the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The modelled climatic response to the 18.6-year lunar nodal cycle and its role in decadal temperature trends
- 1School of Environmental Sciences, University of East Anglia, Norwich NR4 7SQ, United Kingdom
- 2Climatic Research Unit, University of East Anglia, Norwich NR4 7SQ, United Kingdom
- 3School of Mathematics, University of East Anglia, Norwich NR4 7SQ, United Kingdom
- 4National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading RG6 6BB, United Kingdom
- 1School of Environmental Sciences, University of East Anglia, Norwich NR4 7SQ, United Kingdom
- 2Climatic Research Unit, University of East Anglia, Norwich NR4 7SQ, United Kingdom
- 3School of Mathematics, University of East Anglia, Norwich NR4 7SQ, United Kingdom
- 4National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading RG6 6BB, United Kingdom
Abstract. The 18.6-year lunar nodal cycle arises from variations in the angle of the Moon’s orbital plane. Previous work has linked the nodal cycle to climate but has been limited, either by the length of observations analysed, or geographical regions considered in model simulations of the pre-industrial period. Here we examine the global effect of the lunar nodal cycle in multi-centennial climate model simulations of the pre-industrial period. We find cyclic signals in global and regional surface air temperature having amplitudes of O (0.1 K), ocean heat uptake and ocean heat content. The timing of anomalies of global surface air temperature and heat uptake are consistent with the so-called slowdown in global warming in the first decade of the 21st century, also displaying warmer than average Arctic surface temperatures at the same time. The lunar nodal cycle causes variations in mean sea level pressure exceeding 0.5 hPa in the Nordic seas region, thus affecting the North Atlantic Oscillation Index during boreal winter. Our results suggest that the contribution of the lunar nodal cycle to global temperature should be negative in the mid-2020s before becoming positive again in the early-2030s, reducing the uncertainty in time at which projected global temperature reaches 1.5 C above pre-industrial levels.
Manoj Joshi et al.
Status: final response (author comments only)
- AC1: 'Comment on egusphere-2022-151', Manoj Joshi, 03 May 2022
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CC1: 'Comment on egusphere-2022-151', Michael Wallace, 05 May 2022
The author invited me to comment.
Informal referencing for "vol/sol" at around line 105.
On that paragraph topic, if the author intends to cite past work relating solar cycle forcing explorations for climate projections with lags of 2 to 3 years or more, it seems incumbent that he cite this work that I authored.
https://www.tandfonline.com/doi/full/10.1080/02626667.2019.1567925?journalCode=thsj20&scroll=top&needAccess=true
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CC2: 'Comment on egusphere-2022-151', Paul PUKITE, 12 May 2022
This interface does not allow image uploads of larger sizes so instead my review of the paper is in a PDF attachment. But since I do not have a good PDF generator, the review can also be found online at => Response to the lunar cycle | GeoEnergyMath.com <=
http://geoenergymath.com/2022/05/12/response-to-the-lunar-cycle/
This link contains images that are expandable via clicking
Good luck, hope it gets published
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RC1: 'Comment on egusphere-2022-151', Anonymous Referee #1, 20 May 2022
The authors try to quantify the effects of one of the lunar cycles on our climate, and in particular on surface temperature and the NAO, via millennial length runs of a coarse coupled model. Although the idea is interesting and thought-provoking, the chosen model is inadequate.
My main issues are:
1.The model’s resolution.
Two degrees is way too coarse to investigate tides. We’re talking about a process that has a typical scale of fewer than 5 km at critical locations, and that is highly dependent on the correct representation of bathymetry.
2.The model’s biases
From line 111, you try and find an explanation for the seemingly “inconsistent” behavior of the Nordic Seas and later that of the Southern Ocean. From line 125, you report on potential but non-significant links with the modelled AMOC. Unfortunately, FORTE2, whose reference Blaker et al. (2020) was missing from the bibliography, has significant biases that most likely impact these two results:
- Warm bias in the Southern Ocean and in the northwest Atlantic;
- Deep mixed layer bias in the Nordic Seas and in the Ross Sea.
Similarly, you ought to verify the biases in the model’s NAO before analyzing its response.
3.The focus on temperature only
In regions that are salinity-controlled (Arctic, Nordic Seas, North Atlantic, Southern Ocean), the change in background diffusivity should primarily affect the salinity. A freshening would likely lead to a surface cooling, and a salinification to enhanced mixing so to a warming of the surface. Maps or timeseries of salinity changes should be shown. Similarly, there is currently no mention of sea ice changes in this paper.
