Reconstructing GRACE-type time-variable gravity from the Swarm satellites

The Gravity Recovery and Climate Experiment (GRACE) mission has enabled mass changes and transports in the hydrosphere, cryosphere and oceans to be quantified with unprecedented resolution. However, while this legacy is currently being continued with the GRACE Follow-On (GRACE-FO) mission there is a gap of 11 months between the end of GRACE and the start of GRACE-FO which must be addressed. Here we bridge the gap by combining time-variable, low-resolution gravity models derived from European Space Agency’s Swarm satellites with the dominating spatial modes of mass variability obtained from GRACE. We show that the noise inherent in unconstrained Swarm gravity solutions is greatly reduced, that basin averages can have root mean square errors reduced to the order of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {cm}$$\end{document}cm of equivalent water height, and that useful information can be retrieved for basins as small as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1000 \times 1000\,\hbox {km}$$\end{document}1000×1000km. It is found that Swarm data contains sufficient information to inform the leading three global mass modes found in GRACE at the least. By comparing monthly reconstructed maps to GRACE data from December 2013 to June 2017, we suggest the uncertainty of these maps to be \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2{-}3\,\text {cm}$$\end{document}2-3cm of equivalent water height.

The Gravity Recovery and Climate Experiment (GRACE) mission has enabled mass changes and transports in the hydrosphere, cryosphere and oceans to be quantified with unprecedented resolution. However, while this legacy is currently being continued with the GRACE Follow-On (GRACE-FO) mission there is a gap of 11 months between the end of GRACE and the start of GRACE-FO which must be addressed. Here we bridge the gap by combining time-variable, low-resolution gravity models derived from European Space Agency's Swarm satellites with the dominating spatial modes of mass variability obtained from GRACE. We show that the noise inherent in unconstrained Swarm gravity solutions is greatly reduced, that basin averages can have root mean square errors reduced to the order of cm of equivalent water height, and that useful information can be retrieved for basins as small as 1000 × 1000 km . It is found that Swarm data contains sufficient information to inform the leading three global mass modes found in GRACE at the least. By comparing monthly reconstructed maps to GRACE data from December 2013 to June 2017, we suggest the uncertainty of these maps to be

2−3 cm of equivalent water height.
For nearly 15 years the Gravity Recovery and Climate Experiment (GRACE) mission allowed us to construct monthly maps of changes in the Earth's gravitational field (and thus mass distribution) with unprecedented accuracy. This data has been used to study glacier and ice sheet mass imbalance 1,2 , hydrological change 3,4 (including floods and droughts 5-7 ) ocean mass change 8,9 , the solid Earth's response to post-glacial unloading [10][11][12] , and the mechanisms of large earthquakes 13,14 . The GRACE mission surpassed its planned duration three times over, with its mass change times series being brought to a close in June 2017, and the satellites being decommissioned later that same year. The GRACE successor mission, GRACE Follow-On (GRACE-FO), was then successfully launched in May 2018, with gravity field models being produced from June 2018 onwards. As a result, there is a gap of 11 months between these missions.
For science applications that require a continuous time series, at least four methods exist that can be used to bridge this gap: (1) GRACE data are simply extrapolated, e.g., by estimating a model consisting of seasonal, trend and acceleration terms based on monthly GRACE level-2 data. This may be combined with a monthly climatology (i.e. averaging all instances of a specific month within the GRACE record). However, this approach is basically equivalent to predicting unobserved gravity models using only knowledge of the past, thus should be used with caution. (2) GRACE data are reconstructed based on data assimilation 15,16 or other elaborate statistical 17 or machine learning 18 methods that explore the correlation of total water storage to observable fields such as precipitation and land or sea surface temperature. Some studies (e.g. Ref. 19 ) have developed pre-GRACE era reconstructions in this way, but so far this method has been limited to specific compartments (e.g. only hydrological storage variations) and often to basin averages or continental areas. (3) GRACE-type surface mass fields are reconstructed from the time-variable deformation of the planet as measured by Global Navigation Satellite System (GNSS) networks, and then inverted through a loading theory (see, e.g., Refs. [20][21][22] ). However, the required networks are sparse in many regions of the world, and GNSS time series for individual stations include many non-loading signals and technique-related errors which are difficult to separate [23][24][25] . (4) Data from other sources such as satellite laser ranging (SLR) or from the European Space Agency (ESA) Swarm 3-satellite formation are used to reconstruct low-degree models of the gravity field [26][27][28] .
