the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
21 Sep 2021
21 Sep 2021
Probabilistic Gridded Seasonal Sea Ice Presence Forecasting using Sequence to Sequence Learning
- 1Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
- 2Ocean, Coastal and River Engineering Research Centre, National Research Council Canada, Ottawa, Canada
- 3Memorial University of Newfoundland, Newfoundland and Labrador, Canada
- 1Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
- 2Ocean, Coastal and River Engineering Research Centre, National Research Council Canada, Ottawa, Canada
- 3Memorial University of Newfoundland, Newfoundland and Labrador, Canada
Abstract. Accurate and timely forecasts of sea ice conditions are crucial for safe shipping operations in the Canadian Arctic and other ice-infested waters. Given the recent observations on the declining trend of Arctic sea ice extent over the past decades due to global warming, machine learning (ML) approaches are deployed to provide accurate short-term to long-term forecasting. This study unlike previous ML approaches in the sea-ice forecasting domain provides a daily spatial map of the probability of ice in the study domain up to 90 days of lead time. The predictions are further used to predict freeze-up/breakup dates and show their capability to capture these events within a valid time period (7 days) at specific locations of interest to communities.
Nazanin Asadi et al.
Status: final response (author comments only)
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RC1: 'Comment on tc-2021-282', Anonymous Referee #1, 27 Oct 2021
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AC1: 'Reply on RC1', Nazanin Asadi, 24 Jan 2022
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2021-282/tc-2021-282-AC1-supplement.pdf
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AC1: 'Reply on RC1', Nazanin Asadi, 24 Jan 2022
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RC2: 'Comment on tc-2021-282', Anonymous Referee #2, 27 Oct 2021
Asadi et al. 2021 - Probabilistic Gridded Seasonal Sea Ice Presence Forecasting using Sequence to Sequence Learning
The authors present a fascinating application of machine learning techniques to better predict ice presence/absence within Hudson Bay, using ERA5 data as an input. The results show promise in helping plan shipping operations around the ice-free season, however, the clarity of these results is lost in lengthy wording. It is recommended that the authors read through the document for grammatical errors and places where the wording of sentences can be made more succinct. This article can become much more impactful and easier to read with more ‘straight to the point' sentences.
General comments:
- Ensure you are consistent using ‘freeze-up’ with a hyphen throughout the document, and choose either ‘breakup’ or ‘break up’ to use throughout the document
- I am aware that it is difficult to phrase sentences when discussing the number of lead days and the two models, however, I found most sentences discussing these topics hard to read. For example, line 149:
‘For example, the top row of Fig 1b shows the accuracy of forecasts launched in January using Basic model for forecast lead days of 1 to 90. E.g., the first top-left box in this figure (Fig 1(b)) corresponds to the average accuracy after 1 day forecast for all forecasts launched between January 1 and January 31, ending in January 2 to April 1 and the second box corresponds to average accuracy of forecasts launched between January 1 and January 31 ending in January 3 to April 2.’
I think it would be easier if you use articles when you are referencing lead days or models. For example: ‘the Basic model’ or ‘a 1 day forecast’. This would make your sentences flow better while reading them, which would communicate your results more efficiently.
- The results section has some statements that are more suited towards the discussion section, however I see your discussion and conclusion section are combined. I’m unsure if the section headers are pre-determined by the journal, but if they are not I would suggest making section 6 ‘Results and Discussion’, and section 7 ‘Conclusion’. This would allow you to discuss your results more in depth as you present them, as I feel like some of your results could be discussed more in depth.
- Throughout the document, you abbreviate some month names and use the full name for others. You should pick one method and stick to it throughout (i.e. always abbreviate or always use the full word).
- There is a comment in the specific comments regarding this, but you should include some discussion regarding the resolution of your results, and how this may impact the use of your results for port-specific operations. I am a little wary of how the land mask may impact how ‘close’ the pixel you use to represent the port is to the actual port in question. A figure representing this may add some clarity.
