Influencing Factors of the Sub-Seasonal Forecasting of Extreme Marine Heatwaves: A Case Study for the Central–Eastern Tropical Pacific

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Review of the manuscript “Influencing factors of the Sub-seasonal Forecasting of Extreme Marine Heatwaves: A Case Study for the Central-eastern Tropical Pacific” by Lin Lin, Yueyue Yu, Chuhan Lu, Guotao Liu, Jiye Wu and Jing-Jia Luo.

 

In this paper, the authors aim to provide an analysis of the forecasting skills of the NUIST CFS1.1 model. To this end, they select 7 extreme MHWs detected on satellite sea surface temperature data in the eastern tropical Pacific ocean and investigate the capability of the model forecast to predict various spatio-temporal characteristics of the events. Their analysis includes an investigation of ENSO forecasts and of the relative contribution of advection and heat fluxes to the selected extreme events, they then evaluate the skills of the forecast in reproducing this variability. While the work seems generally well-executed and insightful, it falls short of convincing on a certain number of aspects because of some lack of clarity. I believe some of the methodology could be reproduced in other studies examining the skills of specific forecasts but to achieve this certain points require clarification and additional editing is recommended (specific remarks follow).

 

One of the first thing I don’t understand well is how are the 7 MHWs selected. The authors describe them as extreme but they exclude the S4 (extreme) metrics from their study. I’m not sure either of how the start and end date of those events are identified. I’m going to provide the authors with a list of specific comments but I would recommend to be more didactic throughout the manuscript.

 

List of specific comments:

- Figure 1: why is there a line at ACC=0.3 and RMSE=1? and why aren’t all the crosses represented?

- lines 257-258: “Additionally, the model fails to predict the second surge in PS>3 and IS>3 during MHW#2 (Figures 3g, h).” The model actually seems to show a delayed peak in march for those metrics.

- Figure 2: “The rapid warming period, characterized by obvious upward trends of IS>1 and IS>2, which also includes the peak of IS>3, is marked by black dashed lines.” is this period determined visually or is there a criteria? (both are fine but you should clarify).

- Figure 6: what is the black line?

- 3.2.1 ENSO forcing: how do you calculate the ACC for ENSO indices?

- line 384: “the rapid warming periods of MHWs” Is it the period defined in Figure 2?

- lines 414-416: “For the severe warming (S>3), Adv's positive contributions persist across all MHW events except MHW#2 (Figure 9c), where insufficient grids reach S>3 and therefore not considered.” How many grid points are considered sufficient? If S3 region is too small to be considered for MHW#2 maybe it shouldn’t appear on the figure.

- line 455-456: “In high-predictive events (MHW#1 and #2), the contribute rate of Adv-v slightly exceeds that of Adv-u.” is it significant?

- Figure 6: MHW#3 and 4 are MHW#6 and 7?

Comments on the Quality of English Language

Here are some suggestion: 

• line 79-81: “It is also utilized by the NUIST CFS1.1 (latest version of Nanjing University of Information Science and Technology Climate Forecast System) model [44–48] that the sub-seasonal forecast skills of seven extreme MHWs in the central and eastern tropical Pacific [49].” please rephrase.
• line 153-154: “it is found that the temporal evolution of IS>1, IS>2, and IS>3 index (shown in Table 1) could 153 best capture the moderate (strong, severe, extreme) warming processes, respectively.” You should remove the parenthesis around strong, severe and extreme here.
• line 207: “Among the seven extreme MHWs occurred” → Among the seven extreme MHWs that occurred.
• lines 215 and 217: locate → located
• line 275: “they locate” → they are located
• lines 497, 504 and 582: “events occurred” → events that/which occurred.

Author Response

Comments 1: Figure 1: why is there a line at ACC=0.3 and RMSE=1? and why aren’t all the crosses represented?

Response 1: Thank you for the valuable comments. We have revised Figure 1 as following.

In Figures 1a~d, the lines with the value of average ACC and RMSE in each panel represent the average forecast skill for S>1, S>2, S>3, and all metrics in MHW#1~7. We revised our expressions correspondingly. Please see pages 6, lines 216–233 in the revised manuscript.

