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

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-scale in 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: 2.Three 50+ Year Retrospective Offline Runs 3.Three Reanalysis Datasets 1.Observations 18 Illinois soil moisture observation sites (1981- present)  S.E. Hollinger & S.A. Isard, 1994 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 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 4.NCEP Climate Forecast System (CFS) Datasets  S.Saha et al 2005

11. VICLBRRR2R1Obstemporal 0.860.810.740.550.710.80 Noah 0.910.860.570.600.83 VIC 0.910.630.490.72 LB 0.730.540.68 RR 0.630.47 R2 0.57 R1 Temporal anomaly correlations averaged over Illinois 0.61 ERA40

12. 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

17. Spatial & temporal anomaly correlations averaged over US spatialNoahVICLBRRR2R1temporal 0.830.810.710.520.48 Noah VIC 0.670.800.700.480.40 VIC LB 0.750.740.730.560.41 LB RR 0.570.600.680.540.33 RR R2 0.460.440.500.480.42 R2 R1 0.410.360.410.320.40 R1

22. Summary I. 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 II. RR has not reached its potential III. 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