Specific comments:
Line 3 – You may be limited on word count in your abstract, but I think it would be helpful if you stated the type of data you are feeding into your ML system to derive these predictions.
Line 3 – recommend changing to “Given the recent observations of the declining trend”
Line 6 – recommend changing to ‘within a 7-day time period’, unless you define why a 7-day time period is ‘valid’ in the manuscript?
Line 8 – The introductory sentence needs a little bit of work. I would recommend removing ‘northern communities’ as you do not speak of them in the rest of the introduction. Maybe focus more on the topic of shipping and why ice forecasting is vital for shipping operations in this introductory sentence. OR add in reference to communities, and why they rely on ice.
Line 12 – Could you expand on what you mean by ‘Typical approaches are usually statistical or dynamical in nature.’? Maybe add a reference to examples of these? I see that you go more in depth in the next paragraph into dynamical forecasts, but what about statistical like you mentioned earlier?
Line 15 – I would recommend splitting this up into two sentences, breaking it up at one of the commas
Line 16 – remove ‘the summer of’ before 2008, as you have already indicated that this study was in the spring and summer
Line 18 – I am not too sure what you mean by ‘skill’. Do you mean the forecasts ability to predict ice? There may be a better way to word this to avoid ambiguity.
Line 20 – It might be nice to list some environmental controlling factors in brackets, like: (i.e. wind speed and direction, tides)
Line 24 – Recommend to change to ‘Both of these approaches determine…’
Line 28 – Change to ‘composed of sea ice concentration data…’
Line 30 – Remove ‘good’
Line 30 – Would help the reader if you included where the mean September sea ice extents from 2017 came from (ice charts? Passive microwave data?)
Line 43 – ‘calibrated probability of ice’: presence or concentration?
Line 64 – Need to define ‘SST’
Line 65 – Doesn’t ERA5 have a 31km resolution? I would state this plainly so the reader knows what resolution your results are.
Line 74 – Would recommend shuffling around this sentence: ‘Shipping traffic is also generated by mining, fishing, tourism and research activities, being mostly confined to the ice-free and shoulder season’.
Line 84 – ‘In Seq2Seq learning, which has successful applications in machine translation’
Line 87 – Recommend to spell out ‘two’
Line 88 – suggest removing ‘part’
Line 92 – In line 54 you use the double wavy equal sign, but here you use a single wavy line. I would recommend picking one and keeping it consistent throughout.
Line 94 – Recommend to change to: ‘The encoder section of the Basic model takes the last three days of environmental conditions as an input’
Line 97 – Remove ‘so as’
Line 99 – May be better to spell out ‘LSTM’ in full form
Line 101 – Recommend rewording the last sentence for clarity: ‘The output to the encoder is a single raster with the same height and width as the input, but a higher number of channels to represent the fully encoded system state.’
Line 115 – Remove ‘so as’ (try and write sentences as simply as possible, i.e. with as little unnecessary words)
Line 128 – Just verify that your quotation is facing the correct way before ‘April’
Line 137 – How did you determine what learning rater and momentum to use?
Line 142 – Suggest to remove ‘coming’, or replace with ‘derived’
Line 151 – I would recommend changing the formats of your dates here: ‘forecasts launched between 1-31 January, ending in 2 January to 1 April, and the second box corresponds to average accuracy of forecasts launched between 1 – 31 January, ending in 3 January to 2 April.
Line 154 – This sentence needs a lot of work: suggest removing ‘very’ and changing ‘on January’ to ‘of January’. As well, are you indicating that the accuracy is close to 100% for both January and the span of January – March (this is not clear)? It would be helpful if you stated the actual accuracies.
Line 155 – This sentence struggles with the same structural problems as the first, I would recommend rewording to something like: ‘In contrast, for forecasts at the beginning of the open water season (June and July), the climate normal struggles to accurately capture the ice cover for lead times of 1 to 50 days likely due to inter-annual variability and the impact of climate change’. You might also want to indicate what climate change has to do with this (i.e. ‘lengthening of the open water period due to climate change’)
Line 165 – This sentence also needs to be reworded, I have underlined grammatical errors: ‘Using additional climate variables for the input of the Augmented model is showing its impact here where in the periods that Basic model is worse than climate normal (Fig 1d), the Augmented model has better accuracy and is closer accuracy to climate normal.’
