the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CRA-LICOM: A global high-frequency atmospheric and oceanic temporal gravity field product (2002–2024)
Abstract. Modeling sub-daily mass changes, dominated by the atmosphere and the oceans, is not only essential for understanding weather and climate change but also serves as a fundamental requirement for nearly all existing terrestrial or space-borne geodetic observations to perform signal separation. Removing these high-frequency mass changes, through the usage of so-called de-aliasing products, is of particular interest for satellite gravity missions such as GRACE and GRACE-FO to prevent the aliasing of short-term mass changes into seasonal and long-term mass variability. However, establishing a global observation network to monitor high-frequency gravity signals is impractical. Thus, ongoing efforts focus on simulating this high-frequency signal by driving atmospheric/oceanic numerical models with specific climate-forcing fields and assimilating observational data. Its realization relies on a complicated system and the uncertainty of obtained results is non-negligible for its dependency on selected forcing field and ocean model.
To explore the signal and uncertainty of de-aliasing products, we establish China’s first de-aliasing computation platform, independently. This is achieved by using the recently released CRA-40 (China’s first generation of atmospheric reanalysis) as forcing fields to drive our in-house 3-D atmospheric integration model and the LASG/IAP (State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics) Climate System Ocean Model 3.0 (LICOM3.0). With this new platform, we reproduce an alternative high-frequency atmospheric and oceanic gravity de-aliasing product, called CRA-LICOM, at 6 hourly and 50 km resolution, covering 2002–2024 at a global scale. The product is freely available at https://doi.org/10.11888/SolidEar.tpdc.302016. Inter-comparisons with the products of GFZ (Deutsches GeoForschungsZentrum) and validations against independent observations have revealed: (i) the current version of CRA-LICOM has well satisfied the requirement of the state-of-the-art satellite gravity missions, as well as other geodetic measurements, and (ii) despite agreement across most areas, considerable uncertainty is found at marginal seas near continental shelves, particularly at high-latitude regions. Therefore, scientific applications that aim to understand the fast-changing global water cycle, as well as mission design of future satellite gravity that seeks accurate gravity de-aliasing, can use our product as a reliable source. The current platform has the potential to be improved in terms of modeling and data assimilation capacity, which will be outlined in this study.
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RC1: 'Review on essd-2025-81', Anonymous Referee #1, 24 Apr 2025
I just read the manuscript "CRA-LICOM: A global high-frequency atmospheric and oceanic temporal gravity field product" submitted by Fan Yang et al. for possible publication in Earth System Science Data. The text describes a non-tidal atmosphere and ocean dealiasing product that is intended to minimize temporal aliasing artifacts in data products obtained from the sensor data of the GRACE/-FO satellite gravimetry missions. The paper is generally well written and fits nicely into the scope of the journal. However, a number of issues need to be resolved before publication can be recommended.
(1) The paper discusses in great detail the tides simulated in CRA-LICOM and compares those tidal signals in both atmosphere and oceans to the signals found in ERA5 and MPIOM. On the other hand, authors are also stating that tides should be treated separately in the gravity field processing (line 52). I agree to that notion and suggest that authors just make sure that no distinct periodic variations associated with tides remain in their CRA-LICOM AOD data. Please note that those tidal lines can differ from the frequencies considered in AOD1B RL07 for reasons associated with, e.g., the differing temporal sampling. Figure 4 should be revised to include figures of all partial tides that were considered as relevant. A 1:1 comparison to AOD1B RL07 can be, however, omitted.
(2) LICOM misses atmospheric surface pressure forcing (line 230) and consequently misses a part of the high-frequency excitation of ocean bottom pressure. This limitation of LICOM should be emphasized more prominently in the article.
(3) The oceanic component of S2 appears to be a consequence of the applied IB correction. It would be adviseable to perform the de-tiding first and apply the IB correction afterwards.
(4) The spin-up period of the model experiment appears to be rather short: please provide a plot comparing the drift in your model experiment with the drift in the various spin-up cycles to demonstrate that there is no artificial drift present in the product.
(5) It is surprising to read that the adopted value for g differs from the WMO constant (line 225). Please explain your choice in more detail.
(6) Line 285: The "Earth's gravity system" reads odd. Please revise.
(7) Line 307: The potential double bookkeeping of S2 has been an issue with AOD1B RL04 and earlier versions. since that time, AOD1B is defined as purely "non-tidal" and all atmospheric tides (including S2) need to be corrected with separate models. Please revise the statement.
(8) Figure 6 is not really insightful, since the signal characteristics are so different between oceans and land. I suggest to explore alternative ways for the comparison with the official AOD1B RL07 product by GFZ.
