Satellite altimeter sea surface heights, in combination with Argo ocean temperature and salinity observations, provide an independent measure of global mean ocean mass (GMOM) change. Over the period ...January 2005 to April 2020, GMOM rates observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GFO) range from 1.88 ± 0.10 to 2.63 ± 0.10 mm/year, compared to 2.85 ± 0.37 mm/year from Altimeter‐Argo. Over much of the GRACE era, GRACE GMOM estimates agree well with Altimeter‐Argo over a broadband of frequencies. However, during the late stage of the GRACE mission (after August 2016) and into the GFO era, differences between GRACE/GFO and Altimeter‐Argo GMOM estimates become substantially larger and systematic, which may be related to the single accelerometer mode of operation during late‐stage GRACE and GFO missions and/or errors of Argo and altimeter data.
Key Points
GRACE/GFO‐derived global ocean mass change agrees generally well with Altimeter‐Argo estimates at seasonal and long‐term scales
The GRACE/GFO and Altimeter‐Argo GMOM differences become substantially larger and systematic during the late‐stage GRACE and GFO periods
The discrepancy is likely related to the single accelerometer mode of the late‐stage GRACE and GFO and/or errors of Argo and altimeter data
Global mean ocean mass change derived from the Gravity Recovery and Climate Experiment (GRACE) gravity solutions generally agrees well with ocean mass change inferred from satellite altimeter sea ...surface height and Argo floats observations during the period January 2005 to December 2015. However, there is a systematic annual phase lag (~10°) between GRACE and Altimeter‐Argo estimates. This phase lag is attributed to the enforced mass conservation in GRACE gravity solutions, in which the ΔC00 coefficients (representing changes in total Earth mass) are set to zero. After a correct implementation of global mass conservation by removing global mean atmospheric mass from the GRACE solutions using atmospheric model predictions, the annual phase lag is nearly completely gone, yielding significantly improved agreement between GRACE and Altimeter‐Argo estimates. In addition, retaining GRACE ΔJ2 coefficients provides better GRACE ocean mass estimates at both seasonal and long‐term time scales.
Key Points
GRACE‐derived global ocean mass change agrees remarkably well with Altimeter‐Argo estimates at both seasonal and long‐term scales
Appropriate implementation of global mass conservation is important in GRACE global mean ocean mass change estimation
Retaining GRACE ΔJ2 coefficients provides better GRACE ocean mass estimates at both seasonal and long‐term scales
Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic ...conditions in past years or even decades. Here we demonstrate that five commonly used conceptual “bucket” rainfall‐runoff models are unable to replicate multiyear trends exhibited by natural systems during the “Millennium Drought” in south‐east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13‐year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model “buckets” are already “emptying” on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall‐runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall‐runoff relationships in the context of a changing climate.
Key Points
Environmental state variables such as groundwater display multiyear trends in response to sustained climate anomalies
We show that five commonly used conceptual “bucket” rainfall runoff models are unable to replicate such trends
Because of this, the models fail to extrapolate realistically to different climatic conditions, compromising runoff projections
Plain Language Summary
It is common in science to use a mental picture or metaphor that simplifies a complex phenomenon. A common metaphor used for a water supply catchment is that of a leaky bucket. When it rains, the bucket fills up; when it does not rain for a while, the bucket empties due to evaporation and water used by trees; and leaking water is like river flow. Computer models based on variants of this metaphor are common and can provide predictions of how much streamflow might occur under future scenarios. This paper explores limitations of the bucket metaphor and associated models. Recently, during a 13‐year drought in Australia, river catchments gradually started to dry up. With each passing year, the depth to groundwater increased gradually as the water used by trees was not replenished by rainfall. We compare this long, slow behavior to that of five commonly used “bucket” models. The models do not show the long, slow drying up—they only show the seasonal ups and downs, and their predictions of streamflow over the drought are poor. This is surprising, and it means we should choose our models carefully and seek out models that can simulate this behavior and its impact on streamflow.
