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Intercomparison of US Land Surface Hydrologic Cycles from Multi-analyses & Models

Intercomparison of US Land Surface Hydrologic Cycles from Multi-analyses & Models. Yun Fan & Huug van den Dool CPC/NCEP/NOAA. NOAA 30th Annual Climate Diagnostic & Prediction Workshop, 27 October, 2005, State College, PA. Outline. Motivation Data

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Intercomparison of US Land Surface Hydrologic Cycles from Multi-analyses & Models

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  1. Intercomparison of US Land Surface Hydrologic Cycles from Multi-analyses & Models Yun Fan & Huug van den Dool CPC/NCEP/NOAA NOAA 30th Annual Climate Diagnostic & Prediction Workshop, 27 October, 2005, State College, PA

  2. Outline • Motivation • Data • Soil moisture annual cycle & long-term variability over Illinois • Spatial & temporal correlations over CONUS • Annual land surface hydrologic cycles • CFS land surface predictability • Summary

  3. Motivation Soil Moisture (SM): one of key factors in environmental processes, such as meteorology, hydrology & et al. Accurate SM is important for Weather & climate prediction. Long-term large-scalein situ measurement not yet established Remote sensing – promising but immature Calculated SM: depends on quality of forcing & models Questions: • Skills of soil moisture data sets • Land surface hydrologic predictability of CFS • Existing problems & possible reasons

  4. 8 Land Surface Datasets: • Observations • 18 Illinois soil moisture observation sites(1981- present) • S.E. Hollinger & S.A. Isard, 1994 2. Three 50+ Year Retrospective Offline Runs • Noah - Noah LSM Retrospective N-LDAS Run (1948-1998) – present • Y. Fan, H, van del Dool, D. Lomann & K. Mitchell, 2003 • VIC - VIC LSM Retrospective N-LDAS Run (1950-2000) • E. Maurer, A. Wood, J. Adam, D. Lettenmaier & B. Nijssen, 2002 • LB - CPC Leaky Bucket Soil Moisture Dataset • J. Huang, H. van den Dool & K. Georgakakos, 1996, Y. Fan & H. van den Dool, 2004 3. Three Reanalysis Datasets • RR - North American Regional Reanalysis (1979 - present) • F. Mesinger et al, 2003, 2005 • R1 – NCEP-NCAR Global Reanalysis I (1948 - present) • E. Kalnay et al, 1996 & R. Kistler et al 2001 • R2 – NCEP-DOE Global Reanalysis II (1979 - present) • M. Kanamitsu et al, 2002 • NCEP Climate Forecast System (CFS) Datasets • S.Saha et al 2005

  5. Temporal anomaly correlations averaged over Illinois 0.61ERA40

  6. dW(t)/dt: soil water storage change P(t): precipitation E(t): evaporation R(t): surface runoff G(t): subsurface runoff Res=P-E-R-G-dW/dt

  7. Spatial & temporal anomaly correlations averaged over US

  8. Summary • By overall mean annual cycle & interannual variability 1. Offline retrospective runs are generally better than reanalyses Noah < = > VIC LB RR R2 R1 Good --------------------------------------------------> poor 2. All other models (except Noah) either too dry and or too large annual cycle 3. Three reanalyses (RR > R2 > R1) shown steadily improvements • RR has not reached its potential • CFS (land surface soil moisture) 1. Good prediction skill (cr > 0.6, against to R2) for up to 5 months 2. Dry bias increase & delayed anomalies with lead time increase IV. Looking forward to R3

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