., 2009. Attribution of projected changes in atmospheric moisture transport in the Arctic: A self-organizing map perspective. Journal of Climate, 22(15), pp.4135-4153.
Citation: https://doi.org/10.5194/egusphere-2023-3033-RC2 - AC1: 'Reply on RC2', Sina Mehrdad, 28 Mar 2024
- Baseline machine learning model comparison. This study presents an innovative application of convolutional autoencoders (AEs), which appears to be one of the first attempts in studying Arctic response to radiative forcing. However, I am still unclear about the motivation behind choosing convolutional AEs. For example, the authors mentioned that in Lines 538-539 that the FAE is a compelling method for generating a concise and informative representation, but did not elaborate on compare to what. Have the authors compared the performance of convolutional AEs with other machine learning or statistical methods? For example, similar clustering analyses could be conducted using self-organizing maps (SOMs), which are computationally less expensive than training convolutional AEs. SOMs have been used in studying atmospheric moisture transport in the Arctic or large-scale atmospheric circulations (e.g., Skific et al. 2009; Lee 2017). Additionally, principal component analysis (PCA) or empirical orthogonal function (EOF) analysis could be employed for clustering tests. Why not start with these simpler methods before diving into complex deep learning models? However, if the authors can demonstrate that convolutional AEs outperform SOMs or PCA/EOF, it would strengthen the justification for using convolutional AEs in this study. Perhaps the authors could consider quickly implementing these simpler methods and comparing the results with those obtained from convolutional AEs.
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Dörthe Handorf
Ines Höschel
Khalil Karami
Johannes Quaas
Sudhakar Dipu
Christoph Jacobi
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(70821 KB) - Metadata XML