the climate change in south america due to a doubling in the co2 concentration: intercomparison of...

INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 17, 377–398 (1997)

THE CLIMATE CHANGE IN SOUTH AMERICA DUE TO A DOUBLINGIN THE CO2 CONCENTRATION: INTERCOMPARISON OF GENERAL

CIRCULATION MODEL EQUILIBRIUM EXPERIMENTS

JUAN. C. LABRAGA

Consejo Nacional de Investigaciones Cientı´ficas y Te´cnicas, Centro Nacional Patago´nico, (9120) Puerto Madryn, Chubut, Argentinaemail: [email protected]

Received 18 July 1995Revised 1 July 1996

Accepted 11 July 1996

ABSTRACT

Climatic mean sea-level (MSL) pressure, temperature, and precipitation fields simulated by five general circulation models arecompared with observations within the area of the South American subcontinent in order to assess their accuracy. Equilibriumclimate experiments with doubled atmospheric CO2 concentration are also intercompared to find consistent climate trendpatterns in the region. Results are analysed through statistical methods to appreciate the relative model performance at aregional scale. Descriptive analysis is applied to evaluate each model’s ability to simulate outstanding climate features.Statistical analysis of control runs indicates comparable and acceptable model performance in seasonal MSL pressure field andsurface temperature field simulations, but less satisfactory results in precipitation. Descriptive analysis reveals that some of thelocal climate characteristics, such as the location of the summer continental warm centre, and the seasonal variation in themean latitudinal pressure gradient at the southern end of the continent, are not simulated adequately. Regarding precipitation,even those models that perform better in the statistical comparison fail to simulate some of the major precipitation regimes,either in mean magnitude or seasonality of the rainfall rate. Comparison of the 26CO2 equilibrium experiments allow us toestablish some related and physically consistent trend patterns. Among them: (i) a southward shift in two main pressuresystems, the summer continental low and the Pacific anticyclone, consistent with a simulated displacement in the arid zone ofcentral Chile and western Argentina, and a noticeable warm-up in the region; (ii) maximum warming in Paraguay andsouthern Brazil up to the Atlantic coast and concurrent weakening of two pressure systems, the Atlantic anticyclone and thecontinental high, during winter; (iii) precipitation increase in the tropical central and eastern part of the continent south ofabout 15�S, and an opposite trend to the north of this latitude, coincident with a maximum warming in the lower Amazonbasin and northern coast of South America, and an intensification of the longitudinal pressure difference between theequatorial Pacific and Atlantic Oceans during summer; (iv) enhanced convective activity in the Inter Tropical ConvergenceZone (ITCZ) along the Pacific coast of South America north of the Equator; (v) precipitation increase in the southern end ofthe continent, consistent with increasing warming at higher latitudes.# 1997 by the Royal Meteorological Society. Int. J.Climatol. 17, 377–398 (1997)

(No. of Figs: 12 No. of Tables: 7 No. of Refs: 22)

KEY WORDS: South America; climate change; general circulation modelling; GCM comparisons; surface variables

1. INTRODUCTION

Planning adaptive or preventive strategies to confront the regional impact of the global climate change due toincreasing greenhouse gas emissions usually involves the development of scenarios of the future climate. One ofthe most powerful tools presently available for that purpose is the atmospheric general circulation model (GCM).

All GCMs are founded essentially in the same basic physical laws. Expressed as a set of partial differentialequations, they govern the time evolution of atmospheric variables. However, diverse methods are applied in thetime and space discretization of the continuous model equations. Moreover, there are various approaches in theparameterization of physical subgrid-scale processes. Their individual accuracy is difficult to estimate becausemost of them are non-linearly coupled. Intercomparison and validation of model results is a useful procedure tofind the way toward future improvements, and to obtain a better understanding of the climate system and itspossible evolution.CCC 0899-8418/97/040377-22 $17.50# 1997 by the Royal Meteorological Society

A reference study which compares 14 GCM climate experiments and provides detailed reference to previoussimilar works can be found in Boeret al. (1991). Intercomparison of similar numerical experiments from areduced number of GCMs has been used previously in describing the possible future climate of SouthernHemisphere continents (Mullan and Renwick, 1990; Burgoset al., 1991; Whetton and Pittock, 1991). Acomprehensive assessment of GCM performance over the Southern Hemisphere as a whole is still pending andwould be very valuable. Possibilities and current state of the art of climate simulation in this part of the world areanalysed in Henderson-Sellers and Giambelluca (1995).

Statistical and descriptive procedures are used here to evaluate the accuracy of five GCM numericalexperiments simulating the contemporary climate in South America. Doubled CO2 equilibrium climateexperiments are also intercompared, and some consistent trend patterns are identified in the region. The area ofstudy includes the whole South American subcontinent and adjacent oceans, stretching out from 20�N to 60�S,and from 20�W to 100�W.

