multimodel ensemble reconstruction of drought over the continental u s
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Multimodel Ensemble Reconstruction of Drought over the Continental U.S. Aihui Wang 1 , Dennis P. Lettenmaier 1 , Sarith Mahanama 2 , and Randal D. Koster 2 for presentation at Climate Diagnostics and Prediction Workshop Tallahassee, FL October 24 2007

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multimodel ensemble reconstruction of drought over the continental u s
Multimodel Ensemble Reconstruction of Drought over the Continental U.S

Aihui Wang1, Dennis P. Lettenmaier1,

Sarith Mahanama2, and Randal D. Koster2

for presentation at

Climate Diagnostics and Prediction Workshop

Tallahassee, FL

October 24 2007

1Department of Civil and Environmental Engineering,

University of Washington, Seattle, WA 98195

2NASA Goddard Space Flight Center, Greenbelt MD 20771

outline
Outline
  • Motivation
  • Models
  • Methodology
  • Results
  • Summary
motivation

Widely used, but link to direct observations (e.g., of soil moisture) is weak – hence reliance on indirect methods, such as PDSI.

  • Need for reproducible basis for identifying drought-affected regions.
  • Land surface model representations of soil moisture (and runoff) offer an alternative means for estimating severity, frequency, duration, and variability of current droughts, and linking them to the climatology of observed droughts.
Motivation
models
Models
  • VIC: Variable Infiltration Capacity Model

(Liang et al. 1994)

  • CLM3.5: Community Land Model version 3.5

(Oleson et al. 2007)

  • NOAH LSM: NCEP, OSU, Air Force, Hydrol. research lab

(Mitchell et al. 1994, Chen and Mitchell 1996)

  • Catchment LSM: NASA Seasonal-to-Interannual Prediction Project (NSIPP) LSM

(Koster et al. 2000; Ducharne et al. 2000)

slide6
Data
  • Daily precipitation and temperature max-min, other land surface variables (downward solar and longwave radiation, near-surface humidity, and wind) derived via index methods. Methods as described in Maurer et al. (2002). Data duration is from 1915-2003, and period of analysis is 1920-2003 . Spatial resolution 0.5 (3322 land grid cells), domain conterminous United States.
  • Soil and vegetation parameters are from different sources for different models (generally NLDAS), as provided by model developers. Other parameters are model standard setup.
slide7

The challenge: Different land schemes have different soil moisture dynamics

Model simulated

soil moisture at cell

(40.25N, 112.25W)

slide8

Solution: Normalized total column soil moisture

  • For each model, total column soil moisture was expressed as percentiles (hence by construct, uniformly distributed from zero to one).
  • Percentiles were estimated for each model by month, using simulated total column soil moisture for the period 1920-2003.
  • Percentiles were computed using the Weibull plotting position formula.
ensemble methods
Ensemble methods

Two ensemble methods were used:

Ensemble-1: averaged 4 modeled soil moisture percentiles of each grid cell on monthly scale.

Ensemble –2: first, normalized column total soil moisture

modeled by 4 models individually; second, averaged those normalized soil moisture of each grid cell in 4 models; third, calculated percentiles of those averaged values .

multimodel comparison soil moisture as percentiles

July 1934

Multimodel comparison –soil moisture as percentiles

VIC

CLM3.5

NOAH

Catchment

Ensemble-2

Ensemble-1

november 1952
November 1952

VIC

CLM3.5

NOAH

Catchment

Ensemble-1

Ensemble-2

october 1963
October 1963

CLM3.5

VIC

NOAH

Catchment

Ensemble-1

Ensemble-2

february 1977
February 1977

CLM3.5

VIC

NOAH

Catchment

Ensemble-1

Ensemble-2

june 1988
June 1988

CLM3.5

VIC

NOAH

Catchment

Ensemble-2

Ensemble-1

june 2002
June 2002

CLM3.5

VIC

NOAH

Catchment

Ensemble-2

Ensemble-1

conclusions
Conclusions
  • Current drought products suffer from the absence of reproducible, objective methods for identifying drought extent and severity.
  • Although widely used, PDSI has well-known shortcomings, especially the absence of a strong link to physical processes
  • Land surface parameterizations, such as the family of NLDAS models, avoid these shortcomings. However, soil moisture, a key drought-related variable, is model-dependent
  • Multi-model estimates of soil moisture, appropriately normalized, address all of the above shortcomings. When forced with common observations, major drought events appear to be plausibly reproduced by the individual models, and two methods of combining results into a multi-model ensemble.
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