We develop a 3-D finite-element model to study the viscoelastic response of a compressible Earth to surface loads. The effects of centre of mass motion, polar wander feedback, and self-consistent ...ocean loading are implemented. To assess the model's accuracy, we benchmark the numerical results against a semi-analytic solution for spherically symmetric structure. We force our model with the ICE-5G global ice loading history to study the effects of laterally varying viscosity structure on several glacial isostatic adjustment (GIA) observables, including relative sea-level (RSL) measurements in Canada, and present-day time-variable gravity and uplift rates in Antarctica. Canadian RSL observations have been used to determine the Earth's globally averaged viscosity profile. Antarctic GPS uplift rates have been used to constrain Antarctic GIA models. And GIA time-variable gravity and uplift signals are error sources for GRACE and altimeter estimates of present-day Antarctic ice mass loss, and must be modelled and removed from those estimates. Computing GIA results for a 3-D viscosity profile derived from a realistic seismic tomography model, and comparing with results computed for 1-D averages of that 3-D profile, we conclude that: (1) a GIA viscosity model based on Canadian relative sea-level data is more likely to represent a Canadian average than a true global average; (2) the effects of 3-D viscosity structure on GRACE estimates of present-day Antarctic mass loss are probably smaller than the difference between GIA models based on different Antarctic deglaciation histories and (3) the effects of 3-D viscosity structure on Antarctic GPS observations of present-day uplift rate can be significant, and can complicate efforts to use GPS observations to constrain 1-D GIA models.
Gravity fields produced by the Gravity Recovery and Climate Experiment (GRACE) satellite mission require smoothing to reduce the effects of errors present in short wavelength components. As the ...smoothing radius decreases, these errors manifest themselves in maps of surface mass variability as long, linear features generally oriented north to south (i.e., stripes). The presence of stripes implies correlations in the gravity field coefficients. Here we examine the spectral signature of these correlated errors, and present a method to remove them. Finally, we apply the filter to a model of surface‐mass variability to show that the filter has relatively little degradation of the underlying geophysical signals we seek to recover.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are used to estimate monthly changes in total water storage across the Middle East during February 2003 to December ...2012. The results show a large negative trend in total water storage centered over western Iran and eastern Iraq. Subtracting contributions from the Caspian Sea and two large lakes, Tharthar and Urmiah, and using output from a version of the CLM4.5 land surface model to remove contributions from soil moisture, snow, canopy storage, and river storage, we conclude that most of the long‐term water loss is due to a decline in groundwater storage. By dividing the region into seven mascons outlined along national boundaries and fitting them to the data, we find that the largest groundwater depletion is occurring in Iran, with a mass loss rate of 25 ± 3 Gt/yr during the study period. The conclusion of significant Iranian groundwater loss is further supported by in situ well data from across the country. Anthropogenic contributions to the groundwater loss are estimated by removing the natural variations in groundwater predicted by CLM4.5. These results indicate that over half of the groundwater loss in Iran (14 ± 3 Gt/yr) may be attributed to human withdrawals.
Key Point
Using satellite gravity data for groundwater monitoring across the Middle East
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Using satellite gravimetric and altimetric data, we examine trends in water storage and lake levels of multiple lakes in the Great Rift Valley region of East Africa for the years 2003–2008. GRACE ...total water storage estimates reveal that water storage declined in much of East Africa, by as much as
60
mm
year
, while altimetric data show that lake levels in some large lakes dropped by as much as 1–2
m. The largest declines occurred in Lake Victoria, the Earth’s second largest freshwater body. Because the discharge from the outlet of Lake Victoria is used to generate hydroelectric power, the role of human management in the lake’s decline has been questioned. By comparing catchment water storage trends to lake level trends, we confirm that climatic forcing explains only about 50decline. This analysis provides an independent means of assessing the relative impacts of climate and human management on the water balance of Lake Victoria that does not depend on observations of dam discharge, which may not be publically available. In the second part of the study, the individual components of the lake water balance are estimated. Satellite estimates of changes in lake level, precipitation, and evaporation are used with observed lake discharge to develop a parameterization for estimating subsurface inflows due to changes in groundwater storage estimated from satellite gravimetry. At seasonal timescales, this approach provides closure to Lake Victoria’s water balance to within
17
mm
month
. The third part of this study uses the water balance of a downstream water body, Lake Kyoga, to estimate the outflow from Lake Victoria remotely. Because Lake Kyoga is roughly 20 times smaller in area than Lake Victoria, its water balance is strongly influenced by inflow from Lake Victoria. Lake Kyoga has been shown to act as a linear reservoir, where its outflow is proportional to the height of the lake. This model can be used with satellite altimetric lake levels to estimate a time series of Lake Victoria discharge with an rms error of about
134
m
3
s
.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In this study, we estimate a time series of geocenter anomalies from a combination of data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission and the output from ocean models. ...A matrix equation is derived relating total geocenter variations to the GRACE coefficients of degrees two and higher and to the oceanic component of the degree one coefficients. We estimate the oceanic component from two state‐of‐the‐art ocean models. Results are compared to independent estimates of geocenter derived from other satellite data, such as satellite laser ranging and GPS. Finally, we compute degree one coefficients that are consistent with the processing applied to the GRACE Level‐2 gravity field coefficients. The estimated degree one coefficients can be used to improve estimates of mass variability from GRACE, which alone cannot provide them directly.