River runoff is estimated as a water budget residual using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage time series, ERA5 reanalysis data, and precipitation observations ...for January 2003 through December 2015 for the Obidos upstream drainage basin and for the entire Amazon basin. Estimated runoff based on the water budget agrees remarkably well with in situgauge observations at Obidos, especially at seasonal time scales, with nearly perfect phase agreementbut slightly larger seasonal amplitude. The discrepancy in the seasonal amplitude maybe attributed to underestimation of river gauge runoff during the wet season when water overflows the riverbanks. The ERA5 model appears to overestimate long-term mean evapotranspiration in the Amazon by ~2 cm/monthb ased on comparisons with precipitation and runoff observations. Using precipitation data based on satellites and gauge observations relative to gauge observations alone improved agreement between water budget runoff estimates and in situ
runoff observations. Seasonal variations in ERA5 simulated runoff are about twice as large as those from in situobservations and show a large phase lag as well. Water budget based runoff for the entire Amazon (~ 7,200 km3averaged 29over the 13 yr period) is significantly larger than observed runoff (~5,700 km3) at notably larger than previous estimates for the entire Amazon. These differences may be partly related to submarine runoff from the Amazon basin that cannot be captured by surface gauges.
Climate model estimates show significant groundwater depletion during the 20th century, consistent with global mean sea level (GMSL) budget analysis. However, prior to the Argo float era, in the ...early 2000’s, there is little information about steric sea level contributions to GMSL, making the role of groundwater depletion in this period less certain. We show that a useful constraint is found in observed polar motion (PM). In the period 1993–2010, we find that predicted PM excitation trends estimated from various sources of surface mass loads and the estimated glacial isostatic adjustment agree very well with the observed. Among many contributors to the PM excitation trend, groundwater storage changes are estimated to be the second largest (4.36 cm/yr) toward 64.16°E. Neglecting groundwater effects, the predicted trend differs significantly from the observed. PM observations may also provide a tool for studying historical continental scale water storage variations.
Plain Language Summary
Melting of polar ice sheets and mountain glaciers has been understood as a main cause of sea level rise associated with contemporary climate warming. It has been proposed that an important anthropogenic contribution is sea level rise due to groundwater depletion resulting from irrigation. A climate model estimate for the period 1993–2010 gives total groundwater depletion of 2,150 GTon, equivalent to global sea level rise of 6.24 mm. However, direct observational evidence supporting this estimate has been lacking. In this study, we show that the model estimate of water redistribution from aquifers to the oceans would result in a drift of Earth's rotational pole, about 78.48 cm toward 64.16°E. In combination with other well‐understood sources of water redistribution, such as melting of polar ice sheets and mountain glaciers, good agreement with PM observations serves as an independent confirmation of the groundwater depletion model estimate.
Key Points
Earth's pole has drifted toward 64.16°E at a speed of 4.36 cm/yr during 1993–2010 due to groundwater depletion and resulting sea level rise
Including groundwater depletion effects, the estimated drift of Earth's rotational pole agrees remarkably well with observations
Over the past decade, the rate of global mean sea level (GMSL) rise is about 3.5 mm/year. Terrestrial water/ice mass loss to the oceans and ocean volume expansion explain about 3.1 mm/year, ...indicating that the GMSL budget is not been fully understood. Past estimates from Gravity Recovery and Climate Experiment (GRACE) data have indicated that terrestrial water storage (TWS) is increasing and is thus a mitigating contributor to GMSL rise. However, TWS estimates from GRACE are uncertain mostly due to limitations in GRACE estimates of degree‐1 and degree‐2 order‐0 spherical harmonic coefficients. We obtain an improved estimate of the TWS contribution to GMSL change using revised GRACE estimates of these low‐degree coefficients. For the period 2005–2015, we find that TWS makes an additional contribution to GMSL rise of about 0.32 ± 0.02 mm/year, mostly associated with a TWS decrease. This revised estimate is sufficient to nearly balance the budget of GMSL rise.