Comments 2: lines 257-258: “Additionally, the model fails to predict the second surge in P_S>3 and I_S>3 during MHW#2 (Figures 3g, h).” The model actually seems to show a delayed peak in march for those metrics.

Response 2: Thank you for pointing this out. Please see page 8, lines 271–274 in the revised manuscript.

Comments 3: Figure 2: “The rapid warming period, characterized by obvious upward trends of I_S>1 and I_S>2, which also includes the peak of I_S>3, is marked by black dashed lines.” is this period determined visually or is there a criteria? (both are fine but you should clarify).

Response 3: Thank you for pointing this out. We revised our expressions correspondingly. Please see pages 8–9, lines 279–281 in the revised manuscript.

Comments 4: Figure 6: what is the black line.

Response 4: Thank you for pointing this out. We revised our expressions correspondingly. Please see page 12, lines 332–335 in the revised manuscript.

Comments 5: 3.2.1 ENSO forcing: how do you calculate the ACC for ENSO indices?

Response 5: Thank you for pointing this out. The ACC and RMSE for ENSO indices are calculated as below. We revised our expressions correspondingly. Please see page 14, lines 382–384 in the revised manuscript.

Comments 6: line 384: “the rapid warming periods of MHWs” Is it the period defined in Figure 2?

Response 6: Agree. Thank you for pointing this out. We revised our expressions correspondingly. Please see page 15, lines 410–415 in the revised manuscript.

Comments 7: lines 414-416: “For the severe warming (S>3), Adv's positive contributions persist across all MHW events except MHW#2 (Figure 9c), where insufficient grids reach S>3 and therefore not considered.” How many grid points are considered sufficient? If S3 region is too small to be considered for MHW#2 maybe it shouldn’t appear on the figure.

Response 7: Thank you for the valuable comments. The percentage of grid points experiencing severe warming in MHW#1, #2, #6 and #7 is 8.98%, 2.92%, 33.83%, 20.12%, respectively. Since the percentage of grid points experiencing severe warming in MHW#2 is less than 5% of the total region, MHW#2 is excluded in Figure 9c. The revised Figure 9 is as following.

We revised the expressions correspondingly. Please see page 17, lines 476–478 in the revised manuscript.

Comments 8: line 455-456: “In high-predictive events (MHW#1 and #2), the contribute rate of Adv-v slightly exceeds that of Adv-u.” is it significant?

Response 8: Thank you for pointing this out. The conclusion that Adv-v's contribution rate is slightly higher than Adv-u's is significant. We added the expressions correspondingly. Please see page 17, lines 484–486 in the revised manuscript.

Comments 9: Figure 6: MHW#3 and 4 are MHW#6 and 7?

Response 9: Thank you for pointing this out. I’m sorry that I mistakenly marked MHW#6 and #7 as MHW#3 and #4 in Figure 10. We revised the Figure 10 correspondingly.

Point 1: line 79-81: “It is also utilized by the NUIST CFS1.1 (latest version of Nanjing University of Information Science and Technology Climate Forecast System) model [44–48] that the sub-seasonal forecast skills of seven extreme MHWs in the central and eastern tropical Pacific [49].” please rephrase.

Response 1: Corrected. Please see page 2, lines 79–86 in the revised manuscript.

Point 2: line 153-154: “it is found that the temporal evolution of I_S>1, I_S>2, and I_S>3 index (shown in Table 1) could best capture the moderate (strong, severe, extreme) warming processes, respectively.” You should remove the parenthesis around strong, severe and extreme here.

Response 2: Corrected. Please see page 4, lines 156–158 in the revised manuscript.

Point 3: line 207: “Among the seven extreme MHWs occurred” → Among the seven extreme MHWs that occurred.

Response 3: Corrected. Please see page 5, lines 211–214 in the revised manuscript.

Point 4: lines 215 and 217: locate → located

Response 4: Corrected. Please see page 6, lines 220–226 in the revised manuscript.

Point 5: line 275: “they locate” → they are located

Response 5: Corrected. Please see page 9, lines 290–291 in the revised manuscript.

Point 6: lines 497, 504 and 582: “events occurred” → events that/which occurred.

Response 6: Corrected. Please see pages 20 and 21, lines 526–527, 533–536, 610–613 in the revised manuscript.

