Interannual Variability of Great Plains Summer Rainfall in Reanalyses and

NCAR and NASA AMIP-like Simulations

Alfredo Ruiz-Barradas

Sumant NigamDepartment of

Atmospheric and Oceanic ScienceUniversity of Maryland

5th International Scientific Conference on the Global Energy and Water CycleOrange County, California, USA

June 20-24, 2005

Motivation

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

• 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• 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

Standard Deviation of monthly rainfall during summer (JJA)

Blue box is used to define the Great Plains Precipitation (GPP) Index:Area-averaged precipitationanomalies.

•NARR assimilates verywell precipitation

•Quasi-realistic variabilityin global reanalyses

•Models better than globalreanalyses

Smoothed GPP Indices during the warm-season months

1993

19881950s 1970s

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-MexicoUS-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

Warm-season regressions of monthly GPP indices onPRECIPITATION

0.8 0.9

0.6 1.2

1.0 1.0

•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

Warm-season regressions of monthly GPP indices onSTATIONARY MOISTURE FLUXES 0.8

0.6 0.4

0.3 0.4

NARR•Southerly moisture fluxes from the Gulf of Mexico and Caribbean Sea converging over central US. •Westerly moisture fluxes from southwestern states

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

Warm-season regressions of monthly GPP indices onTRANSIENT MOISTURE FLUXES 0.0

0.0 0.1

-0.0

Transients carry moisture from the southeast to the northwest of the region,especially in NARR and ERA-40.

Warm-season regressions of monthly GPP indices onTOTAL MOISTURE FLUXES 0.7

0.6 0.5

0.4

•Total moisture fluxes keep the circulation features from the stationary component.

•Maximum of MFC is now centered in the region

Warm-season regressions of monthly GPP indices onEVAPORATION

0.2 0.1

-0.1 0.2

0.8 0.7

CI=1/3 of that in P & MFC

•NARR and GOLD have similar structure and amplitude of anomalies

•Reanalyses EVAPORATION anomalies are ~a third of MFC anomalies (except in NCEP).

•Simulated EVAPORATION anomalies are ~twice the MFC anomalies!!

Correlation between July’s rainfall and preceding and succeeding monthly rainfall.

US-MexicoLow dependence on previousmonths rainfall.

CAM3.0Dependence of previous months rainfall is comparableto reanalyses.

ReanalysesModerate dependence onprevious months rainfall.

NSIPPVery high dependence on previous months rainfall

Warm-season regressions of monthly GPP index onSURFACE RADIATION & TEMPERATURE

0.4 -0.8

-2.0 -2.0

-1.6 -1.4

SW anomalies are veryclose in NARR and both models, however, LH anomalies are ~ 3x largerin models:

NARR CAM3.0 NSIPPSW -5.1 -5.2 -4.9LH -5.8 -22.1 -19.9

Large evaporation in modelsinduces large surface cooling,decreased upward LW (increased LW anomalies), increased SH from the atm to the sfc and a total negativesurface energy balance:

NARR CAM3.0 NSIPPLW 4.5 10.4 10.0SH 6.8 14.8 13.6EB 0.4 -2.0 -1.6T -0.8 -2.0 -1.4

Conclusions•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 leastin the context of variability over the Great Plains).

References

• 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.