the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Apr 2024
The Seasonal Variability in the Semidiurnal Internal Tide; A Comparison between Sea Surface Height and Energetics
Abstract. We investigate the seasonal variability of the semidiurnal internal tide steric sea surface height (SSSH) and energetics using 8-km global Hybrid Coordinate Ocean Model (HYCOM) simulations with realistic forcing and satellite altimeter data. In numerous previous studies, SSSH has been used to explore the seasonal changes in internal tides. For the first time, we compare the seasonal variability of the semidiurnal internal tide SSSH with the seasonal variability of the semidiurnal baroclinic energetics. We explore the seasonal trends in SSSH variance, barotropic to baroclinic conversion rate, kinetic energy, available potential energy, and pressure flux for the semidiurnal internal tides. We find that the seasonal cycle of monthly semidiurnal SSSH variance in the Northern Hemisphere is out of phase with the Southern Hemisphere. This north-south phase difference and its timing are in agreement with altimetry. The amplitudes of the seasonal variability in SSSH variance are about 10–15 % of their annual-mean values when zonally averaged. The normalized amplitude of the seasonal variability is higher for the SSSH variance than for the energetics. The largest seasonal variability is observed in Georges Bank and the Arabian Sea, where the seasonal trends of monthly SSSH variance and energetics are in phase. However, outside these hotspots, the seasonal variability in semidiurnal energetics is out of phase with semidiurnal SSSH variance and a clear phase difference between the Northern and Southern Hemispheres is lacking. While the seasonal variability in semidiurnal energy is driven by seasonal changes in barotropic to baroclinic conversion, semidiurnal SSSH variance is also modulated by seasonal changes in stratification. Surface intensified stratification at the end of summer enhances the surface perturbation pressures, which enhance the SSSH amplitudes.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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CC1: 'Long-period tides', Paul PUKITE, 13 Apr 2024
Instead of semidurnal, diurnal, and mixed semidiurnal, why not concentrate on long period tides (such as Mf, nodal, etc)? Those may be much weaker, but the sluggish responsiveness of the immense internal ocean inertia is a match for the slower more gradual forcing,
Citation: https://doi.org/10.5194/egusphere-2024-1032-CC1 -
AC1: 'Reply on CC1', Harpreet Kaur, 30 Jul 2024
Our focus in this manuscript is on studying the seasonal variability in the semidiurnal internal tides in the global ocean, which contain more than 70% of all tidal energy (Egbert and Ray, 2003). We agree that the variability in longer-period tides may also be interesting, but this is saved for another paper. Considering that the frequency of internal tides in the stratified ocean must be greater than the inertial frequency f and lower than buoyancy frequency N, f < ω < N, the long-period internal tides should only occur in a narrow band around the equator.
Citation: https://doi.org/10.5194/egusphere-2024-1032-AC1
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AC1: 'Reply on CC1', Harpreet Kaur, 30 Jul 2024
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RC1: 'Comment on egusphere-2024-1032', Anonymous Referee #1, 06 May 2024
The study of semidiurnal SSH and energetics through HYCOM simulation offers valuable understanding of seasonal variability in the semidiurnal internal tide, , yieding fruitful insights. However, the assertion that seasonal variability in stratification at the generation site affects barotropic to baroclinic conversion in the Arabian Sea and Georges Bank prompts inquiry into the reasons behind the pronounced seasonal variability in these regions compared to other oceanic areas.
Citation: https://doi.org/10.5194/egusphere-2024-1032-RC1 - AC2: 'Reply on RC1', Harpreet Kaur, 30 Jul 2024
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RC2: 'Comment on egusphere-2024-1032', Anonymous Referee #2, 12 May 2024
This study investigates the seasonal variability of semidiurnal internal tide steric sea surface height (SSSH) and energetics, comparing trends across hemispheres and geographical hotspots like Georges Bank and the Arabian Sea. Northern and Southern Hemisphere SSSH variance exhibits a phase difference, consistent with altimetry. Seasonal changes in barotropic to baroclinic conversion drive energy variability, while SSSH variance is influenced by seasonal stratification changes. These results are valuable in the internal tide seasonality study. However, there are some questions that need to be discussed. Two main questions are: 1 Why were two simulations run and 2 Is a one-year simulation long enough to do these seasonality analyses?
