Mitglie

d d

er

Helm

holtz-G

em

ein

schaft

Multivariate Land Data Assimilation at Global Scale

Xujun Han1,2, Harrie-Jan Hendricks Franssen1,2, Carsten Montzka1,2, Harry Vereecken1,2, Rui Jin3,4, Xin Li3,4

1.Forschungszentrum Jülich, Agrosphere (IBG 3), Leo-Brandt-Strasse, 52425 Jülich, Germany

2.Centre for High-Performance Scientific Computing in Terrestrial Systems, Leo-Brandt-Strasse, 52425 Jülich, Germany

3.Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, PR China

4.CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, PR China

Figure 2. Average latent heat flux of FLUXNET(MTE)-a, CLM Openloop

simulation-b, data assimilation (ESA_SM-c or GRACE-d) on 01-2009

Community Land Model

CRUNCEP

ForcingVegetation Soil

ESA Soil

Moisture

GRACE Data Assimilation

Figure 1. Global land data assimilation with community land model

a b

c d

Summary

1. ESA CCI soil moisture product and GRACE terrestrial water storage

anomalies were assimilated into the Community Land Model (Fig. 1) using

the Local Ensemble Transform Kalman Filter.

2. The FLUXNET-MET latent heat flux and runoff data (Global Runoff Data

Centre) were used to evaluate the results of different scenarios.

3. Fig. 2 shows the average latent heat flux on 01-2009, the zonal average of

latent heat flux on 01-2009 is summarized in Fig. 3. The openloop

simulation of CLM shows very good performance compared with

FLUXNET-MTE. And two data assimilation scenarios (ESA_SM and

GRACE) improved the results of latent heat flux further. Because most areas

of northern hemisphere were not updated in data assimilation due to the low

temperature condition in January and February, the impacts of data

assimilation on the southern hemisphere are more obvious.

4. The differences among different scenarios of data assimilation and openloop

simulation are showed in Fig. 4 and Fig. 5 for latent heat flux, runoff and soil

moisture (10 cm and 50 cm), respectively.

5. The leaf area index, snow water equivalent measurement and other sources of

observation could also be used in the data assimilation to improve the results

further.

Figure 3. Zonal average of latent heat flux on 01-2009

Figure 4. Difference of latent heat flux (upper) and runoff (lower) between

data assimilation (ESA_SM-a,c or GRACE-b,d) and CLM Openloop

simulation on 01-2009

Figure 5. Difference soil moisture at 10 cm (upper) and 50 cm (lower)

between data assimilation (ESA_SM-a,c or GRACE-b,d) and CLM

Openloop simulation on 01-2009

Acknowledgements:The authors would also like to thank ESA Dragon 3 program (Project ID 10649),

DFG (Deutsche Forschungsgemeinschaft) Forschergruppe 2131; NSFC (National

Science Foundation of China) project (grant number: 41271357, 91125001);

Transregional Collaborative Research Centre 32, financed by the German Science

foundation; Supercomputing facilities of Forschungszentrum Julich (JUROPA)

GRACE land data were processed by Sean Swenson, supported by the NASA

MEaSUREs Program, and are available at http://grace.jpl.nasa.gov

Contact:https://github.com/daspy/daspy --- x.han@fz-juelich.de

References:Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of

FLUXNET eddy covariance observations: validation of a model tree ensemble approach

using a biosphere model, Biogeosciences, 6, 2001-2013, 2009.

Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial water storage

estimates, Water Resour Res, 48, 2012.

Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., de Jeu, R. A. M., Wagner, W., McCabe, M. F.,

Evans, J. P., and van Dijk, A. I. J. M.: Trend-preserving blending of passive and active

microwave soil moisture retrievals, Remote Sens Environ, 123, 280-297, 2012.

Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I.

J. M., McCabe, M. F., and Evans, J. P.: Developing an improved soil moisture dataset by

blending passive and active microwave satellite-based retrievals, Hydrology and Earth

System Sciences, 15, 425-436, 2011.

Swenson, S. and Wahr, J.: Post-processing removal of correlated errors in GRACE data,

Geophys Res Lett, 33, 2006.

Wagner, W., Dorigo, W., de Jeu, R., Fernandez, D., Benveniste, J., Haas, E., and Ertl, M.:

Fusion of active and passive microwave observations to create an essential climate

variable data record on soil moisture, XXII ISPRS Congress, Melbourne, Australia, 2012.

315-321, 2012.

a b

c d

a b

c d