Line 169 – Double check if it should be ‘the climate normal’ or ‘climate normal’
Line 179 – Spell out ‘April’ fully, as you have spelled out every other month
Line 202 – ‘Observations’ should not be capitalized
Figure 4 – Include units for Latitude and Longitude, and capitalize the words in your legend
Figure 5 – Units for lat and long
Line 212 – ‘Figure 5 and 6 show the overall…’
Line 215 – ‘The freeze-up accuracy maps at Fig 5 show that except the Basic model’s prediction at 30 lead day (Fig 6b), other maps are showing similar patterns of accuracy.’ This sentence needs reworking – would recommend flipping the sentence, so you are presenting the positive results first, then adding on the Basic model’s prediction after.
Line 221 – ‘compared’ instead of ‘comparing’
Line 222 – Capitalize ‘fig 6a’
Figure 6 - Units for lat and long
Line 227 – I would recommend changing all of your dates to the format: ‘1 Oct to 31 Jan’. This is a more standard way of presenting dates and is more simplistic.
Line 233 – ‘Compared’ instead of ‘comparing’
Line 234 – Change to ‘its accuracy over the breakup season…’
Line 235 – Since you discuss the break up at three sample ports, and present the results in Figures 8 and 9, I think it would be important to include a map of these three locations, indicating which pixels you use to extract this data. I am curious how the land mask affects the data, i.e. how close are the pixels you are using to the actual port? Since you are using 31km ERA5 data, I would suspect that the pixel you chose to represent each port is actually a distance away from the actual dock. In the end, I guess I am a little wary of how applicable your results are to local communities, as they are likely more impacted by ice break up on a smaller scale along the coast (for hunting and travel), whereas shipping operations are more concerned of the large scale ice break up along shipping corridors. Some discussion of how the scale of your results impacts how they are used by different groups may help address this.
Line 237 – Capitalize ‘figures’
Line 242 – Would recommend moving the figure reference to the end of the sentence, and putting it in brackets OR starting the sentence with ‘In figure 8, 30 lead day predictions for freeze-up are more…’
Line 242 – Any idea why this is? I am curious why the predictions varied at the different town ports and would think a discussion of this would add to your paper.
Line 252 – If you have space in your word count, I would recommend listing the 8 variables used in the Basic model, and the other variables added to the Augmented. This would help refresh the reader’s memory as to how these two models vary.
Figures 8 and 9 – If possible, the font size should be increased, particularly for your axis labels. This might take some reorganizing of your figure boxes – maybe you could rotate the ‘model’ and ‘day’ labels on the far left of your figures?
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AC2: 'Reply on RC2', Nazanin Asadi, 24 Jan 2022
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2021-282/tc-2021-282-AC2-supplement.pdf
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AC2: 'Reply on RC2', Nazanin Asadi, 24 Jan 2022
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RC3: 'Comment on tc-2021-282', Anonymous Referee #3, 23 Nov 2021
Asadi et al.:
Probabilistic Gridded Seasonal Sea Ice Presence Forecasting using Sequence to Sequence LearningThe authors present a new approach for forecasting sea ice presence in the Hudson Bay area using machine learning techniques. The study presents models which use the Sequence-to-Sequence Learning framework to predict probabilities of sea ice presence for up to 90 days lead time. The authors suggest two somewhat different models, which are applied in hindcasting experiments, where they exhibit slightly more skill than "climate normal"-predictions especially in the breakup season. The models are also evaluated for their ability to predict freeze-up dates and breakup dates.