- Line 86. Is there any specific reason to select this period to study the seasonality?
- Line 87. If realistic atmosphere forcing was used, is there enough time to spine up the model, to make the model balance?
- Line 87. Why there are only 5 tidal components are selected? Do more components make more sense?
- Line 95. Is this ratio a global average or from a specific region? Would the ratio show a big difference among different regions? So maybe including more tidal components would be better.
- Line 99. I didn't get why the author set two simulations (Expt 6.1 and 18.5) with different tidal forcing and under different time coverage. Is there a good reason to explain?
- Line 106. What’s the reason for selecting this period?
- Line 109. “Steric SSH is computed inline during the HYCOM simulation”. Could the author explain more details of this sentence?
- Line 112. What does the “both” refer to?
- Function 1. Explain parameter h
- Line 128. If my understanding is right, the number 30.42 is calculated by 365/12. However, the author mentioned it is 5 years, so does that mean that using (365*4+366/12) =30.43 makes more sense?
- Line 143. Could you explain why the approximation is 0?
- Line 164. February has 696 hours, which looks close to the hours in other months. How was the outlier created?
- Line 203. Why are the thermobaric instabilities not shown in energy flux? Do these instabilities exist both in Expt6.1 and Expt18.5?
- Line 219. How was the phase was calculated? What's the meaning of the positive and negative phases?
- Line 224. Is one year long enough to get reliable seasonal variability?
- Figure 3. Looks like there is a line at 30 degrees N. At the north of this line, winter and spring shows positive, while at the south of this line is negative. Is there any reason for this?
- Line 260. What’s the percentage described?
- Line 263. Is one year of simulation long enough to get the phase lag conclusion?
- Line 266-267. Can you do more explain why the conversion rate is the primary factor of the seasonal variability?
- Section 3.4. For the comparison between satellite and simulation. Have you compared the exact energetic value (for example, the energy or energy flux)? Do they under the same magnitude?
- Line 281. “As” should be “as”?
- Figure 6. Did you use the 1992-2017 satellite data to calculate the harmonic parameters? Is the figure 6 (f) can be interpolated from the Figure 4 (d)? Could you mention which experiment you used in the figure caption (for all figures applicable)
Citation: https://doi.org/10.5194/egusphere-2024-1032-RC2 - AC3: 'Reply on RC2', Harpreet Kaur, 30 Jul 2024
- AC4: 'Reply on RC2', Harpreet Kaur, 30 Jul 2024
Interactive discussion
Status: closed
-
CC1: 'Long-period tides', Paul PUKITE, 13 Apr 2024
Instead of semidurnal, diurnal, and mixed semidiurnal, why not concentrate on long period tides (such as Mf, nodal, etc)? Those may be much weaker, but the sluggish responsiveness of the immense internal ocean inertia is a match for the slower more gradual forcing,
Citation: https://doi.org/10.5194/egusphere-2024-1032-CC1 -
AC1: 'Reply on CC1', Harpreet Kaur, 30 Jul 2024
Our focus in this manuscript is on studying the seasonal variability in the semidiurnal internal tides in the global ocean, which contain more than 70% of all tidal energy (Egbert and Ray, 2003). We agree that the variability in longer-period tides may also be interesting, but this is saved for another paper. Considering that the frequency of internal tides in the stratified ocean must be greater than the inertial frequency f and lower than buoyancy frequency N, f < ω < N, the long-period internal tides should only occur in a narrow band around the equator.
