J. A. Marengo, R. Laborbe, M. Valverde

1. Comparisons between observed and modeled tendencies in precipitation and temperature extremes in South America during the XX Century (IPCC-AR4 20C3M) J. A. Marengo, R. Laborbe, M. Valverde Centro de Previsão de Tempo e Estudos Climaticos, CPTEC, Brazil M. Rusticucci, Olga Peñalba Universidad de Buenos Aires, Argentina Madeleine Renom Universidad de la Republica, Uruguay

2. Introduction The purpose of this study is to later establish how IPCC global models simulate climate extreme indices over South America. If several runs were available for a model, a mean over the runs has been applied before starting the analysis. Other simulated output are now available for other models on the IPCC internet site. 3 steps have been done in order to get a better feel of different models outputs. 1-Maps of the simulated trend of extreme climate indices over 40 years (1960- 2000). 2-Correlation of in-situ times series and extracted model time series at stations positions. 3-Visualization of graphic representing the plot of observed and simulated time series at random locations.

3. IPCC AR4 models (Li et al. 2005)

4. 20C3M This experiment consists of 5-member ensemble simulations of the 20th Century climate (starting from mid-19th Century).The initial state of each member simulation is taken from the different states of the pre-industrial control experiment at 1, 51, 101, 151, and 200 years. The forcing agents of the experiment are the historical record of (or estimated) greenhouse gases (CO2, CH4, N2O and CFCs), sulfate aerosol direct effects, volcanoes and solar forcing.

5. Solar forcing for the 20C3M experiment The effect of solar activity is input by specifying the solar constant. The data are based on the estimation by Lean et al. (1995) which are provided at from NASA/GISS. Temporal change of the solar constant is plotted below. Volcanic forcing for 20C3M experiment The effect of stratospheric aerosols due to the volcanic activity is reflected with substitution as the solar irradiance reduction at the model’s top of atmosphere (by changing the solar constant). The magnitude of the reduction is linearly scaled so that the global averaged radiative forcing fits to the estimation by Sato et al. (1993)

6. Extreme indices used Extreme indices were derived from IPCC models (IPCC web site) and from observations in South America [Frisch et al. 2002, Vincent et al. 2005, Haylock et al. 2003)]

7. Maps of extreme indices trend-MIROC 3.2 hires The same analysis than the previous work on MIROC3.2 will be later applied to the CCSM3 and GFDL: • We have computed the tendency of several indices over the period 1960-2000 (40 years) using a linear regression. A Student t-test has been applied to evaluate the significance of the previous result using 90% of significance. • On the maps, the black lines enclose regions of significance trends from the student t-test at 5% level • The simulated time series to the closest data grid of every station have been extracted. • The order of magnitude from the simulated data of SDII indice for the 3 models is very different (10^-5 of difference!!). The units are probably not the same. Nothing is mentioned in the IPCC erratum web page. The correlation between observed data has not been done.

8. Warm Nights Tn90 MIROC3.2 hires Vincent et al. (2005)

9. Heavy precipitation Days (>10mm) - R10 MIROC 3.2 hires Haylock et al. (2005)

10. Very Wet Day Precipitation - R95p MIROC 3.2 hires Haylock et al. (2005)

11. Analyses of 3 IPCC models • The time series of stations and of the 3 models CCSM3, GFDL and MIROC3.2 (hires) have been extracted. We have plotted on the same graph the longest station time serie available and the corresponding model time serie. • For each station, some indices have been studied: Tn90, R10, R95t and CDD. The first one is based on temperature, the others on precipitation. • This allowed us to represent on the same page the data of the 3 models for 2 indices (based on the same variable). • The Spearman correlation has been performed between the simulated and observed time series. • The results have been plot on maps where every point represents a station position and the value is the correlation between the 2 series. • As the standard correlation, the spear correlation gives a number between -1 and 1 were 1 is the perfect correlation and -1 the anticorrelation.