The study has a clear motivation, is well structured, and applies new (as to my knowledge) methods in a promising way. The text is short and precise. The general setup of the experiments is described well, however, as I'm not an expert in ML, I cannot judge the parts of the paper that go into technical details of the ML process. The results are presented clearly, but I miss a broader discussion of the results and which conclusions can be drawn from them. Especially as the motivation of the study is to develop new methods to support maritime users with new operational forecast products, a comparison to existing products would be valuable. Also a short assessment/discussion of the applicability to operational forecasting is missing in my opinion.
Please find here some general comments, followed by comments related to specific line numbers. Text in quotation marks after the given line number refers to the original text of the manuscript. Text in quotation marks in the following line is my suggestion of how to replace the original text.
%%%%%%%%%%%%%%%%%%%%
General comments
%%%%%%%%%%%%%%%%%%%%A)
The captions of Figures 1-3 do not only explain the figure but also contain statements about the shown results. This is a new approach to me. If the journal allows for that, I would not object, but I don't think it is common style.B)
For Figure 4 you compare predicted ice presence with observed ice presence. The observations are calculated from SIC from ERA5 by using a threshold of 15 %, while the ice presence from the forecasted probabilities is calculated with a threshold of 50 %. As the model is based and trained on SIC from ERA5, why do you use a threshold of 50 % and not of 15 %? Or at least the same threshold for both?C)
I understand that the motivation of your study is (at least partly and in the long run) to improve operational forecasting of sea ice conditions in the Hudson Bay area. In line 254-257 you explain the technical advantages of the ML approach compared to standard numerical models (=reduced computational costs). The paper would benefit from also looking into the results/skill of the ML models compared to standard models. Is your approach not only faster but also better than currently used forecasting systems? Or is it so much faster that it is useful despite of a possibly lower quality? Or is it worse/better only for some lead times? It would be interesting to see how your model compares e.g. to the S2S-forecast of ECMWF (up to 60 day forecast at 1/4 degree resolution).
https://www.ecmwf.int/en/forecasts/dataset/sub-seasonal-seasonal-prediction
https://apps.ecmwf.int/datasets/data/s2s-realtime-daily-averaged-ecmf/levtype=sfc/type=cf/
Comparing your models to the climate normal is a very good and valid first step. The comparison to a numerical forecast would however be a very interesting addition.D)
With the Basic and Augmented models you introduce two approaches, which you compare with each other throughout the paper. However, I don't find a conclusion/discussion about which of the two models you would suggest in the end. Is it worth the effort of the Augmented model, which needs more input data, or is the Basic model sufficient for the purpose? Or are both needed, for different purposes? Maybe you can here also explain/speculate why the Augmented model is considerably worse than the Basic model in Figure 2c. [This text could extend the summary given in lines 258-265.]E)
For your hindcasts you use input data from ERA5, which is a reanalysis product that is not available in real time. Hence, when one wants to apply your method for forecasting future conditions, other input data need to be used. It would be good to elaborate on this topic in a paragraph in the Discussion or Outlook sections. Is it difficult/problematic to switch to other input data? Can the trained monthly models be applied if the forecast is started based on other data for the 3 historical days? And/Or what else is still needed before your models can be used for operational forecasting? This would be a good topic for an Outlook-section/paragraph.
%%%%%%%%%%%%%%%%%%
Specific comments
%%%%%%%%%%%%%%%%%%Title
##############
In the introduction you mention that the novelty is that your forecast is "spatiotemporal". Hence I wonder why you don't use this word in the title.Abstract
##############2-3
Would ML approaches be less important without global warming? Suggestion: Remove "Given ... global warming".4 "a daily spatial map"
Isn't the clue of your study that you provide several "daily spatial mapS", namely 90 for a 90-day forecast?
1 Introduction
#################9, 10, 12, 14, 82
Be more clear on the terms "short-term", "longer term", "seasonal" and "medium-term" forecasting.16
Maybe mention the lead-time used in Zhang et al. (2008)21 "governing"
Do the equations govern the physics? I'd suggest "describing"24 "This is a key advantage of..."