The last method would provide an independent and direct approach to gravity field and mass change estimation, and SLR-, Swarm-and GRACE-results can be compared during the overlap period of December 2013 to

Time-variable gravity from Swarm
Monthly Swarm reconstructions. We have reconstructed monthly Swarm geopotential solutions and surface mass change maps (measured in e.w.h., equivalent water height) from December 2013 to December 2018 using principal component analysis (PCA 36 ). PCA decomposes the time series of GRACE e.w.h. maps into orthogonal spatial patterns, each scaled by uncorrelated time series. We utilize the GRACE-derived patterns in order to represent two variants of the monthly Swarm solutions, referred to as (1) "Swarm reconstruction" and (2) " Swarm reconstruction residual " [see explanation in Methods; if not mentioned explicitly, the results are shown for the Swarm reconstruction (1)]. As an example, Fig. 1 shows monthly surface mass change maps for March 2016 from GRACE (top), Swarm-only (center), and Swarm projected to the leading three GRACEmodes (bottom), each for d/o 12 (left column) and for d/o 40 (right column). The low-degree solution shows that Swarm-only gravity recovery (Fig. 1c) is able to capture some major patterns of the more precise GRACE solution (Fig. 1a), but at monthly time-scale noise is ubiquitous. The Swarm reconstruction (Fig. 1e) constrained by the GRACE spatial patterns is closer to the GRACE monthly solution, with a global root mean square error (RMSE) of 0.022 m compared to the Swarm-only solution (RMSE 0.092 m ). The improvement through the reconstruction approach is even more striking when we compare d/o 40 solutions (Fig. 1b,d,f); the Swarm-only solution contains mostly noise (RMSE 0.39 m ), while the reconstructed field is very similar to the GRACE solution (RMSE 0.035 m ). For the selected month, the Swarm reconstruction residual variant shows a slightly larger RMSE of 0.035 m for d/o 12 and a slightly lower RMSE of 0.030 m for d/o 40, as compared to the Swarm reconstruction, based on the full signal. Figure 2 provides an assessment of the errors of the method proposed here compared to GRACE. The maps in Fig. 2a,b visualize the spatial error of the Swarm reconstruction, while Fig. 2c,d depict the error of the reconstructed residual solution. Errors are below 10 cm for most regions. Largest RMS errors are found in the Amazon basin, where the large hydrological signal can neither be fully captured by Swarm nor fully represented in three modes. Moreover, climate phenomena like El Niño and La Niña lead to propagating mass change that also cannot be explained with only three modes (e.g., drought conditions in Northern South America towards the end of 2015). The degree and order 12 and 40 error maps appear broadly similar, with larger errors for higher degrees. Furthermore, the Swarm reconstruction residual variant is comparable to the Swarm reconstruction solution, with, in general, slightly smaller RMSE values over land and slightly larger RMSE values over the ocean and Antarctica. When we assess the global mean RMSE as a function of time (Fig. 2e,f) 37 , an effect which was mitigated through receiver updates in the early mission phase 38 . The reconstruction is much less affected by ionospheric disturbances as compared to the monthly Swarm-only solutions.
