Global soil moisture bimodality in satellite observations and climate models

L. Vilasa*, D. G. Miralles, R. A.M. De Jeu, A. J. Dolman

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

A new diagnostic metric based on soil moisture bimodality is developed in order to examine and compare soil moisture from satellite observations and Earth System Models. The methodology to derive this diagnostic is based on maximum likelihood estimator encoded into an iterative algorithm, which is applied to the soil moisture probability density function. This metric is applied to satellite data from the Advanced Microwave Scanning Radiometer for the Earth Observing System and global climate models data from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show high soil moisture bimodality in transitional climate areas and high latitudes, potentially associated with land-atmosphere feedback processes. When comparing satellite versus climate models, a clear difference in their soil moisture bimodality is observed, with systematically higher values in the case of CMIP5 models. These differences appear related to areas where land-atmospheric feedback may be overestimated in current climate models.

Original languageEnglish
Pages (from-to)4299-4311
Number of pages13
JournalJournal of Geophysical Research. Atmospheres
Volume122
Issue number8
Early online date17 Apr 2017
DOIs
Publication statusPublished - 27 Apr 2017

Funding

The authors acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S1) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordi nating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. CMIP5 model data can be accessed from its data por tal “http://cmip-pcmdi.llnl.gov/cmip5/ data_portal.html”. The authors also acknowledge the creators of the LPRM/AMSR-E/Aqua Daily L3 Surface Soil Moisture data set: the Vrije Universiteit Amsterdam and NASA GSFC and its publisher, the Goddard Earth Sciences Data and Information Services Center (GES DISC). LPRM AMSR-E data can be downloaded from ftp server “ftp://hydro1.sci.gsfc.nasa.gov/data/ s4pa/WAOB/”. L.U. Vilasa acknowledges the financial support from the Netherlands Organization for Scientific Research through NWO China Water/84200008/Land Atmos project. D. G. Miralles acknowledges support from the European Research Council (ERC) under grant agreement 715254 (DRY-2- DRY). A.J. Dolman acknowledges sup port from the program of the Netherlands Earth System Science Centre (NESSC), financially supported by the Ministry of Education, Culture and Science (OCW) (grant 024.002.001). The contribution of R.A.M. de Jeu was funded by the European Space Agency Climate Change Initiative for Soil Moisture (contract 4000104814/11/I-NB).

FundersFunder number
European Space Agency Climate Change Initiative for Soil Moisture4000104814/11/I-NB
Horizon 2020 Framework Programme715254
European Research Council
Ministerie van Onderwijs, Cultuur en Wetenschap024.002.001
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Netherlands Earth System Science Centre

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