In 2001 the Intergovernmental Panel on Climate Change projected the contribution to sea level rise from the Greenland ice sheet to be between -0.02 and +0.09 m from 1990 to 2100 (ref. 1). However, ...recent work has suggested that the ice sheet responds more quickly to climate perturbations than previously thought, particularly near the coast. Here we use a satellite gravity survey by the Gravity Recovery and Climate Experiment (GRACE) conducted from April 2002 to April 2006 to provide an independent estimate of the contribution of Greenland ice mass loss to sea level change. We detect an ice mass loss of 248 ± 36 km3 yr-1, equivalent to a global sea level rise of 0.5 ± 0.1 mm yr-1. The rate of ice loss increased by 250 per cent between the periods April 2002 to April 2004 and May 2004 to April 2006, almost entirely due to accelerated rates of ice loss in southern Greenland; the rate of mass loss in north Greenland was almost constant. Continued monitoring will be needed to identify any future changes in the rate of ice loss in Greenland.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Using measurements of time-variable gravity from the Gravity Recovery and Climate Experiment satellites, we determined mass variations of the Antarctic ice sheet during 2002-2005. We found that the ...mass of the ice sheet decreased significantly, at a rate of 152 ± 80 cubic kilometers of ice per year, which is equivalent to 0.4 ± 0.2 millimeters of global sea-level rise per year. Most of this mass loss came from the West Antarctic Ice Sheet.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Ground‐based measurements of active layer thickness provide useful data for validating/calibrating remote sensing and modeling results. However, these in situ measurements are usually site‐specific ...with limited spatial coverage. Here we apply interferometric synthetic aperture radar (InSAR) to measure surface deformation over permafrost on the North Slope of Alaska during the 1992–2000 thawing seasons. We find significantly systematic differences in surface deformation between floodplain areas and the tundra‐covered areas away from the rivers. Using floodplain areas as the reference for InSAR's relative deformation measurements, we find seasonally varying vertical displacements of 1–4 cm with subsidence occurring during the thawing season and a secular subsidence of 1–4 cm/decade. We hypothesize that the seasonal subsidence is caused by thaw settlement of the active layer and that the secular subsidence is probably due to thawing of ice‐rich permafrost near the permafrost table. These mechanisms could explain why in situ measurements on Alaskan North Slope reveal negligible trends in active layer thickness during the 1990s, despite the fact that atmospheric and permafrost temperatures in this region increased during that time. This study demonstrates that surface deformation measurements from InSAR are complementary to more traditional in situ measurements of active layer thickness, and can provide new insights into the dynamics of permafrost systems and changes in permafrost conditions.
The measurement of temporal changes in active layer thickness (ALT) is crucial to monitoring permafrost degradation in the Arctic. We develop a retrieval algorithm to estimate long‐term average ALT ...using thaw‐season surface subsidence derived from spaceborne interferometric synthetic aperture radar (InSAR) measurements. Our algorithm uses a model of vertical distribution of water content within the active layer accounting for soil texture, organic matter, and moisture. We determine the 1992–2000 average ALT for an 80 × 100 km study area of continuous permafrost on the North Slope of Alaska near Prudhoe Bay. We obtain an ALT of 30–50 cm over moist tundra areas, and a larger ALT of 50–80 cm over wet tundra areas. Our estimated ALT values match in situ measurements at Circumpolar Active Layer Monitoring (CALM) sites within uncertainties. Our results demonstrate that InSAR can provide ALT estimates over large areas at high spatial resolution.
Key Points
Thawing of active layer causes surface subsidence
Surface subsidence from InSAR can be used to estimate active layer thickness
Our active layer thickness estimates closely match ground measurements
Accuracy of GRACE mass estimates Wahr, John; Swenson, Sean; Velicogna, Isabella
Geophysical research letters,
March 2006, Volume:
33, Issue:
6
Journal Article
Peer reviewed
Open access
The GRACE satellite mission is mapping the Earth's gravity field at monthly intervals. The solutions can be used to determine monthly changes in the distribution of water on land and in the ocean. ...Most GRACE studies to‐date have focussed on producing maps of mass variability, with little discussion of the errors in those maps. Error estimates, though, are necessary if GRACE is to be used as a diagnostic tool for assessing and improving hydrology and ocean models. Furthermore, only with error estimates can it be decided whether some feature of the data is real, and how accurately that feature is determined by GRACE. Here, we describe a method of constructing error estimates for GRACE mass values. The errors depend on latitude and smoothing radius. Once the errors are adjusted for these factors, we find they are normally‐distributed. This allows us to assign confidence levels to GRACE mass estimates.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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