Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

Paul A. Dirmeyer * George Mason University, Fairfax, Virginia

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Jiexia Wu * George Mason University, Fairfax, Virginia

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Holly E. Norton * George Mason University, Fairfax, Virginia

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Wouter A. Dorigo Vienna University of Technology, Vienna, Austria
Laboratory of Forest and Water Management, Ghent University, Ghent, Belgium

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Steven M. Quiring Texas A&M University, College Station, Texas

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Trenton W. Ford Southern Illinois University, Carbondale, Illinois

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Joseph A. Santanello Jr. ** NASA Goddard Space Flight Center, Greenbelt, Maryland

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Michael G. Bosilovich ** NASA Goddard Space Flight Center, Greenbelt, Maryland

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Michael B. Ek NOAA/National Centers for Environmental Prediction, College Park, Maryland

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Randal D. Koster ** NASA Goddard Space Flight Center, Greenbelt, Maryland

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Gianpaolo Balsamo European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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David M. Lawrence National Center for Atmospheric Research, Boulder, Colorado

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Abstract

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

Corresponding author address: Paul A. Dirmeyer, Center for Ocean–Land–Atmosphere Studies, George Mason University, 4400 University Dr., Mail Stop 6C5, Fairfax, VA 22030. E-mail: pdirmeye@gmu.edu

Abstract

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

Corresponding author address: Paul A. Dirmeyer, Center for Ocean–Land–Atmosphere Studies, George Mason University, 4400 University Dr., Mail Stop 6C5, Fairfax, VA 22030. E-mail: pdirmeye@gmu.edu
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