The ability of GCMs to simulate accurately the basic characteristics of our climate determines the degree ofconfidence in their results about possible climate modifications. However, it should be stressed that this is anecessary, but not sufficient condition for the accuracy of climate change simulations (Boeret al., 1991).

The intercomparison was restricted to five GCMs in their state of development at the time of this study. TheGCMs are subjected to continuous improvements. This emphasizes the relative character of any appreciationabout their relative efficiency.

2. MAIN CHARACTERISTICS OF THE GCMs

The models used in this work are BMRC, Bureau of Meteorology Research Center, Australia (Hartet al., 1990);CCC, Canadian Climate Centre, Canada (McFarlaneet al., 1992); CSIRO9, Division of Atmospheric Research,Commonwealth Scientific and Industrial Research Organization, Australia (McGregoret al., 1993); GFDLH,Geophysical Fluid Dynamics Laboratory, USA (Houghtonet al., 1990); UKMOH, United KingdomMeteorological Office, UK (Houghtonet al., 1990).

A complete account of the physical parameterizations and numerical procedures can be found in the previousreferences, as well as descriptions of current climate global simulations. Common model characteristics andspecific features related to differences between each model’s performance that have been found throughout thiswork should be mentioned at this stage.

Spatial discretization of the model’s equations is accomplished by expansion in spherical harmonics (spectralmodels) in the horizontal, and finite differences (finite elements in the CCC) in the vertical, except for theUKMOH which applies finite difference exclusively. Truncation order of expansion, resolution of the transformgrid used for calculating model physics, and number of vertical levels of each model are indicated in Table I.

All models include a single mixed-layer ocean model and a thermodynamic sea-ice model. TheQ-fluxcorrection is a common approach to take into account the absence of currents in the simplified mixed-layer oceanmodel (Whetton and Pittock, 1991). Basically, theQ-flux correction is the amount of additional heat fluxnecessary to attain the observed sea-surface temperature (SST) annual variation. This correction is appliedequally in 26CO2 experiments, representing a major limitation in climate change simulations.

The representation of the surface topography varies widely amongst models according to their horizontalresolution and grid-point locations. In the GFDLH and UKMOH models, the Andes mountain range and other

Table I. GMC horizontal and vertical resolution

BMRC CCC CSIRO9 GFDLH UKMOH

Spectral truncation R21 T32 R21 R30 Finite difference modelHorizontal resolution

(latitude by longitude)3�2�65�6� 3�75�63�75� 3�265�6� 2�25�63�75� 2�5�63�75�

(Number of grid-points) (3584) (4608) (3584) (7680) (6912)Number of vertical levels 9 10 9 9 11

378 J. C. LABRAGA

important topographic features in the subcontinent are better represented than in the other models owing to theirrelatively higher resolution.

3. COMPARISON OF GCM CONTROL RUNS WITH OBSERVATIONS

A two-fold assessment procedure of control runs has been adopted. First, statistical comparison between observedand simulated fields provides a regional and quantitative measure of model performance. Second, sets of climatefeatures in the observed MSL pressure, surface temperature and precipitation fields, relevant to the presentclimate, are identified, and each model’s ability to simulate them is evaluated descriptively. As each feature canbe related to dominant physical processes (i.e. features in the precipitation field related to convective activity inthe ITCZ), this procedure can help to appreciate relative model efficiency regarding the simulation of thesespecific processes.

3.1. Comparative statistics

Computed RMS error between observed and modelled mean sea-level pressure, surface temperature, andprecipitation fields are presented in Table II as an objective measure of accuracy in control run results within theregion of study. Complementary information is provided by correlation computation, which gives a quantitativemeasure of correspondence between observed and simulated field patterns. All calculations were carried out afterinterpolating model output onto a 3�18� latitude by 5�63� longitude common grid. A weighting function was usedto take into account the latitudinal variation of the area represented by grid-points.

The region of comparison was limited in each variable according to the following criteria. Observedcontinental precipitation fields are more reliable than the oceanic fields owing to the large amount of data uponwhich the former are based. For this reason, precipitation statistics comprise continental grid-points only.Temperature RMS error and correlation computations were also restricted to continental grid-points. As alreadymentioned, theQ-flux correction constrains models to simulate closely observed annual SST variation. Toinclude oceanic grid-points in the intercomparison would mask the real model efficiency. Because pressuresystems located in oceanic regions, and their associated circulation patterns, have a remarkable influence uponcontinental climate, statistical computations for the pressure field comprise the whole set of grid-points.

