Peer review of: Emergent constraints on future projections of the western North Pacific Subtropical High (2020)

Peer Review File 

Reviewers' comments first round: 
 
Reviewer #1 (Remarks to the Author): 
 
The paper is well written with sound methodology and contains useful scientific information on the 
North Pacific Subtropical High. The paper is recommended for publication with minor suggestions. 
 
Suggestions: 
A recent study (Preethi etal 2017a) has shown that the North Pacific Subtropical High has shifted 
westwards about 5-7 degrees of longitudes during recent decades. A further study (Preethi etal 
2017b) showed that this High depicts an zonal oscillatory behavior with east-west-east shifts in the 
future projections. The authors could consider citing these papers: 
 
References: 
Preethi B, M Mujumdar, RH Kripalani, A Prabhu, R Krishnan 2017a: Recent trends and tele-
connections among South and East Asian summer monsoons in a warming environment. Climate 
Dynamics, 48, 2489-2505. 
 
Preethi B, M Mujumdar, A Prabhu, RH Kripalani 2017b: Variability and tele-conections of South and 
East Asian summer monsoons in present and future projections of CMIP5 climate models. Asia-
Pacific J of Atmos Sc, 53(2), 303-325 
 
 
 
Reviewer #2 (Remarks to the Author): 
 
Review of manuscript #NCOMMS-19-3009875 entitled “Emergent constraints on future projections 
of the western North Pacific Subtropical High”. 
 
Chen et al. use an ensemble of global climate models to identify a reasonably strong intermodel 
relationship between historical sea surface temperatures and projected changes to the western 
North Pacific Subtropical High (WNPSH). Observations of sea surface temperature across two 
regions are then used in conjunction with this relationship in an attempt to narrow intermodel 
spread in the leading modes of the WNPSH projection. The authors use this constraint to conclude 
that the WNPSH will strengthen under future warming. This anticipated intensification is shown to 
have major implications for East Asian climate. The research is novel and would be of interest to 
the climate community. However, the manuscript is fairly difficult to read. I have pointed out 
several instances of this below. I recommend asking a native English speaker to assist with 
proofreading during revision. 
 
Major comments: 
 
1) Other reviewers will be better informed to comment on the underlying large-scale dynamics, 
but the authors do a good job of highlighting the existence of a relationship between historical sea 
surface temperature patterns and projected changes to the WNPSH. However, there are additional 
steps that would help to solidify the proposed emergent constraint. The statistical methodology is 
generally adequate to the current level of development for emergent constraints, although the 
emergent projection can be improved through the incorporation of a methodology such as that of 
Bowman et al. (2018, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL080082). 
Given that the authors have a series of observational datasets (and thus, can determine 
observational uncertainty), a more robust projection of the optimal PC1 and PC2 can be derived 
using this methodology. 
 
2) Similarly, it is important to know what factors (model processes/characteristics or 
parameterizations) are driving the intermodel spread in simulated present-day SST patterns across 
the T1 and T2 regions. Without this knowledge, it is difficult for modelling centers to attempt to fix 
the bias and thus, improve future projections. While a full investigation of these factors is outside 
the scope of this study, a preliminary analysis would be highly beneficial. Given that space is 
limited, some simple scatterplots related to this (between simulated historical SST patterns and 
relevant factors/processes) could be shown as Supplemental Figure(s). 
 
3) Role of internal variability: The authors do not state whether they are using the first ensemble 
member for each CMIP5 model or not? Assuming that this is the case, a note should be added to 
the Methods. Furthermore, it brings up the question of how much sensitive the results are to 
internal variability. I am interested in seeing how sensitive a model’s placement on Figure 2 is to 
the choice of ensemble member. This simple analysis could be performed for a single model with 
several CMIP5 realizations and added as a supplemental figure if the results warrant inclusion. 
 
Specific comments: 
 
L40: Change “despite of intensive” to “despite a number of intensive” 
 
L47: Remove “state-of-the-art” here, this is already stated in the paragraph above. 
 
L50: Add “the” before “surface warming pattern” 
 
L56 and L64: Change “projection” to “projections” 
 
L57: Change “75% uncertainty” to “75% of the uncertainty” 
 
L66: Change “consistence” to “consistency”. I am also confused what is meant by model 
consistency in this context, does it have to do with the sign of future change? Please clarify and 
rephrase in the text. 
 
L70: Remove “totally”. 
 
FigS1: Add a panel showing the historical SLP over the same region to give the reader more 
context. 
 
L83: change “present-day simulation” to “historical simulation” throughout. 
 
Fig 1: State what the interval between contours represents, is it 1 mm/day and 1% change in 
cloud fraction? Also, why is the mean SST being taken over different areas (30-30 and 60-60) 
before being regressed onto the principal components? 
 
L88: Use “It has been shown” or “It is known”. 
 
L89: Change “too few low-cloud covers” to “a lack of low clouds” 
 
L92: Add some information about the pre-industrial simulation(s) to the Methods. Is this the PI-
control scenario from each CMIP5 model? 
 
L99-100: I thought the stratocumulus cloud regions had a cold bias (as stated on L85-86)? Please 
clarify. 
 
