# Greenland Ice Sheet Elevation Change From CryoSat-2 and ICESat-2

Nitin Ravinder, Andrew Shepherd, Inès Otosaka, Thomas Slater, Alan Muir, Lin Gilbert

► To cite this version:

Nitin Ravinder, Andrew Shepherd, Inès Otosaka, Thomas Slater, Alan Muir, et al.. Greenland Ice Sheet Elevation Change From CryoSat-2 and ICESat-2. Geophysical Research Letters, 2024, 51 (24), 10.1029/2024GL110822. [https://doi.org/10.1029/2024GL110822](https://doi.org/10.1029/2024GL110822)

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Distributed under a Creative Commons Attribution 4.0 International License
# Geophysical Research Letters®

## RESEARCH LETTER

10.1029/2024GL110822

Thomas Slater was formerly at University of Leeds.

**Key Points:**

* Greenland Ice Sheet elevation change between 2018 and 2022 from CryoSat-2 and ICESat-2 was -11.4 ± 0.8 and -11.7 ± 1.3 cm/yr, respectively

* Ablation zone seasonal cycle amplitude between 2018 and 2022 from CryoSat-2 and ICESat-2 was 62.9 ± 26.5 and 59.4 ± 24.4 cm, respectively

* Volume change between 2010 and 2022 was -196 ± 37 km³/yr with an interannual variability of 129 km³/yr

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

N. Ravinder,
eenr@leeds.ac.uk

**Citation:**

Ravinder, N., Shepherd, A., Otosaka, I., Slater, T., Muir, A., & Gilbert, L. (2024). Greenland Ice Sheet elevation change from CryoSat-2 and ICESat-2. *Geophysical Research Letters*, *51*, e2024GL110822.
https://doi.org/10.1029/2024GL110822

Received 14 JUN 2024

Accepted 28 OCT 2024

© 2024. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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## Greenland Ice Sheet Elevation Change From CryoSat-2 and ICESat-2

Nitin Ravinder¹ ID, Andrew Shepherd² ID, Inès Otosaka² ID, Thomas Slater² ID, Alan Muir³,⁴ ID, and Lin Gilbert⁴ ID

¹School of Earth and Environment, University of Leeds, Leeds, UK, ²Centre for Polar Observation and Modelling, Department of Geography and Environmental Science, Northumbria University, Newcastle Upon Tyne, UK, ³Centre for Polar Observation and Modelling, Department of Earth Sciences, University College London, London, UK, ⁴Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London, London, UK

**Abstract** Although fluctuations in ice sheet surface mass balance lead to seasonal and interannual elevation changes, it is unclear if they are resolved differently by radar and laser satellite altimeters. We compare methods of computing elevation change from CryoSat-2 and ICESat-2 over the Greenland Ice Sheet to assess their consistency and to quantify recent change. Solutions exist such that interannual trends in the interior and the ablation zone agree to within -0.2 ± 1.5 and 3.3 ± 6.0 cm/yr, respectively, and that seasonal cycle amplitudes within the ablation zone agree to within 3.5 ± 38.0 cm. The agreement is best in the north where the measurements are relatively dense and worst in the southeast where the terrain is rugged. Using both missions, we estimate Greenland lost 196 ± 37 km³/yr of volume between 2010 and 2022 with an interannual variability of 129 km³/yr.

**Plain Language Summary** The polar ice sheets are reacting to climate warming. Changes in their height can be used to study changes in their snowfall, surface melting, glacier flow, and sea level contribution. Although satellite altimeters are able to detect changes in ice sheet height, it is not clear whether these changes are sensed differently by laser and radar systems. Using four years of coincident measurements recorded by ESA's CryoSat-2 and NASA's ICESat-2, we show that radar-laser differences at the ice sheet scale are, in fact, a small proportion (<10%) of the changes in height that are taking place. This means that either system can be used with confidence to study the effects of climate change on the polar ice sheets. At smaller spatial scales, the remaining differences are still important and should be investigated further so that we can understand their causes.