Table II. Annual and seasonal pattern correlation and RMS error (in parentheses) of the simulated MSL pressure, surfacetemperature and precipitation fields in the South American subcontinent

Annual DJF MAM JJA SON

Pressure (hPa) BMRC 0�97 (3�4) 0�94 (4�3) 0�97 (2�7) 0�96 (3�4) 0�95 (4�2)CCC 0�98 (2�3) 0�97 (3�0) 0�97 (3�2) 0�97 (2�4) 0�97 (2�4)CSIRO9 0�96 (2�5) 0�92 (3�7) 0�96 (2�5) 0�96 (2�8) 0�96 (3�1)GFDLH 0�97 (5�3) 0�95 (4�9) 0�96 (5�7) 0�98 (6�5) 0�97 (5�0)UKMOH 0�95 (3�0) 0�89 (4�8) 0�95 (2�9) 0�95 (2�9) 0�95 (3�3)

Temperature (�C) BMRC 0�91 (2�6) 0�77 (3�1) 0�90 (2�7) 0�94 (3�2) 0�90 (3�0)CCC 0�92 (2�7) 0�86 (2�8) 0�93 (2�7) 0�95 (3�0) 0�91 (2�8)CSIRO9 0�88 (2�8) 0�76 (2�9) 0�88 (2�7) 0�93 (3�1) 0�87 (3�3)GFDLH 0�92 (3�4) 0�83 (3�5) 0�92 (3�5) 0�94 (3�8) 0�92 (3�5)UKMOH 0�95 (2�4) 0�91 (2�3) 0�96 (2�2) 0�96 (3�2) 0�93 (3�5)

Precipitation (mm day71) BMRC 0�34 (2�6) 0�27 (3�7) 0�44 (3�5) 0�69 (2�3) 0�23 (2�8)CCC 0�37 (2�3) 0�27 (3�6) 0�42 (3�2) 0�80 (2�0) 0�50 (2�3)CSIRo9 0�68 (1�7) 0�66 (2�5) 0�65 (2�5) 0�61 (2�5) 0�73 (2�1)GFDLH 0�28 (3�7) 0�21 (5�6) 0�39 (4�3) 0�58 (2�8) 0�38 (4�0)UKMOH 0�67 (2�0) 0�75 (2�6) 0�67 (2�8) 0�71 (2�6) 0�66 (2�2)

SOUTH AMERICA CLIMATE CHANGE 379

The noticeable departure between observed pressure values and those simulated by the GFDLH in the area ofthe subtropical anticyclones (Figure 1) is reflected in a relatively higher RMS error. However, all patterncorrelation values in this field are quite similar, and are the highest amongst the surface variables considered. Itsseasonal variation is also very small.

The RMS errors in the surface temperature field are quite similar amongst models, although relatively higher inthe GFDLH model. The magnitude and range of pattern correlation values indicate, as in the previous variable, anacceptable and homogeneous model performance in the simulation of the thermal field.

Precipitation pattern correlation is in general rather low, and substantial differences can be appreciatedbetween models. The CSIRO9 and the UKMOH models present the highest correlation and lowest RMS error inthis field. In these models, statistics show little variation among the seasons, indicating a more even performancethan in the other models.

3.2. Descriptive comparison of control runs

Previous statistical analysis provides regional measures of performance. However, to verify the correctsimulation of outstanding features in surface variable, climate fields will provide additional confidence insumulated climate changes involving these features, and reassurance about their physical consistency.

3.2.1. MSL pressure field simulation.The MSL pressure is considered a very important variable in modelintercomparison, because it represents an integrated balance between dynamic and thermodynamic processes(Boeret al., 1991). Most of the pressure field characteristics described below have a dominant influence upon theregional climate, and models are expected to reproduce them closely.

Analysis of the MSL pressure fields from the European Centre for Medium-range Weather Forecast data set,1985–1990 (ECMWF, 1993), are shown in Figures 1 and 2 for summer (DJF) and winter (JJA) respectively,together with model results. The following climatic characteristics of the MSL pressure field deserve specialattention:

(i) seasonal variations in location and intensity of the South Pacific and South Atlantic Oceans’ anticyclones;(ii) location and typical central magnitude of the summer continental low-pressure system;(iii) seasonal variation in the latitudinal pressure gradient in the southern part of the continent (typical

magnitudes are shown in Table III, and location of representative points in Figures 1 and 2);(iv) perennial pressure imbalance between the Pacific and the Atlantic oceans in the 10�N–10�S equatorial belt

(pressure is 2 to 3 hPa lower in the Pacific Ocean than in the Atlantic Ocean throughout the year betweenrepresentative points indicated in Figures 1 and 2).

A detailed description of each of the previous climatic features can be found in Schwerdtfeger (1976), and vanLoon et al., (1972).

Characteristic magnitudes and seasonal variation of the above pressure field patterns are in general adequatelysimulated by each of the models. However, a few exceptions can be readily appreciated.

Maximum MSL pressure values in the area of the subtropical anticyclones are overestimated in the GFDLHmodel results. This bias was noticed by Boeret al (1991) in a global intercomparison with a lower resolutionversion of the same model.

