the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Apr 2026
A global high-resolution temperature and salinity reconstruction by spatiotemporal multiscale correlations and dynamic height constraints
Abstract. High-resolution temperature and salinity (T & S) gridded datasets are essential for exploring large and mesoscale ocean phenomena. In this study, a global, weekly T & S gridded dataset with a horizontal resolution of 1/4° and a depth range of 0–1500 m from 2005 to 2023 is reconstructed using a four-dimensional multigrid analysis (4D-MGA) framework. With minimal prior statistical assumptions, the 4D-MGA efficiently extracts information from in-situ T & S profiles and satellite-observed sea level anomalies by integrating multiscale spatiotemporal correlation and physical constraints. The results show that the 4D-MGA product successfully delivers a credible, high-resolution analysis that combines robustness with reliable mesoscale information. Specifically, our product exhibits a lower root mean square error and excellent unbiased performance on a global scale when compared with the ARMOR3D product and the GLORYS reanalysis. http The product is then applied to investigate the linear trends of geostrophic transport within five key sections in WBCs. The 4D-MGA product is a subset of the high-resolution, objective analysis, gridded dataset for oceans, and it has the potential to advance our understanding of ocean dynamics and climate change. The weekly reconstructed dataset (4D-MGA) is freely available at https://doi.org/10.5281/zenodo.19378150.
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Status: open (until 21 Jun 2026)
- RC1: 'Comment on essd-2026-261', Anonymous Referee #1, 02 May 2026 reply
Data sets
1/4° global temperature and salinity objective analysis using 4D-MGA Haowen Wu et al. https://doi.org/10.5281/zenodo.19378150
Model code and software
1/4° global temperature and salinity objective analysis using 4D-MGA Haowen Wu et al. https://doi.org/10.5281/zenodo.19378150
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- 1
Li et al. 2026 presented a temperature-salinity dataset with a 4D-MGA method using WOD in weekly 0.25-deg resolutions in the global oceans from 2005 to 2023, compared with datasets from ARMOR3D and GLORYS. The analyzed temperature and salinity distributions shows reasonable magnitude of geostrophic currents at western boundaries. The datasets should be helpful to the community and could be published in ESSD. However, I am unable to accept the manuscript in the present form due to numerous questions when I go through the manuscript. My major concerns are (1) OISST dataset is an observation-based analysis filled to the global ocean, which is not suitable to ingest into the 4D-MGA as observations, (2) AVISO SLA is ingested into 4D-MGA while is compared with as independent data, (3) Comparisons with ARMOR3D and GLORYS may also be problematic since it is not mentioned whether they also ingest AVISO and OISST datasets, (4) Comparison against WOA23 appears independent, but actually WOA23 and WOD23 are not independent, (5) Comparisons with selected months and years may misleading, and should be done over the entire period of 2005-2023, (6) Many statements should be carefully clarified as detailed below.
Abstract: It should be stated that 4D-MGA uses WOD24.
L14, delete “http”
L14-15, add descriptions of the linear trend of WBCs
L15-18, it is better to be removed in “Abstract”
In introduction section, should the authors describe the 4D-Var that is popular in data assimilation?
L86-90, it is problematic to include DOISSTv2.1 and AVISO as “observations” while they are actually observation-based analyses, not real observations.
L112, what is “current grid layer”? Is it the vertical layers of the 4D-MGA system? How many vertical layers has the 4D-MGA, and what is the thickness for each layer?
Figure 1, what is exactly the “adjustment for SST” at different layers?
L156, what is the “reference observation”, and where has it been described? Note ARMOR3D and GLORYS in section 2.1.3 can only be used for intercomparison, not as reference observation.
L166, “WOD23 observations as the reference” cannot be used for Validation since it has been used in 4d-MGA as described in L78.
Figure 2. Are the RMSEs averaged from the surface to the bottom of the ocean?
L168-175, the low RMSEs in 4D-MGA should directly result from using the WOD23 since WOD23 is not independent from 4D-MGA, while ARMOR3D and GLORYS may not use the same WOD23 data or with different QC procedures or with different analysis resolutions horizontally and vertically.
L180, why WOA23 is compared in vertical in Figure 3 but not in Figure 2? Why the RMSEs and biases in WOA23 are much larger than 4D-MGA? “WOA” in Figure legend should be WOA23.
Figure 4, the “SLA observations” is not a correct term, the AVISO SLA analysis or observation-based AVISO SLA analysis should be used.
Figure 4, Could the low RMSEs and higher ACCs in 4D-MGA could directly result from the ingestion of DOISST product, while others are not?
L227-229, what are the references for these ACCs and RMSEs?
Figure 5, I doubt it is proper to compare with AVISO, which has been ingested into the 4D-MGA.
Figure 6 (i,j), What does “WBCs” represent, EAC and BMC? Or includes those in Figure 5? Is “BMC” the Brazil Current, and what does “M” mean?
L230, “respectively” in Kuroshio, Gulf Stream, AC, EAC, and BC. It would be better if these currents can be compared with those derived from other products ARMOR3D and GLORYS.
L280, why these month and year are selected?
L289, “randomly sampling 30% and 60%”, how about the OISST used in 4D-MGA? Should it also be reduced by 30% and 60%?
Figure 8, please clarify the statement “the linear correlation coefficient
between the linear trend and the mean anomaly”. The abbreviations of KOE, ARC, and BMC should be revised throughout the manuscript as KE, AC, and BC.
Figure 9, (1) quality/resolution is bad, (2) what is “background”, (3) what is the reference? Are 4D-MGA compared with independent non-sampled/ingested data? (4) It should be very helpful if the same comparisons are done for ARMOR3D and GLORYS. (5) “shaded area represents ±1 standard deviation” of 30%, 60%, or background? I guess there are three different shadings but are hard to identify. (6) Circles, triangles, and squares are not exactly on the time axis of Jan, Apr, Jul, and Oct, is this intentional designed? (7) What do the error bars represent for 2005, 2014, or 2023?
L299-301, very interesting. Generally speaking, an analysis system can filter out noises and random errors in observations, and the comparison of analysis against those noisy observations may not be problematic. A better way of evaluation is to filter out noises in observations such that the STDs in filtered observations are equivalent to that in analysis (Huang, B., X. Yin, T. Boyer, C. Liu, M. Menne, Y. D. Rao, T. Smith, R. Vose, H.-M. Zhang, 2025: Extended Reconstructed Sea Surface Temperature Version 6 (ERSSTv6): Part II. Upgrades on Quality Control and Large-scale Filter. J. Climate, 38, 1123-1136. https://doi.org/10.1175/JCLI-D-24-0185.1).
L310, what are “temporal grid layers”? are they same as “temporal layers” in section 2.3?