Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/american-meteorological-society/plant-and-soil-parameter-caused-uncertainty-of-predicted-surface-mADFWBO470
References (51)
-
L. Luo, A. Robock, K. Vinnikov, C. Schlosser, A. Slater, A. Boone, H. Braden, P. Cox, P. Rosnay, R. Dickinson, Yongjiu Dai, Q. Duan, P. Etchevers, A. Henderson‐sellers, N. Gedney, Ye. Gusev, F. Habets, Jinwon Kim, E. Kowalczyk, K. Mitchell, O. Nasonova, J. Noilhan, A. Pitman, J. Schaake, A. Shmakin, T. Smirnova, P. Wetzel, Y. Xue, Zong‐Liang Yang, Q. Zeng (2003)
Effects of frozen soil on soil temperature, spring infiltration, and runoff: Results from the PILPS 2(d) experiment at Valdai, RussiaJournal of Hydrometeorology, 4
-
N. Mölders, U. Haferkorn, J. Döring, G. Kramm (2003)
Long-term investigations on the water budget quantities predicted by the hydro-thermodynamic soil vegetation scheme (HTSVS) – Part I: Description of the model and impact of long-wave radiation, roots, snow, and soil frostMeteorology and Atmospheric Physics, 84
-
R. Avissar (1991)
A statistical-dynamical approach to parameterize subgrid-scale land-surface heterogeneity in climate modelsSurveys in Geophysics, 12
-
A. Arakawa, V. Lamb (1977)
Computational Design of the Basic Dynamical Processes of the UCLA General Circulation Model, 17
-
R. Jackson, J. Canadell, J. Ehleringer, H. Mooney, O. Sala, E. Schulze (1996)
A global analysis of root distributions for terrestrial biomesOecologia, 108
-
J. Dorman, P. Sellers (1989)
A Global Climatology of Albedo, Roughness Length and Stomatal Resistance for Atmospheric General Circulation Models as Represented by the Simple Biosphere Model (SiB)Journal of Applied Meteorology, 28
-
P. Jarvis (1976)
The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the FieldPhilosophical Transactions of the Royal Society B, 273
-
D. Pollard, S. Thompson (1995)
Use of a land-surface-transfer scheme (LSX) in a global climate model: the response to doubling stomatal resistanceGlobal and Planetary Change, 10
-
Z. Toth, E. Kalnay (1993)
Ensemble Forecasting at NMC: The Generation of PerturbationsBulletin of the American Meteorological Society, 74
-
G. Kramm, R. Dlugi, N. Mölders, H. Mulled (1970)
Numerical Investigations Of The Dry Deposition Of Reactive Trace Gases, 3
-
B. Hicks, D. Baldocchi, T. Meyers, R. Hosker, D. Matt (1987)
A preliminary multiple resistance routine for deriving dry deposition velocities from measured quantitiesWater, Air, and Soil Pollution, 36
-
J. Dudhia (1993)
A Nonhydrostatic Version of the Penn State–NCAR Mesoscale Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold FrontMonthly Weather Review, 121
-
P. Sellers, Y. Mintz, Y. Sud, A. Dalcher (1986)
A Simple Biosphere Model (SIB) for Use within General Circulation ModelsJournal of the Atmospheric Sciences, 43
-
S. Grunwald, D. Rooney, K. Mcsweeney, B. Lowery (2001)
Development of pedotransfer functions for a profile cone penetrometerGeoderma, 100
-
B. Cosby, G. Hornberger, R. Clapp, T. Ginn (1984)
A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of SoilsWater Resources Research, 20
-
A. Betts, J. Ball (1997)
Albedo over the boreal forestJournal of Geophysical Research, 102
-
G. Bonan, K. Oleson, M. Vertenstein, S. Levis, X. Zeng, Yongjiu Dai, R. Dickinson, Zong‐Liang Yang (2002)
The land surface climatology of the community land model coupled to the NCAR community climate modelJournal of Climate, 15
-
A. Henderson‐sellers, Zong‐Liang Yang, R. Dickinson (1993)
The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3Bulletin of the American Meteorological Society, 76
-
J. Deardorff (1978)
Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetationJournal of Geophysical Research, 83
-
N. Mölders (2001)