According to reference values shown in Table III, the observed pressure gradient in the southern part of thecontinent decreases from summer to winter. This is an important feature in the pressure field, which determinesthe characteristic annual wind regime in the extreme south of the continent (Prohaska, in Schwedtfeger, 1976),and it seems to be captured only by the CCC model. However, overestimated values of the pressure gradient inboth seasons are found in their results.

3.2.2. Surface temperature field simulation.Observed DJF and JJA mean surface temperature fields derivedfrom the Legates and Willmott (1992) data set are shown in Figures 3 and 4, together with model results.

380 J. C. LABRAGA

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Some of the most relevant climatic features in the surface thermal field taken into account in theintercomparison are as follows.

(i) Location and central magnitude of the summer continental warm centre (Table IV).(ii) Seasonal variation of the meridional temperature gradient at the southern end of the continent. Observed

temperature differences along the continental axis between 20�S and 50�S are presented in Table V, andrepresentative points are indicated in Figures 3 and 4.

Figure 3. Summer (DJF) mean surface temperature field (�C). Isotherms each 5�. (a) Observed data, Legates and Willmott (1992); modelresults of (b) BMRC; (c) CCC; (d) CSIRO9; (e) GFDLH; (f) UKMOH

Table III. Latitudinal pressure gradient between 45� and 55�S, at 70�W (hPa�Latitude71)

Season Observed BMRC CCC CSIRO9 GFDLH UKMOH

Summer 1�4 1�1 1�8 1�4 1�4 1�2Winter 1�2 1�4 1�6 1�7 1�8 1�8


(iii) Location and central magnitude of the winter continental cold centre in the southern part of the continent.(iv) Seasonal variation in the mean temperature difference between south-eastern and north-eastern Brazil.

Temperature differences are shown in Table V, and representative points are indicated in Figures 3 and 4.(v) Influence of terrain elevation upon spatial temperature variations along the Andes mountain range and main

topographic features. Particularly noticeable is a secondary cold centre in the Bolivian Altiplano (Table VI).

Figure 4. As for Figure 3, but for winter (JJA)

Table IV. Summer continental warm centre location and typical magnitude. Observed values according toProhaska (in Schwerdtfeger, 1976)

Observed BMRC CCC CSIRO9 GFDLH UKMOH

Latitude 23�S 27�S 27�S 27�S 25�S 25�SLongitude 60�W 57�W 58�W 56�W 60�W 60�WMaximum temperature 29�C 32�C 28�C 30�C 33�C 32�C

384 J. C. LABRAGA

A thorough description of the previous climatic characteristics can be obtained in Schwerdtfeger’s (1976)description of this surface field.

It should be remembered that what is called ‘model surface temperature’ is in fact the air temperature atvarying heights above the ground according to model configuration. This is not strictly equivalent to the observedscreen-height temperature. Because only gross features in the temperature field are considered, no reduction to acommon level has been attempted.

The summer continental warm centre in the Paraguayan Chaco, although its magnitude is simulated adequatelyin control runs, tends to be located to the south-east of its real position in those models with a relatively lowerresolution, such as the BMRC, CCC, and CSIRO9 (Table IV). The warm tongue depicted by the isotherms to thesouth of the maximum is also shifted to the east. Model representations of the conspicuous Andes mountain rangeat about 20�S is lower than in reality and excessively extended in the east–west direction (topography is notshown here). Thus, east of the Andes fictitious terrain heights induce a south-eastward shift of the temperaturemaximum. This effect also appears as a noisy temperature pattern over the eastern Pacific Ocean between 20�Sand 30�S in the CSIRO9 model results.

Meridional temperature differences along the continental axis increase from north to south during summer, andreverse during winter. This feature is simulated correctly by all models (Table V). However, temperaturedifferences at low latitudes during summer are underestimated in the BMRC and CSIRO9 models owing to thesouth-eastward shift in the summer continental maximum. An overestimated winter cold centre in the southernend of the continent in the BMRC, GFDLH, and UKMOH models, produces greater meridional temperaturedifferences than observed at high latitudes.

All models indicate higher north–south temperature differences in winter than in summer in eastern Brazil, ingood correspondence with observations (Table V). However, the BMRC, CCC and CSIRO9 models shownegligible or even negative summer temperature differences, owing to the south-eastward shift in the continentalmaximum.

Mean temperature in the relatively cold Bolivian Altiplano tends to be lower than observed, as the maximumheight of the local topography represented by each model increases (see Table VI).