12. MIROC3.2 hires GFDL CCSM3 Simulated trends and correlation with station data of Tn90 index Trends Correlations

13. MIROC3.2 hires GFDL CCSM3 Simulated trends and correlation with station data of R10 index Trends Correlations

14. MIROC3.2 hires GFDL CCSM3 Simulated trends and correlation with station data of R95t index Trends Correlations

15. Anomaly = mean(1961-2000) - mean(1961-1990)Anomalies over 40 years - Tn90

16. Anomaly = mean(1961-2000) - mean(1961-1990)Anomalies over 40 years - R10

17. Anomaly = mean(1961-2000) - mean(1961-1990)Anomalies over 40 years - R95t

18. Time series of Tn90 and R10- Agua Funda (SE Brazil) 3 models : CCSM3, GFDL and MIROC3.2 hires -FD -Tn90 CCSM GFDL MIROC Tn90 Corr=0.20 Corr=0.30 Corr=0.03 OBSV Model R10 Corr=-0.14 Corr=-0.04 Corr=-0.03

19. Time series of Tn90 and R10– Izobamba (Andean Ecuador) 3 models : CCSM3, GFDL and MIROC3.2 hires CCSM3 GFDL MIROC3.2 hires Tn90 Corr=0.51 Corr=0.45 Corr=0.57 R10 Corr=-0.11 Corr=0.04 Corr=0.23

20. CDD (2070-80)-(2001-2010) CDD (2090-80)-(2001-2010) R10 (2070-80)-(2001-2010) R10 (2070-80)-(2001-2010) Model: miroc3_2_hires. Scenario: 1pctto2x (2XC02) Run: (2001/2080) Scenario: Picntrl (control run) Run: (2001/2100) (Source: Nuñez and Blázquez)

21. CDD for the Picntrl scenario (blue) and for the 1pctto2x scenario (red). R10 for the Picntrl scenario (blue) and for the 1pctto2x scenario (red). (Source: Nuñez and Blázquez)

22. Tn90 (%) R95T (%) R10 (days) CDD (days) Multi-model averages of spatial patterns of change under A1b. (2080-2099 minus 1980-1999). Stippled regions correspond to areas where at least four of the eight model concur in determining that the change is statistically signicant (Tebaldi et al. 2003)

23. SUMMARY-from IPCC C203M coupled models • Hard to compare observations (especially rainfall) with indices derived from AOGCMs, However, these models seem to capture well the warming trends • The best correlation for all models are given with Tn90 (warm nights) indice based on temperature. The time series show that models are good at simulating the magnitude and the tendency of temperature indices (especially Tn90) • Problems with observations-quality and coverage • The resolution of the models is linked with spatial variability. However, higher resolution does not seems to imply better correlation • It seems MIROC3.2 usually obtains higher indices values than other models. CCSM3 gets a wider range of outputs • When the spatial variability of the variable is weak, all the models are more or less equivalent. (cdd-r10) • All the models seem to have on positive bias for r95t

24. Things to do.. • Extend and update observational network (Concentrate on one region?, entire South America?) • Use field correlation instead of point correlation (e.g. Kiktev et al. 2003-J. Climate). • Analyze years with extreme signals in the Coupled models (say, a very strong El Nino years produced by the model) to see what happen with extremes in that year. • Limitations in the comparison between grid box model and point station data. Limitations in comparing time series of observed and modelled rainfall extreme indices. May be we need AGCMs forced with observed SSTs. • So: use data from the C20C experiment (so far, only HadAM3 daily model data is available)C20C is for AGCM only, IPCC C203M is for AOGCM). With C20C we can study some extreme events (ENSO and observed dry years). • Write papers…………….

25. International Climate of the Twentieth Century Project(C20C)Project initiated by Hadley CentreAgreed set of runs, diagnostics and special projectsGoal:Characterize climate variability and predictability of the last ~130 years through analysis of observational data and ocean-forced atmospheric general circulation models (AGCM)

26. C20C Phases • Phase 1: SST and sea ice • Hadley Centre provides HadISST1.1 SST and sea ice data set as lower boundary conditions • Integrate over 1871-2002 (at least 1949-2002) • Ensembles of at least 4 members • Phase 2: atmospheric composition • Greenhouse gases – CO2, O3, etc. • Aerosols (volcanic) • Solar variability • Phase 3: land surface variability • specified evolution of soil wetness and vegetation

27. Participating Groups • Bureau of Meteorology Research Centre – Australia • Center for Climate System Research (Univ. Tokyo)– Japan • Center for Ocean-Land-Atmosphere Studies – USA • Climate Prediction Division (JMA) – Japan • Commonwealth Science and Industrial Research Organization – Australia • CPTEC - Brazil • Department of Natural Resources (Queensland) – Australia • Hadley Centre – UK • Meteorological Research Institute (JMA) – Japan • NASA Goddard Space Flight Center – USA • National Institute for Water and Atmospheric Research – New Zealand • Seoul National Univ. – Korea • Univ. of California at Los Angeles – USA • National Climate Center – China • Main Geophysical Observatory – Russia • Interested parties: • MeteoFrance – France • National Center for Atmospheric Research – USA • International Centre for Theoretical Physics – Italy