Interannual Variability during summer (DJF) in Observations and in the COLA model J. Nogues-Paegle (University of Utah) C. Saulo and C. Vera (University of Buenos Aires) B. Kirtman and V. Misra (George Mason University and Center for Ocean-Land-Atmosphere Studies) Emphasis on South American response to ENSO and South Atlantic surface temperature variability

Outline Comparison of DJF and inter-annual variability of rainfall and low-level winds (CMAP, Reanalysis 1 and 2 and seasonal runs of the COLA model forced with observed SSTs) Comparison of DJF and inter-annual variability of rainfall and low-level winds (CMAP, Reanalysis 1 and 2 and seasonal runs of the COLA model forced with observed SSTs) How do the different analyses reproduce the SOI signal over South America? How do the different analyses reproduce the SOI signal over South America? Impact of South Atlantic SST’s on South American variability Impact of South Atlantic SST’s on South American variability

Data Sets CMAP: Climate Prediction Center Merged Analysis of Precipitation, on a 2.5 x 2.5 grid, obtained using gauge-based monthly analysis and satellite estimates (Xie and Arkin 1997) CMAP: Climate Prediction Center Merged Analysis of Precipitation, on a 2.5 x 2.5 grid, obtained using gauge-based monthly analysis and satellite estimates (Xie and Arkin 1997) REAN 1 and 2, both run atT62, 28 levels with mostly same raw observational data. R-2 was done to correct some errors in R 1 (such as insertion of PAOBs at wrong longitudes), and includes replacement of model precipitation at the land surface with a pentad precipitation estimate similar to CMAP to maintain a reasonable soil wetness (Kanamitsu et al., 2002) REAN 1 and 2, both run atT62, 28 levels with mostly same raw observational data. R-2 was done to correct some errors in R 1 (such as insertion of PAOBs at wrong longitudes), and includes replacement of model precipitation at the land surface with a pentad precipitation estimate similar to CMAP to maintain a reasonable soil wetness (Kanamitsu et al., 2002) COLA model run at T62L28 with observed SSTs, for 1981 through 2003 for DJFM (Misra and Marx, 2004) available at COLA model run at T62L28 with observed SSTs, for 1981 through 2003 for DJFM (Misra and Marx, 2004) available at

DJF averages

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

In version 2, the convective rain evaporation was decreased

Inter-annual Variability and signal to noise ratio of COLA 9 member ensemble experiment

Interannual Standard deviation for DJF 925 mb V (m/s) Reanalysis 1 COLA model v2

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

Inter-annual Standard Deviation DJF 500 mb Heights (m) Reanalysis 1 COLA model v2 (m)

Response to equatorial Pacific SSTs (ENSO)

Corr. Coef. ( SOI, PC) (-.73) (-.80) (-.81) (.76) % VAR explained By first 5 EOFs

Correlation Coefficients between SOI index and various gridded estimates of precipitation

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

Response to South Atlantic surface temperatures

Midsummer Low-Level Circulation and Precipitation in Subtropical South America and Related Sea Surface Temperature Anomalies in the South Atlantic MOIRA E. DOYLE and VICENTE R. BARROS, J of Climate, 2002

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

CORR of SA ST with CMAP, REAN 1 and 2, and COLA v2

COR of ST SA with Z 500 mb for REAN (top) and COLA model (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.

CONCLUSIONS 1)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 figures

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

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

850 mb Temperature - DJF