## 1. Introduction

Arctic air temperatures have warmed at nearly four times the global average rate in the past few decades (Rantanen et al., 2022), leading to dramatic changes in the regional ice cover (Intergovernmental Panel on Climate Change, 2023). The Greenland Ice Sheet (GrIS) has lost mass progressively during the satellite era, raising the global sea level by 13.6 ± 1.3 mm since 1992 (Otosaka et al., 2023). Although satellite altimetry is a powerful tool for documenting the effects of changing ice sheet flow (Flament & Rémy, 2012; Khan, Choi, et al., 2022; Pritchard et al., 2009) and surface mass balance (SMB) (Davis et al., 2005; Gray, 2021; Slater et al., 2021; Smith et al., 2022), it is unclear whether there are differences between the capabilities of radar and laser systems (Lai & Wang, 2022; Yang et al., 2022). Nevertheless, both techniques have been widely employed to measure fluctuations in ice sheet surface elevation since the 1990s (Davis & Ferguson, 2004; Sandberg Sørensen et al., 2018; Shepherd et al., 2019; Sørensen et al., 2011; Wingham et al., 1998), and so a detailed comparison of their capabilities is timely.

Satellite altimeters operate by transmitting electromagnetic radiation toward the Earth's surface and recording the travel time and distribution of the backscattered signal, the latter of which is related to the terrain and scattering properties of the area illuminated within the altimeter footprint (Rémy et al., 2015). Measurements have been acquired over the GrIS using a series of satellites since the 1980s, including missions with Ku-Band (SeaSat, GEOSat, ERS, ENVISAT, CryoSat-2, Sentinel-3) and Ka-band radar (SARAL) and laser sensors (ICESat and ICESat-2). Some of the energy transmitted by Ku-band radar has been shown to penetrate to depths of up to 10 m in dry snow (Rémy et al., 2015), which means the technique is relatively insensitive to short-term height fluctuations. This interaction depends upon the physical properties of the snowpack, such as grain size, density, and
liquid water content, which vary in space and time (Matzler, 1996). Although penetration leads to a more stable scattering horizon, it tends to be lower than the ice sheet surface, and so retracking algorithms and corrections have been developed to compensate (Davis, 1997; Helm et al., 2014; Slater et al., 2019). In contrast, Ka-band radar and laser altimeters track closer to the ice sheet surface because they operate at shorter wavelengths (Markus et al., 2017; Verron et al., 2015) that are observed to penetrate only tens of centimeters (Otosaka et al., 2020; Rémy et al., 2015; Studinger et al., 2023). While they offer a more direct measurement of the ice sheet surface, they are more sensitive to short-term changes in height and laser altimeters are also impacted by cloud cover and drifting snow (Markus et al., 2017).

CryoSat-2 and ICESat-2 were launched in 2010 and 2018, respectively, and there are now more than four years of overlap between their measurement records. The capability of these missions to study challenging terrain provides a unique opportunity to compare the performance of radar and laser altimeters over the GrIS. Previous studies have made use of these missions to measure GrIS elevation change (Lai & Wang, 2022; Winstrup et al., 2024; Yang et al., 2022). However, detailed assessments of radar and laser altimeter differences have been limited to ENVISAT and ICESat; these missions had different ground footprints and orbital sampling with ICESat operating only episodically between 2003 and 2009, and the comparison was restricted to measurements acquired above Greenland's equilibrium line altitude (Sørensen et al., 2015). In this study, we compute rates of elevation changes and seasonal fluctuations across the GrIS from CryoSat-2 radar altimetry and ICESat-2 laser altimetry and compare these signals during their overlapping period to assess their similarities and differences.

## 2. Data and Methods

We use the ATL06 Version 5 product for ICESat-2 (Smith et al., 2021) and the Baseline-B land ice product from the CryoSat ThEMatic PrOducts (CryoTEMPO) project (Andersen, 2022) for CryoSat-2 (Text S1 in Supporting Information S1). CryoSat-2 has two modes of operation over the GrIS: Low Resolution Mode in the interior areas, where it operates as a conventional pulse limited altimeter, and Synthetic Aperture Radar Interferometric mode over the margins (Figure S1 in Supporting Information S1), where the interferometric capabilities are better suited to map the complex terrain (Wingham et al., 2006). While CryoSat-2 and ICESat-2 have a drifting and a repeating orbit, respectively, both sample the ice sheet surface more thoroughly with between 6 and 50 times finer ground footprints than past altimeters (Levinsen et al., 2016; Smith et al., 2019; Wingham et al., 2006).

