Systematic Bias in Land Surface Models

A neural network–based flux correction technique is applied to three land surface models. It is then used to show that the nature of systematic model error in simulations of latent heat, sensible heat, and the net ecosystem exchange of CO2 is shared between different vegetation types and indeed different models .B y manipulating the relationship between the dataset used to train the correction technique and that used to test it, it is shown that as much as 45% of per-time-step model root-mean-square error in these flux outputs is due to systematic problems in those model processes insensitive to changes in vegetation parameters. This is shown in the three land surface models using flux tower measurements from 13 sites spanning 2 vegetation types. These results suggest that efforts to improve the representation of fundamental processes in land surface models, rather than parameter optimization, are the key to the development of land surface model ability.

[1]  Gabriel Abramowitz,et al.  Towards a benchmark for land surface models , 2005 .

[2]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[3]  Prashant D. Sardeshmukh,et al.  The Diagnosis of Mechanical Dissipation in the Atmosphere from Large-Scale Balance Requirements , 1992 .

[4]  Ray Leuning,et al.  A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy. II. Comparison with measurements , 1998 .

[5]  Kuolin Hsu,et al.  Neural Error Regression Diagnosis (NERD): A Tool for Model Bias Identification and Prognostic Data Assimilation , 2006 .

[6]  Kuolin Hsu,et al.  Self‐organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis , 2002 .

[7]  A. Pitman.  The evolution of, and revolution in, land surface schemes designed for climate models , 2003 .

[8]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[9]  Robert Clement,et al.  On the validation of models of forest CO2 exchange using eddy covariance data: some perils and pitfalls. , 2005, Tree physiology.

[10]  R. Valentini,et al.  A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization , 2003 .

[11]  Willem Bouten,et al.  Analysing forest transpiration model errors with artificial neural networks , 2001 .

[12]  R. Dickinson,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1993 .

[13]  Willem Bouten,et al.  Water and carbon fluxes above European coniferous forests modelled with artificial neural networks , 1999 .

[14]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[15]  S. Sorooshian,et al.  Effective and efficient algorithm for multiobjective optimization of hydrologic models , 2003 .

[16]  J. Albertson,et al.  Diagnosing model error in canopy‐atmosphere exchange using empirical orthogonal function analysis , 2006 .

[17]  Peter A. Coppin,et al.  Parameter estimation in surface exchange models using nonlinear inversion: how many parameters can we estimate and which measurements are most useful? , 2001 .

[18]  R. Dickinson,et al.  The Common Land Model , 2003 .

[19]  A. J. Pitman,et al.  What are the limits to statistical error correction in land surface schemes when projecting the future? , 2005 .

[20]  Sallie Keller-McNulty,et al.  Workshop on Statistical Approaches for the Evaluation of Complex Computer Models , 2002 .

[21]  Mariana Vertenstein,et al.  The Community Land Model's Dynamic Global Vegetation Model (CLM-DGVM): Technical description and user's guide , 2004 .

[22]  R. Leuning,et al.  A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy I:: Model description and comparison with a multi-layered model , 1998 .

[23]  Ernst Linder,et al.  Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations , 2005 .

[24]  P. Sellers,et al.  Testing the Simple Biosphere model (SiB) using point micrometeorological and biophysical data , 1987 .

[25]  Christian L. Keppenne,et al.  Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction , 2005 .

[26]  Andrew E. Suyker,et al.  Gap filling strategies for long term energy flux data sets , 2001 .

[27]  W. Oechel,et al.  Energy balance closure at FLUXNET sites , 2002 .

[28]  R. Todling,et al.  Data Assimilation in the Presence of Forecast Bias: The GEOS Moisture Analysis , 2000 .

[29]  Peter E. Thornton,et al.  Technical Description of the Community Land Model (CLM) , 2004 .

[30]  Suranjana Saha,et al.  Response of the NMC MRF Model to Systematic-Error Correction within Integration , 1992 .