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Haibin Li and Alan Robock Department of Environmental Sciences, Rutgers University

Evaluation of Reanalysis Soil Moisture Simulations Using Updated Chinese Observations for 1981-1999. Haibin Li and Alan Robock Department of Environmental Sciences, Rutgers University Suxia Liu and Xingguo Mo

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Haibin Li and Alan Robock Department of Environmental Sciences, Rutgers University

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  1. Evaluation of Reanalysis Soil Moisture SimulationsUsing Updated Chinese Observations for 1981-1999 Haibin Li and Alan Robock Department of Environmental Sciences, Rutgers University Suxia Liu and Xingguo Mo Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences Pedro Viterbo European Centre for Medium-Range Weather Forecasting

  2. Updated Chinese soil moisture Application – evaluation of reanalysis soil moisture by observations Question: are model produced soil moisture data sets reliable? If not, what are the deficiencies. Conclusions Outline

  3. Station Distribution Data Quality Updated soil moisture from China

  4. Station Distribution and Data Quality • Measurements: • Mass (%) to volume (%) • 3 times per month (8th, 18th, 28th ) • c) From 1981-1999 • d) 11 vertical levels:0-5 cm, 5-10 cm,10- 20 cm, and each 10-cm layer down to 1 m (Robock et al., 2000)

  5. Sample Plant Available Soil Moisture

  6. ERA40, NCEP/NCAR Reanalysis (R-1) and NCEP/DOE Reanalysis (R-2) soil moisture data sets for 1981-1999 Top 1 m soil moisture was calculated for comparison Emphasis: interannual and seasonal variability Application -- Model Evaluation

  7. Reanalysis Reanalyze historical data using state-of-the-art models. (http://dss.ucar.edu/pub/reanalyses.html) R-1 (Kistler et al., 2000) Soil moisture relaxed to the Mintz and Serafini climatology with a 60-day time scale. R-2 (Kanamitsu et al., 2002) Uses observed precipitation rather than model-generated precipitation, so no nudging was required for deep soil wetness. ERA40 (Douville et al., 2000) Uses an optimal interpolation technique to nudge soil moisture based on 2-m relative humidity and temperature. Reanalysis and soil moisture nudging

  8. Strategy – Point comparison • 10 stations were selected: Western: 15 Central: 20, 21, 31, 33 ,36 Northern: 9, 23, 24, 29 • Corresponding grid values were extracted for each model

  9. R-2 R-1 ERA40 Observed Soil Moisture Time Series • Western Station (#15): R-1 and ERA40 produce nearly constant soil moisture. • R-1 has little interannual variability. • R-2 produces negative biases.

  10. Time Series Correlations • R-2 shows improvements than R-1 with better seasonal cycle. • ERA40 has better variation than R-2 and R-1.

  11. R-2 R-1 ERA40 Observed Seasonal Cycle • Station 15: weak seasonal cycle. • R-1: the amplitude of seasonal cycle is too large. • R-2: improved seasonal cycle but monthly average is low.

  12. Soil Moisture Evolution 1) R-2: too dry 2) R-1: constant soil moisture in winter

  13. Anomaly R-2: Predictable anomaly pattern. What about temporal scale?

  14. Temporal Scale Theory: (Delworth and Manabe,1988) Temporal Scale (unit: months) R-1 and ERA40 have similar temporal scale to observations. Temporal scales of top layer don’t show too much difference, deep layer is responsible.

  15. R-2 shows improved climatology and interannual variability than R-1. There may exist systematic biases in R-2. ERA40 has better soil moisture anomaly. ERA40 and R-1 have similar temporal scale with respect to observations, temporal scale for R-2 is too long. Land surface model needs to be updated in R-1 and R-2. Conclusions

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