Other comments:
Inconsistent usage of “high latitudes”. Line 69-70 for example, it excludes the Arctic (which is extremely stratified).
Figures 5:8 are very hard to read. Use a pseudocolor plot, or at least filled contours.
All figures are at the end of the document, and their captions are on a different page. It is really uncomfortable to view on a screen. The EGUsphere now (finally!) recommends that figures and their captions be in the text, closest to where they are discussed.
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CC3: 'Reply on RC1', Paul PUKITE, 23 May 2022
"Two degrees is way too coarse to investigate tides. We’re talking about a process that has a typical scale of fewer than 5 km at critical locations, and that is highly dependent on the correct representation of bathymetry."
Misconception here. Lunar tidal forcing has a global effect and causes measurable changes in the Earth's rotation rate, as evidenced by length of day (LOD) data which shows significant impacts of monthly and fortnightly tidal cycles. Being a fluid, the ocean has a more complex response to inertial forcing, leading to sloshing, especially along the thermocline where a reduced effective gravity exists. So can't assume the diurnal tidal properties alone.
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AC2: 'Reply on RC1', Manoj Joshi, 02 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/egusphere-2022-151/egusphere-2022-151-AC2-supplement.pdf
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CC4: 'Comment on egusphere-2022-151', Mikh Kova, 05 Jul 2022
Soory to criticise you but... The lunar infleunce on cliumate is described at https://journalijecc.com/index.php/IJECC/article/view/30803 What determines ccyles is not so much lunar nodes but New/Full Moon-perigees described at https://www.aimspress.com/article/doi/10.3934/geosci.2021034 Lunar nodes' influence is only secondary. Given the circumstacnes surrounding 2010 - 2016, no conclusion should be based on these years; these six years make a once in a mileinium event.
- EC1: 'Reply on CC4', Axel Kleidon, 26 Aug 2022
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CC5: 'Comment on egusphere-2022-151', Adam Blaker, 18 Aug 2022
Review of “The modelled climatic response to the 18.6-year lunar nodal cycle and its role in decadal temperature trends” by Manoj Joshi et al.
The paper presents a brief report on the climatic response of a low-resolution OAGCM to the variation in tidal mixing caused by the 18.6 year lunar nodal cycle. A spatial and time varying field of modified ocean diffusion is employed to represent the variation in tidal mixing, including two variants to reflect uncertainty in the vertical. This resulting experiment is simple, but sufficient to draw conclusions that warrant further study.
My impression is that the presented paper does the minimum necessary to draw attention to the potential importance of the 18.6 year lunar nodal cycle in the context of climate projections and hiatus/surge events. The authors propose that parameterisation of the lunar nodal cycle should be implemented in 1D integrated assessment models and decadal-scale forecast systems, and I am inclined to agree.
I have some concerns that I would like to see addressed prior to publication.
Major comments:
The authors create a map of ocean diffusion amplitude modulation based on the geographical distribution of the RMS current velocity and the nodal amplitudes. However, these are the barotropic tides. Around 2/3 of the power input to surface tides is lost in the shallow seas, whilst the remaining 1/3 generate internal tides (see e.g. Ferrari and Wunsch, 2009; de Lavergne, 2019). I believe it is the latter which the authors intended to parameterise in the model, and I therefore have concerns about the spatial distribution given in Figure 1.
The geographical distribution of internal tidal energy dissipation is strongly influenced by bathymetry. The map of tidal dissipation produced by de Lavergne et al. (2018, 2019) clearly shows the influence of bathymetry. This prompts two questions:
- Why did the authors not use such a map in their parameterisation?
- How would the results differ if the dissipation used this sort of geographical distribution?
Such a change in the geographical distribution would likely affect many of the regional results, but it is harder to gauge the impact on the global quantities such as surface temperature and ocean heat uptake.
I believe the importance of the result in the context of the recent hiatus in global temperature and ocean heat uptake is overstated. Hedemann et al. 2017 (cited on line 150) define an ocean surface layer that is 100m thick. Fluxes of heat into the ocean are given as fluxes through 100m, not the ocean surface, and are consequently much smaller. Estimates of increased ocean heat uptake (through the ocean surface) during the 2000s are typically 0.7 +/- 0.3 W m−2 (Drijfhout et al. 2014). The average flux you report (~0.07 +/- 0.07 W m-2) is therefore sufficient to explain one tenth of the hiatus.
Minor comments:
Line 35: miss-spelt Yndestad.
Line 89: remove “opposites” given in parenthesis to improve readability. They are unnecessary due to the last sentence in the paragraph.
