the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2024
Verifying the relationships among the variabilities of summer precipitation extremes over western Japan in the d4PDF climate ensemble, monsoon activity, and Pacific sea surface temperature
Abstract. Upper 99th percentile hourly and 90th percentile daily rainfall over western Japan was calculated for June–July every year, using two observation-based and one simulation-based datasets. These were 54 rain-gauges over the 1952–2022 period, 1 km resolution radar/rain-gauge merged precipitation data over the 2006–2022 sub-period, and the 5 km resolution d4PDF (database for Policy Decision-making for Future climate changes) climate ensemble over the 1952–2010 sub-period. Grid-points over western Japan were clustered by applying the HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) algorithm. Spearman correlation was calculated between rainfall extremes of the clusters, and standardised scores of four modes from the rotated extended Principle Component Analysis of Pacific Sea Surface Temperature (SST) anomalies. These modes represent ENSO (El Niño-Southern Oscillation) growth, ENSO decay, warming trend, and PDV (Pacific Decadal Variability). Based on the clustering, 10 sub-regions were selected for analysis. The correlation coefficients between rainfall extremes and SST modes were at most moderate (|R| ⩽ 0.60) over most sub-regions, reflecting correlation with ENSO decay and warming trend, both directions with a spatial pattern for ENSO growth, and anti-correlation with PDV. These relationships could be partially explained through the strength and location of the monsoon jet in relation to different ENSO phases. d4PDF reflected similar relationships for the first three modes, although it showed both spatial and strength biases. Correlation between rainfall extremes and ENSO decay was likely excessively strong in d4PDF due to the regional climate model's over-response to the monsoon jet wind speed. For the PDV mode, the model could not reproduce the observed relationship of spatially widespread anti-correlation with rainfall extremes. Based on the mostly weak long-term correlations, we concluded that individual SST modes modulated rainfall extremes but were not controlling factors in their occurrence. However, we hypothesize that multiple modes may stack in ways that greatly strengthen their modulating effect, and recommend further investigation in this using sensitivity simulations on case studies.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2024-1304', Anonymous Referee #1, 12 Jul 2024
This manuscript investigates the interannual variations of the observed precipitation extremes over the western Japan during June and July with the three datasets, i.e., the Radar-AMeDAS during 2006–2022, the 54 rain gauge data during 1952–2022, and the 5km mesh 10-member RCM simulations covering the 59-year period 1952–2010. Differences in the data periods result in an interpretation of the results being difficult and ambiguous. Since the correlation coefficients are discussed and not climate values, data from the same time period should be used. Furthermore, the short 17-year time period of the Radar-AMeDAS DATA is questionable in discussing clustering calculations and inter-annual variations. There is no rationale for comparing Radar-AMeDAS and RCM simulations because the model is forced with the observed SSTs, and therefore should be compared to data between the same periods. However, in this case, the common period is only 5 years, 2006-2010. Therefore, clustering and subsequent analysis should be performed on the rain-gauge data for the period 1952-2010, the same period as the d4PDF.
I recommend to re-submit the manuscript. One choice would limit the analysis to an evaluation of model performance by comparing long-term rain-gauge and d4PDF simulations.
Citation: https://doi.org/10.5194/egusphere-2024-1304-RC1 -
AC1: 'Reply on RC1', Shao-Yi Lee, 22 Aug 2024
Dear reviewer, thank you for your comments. If the manuscript is sent to revision, it may take another 6 weeks, but we would like to share our progress with you.
We agree with your comment and indeed find it difficult to interpret our results due to the differences in time period. Radar-AMeDAS was used despite its short 17-year period because of its high-resolution spatial coverage, but as you have pointed out there is only a common period of 5 years. As you have suggested, we have performed a clustering on rain-gauges for the 1952-2010 period, identical to the simulation period. Furthermore, this was done for the ~130 rain-gauges over a larger region over Japan, compared to ~50 in western Japan previously. Below is the result for 99th upper percentile hourly rainfall, and a minimum cluster size setting of 3. This resulted in 16 classes, but also quite a number of unclassified rain-gauges. (Due to the image limitation, the quality is not so good.) From here, we should be able to compare identical regions in the simulations compared to the regions covered by the rain-gauges.
Citation: https://doi.org/10.5194/egusphere-2024-1304-AC1
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AC1: 'Reply on RC1', Shao-Yi Lee, 22 Aug 2024
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RC2: 'Comment on egusphere-2024-1304', Anonymous Referee #2, 01 Aug 2024
This paper investigates relationships between precipitation extremes over western Japan and major SST modes over the Pacific based on observational data, and it tries to explain the relationships through the modulation of monsoon activity. Then, the paper verifies the representation of the obtained relationships in d4PDF data. I found it difficult to evaluate this paper due to several issues listed below during the review. Therefore, I would like to reassess the significance of this paper after the authors have addressed these issues.
First, this paper seems to have incorrect notations in the figure numbers and similar references listed below. Authors should carefully proofread the manuscript before submitting it.
- Lines 350: Supp. Figs S3-3a -> Supp. Figs S1-3a ?
