Interannual Variability Interannual Variability during summer (DJF) in during summer (DJF) in Observations and in the Observations and in the

COLA model COLA model J. Nogues-Paegle J. Nogues-Paegle (University of Utah)(University of Utah)

C. Saulo and C. Vera C. Saulo and C. Vera (University of Buenos (University of Buenos Aires)Aires)

B. Kirtman and V. MisraB. Kirtman and V. Misra (George Mason (George Mason University and Center for Ocean-Land-Atmosphere University and Center for Ocean-Land-Atmosphere

Studies)Studies)

Emphasis on South American response to ENSO and South Atlantic surface temperature variability

OutlineOutline Comparison of DJF and inter-annual Comparison of DJF and inter-annual

variability of rainfall and low-level winds variability of rainfall and low-level winds (CMAP, Reanalysis 1 and 2 and seasonal (CMAP, Reanalysis 1 and 2 and seasonal runs of the COLA model forced with runs of the COLA model forced with observed SSTs) observed SSTs)

How do the different analyses reproduce How do the different analyses reproduce the SOI signal over South America?the SOI signal over South America?

Impact of South Atlantic SST’s on South Impact of South Atlantic SST’s on South American variabilityAmerican variability

Data SetsData Sets CMAP: Climate Prediction Center Merged Analysis of CMAP: Climate Prediction Center Merged Analysis of

Precipitation, on a 2.5 x 2.5 grid, obtained using Precipitation, on a 2.5 x 2.5 grid, obtained using gauge-based monthly analysis and satellite estimates gauge-based monthly analysis and satellite estimates (Xie and Arkin 1997) (Xie and Arkin 1997)

REAN 1 and 2, both run atT62, 28 levels with mostly REAN 1 and 2, both run atT62, 28 levels with mostly same raw observational data. R-2 was done to same raw observational data. R-2 was done to correct some errors in R 1 (such as insertion of correct some errors in R 1 (such as insertion of PAOBs at wrong longitudes), and includes PAOBs at wrong longitudes), and includes replacement of model precipitation at the land replacement of model precipitation at the land surface with a pentad precipitation estimate similar surface with a pentad precipitation estimate similar to CMAP to maintain a reasonable soil wetness to CMAP to maintain a reasonable soil wetness (Kanamitsu et al., 2002) (Kanamitsu et al., 2002)

COLA model run at T62L28 with observed SSTs, for COLA model run at T62L28 with observed SSTs, for 1981 through 2003 for DJFM (Misra and Marx, 2004) 1981 through 2003 for DJFM (Misra and Marx, 2004) available at available at http://dataportal.ucar.edu:9191/dods/v270 http://dataportal.ucar.edu:9191/dods/v270

DJF averages

CMAP rainfall estimates (mm/day) from CMAP rainfall estimates (mm/day) from CMAP (left) and NCEP/NCAR CMAP (left) and NCEP/NCAR reanalysis 1 (right)reanalysis 1 (right)

In version 2, the convective rain evaporation was decreased

Inter-annual Variability and signal to noise ratio of COLA9 member ensemble experiment

Interannual Interannual Standard deviation Standard deviation

for DJFfor DJF925 mb V (m/s)925 mb V (m/s)

Reanalysis 1 Reanalysis 1

COLA model v2COLA model v2

COLA 925 mb V Stand. COLA 925 mb V Stand. Dev (m/s)Dev (m/s)

Inter-annual Standard Inter-annual Standard Deviation DJF 500 mb Deviation DJF 500 mb

Heights (m)Heights (m)

Reanalysis 1 Reanalysis 1

COLA model COLA model v2v2

(m)

Response to equatorial Pacific SSTs (ENSO)

Corr. Coef.( SOI, PC)

(-.73) (-.80)

(-.81) (.76)

2217 8 6 5

% VAR explainedBy first 5 EOFs

4913 8 5 4

231710 7 6

3314 9 6 4

Correlation Coefficients between SOI index Correlation Coefficients between SOI index and various gridded estimates of and various gridded estimates of

precipitation precipitation

Principal Components for CMAP PC 2 and 4 also exhibit Principal Components for CMAP PC 2 and 4 also exhibit significant correlations ( > .32 in magnitude) with the SOI significant correlations ( > .32 in magnitude) with the SOI ( -.44 for PC 2 and .36 for PC 4). This is not the case for ( -.44 for PC 2 and .36 for PC 4). This is not the case for precipitation estimates from reanalysis or COLA model runs.precipitation estimates from reanalysis or COLA model runs.

