Quantifying Contribution of Atmospheric Circulation to Precipitation Variability and Changes in the U.S. Great Plains and Southwest Using Self Organizing Map &ndash; Analogue

Zhuang, Yizhou; Fu, Rong

doi:https://doi.org/10.5194/egusphere-2023-626

Preprints

https://doi.org/10.5194/egusphere-2023-626

Preprints

07 Jun 2023

| 07 Jun 2023

Quantifying Contribution of Atmospheric Circulation to Precipitation Variability and Changes in the U.S. Great Plains and Southwest Using Self Organizing Map – Analogue

Yizhou Zhuang and Rong Fu

Abstract. The Great Plains and Southwest regions of the U.S. are highly vulnerable to precipitation-related climate disasters such as droughts and floods. In this study, we propose a self-organizing map–analogue (SOMA) approach to empirically quantify the contribution of atmospheric circulation (mid-tropospheric geopotential and column moisture transport) to the regional precipitation anomalies, variability, and multi-decadal changes. Our results indicate that atmospheric circulation contributes significantly to short-term precipitation variability, accounting for 54–61 % of the total variance and 62–68 % of the amplitude of the mean precipitation anomalies in these regions, though these contributions vary significantly across seasons. The remaining variance is largely influenced by thermodynamically driven factors. As indicated in previous research, Pacific Decadal Oscillation (PDO) is one of the major climate modes influencing the long-term multi-decadal variation of precipitation. By contrasting three multi-decadal periods (1950–1976, 1977–1998, 1999–2021) with shifting PDO phases and linking the phase shift to circulation SOM nodes, we found that circulation changes contribute considerably to the multi-decadal changes of precipitation anomaly in terms of the mean and probability of dry and wet extremes, especially for the Southern GP and Southwest. However, these circulation-induced changes are not totally related to the PDO phase shift (mostly less than half), atmospheric internal variability or anthropogenically induced changes in circulation can also be potential contributors. Our approach improves upon flow analogue and SOM-based methods and provides insights into the contribution of atmospheric circulation to regional precipitation anomalies and variability.

Received: 31 Mar 2023 – Discussion started: 07 Jun 2023

Download & links

Preprint (PDF, 7783 KB)

Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Preprint (7783 KB)

Download & links

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Journal article(s) based on this preprint

06 Feb 2024

Quantifying the contribution of atmospheric circulation to precipitation variability and changes in the US Great Plains and southwest using self-organizing map–analogue

Yizhou Zhuang and Rong Fu

Atmos. Chem. Phys., 24, 1641–1657, https://doi.org/10.5194/acp-24-1641-2024,https://doi.org/10.5194/acp-24-1641-2024, 2024

Short summary

Yizhou Zhuang and Rong Fu

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-626', Anonymous Referee #1, 16 Jun 2023

Review of: Quantifying Contribution of Atmospheric Circulation to Precipitation Variability and Changes in the U.S. Great Plains and Southwest Using Self Organizing Map – Analogue

OVERALL COMMENTS:

This is a really interesting paper. I think this is a valuable contribution. I am a big proponent of the use of classification techniques (and SOMs) in helping to analyze and visualize the complex relationships between atmosphere and surface impacts – and this is an excellent example. However, I have some semi-major and minor comments that I detail below, that I would like the authors to address and consider, prior to publication. Also, this manuscript might need some slight language editing.

MAJOR COMMENTS:

I think the dynamic vs. thermodynamic dichotomy is a bit misleading. This is because z500 and IVT are going to have thermally-related variability inherent to them, right? While you only explicitly include z500 heights and IVT on each day into the classification, that does not necessarily mean that many/all other environmental variables (e.g. 2m temperature, 850t, 2m dew points, 925winds, SLP, and everything else) are not also ‘indirectly’ playing a role in classifying a day’s weather. That is to say, classification is implicitly wholistic – it categorizes the *wholistic/synergistic* environment over a particular time period (herein, a day). While this is not an issue in most research using classification (and is arguably a benefit of using classification in applied research), herein, when you are trying to use classification to de-couple the dynamics portion from thermodynamics, I think it is problematic. I just cannot de-couple this in this manner… To me, what you are actually calculating here is not dynamic vs. thermodynamic contributions, but rather the variability that can be accounted for using this categorization (i.e. this SOMA-based model) and the residual variability that cannot – and both the SOM and the residual contain both dynamic and thermodynamic contributions within them. That is, you do not have P’_dyn vs. P’_the, but rather more like P’_SOMA vs. P’_residual. In this sense, there is nothing inherently wrong with what you did, just the way you interpreted it, and the dynamic vs. thermodynamic is a misnomer. But, perhaps I can be convinced otherwise.