- Lines 351-355: Similar mistakes as above.
- Supplementary Material Section 1: Supplementary Figure S2-0 -> Supplementary Figure S1-0 ?
- Supplementary Material Section 1: Similar mistakes in other figure captions in this section.
- The caption of Supplementary Table S2-1: “Meteorological stations used, in three columns, with names in English and Japanese. Years listed with the shaded station are those with insufficient data and not considered.” It seems that this explanation does not fully correspond with Table S2-1.
- Supplementary Material Section 2: Figure S2-2 -> Supplementary Figure S2-1 ?
- Supplementary Material Section 4: Table S3-1 -> Supplementary Figure S4-1 ?
- Supplementary Material Section 4: Placing Figure S4-5 just below Figure S4-4 would be better.
- Supplementary Table S6-1a, S6-1b, S6-2a, and S6-2b: What does “ENSO-NC” mean in the rightmost column? There is no explanation of this term in the table caption and the manuscript.
- Line 9 in the caption of Supplementary Table S6-2a: 99.9th percentile hourly rainfall -> 99th percentile daily rainfall
- Line 174: dJF -> DJF
- Line 175: Djf -> DJFSecond, I think Table 2 is the most crucial result in this paper; however, I could not understand what type of observational data the presented results are based on and which period they covered. Please note this information in the table caption. In addition, I could not understand the results easily because the results of observation and d4PDF are displayed in layers with complex notations. I would like to ask the authors to present the observations and d4PDF separately.
Third, I am concerned about the difference in the periods of the Radar-AMeDAS data and the d4PDF data in interpreting the results. Since the overlapping period between the Radar-AMeDAS data and the d4PDF data is short, it would be better to focus on comparing the ground observation data with the d4PDF data. There is no problem with using the Radar-AMeDAS data as supplementary data. Another choice is that using AMeDAS data would provide more spatially dense observational information since the late 1970s.
Fourth, in this paper, many figures and tables are presented in the supplementary section and are cited in the main text. However, I do not believe all these figures and tables are necessary to reach the paper's conclusions. Presenting numerous results with little significance only wastes the reader's time. Please carefully select the figures and tables to be included in the paper. Associated with this point, it seems that the analysis of Ph99.9 and Pd99 has a minor role in this paper, so I think this analysis could be omitted.
[Other comments]
1. In this paper, the SST mode four is interpreted as the Pacific Decadal Variability mode. Please show a temporal correlation between this mode and a well-known climate index, such as the IPO or PDO index, which would be available on a website.
2. Line 533: observations -> JRA55Citation: https://doi.org/10.5194/egusphere-2024-1304-RC2 -
AC2: 'Reply on RC2', Shao-Yi Lee, 22 Aug 2024
Dear reviewer, thank you for your comments. If the manuscript is sent for revision, it will be another 6 weeks, so we would like to update you on the current progress.
First of all, we would like to apologise for incorrect references/notations and general poor readability of the manuscript. It went through 3 major rewritings and the writing has become confused, so we will try to correct the errors and improve its readability.
A large part of the confusing presentation may be trying to compare d4PDF with both radar and rain-gauges. As you have pointed out, the overlapping period of radar-AMeDAS and d4PDF is short. This issue was also raised by reviewer 1. We will focus on comparing rain-gauges with d4PDF for identical periods, and this should improve the interpretability of the results.
After excluding radar-AMeDAS, then improving the d4PDF data-processing method (described later), it is possible to cluster 5km d4PDF over a larger area of Japan in one step. (Previously, the raw RCM data was loaded into GrADs which interpolated it onto a lon-lat grid, which resulted in a larger volume of data. Now, the grid-points from the smaller native RCM grid are used. The grid-points inside a convex hull over major Japanese islands are cropped, then any significant correlation with nearby points compressed as a sparse matrix.) The results of clustering the 99th upper percentile hourly rainfall are shown below. This can be compared with the results based on rain-gauges. In both figures, the minimum cluster size is set to 3. The interannual variability of rainfall can be compared between d4PDF and rain-gauges by selecting points covered by the rain-gauge clusters.
With regards to whether SST 4 can be interpreted as PDO, we have calculated a time series of PDO index (https://www.ncei.noaa.gov/access/monitoring/pdo/) for 1952-2010. The mean of 5 seasons centred on JJA of the year was taken, using a similar treatment to SST, where each PCA sample was 5 seasons concatenated. Below is the comparsion of the two. Black is the PDO index. Red is standardised score of SST mode 4. The Spearman correlation between the two is 0.55, statistically significant at alpha=5%. It is not an extremely strong correlation but better than the correlation between PDO index and other 3 modes, which are of magnitude 0.31 and not statistically significant. There seems to be a lot of higher frequency signal still in SST mode 4, although the lower frequency seems to generally match the PDO index. This may be why the correlation is only 0.55. We can also do a direct correlation between the rainfall extremes and the PDO index.
Citation: https://doi.org/10.5194/egusphere-2024-1304-AC2
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AC2: 'Reply on RC2', Shao-Yi Lee, 22 Aug 2024
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