Plain Language Summary
During the last decade, the rate of global mean sea level (GMSL) rise is about 3.5 mm/year. Ocean volume increase due to thermal expansion has contributed to GMSL rise by about 1.3 mm/year. Recent estimates of terrestrial water and ice melt inflow to the oceans can explain about 1.8 mm/year, so the sum of ocean mass and volume increase (3.1 mm/year) does not explain the total observed GMSL rise (3.5 mm/year). The missing contribution to GMSL rise, about 0.4 mm/year, is a significant water volume, similar in size to the contribution from melting Antarctic ice. In this study, we show that loss of water stored on land (terrestrial water storage (TWS)) accounts for most of the missing contribution to GMSL rise (about 0.3 mm/year). Previous TWS estimates using satellite gravity data were flawed due to various limitations, which are corrected in this study.
Key Points
Sea level rise from terrestrial water storage (TWS) change has been underestimated in previous estimates using GRACE data
During 2005–2015, TWS change has contributed to global mean sea level rise by about 0.32 ± 0.02 mm/year
Global mean sea level has increased about 3 mm/yr over several decades due to increases in ocean mass and changes in sea water density. Ocean mass, accounting for about two-thirds of the increase, ...can be directly measured by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) satellites. An independent measure is obtained by combining satellite altimetry (measuring total sea level change) and Argo float data (measuring steric changes associated with sea water density). Many previous studies have reported that the two estimates of global mean ocean mass (GMOM) change are in good agreement within stated confidence intervals. Recently, particularly since 2016, estimates by the two methods have diverged. A partial explanation appears to be a spurious variation in steric sea level data. An additional contributor may be deficiencies in Glacial Isostatic Adjustment (GIA) corrections and degree-1 spherical harmonic (SH) coefficients. We found that erroneous corrections for GIA contaminate GRACE/GFO estimates as time goes forward. Errors in GIA corrections affect degree-1 SH coefficients, and degree-1 errors may also be associated with ocean dynamics. Poor estimates of degree-1 SH coefficients are likely an important source of discrepancies in the two methods of estimating GMOM change.
River discharge is a critical component for understanding hydrological processes and sustainable management of water resources. The importance of discharge observation has increased due to its ...potential extreme variation resulting from the projected climate change and stronger variability of precipitation and temperature in some large basins. However, inherent difficulties in ground-based observations and decreasing number of gauge stations hinder accurate measurement of global river discharge and its spatio-temporal variations. Various remote sensing methods have been examined as alternatives, however, they require ground measurements to convert their proxy measurements into the actual river discharge. In this study, we estimate the discharge at the Óbidos station and the mouth of the Amazon basin using the water storage variations derived from GRACE gravity data without relying on any auxiliary ground observations. We extract the water mass signal along the main stem of the river by applying the Empirical Orthogonal Function (EOF) for water storage variations over the basin. The relative water storage variations along the main stem derived from the EOF decomposition are highly correlated with in-situ discharge at the Óbidos. However, in high water season, the GRACE-based discharge is estimated larger than the in-situ observations, and the difference is particularly significant during the 2009 extreme flood season. We argue that the in-situ river discharge in 2009 was underestimated due to the missed water volume for the flow detouring around the Óbidos gauge station during the high-flow event. Net river discharge of the Amazon Basin to Atlantic Ocean is also estimated, and its annual discharge is about 23% larger than that of the Óbidos. In particular, 2009 river discharge to Atlantic Oceans is estimated as 1050Gton.
•We estimate river discharge at the Óbidos station using GRACE.•Discharge from GRACE is larger than that from in-situ observation during flooding.•We argue that in-situ observation missed some flooding flow.•River discharge at the Amazon Basin mouth is also estimated for the first time.
Error Assessment of GRACE and GRACE Follow‐On Mass Change Chen, Jianli; Tapley, Byron; Tamisiea, Mark E. ...