INDIVIDUAL COMMENTS:

Line 100-102: Need a bit of clarity here… the daily standardized anomaly is applied to the pentad moving average filter? Or the opposite? Or are these two things done separately?

Line 102:; Why 1950 to 1999 for the climo? Why not the entire period, or the most recent 30-year climate normal period? Or a period that ends in the present (2021)?

Line 131: The way this is written is still slightly confusing in terms of how many dimensions you actually have here. It is a 2-dimensional data matrix with a size of 3782-by-26,280 (with perhaps a few leap-days in there), correct?

Line 160: While I am fine with the way you did this, just as a note, if trained using a batch process, then slightly different input vectors might be used and thus, result in different final clustering solutions, even with all the other ‘settings’ the same. So, you might want to run each node number multiple (10-20) times, and then average their QE, TE and CE.

Figure 2: It would be nice if you could incorporate the seasonality of frequency of each atmospheric pattern into this graphic instead of having a separate figure 3.

Figure 3: I think the y-axis on these should be identical, so we can tell which ones are more/less frequent overall.

Figure 4: Again, I think the y-axis on these should all be identical.

Line 226 and Figure 5: I rarely see CAPE and CIN in standardized values, but rather in their more-traditional units. I think you need to be careful here, as for example, a lower than average CAPE for a location that has pretty high average CAPE normally, might still mean that the atmosphere is pretty unstable. Also, are these values deseasonalized?

Line 230: D1 is perhaps suppressing convective development, but convective *initiation* (i.e. the ‘triggering lifting mechanism’) is as somewhat separate ingredient.

Line 235: the soil moisture thing comes out of no-where…. I don’t disagree, but, I do suggest a citation for this.

Line 274-275: I think you need to be careful with how this sentence is worded. I agree that the dynamic factors are likely the major contributors, but that statement is predicated upon how you specifically defined “dynamics” herein (with z500 and IVT SOMs). If you had chosen different variables to represent “dynamics” (e.g. SLP) would this statement still hold? Would the statement be weaker? Would it be stronger?

Figure 8: Very interesting figure! I like it. However, why are the types color-coded the way they are? Is there a reason? It looks roughly like wet is green, and dry is red/orange/yellow, but is there a specific method for this?

Figure 9: Again, these subplots should have the same y-axis.

Line 314: “… larger or smaller than 90%...” - wouldn’t that be ALL of them?

Line 316: yes, but this is partly because PDO is not as often >0.5 or <-0.5 during these months, and thus, less sample size, right? And why the +/- 0.5 thresholds… why not 1.0, or 0.25??

Line 321: Perhaps I am missing something here, but, just because these nodes have been found to be related to PDO, doesn’t necessarily mean that this represents the “PDO-related dynamic contribution” to P’ … moreover the way that you have constructed this, makes it impossible for PDO to contribute MORE than the SOM-based dynamic component. That is, it could very well be that PDO is a more-dominant factor (more dominant than the SOM patterns), but this methodology would not allow for that. I think there is a better way to tease out how PDO is contributing to P’ in these areas. At best, perhaps you can say that this is showing the combined contribution of the SOM-patterns that have been found to be most influenced by the PDO.

Also, who is to say that this represents PDO-only related contributions to P’? What if you did this for AMO and AMO phases?? Or the IPO? In many ways, the SOM patterns you define are simply the regional-scale manifestation of hemispheric- to global-scale variability in the multiple internal climate oscillations occurring simultaneously (PDO/AMO/IPO/ENSO and that of all other teleconnections/oscillations at various periodicities).