Suggestion: "This disadvantage can be overcome by using..."27 "to perform"
"for"29 "Their results are"
"The results were"29, 31
In line 29 you write that the model predicts sea ice concentration but in line 31 you present results for ice extent. I'm not sure if one can assume that everyone knows the relation between SIC and sea ice extent.32 "September sea ice minimum."
minimum extent? minimum thickness?33 "This study"
This is misleading because it could mean your own study. Better use "They" or "Hovath et al. (2020)".34 "was found the uncertainty"
"was found that the uncertainty"38 "that is closer to what is proposed here"
As we don't know yet, what you will propose, this information is not very useful here.49 "probability of ice at"
"probability of ice presence at"2 Data
##################56 "data from 1985-2017 is"
"data from 1985-2017 are"65 "the following input variables"
"the following 8 input variables". This helps explaining the number 8 in line 96.67 "V-Component"
"V-component"67
replace "and" before "landmask" by comma3 Study region
#######################
Here would be a good place for a map which also indicates the ports used later on.76
Remove the parenthesis if Foxe Basin can be shown in a map.79 "Recent decades"
For me, 1985-2017 includes several "recent decades" and one could get the impression that lines 77-79 are not valid for "recent decades". So maybe consider re-phrasing "recent decades" to "In recent years" or using the term "trend"?4 Forecast model architecture
##########################85/86 "sequence of inputs"/"sequence of outputs"
It would be helpful if you could mention (maybe in a new sentence) some examples for "input" and "output" for the application in this study. I guess input includes SST, t2m, winds, etc. and output is ice presence probability?86 "consist"
"consists"88
Does "desired length" in your application mean number of variables or number of grid cells or number of forecasted days? It is good with a general explanation of the Seq2Seq method like you do here, but for someone not from the ML field, it would also be nice to directly get examples about how the method can be understood for the application of sea ice forecasting.89-90
I first understood this sentence such that the encoder part would be called Basic model and the decoder part would be the Augmented model. Can you phrase it differently to make it more clear also for non ML-experts?94 "three days of environmental conditions"
Shouldn't it read "environmental conditions of the last three days"?96
Maybe explain why you call the number of input variables "C":
"and C is the number of channels, in this case the total number of input variables (here 8)."98 "sequence of extracted feature grid"
Are the feature grids what was called "environmental patterns" in the previous sentence? If so, could you use the same term? If not, could you explain how to get from one to another?98 "the sequence are"
"the sequence is"97-113
As I don't have a background in AI/ML, I unfortunately don't understand the setup of the model in detail. However, as I can follow the general concept, e.g. what is input and what is output, I think it is OK to keep the text as it is if the targeted audience is AI/ML experts more than sea ice modellers.115
I miss a sentence about why you suggest an additional model. What is the (expected) problem with the Basic model or which benefits do you expect from the Augmented model?116 "(e.g., 60 or 90 days)"
remove comma117 "t2m, u10 and v10"
Why do you use exactly these variables? I could imagine that climate normals of e.g. sea ice concentration or sea surface temperature could also be beneficial to correctly predict sea ice presence.5 Description of Experiments
#############################124 "required"
"requires"125 "assess"
"assesses"130 "the model from year i-1"
"the model for year i-1"130
End the sentence after "i-2" and start a new one.136 and 140
Are "ML models" (line 136) and "neural network model" (line 140) something different? If not, use the same word.142 "3 months of year"
"3 months of each year"141 "thresholded at 15%"
This is unclear to me. Do you apply the threshold to convert to ice presence?131, 132, 142
In line 142 you talk about "test procedure". Is this the same as "validation" mentioned above?6.1 Presence of Ice Forecasts
##########################145 "6.1 Presence of Ice Forecasts"
Shouldn't it be "Forecasts of Ice Presence"?147 "test set"
What is this? I don't think you have introduced this term before.148
How do you calculate accuracy from the binary forecast map?148, 149, 150 etc.