3.2.3. Precipitation field simulation.Precipitation is one of the most critical test variables in any modelintercomparison. An additional difficulty appears in the area considered due the large number and diversity ofidentified precipitation regimes in South America, most of them related to regional circulation patterns and localtopographic features (Schwerdtfeger, 1976). Their correct simulation represents a serious challenge for even the

Table V. Temperature differences along the continental axis betweenpoints A(20�S 60�W), B(35�S 65�W) and C(50�S 70�W), and betweennorth-eastern and south-eastern Brazil, points D(5�S 40�W) and

E(25�S 50�W)

AÿB BÿC DÿE(�C) (�C) (�C)

Summer Observed 5 10 2BMRC 1 17 ÿ3CCC 5 10 0CSIRO9 3 8 ÿ2GFDLH 7 10 ÿ2UKMOH 7 13 2

Winter Observed 12 4 8BMRC 14 6 5CCC 13 2 5CSIRO9 10 3 5GFDLH 16 6 3UKMOH 15 6 9


most sophisticated GCM. Only broad features in precipitation seasonality and annual mean rate are considered inthe following intercomparison.

Observed mean annual rainfall rate and rainfall seasonality maps were derived from the Legates and Willmott(1992) data set, and are shown in Figures 5 and 6 respectively, together with model results.

A brief characterization of the most extended precipitation regimes is presented below, and a full descriptioncan be found in Schwerdtfeger (1976).

(i) An extended corridor of arid and semi-arid lands crosses South America from north-west to south-east, fromthe southern end of Ecuador and Pacific coast of Peru, along central Chile and western Argentina, up to theAtlantic coast in the far south of the continent (see 2 mm day71 in Figure 5(a) isohyet).

(ii) The Pacific winter-rains regime dominates along the coast of Chile south of about 33�S and extends itsinfluence over the eastern slope of the Andes and Patagonia tablelands from about 35�S to the south (seesouthernmost 0�5 isopleth in Figure 6(a)).

(iii) The subpolar maritime precipitation regime dominates the southern end of the continent. Rains are abundantduring the whole year and slightly predominant during summer.

(iv) The tropical precipitation regime prevails on the eastern Andean slope, in central and southern Peru, Boliviaand north-western Argentina. It is characterized by a short summer maximum and a definite dry and longwinter season (see 0�8 isopleth in Figure 6(a)). The tropical precipitation regime moves gradually into thesubtropical regime to the east and south-east, and into the southern tropical regime to the north-east. Theregion dominated by the latter three tropical regimes receives a wide range of total annual precipitation,most of it during the Southern Hemisphere summer months. The area is approximately encircled by the 0�65isopleth in Figure 6(a).

(v) Along the northern and north-eastern coast of South America, the annual movement of the ITCZ is clearlyfelt in the precipitation regime. On the Caribbean coast of Colombia and Venezuela maximum rainfalloccurs during September–October when the ITCZ is in its extreme northerly position. On the Atlantic coastof Guyana, Surinam and French Guiana, the maximum occurs during May–June and December–January,and on the north-eastern coast of Brazil during April.

(vi) In the lowlands of Colombia and the Llanos of central Venezuela, the northern extremity of Brazil andsouthern Guyana, a single maximum (from April to November) precipitation regime dominates, the northtropical regime (see 0�35 isopleth in Figure 6(a)).

(vii) The tropical mountain regime prevailing in the Andean region along Ecuador, west of Colombia and north-west of Venezuela is characterized by a double maximum during spring and autumn, barely separated by arelative minimum during the central months of the year. Locations in this area report some of the highestannual totals in the subcontinent (see 8�0 mm day71 isohyet on the northern Pacific coast in Figure 5(a)).

Some of the above major precipitation regimes are not well simulated in extent, magnitude or seasonality ofprecipitation. The most noticeable departures are the following.

Table VI. Bolivian Altiplano cold centre. Minimum temperature, location, and localmaximum height of model topography

LocationSummer Winter Height

(�C) (�C) Longitude Latitude (m)

Observed 15 10 68�W 20�S >3500BMRC 18 10 69�W 18�S 2000CCC 12 8 68�W 20�S —CSIRO9 18 12 68�W 17�S 2000GFDLH 9 5 70�W 17�S 3000UKMOH 12 3 70�W 17�S 3500

386 J. C. LABRAGA

The corridor of arid and semi-arid lands is modelled with excessive extension toward southern Brazil by theBMRC, GFDLH and UKMOH models, covering part of the area where the subtropical regime prevails in ourclimate. The GFDLH model also exhibits an annual rainfall distribution rather opposite to that observed.

Models with higher horizontal resolution, such as the UKMOH and GFDLH, can better represent the intenseprecipitation gradient on the eastern slope of the Andes in southern Argentina, whereas those with lowerresolution tend to overestimate rainfall rates in that region.

It is possible to discern the Pacific winter rains from the subpolar maritime regimes in all model results, thoughprecipitation rates are rather underestimated in both regimes.

The BMRC, CCC, and GFDLH models tend to overestimate the amount of rainfall on the Andean heights ofthe region dominated by the tropical summer rains regime.

Figure 5. Mean annual precipitation field (mm day71). Isohyets each 2 mm day71. (a) Observed data, Legates and Willmott (1992); modelresults of (b) BMRC; (c) CCC; (d) CSIRO9; (e) GFDLH; (f) UKMOH


None of the models adequately capture the north tropical regime, either in its typical spatial distribution orseasonality of precipitation.

