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Interannual Variability of Great Plains Summer Rainfall in Reanalyses and NCAR and NASA AMIP-like Simulations. Alfredo Ruiz-Barradas Sumant Nigam. Department of Atmospheric and Oceanic Science University of Maryland.

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Slide1 l.jpg

Interannual Variability of Great Plains Summer Rainfall in Reanalyses and NCAR and NASA AMIP-like Simulations

Alfredo Ruiz-Barradas

Sumant Nigam

Department of

Atmospheric and Oceanic Science

University of Maryland

5th International Scientific Conference on the Global Energy and Water Cycle

Orange County, California, USA

June 20-24, 2005


Motivation l.jpg
Motivation Reanalyses and NCAR and NASA AMIP-like Simulations

To better know the structure and mechanisms of precipitation variability in nature and models

At issue:

  • Model validation

  • Relative contributions of local (evaporation) and remote (moisture fluxes) water sources

  • SST-Circulation-Hydroclimate linkages


Outline l.jpg
Outline Reanalyses and NCAR and NASA AMIP-like Simulations

  • The data sets.

  • Precipitation variability over the Great Plains.

  • Structure of hydroclimate fields and their relative contributions associated with precipitation anomalies.

  • Implications on the surface energy balance.

  • Conclusions.

  • References.


Data sets l.jpg
Data Sets Reanalyses and NCAR and NASA AMIP-like Simulations

  • North American Regional Reanalysis (NARR): 1979-1998.

  • ECMWF Global Reanalysis (ERA-40): 1958-1998.

  • NCEP Global Reanalysis (NCEP): 1950-1998

  • AMIP integrations from:

    • NCAR’s Community Atmospheric Model, version 3.0 (CAM3.0): 1950-1998

    • NASA’s Seasonal-to-Interannual Prediction Project Model (NSIPP): 1950-1998.

  • CPC’s US-Mexico retrospective precipitation analysis

    (US-Mexico): 1950-1998

  • COLA’s Global Offline Land Surface Data set (GOLD): 1979-1998


Slide5 l.jpg

Standard Deviation of Reanalyses and NCAR and NASA AMIP-like Simulations

monthly rainfall during summer (JJA)

  • NARR assimilates very

    • well precipitation

  • Quasi-realistic variability

    • in global reanalyses

  • Models better than global

    • reanalyses

Blue box is used to define

the Great Plains

Precipitation (GPP) Index:

Area-averaged precipitation

anomalies.


Slide6 l.jpg

Smoothed GPP Indices during the warm-season months Reanalyses and NCAR and NASA AMIP-like Simulations

1993

1950s

1970s

1988

Monthly JJA STD (mm/day)

US-Mexico NARR ERA-40 NCEP CAM3.0 NSIPP

0.90 0.81 0.66 1.21 0.96 0.99

Correlations wrt US-Mexico

US-Mexico NARR ERA-40 NCEP CAM3.0 NSIPP

Monthly JJA 1 0.99 0.71 0.53 0.11 -0.09

Smoothed 1 0.99 0.55 0.33 0.25 0.06


Slide7 l.jpg

Warm-season regressions Reanalyses and NCAR and NASA AMIP-like Simulations

of monthly GPP indices on

PRECIPITATION

0.9

0.8

  • Regionally confined

  • anomalies in NARR &

  • US-Mexico

  • Sub-continental scale

  • anomalies in ERA-40 and

  • NCEP

  • Simulated anomalies are

  • closer to observations than

  • global reanalyses

1.2

0.6

1.0

1.0


Slide8 l.jpg

Warm-season regressions Reanalyses and NCAR and NASA AMIP-like Simulations

of monthly GPP indices on

STATIONARY MOISTURE

FLUXES

0.8

  • NARR

  • Southerly moisture fluxes

  • from the Gulf of Mexico and

  • Caribbean Sea converging

  • over central US.

