Evaluation of the WCRP-CMIP3 model simulations in the La Plata basin

METEOROLOGICAL APPLICATIONSMeteorol. Appl. 15: 497–502 (2008)Published online 29 September 2008 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/met.98

Evaluation of the WCRP-CMIP3 model simulations in theLa Plata basin

Gabriel Silvestri* and Carolina VeraCIMA/UBA-CONICET, Buenos Aires Argentina

ABSTRACT: In this article, an assessment of the climate simulations for the La Plata Basin (LPB) by the Phase 3 of theWorld Climate Research Programme – Coupled Model Intercomparison Project is made. The LPB covers a wide area oversouth-eastern South America, and is the fifth largest basin in the world in terms of geographical extent.

It was found that the observed warm summer – cold winter pattern of temperature is well represented by the climatemodels but they have deficiencies in reproducing the monthly mean temperature values. The largest differences take placeduring spring and summer, seasons in which the models exhibit temperatures higher than observations.

Models are able to represent the annual cycle of precipitation in the north of the LPB but some deficiencies are observedin the centre and south. In addition, there is an overestimation of heavy (light) rain events at the northern (central andsouthern) portion of the basin.

Results also show that the projected precipitation future change is positive in autumn and winter in the northern LPBas well as in most of the year in the centre and south of the basin. The precipitation distributions show an increase(decrease) in the frequency of low rainfall (heavy rainfall) events in the north of LPB and opposite change in the centreand south. Nevertheless, considerable inter-model variability is found in the representation of the precipitation features atboth present and future climate that make quite uncertain the quantification of those projected changes. Copyright 2008Royal Meteorological Society

KEY WORDS WCRP-CMIP3 models; La Plata basin; climate change; South American climate

Received 27 February 2008; Revised 21 July 2008; Accepted 22 July 2008

1. Introduction

The La Plata Basin (LPB) extends along a wide regionover southeastern South America between 15 and 40 °Sto the east of the Andes Mountains (Figure 1). The LPBcovers about 3.2 million km2 over Argentina, Bolivia,Brazil, Paraguay and Uruguay having a fundamental rolein the economy of these countries. The LPB regionencompasses around 136 million people representing57% of the combined population of the five countriesand 41% of South America. Also, 60% of the combinedGNP of the five countries that represents 41% of SouthAmerica is generated in the LPB. Agricultural activi-ties are the ones that contribute most to the GNP ofthe LPB region. The major products, mostly for export,are soybean, maize, cotton, sugar/alcohol (sugarcane),timber (planted forests), meat (planted and native pas-tures), rice, wheat, coffee, and orange. In 2005, the com-bined soybean production of Argentina and Brazil wasthe largest in the world (Song et al., 2007). Hydropowerplants in the LPB produce around 95% of the energyused in Paraguay, 25% in Brazil and 40% in Argentina.It is then evident that knowing the future evolution ofthe climate in the area of the LPB is a topic of vital

* Correspondence to: Gabriel Silvestri, CIMA/UBA-CONICET,Argentina. E-mail: [email protected]

importance for the region. The multi-model climate sim-ulations obtained from the Phase 3 of the World ClimateResearch Programme – Coupled Model IntercomparisonProject (WCRP-CMIP3) currently make such a studypossible.

The ability of seven WCRP-CMIP3 models to repre-sent the observed climatological seasonal precipitationin South America during the period 1970–1999 wasassessed by Vera et al. (2006a). They showed that most ofthe models are able to reproduce the basic characteristicsof the precipitation seasonal cycle, such as the northwest-ward and southeastward migration of precipitation overtropical South America and the precipitation maximumobserved over the southern Andes. Nevertheless, thereare large discrepancies in the simulation of the intensityand location of South Atlantic convergence zone, whichis a quasi-stationary band of convection that extends fromcentral Brazil south eastward into the Atlantic Ocean(e.g. Kodama, 1992; Lenters and Cook, 1995). Also, themodels still have problems in reproducing quantitativelyaccurate seasonal precipitation amounts over the mainbasins of the continent, such as the Amazon basin andthe LPB. The continental analysis of the climate changeoutputs for the SRESA1B scenario made by Vera et al.(2006a) shows a substantial agreement among models inprecipitation changes for the period 2070–2099 relativeto 1970–1999, mainly characterized by: (1) an increase

Copyright 2008 Royal Meteorological Society

498 G. SILVESTRI AND C. VERA

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of summer precipitation over the northern Andes andsoutheastern South America, (2) a decrease of winter pre-cipitation over most of the continent, and (3) a decreaseof precipitation along the southern Andes for all seasons.