Along the Andes mountain range in western Colombia most of the models locate one of the rainiest continentalregions, in correspondence with observations. Magnitudes simulated by the CSIRO9, BMRC and CCC modelsapproach climatic values, ranging from 8 to 10 mm day71, but they are higher than 20 mm day71 in theUKMOH and even higher in the GFDLH.

Simulated precipitation seasonality throughout the northern and north-eastern coast of South America comparereasonably well with observations. This suggests that models are capable of reproducing the dynamics of theITCZ, which governs the precipitation regime throughout the region. Regarding the spatial distribution of rainfallin the area, there is general agreement about lower precipitation along the Caribbean coast, and the UKMOH isable to reproduce the minimum in the north-east of Brazil in accordance with observations.

Figure 6. Fraction of the total annual precipitation received from October to March. Isopleths each 0�15. (a) Observed data, Legates andWillmott (1992); model results of (b) BMRC; (c) CCC; (d) CSIRO9; (e) GFDLH; (f) UKMOH

388 J. C. LABRAGA

4. INTERCOMPARISON OF 26CO2 EQUILIBRIUM CLIMATE SIMULATIONS

In this section results of 26CO2 equilibrium experiments are intercompared in the South American region, tofind concurrent and consistent trend patterns in the surface variables previously considered.

Global results of 26CO2 equilibrium climate simulations have been discussed elsewhere for each of themodels considered here (Houghtonet al., 1990, 1992; Boeret al., 1992; Colmanet al., 1994). The same set ofmodels have been used previously by Whetton and Pittock (1993) and Whettonet al. (1993), to estimae howclimate may change in the future in the Australian region.

4.1. Comparative statistics in the 26CO2 experiments

Between-model pattern correlation and RMS difference have been computed in order to provide a regionalmeasure of consistency between doubled CO2 equilibrium experiment results. As in the previous comparison(section 3.1), continental grid-points were considered in surface temperature and precipitation computations, andthe whole set of grid-points in MSL pressure computations. Results are shown in Table VII.

Difference fields (26CO2 minus 16CO2 experiments) instead of 26CO2 fields were used in calculations,because attention is focused on consistency among simulated change patterns. Combined between-model highcorrelation and low RMS difference allow definition of a subset of models whose simulated change patterns aremore similar than the others. For instance, the BMRC, CSIRO9 and UKMOH form a subset of models regardingsimulated precipitation change similarity, the BMRC, CCC, and CSIRO9 for MSL pressure, and the BMRC,CCC and GFDLH for temperature.

4.2. Descriptive comparison of 26CO2 experiments

Homogeneous model performance have been found in section 3.1 regarding current climate simulations ofMSL pressure and surface temperature fields. This was not the case with precipitation. Thus, results of 26CO2

experiments of the best performing models in control runs are discussed with more detail.Change patterns indicated in the following should not be considered as climate predictions, but as indicators of

possible broad regional climate trends. This is due to limitations regarding both model capability and the natureof the so-called equilibrium experiments.

Table VII. Between-model pattern correlation and RMS difference (in brackets) for mean annual difference fields (26CO2

minus 16CO2 experiments)

BMRC CCC CSIRO9 GFDLH UKMOH

Pressure (hPa) BMRC 0�52 0�55 0�17 0�51CCC [0�44] 0�49 ÿ0�14 0�15CSIRO9 [0�35] [0�49] 0�42 0�48GFDLH [0�55] [0�80] [0�48] 0�21UKMOH [0�52] [0�70] [0�58] [0�79]

Temperature(�C) BMRC 0�46 0�42 0�70 0�01CCC [1�39] 0�51 0�66 0�15CSIRO9 [2�67] [1�49] 0�40 0�37GFDLH [1�46] [0�56] [1�40] 0�04UKMOH [2�32] [1�26] [0�85] [1�20]

Precipitation (mm dayÿ1) BMRC ÿ0�43 0�54 0�24 0�36CCC [0�87] ÿ0�16 ÿ0�06 ÿ0�10CSIRO9 [0�68] [1�07] 0�26 0�44GFDLH [0�79] [1�13] [0�99] 0�06UKMOH [0�95] [1�25] [0�98] [1�23]


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(JJA

)


4.2.1. MSL pressure in the 26CO2 equilibrium climate. The MSL pressure difference fields (26CO2 minus16CO2 numerical experiments) are presented in Figures 7 and 8, for DJF and JJA respectively.

Simulations, in general, indicate a region of positive pressure trend in the Pacific Ocean to the south of thesemi-permanent anticyclone, which follows its seasonal displacement. The central and northern part of the highpressure system is affected by negative pressure trends. The combined effects resemble a southward shift in thePacific Ocean anticyclone. This is consistent with the suggestion of Pittock and Salinger (1982) of a possiblesouthward migration in the subtropical high pressure belt and mid-latitude westerlies for a warmer climate. Thehypothesis was based on an expected greater warming at high latitudes, which results in a reduced meridionaltemperature gradient.