  • Westerly moisture fluxes

  • from southwestern states

0.6

0.4

  • Global Reanalyses

  • ERA-40,especially, has

  • both pathways

  • Models

  • CAM3.0 has very weak

  • transport from the Gulf of

  • Mexico

  • NSIPP has stronger fluxes

  • from the Gulf of Mexico

  • None of the models has

  • westerly fluxes

0.3

0.4


Slide9 l.jpg

Warm-season regressions Reanalyses and NCAR and NASA AMIP-like Simulations

of monthly GPP indices on

TRANSIENT MOISTURE

FLUXES

0.0

Transients carry moisture

from the southeast to the

northwest of the region,

especially in NARR and

ERA-40.

0.0

0.1

-0.0


Slide10 l.jpg

Warm-season regressions Reanalyses and NCAR and NASA AMIP-like Simulations

of monthly GPP indices on

TOTAL MOISTURE

FLUXES

0.7

  • Total moisture fluxes keep

  • the circulation features

  • from the stationary

  • component.

  • Maximum of MFC is now

  • centered in the region

0.5

0.6

0.4


Slide11 l.jpg

Warm-season regressions Reanalyses and NCAR and NASA AMIP-like Simulations

of monthly GPP indices on

EVAPORATION

0.1

0.2

  • NARR and GOLD have

  • similar structure and

  • amplitude of anomalies

-0.1

0.2

  • Reanalyses EVAPORATION

  • anomalies are ~a third of

  • MFC anomalies (except in

  • NCEP).

  • Simulated EVAPORATION

  • anomalies are ~twice the

  • MFC anomalies!!

0.8

0.7

CI=1/3 of that in P & MFC


Slide12 l.jpg

Correlation between July’s Reanalyses and NCAR and NASA AMIP-like Simulations

rainfall and preceding and

succeeding monthly rainfall.

US-Mexico

Low dependence on previous

months rainfall.

Reanalyses

Moderate dependence on

previous months rainfall.

CAM3.0

Dependence of previous

months rainfall is comparable

to reanalyses.

NSIPP

Very high dependence on

previous months rainfall


Slide13 l.jpg

Warm-season regressions Reanalyses and NCAR and NASA AMIP-like Simulations

of monthly GPP index on

SURFACE RADIATION

& TEMPERATURE

SW anomalies are very

close in NARR and both

models, however, LH

anomalies are ~ 3x larger

in models:

-0.8

0.4

NARR CAM3.0 NSIPP

SW -5.1 -5.2 -4.9

LH -5.8 -22.1 -19.9

-2.0

-2.0

Large evaporation in models

induces large surface cooling,

decreased upward LW

(increased LW anomalies),

increased SH from the atm

to the sfc and a total negative

surface energy balance:

NARR CAM3.0 NSIPP

LW 4.5 10.4 10.0

SH 6.8 14.8 13.6

EB 0.4-2.0 -1.6

T -0.8 -2.0 -1.4

-1.4

-1.6


Conclusions l.jpg
Conclusions Reanalyses and NCAR and NASA AMIP-like Simulations

  • Reanalyses suggest that remote water sources (moisture fluxes) dominate over local water sources (evaporation) in the generation of interannual rainfall variability over the Great Plains during the warm-season.

  • Models put a premium on local water sources (precipitation recycling).

  • Deficient simulation of moisture pathways feeding the Great Plains.

  • In consequence: regional hydroclimate simulations and predictions remain challenging for global models (at least

    in the context of variability over the Great Plains).


References l.jpg
References Reanalyses and NCAR and NASA AMIP-like Simulations

  • Nigam, S., and A. Ruiz-Barradas, 2005: Seasonal hydroclimate variability over North America in ERA-40, Regional Reanalysis and AMIP simulations. Submitted to J. Climate.

  • Ruiz-Barradas, A., and S. Nigam, 2005a: Warm-season Precipitation Variability over the US Great Plains in Observations, NCEP and ERA-40 Reanalyses, and NCAR and NASA Atmospheric Simulations. J. Climate., 18, 1808-1829.

  • ______, ______, 2005b: Great Plains Hydroclimate Variability: The View from the North American Regional Reanalysis. Submitted, J. Climate.


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