The analysis of the uncertainties in the climate changeprojections in South America and particularly in the LPB,associated with differences in the climate representationby different climate models and different natural (e.g.solar irradiance and volcanic aerosols) and anthropogenic(e.g. greenhouse gases, sulfate aerosols and ozone) cli-mate forcing has not received enough attention. Usingmulti-model ensemble means Giorgi and Bi (2005a,b)and Giorgi (2006) show that central South America seemsto have a moderate response to global change comparedto other regions of the world. It is not clear, though, ifsuch a response is hidden by a large dispersion amongmodels in representing the climate change over this par-ticular region. Therefore, the aim of this paper is toprovide a detailed description of the ability of the WCRP-CMIP3 models to reproduce the present climate featuresin the LPB, paying particular attention to the assessmentof the inter-model dispersion and its impact on the climatechange regional projections.

2. Data and models

2.1. Data

Observed monthly mean precipitation and temperaturedata from 25 stations, available from the Meteorologi-cal Services of the region and archived at the NationalCenter for Atmospheric Research (NCAR) were used(Figure 1). The analysis is restricted to the period1979–1999 considering the availability and quality ofregional information.

2.2. WCRP-CMIP3 models

The set of climate simulations WCRP-CMIP3 wasgenerated by the modelling groups of the World ClimateResearch Program for the IPCC-AR4 and it is avail-able at the Program for Climate Model Diagnosis andIntercomparison (PCMDI, http://www-pcmdi.llnl.gov/ipcc/about ipcc.php). The outputs of seven of thoseglobal models have been analysed in this study: CNRM-CM3 (Salas-Melia et al., 2005), ECHAM5/MPI-OM(Roeckner et al., 2003), GFDL-CM2.0 (Delworth et al.,2006), GISS-EH (Schmidt et al., 2006), IPSL-CM4(Marti et al., 2005), MIROC-3.2 (Hasumi and Emori,2004) and MRI-CGCM2.3.2a (Yukimoto et al., 2006).Detailed documentation about these models and simu-lation forcing can be found at http://www-pcmdi.llnl.gov/ipcc/model documentation/ipcc model documentation.php. The ‘climate of the 20th Century experiment’(20C3M) is used to describe the present climate (1979–1999) and the ‘720 ppm stabilization experiment’(SRESA1B) represents the future conditions (2070–2099).

3. Present climate

There is an abundant bibliography describing the mainaspects of rainfall and temperature over South America(e.g. Vera et al., 2006b, and references therein) and adetailed analysis of the hydrological cycle in the LPB canbe found in Berbery and Barros (2002). Therefore, only abrief description of these characteristics is made here. Theanalysis is focused on the comparison between the ensem-ble of model simulations (hereafter ENSEM) of presentclimate and observations (OBS) at the northern, centraland southern portions of the LPB, hereafter referred asnorth, centre and south, respectively (Figure 1).

3.1. Temperature

The annual cycles of observed and modelled tempera-ture are shown in Figure 2. The observed annual cyclein region north is characterized by a short cold periodin June–July when the mean temperature is almost 4 °Clower than in summer (Figure 2(a)). The warm sum-mer – cold winter pattern is well represented in generalby ENSEM. In particular, from January to June the dif-ference between ENSEM and OBS is smaller than 0.7 °C.On the other hand, during the period August–Decemberthe largest differences between ENSEM and OBS areobserved, while large model dispersion is also evident.The difference ENSEM minus OBS is maximum aroundSeptember and October with magnitudes between 2 and3 °C.

In the centre of the region, the amplitude of almost10 °C in the observed annual cycle is overestimated byENSEM (Figure 2(b)). In fact, from September to Febru-ary all models overestimate the observed temperatureand, thus, ENSEM is 2–3 °C warmer than OBS. On thecontrary, three or more models simulate temperatures

Copyright 2008 Royal Meteorological Society Meteorol. Appl. 15: 497–502 (2008)DOI: 10.1002/met

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Figure 2. Observed (solid black line), modelled (grey lines) andENSEM (dotted black line) monthly mean temperature in the north

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lower than those observed from March to July and the dif-ferences ENSEM minus OBS are negative (lower in mag-nitude than 1.8 °C). Although in some models the mini-mum during winter takes place in May–June, ENSEM isable to reproduce it in June–July as in OBS.

