Interannual Variability of Warm-Season Rainfall over the US Great Plains in NASA/NSIPP and NCAR/CAM2.0 AMIP Simulations

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Interannual Variability of Warm-Season Rainfall
over the US Great Plains in NASA/NSIPP and
NCAR/CAM2.0 AMIP Simulations

• By
• Alfredo Ruiz-Barradas and Sumant Nigam
• Department of Meteorology
• University of Maryland
• 
  December 11, 2003

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Goal

• To assess interannual variability of
  precipitation over North America in AMIP-like
  runs of CAM2.0 and NSIPP models during summer
  months (June, July, August).

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Outline

• Data
• JJA Climatology
• Interannual Variability
• Remarks

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From TV News it seems we have the flood of the
century every year
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Data

• Precipitation
• Retrospective US and Mexico analysis.
• Hulme (University of East Anglia) data set.
• Xie/Arkin precipitation data set.
• NCEP ERA40 Reanalyses.
• SST from Hadley Center.
• NCEP ERA40 Reanalyses.
• AMIP simulations (ens05 mean) from the NSIPP
  model.
• AMIP simulation (case newsstamip06) from the CAM
  model.

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Data

• Reanalysis and simulations extrapolated to a
  5?2.5? grid on 17 pressure levels.
• Monthly climatology for the 1950-1998 period.
• Monthly anomalies wrt 1950-1998 climatology.
• JJA is the mean of June, July, August.
• Assessment through
• Standard Deviation
• Precipitation Index
• Multivariate analysis

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CLIMATOLOGY

• OF
• MOISTURE FLUXES

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Remarks Climatology

• Vertically Integrated Moisture Fluxes 1)
  Observations in agreement mean southerly
  moisture fluxes from MFD in the Gulf of Mexico
  and Caribbean Sea toward MFC in the GP output of
  moisture fluxes by transients from the GP region
  to the N and NE. 2) Simulations reproduce
  observed features at different extent with NSIPP
  and CAM having problems to capture both MFC and
  southerly moisture flux.
• Precipitation 1) No-reanalysis data sets agree
  very well. 2) NCEP Reanalysis overestimate
  precipitation ERA-40 is reasonably well. 3)
  Shifted maximum in simulations W in NSIPP, E in
  CAM.

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INTERANNUAL VARIABILITY

• OF PRECIPITATION

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Remarks Variability

• Precipitation 1) No-reanalysis data sets agree
  very well. 2) NCEP has larger variability than
  observations ERA-40 has reasonably variability
  but maximum is to the W of the GP. 3) Maximum of
  STD is shifted to the W in NSIPP and to the E in
  CAM.
• Indices 1) ERA40 has larger correlation with
  no-reanalysis indices than NCEP has. 2)
  Simulations disagree with each other and with
  verifying no-reanalysis observations. 3)
  Simulations suggest that precip over the GP
  region is largely of convective nature. However
  ERA-40 indicates that large-sacle precipitation
  is equally important!!

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REGRESSING INDICES
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Remarks Regressions

• GP precip anomalies are associated with mean
  southerly MF from the Gulf of Mexico and
  Caribbean Sea, as well as mean MFC. Transients
  enhance precip in the N and reduce it in the S of
  the region.
• Simulations disagree between them, NSIPP is
  closer to observations but with max of precip to
  the W of the max of MFC CAM however shows MF
  from the Pacific!!
• GP precip anomalies are linked to Pacific SSTs in
  both observations and simulations.
• A wave-train with lows over the oceans and
  central US present in observations is weakly
  captured in simulations.

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MULTIVARIATE ANALYSIS

• PrecipSfcTmpSfcPress

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JJA vs MJJA or JJAS

• REOF OF SST?(700)

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Remarks

• Multivariate analysis indicates
• Great Plains precipitation variability is the
  main mode of summer variability in observations
• This is however not the case in both model
  simulations
• Wet/dry events are cold/warm events in both
  observed and simulated summers.
• Part of the GP precip variability seems to be
  forced by the atmosphere. Transition months
  affect the structure of what is defined as
  summer.