Line 98 and onwards: refers to “global mean surface temperature Tg”, whilst the plot titles in Figure 4 refer to “Tsurf”. It is ambiguous what “surface temperature” refers to. In the preceding paragraph I was (I think rightly) taking this to be the “sea surface temperature” (SST). However, I think this and subsequent references might be to “surface air temperature” (SAT; due e.g. to the presence of contours over land in figures 5 and 6). Please clarify throughout.
Line 102: please supply “(vol/sol refs here)”.
Line 104: relating to my earlier comment, it is important to determine whether the quantity presented in Figure 4 is SST or SAT. If SAT then the contribution from the land will likely dominate the variability. If SST, does the variability arise from the summer months? In either case, I think a caveat drawing the reader’s attention to the simple ice representation in FORTE2 would be advisable.
Line 110: remove ‘though’
Line 111: Is the inconsistency in the Nordic Seas caused/dominated by variation in the ice cover, rather than the lunar tidal variation in the experiment?
Line 120: Missing close “)”
Line 125: switch order of the last two sentences in this paragraph.
Line 144: insert “a” > “…less of a global…”
Check references: missing Blaker et al. (2020)
Line 267/8: two mentions of “380 years” which seems to contradict the 760 years mentioned on line 80.
Line 279: duplicate “in in”
References:
de Lavergne, C., Falahat, S., Madec, G., Roquet, F., Nycander, J., Vic, C. (2019), Toward global maps of internal tide energy sinks. Ocean Modelling, 137, 52-75. doi:10.1016/j.ocemod.2019.03.010.
de Lavergne Casimir, Falahat Saeed, Madec Gurvan, Roquet Fabien, Nycander Jonas, Vic Clément (2018), Global maps of internal tide generation and dissipation. SEANOE. https://doi.org/10.17882/58105
Drijfhout, S. S., A. T. Blaker, S. A. Josey, A. J. G. Nurser, B. Sinha, and M. A. Balmaseda (2014), Surface warming hiatus caused by increased heat uptake across multiple ocean basins, Geophys. Res. Lett., 41, 7868–7874, doi:10.1002/2014GL061456.
Ferrari R. and C. Wunsch (2009), Ocean Circulation Kinetic Energy: Reservoirs, Sources, and Sinks, Annual Review of Fluid Mechanics, 41:1, 253-282
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RC3: 'Reply on CC5', Adam Blaker, 23 Aug 2022
- AC5: 'Reply on RC3', Manoj Joshi, 02 Dec 2022
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RC2: 'Comment on egusphere-2022-151', Adam Blaker, 23 Aug 2022
Review of “The modelled climatic response to the 18.6-year lunar nodal cycle and its role in decadal temperature trends” by Manoj Joshi et al.
The paper presents a brief report on the climatic response of a low-resolution OAGCM to the variation in tidal mixing caused by the 18.6 year lunar nodal cycle. A spatial and time varying field of modified ocean diffusion is employed to represent the variation in tidal mixing, including two variants to reflect uncertainty in the vertical. This resulting experiment is simple, but sufficient to draw conclusions that warrant further study.
My impression is that the presented paper does the minimum necessary to draw attention to the potential importance of the 18.6 year lunar nodal cycle in the context of climate projections and hiatus/surge events. The authors propose that parameterisation of the lunar nodal cycle should be implemented in 1D integrated assessment models and decadal-scale forecast systems, and I am inclined to agree.
I have some concerns that I would like to see addressed prior to publication.
Major comments:
The authors create a map of ocean diffusion amplitude modulation based on the geographical distribution of the RMS current velocity and the nodal amplitudes. However, these are the barotropic tides. Around 2/3 of the power input to surface tides is lost in the shallow seas, whilst the remaining 1/3 generate internal tides (see e.g. Ferrari and Wunsch, 2009; de Lavergne, 2019). I believe it is the latter which the authors intended to parameterise in the model, and I therefore have concerns about the spatial distribution given in Figure 1.
The geographical distribution of internal tidal energy dissipation is strongly influenced by bathymetry. The map of tidal dissipation produced by de Lavergne et al. (2018, 2019) clearly shows the influence of bathymetry. This prompts two questions:
- Why did the authors not use such a map in their parameterisation?
- How would the results differ if the dissipation used this sort of geographical distribution?
Such a change in the geographical distribution would likely affect many of the regional results, but it is harder to gauge the impact on the global quantities such as surface temperature and ocean heat uptake.
