# Presentation: 233792

## 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**

## 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**

## 8 Land Surface Datasets:

- 2.	Three 50+ Year Retrospective Offline Runs

- 3.	Three Reanalysis Datasets

- 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***

- NCEP Climate Forecast System (CFS) Datasets
**S.Saha et al 2005**

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## Temporal anomaly correlations averaged over Illinois

| 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**

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## 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 |

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## 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**** **
**   **