the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Barents-2.5km v2.0: An operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard
Yvonne Gusdal
Edel Rikardsen
Marina Duran Moro
Jostein Brændshøi
Nils Melsom Kristensen
Sindre Fritzner
Keguang Wang
Ann Kristin Sperrevik
Martina Idžanović
Thomas Lavergne
Jens Debernard
Kai H. Christensen
Abstract. An operational ocean and sea ice forecast model, Barents-2.5, is implemented at MET Norway for short-term forecasting at the coast off Northern Norway, the Barents Sea, and waters around Svalbard. Primary forecast parameters are the sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model is also a substantial input for drift modeling of pollutants, ice berg, and in search-and-rescue pertinent applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an Ensemble Prediction System with 24 daily realizations of the model state. SIC, SST and in-situ hydrography are constrained through the Ensemble Kalman Filter (EnKF) data assimilation scheme executed in daily forecast cycles with lead time up to 66 hours. While the ocean circulation is not directly constrained by assimilation of ocean currents, the model ensemble represents the given uncertainty in the short-term current field by retaining the current state for each member throughout forecast cycles. Here we present the model setup and a validation in terms of SIC, SST and in-situ hydrography. The performance of the ensemble to represent the models uncertainty, and the performance of the EnKF to constrain the model state are discussed, in addition to the model’s forecast capabilities for SIC and SST.
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Johannes Röhrs et al.
Status: closed
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RC1: 'Comment on gmd-2023-20', Benjamin Barton, 19 Apr 2023
Review of "Barents-2.5km v2.0: An operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard"
General Comments
This paper presents the development methods and validation of the Barents-2.5km v2.0 forecast model. The new version of the Barents-2.5km model has been upgraded to include a 24 member Ensemble Prediction System. The data assimilation has also been upgraded from only sea ice concentration to additionally including sea surface temperature and hydrographic profiles.
Overall the paper shows the forecast system has promise as a useful tool for various applications that require predictions of ocean variables in the Barents, Norwegian and Greenland Sea. This is particularly important for the marginal ice zone areas and the paper shows the challenge and skill required for accuracy. Some of the description of the model forecast skill is hard to follow. Analysis and validation of currents are presented in a separate paper which means there is not a complete picture here. There is also no comparison to v1.0 of the Barents-2.5km model. There are a few points that need attention that I detail below.
Specific Comments
Line 7-9: Currents are only briefly mentioned in the paper and seems to be a larger part of another paper. The abstract should be used to discuss what is in the paper.
Line 108-110: Have you considered using the elastic-plastic anisotropic (EAP) rheology scheme in CICE (Heorton et al., 2018)? Why was EVP chosen?
Line 291: You should mention the SST RMSE July peak.
Lines 293-296: The model is compared against the same observations that are entrained through the data assimilation system. I understand that the observations are considered independent because they are compared with a previous forecast. This has value in quantifying the quality of the prediction for assimilated variables. However, I think the quality of the paper could be improved if you make comparison with un-assimilated data, perhaps sea surface height which could give an indication of current quality.
Line 300-305: Please add the SIC ice charts dataset to the methods and reference.
Line 312-314: Sea ice concentration tends to increase rapidly when there is no data for assimilation. It seems to be reliant of the SIC assimilation data. I see you've made a point about why this happens in Line 422 but I think it would be better to make that point here or rephrase the bit around Line 422.
Line 330-334: Why does the model have greater RMSE for SST up to 12h lead time than the persistence, deterministic or trend with observations?
Line 347-358: Describe the validation/analysis technique in the methods. Same goes for other validation methods. What do the bins represent?
Line 374-377: What are analysis increments? This needs to be clearer.
Line 380: How is the spread calculated? Is it the standard deviation of the ensemble?
Line 392-393: What are spread increments? It also does not say what they are in Figure 12 caption.
Line 442-446: I do not understand Figures 10 and 11 enough. Why is the spread in SST satisfactory? Why is the spread in SIC too low?
Line 471-472: How does the Barents-2.5km v2.0 of the model compare against original Barents-2.5km? Can you quantify the improvement in accuracy that users can expect?
Table 1: This could be quoted in the text instead of a table.
Technical Corrections
Line 307-308: Isn't it 3 periods? January, March and May 2022 in Figure 8 (not Figure 7).
Line 423-424: "looses skill within few days without DA. Deterioration of model skill within few days" typo "a few" for both "few".
Table 2: caption typo ")".
Line 478: "each satellite swath is compare to the model" typo "compared".
Figure 6: Please note the figures that the regions are used for in the caption.
Figures 7, 8 and 9: the text in the figure is too small to comfortably read. Please add ME and RMSE to the respective y-axis labels.
Figure 10: I do not understand what this is showing. The caption is too brief and the axis labels are unhelpful.
Figure 12: Please improve the caption with more detail. What are the black regions in g and h?.
References
Heorton HDBS, Feltham DL, Tsamados M. 2018. Stress and deformation characteristics of sea ice in a high-resolution, anisotropic sea ice model. Philos Trans A Math Phys Eng Sci. 28;376(2129):20170349. doi: 10.1098/rsta.2017.0349.
Citation: https://doi.org/10.5194/gmd-2023-20-RC1 - AC1: 'Reply on RC1', Johannes Röhrs, 25 May 2023
-
RC2: 'Comment on gmd-2023-20', Anonymous Referee #2, 23 Apr 2023
-
AC2: 'Reply on RC2', Johannes Röhrs, 26 May 2023
We would like to thank the reviewer for studying our manuscript and providing usefull feedback. Please find answers to your questions in the attached document. We look forward to present a revised manuscript taking account for the issues raised by both reviewers.