Basin averages. In order to assess the impact of the new approach for typical regional applications, we here compare area-mean mass changes (in terms of e.w.h.) for basins of various size. Figure 3 shows basin averages (for the locations, see Fig. 4) for the Antarctic ice sheet, the Amazon basin, the Mississippi basin, the Greenland ice sheet and the Ganges basin derived from GRACE, Swarm-only and the Swarm reconstruction for d/o 12 and 40. Tables 1 and 2 give an overview of trend and RMSE estimates and also include a six-parameter GRACE model (constant, trend, and annual and semiannual terms). The first observation from Tables 1 and 2 is that, during the Swarm time frame considered here, the monthly GRACE-derived mass anomalies reveal a quite regular behaviour for all regions considered, without larger (e.g. ENSO-related) temporal anomalies and without trend changes or similar obvious interannual changes. The GRACE six-parameter signal model follows the monthly GRACE solution within RMSE 2-8 cm, which is quite moderate when compared to ENSO years. It is thus not surprising that this simple GRACE-based prediction model already provides a good fit, however this is obviously due to the low variability in the considered time frame and therefore cannot invalidate methods that rely on other data such as Swarm.
Trends derived from a 4-year period cannot provide significance for assessing geophysical processes, but they can be compared to each other. We notice, e.g., that for the Amazon basin the GRACE trend within the Swarm period does not fit well to the overall GRACE trend (which one would have to use when relying on GRACE www.nature.com/scientificreports/ interpolation) while for other regions the overall GRACE trend represents a good predictor for the Swarm period. Again, this is due to only very moderate interannual variability in this timeframe. We notice that trends from a Swarm-only monthly solution are generally larger as compared to the reconstruction approach, which is in line with the methodology. We find that Swarm-only trends appear unrealistically large for the Antarctica and Amazon regions, in particular for d/o 40, while all other approaches including the reconstructions fit to GRACE trends. Results are diverse for the smaller regions.
As for the RMSE with respect to the monthly GRACE solution, the Swarm-monthly solutions generally shows the largest misfit, followed by the Swarm reconstruction and then the Swarm reconstruction residual with removing-restoring GRACE trends. This is particularly striking for the d/o 40 comparisons where the latter approach indeed outperforms the GRACE 6-parameter model in almost all regions and sub-intervals. This is encouraging, but it may be expected due to the mentioned regular behaviour of mass change during the time frame. It is also worth noting that the Swarm reconstruction without making use of GRACE-trends is much closer to GRACE as compared to the Swarm-monthly result.
For the entire Antarctic ice sheet, GRACE shows a mass loss until mid-2016. Afterwards, the time series shows a slight mass gain until November 2016, followed by a drop of approximately 20 cm . This drop is likely an artifact due to the poorer quality of GRACE data caused by the lack of accelerometer data from GRACE B for this period 39  . These rates are predominately driven by melting glaciers in West Antarctica and the Antarctic peninsula, while the East Antarctic ice sheet appears more stable. As expected, the monthly Swarm-only solutions suffer from considerable noise, in particular in the beginning. However, for d/o 12, the RMSE between GRACE and Swarm is reduced          In general, the signal-to-noise ratio in Greenland is quite low, as can be seen in Fig. 3. The Ganges basin is the smallest study region considered here (approx. 1100 km × 900 km ), even though our results suggest that future work could concentrate on smaller basins. Apart from the seasonal signal in the GRACE data, one can see a slight decline of total water storage in the GRACE-FO era. For example, Ref. 42 and 43 describe interannual water storage variability to human water usage, extreme precipitation (floods and droughts) and large-scale ocean-atmospheric interactions such as El Niño and Indian Ocean Dipole, yet these are all dwarfed by the huge seasonal signal. We find that for d All trend and RMSE values are summarized in Tables 1 and 2. A common approach to bridge the GRACE gaps is to interpolate or extrapolate previous and subsequent GRACE solutions. However, it can be argued that the skill of such simple extrapolation approaches inevitably depends on how "regular" the true surface mass field evolves in time, and that scientifically meaningful results are often related to anomalous or episodic events. On the basis of comparisons to GRACE-derived basin averages, our findings are that monthly Swarm reconstructions should generally be preferred to a simple six-parameter GRACE model (constant, trend, annual and semiannual terms), although we admit that the simple model, in certain situations, is sufficient. A detailed analysis can be found in Section S4 of the Supplement.

