Assessing Global Water Storage Variability from GRACE: Trends, Seasonal Cycle, Subseasonal Anomalies and Extremes

Abstract

Throughout the past decade, the Gravity Recovery and Climate Experiment (GRACE) has given an unprecedented view on global variations in terrestrial water storage. While an increasing number of case studies have provided a rich overview on regional analyses, a global assessment on the dominant features of GRACE variability is still lacking. To address this, we survey key features of temporal variability in the GRACE record by decomposing gridded time series of monthly equivalent water height into linear trends, inter-annual, seasonal, and subseasonal (intra-annual) components. We provide an overview of the relative importance and spatial distribution of these components globally. A correlation analysis with precipitation and temperature reveals that both the inter-annual and subseasonal anomalies are tightly related to fluctuations in the atmospheric forcing. As a novelty, we show that for large regions of the world high-frequency anomalies in the monthly GRACE signal, which have been partly interpreted as noise, can be statistically reconstructed from daily precipitation once an adequate averaging filter is applied. This filter integrates the temporally decaying contribution of precipitation to the storage changes in any given month, including earlier precipitation. Finally, we also survey extreme dry anomalies in the GRACE record and relate them to documented drought events. This global assessment sets regional studies in a broader context and reveals phenomena that had not been documented so far.

A global reconstruction of climate‐driven subdecadal water storage variability

“…At each temporal scale, some processes may be more relevant than others. For example, decadal trends can be controlled by groundwater depletion [Döll et al, 2014;Chen et al, 2016] or ice melt [Jacob et al, 2012;Velicogna et al, 2014], whereas short-term anomalies are usually related to fluctuations of the relevant atmospheric drivers [Humphrey et al, 2016]. We therefore decompose the TWS signal in order to relate changes in water storage to changes in atmospheric drivers at the temporal scales at which they are the most relevant.…”

“…Here we define the linear trend as the Theil-Sen slope of the original time series [Sen, 1968]. The three remaining components are obtained with the Seasonal-Trend decomposition based on Loess procedure (STL) [Cleveland et al, 1990] adapted for unevenly spaced time series [Humphrey et al, 2016].…”

“…In a global comparison between decomposed GRACE time series and atmospheric drivers, Humphrey et al [2016] showed that applying an exponential decay filter to daily precipitation time series before averaging them to monthly resolution greatly improved their correlation with subseasonal TWS anomalies (GRACE subseas ). In addition, the interannual variability (GRACE inter ) was shown to correlate well with interannual precipitation and temperature anomalies in many regions of the world.…”

“…The decay parameter τ conditions the steepness of the exponential decay filter applied to the daily precipitation time series before it is averaged to the monthly resolution. Additional information concerning the mathematical formulation of this exponential decay filter can be found in Humphrey et al [2016] and is further elaborated in supporting information S1. Finally, parameters β 1 and β 2 correspond to calibrated scaling coefficients and ϵ denotes an error term, which is discussed later.…”

“…While TWS anomalies obviously depend on precipitation, the statistical link with temperature anomalies is likely a proxy for evapotranspiration, as discussed in Humphrey et al [2016]. Note that other explanatory variables such as river discharge, sea surface temperature, or basin-scale averages of precipitation and temperature have been used successfully in a regional context [Becker et al, 2011;Forootan et al, 2014;Reager and Famiglietti, 2013].…”