We estimate Greenland surface height change from both missions by locating elevation measurements within regular 5 km grid cells and using an iterative least squares model fit (Slater et al., 2021) to separate elevation fluctuations due to topography, satellite heading, and time (Text S2, Figure S2 in Supporting Information S1). At each iteration of the model fit, we exclude outliers greater than a multiple (N) of the standard deviation (SD) of the remaining measurements. We apply this approach to data acquired by both the ICESat-2 and CryoSat-2 missions independently. We then aggregate changes in elevation over fixed time intervals to generate a time series of elevation changes within each grid cell. At each epoch, we interpolate the observed elevation changes to unobserved areas (Shepherd et al., 2019), and compute averaged time series within the following regions: (a) the entire GrIS, (b) the ice sheet ablation zone (a 196,700 km² area below the equilibrium line, where SMB is negative) (Slater et al., 2021), (c) the interior (a 1,523,175 km² area above the equilibrium line), and also (d) the seven principal drainage sectors of the ice sheet (Mouginot & Rignot, 2019). Within the regions separated by CryoSat-2's modes of operation (Figure S1 in Supporting Information S1), we do not find the comparison between both missions to be significantly different from the interior and the ablation zone. We then derive rates of elevation change by applying a linear regression to time series within individual grid cells and averaged within each region. In addition, we calculate annual seasonal amplitudes by averaging the summer and winter height change determined as the peak to trough difference within summer (1st April to 30th September) and winter periods (1st October to 31st March), respectively (Slater et al., 2021). We focus our comparison of seasonal amplitudes on the ablation zone because seasonal changes in the interior are small (12.1 ± 10.3 cm) in comparison (61.2 ± 26.9 cm).

## 3. Sensitivity Analysis

We investigate the influence of three main model fit parameters - the degree of outlier exclusion, the epoch length, and the interpolation distance - on interannual and seasonal changes in elevation by performing a sensitivity analysis. The impacts of parameter choices are explored in the ice sheet interior and in the ablation zone during the
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Sensitivity of Greenland Ice Sheet interannual (b), (d) and seasonal (f) elevation changes determined from CryoSat-2 (blue) and ICESat-2 (green) to a range of processing parameters, and the percentage area sampled by varying the parameter on x-axis alone ((a), (c) we use a 30-day epoch length and (e) we apply 0 km interpolation); stars indicate the optimal scenarios. The gray-levels on b,d and f illustrate the degree of outlier exclusion (light to dark from 2 to 4 times SD). For interannual changes (b), (d), line types indicate the epoch length (solid = 30-day, dotted = 60-day and dashed = 91.25-day). For seasonal changes (f), line types indicate the interpolation distance (solid = 0 km, dotted = 25 km and dashed = 50 km). Refer to Figure S5 in Supporting Information S1 for other sensitivity plots.</figcaption></figure>

4-year overlap period between CryoSat-2 and ICESat-2 (Figure 1, Text S3 and Figure S5 in Supporting Information S1). We vary the outlier exclusion limit from 2 to 4 times the SD, the epoch window from 30-day to 91.25-day periods, and the interpolation distance from 0 to 50 km, generating an ensemble of 27 elevation change scenarios for each mission; the total ice sheet area sampled for each scenario is determined by the combination of epoch window and interpolation distance.

The interpolation distance has the largest influence on elevation trends in the interior and in the ablation zone (Figure 1). This effect is more pronounced for ICESat-2 owing to the larger unobserved areas left by its repeating orbit in comparison to the drifting orbit of CryoSat-2. At an interpolation distance of 25 km, we find the maximum overlap between CryoSat-2 and ICESat-2 in the interior (Figure 1). This leads to small differences of -0.2 ± 1.5 and 3.3 ± 6.0 cm/yr in the interior and ablation zone, respectively, both of which are a small fraction (6%) of the observed trend. Thus, we apply an interpolation distance of 25 km everywhere for consistency. The seasonality of elevation changes in the ablation zone is primarily influenced by epoch window size (Figure 1) and to a lesser extent by the outlier exclusion limit (Figure S5 in Supporting Information S1). Seasonal amplitudes increase with decreasing epoch lengths and increasing outlier exclusion limits. At the shortest epoch window of 30 days, we find the smallest differences (3.5 ± 38.0 cm or 5.7% of the observed signal) in seasonal amplitudes determined by CryoSat-2 and ICESat-2 using an outlier exclusion limit of 3 times the SD for both. Therefore, we apply a 30-day epoch window with a large outlier exclusion limit to retain much of the seasonality.

