Assessment of soil moisture fields from imperfect climate models with uncertain satellite observations
- 1School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK
- 2Geological Sciences Division, British Antarctic Survey, Cambridge, CB3 0ET, UK
- 3Department of Hydrology and Geo-Environmental Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- 4Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, 1040, Vienna, Austria
Abstract. We demonstrate that global satellite products can be used to evaluate climate model soil moisture predictions but conclusions should be drawn with care. The quality of a limited area climate model (LAM) was compared to a general circulation model (GCM) using soil moisture data from two different Earth observing satellites within a model validation scheme that copes with the presence of uncertain data. Results showed that in the face of imperfect models and data, it is difficult to investigate the quality of current land surface schemes in simulating hydrology accurately. Nevertheless, a LAM provides, in general, a better representation of spatial patterns and dynamics of soil moisture compared to a GCM. However, in months when data uncertainty is higher, particularly in colder months and in periods when vegetation cover is too dense (e.g. August in the case of Western Europe), it is not possible to draw firm conclusions about model acceptability. For periods of higher confidence in observation data, our work indicates that a higher resolution LAM has more benefits to soil moisture prediction than are due to the resolution alone and can be attributed to an overall enhanced representation of precipitation relative to the GCM. Consequently, heterogeneity of rainfall patterns is better represented in the LAM and thus adequate representation of wet and dry periods leads to an improved acceptability of soil moisture (with respect to uncertain satellite observations), particularly in spring and early summer. Our results suggest that remote sensing, albeit with its inherent uncertainties, can be used to highlight which model should be preferred and as a diagnostic tool to pinpoint regions where the hydrological budget needs particular attention.