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Bohua Huang, E. Schneider (1995)
The Response of an Ocean General Circulation Model to Surface Wind Stress Produced by an Atmospheric General Circulation ModelMonthly Weather Review, 123
(1999)
The Global Soil Wetness
(1997)
A two - dimensional implementation of the Simple Biosphere ( SiB ) model
M. Ji, Arun Kumar, A. Leetmaa (1994)
An experimental coupled forecast system at the national meteorological center: some early resultsTellus A, 46
B. Kirtman, J. Shukla, Bohua Huang, Zhen-Liang Zhu, E. Schneider (1997)
Multiseasonal predictions with a coupled tropical ocean-global atmosphere systemMonthly Weather Review, 125
Y. Xue, P. Sellers, J. Kinter, J. Shukla (1991)
A Simplified Biosphere Model for Global Climate StudiesJournal of Climate, 4
K. Kondrat'ev, L. D'iachenko, V. Kozoderov (1988)
The earth radiation budget
P. Dirmeyer, F. Zeng, A. Ducharne, J. Morrill, R. Koster (2000)
The Sensitivity of Surface Fluxes to Soil Water Content in Three Land Surface SchemesJournal of Hydrometeorology, 1
D. Entekhabi, I. Rodríguez‐Iturbe, R. Bras (1992)
Variability in Large-Scale Water Balance with Land Surface-Atmosphere InteractionJournal of Climate, 5
P. Dirmeyer (2000)
Using a global soil wetness dataset to improve seasonal climate simulationJournal of Climate, 13
(1996)
The effect of the cumulus convection on the climate of the COLA general circulation model
H. Douville, P. Viterbo, J. Mahfouf, A. Beljaars (2000)
Evaluation of the Optimum Interpolation and Nudging Techniques for Soil Moisture Analysis Using FIFE DataMonthly Weather Review, 128
P. Xie, P. Arkin (1997)
Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model OutputsBulletin of the American Meteorological Society, 78
J. Charney (1975)
Dynamics of deserts and drought in the SahelQuarterly Journal of the Royal Meteorological Society, 101
R. Koster, M. Suárez, M. Heiser (2000)
Variance and Predictability of Precipitation at Seasonal-to-Interannual TimescalesJournal of Hydrometeorology, 1
J. Sela (1980)
Spectral Modeling at the National Meteorological CenterMonthly Weather Review, 108
R. Koster, M. Suárez (1995)
Relative contributions of land and ocean processes to precipitation variabilityJournal of Geophysical Research, 100
A. Betts, P. Viterbo, E. Wood (1998)
Surface Energy and Water Balance for the Arkansas-Red River Basin from the ECMWF ReanalysisJournal of Climate, 11
P. Dirmeyer, A. Dolman, N. Sato (1999)
The Pilot Phase of the Global Soil Wetness ProjectBulletin of the American Meteorological Society, 80
E. Schneider, Zhen-Liang Zhu, B. Giese, Bohua Huang, B. Kirtman, J. Shukla, J. Carton (1997)
Annual Cycle and ENSO in a Coupled Ocean-Atmosphere General Circulation ModelMonthly Weather Review, 125
S. Manabe, Rj Er, M. Spelman, K. Bryan (1991)
Transient responses of a coupled ocean-atmosphere model to gradual changes of atmospheric CO2
(1992)
Seasonal variation of the surface radiation budget derived from ISCCP - C 1 data
Powder Mill Road
Y. Xue, F. Zeng, C. Schlosser (1996)
SSiB and its sensitivity to soil properties-A case study using HAPEX-Mobilhy data, 13
R. Reynolds, Thomas Smith (1994)
Improved Global Sea Surface Temperature Analyses Using Optimum InterpolationJournal of Climate, 7
A coupled land––atmosphere climate model is examined for evidence of climate drift in the land surface state variable of soil moisture. The drift is characterized as pathological error growth in two different ways. First is the systematic error that is evident over seasonal timescales, dominated by the error modes with the largest saturated amplitude: systematic drift. Second is the fast-growing modes that are present in the first few days after either initialization or a data assimilation increment: incremental drift. When the drifts are robust across many ensemble members and from year to year, they suggest a source of drift internal to the coupled system. This source may be due to problems in either component model or in the coupling between them. Evidence is presented for both systematic and incremental drift. The relationship between the two types of drift at any given point is shown to be an indication of the type and strength of feedbacks within the coupled system. Methods for elucidating potential sources of the drift are proposed.
Journal of Hydrometeorology – American Meteorological Society
Published: May 17, 2000
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