I believe the importance of the result in the context of the recent hiatus in global temperature and ocean heat uptake is overstated. Hedemann et al. 2017 (cited on line 150) define an ocean surface layer that is 100m thick. Fluxes of heat into the ocean are given as fluxes through 100m, not the ocean surface, and are consequently much smaller. Estimates of increased ocean heat uptake (through the ocean surface) during the 2000s are typically 0.7 +/- 0.3 W m−2 (Drijfhout et al. 2014). The average flux you report (~0.07 +/- 0.07 W m-2) is therefore sufficient to explain one tenth of the hiatus.
Minor comments:
Line 35: miss-spelt Yndestad.
Line 89: remove “opposites” given in parenthesis to improve readability. They are unnecessary due to the last sentence in the paragraph.
Line 98 and onwards: refers to “global mean surface temperature Tg”, whilst the plot titles in Figure 4 refer to “Tsurf”. It is ambiguous what “surface temperature” refers to. In the preceding paragraph I was (I think rightly) taking this to be the “sea surface temperature” (SST). However, I think this and subsequent references might be to “surface air temperature” (SAT; due e.g. to the presence of contours over land in figures 5 and 6). Please clarify throughout.
Line 102: please supply “(vol/sol refs here)”.
Line 104: relating to my earlier comment, it is important to determine whether the quantity presented in Figure 4 is SST or SAT. If SAT then the contribution from the land will likely dominate the variability. If SST, does the variability arise from the summer months? In either case, I think a caveat drawing the reader’s attention to the simple ice representation in FORTE2 would be advisable.
Line 110: remove ‘though’
Line 111: Is the inconsistency in the Nordic Seas caused/dominated by variation in the ice cover, rather than the lunar tidal variation in the experiment?
Line 120: Missing close “)”
Line 125: switch order of the last two sentences in this paragraph.
Line 144: insert “a” > “…less of a global…”
Check references: missing Blaker et al. (2020)
Line 267/8: two mentions of “380 years” which seems to contradict the 760 years mentioned on line 80.
Line 279: duplicate “in in”
References:
de Lavergne, C., Falahat, S., Madec, G., Roquet, F., Nycander, J., Vic, C. (2019), Toward global maps of internal tide energy sinks. Ocean Modelling, 137, 52-75. doi:10.1016/j.ocemod.2019.03.010.
de Lavergne Casimir, Falahat Saeed, Madec Gurvan, Roquet Fabien, Nycander Jonas, Vic Clément (2018), Global maps of internal tide generation and dissipation. SEANOE. https://doi.org/10.17882/58105
Drijfhout, S. S., A. T. Blaker, S. A. Josey, A. J. G. Nurser, B. Sinha, and M. A. Balmaseda (2014), Surface warming hiatus caused by increased heat uptake across multiple ocean basins, Geophys. Res. Lett., 41, 7868–7874, doi:10.1002/2014GL061456.
Ferrari R. and C. Wunsch (2009), Ocean Circulation Kinetic Energy: Reservoirs, Sources, and Sinks, Annual Review of Fluid Mechanics, 41:1, 253-282
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AC3: 'Reply on RC2', Manoj Joshi, 02 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/egusphere-2022-151/egusphere-2022-151-AC3-supplement.pdf
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RC4: 'Comment on egusphere-2022-151', Anonymous Referee #3, 24 Aug 2022
Review for “The modelled climatic response to the 18.6-year lunar nodal cycle and its role in decadal temperature trends” by Joshi et al.
The authors examine the impact of lunar nodal cycle with a period 18.6 years on the Earth’s climate. To do this, they use a relatively simple model with parametrized effects of the lunar nodal cycle. They show that there is a cyclic signal related to the lunar nodal cycle in global/regional air-temperature and in the mean sea level pressure, resembling the North Atlantic Oscillation (NAO). They also mention that global warming hiatus earlier this century may have been partly caused by this, and mention that similar events like this are expected in the future. While I find the topic interesting and I think effects like this should be explored further, I also think that the manuscript requires major revision before it can be published.
Major comments:
I think that discussion could be more thorough, i.e., results/discussion sections should be expanded.
- For example, how does lunar nodal cycle impact on global/regional mean temperature, NAO etc. compare with other processes that control decadal-multidecadal indices. Is it more or less important for climate system variability than other processes? Or perhaps the lunar nodal cycle is a cause for some of the variability? Maybe the different variabilities are out-of-phase and/or uncorrelated? Much like other comments I have seen, I agree that the results in this paper are overstated, also given the simplicity of the experiments.