Validation with GPS.
In order to validate the new approach with independent data, we analyze time series from twenty globally distributed GPS (Global Positioning System) sites. Vertical GPS displacements were pre-processed and corrected for non-tidal atmospheric, non-tidal oceanic and post-glacial rebound effects, as described in the Data section of the Supplement. Mass redistribution estimates derived from GRACE, Swarmonly, Swarm-reconstructed and Swarm-reconstructed residual fields of maximum d/o 12 were converted to vertical deformation using elastic loading theory (Fig. 4). Subsequently, we derived linear trends and annual amplitudes from daily GPS displacements, GPS displacements averaged to monthly time scale, and displacements estimated for GRACE, Swarm-only, Swarm-reconstructed and Swarm-reconstructed residual data.
We find that both Swarm-reconstructed and GRACE-predicted displacements reproduce well inter-annual signals observed by GPS, while the fits for trends and at the annual timescale are moderate. Inter-annual variations are large in particular for the Amazon, as noticed by GPS, and they are captured by GRACE and Swarmreconstructions. Similarly we find a good agreement at interannual timescales for the Mississippi basin where the hydrological signals are also large. GPS uplift or subsidence trends which may be caused, next to mass loading, by a plethora of other geophysical or anthropogenic effects are difficult to compare. At the annual timescale no clear picture emerges: gravity solutions from the Swarm reconstruction approach seem to outperform monthly Swarmonly solution for some regions with large signal while for others the Swarm-only monthly solutions appear closer to GPS. The Swarm-reconstructed residual variant, which is applied in remove-restore mode, will provide fits very close to the GRACE-predicted loading and thus performs well where we know, from other studies, that GRACE fits well to GPS; yet neither Swarm-only nor Swarm reconstructed solutions appear to fit GPS worse.
Validation with satellite laser ranging (SLR). Another way of validating the global gravity solutions is by assessing how well they would allow the prediction of the orbits of other satellites. Here, we use the Swarm-derived gravity fields for computing post-fit satellite laser ranging (SLR) range residuals to five SLR satellites; Lageos 1 (altitude 5860 km ), Lageos 2 (altitude 5620 km ), Ajisai (altitude 1490 km ), Starlette (altitude 800 km−1100 km ) and Stella (altitude 810 km ). Lageos data is processed in orbit arcs of 10 days, while for Ajisai, Starlette and Stella 3-day arcs are chosen due to limitations in modeling the atmospheric drag 44 . Figure 5 shows the residuals computed with (a) monthly Swarm-only gravity fields and (b) the reconstruction approach. In both cases, the time-variable gravity fields are truncated at d/o 12 and the same static background model 45 has been applied for d/o 13-120. As already mentioned, the quality of the monthly Swarm-only solutions is affected by ionospheric disturbances in 2014 and 2015 37 , which can clearly be seen in Fig. 5a. SLR observations form Ajisai, Stella and Starlette do not match the Swarm-only gravity field during the beginning of the Swarm mission, leading to residuals of several decimeters. The SLR residuals reveal (again) that the reconstruction approach does not seem to be affected by the ionospheric disturbances. Starting in 2016, the residuals of both approaches get more similar, but those of the reconstruction are still lower. As Lageos 1/2 fly in a higher orbit than the other three satellites, they are less sensitive to details of the gravity field. Thus, the improvement when using the Swarm reconstruction can mainly been seen when looking at Ajisai, Stella and Starlette. In summary, the analysis of SLR range residuals confirms the capability of the reconstruction approach to improve timevariable gravity fields from Swarm.