On the uncertainty in mesoscale modeling caused by surface parametersMeteorology and Atmospheric Physics, 76
-
D. Collins, R. Avissar (1994)
An Evaluation with the Fourier Amplitude Sensitivity Test (FAST) of Which Land-Surface Parameters Are of Greatest Importance in Atmospheric ModelingJournal of Climate, 7
-
Z. Toth, Yuejian Zhu, T. Marchok (2001)
The Use of Ensembles to Identify Forecasts with Small and Large UncertaintyWeather and Forecasting, 16
-
D. Verseghy (2007)
Class—A Canadian land surface scheme for GCMS. I. Soil modelInternational Journal of Climatology, 11
-
G. Panin, G. Tetzlaff, A. Raabe (1998)
Inhomogeneity of the Land Surface and Problems in theParameterization of Surface Fluxes in Natural ConditionsTheoretical and Applied Climatology, 60
-
M. Wilson, A. Henderson‐sellers, R. Dickinson, P. Kennedy (1987)
Sensitivity of the biosphere-atmosphere transfer scheme (BATS) to the inclusion of variable soil characteristicsJournal of Applied Meteorology, 26
-
J. Reisner, R. Rasmussen, R. Bruintjes (1998)
Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale modelQuarterly Journal of the Royal Meteorological Society, 124
-
Song‐You Hong, H. Pan (1996)
Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast ModelMonthly Weather Review, 124
-
A. Henderson‐sellers (1993)
A Factorial Assessment of the Sensitivity of the BATS Land-Surface Parameterization SchemeJournal of Climate, 6
-
Douglas Miller, R. White (1998)
A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology ModelingEarth Interactions, 2
-
S. Meyer (1975)
Data analysis for scientists and engineers -
M. Tracton, E. Kalnay (1993)
Operational Ensemble Prediction at the National Meteorological Center: Practical AspectsWeather and Forecasting, 8
-
J. Noilhan, S. Planton (1989)
A Simple Parameterization of Land Surface Processes for Meteorological ModelsMonthly Weather Review, 117
-
E. Kreyszig (1998)
Statistische Methoden und ihre Anwendungen -
Y. Shao, A. Henderson‐sellers (1996)
Modeling soil moisture: A Project for Intercomparison of Land Surface Parameterization Schemes Phase 2(b)Journal of Geophysical Research, 101
-
K. Wilson, A. Goldstein, E. Falge, M. Aubinet, D. Baldocchi, P. Berbigier, C. Bernhofer, R. Ceulemans, H. Dolman, C. Field, A. Grelle, A. Ibrom, B. Law, A. Kowalski, T. Meyers, J. Moncrieff, R. Monson, W. Oechel, J. Tenhunen, R. Valentini, S. Verma (2002)
Energy balance closure at FLUXNET sitesAgricultural and Forest Meteorology, 113
-
N. Mölders, W. Rühaak (2002)
On the impact of explicitly predicted runoff on the simulated atmospheric response to small-scale land-use changes—an integrated modeling approachAtmospheric Research, 63
-
A. Slater, C. Schlosser, C. Desborough, A. Pitman, A. Henderson‐sellers, A. Robock, K. Ya, K. Mitchell, A. Boone, H. Braden, F. Hen, P. Ox, P. Rosnay, R. Dickinson, Q. Duan, J. Entin, N. Gedney, Jinwon Kim, V. Oren, E. Kowalczyk, O. Nasonova, J. Noilhan, S. Schaake, A. Shmakin, D. Verseghy, P. Etzel, Y. Ue, Qingdong Zeng (2001)
The Representation of Snow in Land Surface Schemes: Results from PILPS 2(d)Journal of Hydrometeorology, 2
-
Chen (1997)
Cabauw experimental results from the Project of Intercomparison of Land Surface Schemes (PILPS).J. Climate, 10
-
R. Avissar, R. Pielke (1989)
A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorologyMonthly Weather Review, 117
-
M. Jin, S. Liang (2006)
An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing ObservationsJournal of Climate, 19
-
N. Mölders, J. Walsh (2004)
Atmospheric response to soil-frost and snow in Alaska in MarchTheoretical and Applied Climatology, 77
-
Fei Chen, J. Dudhia (2001)
Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and SensitivityMonthly Weather Review, 129
-
G. Grell (1993)
Prognostic evaluation of assumptions used by cumulus parameterizationsMonthly Weather Review, 121
