the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Monitoring the regional Ocean Heat Content change over the Atlantic Ocean with the space geodetic approach
Victor Rousseau
Robin Fraudeau
Matthew Hammond
Odilon Joël Houndegnonto
Michaël Ablain
Alejandro Blazquez
Fransisco Mir Calafat
Damien Desbruyères
Giuseppe Foti
William Llovel
Florence Marti
Benoît Meyssignac
Marco Restano
Jérôme Benveniste
Abstract. The estimation of the regional Ocean Heat Content (OHC) is essential for climate analysis and future climate predictions. In this study, we propose a method to estimate and propagate uncertainties in regional OHC changes. The OHC is estimated with space geodetic steric data corrected from salinity variations estimated with in situ measurements. A variance-covariance matrix method is used to propagate uncertainties from space geodetic data to the OHC change. The integrated OHC change over the Atlantic basin is 0.17 W m-2 which represents 21 % of the global OHC trend, with significant trends observed in 52 % of the Atlantic basin. Uncertainties in OHC trends are mainly attributed to manometric sea level change uncertainties. We validate our space geodetic OHC estimates at two test sites, representing the subtropical and subpolar regions of the North Atlantic, highlighting their importance in understanding climate dynamics. Our results show good agreement between space geodetic estimates and in situ measurements in the North Atlantic region. The space geodetic OHC trends reveal a warming pattern in the southern and western parts of the North Atlantic, particularly in the Gulf Stream region, while the northeastern part exhibits cooling trends. Overall, our study provides valuable insights and a new framework to estimate regional OHC change and its uncertainties, contributing to a better understanding of the Earth's climate system and its future projections. The space geodetic OHC change product (version 1.0) is freely available at https://doi.org/10.24400/527896/a01-2022.012 (Magellium/LEGOS, 2022)
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Victor Rousseau et al.
Status: open (until 02 Nov 2023)
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CC1: 'Comment on essd-2023-236', Armin Koehl, 28 Aug 2023
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The manuscript describes a technique to estimate heat content changes from satellite data together with in situ data. The estimate include uncertainty estimation and a comparison to alternative estimates. The product is available to the public, and as such the manuscript is worth to be published. However, I feel that the methodology is not sufficiently complete or well enough described to allow the reader to reproduce the estimation of the ocean heat content (OHC).
The large contribution to the error estimate by manometric contribution is surprising given that this component shows very little contribution to the dynamic sea level change (see for instance Landerer etal (doi.org/10.1175/JPO3013.1) or Couldrey et al (doi.org/10.1007/s00382-020-05471-4)). Dynamic sea level certainly does not contain the important signals related to melting, groundwater change and GIA.
However, this outcome is an argument against this technique and suggest that it may be actually inferior to calculating heat content change directly from temperature data.There is discussion about the different contributions to the uncertainty in the conclusion. Data gaps are reported as the main issue with the technique, while the estimated uncertainties relate mostly to the manometric contribution from the gravity missions. Their procedure was introduced in several ways to be superior to alternative estimates based on for instance temperature data alone, but then their product is evaluated with these kind of products. In the end, I felt a bit lost as how to evaluate the quality and merits of the product and what the true uncertainties are. One merit is the estimate of uncertainty but then I get the impression that these cannot be trusted since the true uncertainty may relate also to contributions not considered. The authors dedicated a substantial part of the conclusion to the discussion of uncertainties, and I believe it will be hard to provide additional details. However as detailed below, there are some places, for instance in the introduction, the comparison to alternative estimates and the presentation of the uncertainies, where some more details or interpretations could be presented.
Details:L 38 What is the role of salinity here?
L68 Why is this uncertainty estimate sufficient ?
Doesn't the contribution of the halosteric signal to the uncertainty need to be considered too? estimating a signal from the difference instead directly from the thermosteric sounds at first a bad idea since the uncertainties add. Add a argument why your approach is still a good idea.L 61-68 Maybe it would be useful to contrast advantages with disadvantage. The full depth estimate of the thermosteric signal assumes that the halosteric signal is known or zero which is not the case. Maybe anticipate some of the caveates discussed later, to provide a more blanced view.
L 78 Please define abbreviation TSSL
eq (2) There needs to be different identifiers for TSSL from temperature and from the geodetic approach.
L 87-88 Since the expansion coefficient is strongly temperature dependent, it is important to state the reference state assumed for the expansion coefficient. Assuming is 0C and 35 PSU would be a very bad choice, because coefficients near the reference state will be very different from the actual coefficient in a substantial part of the ocean. I think (2) makes only sense for a linear density equation, because expansion coefficient may be very different for a whole range from 0 to the actual temperature, while the changes may be mostly be around the actual temperature and very different from 0.
l 31 Not clear how the changing temperature is used to calculate halosteric sea level.
l 143 How is merging done?
l 153-154 Some extra details would be helpful since the brief introduction above gave the impression that this may not have been done in the best way,
Section 3.3.3 Also for the validation products it would be useful to know how the salinity data has been used.
L 199 Please specify in what way the method was extended. Check the grammar of the sentence.
l 201-202 Not clear how the variance covariance matrices are defined. Please provide a definition, is covariance only in time? What do the two arguments t stand for?
l 208, eq (5) Not clear which terms are in that budget. What is i indicating?
l 210 Not clear how this works, this is dispersion in time?
Maybe also a comment on how independent the members of the ensemble are and how that affects the estimate.l 211 How is the additional uncertainty computed?
eq (6) Not clear to me over which dimension the cov is calculated that the result depends on i,j,lat, lon and t. And why there are two t arguments.
eq (9) why to the power of t, where does it come from? Or is is a superscipt, maybe transposed with the period indicating the dot product ?
l 235-239, eq(10) not clear where the uncertainty is formulated or is \hat(beta) the uncertainty. It is described as estimator of beta. What is X in (10)?
Figure 1 What is the time period? April 2002 to Dec 2020 for all estimates here?
l 249 What is the expectation here. The hypothesis from the Introduction is that the present estimate is the better product than NOC or ISAS21. How does one use this comparison to validate this assumption? I can believe that OVIDE is the better yet sparse product that can be used to validate your product, if you show that your product has smaller rms to OVIDE than ISAS21 you could make a point, otherwise one could point to greater detail but also larger noise in your product. I am missing here some conclusions for this comparison.
I only take that it is an alternative, but that's not how you started out.l 288-289 Isn't the given reason and the pattern the same thing?
l 297 Where is the value 3 Wm2 shown? Maybe it would be better to show the actual value of the contributions to the uncertainty rather then the percent due to MAN.
l 307-308 Doesn't the evaluation assume ISAS21 to be the better product, contradicting the premise of the manuscript ?
l 317-318 Maybe useful to report also the correlation between ISAS21 and OVIDE
Fig.5 Add longitude range from the text here.
L 353 Isn't 15 W/m2 only for the subtropical gyre while negative values apply for the subpolar gyre? Maybe you want to say order 15W/m2?
L 360-361 I think nothing has shown so far. The information comes in the next sentence. Maybe you want to reformulate these sentences and discuss Figs E1/F1 a little more.
Table 1 not clear what time_covar is.
Citation: https://doi.org/10.5194/essd-2023-236-CC1
Victor Rousseau et al.
Data sets
Atlantic OHC from space: Heat content change over the Atlantic Ocean by space geodetic approach Magellium/LEGOS https://doi.org/10.24400/527896/A01-2022.012
Victor Rousseau et al.
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