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

Reviewer Report

Title: Influencing Factors of the Sub-seasonal Forecasting of Extreme Marine Heatwaves: A Case Study for the Central-eastern Tropical Pacific

Authors: Lin Lin, Yueyue Yu, Chuhan Lu, Guotao Liu, Jiye Wu, Jing-Jia Luo

Journal: Remote Sensing

 

Major Strengths

1. Timely and Relevant Topic: The study addresses a crucial issue in climate forecasting, particularly the challenges of sub-seasonal MHW prediction, which is highly relevant given the increasing frequency of MHWs due to climate change.
2. Robust Methodology: The study employs a comprehensive heat budget analysis and statistical evaluation of forecast performance, ensuring a solid basis for the conclusions.
3. Clear Categorization of MHW Events: The classification of MHWs into different predictability levels allows for a systematic comparison of forecasting accuracy and underlying oceanic and atmospheric drivers.
4. Comprehensive Data Sources: The use of NOAA OISSTV2 and ORAS5 datasets, along with the NUIST CFS1.1 model outputs, enhances the reliability of the study.

 Major Concerns and Recommendations

• Clarification of Predictability Criteria: The manuscript categorizes MHW events into high, moderate, and low predictability based on anomaly correlation coefficients (ACC) and root mean square errors (RMSE). However, the threshold values for these classifications are not explicitly defined. Providing a clear numerical criterion for each category would enhance transparency.
• Impact of ENSO Forecasting on MHW Predictability: The study notes that a robust ENSO forecast does not necessarily lead to accurate MHW forecasts. It would be beneficial to elaborate on the potential mechanisms driving this discrepancy. Are there specific ENSO phases or teleconnections that impact forecast skill?
• Model Bias in Heat Flux Contributions: The study finds that the NUIST CFS1.1 model struggles to capture the air-sea heat flux (Q) contributions accurately. More discussion on possible model improvements, such as parameterization schemes or data assimilation techniques, could strengthen the manuscript.
1. Spatial Resolution and Forecast Skill:
• The manuscript mentions that data are interpolated to a 1.125° × 1.125° grid. Given the fine-scale nature of MHWs, could the resolution affect the forecast accuracy? If so, would using higher-resolution reanalysis data improve results?
• Graphical Representation of Forecast Errors: The figures comparing observed and forecasted MHW metrics are informative, but additional visualizations highlighting forecast errors (e.g., bias maps or Taylor diagrams) could provide a clearer assessment of model performance.

 Minor Issues and Suggestions

• Grammar and Clarity: Several sentences are lengthy and complex. Simplifying sentence structures and improving clarity would enhance readability. Example: “The concept of 'mega ENSO' could capture a broader range of SST variability compared to traditional ENSO indices” — consider rewording for clarity.
• Consistency in Terminology:  The manuscript sometimes uses different terms interchangeably (e.g., “heat convergence,” “heat advection”). Standardizing terminology would improve clarity.
• Missing References: Several references are cited but not fully listed in the bibliography. Ensuring all citations are properly included would improve the manuscript's completeness.

 Recommendation: Minor Revisions

Comments for author File: Comments.pdf

Author Response

Comments 1: Clarification of Predictability Criteria: The manuscript categorizes MHW events into high, moderate, and low predictability based on anomaly correlation coefficients (ACC) and root mean square errors (RMSE). However, the threshold values for these classifications are not explicitly defined. Providing a clear numerical criterion for each category would enhance transparency.

Response 1: Thank you for pointing this out. Due to that there are only seven MHWs selected, it is difficult to define threshold values of ACC and RMSE. Combing the arrangement of sub-season forecast skill of MHWs (from MHW#1 to #7) and Figure 1, it can be seen that MHW#1 and #2 are best forecasted with NUIST CFS1.1 model, thus, they are categorized as high-predictive MHWs. Similarly, MHW#6 and #7 are the worst forecasted and they are categorized as low-predictive MHWs. The remaining (MHW#3~5) are categorized as moderate-predictive MHWs. We added our expressions correspondingly. Please see page 6, lines 216–229 in the revised manuscript.

Comments 2: Impact of ENSO Forecasting on MHW Predictability: The study notes that a robust ENSO forecast does not necessarily lead to accurate MHW forecasts. It would be beneficial to elaborate on the potential mechanisms driving this discrepancy. Are there specific ENSO phases or teleconnections that impact forecast skill?

