the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 May 2023
Impact of AMV on rainfall intensity distribution and timing of the West African Monsoon in DCPP-C-like simulations
Abstract. Previous studies agree on an impact of the Atlantic Multidecadal Variability (AMV) on total seasonal rainfall amounts over the Sahel. However, whether and how AMV affects the distribution of rainfall or the timing of the West African Monsoon is not well known. Here we analyze daily rainfall outputs from atmosphere-ocean coupled models. Models show dry biases over the Sahel, where the mean intensity is consistently smaller than observations, and wet biases over the Guinea Coast, where they simulate too many rainy days. In addition, most models underestimate the average length of the rainy season over the Sahel, some due to a too late monsoon onset and others due to a too early cessation. In response to a persistent positive AMV pattern imposed in the Atlantic, following a protocol largely consistent with the one proposed by the Component C of the Decadal Climate Prediction Project (DCPP-C), models show an enhancement in total summer rainfall over West African land mass, including the Sahel. Both the number of wet days and the intensity of daily rainfall events are enhanced over the Sahel. The former explains most of the changes in seasonal rainfall in the northern fringe, while the latter is more relevant in the southern region, where higher rainfall anomalies occur. This dominance is connected to the changes in the number of days per type of event: the frequency of both moderate and heavy events increases over the Sahel’s northern fringe. Conversely, over the southern limit, it is mostly the frequency of heavy events which is enhanced, affecting the mean rainfall intensity there. Extreme rainfall events are also enhanced over the whole Sahel in response to a positive phase of the AMV. Models with stronger negative biases in rainfall amounts tend to show weaker changes in response to AMV, suggesting systematic biases could affect the simulated responses. The monsoon onset over the Sahel shows no clear response to AMV, while the demise tends to be delayed and the overall length of the monsoon season enhanced between 2 and 5 days with the positive AMV pattern. The effect of AMV on the seasonality of the monsoon is more consistent to the West of 10º W, with all models showing a statistically significant earlier onset, later demise and enhanced monsoon season with the positive phase of the AMV. Our results suggest a potential for the decadal prediction of changes in the intraseasonal characteristics of rainfall over the Sahel, including the occurrence of extreme events.
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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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Preprint
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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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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-962', Anonymous Referee #1, 17 Aug 2023
Review of the manuscript 'Impact of AMV on rainfall intensity distribution and timing of the
West African Monsoon in DCPP-C-like simulations'by Mohino et al.
The authors examine the influence of the Atlantic Multidecadal Variability (AMV) on intraseasonal precipitation characteristics
by analyzing a series of model simulations following a commonly used protocol.
They analyze the biases shown by the models and estimate the impact
of AMV by comparing 10-year averaged AMV+ and AMV- experiments.
Models show consistent bias patterns in the summer JAS seasonal total rainfall amounts, number of rainy days and mean rainfall
intensity, with an underestimation over the Sahel and an overestimation to the south, especially over the Guinea Coast.
The models analyzed show high agreement in the response of West African
rainfall to a positive phase of the AMV. This response involves a general increase in JAS seasonal rainfall amounts
with higher changes in the southern Sahel, typically close to 10N, and weaker ones to the North.
The latter are mainly related to an increase in the number of rainy days due to the enhancement of all types of rainfall events,
moderate, heavy and extreme. The stronger changes observed in the southern part of the Sahel are better explained by an
increase in the mean intensity of rainfall, as the number of heavy and extreme rainfall events grows, while those for moderate
changes little or it even decreases.Comments:
- introduction: causes of the AMV: please note that AMV variability might also be caused in a model that does not include ocean circulation changes.
Please see Clement et al. DOI: 10.1126/science.aab398- method: onset and demise of the wet season: 'The daily rainfall anomaly is obtained as the rainfall for each day minus the
long-term climatological mean daily rainfall using all available years in the observations and all years and ensemble members
in both experiments, AMV+ and AMV-, in the models.' I find this description confusing. I would prefer to rewrite the sentence.
Do you mean: The daily rainfall anomaly is obtained as the climatological rainfall for each day minus the annual mean rainfall using .....- statistical significance: 'To test whether the change in a given quantity is statistically significant we apply the parametric test for differences of means
under independence (Wilks, 2019)' Please describe more precisely what kind of parametric test you have used. I assume you have used a t-test.
