the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Mar 2024
Dynamic MJO forecasts using an ensemble subseasonal-to-seasonal forecast system of IAP-CAS model
Abstract. The Madden-Julian Oscillation (MJO) is a crucial predictability source on a sub-seasonal to seasonal (S2S) timescale. Therefore, the models participating in the WWRP/WCRP S2S prediction project focus on accurately predicting and analyzing the MJO. This study provided a detailed description of the configuration within the IAP-CAS S2S forecast system. We assessed the accuracy of the IAP-CAS model's MJO forecast using traditional RMM analysis and cluster analysis. Then, we explained the reasons behind any bias observed in the MJO forecast. Comparing the 20-year hindcast with observations, we found that the IAP-CAS ensemble mean has a skill of 24 days. However, there is still room for improvement in the ensemble spread. To examine the MJO structure in detail, we used cluster analysis to classify the MJO events during boreal winter into four types: fast-propagating, slow-propagating, standing, and jumping patterns of MJO. The model exhibits biases of overestimated amplitude and faster propagation speed in the propagating MJO events. Upon further analysis, it was found that the model forecasted a wetter background state. This leads to more intense forecasted convection and stronger coupled winds, especially in the fast MJO events. However, the horizontal moisture advection effect for eastward propagation is overestimated in IAP-CAS due to the wetter state and more substantial MJO circulations, which results in a faster MJO mode. These findings show that the IAP-CAS skilfully forecasts signals of MJO and its propagation, and they also provide valuable guidance for improving the current MJO forecast by developing the ensemble system and moisture forecast.
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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.
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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.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1', Fei Liu, 29 Apr 2024
I have thoroughly enjoyed reading this fascinating work and am delighted to see that the IAP-CAS model has achieved a good prediction skill for the MJO, with a 24-day skill in the 20-year hindcast. It is important to investigate the predicted amplitude and propagation of MJOs, as highlighted by Chidong Zhang in the 2024 EGU meeting (personal communication)： The stable eastward propagation, with an average speed of about 5 m/s, is the most important feature of the MJO for sub-seasonal prediction. Additionally, the physical explanation presented for the prediction bias in terms of enhanced MJO amplitude and faster eastward propagation is also reasonable. This paper is well-organized, and the interesting findings justify its publication in Geoscientific Model Development. I have some suggestions and comments for the authors to further improve their work.
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It appears that the IAP-CAS model has already been involved in the S2S project, and we can download the hindcast data from the S2S project. Is the model used in this work the same version as the one from the S2S project? Why not create a figure comparing the prediction skill among all S2S models? Some studies have conducted such comparisons, and it is necessary to present the average and best skill among the current S2S models.
 Was the prediction skill of 24 days calculated for the annual MJO or for the boreal winter MJO? It is important to clearly state whether the main conclusions are for the annual mean or for the boreal winter. (Sometimes you show the results for the annual mean, while some figures were drawn for the boreal winter), as S2S models exhibit a significant annual cycle in the prediction of MJO. Similar checks are also necessary for other presentations. For example, in Lines 168-170, you only have 16 ensembles since 2019, while in Fig. 3, you also presented 16 ensembles for the long period of 1999-2018.   Â
Line 42: The impact of MJO on sub-subseasonal prediction of each sub-monsoon precipitation has been well discussed (Liu et al., 2022), and should be referenced.
Liu, F., Wang, B., Ouyang, Y. et al. Intraseasonal variability of global land monsoon precipitation and its recent trend. npj Clim Atmos Sci 5, 30 (2022). https://doi.org/10.1038/s41612-022-00253-7.Â
Lines 117-119: There were many phenomena that affect the MJO propagation. I suggest deleting this statement as it is not directly related to this work.
 Lines 145: I cannot follow why you use the 10-day forecast nudging from GFS forecast. Should we attribute the good prediction skill of 24 days to IAP-CAS or GFS? Lines 245: Was this underdispersive due to weak initial perturbation of the time-lag method?Â
Lines 306-310: You can calculate the phase speed in this Hovmöller diagram directly. I have a different explanation for the phase speed difference. In Fig. 6, the predicted zonal scale of the MJO, represented by the easterly wind anomalies to the east of the MJO convective center, covers a larger region than observed, which is more obvious for the slow-propagating mode. The moist central Pacific in IAP-CAS overestimates the zonal scale of the MJO, which will increase the eastward propagation speed of the MJO, since the phase speed is inversely proportional to the wave number, as shown in previous work (Wang et al. 2019Sci. Adv. Diversity of MJO). The increased MSE tendency to the east of the MJO can explain the increased amplitude of the MJO, rather than the propagation speed. Let's make an assumption: for the same speed, the stronger MJO also has a larger MSE tendency to the east than the weaker MJO.
Citation: https://doi.org/10.5194/egusphere-2024-1-RC1 -
AC1: 'Reply on RC1', Yangke Liu, 13 May 2024
Dear Prof. Liu,
Thank you for your feedback. Please find attached our detailed response to your comments and suggestions.
If you have any further questions or require additional clarification, please do not hesitate to contact me.