I would put a period after the abbreviated "Fig" -> "Fig."150 "in this figure (Fig 1(b))"
"in Fig. 1b"150 "the first top-left"
"the first (top-left)151 "after 1 day forecast"
"after a 1-day forecast"152-153
Why April 1 and April 2? Wouldn't a 1-day forecast started on January 31 end on February 1, and a 2-day forecast on February 2?154 "month on January"
"month of January"155 "consistently"
"constantly"156
Mention the sub-figure number you are talking about.158 "Fig 1d"
"Fig. 1d and 1e"158 "significantly"
Did you do a statistical test whether it is significant? Otherwise maybe remove the word.160 "early lead times"
"short lead times"163 "Climate normal"
Stay consistent with capital C or not.163-164
Comparing the Augmented model with the climate normal (Fig. 1e), I don't see an improvement for March/April. I see the Augmented model is better than the Basic model, but actually it is just 'less bad' compared to the climate normal. The accuracy of the Augmented model is not higher than of the climate normal. (You explain this later, so maybe make clear that this sentence only deals with Fig. 1f and not with Fig. 1e.166 "(Fig 1d)"
"(dark areas in Fig. 1d)"167
Remove "accuracy" at the beginning of the line.167 "90 lead day"
"90 lead days"167
The "For example..."-sentence is not complete, there is no verb.169
Consider to start a new paragraph for the Brier score.169
(related to comment for line 148): What is "probabilistic accuracy" compared to "accuracy"?173
Remove "Also"174 "Pt is the model prediction"
Maybe add "... of ice presence probability"174 "represents"
"presents"/"shows"177 "The pattern observed"
"observed can easily be mixed with observations, so maybe say "The resulting pattern"178
End sentence after "both models" and start a new one for the differences.178 "(2c)"
"(Fig. 2c)"179 "longer lead days"
"longer lead times"184 "For early lead days"
"For short lead times"185 "lead day"
"lead days" (make sure to do it consistently throughout the paper, e.g. line 211 and in caption of Figure 3)184-187
I find this sentence is too long. Also, there is inconsistency of the used terms "forecasted probability" and "forecasted probabilities".190
When first reading the sentence, it sounds like monthly averaging would make it impossible to provide information on a map. Maybe you can clarify it with "Monthly averaged and domain-integrated accuracy values ..."194-195
To simplify the explanation of the different dates, I suggest to add the dates in the caption of the subfigures 4a-4c, e.g. "(a) 5 June 2014 (after 30 days)"194 "given data"
"given date"198 "and and"
"and the"200-201
I don't see in the figures that the Basic model would have "increased ice presence probability in the northern part of the domain". If it is important, highlight the area in the plots.202 "Observations"
"observations"201-202
"the agreement ... to be in good agreement" Too many agreements.Figure 1 and 2:
I would suggest to use a diverging colormap when differences are displayed. This makes it easy to see where zero is located and it makes it more clear which plots display differences and which plots display absolute values.Caption of Figure 1:
"Model performance and improvements": Why not call it "Accuracy"?Caption of Figure 2:
"Brier score of the Basic model (a) and the Augmented model (b) as a function of lead time. Their score difference is shown in (c). Most differences are observed in breakup and freeze-up seasons."Figure 3:
- It would be nice if the aspect ratio of x and y axis was 1, so that the dashed line would be at 45 degree.
- for the text in the legend I suggest "xx lead days" instead of "xx Lead Day"Caption of Figure 3:
- Would be nice to remind the reader (especially those who only look at the figures and don't read the text) that you are talking about ice presence probabilities/frequencies.Figure 4:
- In order to compare probability maps with ice presence maps it would be nice (if possible) if the plots would have the same size, i.e. smaller plots for those which have no colorbar.
- I would prefer if the height of the colorbar was the same as the height of the map-plot.