Citation: https://doi.org/10.5194/egusphere-2023-626-RC1
- AC1: 'Reply on RC1', Yizhou Zhuang, 01 Oct 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-626/egusphere-2023-626-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-626-AC1
RC2:
'Comment on egusphere-2023-626', Anonymous Referee #2, 26 Jun 2023

GENERAL COMMENTS:
Overall, this paper can be great contribution to our science community. The authors come up with the novel approach for quantifying the contribution of atmospheric circulation to the recent precipitation variability in US. They combine two statistical techniques - Self Organizing Map (SOM) and circulation analogue - to effectively visualizing circulation pattern as in SOM and quantifying the dynamical contribution as exact as in circulation analogue. Using this new SOM-Analogue (SOMA) approach, they investigate the link between circulation patterns and daily precipitation anomalies in different seasons and PDO phases. The results provide the valuable insight how the specific circulation pattern leads to the regional precipitation anomalies in US. However, I think there are some major issues in the development of new SOMA approach and the analysis regarding PDO-related contributions. I would suggest the authors to address below major/minor comments before the publication.

MAJOR (SPECIFIC) COMMENTS:
Decomposition of dynamic and thermodynamic contribution
The variables Z500 and IVT are used for elucidating the dynamic contribution to the precipitation anomalies. However, IVT is multiplication of wind vector (dynamic variable) and moisture (thermodynamic variable), so the SOMA from IVT would also contain the thermodynamic contribution. To decompose the dynamic and thermodynamic contribution, I think dynamic variables (e.g., velocity potential) has to be used instead of IVT. Or, rather than focusing on the decomposition of dynamic/thermodynamic contribution, the authors can express the same results as the contribution from the moist circulation and the residual. In my opinion, the interesting science in this study is to characterize the circulation pattern responsible for the regional precipitation anomalies, and the exact decomposition of thermodynamic and dynamic contribution is kind of secondary interest compared to that.
If the authors want to decompose the thermodynamic/dynamic contribution, then I think new variable should be used for SOMA instead of IVT. Or, authors can express the same results as the contribution from the moist circulation and residual, with revising rest of the manuscript accordingly.

Issues regarding the data and SOMA approach
First, is the observational data used for SOMA detrended or high-pass filtered? In previous studies using circulation analogue (Deser et al. 2016; Lehner et al. 2018), the dynamic contribution is found after the observational data is detrended or high-pass filtered. This is for eliminating the forced thermodynamic contribution in the timeseries and to focus on the dynamic contribution. In this manuscript, it seems there are no mentioning about such data processing. I think the detrend is needed if it is not done, and it should be mentioned if it is done. Or, you may show that such data processing doesn’t matter to your results.
Second, in L197-199, to apply circulation analogue to each BMU node, the authors regress total precipitation anomalies onto the circulation anomalies (PC values), and multiply that regression coefficients to the circulation anomalies (PC values) to get dynamical precipitation changes. I think this can overemphasize the contribution from the circulation anomalies, and authors may need to come up with another approach for this step. The detail is written below.
In previous studies for circulation analogue, the linear coefficients or subsamples to estimate the dynamic precipitation anomalies are calculated only using the circulation anomalies, as you wrote in L205-206. However, here, total precipitation anomalies are directly regressed onto the circulation anomalies in same BMU node to get coefficients, and that regression coefficients are used to estimate the dynamic precipitation anomalies. This is like assuming that total precipitation anomalies are similar to anomalies from the moist circulation (or dynamic contribution) even before the decomposition. I think this would artificially overemphasize the dynamic contribution. So, I recommend you to revise step 4 in L197-199. You may use the linear coefficients for reconstructing target-day circulation from analogue-day circulations to getting target-day dynamic precipitation anomalies from the analogue-day precipitation anomalies in same BMU node, following Deser et al. 2016. I think you should avoid to directly link the total precipitation anomalies to the circulation anomalies to get the coefficients for estimating dynamic contribution.

PDO-related analysis
The precipitation anomalies related to PDO is often linked to the large-scale circulation over the North Pacific (retreat of Aluetian Low/ expansion of subtropical high), and it was less linked to the circulation anomalies within the US. This study can further investigate the teleconnection impact of PDO on the circulation anomalies within US, which will be the valuable contribution for regional teleconnection studies. However, the regional circulation and precipitation anomalies related to PDO will probably sensitive to the detailed pattern of tropical SST pattern (as it is known that the teleconnection within US depends on ENSO diversity), and they will be affected by the other variability (e.g., AMO, ENSO) as well. In this situation, the mere difference of positive and negative phase of PDO with relatively short length of observation would not sufficient to convince the PDO impact in this study. These limitation needs to be addressed in the manuscript.
In spite of above limitations, I think the analysis in this manuscript can be developed further to shed light on understanding the PDO teleconnection impact. The figure 10 is a good starting point where PDO-related local circulation/precipitation anomalies can be hypothesized and analyzed in detail. I suggest the authors to provide the figure for precipitation anomalies corresponding to figure 10, showing the precipitation anomalies linked to each circulation changes between two PDO phase (for three target regions). And then, authors may select the circulation node which shows strong precipitation anomalies, linking the PDO to that circulation and precipitation anomalies. If the PDO-induced teleconnection can explain those circulation pattern with the previously known mechanisms, authors can somewhat convince that their PDO-related precipitation anomalies are indeed PDO-induced. In summary, I think the current analysis/discussion for the PDO impact is not enough, and authors need to investigate the significant circulation pattern in Fig. 10 and try to link those to the PDO-related teleconnections to ensure the PDO impact.

