the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Sep 2024
Numerical Models for Monitoring and Forecasting Sea Ice: a short description of present status
Abstract. The severe changes in climate resulting in the polar oceans getting warmer – with drastic consequences to their physical, biogeochemical and biological state – require forecasting systems that can accurately simulate and skilfully predict the state of the ice cover and its temporal evolution. Sea-ice processes significantly impact ocean circulation, water mass formation and modifications, and air-sea fluxes. They comprise vertical processes, mainly related to thermodynamics, and horizontal ones, due to internal sea ice mechanics and motion. We provide an overview on how these processes can be modelled and how operational systems are working, in combination with data assimilation techniques, to enhance accuracy and reliability. We also emphasize the need for advancing research on improving such numerical techniques by highlighting currents limits and ways forward.
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RC1: 'Comment on sp-2024-24', Anonymous Referee #1, 19 Oct 2024
General comments:
This paper provides an overview of sea ice process, modeling, and short-term forecast by referring to previous literature including the most recent papers. It would be very helpful for general readers who are not familiar with the sea ice modeling and forecast but are interested in their product. Since this paper focuses on the general description of the past studies, there are no new values to be added in the sea ice research community. Overall, the paper is well written and organized with some evidence, but there are a few more things to be added in the paper which would help further improve the understanding of the sea ice process, modeling, and forecast. Below are major and more specific comments on this paper.
1. Biogeochemistry (L65-68)
The authors mentioned only about the sea animals and algae around the sea ice, but the sea ice also plays an important role in the exchange of natural and anthropogenic gases such as carbon dioxides and aerosols that are crucial sources of nutrients for the phytoplankton and other sea life below the sea ice. Could the authors expand the section a bit more by adding a few more sentences on the sea ice role in the biogeochemical cycle (e.g., carbon and nutrients)?
2. Model bias and further improvement (L107-L114)
At the end of this paper, the authors discussed the model bias in forecasting the sea ice edge and boundary between the first and multi-year ice, but what are the underlying causes of these model biases (e.g., model physics, resolutions, ensemble members, data assimilation techniques, and/or observation)? Also, what measures can the sea ice modeling community undertake the most to reduce the forecast errors? The authors suggested two research thrusts in the end, but I could not find the link to these errors, wondering how these suggestions can help resolve the model biases.
Specific comments:
L20: To meet this goal,
L39, 41: “birthday party” is a bit narrative. Is it a well-accepted expression in the scientific community?
L40: This process is called “brine rejection”, so you may add this word in the sentence.
L60: I am a bit confused. Does it mean a positive feedback, because the waves get amplified with smaller ice floes and generate more ice floes with smaller scales.
Figure 1: Do you have any satellite observation map to validate the model simulation? This would help readers to understand how reasonably the model reproduces the sea ice thickness.
Table 1: “SIUV motions” should be “SIUV velocities”. What is “SID”? Also, remove “**” in the area of the IcePOM in the table.
Citation: https://doi.org/10.5194/sp-2024-24-RC1 -
RC2: 'Comment on sp-2024-24', Anonymous Referee #2, 24 Oct 2024
General comments:
This manuscript provides a brief overview of recent developments in numerical models for sea-ice. It covers fundamental physical processes, modelling approaches, and operational systems with data assimilation techniques, and provides a summary of modern numerical models and operational system for sea ice. The manuscript also draws attention to the challenges and concerns in developing sea ice modeling, numerical solvers, and machine learning applications. The manuscript is concise and informative, thus suitable for publication.
Specific comments:
Line 60: "Smaller ice floes offer more reflecting edges and are more efficient at attenuating waves." This statement is incomprehensive as it only mentions reflection or scattering, but not dissipation through multiple energy non-conservative processes in the ice effects on waves, which is also mentioned in Squire (2020).
Technical corrections:
Regarding Chapter 2's organization: The "Overview of processes in sea ice" currently includes sections that aren't strictly processes (numerical models and data assimilation). I would recommend that the author consider restructuring the technical sections into a new chapter regarding the current modeling approaches.
In Table 1, it mentions "*Output interpolated to 9 km" in the caption, but the corresponding entry isn't shown in the table. Moreover, the links for GIOPS and RIOPS are not precise, and Met Office FOAM may also need a relevant link.
Citation: https://doi.org/10.5194/sp-2024-24-RC2
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