I am wondering if this test is applicable for extreme values (e.g. Figure 14), because the populations must be normally distributed?- The color bar (magnitude and units) in some plots might be wrong. I think they do not fit to the caption. Please have a look e.g. at Figure 2 and Figure 5.
The caption of Figure 2 says mm/day, but in the figure it says days. I have the impression that also the magnitude of the color-bar is not correct.- wondering if it might be useful to also compare the precipitation PDFs of the observations and simulations for some key areas.
Citation: https://doi.org/10.5194/egusphere-2023-962-RC1 - AC1: 'Reply on RC1', Elsa Mohino, 11 Sep 2023
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RC2: 'Comment on egusphere-2023-962', Anonymous Referee #2, 19 Sep 2023
Review of “Impact of AMV on rainfall intensity distribution and timing of the West African Monsoon in DCPP-C-like simulations” by Mohino et al.
Authors have analysed the impact of AMV on West African precipitation (monsoon season length, intensity and spatial changes) in a small set of model simulations of different characteristics using different SST forcing following 2 AMV protocols, model physics, and different resolutions. Authors found a coherent impact of AMV on WAM precipitation among the simulations. Under AMV+, the increased precipitation change found in the southern part of WAM has been explained through the increase in wet days and extreme events relative to the northern counterpart.
Major: the statistical analysis performed is overall convincing, although I do have some doubts about the robustness of the conclusions because the chosen simulations do not allow a clear understanding if differences are arising because the model resolution, the DCPP-C or PRIMAVERA protocol, because the model physics (being usually very large for precipitation) or AMV phases. The simulations used in this study run under different model version and this makes the comparison very complicated. Additionally, is it not really clear to me the premise of the study. Also, I did not fully get what is the added value of using these simulations instead of CMIPs or other type of simulations which perform AMV decently. I guess that the most important stuff here is the bias assessment due to AMV phases that could help to understand overall north-south or east-west precip biases in WAM. I invite authors to revise the manuscript trying to calibrate better the focus of the study. Also, mechanisms for explaining the biases are not investigated at all, and I think that would be nice to know more about where the differences among models come from.
Abstract:
Ln 3: better to state immediately what kind of dataset you are using (e.g. CMIPs… or other…). It is not clear what models are you talking about now because later on you introduce DCPPs…
Ln 7:”… models…” once again, not clear what kind of coupled models are you talking about.
Ln 9: “land mass”… I would change in “over land”.
Ln 15: “stronger negative biases” relative to what? Observations? Please clarify.
Ln 38 “Brazil’s Nordeste” North-east Brazil maybe sounds better in this context, because Nordeste usually refers to socio-cultural division. It is up to you.
Ln 41 “… AMV can also promote”. Positive or negative AMV?
Ln 47 Again, what AMV phase is promoting enhanced summer seasonal rainfall … of Sahel?
Datasets:
Ln 113: what do you mean here for ensemble members? How many?
Ln 125: you can include some info about DCPP-C and PRIMAVERA protocol and simulations in the abstract, so it is clear since the beginning on what product your analysis is based on.
Perhaps I have missed it somewhere, but are the simulations running with different SST protocols under piControl or historical set up?
Metrics:
I have a question about the method: would it be easier and more appropriate to count the wet days and then calculating the mean intensity, considering the length of the monsoon season calculating the onset and the withdrawn of the WAM? I think that referring to standard metrics used in the vast literature on monsoons would ease eventual comparison with other studies without introducing new metrics. How would the number of wet days you have introduced compare with the common monsoon season length?
See Sultan and Janicot 2003, https://journals.ametsoc.org/view/journals/clim/16/21/1520-0442_2003_016_3407_twamdp_2.0.co_2.xml?tab_body=fulltext-display
Statistics:
I did not really get what statistical test have you used. Would a parametric test be appropriate considering the few data you have?
Results:
Figure 2: Label panel b and d: How CNRM-CM6-1 black and blue differ? Is it just a different simulation with different protocol but with the same model? And also, how do EC-Earth3 and 3P-HR differ? Just resolution or also something else?
What is the east-west bias in CNRM due to?
Ln 200: it would be great also to show SST north-south SST bias in all datasets (in the supplementary for example).
Ln 200 on: discussing biases in tropical precipitations among CMIP generations you can refer to Fiedler et al., 2020: Fiedler, S., Crueger, T., D’Agostino, R., Peters, K., Becker, T., Leutwyler, D., ... & Stevens, B. (2020). Simulated tropical precipitation assessed across three major phases of the Coupled Model Intercomparison Project (CMIP). Monthly Weather Review, 148(9), 3653-3680.