Best regards,
Qing Bao
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AC1: 'Reply on RC1', Yangke Liu, 13 May 2024
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RC2: 'Comment on egusphere-2024-1', Lucas Harris, 02 May 2024
Please see my review in the supplement. I think the paper is solid and the analysis is interesting, although the system itself isn't particularly distinguished from S2S systems.
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AC2: 'Reply on RC2', Yangke Liu, 13 May 2024
Dear Lucas Harris,
Thank you for your feedback. Please find attached our detailed response to your comments and suggestions.
If you have any further questions or require additional clarification, please do not hesitate to contact me.
Best regards,
Qing Bao
-
AC2: 'Reply on RC2', Yangke Liu, 13 May 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1', Fei Liu, 29 Apr 2024
I have thoroughly enjoyed reading this fascinating work and am delighted to see that the IAP-CAS model has achieved a good prediction skill for the MJO, with a 24-day skill in the 20-year hindcast. It is important to investigate the predicted amplitude and propagation of MJOs, as highlighted by Chidong Zhang in the 2024 EGU meeting (personal communication)： The stable eastward propagation, with an average speed of about 5 m/s, is the most important feature of the MJO for sub-seasonal prediction. Additionally, the physical explanation presented for the prediction bias in terms of enhanced MJO amplitude and faster eastward propagation is also reasonable. This paper is well-organized, and the interesting findings justify its publication in Geoscientific Model Development. I have some suggestions and comments for the authors to further improve their work.
Â
It appears that the IAP-CAS model has already been involved in the S2S project, and we can download the hindcast data from the S2S project. Is the model used in this work the same version as the one from the S2S project? Why not create a figure comparing the prediction skill among all S2S models? Some studies have conducted such comparisons, and it is necessary to present the average and best skill among the current S2S models.
 Was the prediction skill of 24 days calculated for the annual MJO or for the boreal winter MJO? It is important to clearly state whether the main conclusions are for the annual mean or for the boreal winter. (Sometimes you show the results for the annual mean, while some figures were drawn for the boreal winter), as S2S models exhibit a significant annual cycle in the prediction of MJO. Similar checks are also necessary for other presentations. For example, in Lines 168-170, you only have 16 ensembles since 2019, while in Fig. 3, you also presented 16 ensembles for the long period of 1999-2018.   Â
Line 42: The impact of MJO on sub-subseasonal prediction of each sub-monsoon precipitation has been well discussed (Liu et al., 2022), and should be referenced.
Liu, F., Wang, B., Ouyang, Y. et al. Intraseasonal variability of global land monsoon precipitation and its recent trend. npj Clim Atmos Sci 5, 30 (2022). https://doi.org/10.1038/s41612-022-00253-7.Â
Lines 117-119: There were many phenomena that affect the MJO propagation. I suggest deleting this statement as it is not directly related to this work.
 Lines 145: I cannot follow why you use the 10-day forecast nudging from GFS forecast. Should we attribute the good prediction skill of 24 days to IAP-CAS or GFS? Lines 245: Was this underdispersive due to weak initial perturbation of the time-lag method?Â
Lines 306-310: You can calculate the phase speed in this Hovmöller diagram directly. I have a different explanation for the phase speed difference. In Fig. 6, the predicted zonal scale of the MJO, represented by the easterly wind anomalies to the east of the MJO convective center, covers a larger region than observed, which is more obvious for the slow-propagating mode. The moist central Pacific in IAP-CAS overestimates the zonal scale of the MJO, which will increase the eastward propagation speed of the MJO, since the phase speed is inversely proportional to the wave number, as shown in previous work (Wang et al. 2019Sci. Adv. Diversity of MJO). The increased MSE tendency to the east of the MJO can explain the increased amplitude of the MJO, rather than the propagation speed. Let's make an assumption: for the same speed, the stronger MJO also has a larger MSE tendency to the east than the weaker MJO.
Citation: https://doi.org/10.5194/egusphere-2024-1-RC1 -
AC1: 'Reply on RC1', Yangke Liu, 13 May 2024
Dear Prof. Liu,
Thank you for your feedback. Please find attached our detailed response to your comments and suggestions.
If you have any further questions or require additional clarification, please do not hesitate to contact me.
Best regards,
Qing Bao
-
AC1: 'Reply on RC1', Yangke Liu, 13 May 2024
-
RC2: 'Comment on egusphere-2024-1', Lucas Harris, 02 May 2024
Please see my review in the supplement. I think the paper is solid and the analysis is interesting, although the system itself isn't particularly distinguished from S2S systems.
-
AC2: 'Reply on RC2', Yangke Liu, 13 May 2024
Dear Lucas Harris,
Thank you for your feedback. Please find attached our detailed response to your comments and suggestions.
If you have any further questions or require additional clarification, please do not hesitate to contact me.
Best regards,
Qing Bao
-
AC2: 'Reply on RC2', Yangke Liu, 13 May 2024
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Cited
Yangke Liu
Qing Bao
Bian He
Xiaofei Wu
Jing Yang
Yimin Liu
Guoxiong Wu
Siyuan Zhou
Yao Tang
Ankang Qu
Yalan Fan
Anling Liu
Dandan Chen
Zhaoming Luo
Xing Hu
Tongwen Wu
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(8521 KB) - Metadata XML