Interannual and seasonal changes occur over long and short time scales and are more sensitive to spatial (interpolation distance) and temporal (epoch window size) sampling, respectively. While a larger outlier exclusion limit will retain seasonality, it may destabilize interannual trends by adding noise to the linear regression. Therefore, we choose different scenarios for interannual and seasonal changes that optimize the agreement between CryoSat-2 and ICESat-2 and maximize the spatiotemporal sampling (Table S1 in Supporting Information S1). For interannual trends, we choose scenarios with a 60-day epoch length, 25 km interpolation distance, and outlier exclusion limit of 2 and 3 times the SD for CryoSat-2 and ICESat-2, respectively. This leads to excellent agreement between both missions in the ablation zone (to within 3.3 ± 6.0 cm/yr) and a near-complete coverage of the ice sheet area (more than 90%) due to the spatiotemporal sampling (Figure 2, Table S2 in Supporting Information S1). To measure seasonal amplitudes, we use a different set of parameters: a 30-day epoch window and outlier exclusion limit of 3 times the SD (Slater et al., 2021); we choose a 50 km interpolation distance to compensate for reduced coverage at 30-day epochs.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Rates of elevation change averaged from CryoSat-2 and ICESat-2 between 2018 and 2022 (a). Time series of bi-monthly elevation changes (b) from CryoSat-2 (blue) and ICESat-2 (red); the preferred scenarios are shown in dark lines with the shaded bands showing the respective uncertainties, and the ensemble scenarios are in shaded lines. Amplitudes of seasonal cycle averaged from both missions (c). A 25 × 25 km median filter is applied to both (a) and (c) for visualization. Refer to Figure S10 in Supporting Information S1 for scatter plots.</figcaption></figure>

**4. Interannual and Seasonal Elevation Change**

We compare CryoSat-2 and ICESat-2 bi-monthly time series of elevation changes to assess their ability to measure temporal fluctuations in ice sheet surface elevation (Figure 2b). Across the GrIS as a whole, bi-monthly variations between CryoSat-2 and ICESat-2 are highly correlated ($R^2 = 0.9$) and agree with a root mean square difference (RMSD) of 5.4 cm (we calculate all differences as CryoSat-2 - ICESat-2). In the ablation zone, we find estimates from the two missions are also highly correlated ($R^2 = 0.97$) with an RMSD of 12.6 cm. The strong agreement is in part thanks to the summertime melting that takes place in the ablation zone, which drives large intra-annual fluctuations in elevation and minimizes the effect of radar penetration (Slater et al., 2021). In the interior of the GrIS, estimates agree to within 5.0 cm, but with slightly reduced correlation ($R^2 = 0.75$), likely because of the increased variability in the different scattering horizons recorded by the radar and laser sensors in this region (Otosaka et al., 2020; Slater et al., 2019).

Between 2018 and 2022, interannual trends from the CryoSat-2 and ICESat-2 over GrIS agree within their respective uncertainties (−11.4 ± 0.8 and −11.7 ± 1.3 cm/yr; Tables S3 and S4 in Supporting Information S1). Interannual trends from both missions agree to within 1 cm/yr across 94% of grid cells and the mean difference is −0.3 ± 1.8 cm/yr. The difference is smaller across the ice sheet interior (−0.2 ± 1.5 cm/yr) than in the ablation zone (3.3 ± 6.0 cm/yr) where the elevation changes are much larger; in both regions this difference is a small fraction of the overall signal (3.2% and 6.1%, respectively).