Some differences between model results are appreciated regarding seasonal magnitude and extension of thepressure change pattern in the region of the Atlantic Ocean anticyclone. A southward shift in this pressure systemdoes not emerge, within the area of study, as definite as in the Pacific Ocean. Decreasing pressure trend north ofabout 40�S during winter, consequent weakening of the Atlantic anticyclone and of the winter continental highpressure systems are common results of the BMRC, CCC, and CSIRO9 models.

There is general agreement among model results about a noticeable southward shift and intensification of thesummer continental low pressure system in the 26CO2 equilibrium climate.

The longitudinal pressure gradient in the equatorial belt shows a sensible strengthening during the SouthernHemisphere summer (between 0�5 and 1 hPa), according to coincident results of the BMRC, CSIRO9, andUKMOH models. This pattern could be related to a deeper southward penetration of the ITCZ into the central andeastern part of the continent in the 26CO2 equilibrium climate.

A noticeable drop in the MSL pressure along the northern part of the continent is indicated by the CSIRO9 andUKMOH models, analysed in the following sections with regard to concurrent changes in the other two variables.

4.2.2. Mean surface temperature field in the 26CO2 equilibrium climate. Summer and winter analysis ofsurface temperature difference fields (26CO2 minus 16 CO2 experiments) are presented in Figures 9 and 10respectively.

A wide range of mean continental temperature increments are simulated by the models, from 4�2�C to 1�5�C. Acommon result is a greater warming over the continent than in the surrounding oceans at a given latitude, beingmore evident south of approximately 20�S during summer, and south of 10�S during winter.

Three areas of relative maximum continental warming are consistently indicated in model results.

(i) In the semi-arid lands of central Chile and western Argentina, between 20�S and 40�S, the temperatureincrement during summer and autumn is as high as 5�C. Enhanced greenhouse effect over those regions withan already limited soil-water availability leads to higher warming rates due to their restricted evaporationcapacity (Manabeet al., 1981).

(ii) In the middle and lower Amazon basin up to the northern Atlantic coast of South America, the UKMOH andCSIRO9 models indicate an area of maximum warming varying in position during the year. The centre ofthis maximum, ranging in intensity from 4�C to 7�C, is rather north of the Equator from June to November.Its displacement seems to be related with that of the ITCZ. The same region exhibits a noticeable negativetrend in precipitation and MSL pressure.

(iii) In southern Brazil and Paraguayan Chaco, the CSIRO9, CCC and GFDLH models show a relative maximumwarming of about 5�C during winter. This maximum is also appreciable during autumn (correspondingfigures are not shown here). Its influence extends toward the Atlantic coast, and could be related to theabove-mentioned weakening of the Atlantic anticyclone and winter continental high pressure in the 26CO2

equilibrium climate.

All models exhibit the highest oceanic temperature increments south of about 50�S. This is more noticeable inthe South Atlantic Ocean to the south-east of the continent during winter. Maxima of about 13�C to 14�C aremodelled by the GFDLH and BMRC. Warming is also very intense during spring. The noticeable temperatureincrement in this area could be attributed to several amplifying factors (see Mitchell, 1989): (i) the positivetemperature–albedo feedback in the vicinity of snow- or ice-covered regions, (ii) a more effective warming as a

392 J. C. LABRAGA

result of a low-level thermal inversion, and (iii) a more efficient transport of latent heat from the tropics to highlatitudes in a warmer and moister atmosphere. However, it is worth mentioning that recent transient experimentswith coupled atmosphere–ocean models indicate lower warming rates in the southern oceans surroundingAntarctica than do equilibrium experiments (Houghtonet al., 1990, 1992).

4.2.3. Precipitation in the 26CO2 equilibrium climate. A better performance can be attributed to CSIRO9and UKMOH models in the simulation of current climate precipitation according to results in sections 3.2 and3.2.3. Changes in this field concurrently simulated by these models are analysed with more detail, as possibletrend indicators at the seasonal scale.

(i) Changes in the mean annual precipitation field. Mean annual precipitation fields (26CO2 minus 16CO2

equilibrium experiments) are shown in Figure 11.

Figure 9. Summer (DJF) mean surface temperature difference between the 26CO2 and 16CO2 climate simulations (�C). Isotherms each 1�C.Model results of (a) BMRC; (b) CCC; (c) CSIRO9; (d) GFDLH; (e) UKMOH


Most of the models simulate negative precipitation trends on a strip of land parallel to the northern coastof the continent. In the lower basin of the Amazon river, the CSIRO9 and UKMOH models indicate anannual average decrease higher than 1 mm day71. This is the region where the dynamics of the ITCZdominates the precipitation regime in the present climate. South of this region and up to about 25�S, theBMRC, CSIRO9, and UKMOH models simulate increasing annual rainfall rates of variable magnitude.

