TY - JOUR
T1 - The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins
AU - Yang, Zong-Liang
AU - Niu, Guo-Yue
AU - Mitchell, Kenneth E.
AU - Chen, Fei
AU - Ek, Michael B.
AU - Barlage, Michael
AU - Longuevergne, Laurent
AU - Manning, Kevin
AU - Niyogi, Dev
AU - Tewari, Mukul
AU - Xia, Youlong
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was funded by NASA grants NAG5-10209, NAG5-12577, NNX07A79G, NNX 08AJ84G, and NNX09AJ48G, NOAA grant NA07OAR4310076, a KAUST grant, and National Natural Science Foundation of China Project 40828004. We thank Robert E. Dickinson for reading the manuscript and the Texas Advanced Computing Center (TACC) for providing us with computational resources.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2011/6/24
Y1 - 2011/6/24
N2 - The augmented Noah land surface model described in the first part of the two-part series was evaluated here over global river basins. Across various climate zones, global-scale tests can reveal a model's weaknesses and strengths that a local-scale testing cannot. In addition, global-scale tests are more challenging than local- and catchment-scale tests. Given constant model parameters (e. g., runoff parameters) across global river basins, global-scale tests are more stringent. We assessed model performance against various satellite and ground-based observations over global river basins through six experiments that mimic a transition from the original Noah LSM to the fully augmented version. The model shows transitional improvements in modeling runoff, soil moisture, snow, and skin temperature, despite considerable increase in computational time by the fully augmented Noah-MP version compared to the original Noah LSM. The dynamic vegetation model favorably captures seasonal and spatial variability of leaf area index and green vegetation fraction. We also conducted 36 ensemble experiments with 36 combinations of optional schemes for runoff, leaf dynamics, stomatal resistance, and the β factor. Runoff schemes play a dominant and different role in controlling soil moisture and its relationship with evapotranspiration compared to ecological processes such as β the factor, vegetation dynamics, and stomatal resistance. The 36-member ensemble mean of runoff performs better than any single member over the world's 50 largest river basins, suggesting a great potential of land-based ensemble simulations for climate prediction. Copyright © 2011 by the American Geophysical Union.
AB - The augmented Noah land surface model described in the first part of the two-part series was evaluated here over global river basins. Across various climate zones, global-scale tests can reveal a model's weaknesses and strengths that a local-scale testing cannot. In addition, global-scale tests are more challenging than local- and catchment-scale tests. Given constant model parameters (e. g., runoff parameters) across global river basins, global-scale tests are more stringent. We assessed model performance against various satellite and ground-based observations over global river basins through six experiments that mimic a transition from the original Noah LSM to the fully augmented version. The model shows transitional improvements in modeling runoff, soil moisture, snow, and skin temperature, despite considerable increase in computational time by the fully augmented Noah-MP version compared to the original Noah LSM. The dynamic vegetation model favorably captures seasonal and spatial variability of leaf area index and green vegetation fraction. We also conducted 36 ensemble experiments with 36 combinations of optional schemes for runoff, leaf dynamics, stomatal resistance, and the β factor. Runoff schemes play a dominant and different role in controlling soil moisture and its relationship with evapotranspiration compared to ecological processes such as β the factor, vegetation dynamics, and stomatal resistance. The 36-member ensemble mean of runoff performs better than any single member over the world's 50 largest river basins, suggesting a great potential of land-based ensemble simulations for climate prediction. Copyright © 2011 by the American Geophysical Union.
UR - http://hdl.handle.net/10754/599889
UR - http://doi.wiley.com/10.1029/2010JD015140
UR - http://www.scopus.com/inward/record.url?scp=79959819353&partnerID=8YFLogxK
U2 - 10.1029/2010JD015140
DO - 10.1029/2010JD015140
M3 - Article
SN - 0148-0227
VL - 116
JO - Journal of Geophysical Research
JF - Journal of Geophysical Research
IS - D12
ER -