Conclusions
We find that the Swarm time series have the potential to fill the gap between GRACE and GRACE-FO. They follow the GRACE solutions well, but show an elevated level of noise as compared to GRACE, as expected. The two variants of the new reconstruction approach reduce the variance further and improve Swarm results, as we show during the overlap period with GRACE. Our main result is that for the recent period (mid-2017 til mid-2018) which is not covered by GRACE, mass change in all major basins occurs quite regularly compared to earlier GRACE results, as could have been predicted from a climatology. For the Mississippi basin, we find a RMSE of 0.09 m for the monthly Swarm-only solutions until d/o 12 and 0.04 m for the reconstruction approach.
The improvement becomes even more evident when we consider higher degree solutions: while we suggest that the monthly d/o 40 Swarm-only solutions should rather not be utilized to full resolution, the reconstruction provides reasonable results (RMSE Mississippi: 0.11 and 0.04 m ). In general, the RMSE with respect to GRACE appears higher in the early Swarm years because of the strong ionospheric activity that affected Swarm precise orbit determination. Global reconstructed maps of equivalent water height are much closer to GRACE as compared to monthly Swarm maps. An independent validation with five SLR satellites confirms that the reconstruction approach can improve time-variable gravity fields from Swarm. Furthermore, comparison to GPS vertical displacements suggests that Swarm-reconstructed deformation fields can cover most of the GPS-observed displacements; relevant for regions where hydrology-induced deformation dominates other effects that GPS is sensitive to.
In this work we have demonstrated a new alternative to simply extrapolating GRACE results in order to fill the GRACE-GRACE-FO mission gap. While this was already possible up to d/o 12 with Swarm gravity data, here we prove that one can achieve higher resolution with a reconstruction approach. Of course this comes at the expense that we have to prescribe GRACE-derived spatial patterns. However, comparing approaches in the overlap period suggests that the new approach clearly outperforms Swarm-only monthly solutions at least for budget studies. We present two variants of the approach: in the first one the entire signal is used in the Principal Component Analysis, leading to a larger flexibility. In the second variant, the PCA is based on GRACE and Swarm residuals, resulting in solutions closer to a 6-parameter GRACE signal model. In other words, this can be viewed as combining GRACE inter-/extrapolation with Swarm analysis of the residual mass fields.
Errors of the reconstruction approach are significantly larger than with GRACE, which was to be expected, but they are considerably smaller than those from Swarm-only data. Our results show that at least for some regions the observed signals are significantly larger than the noise, and scientific interpretation of these signals will provide new insight, in the absence of other methodes. This work will be valuable for those researchers who have used GRACE results at large-scale or basin-averaged level in the past, for example in order to close the water balance and resolve for evapotranspiration or evaporation minus precipitation [46][47][48][49] . Another large field of application is the study of the global sea level budget, where it is of utmost importance to partition the altimetric sea level change ( ≈ 3 mmyr −1 ) into steric and mass-driven contributions 50 . It would be important here to establish consistency across the GRACE / GRACE-FO era by providing a Swarm estimate. Finally, we believe the newly developed Swarm solutions can provide a blueprint for potential future gaps in the GRACE-FO mission, or between GRACE-FO and a follow-on mission, if required. They could also be applied with other LEOs in order to reconstruct temporal gravity field variability prior to the GRACE mission.

Methods
Principal component analysis (PCA). We pursue two variants using a principal component analysis (PCA 36 ) to reconstruct monthly Swarm geopotential solutions: (1) "Swarm reconstruction" and (2) " Swarm reconstruction residual ". For the Swarm reconstruction (1), we represent the monthly GRACE-derived surface mass changes through a series of m mode time series ( a i (t) ; equals the ith column of A ) which each scale a corresponding spatial pattern, c.f. Eq. (1). We apply PCA to the monthly e.w.h. grids, following Ref. 29 . PCA decomposes time series of e.w.h. maps, here collected in the n × m matrix X , with n epochs and the m grid points, into uncorrelated temporal modes (the principal components, contained in A = UD , with U being the n × m matrix of eigenvectors of X T X , and the squared singular values on the diagonal of m × m matrix D ) and orthogonal spatial patterns or Empirical Orthogonal Functions, EOFs (eigenvectors v i of XX T ) contained in the m × m matrix V . The patterns are typically ordered according to how much variability in the original data they explain, and often few are sufficient to retain the underlying information 26 . Equation (1) can be recast as While it is clear that EOF modes and patterns do not necessarily isolate independent physical processes (see discussion in Ref. 32 ), the method has been proven capable of effectively rejecting GRACE noise and unphysical stripes 29 and identifying correlations of total water storage variability with climate modes 34,51 , whose indicators are often defined through PCA of meteorological fields.