The seasonal cycle amplitude is also overestimated byENSEM in the south of the region (Figure 2(c)). ENSEMis 3–5 °C warmer than OBS in September–February.During this period, all models exhibit temperature higherthan observations excepting IPSL, although the modeldispersion is considerably large. On the contrary, duringMarch–August, the differences ENSEM minus OBS arelower than 1.5 °C and most of the models underestimatethe observed values.

3.2. Precipitation

The model representations of the annual cycle, includ-ing information of the inter-model dispersion and the

histograms of distribution, are shown in Figures 3 and 4.The intervals in the distribution diagrams were definedconsidering the distribution of 10, 50 and 90% ofobserved precipitation. Therefore, high (low) precipita-tion can be considered as precipitation higher (lower) thanthe limit of 90% (10%) of such a distribution.

In the north of the region, the annual cycle of pre-cipitation is characterized by a rainy summer and drywinter (Figure 3(a)). ENSEM overestimates the observedprecipitation in October–February and underestimates itin March–September. However, the rainy summer – drywinter pattern is reproduced by ENSEM with magnitudesthat differ by more than 35% with OBS only in the periodApril–June. As a result, there is a remarkable agreementbetween the annual mean precipitation from ENSEM andOBS with a difference of only 1%. The inter-model dis-persion is largest during the rainy season. The histogram

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Figure 3. Observed (solid black line), modelled (grey lines) andENSEM (dotted black line) monthly mean precipitation in the north

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Figure 4. Observed (grey bar), modelled (dotted lines) and ENSEM(black bar) distribution of precipitation in the north (a), centre (b) andsouth (c) of the LPB. For each model, the distribution is indicatedas follow. CNRM, black square; GFDL, grey square; GISS, whitesquare; IPSL, black dot; MIROC, grey dot; MPI, white dot; MRI,

black triangle.

of distribution reveals that ENSEM is not able to repro-duce episodes of low precipitation and it overestimatesthe occurrence of high precipitation events (Figure 4(a)).The diagram shows that ENSEM produces double theamount of high precipitation events regarding observa-tions.

The observed annual wave in the centre of the regionsuggests a double maximum-like structure that is notreproduced by ENSEM (Figure 3(b)). In fact, ENSEMrepresents a rainy summer – dry winter pattern withmagnitudes lower than observations in all month. FromMarch to October the differences are 36–77%, in Novem-ber–February they are lower than 33% and the ENSEM

annual mean precipitation is 45% lower than observa-tions. The underestimation of the observed precipitationby ENSEM is a consequence of most models produc-ing less precipitation than OBS. Largest inter-model dis-persion is evident between February and March. Thedistribution diagram, reveals that models have problemsrepresenting the occurrence of high precipitation eventsover this particular region, overestimating the number ofcases with low precipitation (Figure 4(b)). Consequently,the representation of both extremes by ENSEM is notgood.

In the south of the region, the observed annual cycleclearly describes a double maximum pattern with rainyperiods in autumn (March–April) and spring (Octo-ber–November) and a dry season in winter (June–August) (Figure 3(c)). ENSEM is not able to reproducethis characteristic. In fact, the annual wave described byENSEM is a rainy summer – dry winter pattern withmagnitudes 40–60% lower than the observations (theENSEM annual mean precipitation is 51% lower thanOBS). The precipitation underestimation is observed inessentially most of the models. In fact, just one of them isable to represent the double maximum structure. Regard-ing the distribution of precipitation, as was found for thecentre of the region, models are not able to reproducethe observed number of high precipitation events whilethey produce more cases of low-rain (Figure 4(c)). As aconsequence, ENSEM has strong problems representingevents of intense precipitation and it overestimates theoccurrence of low rainfall episodes.

4. Future climate

Figure 5 shows the differences between the present andfuture precipitation annual cycle and the correspondinginter-model variability for the three considered regions.The precipitation change as depicted by the differencesin the model ensemble mean is positive in autumn andwinter in the northern LPB as well as in most of the yearexcept early spring in the centre and south. Nevertheless,the fact that the inter-model dispersion is considerablylarge not only for present but also for future climate con-ditions makes such mean precipitation changes quantita-tively uncertain. In addition, the analysis of the changesin the precipitation distribution as represented by theENSEM shows an increase (decrease) in the frequencyof the low-rain (heavy rain) events in the north of thebasin and an opposite change in the centre and south.Although Figure 5 shows that such frequency changes inthe three regions are not significantly different from thecorresponding inter-model variability.