Because we find excellent agreement between interannual trends derived from CryoSat-2 and ICESat-2 during their overlap period, we produce an estimate of the elevation trend derived from the average of both missions (Figure 2a, Table S5 in Supporting Information S1). This shows that thinning is prominent along the western and southern margins of Greenland, in agreement with previous studies (Khan, Bamber, et al., 2022; McMillan et al., 2016; Sandberg Sørensen et al., 2018). Thinning during the period 2018–2022 is dominated by the significant 2019 summer melting event (Slater et al., 2021; Tedesco & Fettweis, 2020), with ice thinning extending further inland compared to the 12-year thinning rates (Figures S6–S9 in Supporting Information S1), especially in northern Greenland where the ablation zone has expanded (Noël et al., 2019). Between 2018 and 2022, we estimate an average thinning rate of 11.6 ± 1.6 cm/yr across the GrIS. While the interior of the GrIS thinned at an average rate of 6.3 ± 1.2 cm/yr, the ablation zone thinned 9 times faster at an average rate of 54.3 ± 5.7 cm/yr.
<caption>**Table 1**</caption>

Annual Volume Changes (km³) Observed Over Greenland Between September 2010 and September 2018 From CryoSat-2 (Regular) and September 2018 and September 2022 From a Combination of CryoSat-2 and ICESat-2 (Bold) and Mean and SD for 2010–2022 (Bold)

<table><thead><tr><th>Year</th><th>Ablation</th><th>Interior</th><th>Whole area</th></tr></thead><tbody><tr><td>2010–2011</td><td>-124 ± 8</td><td>-154 ± 7</td><td>-277 ± 11</td></tr><tr><td>2011–2012</td><td>-159 ± 6</td><td>-250 ± 7</td><td>-409 ± 9</td></tr><tr><td>2012–2013</td><td>-88 ± 6</td><td>18 ± 7</td><td>-70 ± 9</td></tr><tr><td>2013–2014</td><td>-125 ± 6</td><td>-20 ± 7</td><td>-145 ± 9</td></tr><tr><td>2014–2015</td><td>-99 ± 6</td><td>-72 ± 6</td><td>-170 ± 9</td></tr><tr><td>2015–2016</td><td>-153 ± 6</td><td>-72 ± 6</td><td>-225 ± 9</td></tr><tr><td>2016–2017</td><td>-64 ± 6</td><td>60 ± 6</td><td>-4 ± 6</td></tr><tr><td>2017–2018</td><td>-105 ± 6</td><td>34 ± 6</td><td>-71 ± 9</td></tr><tr><td><strong>2018–2019</strong></td><td>-209 ± 11</td><td>-255 ± 11</td><td>-464 ± 16</td></tr><tr><td><strong>2019–2020</strong></td><td>-112 ± 9</td><td>-69 ± 9</td><td>-181 ± 13</td></tr><tr><td><strong>2020–2021</strong></td><td>-96 ± 10</td><td>-91 ± 10</td><td>-187 ± 14</td></tr><tr><td><strong>2021–2022</strong></td><td>-65 ± 10</td><td>-79 ± 10</td><td>-144 ± 14</td></tr><tr><td>Mean for 2010-2022</td><td>-117 ± 11</td><td>-79 ± 28</td><td>-196 ± 37</td></tr><tr><td>SD for 2010-2022</td><td>40</td><td>96</td><td>129</td></tr></tbody></table>

Among the principal drainage basins (Table S5 in Supporting Information S1), we find the NW basin is thinning most (21.6 ± 1.8 cm/yr), on average, while the NE basin remains stable (0.7 ± 1.0 cm/yr).

The prominent seasonal cycle in the ablation zone elevation change is captured well by both missions. The mean amplitude of the seasonal cycle observed by CryoSat-2 and ICESat-2 agree well within their respective uncertainties (62.9 ± 26.5 and 59.4 ± 27.2 cm, respectively). The elevation changes across seasonal periods are highly correlated during both summer (April to September, R² = 0.94) and winter (October to March, R² = 0.91) months. The mean seasonal amplitudes have a difference of 3.5 ± 38.0 cm that varies slightly over summer (3.8 cm) and winter (3.2 cm) periods.

Given the excellent agreement we find between seasonal fluctuations from CryoSat-2 and ICESat-2, we examine the variations in summer and winter fluctuations in the ablation zone by again computing an average from both missions (Table S6 in Supporting Information S1). The combined average seasonal amplitude is 61.1 ± 26.8 cm between 2018 and 2022. The summer of 2019 led to the largest thinning of 105.9 ± 27.2 cm owing to warm, moist air advecting north and reduced cloud cover in the south along the western margins (Tedesco & Fettweis, 2020) which caused strong melting. Excluding the CE and SE basins which have small ablation areas (6.4% and 2.5% of the total ablation zone area, respectively), average seasonal amplitudes are largest in CW (105.7 ± 35.6 cm) and SW (98.5 ± 27.8 cm) and smallest in NO (46.4 ± 23.5 cm) and NE (48.6 ± 20.4 cm) (Table S6 in Supporting Information S1), consistent with previous findings (Slater et al., 2021).