Discussion:
Ln: 430 – 436: It is nice to see some proposed explanations for these biases. It would be great also to see some figures about, related to the simulations you have used, especially on soil moisture, otherwise it is just speculation.
I also suggest to shorten and make clearer the main findings in this section.
From your analysis, is it possible to understand if the type of storms is expected to change, e.g., increasing the formation of Mesoscale Convective Systems (MCS)? See here: Fitzpatrick, R. G., Parker, D. J., Marsham, J. H., Rowell, D. P., Guichard, F. M., Taylor, C. M., ... & Tucker, S. (2020). What drives the intensification of mesoscale convective systems over the West African Sahel under climate change?. Journal of Climate, 33(8), 3151-3172.
Citation: https://doi.org/10.5194/egusphere-2023-962-RC2 - AC2: 'Reply on RC2', Elsa Mohino, 09 Oct 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-962', Anonymous Referee #1, 17 Aug 2023
Review of the manuscript 'Impact of AMV on rainfall intensity distribution and timing of the
West African Monsoon in DCPP-C-like simulations'by Mohino et al.
The authors examine the influence of the Atlantic Multidecadal Variability (AMV) on intraseasonal precipitation characteristics
by analyzing a series of model simulations following a commonly used protocol.
They analyze the biases shown by the models and estimate the impact
of AMV by comparing 10-year averaged AMV+ and AMV- experiments.
Models show consistent bias patterns in the summer JAS seasonal total rainfall amounts, number of rainy days and mean rainfall
intensity, with an underestimation over the Sahel and an overestimation to the south, especially over the Guinea Coast.
The models analyzed show high agreement in the response of West African
rainfall to a positive phase of the AMV. This response involves a general increase in JAS seasonal rainfall amounts
with higher changes in the southern Sahel, typically close to 10N, and weaker ones to the North.
The latter are mainly related to an increase in the number of rainy days due to the enhancement of all types of rainfall events,
moderate, heavy and extreme. The stronger changes observed in the southern part of the Sahel are better explained by an
increase in the mean intensity of rainfall, as the number of heavy and extreme rainfall events grows, while those for moderate
changes little or it even decreases.Comments:
- introduction: causes of the AMV: please note that AMV variability might also be caused in a model that does not include ocean circulation changes.
Please see Clement et al. DOI: 10.1126/science.aab398- method: onset and demise of the wet season: 'The daily rainfall anomaly is obtained as the rainfall for each day minus the
long-term climatological mean daily rainfall using all available years in the observations and all years and ensemble members
in both experiments, AMV+ and AMV-, in the models.' I find this description confusing. I would prefer to rewrite the sentence.
Do you mean: The daily rainfall anomaly is obtained as the climatological rainfall for each day minus the annual mean rainfall using .....- statistical significance: 'To test whether the change in a given quantity is statistically significant we apply the parametric test for differences of means
under independence (Wilks, 2019)' Please describe more precisely what kind of parametric test you have used. I assume you have used a t-test.
I am wondering if this test is applicable for extreme values (e.g. Figure 14), because the populations must be normally distributed?- The color bar (magnitude and units) in some plots might be wrong. I think they do not fit to the caption. Please have a look e.g. at Figure 2 and Figure 5.
The caption of Figure 2 says mm/day, but in the figure it says days. I have the impression that also the magnitude of the color-bar is not correct.- wondering if it might be useful to also compare the precipitation PDFs of the observations and simulations for some key areas.
Citation: https://doi.org/10.5194/egusphere-2023-962-RC1 - AC1: 'Reply on RC1', Elsa Mohino, 11 Sep 2023
-
RC2: 'Comment on egusphere-2023-962', Anonymous Referee #2, 19 Sep 2023
Review of “Impact of AMV on rainfall intensity distribution and timing of the West African Monsoon in DCPP-C-like simulations” by Mohino et al.
Authors have analysed the impact of AMV on West African precipitation (monsoon season length, intensity and spatial changes) in a small set of model simulations of different characteristics using different SST forcing following 2 AMV protocols, model physics, and different resolutions. Authors found a coherent impact of AMV on WAM precipitation among the simulations. Under AMV+, the increased precipitation change found in the southern part of WAM has been explained through the increase in wet days and extreme events relative to the northern counterpart.
