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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 - PowerPoint PPT Presentation
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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
//
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
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
//
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
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
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
//
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
Updated soil moisture from China
• Station Distribution
• Data Quality
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
//
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
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)
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
//
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
Sample Plant Available Soil Moisture
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
//
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
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
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
//
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
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.
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
//
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
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
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
//
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
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
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
//
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
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.
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
//
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
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.
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
//
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
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
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
//
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
Anomaly
R-2: Predictable anomaly pattern. R-2: Predictable anomaly pattern.
What about temporal scale?What about temporal scale?
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
//
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
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
)(
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
//
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
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.