MINOR (TECHNICAL) COMMENTS:
L60: Is there any need to use the term “surface parameter”? I think you can just directly mention the precipitation instead of “surface parameter such as precipitation”. It would be great if the terminology in this paragraph is adjusted accordingly.

L60-63: I think it is hard for readers to understand Cassano et al. 2007 (C07) SOM method by reading few sentences here. I suggest you to add and explain the equation 1 and 3 of C07, since they are quite simple. Those equations may be added on the line with simple explanations.

L138: I think it is better to represent the fraction of variances only for the Z500 and IVT combined variability since the EOF analysis is done simultaneously to two variables.

L164: “issmall” to “is small”?

L194-196: Why do you find the analogue day within 91 calendar windows? This might be due to the seasonal dependence of circulation-precipitation relationship, and it is better to mention this somewhere in the text.

L317: “Fig. 9 (1st column) shows the period mean P′ for different seasons and regions” à There are no seasonal information in Fig. 9. You may need to change the text.

L323: “Table2” to “Table1”?

Citation: https://doi.org/10.5194/egusphere-2023-626-RC2
- AC2: 'Reply on RC2', Yizhou Zhuang, 01 Oct 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-626/egusphere-2023-626-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-626-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-626', Anonymous Referee #1, 16 Jun 2023

Review of: Quantifying Contribution of Atmospheric Circulation to Precipitation Variability and Changes in the U.S. Great Plains and Southwest Using Self Organizing Map – Analogue

OVERALL COMMENTS:

This is a really interesting paper. I think this is a valuable contribution. I am a big proponent of the use of classification techniques (and SOMs) in helping to analyze and visualize the complex relationships between atmosphere and surface impacts – and this is an excellent example. However, I have some semi-major and minor comments that I detail below, that I would like the authors to address and consider, prior to publication. Also, this manuscript might need some slight language editing.

MAJOR COMMENTS:

I think the dynamic vs. thermodynamic dichotomy is a bit misleading. This is because z500 and IVT are going to have thermally-related variability inherent to them, right? While you only explicitly include z500 heights and IVT on each day into the classification, that does not necessarily mean that many/all other environmental variables (e.g. 2m temperature, 850t, 2m dew points, 925winds, SLP, and everything else) are not also ‘indirectly’ playing a role in classifying a day’s weather. That is to say, classification is implicitly wholistic – it categorizes the *wholistic/synergistic* environment over a particular time period (herein, a day). While this is not an issue in most research using classification (and is arguably a benefit of using classification in applied research), herein, when you are trying to use classification to de-couple the dynamics portion from thermodynamics, I think it is problematic. I just cannot de-couple this in this manner… To me, what you are actually calculating here is not dynamic vs. thermodynamic contributions, but rather the variability that can be accounted for using this categorization (i.e. this SOMA-based model) and the residual variability that cannot – and both the SOM and the residual contain both dynamic and thermodynamic contributions within them. That is, you do not have P’_dyn vs. P’_the, but rather more like P’_SOMA vs. P’_residual. In this sense, there is nothing inherently wrong with what you did, just the way you interpreted it, and the dynamic vs. thermodynamic is a misnomer. But, perhaps I can be convinced otherwise.

INDIVIDUAL COMMENTS:

Line 100-102: Need a bit of clarity here… the daily standardized anomaly is applied to the pentad moving average filter? Or the opposite? Or are these two things done separately?

Line 102:; Why 1950 to 1999 for the climo? Why not the entire period, or the most recent 30-year climate normal period? Or a period that ends in the present (2021)?