Finally, we estimate Greenland's annual volume changes between 2010 and 2022 across glaciological years starting from 1st September to 31st August (Table 1, Text S5 in Supporting Information S1). From 2018–2019, we use the average from both satellites and for the years preceding, we use CryoSat-2 alone. Between 2010–2011 and 2021–2022, we estimate a volume loss of 196 ± 37 km³/yr across the GrIS. Our estimates agree with past studies (Nilsson et al., 2016; Simonsen & Sørensen, 2017) within uncertainties (Text S5 in Supporting Information S1) and confirms Greenland's sustained contribution to sea level rise that began in the early 2000s (Otosaka et al., 2023). There is significant interannual variability in volume changes in the interior with an SD of 96 km³, likely driven by SMB anomalies. This is particularly from years like 2010–2011, 2011–2012 and 2018–2019, when clear-sky conditions in the summer increased absorption of solar radiation and reduced snowfall, which was accompanied by warm-air advection, causing widespread surface melting (Bevis et al., 2019; Tedesco & Fettweis, 2020). Volume losses are highest in 2011–2012 and 2018–2019, coinciding with the extreme summer melt events (Table 1). By excluding these large melt years, we find the variability in the interior (SD of 53 km³) to be closer to the variability in the ablation zone (SD of 40 km³).

## 5. Discussion

Our sensitivity analysis identifies radar and laser elevation change solutions with far better agreement than have been found in previous comparisons of satellite (Sørensen et al., 2015) or airborne (McMillan et al., 2016; Otosaka et al., 2020; Simonsen & Sørensen, 2017; Slater et al., 2021) measurements. We find agreement between CryoSat-2 and ICESat-2 for our optimal solution (0.3 cm/yr) to be an order of magnitude better across the whole ice sheet than previous comparisons across limited areas (3–18 cm/yr) (McMillan et al., 2016; Simonsen & Sørensen, 2017; Yang et al., 2022). We conclude therefore that it is possible to mitigate the effects of differences in altimeter sensors or temporal disturbances to the surface properties to a large degree.

Residual differences may arise due to uncertainty in the individual data sets, differences in spatiotemporal sampling, or the effects of short-term SMB driven fluctuations in snow and firn which may be sensed differently by radars and lasers. At basin scale we find the agreement between the two missions is best in the NO, NW, and NE basins, which are well sampled by both missions and have relatively simpler terrain compared to the SW and SE basins (Tables S3 and S4 in Supporting Information S1). We find the largest difference (13.2 ± 3.7 cm/yr) in the SE, where the presence of steep slopes further complicates the retrievals of surface elevation from satellite
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Difference between rates of elevation change (a) and amplitude of seasonal cycle (e) from CryoSat-2 and ICESat-2 with their respective histograms (b and f) separated into the principal drainage basins (black outlines (Mouginot & Rignot, 2019)). Differences between elevation change time series from CryoSat-2 and ICESat-2 in the interior (c) and the ablation zone (d); differences between preferred scenarios are indicated by dark and between ensemble scenarios by shaded lines. In (a), the areas in gray outlines highlight differences greater than the mean within its uncertainty. A 25 × 25 km median filter is applied to both (a) and (e) for visualization.</figcaption></figure>

altimetry (Gray et al., 2019). Nevertheless, the agreement is likely to improve as the period of overlap between both missions increases and more measurements become available.

In the ice sheet interior, we find seasonality in the difference between CryoSat-2 and ICESat-2 height changes (Figure 3c). In this region, CryoSat-2 operates as a pulse-limited altimeter and so differences may be in part driven by its coarser spatial sampling as well as the impact of changing snow properties on signal penetration (Amory et al., 2024). The ICESat-2 green laser can potentially penetrate into snow and ice, although the depth remains unclear (Studinger et al., 2023). The CryoSat-2 radar can penetrate a few meters into the firnpack when operating in low resolution mode (Slater et al., 2019). Our delineation of the ice sheet interior also contains the percolation zone, across which the annual melting and refreezing of surface meltwater has been shown to disrupt the radar scattering horizon (Otosaka et al., 2020). While the relatively lower agreement between CryoSat-2 and ICESat-2 seasonal cycles in the interior ($R^2$ = 0.75) suggests that there may be differences due to short-term fluctuations in penetration, the small difference between the interannual trends in this region (–0.2 ± 1.5 cm/yr) indicates that these effects are reduced over longer periods. Improvements in retracking algorithms for radar sensors like CryoSat-2 have been specifically designed to create more stable scattering horizons (Muir, 2022; Nilsson et al., 2016; Slater et al., 2019).