With kind regards
Johannes Röhrs and colleagues
-
AC2: 'Reply on RC2', Johannes Röhrs, 26 May 2023
Status: closed
-
RC1: 'Comment on gmd-2023-20', Benjamin Barton, 19 Apr 2023
Review of "Barents-2.5km v2.0: An operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard"
General Comments
This paper presents the development methods and validation of the Barents-2.5km v2.0 forecast model. The new version of the Barents-2.5km model has been upgraded to include a 24 member Ensemble Prediction System. The data assimilation has also been upgraded from only sea ice concentration to additionally including sea surface temperature and hydrographic profiles.
Overall the paper shows the forecast system has promise as a useful tool for various applications that require predictions of ocean variables in the Barents, Norwegian and Greenland Sea. This is particularly important for the marginal ice zone areas and the paper shows the challenge and skill required for accuracy. Some of the description of the model forecast skill is hard to follow. Analysis and validation of currents are presented in a separate paper which means there is not a complete picture here. There is also no comparison to v1.0 of the Barents-2.5km model. There are a few points that need attention that I detail below.
Specific Comments
Line 7-9: Currents are only briefly mentioned in the paper and seems to be a larger part of another paper. The abstract should be used to discuss what is in the paper.
Line 108-110: Have you considered using the elastic-plastic anisotropic (EAP) rheology scheme in CICE (Heorton et al., 2018)? Why was EVP chosen?
Line 291: You should mention the SST RMSE July peak.
Lines 293-296: The model is compared against the same observations that are entrained through the data assimilation system. I understand that the observations are considered independent because they are compared with a previous forecast. This has value in quantifying the quality of the prediction for assimilated variables. However, I think the quality of the paper could be improved if you make comparison with un-assimilated data, perhaps sea surface height which could give an indication of current quality.
Line 300-305: Please add the SIC ice charts dataset to the methods and reference.
Line 312-314: Sea ice concentration tends to increase rapidly when there is no data for assimilation. It seems to be reliant of the SIC assimilation data. I see you've made a point about why this happens in Line 422 but I think it would be better to make that point here or rephrase the bit around Line 422.
Line 330-334: Why does the model have greater RMSE for SST up to 12h lead time than the persistence, deterministic or trend with observations?
Line 347-358: Describe the validation/analysis technique in the methods. Same goes for other validation methods. What do the bins represent?
Line 374-377: What are analysis increments? This needs to be clearer.
Line 380: How is the spread calculated? Is it the standard deviation of the ensemble?
Line 392-393: What are spread increments? It also does not say what they are in Figure 12 caption.
Line 442-446: I do not understand Figures 10 and 11 enough. Why is the spread in SST satisfactory? Why is the spread in SIC too low?
Line 471-472: How does the Barents-2.5km v2.0 of the model compare against original Barents-2.5km? Can you quantify the improvement in accuracy that users can expect?
Table 1: This could be quoted in the text instead of a table.
Technical Corrections
Line 307-308: Isn't it 3 periods? January, March and May 2022 in Figure 8 (not Figure 7).
Line 423-424: "looses skill within few days without DA. Deterioration of model skill within few days" typo "a few" for both "few".
Table 2: caption typo ")".
Line 478: "each satellite swath is compare to the model" typo "compared".
Figure 6: Please note the figures that the regions are used for in the caption.
Figures 7, 8 and 9: the text in the figure is too small to comfortably read. Please add ME and RMSE to the respective y-axis labels.
Figure 10: I do not understand what this is showing. The caption is too brief and the axis labels are unhelpful.
Figure 12: Please improve the caption with more detail. What are the black regions in g and h?.
References
Heorton HDBS, Feltham DL, Tsamados M. 2018. Stress and deformation characteristics of sea ice in a high-resolution, anisotropic sea ice model. Philos Trans A Math Phys Eng Sci. 28;376(2129):20170349. doi: 10.1098/rsta.2017.0349.
Citation: https://doi.org/10.5194/gmd-2023-20-RC1 - AC1: 'Reply on RC1', Johannes Röhrs, 25 May 2023
-
RC2: 'Comment on gmd-2023-20', Anonymous Referee #2, 23 Apr 2023
-
AC2: 'Reply on RC2', Johannes Röhrs, 26 May 2023
We would like to thank the reviewer for studying our manuscript and providing usefull feedback. Please find answers to your questions in the attached document. We look forward to present a revised manuscript taking account for the issues raised by both reviewers.
With kind regards
Johannes Röhrs and colleagues
-
AC2: 'Reply on RC2', Johannes Röhrs, 26 May 2023
Johannes Röhrs et al.
Data sets
Configuration files for Barents-2.5 Ocean and Ice forecast model Johannes Röhrs https://doi.org/10.5281/zenodo.7607191
Barents-2.5km operational forecast model archive Norwegian Meteorological Institute https://thredds.met.no/thredds/fou-hi/barents_eps.html
Model code and software
metno/metroms: Version 0.4.1 Jens Debernard, Nils Melsom Kristensen, Sebastian Maartensson, Keguang Wang, Kate Hedstrom, Jostein Brændshøi, and Nicholas Szapiro https://doi.org/10.5281/zenodo.5067164
Johannes Röhrs et al.
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