Response to South Atlantic surface temperatures

Midsummer Low-Level Circulation and Precipitation in Subtropical South AmericaMidsummer Low-Level Circulation and Precipitation in Subtropical South Americaand Related Sea Surface Temperature Anomalies in the South Atlanticand Related Sea Surface Temperature Anomalies in the South Atlantic

MOIRA E. DOYLE and VICENTE R. BARROS, J of Climate, 2002MOIRA E. DOYLE and VICENTE R. BARROS, J of Climate, 2002

CORRELATION COEFFICIENT of CMAP prec. in box with surface temperature (1982 - 2003 DJF)

CORR of SA ST with CMAP, REAN 1 and 2, and COLA CORR of SA ST with CMAP, REAN 1 and 2, and COLA v2v2

COR of ST SA COR of ST SA with Z 500 mb with Z 500 mb for REAN (top)for REAN (top)

and COLA and COLA model model

(bottom))(bottom))

SUMMARY

1) DJF precipitation averages from R-1 and 2 do not reproduce the continental maximum at 50-65W. This is not the case for the COLA model. All three estimates have spurious orographic effects over the Andes.

2) None of these estimates reproduce the IA variablity over SESA.

3) COLA model skill concentrates over the equatorial Pacific.

4) The SESA pole of the ENSO signal over South America is poorly reproduced. The ENSO response is overestimated in the EOF variance partition in the COLA model at the expense of other IA signals.

5) SSTs anomalies in the South Atlantic (30-50W 20-30S) evoke a credible response in all analyses. Warm SSTs here are associated with a belt of 500 mb high pressure (25S) that extends through the Pacific and Atlantic oceans. This might be partly ascribed to the ENSO signal.

CONCLUSIONSCONCLUSIONS1)DJF precipitation averages from R-1 and 2 shift southward the

Eq. center of precipitation over the Northern coast and do not reproduce the continental maximum at 50-65W. This is not the case for the COLA model. All three estimates have spurious orographic effects over the Andes.

2) None of these estimates reproduce the IA variablity over SESA.) )

3) COLA model skill concentrates over the equatorial Pacific..4 )The ENSO dipole is poorly reproduced in that the SESA

pole is not apparent (except to a certain extent by the COLA model). Instead, all three estimates show an unreal maximum over the Andes. The ENSO response is overestimated in the EOF variance partition in the COLA model at the expense of other IA signals.

5) SSTs anomalies in the South Atlantic (30-50W 20-30S) evoke a credible response in all analyses. Warm SSTs here are associated with a belt of 500 mb high pressure (25S) that extends through the Pacific and Atlantic oceans.

Other figuresOther figures

Figure. (a) REOF1, (b) REOF2 and (c) REOF4 for DJF rainfall Figure. (a) REOF1, (b) REOF2 and (c) REOF4 for DJF rainfall over South America, explaining 12%, 10.8% and 7.2% of the total over South America, explaining 12%, 10.8% and 7.2% of the total variance by each REOF respectively. Contour interval is 2 non-variance by each REOF respectively. Contour interval is 2 non-dimensional units. Zero contours are omitted. Contours -1 and 1 dimensional units. Zero contours are omitted. Contours -1 and 1 are added in (b)are added in (b)

(a) (b) (c)

COLA v2 (top) COLA v2 (top) v1(bottom)v1(bottom)

COLA v2 COLA v1COLA v2 COLA v1

850 mb Temperature - DJF850 mb Temperature - DJF