Line 131: The way this is written is still slightly confusing in terms of how many dimensions you actually have here. It is a 2-dimensional data matrix with a size of 3782-by-26,280 (with perhaps a few leap-days in there), correct?

Line 160: While I am fine with the way you did this, just as a note, if trained using a batch process, then slightly different input vectors might be used and thus, result in different final clustering solutions, even with all the other ‘settings’ the same. So, you might want to run each node number multiple (10-20) times, and then average their QE, TE and CE.

Figure 2: It would be nice if you could incorporate the seasonality of frequency of each atmospheric pattern into this graphic instead of having a separate figure 3.

Figure 3: I think the y-axis on these should be identical, so we can tell which ones are more/less frequent overall.

Figure 4: Again, I think the y-axis on these should all be identical.

Line 226 and Figure 5: I rarely see CAPE and CIN in standardized values, but rather in their more-traditional units. I think you need to be careful here, as for example, a lower than average CAPE for a location that has pretty high average CAPE normally, might still mean that the atmosphere is pretty unstable. Also, are these values deseasonalized?

Line 230: D1 is perhaps suppressing convective development, but convective *initiation* (i.e. the ‘triggering lifting mechanism’) is as somewhat separate ingredient.

Line 235: the soil moisture thing comes out of no-where…. I don’t disagree, but, I do suggest a citation for this.

Line 274-275: I think you need to be careful with how this sentence is worded. I agree that the dynamic factors are likely the major contributors, but that statement is predicated upon how you specifically defined “dynamics” herein (with z500 and IVT SOMs). If you had chosen different variables to represent “dynamics” (e.g. SLP) would this statement still hold? Would the statement be weaker? Would it be stronger?

Figure 8: Very interesting figure! I like it. However, why are the types color-coded the way they are? Is there a reason? It looks roughly like wet is green, and dry is red/orange/yellow, but is there a specific method for this?

Figure 9: Again, these subplots should have the same y-axis.

Line 314: “… larger or smaller than 90%...” - wouldn’t that be ALL of them?

Line 316: yes, but this is partly because PDO is not as often >0.5 or <-0.5 during these months, and thus, less sample size, right? And why the +/- 0.5 thresholds… why not 1.0, or 0.25??

Line 321: Perhaps I am missing something here, but, just because these nodes have been found to be related to PDO, doesn’t necessarily mean that this represents the “PDO-related dynamic contribution” to P’ … moreover the way that you have constructed this, makes it impossible for PDO to contribute MORE than the SOM-based dynamic component. That is, it could very well be that PDO is a more-dominant factor (more dominant than the SOM patterns), but this methodology would not allow for that. I think there is a better way to tease out how PDO is contributing to P’ in these areas. At best, perhaps you can say that this is showing the combined contribution of the SOM-patterns that have been found to be most influenced by the PDO.

Also, who is to say that this represents PDO-only related contributions to P’? What if you did this for AMO and AMO phases?? Or the IPO? In many ways, the SOM patterns you define are simply the regional-scale manifestation of hemispheric- to global-scale variability in the multiple internal climate oscillations occurring simultaneously (PDO/AMO/IPO/ENSO and that of all other teleconnections/oscillations at various periodicities).

Citation: https://doi.org/10.5194/egusphere-2023-626-RC1
- AC1: 'Reply on RC1', Yizhou Zhuang, 01 Oct 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-626/egusphere-2023-626-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-626-AC1
RC2:
'Comment on egusphere-2023-626', Anonymous Referee #2, 26 Jun 2023

GENERAL COMMENTS:
Overall, this paper can be great contribution to our science community. The authors come up with the novel approach for quantifying the contribution of atmospheric circulation to the recent precipitation variability in US. They combine two statistical techniques - Self Organizing Map (SOM) and circulation analogue - to effectively visualizing circulation pattern as in SOM and quantifying the dynamical contribution as exact as in circulation analogue. Using this new SOM-Analogue (SOMA) approach, they investigate the link between circulation patterns and daily precipitation anomalies in different seasons and PDO phases. The results provide the valuable insight how the specific circulation pattern leads to the regional precipitation anomalies in US. However, I think there are some major issues in the development of new SOMA approach and the analysis regarding PDO-related contributions. I would suggest the authors to address below major/minor comments before the publication.

