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

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

MotivationSoil 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 establishedRemote sensing – promising but immatureCalculated SM: depends on quality of forcing & models

Questions:• Skills of soil moisture data sets• Land surface hydrologic predictability of CFS• Existing problems & possible reasons

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

VIC LB RR R2 R1 Obs temporal

0.86 0.81 0.74 0.55 0.71 0.80 Noah

0.91 0.86 0.57 0.60 0.83 VIC

0.91 0.63 0.49 0.72 LB

0.73 0.54 0.68 RR

0.63 0.47 R2

0.57 R1

Temporal anomaly correlations averaged over Illinois

0.61 ERA40

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

)()()()()( tGtRtEtPdttdW

Spatial & temporal anomaly correlations averaged over US

spatial Noah VIC LB RR R2 R1 temporal

0.83 0.81 0.71 0.52 0.48 Noah

VIC 0.67 0.80 0.70 0.48 0.40 VIC

LB 0.75 0.74 0.73 0.56 0.41 LB

RR 0.57 0.60 0.68 0.54 0.33 RR

R2 0.46 0.44 0.50 0.48 0.42 R2

R1 0.41 0.36 0.41 0.32 0.40 R1

US

S

Tcorrs1

years

t

Scorrt1

SummaryI. 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