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

Global Soil Moisture Data Global Soil Moisture Data

BankBankDepartment of Environmental SciencesDepartment of Environmental Sciences

Rutgers UniversityRutgers University http://climate.envsci.rutgers.edu/soil_moisturehttp://climate.envsci.rutgers.edu/soil_moisture

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Data Collection and Data Collection and DistributionDistributionLand Surface ModelingLand Surface ModelingRemote Sensing Remote Sensing Data AnalysisData Analysis

Alan RobockAlan RobockHaibin LiHaibin Li

Thomas AtkinsThomas AtkinsKonstantin VinnikovKonstantin Vinnikov

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

Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences

Pedro Viterbo

European Centre for Medium-Range Weather Forecasting




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Outline

• 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




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Updated soil moisture from China

• Station Distribution

• Data Quality




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Station Distribution and Data QualityMeasurements:

a) Mass (%) to volume (%)

b) 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)




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Sample Plant Available Soil Moisture




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Application -- Model Evaluation

• 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




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Reanalysis and soil moisture nudging• 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.




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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




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Soil Moisture Time Series

1)1) Western Station (#15): R-1 and Western Station (#15): R-1 and ERA40 produce nearly constant ERA40 produce nearly constant soil moisture.soil moisture.

2)2) R-1 has little interannual R-1 has little interannual variability.variability.

3)3) R-2 produces negative biases.R-2 produces negative biases.

R-2 R-2 R-1R-1

ERA40 ERA40 Observed




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Time Series Correlations

1)1) R-2 shows improvements than R-1 with better seasonal cycle.R-2 shows improvements than R-1 with better seasonal cycle.

2)2) ERA40 has better variation than R-2 and R-1.ERA40 has better variation than R-2 and R-1.




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Seasonal Cycle

R-2 R-2 R-1R-1

ERA40 ERA40 Observed

1)1) Station 15: weak seasonal cycle.Station 15: weak seasonal cycle.

2)2) R-1: the amplitude of seasonal R-1: the amplitude of seasonal cycle is too large.cycle is too large.

3)3) R-2: improved seasonal cycle but R-2: improved seasonal cycle but monthly average is low.monthly average is low.




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Soil Moisture EvolutionSoil Moisture Evolution

1) R-2: too dry1) R-2: too dry

2) R-1: constant soil moisture in 2) R-1: constant soil moisture in

winterwinter




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Anomaly

R-2: Predictable anomaly pattern. R-2: Predictable anomaly pattern.

What about temporal scale?What about temporal scale?




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Temporal Scale

West North Center

MeanStation # 15 9 23 24 29 20 21 31 33 36

OBS 2.9 3.3 0.7 1.0 5.7 3.4 2.8 2.3 3.9 2.9 2.9(±1.3)

ERA40 6.2 13.5 3.9 2.9 2.5 1.1 1.3 2.8 6.2 3.8 4.4(±3.4)

ERA40* 6.4 10.8 7.1 13.3 3.7 1.9 2.1 2.8 3.9 2.3 5.4(±3.7)

R-1 2.0 2.2 1.9 1.8 1.8 3.0 1.4 2.2 2.6 1.9 2.1(±0.4)

R-1* 3.1 1.8 1.8 1.6 1.9 1.9 1.3 1.9 2.6 2.0 2.0(±0.5)

R-2 1.9 3.7 3.2 4.1 6.1 2.6 4.4 5.8 8.4 6.2 4.7(±1.9)

R-2* 2.6 8.7 7.3 6.6 11.6 7.3 8.1 8.3 8.5 7.1 7.6(±2.1)

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.

Theory: (Theory: (Delworth and Manabe,1988)Delworth and Manabe,1988) T

t

etr

)(




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Conclusions

• 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.

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

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