MAJOR (SPECIFIC) COMMENTS:
Decomposition of dynamic and thermodynamic contribution
The variables Z500 and IVT are used for elucidating the dynamic contribution to the precipitation anomalies. However, IVT is multiplication of wind vector (dynamic variable) and moisture (thermodynamic variable), so the SOMA from IVT would also contain the thermodynamic contribution. To decompose the dynamic and thermodynamic contribution, I think dynamic variables (e.g., velocity potential) has to be used instead of IVT. Or, rather than focusing on the decomposition of dynamic/thermodynamic contribution, the authors can express the same results as the contribution from the moist circulation and the residual. In my opinion, the interesting science in this study is to characterize the circulation pattern responsible for the regional precipitation anomalies, and the exact decomposition of thermodynamic and dynamic contribution is kind of secondary interest compared to that.
If the authors want to decompose the thermodynamic/dynamic contribution, then I think new variable should be used for SOMA instead of IVT. Or, authors can express the same results as the contribution from the moist circulation and residual, with revising rest of the manuscript accordingly.

Issues regarding the data and SOMA approach
First, is the observational data used for SOMA detrended or high-pass filtered? In previous studies using circulation analogue (Deser et al. 2016; Lehner et al. 2018), the dynamic contribution is found after the observational data is detrended or high-pass filtered. This is for eliminating the forced thermodynamic contribution in the timeseries and to focus on the dynamic contribution. In this manuscript, it seems there are no mentioning about such data processing. I think the detrend is needed if it is not done, and it should be mentioned if it is done. Or, you may show that such data processing doesn’t matter to your results.
Second, in L197-199, to apply circulation analogue to each BMU node, the authors regress total precipitation anomalies onto the circulation anomalies (PC values), and multiply that regression coefficients to the circulation anomalies (PC values) to get dynamical precipitation changes. I think this can overemphasize the contribution from the circulation anomalies, and authors may need to come up with another approach for this step. The detail is written below.
In previous studies for circulation analogue, the linear coefficients or subsamples to estimate the dynamic precipitation anomalies are calculated only using the circulation anomalies, as you wrote in L205-206. However, here, total precipitation anomalies are directly regressed onto the circulation anomalies in same BMU node to get coefficients, and that regression coefficients are used to estimate the dynamic precipitation anomalies. This is like assuming that total precipitation anomalies are similar to anomalies from the moist circulation (or dynamic contribution) even before the decomposition. I think this would artificially overemphasize the dynamic contribution. So, I recommend you to revise step 4 in L197-199. You may use the linear coefficients for reconstructing target-day circulation from analogue-day circulations to getting target-day dynamic precipitation anomalies from the analogue-day precipitation anomalies in same BMU node, following Deser et al. 2016. I think you should avoid to directly link the total precipitation anomalies to the circulation anomalies to get the coefficients for estimating dynamic contribution.

PDO-related analysis
The precipitation anomalies related to PDO is often linked to the large-scale circulation over the North Pacific (retreat of Aluetian Low/ expansion of subtropical high), and it was less linked to the circulation anomalies within the US. This study can further investigate the teleconnection impact of PDO on the circulation anomalies within US, which will be the valuable contribution for regional teleconnection studies. However, the regional circulation and precipitation anomalies related to PDO will probably sensitive to the detailed pattern of tropical SST pattern (as it is known that the teleconnection within US depends on ENSO diversity), and they will be affected by the other variability (e.g., AMO, ENSO) as well. In this situation, the mere difference of positive and negative phase of PDO with relatively short length of observation would not sufficient to convince the PDO impact in this study. These limitation needs to be addressed in the manuscript.
In spite of above limitations, I think the analysis in this manuscript can be developed further to shed light on understanding the PDO teleconnection impact. The figure 10 is a good starting point where PDO-related local circulation/precipitation anomalies can be hypothesized and analyzed in detail. I suggest the authors to provide the figure for precipitation anomalies corresponding to figure 10, showing the precipitation anomalies linked to each circulation changes between two PDO phase (for three target regions). And then, authors may select the circulation node which shows strong precipitation anomalies, linking the PDO to that circulation and precipitation anomalies. If the PDO-induced teleconnection can explain those circulation pattern with the previously known mechanisms, authors can somewhat convince that their PDO-related precipitation anomalies are indeed PDO-induced. In summary, I think the current analysis/discussion for the PDO impact is not enough, and authors need to investigate the significant circulation pattern in Fig. 10 and try to link those to the PDO-related teleconnections to ensure the PDO impact.

