Articles | Volume 24, issue 1
Hydrol. Earth Syst. Sci., 24, 381–396, 2020
Hydrol. Earth Syst. Sci., 24, 381–396, 2020

Research article 24 Jan 2020

Research article | 24 Jan 2020

Inter-annual variability of the global terrestrial water cycle

Dongqin Yin and Michael L. Roderick

Data sets

A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010 ( Y. Zhang, M. Pan, J. Sheffield, A. L. Siemann, C. K. Fisher, M. Liang, H. E. Beck, N. Wanders, R. F. MacCracken, P. R. Houser, T. Zhou, D. P. Lettenmaier, R. T. Pinker, J. Bytheway, C. D. Kummerow, and E. F. Wood

NASA GEWEX Surface Radiation Budget P. W. Stackhouse, S. K. Gupta, S. J. Cox, T. Zhang, and J. C. Mikovitz

Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset ( I. Harris, P. D. Jones, T. J. Osborn, and D. H. Lister

Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis ( B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne

Recent decline in the global land evapotranspiration trend due to limited moisture supply ( M. Jung, M. Reichstein, P. Ciais, S. I. Seneviratne, J. Sheffield, M. L. Goulden, G. Bonan, A. Cescatti, J. Chen, R. de Jeu, A. J. Dolman, W. Eugster, D. Gerten, D. Gianelle, N. Gobron, J. Heinke, J. Kimball, B. E. Law, L. Montagnani, Q. Mu, B. Mueller, K. Oleson, D. Papale, A. D. Richardson, O. Roupsard, S. Running, E. Tomelleri, N. Viovy, U. Weber, C. Williams, E. Wood, S. Zaehle, and K. Zhang

E-RUN version 1.1: Observational gridded runoff estimates for Europe, link to data in NetCDF format (69 MB) L. Gudmundsson and S. I. Seneviratne

Short summary
We focus on the initial analysis of inter-annual variability in the global terrestrial water cycle, which is key to understanding hydro-climate extremes. We find that (1) the partitioning of inter-annual variability is totally different with the mean state partitioning; (2) the magnitude of covariances can be large and negative, indicating the variability in the sinks can exceed variability in the source; and (3) the partitioning is relevant to the water storage capacity and snow/ice presence.