-
J. Charney (1975)
Dynamics of deserts and drought in the SahelQuarterly Journal of the Royal Meteorological Society, 101
-
T. Hamill, S. Colucci (1997)
Verification of Eta–RSM Short-Range Ensemble ForecastsMonthly Weather Review, 125
-
G. Kramm, N. Beier, T. Foken, H. Müller, P. Schröder, W. Seiler (1996)
A SVAT scheme for NO, NO2, and O3 — Model description and test resultsMeteorology and Atmospheric Physics, 61
-
G. Gutman, A. Ignatov (1998)
The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction modelsInternational Journal of Remote Sensing, 19
-
F. Calhoun, N. Smeck, B. Slater, J. Bigham, G. Hall (2001)
Predicting bulk density of Ohio Soils from Morphology, Genetic Principles, and Laboratory Characterization DataSoil Science Society of America Journal, 65
-
M. McCumber, R. Pielke (1981)
Simulation of the effects of surface fluxes of heat and moisture in a mesoscale numerical model: 1. Soil layerJournal of Geophysical Research, 86
-
C. Schlosser, A. Slater, A. Robock, A. Pitman, N. Speranskaya, K. Mitchell, A. Boone, H. Braden, Fei Chen, P. Cox, P. Rosnay, C. Desborough, R. Dickenson, Yongjiu Dai, Q. Duan, J. Entin, P. Etchevers, Y. Gusev, F. Habets, Jinwon Kim, V. Koren, E. Kowalczyk, O. Nasonova, J. Noilhan, J. Schaake, A. Shmakin, T. Smirnova, P. Wetzel, Y. Xue, Zong‐Liang Yang (2000)
Simulations of a boreal grassland hydrology at Valdai, Russia: PILPS phase 2(d).Monthly Weather Review, 128
-
R. Clapp, G. Hornberger (1978)
Empirical equations for some soil hydraulic propertiesWater Resources Research, 14
- Publisher
- American Meteorological Society
- Copyright
- Copyright © 2004 American Meteorological Society
- ISSN
- 1520-0493
- DOI
- 10.1175/MWR3046.1
- Publisher site
- See Article on Publisher Site
Abstract
Simulated surface fluxes depend on one or more empirical plant or soil parameters that have a standard deviation (std dev). Thus, simulated fluxes will have a stochastic error (or std dev) resulting from the parameters’ std dev. Gaussian error propagation (GEP) principles are used to calculate the std dev for fluxes predicted by the hydro–thermodynamic soil–vegetation scheme to identify prediction limitations due to stochastic errors, parameterization weaknesses, and critical parameters, and to prioritize which parameters to measure with higher accuracy. Relative errors of net radiation, sensible, latent, and ground heat flux, on average, are 7%, 10%, 6%, and 26%, respectively. The analysis identified the parameterization of thermal conductivity as the dominant influence on the std dev of ground heat flux. For net radiation, critical parameters are vegetation fraction and ground emissivity; for sensible and latent heat fluxes, vegetation fraction. Minimum stomatal resistance and leaf area index dominate the std dev of stomatal resistance for most vegetation and soil types. The empirical parameters with the highest relative error are not necessarily the greatest contributors to the std dev of the predicted flux. Based on the analysis high priority should be given to measurements of vegetation fraction, ground emissivity, minimum stomatal resistance, leaf area index in general, and the permanent wilting point and field capacity for clay and clay loam. Moreover, further specification of clay-type soils and tundra-type vegetation may improve the accuracy of the lower boundary condition in Arctic numerical weather prediction. Since GEP showed itself able to identify critical parameters and (parts of) parameterizations, GEP analysis could form a basis for parameterization intercomparisons and for parameter determination aimed at improving models.
Journal
Monthly Weather Review – American Meteorological Society
Published: Dec 8, 2004