Confronting weather and climate models with observational data from soil moisture networks over the United States

Paul A. Dirmeyer, Jiexia Wu, Holly E. Norton, Wouter A. Dorigo, Steven M. Quiring, Trenton W. Ford, Joseph A. Santanello, Michael G. Bosilovich, Michael B. Ek, Randal D. Koster, Gianpaolo Balsamo, David M. Lawrence

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1049-1067
Number of pages19
JournalJournal of Hydrometeorology
Volume17
Issue number4
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

Keywords

  • Climate models
  • Geographic location/entity
  • Instrumentation/sensors
  • Land surface model
  • Models and modeling
  • North America
  • Observational techniques and algorithms
  • Physical meteorology and climatology
  • Reanalysis data
  • Soil moisture

ASJC Scopus subject areas

  • Atmospheric Science

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