the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The rate of information transfer as a measure of ocean-atmosphere interactions
David Docquier
Stéphane Vannitsem
Alessio Bellucci
Abstract. Exchanges of energy between the ocean and atmosphere are of large importance in regulating the climate system. Here we apply for the first time a relatively novel approach, the rate of information transfer, to quantify interactions between the ocean surface and lower atmosphere over the period 1988–2017 at monthly time scale. More specifically, we investigate dynamical dependencies between sea-surface temperature (SST), SST tendency and turbulent heat flux in satellite observations. We find a strong two-way influence between SST / SST tendency and turbulent heat flux in many regions of the world, with largest values in eastern tropical Pacific and Atlantic oceans, as well as in western boundary currents. The total number of regions with a significant influence of turbulent heat flux on SST and on SST tendency is reduced when considering the three variables, suggesting an overall stronger ocean influence compared to the atmosphere. We also find a relatively strong influence of turbulent heat flux taken one month before on SST. Additionally, an increase in the magnitude of the rate of information transfer and in the number of regions with significant influence is observed when looking at interannual and decadal time scales, compared to monthly time scale.
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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
(7348 KB)
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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.
- Preprint
(7348 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
David Docquier et al.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-942', Anonymous Referee #1, 26 Nov 2022
In physical oceanography it is believed that wind stress drives the ocean, while in dynamical
meteorology the ocean surface is treated as a bottom boundary that influences the
atmosphere. The interaction between the sea surface temperature (SST) and wind stress,
respectively characterizing the sea and the atmosphere at the interface, has become of
enormous interest. In this paper, the authors applied a causality analysis which is built on a
firm physical ground, in contrast to other statistical formalisms, to the study of this problem,
and obtained intriguing new results. Specifically, they found that that the ocean surface (SST
and SST tendency) strongly drives changes in the lower atmosphere (THF) and that the lower
atmosphere also has an important influence on the ocean surface in many regions of the
world, different from the traditional view that ocean-driven regimes largely exist in western
boundary currents and atmospheric-led regimes dominate in the open ocean. In recognition of
the importance of the finding, I hence recommend publication of this manuscript. The
following are just some points that the authors may pay some attention.l.1ï¼ l.13, True. But in this paper, the usage of information transfer/information flow (IF) in
studying the interaction is actually more fundamental. It is the exchange of
entropy/information rather than energy. In statistical physics, entropy plays a role in distributing energy.l.97, the last term may also represent the effect from unobserved processes.
l.100, While mathematically this is correct in terms of Shannon entropy, you may want to be more cautious in interpreting the sign, as it actually may not be explained using the well-known physics.
ll. 130-135. ). “This suggests that SST variability generally increases THF variability, while
THF variability mainly constrains SST variability.” This is good. But be cautious.
ll.189-190. To include more additional variables, make sure they are not nearly parallel;
otherwise the singularity of the covariance matrix could numerically deteriorate the result.
Section 3.3. In studying lagged transfer of information, be careful that only the IF in one way
makes sense—Causality cannot be from the future to the past.
ll.226-229. Indeed ocean-atmosphere interactions become more pronounced at larger time
scale. So these results do make sense.Citation: https://doi.org/10.5194/egusphere-2022-942-RC1 - AC1: 'Reply on RC1', David Docquier, 19 Jan 2023
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RC2: 'Comment on egusphere-2022-942', Milan Palus, 11 Dec 2022
- AC2: 'Reply on RC2', David Docquier, 19 Jan 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-942', Anonymous Referee #1, 26 Nov 2022
In physical oceanography it is believed that wind stress drives the ocean, while in dynamical
meteorology the ocean surface is treated as a bottom boundary that influences the
atmosphere. The interaction between the sea surface temperature (SST) and wind stress,
respectively characterizing the sea and the atmosphere at the interface, has become of
enormous interest. In this paper, the authors applied a causality analysis which is built on a
firm physical ground, in contrast to other statistical formalisms, to the study of this problem,
and obtained intriguing new results. Specifically, they found that that the ocean surface (SST
and SST tendency) strongly drives changes in the lower atmosphere (THF) and that the lower
atmosphere also has an important influence on the ocean surface in many regions of the
world, different from the traditional view that ocean-driven regimes largely exist in western
boundary currents and atmospheric-led regimes dominate in the open ocean. In recognition of
the importance of the finding, I hence recommend publication of this manuscript. The
following are just some points that the authors may pay some attention.l.1ï¼ l.13, True. But in this paper, the usage of information transfer/information flow (IF) in
studying the interaction is actually more fundamental. It is the exchange of
entropy/information rather than energy. In statistical physics, entropy plays a role in distributing energy.l.97, the last term may also represent the effect from unobserved processes.
l.100, While mathematically this is correct in terms of Shannon entropy, you may want to be more cautious in interpreting the sign, as it actually may not be explained using the well-known physics.
ll. 130-135. ). “This suggests that SST variability generally increases THF variability, while
THF variability mainly constrains SST variability.” This is good. But be cautious.
ll.189-190. To include more additional variables, make sure they are not nearly parallel;
otherwise the singularity of the covariance matrix could numerically deteriorate the result.
Section 3.3. In studying lagged transfer of information, be careful that only the IF in one way
makes sense—Causality cannot be from the future to the past.
ll.226-229. Indeed ocean-atmosphere interactions become more pronounced at larger time
scale. So these results do make sense.Citation: https://doi.org/10.5194/egusphere-2022-942-RC1 - AC1: 'Reply on RC1', David Docquier, 19 Jan 2023
-
RC2: 'Comment on egusphere-2022-942', Milan Palus, 11 Dec 2022
- AC2: 'Reply on RC2', David Docquier, 19 Jan 2023
Peer review completion
Journal article(s) based on this preprint
David Docquier et al.
Model code and software
Liang Index to quantify ocean-atmosphere interactions (v1.1) David Docquier https://doi.org/10.5281/zenodo.7074861
David Docquier et al.
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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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