5. Conclusions

The ability of seven WCRP-CMIP3 models in repre-senting the temperature and precipitation in the LPBobserved in the period 1979–1999 was assessed. The


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Figure 5. Present and future ENSEM precipitation and inter-model dispersion for the north (top), centre (middle) and south (bottom) of the LPB.(a), (c) and (e): Annual cycles (present in black and future in grey). (b), (d) and (f): Histograms of distribution (present in white and future in

grey; intervals as in Figure 4).

study focused in the exploration of the skill of the ensem-ble mean in representing those present climate features.The inter-model dispersion and its impact on the pro-jected precipitation by models in a future climate changescenario were also discussed.

It was found that the amplitude of the observed warmsummer – cold winter pattern of the temperature annualcycle is overestimated by ENSEM, especially at thecentral and southern portions of the LPB. Althoughsimulated winters tend to be colder than the observedones, the largest differences take place during spring andsummer, seasons in which models exhibit temperatureshigher than observations.

The rainy summer – dry winter annual cycle of pre-cipitation observed in the northern LPB is well rep-resented by ENSEM but it overestimates the occur-rence of high precipitation events and it is not able toreproduce episodes of low precipitation. In the centraland southern LPB, models have strong problems repro-ducing the observed precipitation. The observed annual

wave describes a double maximum structure that is notreproduced by ENSEM and the magnitude of precipita-tion is underestimated. Moreover, models are not able torepresent in those regions events of intense precipitationoverestimating the occurrence of low-rain episodes.

The fact that the inter-model variability is considerablylarge not only for present but for future climate limitedseriously the quantification of the climate change pro-jections for precipitation in the basin. Caution should betaken in using those climate change projections for vul-nerability and adaptation quantitative analysis.

Acknowledgements

Comments and suggestions provided by two anonymousreviewers and the Editor were very helpful in improvingthis paper. We thank Rejane Georgeault for her assis-tance in constructing the time series. This research was



supported by ANPCyT/PICT04-25269, CONICET/PIP-5400, and CLARIS (EU Project 001454). We acknowl-edge the European project CLARIS (http://www.claris-eu.org) for facilitating the access to the IPCC simulationoutputs. We also acknowledge the international modelinggroups for providing their data for analysis, the Pro-gram for Climate Model Diagnosis and Intercomparison(PCMDI) for collecting and archiving the model data,the JSC/CLIVAR Working Group on Coupled Model-ing (WGCM) and their Coupled Model IntercomparisonProject (CMIP) and Climate Simulation Panel for orga-nizing the model data analysis activity, and the IPCCWG1 TSU for technical support. The IPCC Data Archiveat Lawrence Livermore National Laboratory is supportedby the Office of Science, US Department of Energy.

References

Berbery E, Barros V. 2002. The hydrologic cycle of the La Plata basinin South America. Journal of Hydrometeorology 3: 630–645.

Delworth T, Broccoli A, Rosati A, Stouffer R, Balaji V, Beesley J,Cooke W, Dixon K, Dunne J, Dunne K, Durachta J, Findell K,Ginoux P, Gnanadesikan A, Gordon C, Griffies S, Gudgel R,Harrison M, Held I, Hemler R, Horowitz L, Klein S, Knut-son T, Kushner P, Langenhorst A, Lee H, Lin S, Lu J, Maly-shev S, Milly P, Ramaswamy V, Russell J, Schwarzkopf M, Shevli-akova E, Sirutis J, Spelman M, Stern W, Winton M, Wittenberg A,Wyman B, Zeng F, Zhang R. 2006. GFDL’s CM2 global coupledclimate models – Part 1: Formulation and simulation characteristics.Journal of Climate 19: 643–674.

Giorgi F. 2006. Climate change hot-spots. Geophysical ResearchLetters 33: L08707, DOI:10.1029/2006GL025734.

Giorgi F, Bi X. 2005a. Updated regional precipitation and temperaturechanges for the 21st century from ensembles of recentAOGCM simulations. Geophysical Research Letters 32: L21715,DOI:10.1029/2005GL024288.

