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 language | English |
|---|---|
| Pages (from-to) | 4299-4311 |
| Number of pages | 13 |
| Journal | Journal of Geophysical Research. Atmospheres |
| Volume | 122 |
| Issue number | 8 |
| Early online date | 17 Apr 2017 |
| DOIs | |
| Publication status | Published - 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).
| Funders | Funder number |
|---|---|
| European Space Agency Climate Change Initiative for Soil Moisture | 4000104814/11/I-NB |
| Horizon 2020 Framework Programme | 715254 |
| European Research Council | |
| Ministerie van Onderwijs, Cultuur en Wetenschap | 024.002.001 |
| Nederlandse Organisatie voor Wetenschappelijk Onderzoek | |
| Netherlands Earth System Science Centre |
