1 / 22

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

This study explores the accuracy and predictability of soil moisture data sets and land surface hydrologic cycles in the US. It compares multiple analyses and models, highlighting their strengths and weaknesses. The findings also discuss existing problems and potential reasons for discrepancies. Overall, the study provides valuable insights for weather and climate prediction.

martintodd
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related