Transcript Summer Precipitation and Moisture Fluxes over the US in the NSIPP

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
Goal
• To assess interannual variability of
precipitation over North America in AMIPlike runs of CAM2.0 and NSIPP models
during summer months (June, July, August).
Outline
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Data
JJA Climatology
Interannual Variability
Remarks
From TV News: it seems we have “the flood of the century” every year…
Data
• Precipitation:
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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.
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
CLIMATOLOGY
OF
MOISTURE FLUXES
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.
INTERANNUAL
VARIABILITY
OF PRECIPITATION
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 noreanalysis indices than NCEP has. 2) Simulations
disagree with each other and with verifying noreanalysis 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!!
REGRESSING INDICES
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.
MULTIVARIATE ANALYSIS
Precip+SfcTmp+SfcPress
JJA vs MJJA or JJAS
REOF OF SST+(700)
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”.