- In the Atlantic there is a 15–18-year cycle - see: Årthun, M., Wills, R. C. J., Johnson, H. L., Chafik, L., & Langehaug, H. R. (2021). Mechanisms of Decadal North Atlantic Climate Variability and Implications for the Recent Cold Anomaly, Journal of Climate, 34(9), 3421-3439
- There are obviously also Pacific (inter-)Decadal variability, Atlantic Multidecadal variability, AMOC etc., which are briefly mentioned in the manuscript. See e.g.: Omrani, N.-E., et al., 2022: Coupled stratosphere-troposphere-Atlantic multidecadal oscillation and its importance for near-future climate projection. npj Clim. Atmos. Sci., 5:59
- There are many more papers on the topic that could be further discussed.
- The authors state on l. 120, 125 there is insignificant response for everything, except maybe in MSLP in the Euro-Atlantic. How much variance in the NAO on this specific timescale does nodal cycle represent?
- L. 128-138: I think figures here need some uncertainty estimates. Also, I think this paragraph is overstated – other effects may be stronger than nodal cycle so I would like to caution against implying “nodal cycle will(has) cause(d) this”. While I agree that decadal-multidecadal variability can cause delays in or speed-up the global warming trends (and affect the onset of 1.5 degree warming) I think you must be careful if you are not sure how much other modes of variability will contribute and to what extent – different effects may cancel out and then the statements in this paragraph are less meaningful.
- Fig. 10: I am not sure how you added nodal cycle in for bottom panel in Fig. 10. Did you run the model? Statistically? Please elaborate.
- Also add uncertainty from climate models on top panel.
I think methods should be provided in more detail (use appendix if needed).
- I think that the authors have a control run, but it is never mentioned in the methods.
- On l. 55 they talk about 8 largest tidal constituents – since I am not a tidal expert I find this hard to follow – please elaborate what they are, their timescales, is lunar nodal cycle among them or do you impose it separately (this seems to be the case).
- On l. 65-70 you mention geographical shape of the function – is this based on observations? Which?
- Presumably tidal components are typically parametrized in models?
- On l. 71-77: authors talk about “SCALED” and “CONSTANT” model configurations and say that the former provides underestimations and the latter overestimation. Is there an ideal way of simulating this or are these methods commonly used – what have you simplified here?
- L. 79: how exactly is nodal cycle applied to the model? Please elaborate.
Figures should have better captions – more descriptive – half of the time I am left wondering what is actually plotted. I also think they should be revised.
- Fig. 2,3,4 it is really hard to see if something is out-of-phase/in-quadrature etc. if lines are plotted in different figures – I suggest plotting such lines together in one figure. Or provide more details – maybe Fig. references are incorrect in text or maybe you need to mention “middle panel in Fig. 3” etc.?
- l. 107-117: I cannot say I can follow the text here related to Figs. 5-6. I am not sure where you see out-of-phase relationship between Tsurf and global response (of what?).
- Fig. 7: Top panel does look NAO-like, but bottom panel reminds me more of blocking-like structure. Also, top panel shows perhaps some wave-trains in the Southern Hemisphere. I think this figure can be discussed more.
- Many figures are present, but not discussed enough – either don’t use them or discuss them in more detail.
Is there any observational support for the authors’ claims? Even if it is just 20 years of data (i.e. 1 cycle)?
I agree with the authors’ final statements that such effects (if they are as relevant as the authors claim) should be better represented in climate models.
Minor comments
l. 17: O (0.1K) – are you trying to say that it is of order 0.1K? Then just spell it out.
l. 32: 3.7% and 11.5% - provide reference for the numbers.
l. 42, 174: OAGCM --> AOGCM (?)
l. 98, 99: Tg – is this supposed to be Tsurf? It is not defined anywhere.
l. 100-102: suddenly you talk about solar/volcanic forcing – where is this from?? Reference figure/previous study.
l. 106: ‘later’ --> ‘below’ (?)
l.269: I think top and bottom panel description is reversed.
Fig. 2 caption: Provide units.
All Fig. captions: more details.
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AC4: 'Reply on RC4', Manoj Joshi, 02 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/egusphere-2022-151/egusphere-2022-151-AC4-supplement.pdf
- For example, how does lunar nodal cycle impact on global/regional mean temperature, NAO etc. compare with other processes that control decadal-multidecadal indices. Is it more or less important for climate system variability than other processes? Or perhaps the lunar nodal cycle is a cause for some of the variability? Maybe the different variabilities are out-of-phase and/or uncorrelated? Much like other comments I have seen, I agree that the results in this paper are overstated, also given the simplicity of the experiments.
Manoj Joshi et al.
Data sets
Lunar nodal cycle forcing data M. Joshi, R. Hall, D. Stevens, E. Hawkins https://research-portal.uea.ac.uk/en/datasets/lunar-nodal-cycle-amplitude-modulation-map
Manoj Joshi et al.
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