Journal of geophysical research. Solid earth,
September 2021, 2021-09-00, 20210901, Letnik:
126, Številka:
9
Journal Article
Recenzirano
Odprti dostop
We carry out a comprehensive error assessment of Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GFO) Release‐6 (RL06) solutions from the Center for Space Research (CSR) at the ...University of Texas at Austin, NASA Jet Propulsion Laboratory (JPL), and Geoforschungszentrum (GFZ). The study covers the period April 2002 to August 2020 and uses two different methods, one based upon open ocean residuals (OOR) and the other a Three‐Cornered Hat (TCH) calculation. General results from the two methods are similar. With 300 km Gaussian smoothing OOR RMS errors for CSR, JPL, and GFZ solutions are ∼2.01, 3.19, and 3.67 cm, respectively. With additional decorrelation filtering OOR RMS values are reduced to ∼1.24, 1.53, and 1.69 cm, respectively. TCH analysis also shows that CSR has the lowest noise levels with similar RMS values, and additional decorrelation filtering reduces error levels. TCH may underestimate errors if there are common errors among geophysical background models. Errors in GFO's first two years (25 solutions for 2018.06 to 2020.08) are comparable to those of GRACE when zonal degree 2 and 3 coefficients are replaced by Satellite Laser Ranging estimates. The OOR method reveals mismodeled intra‐seasonal dynamic ocean signals associated with the Argentine Gyre during de‐aliasing, while the TCH method shows differences between ocean tide models near Australia and Antarctica. Both OOR and TCH RMS analysis offer a means to assess the noise level of GRACE/GFO estimated mass change. The actual uncertainty of GRACE/GFO estimate averaged (or totaled) over a given region is also affected by other error sources.
Plain Language Summary
The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GFO) satellite gravity missions provide global measurements of the Earth gravity change, which can be used to study mass redistribution in the Earth system, such as sea level rise, glacial melting, and land water storage change. However, it is difficult to determine the noise level of GRACE/GFO observations. In this study, we estimate GRACE/GFO noise level using two methods. One is based on GRACE/GFO residuals over the ocean, as the true ocean signals can be predicted. The other method is based on the so‐called Three‐Cornered Hat (TCH) calculation. The OOR estimates show that the noise level of three commonly used GRACE/GFO Release‐6 (RL06) gravity solutions, provided by the Center for Space Research (CSR) at the University of Texas at Austin, US NASA Jet Propulsion Laboratory (JPL), and German Research Centre for Geosciences (GFZ), are about 2.01, 3.19, and 3.67 cm, respectively, when 300 km Gaussian smoothing is applied. The TCH calculation shows similar results. Both OOR and TCH estimates suggests that the CSR solutions shown the lowest noise level. The OOR and TCH calculations also capture some geophysical signals that are related errors in background geophysical models used in GRACE/GFO data processing.
Key Points
We provide an error assessment of Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GFO) measurements using the open ocean residual (OOR) and Three‐Cornered Hat (TCH) methods
With 300 km Gaussian smoothing, OOR RMS errors for Center for Space Research (CSR), Jet Propulsion Laboratory (JPL), and Geoforschungszentrum Release‐6 (RL06) are estimated to be ∼2.01, 3.19, and 3.67 cm, respectively
TCH shows similar results, confirming CSR RL06 has lowest noise level, and both methods capture issues with background geophysical models
Terrestrial Water Storage (TWS) changes have been estimated at basin to continental scales from gravity variations using data from the Gravity Recovery and Climate Experiment (GRACE) satellites since ...2002. The relatively low spatial resolution (∼300 km) of GRACE observations has been a main limitation in such studies. Various data processing strategies, including mascons, forward modeling, and constrained linear deconvolution (CLD), have been employed to address this limitation. Here we develop a revised CLD method to obtain a TWS estimate that combines GRACE observations with much higher spatial resolution land surface models. The revised CLD constrains model estimates to agree with GRACE TWS when smoothed. As an example, we apply the method to obtain a high spatial resolution TWS estimate in Australia. We assess the accuracy of the approach using synthetic GRACE data.
Plain Language Summary
The estimation of terrestrial water storage (TWS) changes using gravity recovery and climate experiment (GRACE) satellites suffers from low spatial resolution, making it challenging to interpret local‐scale mass changes. In this study, we improved the sparse resolution of GRACE observations by incorporating high‐resolution land surface models (LSM) that provides detailed hydrological information. Through synthetic experiments, we confirmed the accuracy of our estimations in regional‐ and local‐scale. When applied to real GRACE data, our new TWS estimations show better spatial resolution compared to conventional GRACE products. Further, our estimations consistently yield reliable results although different LSM were used.
Key Points
High‐resolution terrestrial water storage was estimated by combining gravity recovery and climate experiment and land surface models
Our new estimates reduced both land‐ocean and inter‐basin leakages simultaneously
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