Here we suggest to utilize a finite combination of GRACE-derived EOF patterns of surface mass variability in order to represent the monthly Swarm solutions, where l(t) contains all gridded e.w.h. values derived from a given Swarm solution for each monthly epoch t. The terms b i (t) are the Swarm time series that will be multiplied with GRACE patterns and need to be solved for in a least-squares adjustment. It makes sense to resolve only a reduced number m , since Swarm models have less spatial details. Here, we chose m = 3 , which explains ∼ 90% of the signal. The choice of three EOFs is a result of a global analysis of the RMS of the Swarm reconstruction w.r.t GRACE (as can be seen in Fig. 2 for three EOFs) and a regional analysis of basin averages of our study regions and additional further regions (see Section S5 of the Supplement).
The Swarm reconstruction residual variant (2) works analogously, but is is based on GRACE and Swarm residuals. The first step in this approach is to compute a 6-parameter model (constant, trend, annual and semiannual terms) from the whole GRACE(-FO) and Swarm period, respectively and subtract it from the original data. The reconstruction, as explained above, is then computed with the residual data. Finally, the GRACE 6-parameter model is added back to the solution, which consequently consists of the main GRACE signal with added x(t) = v 1 a 1 (t) + v 2 a 2 (t) + · · · + v m a m (t).
(3)  Figure 6 shows the first three dominating spatial patterns and corresponding modes from GRACE until d/o 12. The first mode explains 73.1% of the surface mass variability and represents known long-term trends, such as ice-mass loss over Greenland, Antarctica, and Alaska glaciers due to warming oceans (as already shown by e.g. Refs. 32 or 26 ). The corresponding PC shows an acceleration until 2015, followed by a deceleration towards the end of GRACE, which continues for GRACE-FO. It should be mentioned that the months from November 2016 to June 2017 are of minor quality, due to missing accelerometer data as can be seen in Fig. S2.1 and S2.2 of the Supplement 39 . The second mode ( 14.4% ) captures the large seasonal mass changes in the terrestrial hydrological cycle, while the third mode, also seasonal, contains only 2.8% of the variability. Figure 6 also shows the corresponding temporal modes derived from Swarm (in red), which capture the main signals quite well. An acceleration signal is visible in the first mode which results from the combined acceleration of mass loss over the large ice shields 52 ; the Swarm mode is noisier but captures the trend well. It furthermore confirms the deceleration signal after 2015. The second and the third mode in GRACE describe the annual mass redistribution amplitude and its phase, and they represent inter-annual variability. Apart from 2014, Swarm reproduces those modes well.
Error budget. In this section, we assess the uncertainty of the Swarm reconstruction. The commission error considers the error between the Swarm reconstruction and the GRACE solution (considered as the truth in the overlap period), if we use the same number of EOFs for both: The omission error takes into account that we are not able to reconstruct the full number of EOFs for the reconstruction. We can assess this error by assembling the GRACE signal starting with EOF m + 1 until EOF m: We describe the total error e(t) as (4) e c (t) = [v 1 a 1 (t) + v 2 a 2 (t) + · · · + vmam(t)] − [v 1 b 1 (t) + v 2 b 2 (t) + · · · + vmbm(t)]. (5) e o (t) = vm +1 am +1 (t) + vm +2 am +2 (t) + · · · + v m a m (t).  License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.