Closed loop simulations on recoverability of climate trends in next generation gravity missions. Issue 2 (26th September 2022)

• Record Type: Journal Article
• Authors: Schlaak, M; Pail, R; Jensen, L; Eicker, A
• Is Part Of: Geophysical journal international. Volume 232:Issue 2(2023)
• Page Start: 1083
• Page End: 1098
• Publication Date: 2022-09-26
• Subjects: Hydrology -- Global change from geodesy -- Satellite geodesy -- Satellite gravity -- Time variable gravity -- Numerical modelling; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggac373
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: Gravity field satellite missions are unique observation systems to directly measure mass transport processes on Earth and to gather valuable information for climate research. Next Generation Gravity Missions (NGGMs) are expected to be launched within this decade, setting high anticipation for an enhanced monitoring capability that will improve the spatial and temporal resolutions of gravity observations significantly. They will allow for an evaluation of long-term trends in the Terrestrial Water Storage (TWS) signal. The results of this study are based on a time-series of global changes in soil moisture and snow obtained from future climate projections until the year 2100 of a coupled climate model taking part in the CMIP6 (Coupled Model Intercomparison Project Phase 6). For different mission concepts, namely in-line single-pair missions and a Bender double-pair mission, the recoverability of a time variable mass signal is evaluated, considering realistic noise assumptions, simulated over several decades. The results show that a single-pair mission can fulfill the target requirements for the long-term trend, set by the user community, after 70 yr while a double pair already achieves it after 30 yr of observation. After 100 yr of double-pair constellations the globally averaged RMS (polar areas excluded) improves, compared to a single-pair mission, by a factor of 5 for the linear trend, 2.5 for annual amplitude, and 1.8 for the phase observation. In addition, regional investigations indicate that the simple parameter model consisting of offset, linear trend, and annual signal coefficients, as it was used in this study, in several cases might not be able to capture the whole time-variable signal sufficiently, due to the presence of interannual signals. Hence, advanced, locally more adaptable parameter models need to be considered for a better parametrization of local effects in the future.