Fig 2: I am somewhat surprised to see such strong agreement between the observational 
datasets. I recommend creating a supplemental figure showing maps of their SST patterns over 
the historical period for T1 and T2. 
 
Calculation of T1 and T2 indices: I would like to see more detail regarding how these indices are 
derived. In the Fig 2 caption the authors note that “Negative T1 denotes a reduction of cold tongue 
bias…”, but the language used is confusing. Is the index centered on the model ensemble mean 
value? If so, “anomaly” is more appropriate than “bias”, which is often reserved when comparing 
models to observations. 
 Fig 1ab,3,4: It is difficult to see the land boundary on many of these maps. Perhaps switch the SLP 
contour color to grey and the land boundary to black for figs 1a and b? 
 
L125: I appreciate this discussion of the physical mechanisms behind the constraint, but would 
change the subtitle to “Physical mechanisms backing the constraint”. 
 
L129: “The positive SST warming” should say “The positive SST anomalies” or “The SST warming” 
 
L148: Change “can result larger decrease” to “can result in larger decreases” 
 
L149: change “respond” to “response” 
 
L175: “Reduce the CMIP5 intermodel spread by 75%” 75% of what? Please clarify and rephrase. 
 
L179: Fix “father” 
 
L209-211: Expand on why the global-scale bias in SSTs must be subtracted. 
 
L212-213: There are more robust approaches to estimating the optimal PC1 and PC2 than just 
using the mean of observed T1 and T2 (see main comment #2). 
 
 
 
Reviewer #3 (Remarks to the Author): 
 
COMMENT SHEET 
for 
PAPER NUMBER: NCOMMS-19-3009875 
JOURNAL: Nature Communications 
TITLE: Emergent constraints on future projections of the western North Pacific Subtropical High 
 
The manuscript by Chen et al. present an interesting analysis and interpretation on future 
projection of western North Pacific Subtropical High (WNPSH). The study describes two inter-model 
modes that can explain uncertainties of future changes in WNPSH simulated by CMIP5 climate 
models. They found that the first leading mode is linked to the models’ cold tongue biases in the 
tropical Pacific and the second mode is linked to the warm biases associated with marine 
stratocumulus. They corrected future projection of WNPSH using observational constraints 
estimated through the principal component and the related sea surface temperature pattern. The 
study further suggested the physical mechanisms behind the two leading inter-model modes. 
The paper is well focused, and the study objectives are clear. The finding is new and could be 
potentially important to understand future changes in WNPSH and east Asian monsoon. However, 
although the authors try to present a compelling case, the amount of information provided and the 
failure to support their interpretation weakens their case. The present study is mostly based on 
descriptive analysis, lacks important details, and must be strengthened before it can be accepted 
for publication. The major and minor issues are detailed below. 
 
1.The critical point is the appropriateness of the analysis used in this study. The key analysis in 
this paper is to find optimal principal components (PCs) from the inter-model relationship between 
leading PCs and sea surface temperature (SST) pattern indexes as shown in Fig. 2. However, there 
seems to be circular reasoning in this analysis given that the SST pattern index (i.e., T1 and T2) is 
based on PCs. By the definition of T1, PC1 and T1 are expected to be correlated to each other, and 
thus I’m wondering how the result can be justified to constraint future model prediction of WNPSH. 
 
2.The pattern of cold tongue bias stretching to the coast of Peru as shown in Fig 1c is not similar 
to that of the typical cold tongue bias of climate models. How would the result change if T1 is 
defined by equatorial SST mean in different models? And for the T2, how would be the result if T2 
is defined by area-averaging the warm SST bias in the subtropics? 
 
3.Another important issue is that physical mechanisms suggested in the study are not complete, 
rather the arguments are largely based on previous works and speculation. Particularly, the 
linkage between cold tongue SST bias in the historical run and future warming in the western 
Pacific is not clearly shown. The linkage between warm SST bias (or low cloud bias) and enhance 
land-sea thermal contrast also needs to be strengthened. Isn’t there possibility that models 
showing stronger Arctic amplification can cause stronger low cloud and warm SST biases? The 
causal relationship is still not clear in the mechanism related to PC2. Overall, the mechanism 
section seems to overly speculative given the tenuous arguments presented. 
 
4.The methodology used in this study has not been explained in sufficient detail. The authors 
explained that T1 and T2 are the projections of historical mean SST pattern onto the SST pattern 
associated with EOF1 and EOF2, respectively. Does the projection refer to the regression 
coefficient between them? Is the historical mean SST from the observations subtracted from each 
modeled SST to define T1 and T2? In line 210, the historical mean SST refers to one value 
averaged in the specified area? 
 
5.Other minor comments are as follows. 
-Line 72: EOF2 doesn’t look like enhanced WNPSH. 
-Line 90: fewer  weaker 
-Line 102: What is the degree of freedom to calculate p-value? In the multi-model set used in the 
study, the models rooted in the same family are used, thus it seems the degree of freedom should 
not be simply N-2. 
-Future change in circulation pattern related to EOF2 (Fig. 4) do not really indicate the enhanced 
WNPSH. Again, is the second inter-model mode related to WNPSH? 
-Authors’ argument on heat waves, drought, and flood is unrelated to the topic and probably 
better to be avoided because there is not any analysis on these topics. 
-Last paragraph seems to be redundant and duplicated.