- Make sure to use "Climate normal" or "Climate Normal" consistently.Caption of Figure 4:
- The figure does not illustrate "the May models" but the forecasted conditions. Hence a suggestion for rephrasing: "Ice/water distribution in the model domain as observed and forecasted by Basic and Augmented models for a forecast started on 6 May 2014 and lasting for 30 (a), 50 (b), and 70 (c) days, respectively."6.2 Assessment of operational capability
################################209 "15 continuous days in a row"
Either "continuous" or "in a row" is enough.212
It can be a bit confusing that "accuracy" here is related to freeze-up/breakup while the same word is used in section 6.1 for ice presence. (Well done in line 215.)214 "...prediction is correct."
The reader has to infer that 'correct' is translated to 1 and 'not correct' is translated to 0.215-216
Check the grammar of the sentence.219-212
It is surprising to me that a model can have that much more skill on a 30 day longer lead time. Could you elaborate on possible reasons and/or why the Augmented model is doing a better job? (This should probably go to the Discussion section)222 "breakup prediction ability"
Why not call it "breakup accuracy" in analogy to line 215?222 "are presented"
"is presented"222 "fig 6a"
Fig. 6a227 "variability"
Would "interannual variability" be more clear?227 "models' accuracy"
As climate normal is not really a model, I would remove the word "models'".228 "represented"
"presented"228 "For each prediction... same color."
Suggestion: "The respective trends are shown by dashed lines."229,233 "freeze-up season accuracy"
"season" is not necessary.229 "both lead days"
"both lead times"229 "breakup plots (...)"
"breakup accuracy (Fig. 7c and 7d)230
Move "accuracy" directly after "2%"?232-233
Is this because the Augmented model uses climate information as input data and hence tends to predict more similarly to the climate normal than the (more independent) Basic model does?240 "is different."
"is different (i.e. different x and y axes)."236
Refer here to the (new) overview map for locations of the ports.245 "both models both lead days"
"both models and both lead times"248 "as freeze-up for breakup"
"for breakup as for freeze-up"
Figures 5 and 6:
- Add a space between (a), (b), ... and the subfigure caption text.
- Why did you choose to use a diverging colormap even though the plot does not display differences?
- Remove the grid lines which are drawn around each grid cell in order to make the plot less busy.Caption of figure 6:
- End with a period.Figure 7:
- Add a space between (a), (b), ... and the subfigure caption text.
- You could specify "Freeze-up accuracy" and "Breakup accuracy" also in the y-labels.
- Red and green lines are probably difficult to distinguish for color-blind persons. What about using black for the climate normal and e.g. red and cyan for the two models?Caption of figure 7:
"Dashed lines" instead of "Dotted lines"Figures 8 and 9:
- The little red arrows next to the dots are hard to see and the written year numbers don't allow for getting a quick overview of the distribution of the year (e.g. whether the skill is getting better or worse over time). Did you try to plot the dots using a colormap which represents the different years (i.e. colorful dots, and the "valid area" as gray)? Then you don't need the text labels anymore.
- "freeze-up" instead of "Freeze-up" in x-labels and y-labels.Caption of figure 8 and 9:
- Mention that each dot represents one year.7 Discussion
##############251
In my opinion you should mention somewhere that your model is not (yet) used for forecasting future condition but rather for hindcasting.254 "where it takes"
"which takes"261 "Augmented model showing better scores comparing to"
"Augmented model shows better scores compared to"262 "analysis on"
"analysis of"267 "less disperse"
less disperse than what?267 "to accepted"
"to the accepted"References
#################281 "shipping"
"Shipping"293, 305, 308
Why do you cite preprints of papers that are several years old?297 "S., R., G.,"
The co-author is called "Graversen R"297 "high resolution"
"high-resolution"298, 322
missing volume/issue/page number327 "W.-c."
"W.-C."?-
AC3: 'Reply on RC3', Nazanin Asadi, 24 Jan 2022
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2021-282/tc-2021-282-AC3-supplement.pdf
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AC3: 'Reply on RC3', Nazanin Asadi, 24 Jan 2022
Nazanin Asadi et al.
Nazanin Asadi et al.
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