(9) It would be nice to plot postfit residuals in Figure 8 instead of the prefits. Many of the most prominent features in your plots will disappear so that smaller details become visible (and can be discussed).
Citation: https://doi.org/10.5194/essd-2025-81-RC1 -
RC2: 'Comment on essd-2025-81', Anonymous Referee #2, 02 Jun 2025
This paper presents the development and characteristics of CRA-LICOM, a new global atmosphere-ocean (AO) de-aliasing product for use in satellite-based gravity field computation. Comparison against the widely adopted de-aliasing model by GFZ (AOD1B RL07) and several types of observations show that CRA-LICOM is a reasonably accurate representation of atmospheric and oceanic mass changes on submonthly to interannual time scales. I commend the authors for their comprehensive effort (both in analysis and presentation) and think the manuscript should be considered for publication in ESSD after appropriate revisions. Many of my comments are minor, mostly addressing issues with the writing – see the attached PDF. There are nevertheless a few points that warrant being highlighted in my written assessment:
#1: The description of several key datasets, particularly CRA-40 (Section 2.1) and the GFZ-RL07 AO model (Section 2.2), is too succinct. This makes it hard for a reader to appreciate differences between GFZ RL07 and CRA-LICOM and the underlying atmospheric data assimilation products, i.e., CRA-40 and ECMWF (re)analyses. I suggest that the authors include a brief synopsis of the GFZ modeling strategy and add more details on CRA-40 as a data-constrained reconstruction of the atmospheric state.
#2: Similar to #1, too little detail is given on how the DART data were processed (Sections 2.2.4 and 5.2). The series at individual locations are made up from different recordings, have jumps, drifts, outliers and discontinuities, and primarily measure the tidal signal. Without proper consideration, these effects will corrupt any comparison to model-based or satellite-based OBP fields. Imperfect handling of the in situ data could be another source for the systematic biases noted near line 419.
#3: Section 5.2, also on the comparison of CRA-LICOM against bottom pressure recorders: It is unclear whether this analysis was conducted based on monthly averages or high-frequency (e.g., daily) fields and what the contribution of the dominant seasonal/annual cycle is to the statistics shown in Figure 10. Regarding the authors’ speculation about the vicinity to land (lines 420 to 423), this should not be an issue at d/o 180. I simply recommend removing the passage.
#4: I am surprised by the absence of S2 from the list of eliminated atmospheric tidal constituents (Section 3.1.1). Yes, S2 is not represented properly with 6-hourly sampled model output, but even as a standing wave, it can contribute substantially to the daily surface pressure variability at low latitudes. One way to pull the S2 signal from 6-hourly fields is to form, at each grid point, a daily mean composite (i.e., average over all 00UT data points, all 06UT points, etc.) and subtract that composite, day after day, from the full pressure time series. This will also absorb some of the S1-related variability, but the residual S1 signal is estimated as part of the harmonic analysis in the next step anyways.
#5: The authors’ description of the IB correction (lines 200-201) is imprecise and incomplete. IB refers to a static isostatic response of the ocean to atmospheric pressure loading; that is, air pressure anomalies are compensated instantaneously by vertical displacement of local sea level. The IB effect has no impact on the pressure at the seafloor except for a time-varying, spatially uniform OBP change reflecting the net atmospheric mass change over the ocean. From Figure 4, it appears that the S1, S2, and M2 tides in LICOM, as interpreted on lines 309-317, are largely introduced by the IB correction. This mix-up should be corrected and the corresponding paragraph in the text needs to be reworked.
#6: At the start of Section 5.3, the authors imply that GRACE measures the global ocean mass change (static effects; no currents), while GAD represents dynamic OBP variability related to ocean circulation changes. However, as GAD is imperfect, any de-aliasing product will leave a residual dynamic OBP signal to be picked up by GRACE. The separation made by the authors is therefore not fully correct, at least not the way it is currently formulated.
#7: In several places (e.g., line 364), arguments are made about errors in LICOM in western boundary (current) regions. As far as I can see from Figure 7, there is no systematic degradation of western boundary regions, but instead large RMSE values can be found on continental shelves and in many marginal seas. This in turn points to errors in the model bathymetry, wind stress parameterization or the bottom friction law. I suggest that the discussion in the respective places is modified to consider these points.
#8: Section 6 ‘Limitations’: I suggest to differentiate between structural model uncertainty, parametric uncertainty, and input data uncertainty, at least in the first paragraph of this discussion section.
Data sets
CRA-LICOM: A global high-frequency atmospheric and oceanic temporal gravity field product (2002-2024), National Tibetan Plateau / Third Pole Environment Data Center Liu et al. https://doi.org/10.11888/SolidEar.tpdc.302016
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