Citation: https://doi.org/10.5194/egusphere-2024-1032-AC1
-
AC1: 'Reply on CC1', Harpreet Kaur, 30 Jul 2024
-
RC1: 'Comment on egusphere-2024-1032', Anonymous Referee #1, 06 May 2024
The study of semidiurnal SSH and energetics through HYCOM simulation offers valuable understanding of seasonal variability in the semidiurnal internal tide, , yieding fruitful insights. However, the assertion that seasonal variability in stratification at the generation site affects barotropic to baroclinic conversion in the Arabian Sea and Georges Bank prompts inquiry into the reasons behind the pronounced seasonal variability in these regions compared to other oceanic areas.
Citation: https://doi.org/10.5194/egusphere-2024-1032-RC1 - AC2: 'Reply on RC1', Harpreet Kaur, 30 Jul 2024
-
RC2: 'Comment on egusphere-2024-1032', Anonymous Referee #2, 12 May 2024
This study investigates the seasonal variability of semidiurnal internal tide steric sea surface height (SSSH) and energetics, comparing trends across hemispheres and geographical hotspots like Georges Bank and the Arabian Sea. Northern and Southern Hemisphere SSSH variance exhibits a phase difference, consistent with altimetry. Seasonal changes in barotropic to baroclinic conversion drive energy variability, while SSSH variance is influenced by seasonal stratification changes. These results are valuable in the internal tide seasonality study. However, there are some questions that need to be discussed. Two main questions are: 1 Why were two simulations run and 2 Is a one-year simulation long enough to do these seasonality analyses?
- Line 86. Is there any specific reason to select this period to study the seasonality?
- Line 87. If realistic atmosphere forcing was used, is there enough time to spine up the model, to make the model balance?
- Line 87. Why there are only 5 tidal components are selected? Do more components make more sense?
- Line 95. Is this ratio a global average or from a specific region? Would the ratio show a big difference among different regions? So maybe including more tidal components would be better.
- Line 99. I didn't get why the author set two simulations (Expt 6.1 and 18.5) with different tidal forcing and under different time coverage. Is there a good reason to explain?
- Line 106. What’s the reason for selecting this period?
- Line 109. “Steric SSH is computed inline during the HYCOM simulation”. Could the author explain more details of this sentence?
- Line 112. What does the “both” refer to?
- Function 1. Explain parameter h
- Line 128. If my understanding is right, the number 30.42 is calculated by 365/12. However, the author mentioned it is 5 years, so does that mean that using (365*4+366/12) =30.43 makes more sense?
- Line 143. Could you explain why the approximation is 0?
- Line 164. February has 696 hours, which looks close to the hours in other months. How was the outlier created?
- Line 203. Why are the thermobaric instabilities not shown in energy flux? Do these instabilities exist both in Expt6.1 and Expt18.5?
- Line 219. How was the phase was calculated? What's the meaning of the positive and negative phases?
- Line 224. Is one year long enough to get reliable seasonal variability?
- Figure 3. Looks like there is a line at 30 degrees N. At the north of this line, winter and spring shows positive, while at the south of this line is negative. Is there any reason for this?
- Line 260. What’s the percentage described?
- Line 263. Is one year of simulation long enough to get the phase lag conclusion?
- Line 266-267. Can you do more explain why the conversion rate is the primary factor of the seasonal variability?
- Section 3.4. For the comparison between satellite and simulation. Have you compared the exact energetic value (for example, the energy or energy flux)? Do they under the same magnitude?
- Line 281. “As” should be “as”?
- Figure 6. Did you use the 1992-2017 satellite data to calculate the harmonic parameters? Is the figure 6 (f) can be interpolated from the Figure 4 (d)? Could you mention which experiment you used in the figure caption (for all figures applicable)
Citation: https://doi.org/10.5194/egusphere-2024-1032-RC2 - AC3: 'Reply on RC2', Harpreet Kaur, 30 Jul 2024
- AC4: 'Reply on RC2', Harpreet Kaur, 30 Jul 2024
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Maarten C. Buijsman
Zhongxiang Zhao
Jay F. Shriver
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(9212 KB) - Metadata XML