Major: the statistical analysis performed is overall convincing, although I do have some doubts about the robustness of the conclusions because the chosen simulations do not allow a clear understanding if differences are arising because the model resolution, the DCPP-C or PRIMAVERA protocol, because the model physics (being usually very large for precipitation) or AMV phases. The simulations used in this study run under different model version and this makes the comparison very complicated. Additionally, is it not really clear to me the premise of the study. Also, I did not fully get what is the added value of using these simulations instead of CMIPs or other type of simulations which perform AMV decently. I guess that the most important stuff here is the bias assessment due to AMV phases that could help to understand overall north-south or east-west precip biases in WAM. I invite authors to revise the manuscript trying to calibrate better the focus of the study. Also, mechanisms for explaining the biases are not investigated at all, and I think that would be nice to know more about where the differences among models come from.
Abstract:
Ln 3: better to state immediately what kind of dataset you are using (e.g. CMIPs… or other…). It is not clear what models are you talking about now because later on you introduce DCPPs…
Ln 7:”… models…” once again, not clear what kind of coupled models are you talking about.
Ln 9: “land mass”… I would change in “over land”.
Ln 15: “stronger negative biases” relative to what? Observations? Please clarify.
Ln 38 “Brazil’s Nordeste” North-east Brazil maybe sounds better in this context, because Nordeste usually refers to socio-cultural division. It is up to you.
Ln 41 “… AMV can also promote”. Positive or negative AMV?
Ln 47 Again, what AMV phase is promoting enhanced summer seasonal rainfall … of Sahel?
Datasets:
Ln 113: what do you mean here for ensemble members? How many?
Ln 125: you can include some info about DCPP-C and PRIMAVERA protocol and simulations in the abstract, so it is clear since the beginning on what product your analysis is based on.
Perhaps I have missed it somewhere, but are the simulations running with different SST protocols under piControl or historical set up?
Metrics:
I have a question about the method: would it be easier and more appropriate to count the wet days and then calculating the mean intensity, considering the length of the monsoon season calculating the onset and the withdrawn of the WAM? I think that referring to standard metrics used in the vast literature on monsoons would ease eventual comparison with other studies without introducing new metrics. How would the number of wet days you have introduced compare with the common monsoon season length?
See Sultan and Janicot 2003, https://journals.ametsoc.org/view/journals/clim/16/21/1520-0442_2003_016_3407_twamdp_2.0.co_2.xml?tab_body=fulltext-display
Statistics:
I did not really get what statistical test have you used. Would a parametric test be appropriate considering the few data you have?
Results:
Figure 2: Label panel b and d: How CNRM-CM6-1 black and blue differ? Is it just a different simulation with different protocol but with the same model? And also, how do EC-Earth3 and 3P-HR differ? Just resolution or also something else?
What is the east-west bias in CNRM due to?
Ln 200: it would be great also to show SST north-south SST bias in all datasets (in the supplementary for example).
Ln 200 on: discussing biases in tropical precipitations among CMIP generations you can refer to Fiedler et al., 2020: Fiedler, S., Crueger, T., D’Agostino, R., Peters, K., Becker, T., Leutwyler, D., ... & Stevens, B. (2020). Simulated tropical precipitation assessed across three major phases of the Coupled Model Intercomparison Project (CMIP). Monthly Weather Review, 148(9), 3653-3680.
Discussion:
Ln: 430 – 436: It is nice to see some proposed explanations for these biases. It would be great also to see some figures about, related to the simulations you have used, especially on soil moisture, otherwise it is just speculation.
I also suggest to shorten and make clearer the main findings in this section.
From your analysis, is it possible to understand if the type of storms is expected to change, e.g., increasing the formation of Mesoscale Convective Systems (MCS)? See here: Fitzpatrick, R. G., Parker, D. J., Marsham, J. H., Rowell, D. P., Guichard, F. M., Taylor, C. M., ... & Tucker, S. (2020). What drives the intensification of mesoscale convective systems over the West African Sahel under climate change?. Journal of Climate, 33(8), 3151-3172.
Citation: https://doi.org/10.5194/egusphere-2023-962-RC2 - AC2: 'Reply on RC2', Elsa Mohino, 09 Oct 2023
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Paul-Arthur Monerie
Juliette Mignot
Moussa Diakhaté
Markus Donat
Christopher David Roberts
Francisco Doblas-Reyes
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(58066 KB) - Metadata XML