Optimal averaging of soil moisture predictions from ensemble land surface model simulations

• Published in: Water resources research 2015-11, Vol.51 (11), p.9273-9289
• Main Authors: Crow, W. T., Su, C.-H., Ryu, D., Yilmaz, M. T.
• Format: Article
• Language: English
• Subjects: Climate change; Coefficients; Computer simulation; Covariance; Data; Data acquisition; Data assimilation; Data collection; Data processing; ensemble; land surface modeling; Land surface models; Moisture; Regression analysis; Remote sensing; Rescaling; Satellites; Scaling; Soil; Soil analysis; Soil conditions; Soil moisture; Soil surfaces
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1002/2015WR016944

The correct interpretation of ensemble information obtained from the parallel implementation of multiple land surface models (LSMs) requires information concerning the LSM ensemble's mutual error covariance. Here we propose a technique for obtaining such information using an instrumental variable (IV) regression approach and comparisons against a long‐term surface soil moisture data set acquired from satellite remote sensing. Application of the approach to multimodel ensemble soil moisture output from Phase 2 of the North American Land Data Assimilation System (NLDAS‐2) and European Space Agency (ESA) Soil Moisture (SM) Essential Climate Variable (ECV) data set allows for the calculation of optimal weighting coefficients for individual members of the NLDAS‐2 LSM ensemble and a biased‐minimized estimate of uncertainty in a deterministic soil moisture analysis derived via optimal averaging. As such, it provides key information required to accurately condition soil moisture expectations using information gleaned from a multimodel LSM ensemble. However, existing continuity and rescaling concerns surrounding the generation of long‐term, satellite‐based soil moisture products must likely be resolved before the proposed approach can be applied with full confidence. Key Points: Soil moisture can be predicted from a mulit‐model ensemble Interpretation of the ensemble requires model error covariance information Such information can be obtained using an instrumental variable approach