Giorgi F, Bi X. 2005b. Regional changes in surface climate interannualvariability for the 21st century from ensembles of globalmodel simulations. Geophysical Research Letters 32: L13701,DOI:10.1029/2005GL023002.

Hasumi H, Emori S. 2004. K-1 coupled model (MIROC) description.K-1 Technical Report 1, Centre for Climate System Research,University of Tokyo: Tokyo, 34.

Kodama Y. 1992. Large-scale common features of subtropicalprecipitation zones (the Baiu frontal zone, the SPCZ and SACZ).

I: Characteristics of subtropical frontal zones. Journal of theMeteorological Society of Japan 70: 813–836.

Lenters J, Cook K. 1995. Simulation and diagnosis of the regionalsummertime precipitation climatology of South America. Journalof Climate 8: 2988–3005.

Marti O, Braconnot P, Bellier J, Benshila R, Bony S, Brockmann P,Cadule P, Caubel A, Denvil S, Dufresne J-L, Fairhead L, Filib-erti M, Foujols M, Fichefet T, Friedlingstein P, Gosse H, Grand-peix J, Hourdin F, Krinner G, Levy C, Madec G, Musat I, deNoblet N, Polcher J, Talandier C. 2005. The New IPSL Climate Sys-tem Model: IPSL-CM4. Institut Pierre Simon Laplace: Paris; 84,http://dods.ipsl.jussieu.fr/omamce/IPSLCM4/DocIPSLCM4/FILES/DocIPSLCM4 color.pdf.

Roeckner E, Bauml G, Bonaventura L, Brokopf R, Esch M, Gior-getta M, Hagemann S, Kirchner I, Kornblueh L, Manzini E,Rhodin A, Schlese U, Schulzweida U, Tompkins A. 2003. Theatmospheric general circulation model ECHAM5, Part I: Modeldescription. Max Planck Institute for Meteorology Rep. 349, 127,Available from MPI for Meteorology, Bundesstr. 53, 20146 Ham-burg, Germany.

Salas-Melia D, Chauvin F, Deque M, Douville H, Gueremy J, Mar-quet P, Planton S, Royer J, Tyteca S. 2005. Description and vali-dation of the CNRM-CM3 global coupled model, CNRM workingnote 103.

Schmidt G, Ruedy R, Hansen J, Aleinov I, Bell N, Bauer M, Bauer S,Cairns B, Canuto V, Cheng Y, del Genio A, Faluvegi G, Friend A,Hall T, Hu Y, Kelley M, Kiang N, Koch D, Lacis A, Lerner J,Lo K, Miller R, Nazarenko L, Oinas V, Perlwitz J, Rind D,Romanou A, Russell G, Sato M, Shindell D, Stone P, Sun S,Tausnev N, Thresher D, Yao M-S. 2006. Present day atmosphericsimulations using GISS ModelE: Comparison to in-situ, satellite andreanalysis data. Journal of Climate 19: 153–192.

Song B, Marchant M, Reed M, Xu S. 2007. Market Power and Com-petitive Analysis of Chinese Soybean Import Market. Contributedpaper for the International Agricultural Trade Research Consortium(IATRC), Beijing, http://aede.osu.edu/programs/Anderson/trade/51Song.pdf.

Vera C, Higgins W, Amador J, Ambrizzi T, Garreaud R, Gochis D,Gutzler D, Lettenmaier D, Marengo J, Mechoso C, Nogues-Paegle J, Silva Dias PL, Zhangi C. 2006b. Toward a unified view ofthe American monsoon systems. Journal of Climate 19: 4977–5000.

Vera C, Silvestri G, Liebmann B, Gonzalez P. 2006a. Climate changescenarios for seasonal precipitation in South America fromIPCC-AR4 models. Geophysical Research Letters 33: L13707,DOI:10.1029/2006GL025759.

Yukimoto S, Noda A, Kitoh A, Hosaka M, Yoshimura H, Uchiyama T,Shibata K, Arakawa O, Kusunoki S. 2006. Present-day climate andclimate sensitivity in the Meteorological Research Institute CoupledGCM version 2.3 (MRI-CGCM2.3). Journal of the MeteorologicalSociety of Japan 84: 333–363.


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Evaluation of the WCRP-CMIP3 model simulations in the La Plata basin