In the ablation zone there is also a seasonality to the remaining difference between CryoSat-2 and ICESat-2 elevation changes, with CryoSat-2 detecting the onset of summer and winter 30 days earlier and later than ICESat-2, respectively (Figure 3d). These solutions may be affected by heavy winter snowfall and the subsequent melting of this layer in the following summer - processes which may be resolved differently by radar and laser altimetry (Amory et al., 2024; Noël et al., 2019). While bi-monthly elevation changes from both missions are highly correlated in the ablation zone ($R^2$ = 0.97) and are in excellent agreement (~6% of interannual and seasonal changes), further investigation is needed to understand the remaining differences.

The ablation zone also contains isolated areas of ice dynamical imbalance that are not linear with time. Such signals may be resolved differently depending on the temporal sampling of the measurements, which is different for CryoSat-2 and ICESat-2. For example, we find differences of up to 15.8 ± 7.7 cm/yr between CryoSat-2 and ICESat-2 in the fast-flowing section of Jakobshavn Isbrae which has undergone a period of rapid thinning and
thickening driven by oceanic warming and cooling in the Disko Bay, respectively (Joughin et al., 2020; Kha-
zendar et al., 2019). Fluctuations in height of this nature are not captured by the model fit we use to determine
elevation trends and so the detected signal becomes dependent on the period over which the fit is applied (Text S4
in Supporting Information S1). This could be addressed by using higher order polynomials or digital elevation
models.

**6. Conclusions**

We show that interannual and seasonal changes in elevation of the GrIS determined from CryoSat-2 and ICESat-2 are in strong agreement. Interannual trends are most sensitive to the spatial sampling of the data, whereas the amplitude of the seasonal cycle is most sensitive to the temporal sampling. Grouping the data on a 5 km spatial grid and within 60-day intervals leads to optimal agreement between interannual trends with a 25 km interpolation distance, whereas grouping the data into 30-day intervals leads to optimal agreement for seasonal cycles. The average rates of elevation change of the GrIS between 2018 and 2022 determined from CryoSat-2 and ICESat-2 are −11.4 ± 0.8 cm/yr and −11.7 ± 1.3 cm/yr, respectively - a difference of 0.3 ± 1.8 cm/yr. In the ablation zone, where nearly all of Greenland's ice losses occur, the average rates of elevation change determined from CryoSat-2 and ICESat-2 are −52.7 ± 3.4 and −56.0 ± 4.5 cm/yr, respectively, and the amplitude of the seasonal cycle is 62.9 ± 20.7 and 59.4 ± 24.4 cm, respectively. Combining data from both missions, we estimate that the GrIS has reduced in volume at an average rate of 196 ± 37 km³/yr between 2010 and 2022. Though small, there are remaining differences between the CryoSat-2 and ICESat-2 estimates of elevation change that vary in space and time, and further work is required to better understand them.

Data Availability Statement

Baseline-B of CryoTempo Land Ice thematic product is available for download from European Space Agency CryoSat-2 Science FTP server at https://science-pds.cryosat.esa.int/. Version 005 of ICESat-2 ATL06 product is available through National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/atl06/versions/5. Version 4.1 of ArcticDEM mosaic data is available via the Polar Geospatial Centre FTP server at https://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v4.1/500m/.

Acknowledgments

This work was jointly supported by the UK
National Environment Research Council
Centre for Polar Observation and
Modelling (Grant No: PRESCIENT NE/
Y006178/1), European Union's Horizon
2020-funded PROTECT (Grant No:
869304) project and European Space
Agency-funded Climate Change Initiative
(CCI+): Antarctica (Grant No:
4000126813/19/I-NB).

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## References From the Supporting Information

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