the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jan 2024
Estimating ocean heat content from the ocean thermal expansion parameters using satellite data
Abstract. Ocean heat content (OHC) is a depth-integrated physical oceanographic variable used to precisely measure ocean warming. Because of the limitations associated with in-situ CTD data and Ocean Reanalysis system products, satellite-based approaches have gained importance in estimating the daily to decadal variability of OHC over the vast oceanic region. Efforts to minimize the biases in satellite-based OHC estimates are needed to realize the actual response of the ocean to the brunt of climate change. In the current study, an attempt has been made to better implement the satellite-based ocean thermal expansion method to estimate OHC at 17 depth extents ranging from the surface to 700 m. To achieve this objective, an artificial neural network (ANN) model was developed to derive thermosteric sea level (TSL) from a given dataset of sea surface temperature, sea surface salinity, geographical coordinates, and climatological TSL. The model-derived TSL data were used to estimate OHC changes based on the thermal expansion efficiency of heat. Statistical analysis showed high correlation coefficients and low errors in satellite-derived TSL / OHC at 700 m water depth (N 388469, R 0.9926 / 0.9922, RMSE 1.16 m / 1.56 GJ m-2, MBE -0.1917 m / -0.2400 GJ m-2, MBPE -0.4560 % / -0.0290 %, MAE 0.763 m / 1.029 GJ m-2, and MAPE 2.34 % / 0.13 %) and nearly similar results at the remaining depth extents. These results suggest that the proposed ANN models are capable of accurately estimating OHC changes on real-time data and three-dimensional distribution patterns of depth-integrated OHC trends in the global ocean. In addition, the first-ever attempt to estimate the ocean thermal expansion component (i.e., TSL) from satellite data was successful and the model-derived TSL can be used to obtain high-end sea-level rise products in the global ocean.
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RC1: 'Comment on esd-2024-1', Anonymous Referee #1, 29 Jan 2024
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Review of esd-2024-1, " Estimating ocean heat content from the ocean thermal expansion parameters using satellite data", submitted by Kondeti and Palanisamy for possible publication in Earth System Dynamics.
This manuscript presents a methodology for making maps of ocean heat content (and thermosteric sea level) using an artificial neural network that incorporates Argo CTD data together with what the authors identify as satellite SST and SSS data, as well as the results of the application of that method. There are some very major issues that will need to be resolved prior to publication. Â Specific minor comments follow, first major, then minor, indexed by line number (L) where possible.
1. ince ocean heat content and thermometric anomalies are generally surface-intensified, so integrating from the surface to each depth of mapping does little to reveal how well the method works for mapping ocean heat content or thermometric anomalies at depths below the thermocline. Â In that respect it would be much more useful, and transparent, to map these quantities in distinct pressure or depth layers. Â Typical layers for using Argo might be 0-100 m, 100-300 m, 300-450 m, 450-700 m, 700-1000 m and 1000-2000 m, but others might be chosen if desiring more vertical resolution. Â The statistics for these would be much more revealing of the skill of the method below the surface layers than as it is currently formulated
2. The maps of ocean heat content (or thermometric sea level) are validated only against another machine learning estimate, but there are a number of objectively mapped and machine learning mapped products out there which are more widely used against which this work should be compared. Â See Johnson et al. (2023, https://doi.org/10.1175/BAMS-D-23-0076.2) and/or von Schuckman et al. (2023, https://doi.org/10.5194/essd-15-1675-2023) for an idea of the range of products available.
3. Sea surface height is more directly related to both ocean heat content and of course themosteric anomaly, so why aren't say CNEMS SSH maps, available since 1993, used as the neural network analysis?
4. Why are only Argo data used in the training? There are many more temperature profiles from shipboard CTDs, XBTs, marine mammals, and other sources readily available from the WOD that expand the spatial and temporal coverage Argo data considerably. Furthermore, Argo data should be downloaded directly from an Argo global data assembly centre, not WOD at NCEI, for the most recent version, as the data are subject to delayed-mode quality control and revisited frequently. As a result NCEI archives may be out-of-date. Â
5. The data section covers only the in situ data, and not the fields used for prediction. Please consider moving the description of the SST and SSS "data" into the data section. Also, hopefully SSH data too, as that should certainly be useful in constructing the network. Another point, the ORAS5 "SSS" data are not from satellite measurements! Â Those maps are output of an ocean reanalysis , that is to say, an assimilation of in situ data (including all the Argo profile data) into a numerical ocean model. Â So ORAS5 SSS fields are certainly not independent from Argo data, in fact, they directly incorporate those data. This fact, and the model used and datasets ingested, are all well documented on the ORAS5 websites. Â Please study the documentation and revise the analysis, manuscript, and claims within accordingly.Â
6. Section 4.1. Given the relatively large (100s to 1000s of km) and long (30-120 days) decorrelation scales of ocean temperature anomalies, it is misleading to call or treat these points as "independent". Almost all of them will be well correlated with some of the data used to construct the ANN. Â This probably means the true uncertainties are much higher than estimated here.
7. What is the point of section 5? Â It gives neither new oceanographic information nor insight into the accuracy or usefulness of the maps generated. Â It might better to compare and contrast trends with trends from other products. If a section on the topic is retained, it would be beneficial to recruit a co-author familiar with oceanography, as the section as written is haphazard.
Minor comments
8. L24. 93% is an old estimate at this point. See von Schuckman et al. (2023) for an update.
9. L27. The statement that this understanding is "inevitable" seems an odd word choice. Â It is important, or societally relevant, but that does not make it inevitable.
10. L32-33. Actually, just temperature profiles with depth information are also useful (e.g. XBT data). Measurements of conductivity on all profiles are not strictly necessary.
11. L42-87. This paragraph is very long and rather stream-of-consciousness in style. Consider breaking it up into a few paragraphs covering the different topics to better organize the exposition.
12. L48. There is a recent Zhao manuscript on the subject that should be referenced.
13. L177-178. Here and elsewhere in the manuscript, sentences like "blah blah blah are shown in Figure 3" generally duplicate figure captions and interrupt the flow of ideas. Â It's much better to write a sentence that communicates the main thing the authors want the readers to get out of the figure and refer to it parenthetically.
14. L241-249. Â The more energetic regions of the oceans will of course be more difficult to model. Â It might be appropriate to reference studies that have documented the spatial variability of eddy energy in the oceans, highlighting the western boundary current extensions and Antarctic Circumpolar Current, among other regions.
15. L466. The phrase "Future releases of WOA will certainly resolve the complex patterns..." again seems odd, arguably incorrect, and out of place. Â Please consider deleting or rephrasing this sentence.
Citation: https://doi.org/10.5194/esd-2024-1-RC1
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