MINOR (TECHNICAL) COMMENTS:
L60: Is there any need to use the term “surface parameter”? I think you can just directly mention the precipitation instead of “surface parameter such as precipitation”. It would be great if the terminology in this paragraph is adjusted accordingly.

L60-63: I think it is hard for readers to understand Cassano et al. 2007 (C07) SOM method by reading few sentences here. I suggest you to add and explain the equation 1 and 3 of C07, since they are quite simple. Those equations may be added on the line with simple explanations.

L138: I think it is better to represent the fraction of variances only for the Z500 and IVT combined variability since the EOF analysis is done simultaneously to two variables.

L164: “issmall” to “is small”?

L194-196: Why do you find the analogue day within 91 calendar windows? This might be due to the seasonal dependence of circulation-precipitation relationship, and it is better to mention this somewhere in the text.

L317: “Fig. 9 (1st column) shows the period mean P′ for different seasons and regions” à There are no seasonal information in Fig. 9. You may need to change the text.

L323: “Table2” to “Table1”?

Citation: https://doi.org/10.5194/egusphere-2023-626-RC2
- AC2: 'Reply on RC2', Yizhou Zhuang, 01 Oct 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-626/egusphere-2023-626-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-626-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Yizhou Zhuang on behalf of the Authors (01 Oct 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (19 Oct 2023) by Corinna Hoose

RR by Anonymous Referee #2 (30 Oct 2023)

Suggestions for revision or reasons for rejection

GENERAL COMMENTS:
I deeply appreciate for author’s efforts. After revisions, the manuscripts become clearer and the scientific significance is much more improved. Especially, I can feel that analysis and discussion regarding the PDO impact become way better than before. While I’m now agreeing with most of author’s results, there are some additional points which need to be clarified. After the minor revisions, I’m happy to do reviewing again for publication.

Issues related to detrending
Now I can clearly see that high-frequency noise is removed via the 5-day moving average filter. However, still, I don’t know whether the data is detrended or not. You mentioned that Z500 is subtracted by global-mean, but I think it is extraction of pattern effect rather than the detrending. Since global warming entails some tropical SST pattern and related Z anomalies, I cannot agree that removing global-mean is same with detrending of forced response. In addition, since you include the IVT for the circulation anomalies, I think it inevitably has the effect of the global warming. Indeed, in L406-408 and L416-419, you attribute difference between past and recent PDO impact to the global warming. So, I think your manuscript itself admit that there are forced responses which are not detrended.
Since the analysis regarding the forced responses (such as L406-408 / L416-419) is very interesting, I think it would be great to keep current method. However, since it is different from previous circulation analogue studies, I suggest authors to explicitly mention that the anomalies are not detrended and include the forced responses by global warming.

Analogue part in SOMA methodology
First, it become much easier to read and understand the methodology, which is especially important for this study with new techniques. I have some minor comment with the part 4) in page 7 (L229-231). After getting analogue days from each BMU (SOM) nodes, the authors get regression coefficients by regressing precipitation anomalies directly onto the circulation anomalies. This is definitely different with previous circulation analogue such as Lehner et al. 2018, which get the linear coefficients by finding optimal combinations of analogue-day circulation anomalies to reconstruct target day circulation anomalies. The method in this study (regressing precipitation directly onto the circulation anomalies) would inherently maximize the contribution from the moist circulation, and I thought this would be problematic. However, the high R2 values in Figs. 5,6 would be only possible if the circulation anomalies are indeed important as their assumption, so I’m now agreeing with your regression methods. However, I think you need to explicitly mention that “solving regression problem for P” in this study is different from the circulation analogue in previous studies (Deser et al. 2016; Lehner et al. 2018) for clarity.

Other minor comments:
L11-12: I think the content of “and 62~68% of the amplitude of the mean precipitation anomalies” are quite redundant for abstract. I think this is estimation from beta in Fig. 5b, and similar to results in Fig. 5a, which is already mentioned as 54-61%. I suggest the removal of this phrase.

L18-19: It seems the conjunction is not appropriate here. If I were you, I will use
“However, these circulation-induced changes are not totally related to the PDO phase shift (mostly less than half) since internal variability or anthropogenically induced changes in circulation can also be potential contributors.”