Response 2: Thank you for the valuable comments. The discrepancy in the sub-seasonal forecast skills of ENSO and MHW events may be attributed to the relatively shorter timescales of MHWs and their focus on the extreme warming anomalies, in contrast to the broader and more prolonged nature of ENSO. We added our expressions correspondingly. Please see page 14, lines 393–396 in the revised manuscript.

Moreover, the role of ENSO in forecast skills of MHWs might associate with the location of MHW, shown as lines 396–400 in page 14.

Undoubtedly, ENSO could provide a warm SST background for MHWs’ occurring in the central-eastern tropical Pacific. In the future, we will continue to study the relationship between the specific ENSO phases and other teleconnections and the forecast skill of MHWs. We added our expressions correspondingly.

Comments 3: Model Bias in Heat Flux Contributions: The study finds that the NUIST CFS1.1 model struggles to capture the air-sea heat flux (Q) contributions accurately. More discussion on possible model improvements, such as parameterization schemes or data assimilation techniques, could strengthen the manuscript.

Response 3: Thank you for the valuable comments. The air-sea heat flux (Q) term is calculated with mixed layer depth, sea surface sensible heat flux, latent heat flux, downward shortwave radiation and upward long wave radiation. Thus, Q’s forecast accuracy might be related to the radiation and boundary layer parameterization schemes in NUIST CFS1.1 model. We added our expressions correspondingly. Please see pages 16 and 21, lines 461–463 and 599–600 in the revised manuscript.

Comments 4: Spatial Resolution and Forecast Skill: The manuscript mentions that data are interpolated to a 1.125° × 1.125° grid. Given the fine-scale nature of MHWs, could the resolution affect the forecast accuracy? If so, would using higher-resolution reanalysis data improve results?

Response 4: Agree. Higher-resolution reanalysis data could improve the results of forecast accuracy. However, due to the limitation of current computing resources, the model calculation cannot be realized with high resolution at present. In the future, we will consider using high-resolution data to improve the forecast accuracy. Please see page 3, lines 122–126 in the revised manuscript.

Comments 5: Graphical Representation of Forecast Errors: The figures comparing observed and forecasted MHW metrics are informative, but additional visualizations highlighting forecast errors (e.g., bias maps or Taylor diagrams) could provide a clearer assessment of model performance.

Response 5: Thank you for pointing this out. In this paper, the differences between the forecasts and observations are mostly showed directly together or through calculating the ACC and RMSE of forecasts and observations. In addition, Figure 7 also superimposes the difference field between forecasts and observations.

Point 1: Grammar and Clarity: Several sentences are lengthy and complex. Simplifying sentence structures and improving clarity would enhance readability. Example: “The concept of 'mega ENSO' could capture a broader range of SST variability compared to traditional ENSO indices” — consider rewording for clarity.

Response 1: Thank you for pointing this out. We revised our expressions correspondingly. Following are some examples:

Lines 376-377: Compared to traditional ENSO indices, “mega ENSO” captures a broader range of SST variability [71]. 

Lines 263-266: The MHW#2 event which lasts for 167 days shows similar observational features to MHW#1. Except that there is a secondary surge in P_S>2, I_S>2, P_S>3, I_S>3 from January to March in 1983, which is most pronounced in P_S>3 and I_S>3 (Figure 3).

Lines 263-266: By combining the forecasts of various metrics, it becomes evident that the NUIST CFS1.1 model faces greater challenges in accurately capturing metrics those indicate strong and severe warming.

Point 2: Consistency in Terminology: The manuscript sometimes uses different terms interchangeably (e.g., “heat convergence,” “heat advection”). Standardizing terminology would improve clarity.

Response 2: Thank you for pointing this out. We revised our expressions correspondingly. Please see pages 16,21,22, lines 434,605,645 in the revised manuscript.

Point 3: Missing References: Several references are cited but not fully listed in the bibliography. Ensuring all citations are properly included would improve the manuscript's completeness

Response 3: Corrected. We revised the references correspondingly.

 

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for addressing my review comments in your revised manuscript. I appreciate your thoughtful responses and the improvements made based on the feedback. The revisions enhance the clarity and robustness of the study, and I look forward to seeing it published.