L35-37: After revision, you decided to use “moist circulation vs. residual” rather than “dynamic vs. thermodynamic”. But here, still, you have dynamic vs. thermodynamic perspective. It would be better to change the terms “dynamic vs. thermodynamic” to “moist circulation vs. residual” in all possible places in manuscript for the consistency.

L38-39: I think this study does not attribute the recent trend to anthropogenic warming. This sentence is now adding the confusion.

L49-51: I suggest “atmospheric circulation variability or the PDO-related circulation variability” -> “the PDO-related circulation variability”. Atmospheric circulation variability itself contains the effect from PDO-related circulation anomalies, and this paragraph seems to be more related to the PDO.

L70-73: In Lehner et al. 2018, they use monthly data for circulation analogue, rather than daily data. You need other references which use daily data for circulation analogue. If there are no such study, you may need to mention the time-scale difference.

L80: “daily variability and amplitude of anomaly” -> “daily variability”, since they are similar.

L85: “precipitation anomalies, their variability” -> “daily variability”. In abstract or introduction, giving both of them is adding confusions.

L185-187: Could you explain why the fact that “flow analogue explains a larger portion of observed P’” is the reason for “the flow analogue is better estimate of circulation contribution compared to the C2007”? To me, “larger portion of observed P’’ just means that circulation analogue has larger contribution from the circulation component. I agree with that flow analogue has advantage for the flexible precipitation anomalies to each circulation type, but it is hard to agree with this particular part.

L206-L217: In previous paragraph, author said that they will use the term P_SOMA instead of P_dyn. However, in this paragraph, authors are using P_dyn. It is now adding confusions.

L222: The subscript “i” is used for analogue day in L207 but suddenly used for BMU nodes. It would be better to use different one.

Hide

RR by Anonymous Referee #1 (03 Nov 2023)

ED: Publish subject to minor revisions (review by editor) (22 Nov 2023) by Corinna Hoose

AR by Yizhou Zhuang on behalf of the Authors (01 Dec 2023) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (09 Dec 2023) by Corinna Hoose

AR by Yizhou Zhuang on behalf of the Authors (10 Dec 2023) Author's response Author's tracked changes Manuscript

ED: Publish as is (20 Dec 2023) by Corinna Hoose

AR by Yizhou Zhuang on behalf of the Authors (23 Dec 2023)

Journal article(s) based on this preprint

06 Feb 2024

Quantifying the contribution of atmospheric circulation to precipitation variability and changes in the US Great Plains and southwest using self-organizing map–analogue

Yizhou Zhuang and Rong Fu

Atmos. Chem. Phys., 24, 1641–1657, https://doi.org/10.5194/acp-24-1641-2024,https://doi.org/10.5194/acp-24-1641-2024, 2024

Short summary

Yizhou Zhuang and Rong Fu

Viewed

Total article views: 362 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
240	95	27	362	12	12

HTML: 240
PDF: 95
XML: 27
Total: 362
BibTeX: 12
EndNote: 12

Views and downloads (calculated since 07 Jun 2023)

Month	HTML	PDF	XML	Total
Jun 2023	107	30	11	148
Jul 2023	25	7	2	34
Aug 2023	24	7	0	31
Sep 2023	15	12	2	29
Oct 2023	32	18	7	57
Nov 2023	9	3	1	13
Dec 2023	12	13	3	28
Jan 2024	15	3	1	19
Feb 2024	1	2	0	3

Cumulative views and downloads (calculated since 07 Jun 2023)

Month	HTML	PDF	XML	Total
Jun 2023	107	30	11	148
Jul 2023	25	7	2	34
Aug 2023	24	7	0	31
Sep 2023	15	12	2	29
Oct 2023	32	18	7	57
Nov 2023	9	3	1	13
Dec 2023	12	13	3	28
Jan 2024	15	3	1	19
Feb 2024	1	2	0	3

Viewed (geographical distribution)

Total article views: 354 (including HTML, PDF, and XML) Thereof 354 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 06 Feb 2024

Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Preprint (7783 KB)
Metadata XML

Short summary

This study investigated how atmospheric circulation affects precipitation variability and changes in the US Great Plains (GP) and Southwest (SW). By developing a new method, we found that circulation significantly influences short-term precipitation variability, accounting for 54–61 % of the total variance. Furthermore, circulation changes contribute considerably to the multi-decadal changes in precipitation and its extremes, especially for Southern GP and SW.


Total:	0
HTML:	0
PDF:	0
XML:	0