There is some agreement about precipitation increase along the Pacific coast of South America north ofthe Equator. This region seems to be limited to the east by the heights of the Andes, represented differentlyaccording to the resolution of each model. The magnitude of the increments varies widely amongst models.

Toward the southern end of the continent, the region dominated by the subpolar maritime regime, a smallbut appreciable precipitation increase of about 0�5 mm day71 is a coincident model result.

(ii) Changes in summer and winter precipitation simulated by the CSIRO9 and UKMOH models. DJF and JJAprecipitation difference fields (26CO2 minus 16CO2 numerical experiments) are presented in Figure 12,for the CSIRO9 and UKMOH models.

Figure 10. As for Figure 9, but for winter (JJA)

394 J. C. LABRAGA

Increased rainfall rates along the Pacific coast of Colombia, possibly related with intensified convectiveactivity in the ITCZ, are modelled throughout the year. The magnitude of the increment is much higher in theUKMOH than in the CSIRO9, but it should be recalled that there was a tendency in the former model tooverestimate this maximum in the present climate.

Convective activity in the south tropical central and eastern part of the continent seems to move further southfrom the Equator during DJF than in the present climate. Enhanced precipitation south of 10�S in the UKMOHmodel and of 15�S in the CSIRO9, and decreased precipitation to the north of these latitudes are possibleconsequences of this movement. The area with greater precipitation covers south-eastern Brazil, Paraguay, andnorth-eastern Argentina. During JJA most of the northern coast of the continent is affected by decreasedprecipitation rates. The overall precipitation change suggests a shorter and more southerly penetration of ITCZinto the continent during the warmer months of the year.

Figure 11. Annual mean precipitation difference between the 26CO2 and 16CO2 climate simulations. Solid isohyets each 1 mm day71.Shaded areas indicate negative precipitation trends. Model results of (a) BMRC; (b) CCC; (c) CSIRO9; (d) GFDLH; (e) UKMOH

(mm day71)


In the southern extreme of the continent, the positive rainfall trends mentioned in the previous section occursmainly during the colder months of the year, consistent with increasing warming rates with latitude, particularlyduring winter and spring.

Increased precipitation rates are simulated in the southern part of Bolivia and north-western Argentina east ofthe Andes during summer, whereas decreasing trends are indicated in Chile and western Argentina between 35�Sand 45�S. Both effects can be interpreted as a southward shift in the arid conditions currently prevailing on bothsides of the Andes between 20�S and 33�S.

5. CONCLUSIONS

Results of five GCM equilibrium experiments simulating the current South American climate were comparedwith observations to assess the relative modelling capacity. Doubled CO2 concentration equilibrium climate

Figure 12. Mean precipitation difference between the 26CO2 and 16CO2 climate simulations. Model results for summer of (a) CSIRO9 and(b) UKMOH; model results for winter of (c) CSIRO9 and (d) UKMOH (mm day71). Shaded areas indicate negative precipitation trends

396 J. C. LABRAGA

experiments were also intercompared, to find consistent patterns of climate trends. The analysis was restricted tothree surface variables: MSL pressure, surface temperature and precipitation.

The RMS error and pattern correlation computations, as well as descriptive comparison of model results withobservations, allow determination of the following conclusions.

The five models considered: BMRC, CCC, CSIRO9, GFDLH, and UKMOH, have a comparable andacceptable level of performance in simulating the regional MSL pressure and surface temperature fields.However, some local relevant climate characteristics are not simulated adequately. Among them are the seasonalvariation in the latitudinal pressure gradient at the extreme south of the continent, and the location of the summercontinental warm centre in those models with relatively lower resolution.

All models have a less satisfactory and rather heterogeneous performance regarding the simulation ofprecipitation. The CSIRO9 and UKMOH have shown the lowest RMS error and the highest pattern correlationwhen compared with observed fields. However, even these models do not simulate satisfactorily some of themajor precipitation regimes observed in the continent, either in mean magnitude or seasonality of the rainfall rate.

Due to consensus among models and physical consistency, some of the simulated change patterns deservespecial attention as indicators of possible climate trends.

(i) A southward shift in the summer continental low pressure system and in the Pacific anticyclone, coherentwith a similar displacement in the characteristic arid conditions of central Chile and western Argentina. Anoticeable warm-up in the latter region, and increased precipitation rates in the southern part of Bolivia andnorth-western Argentina east of the Andes during summer.

(ii) A relative maximum warming in southern Brazil and Paraguay up to the Atlantic coast during winter andautumn, possibly related to a weakening of the Atlantic anticyclone and of the continental high pressuresystems north of about 40�S during the same seasons.

(iii) Increasing precipitation in the tropical central and eastern part of the continent south of about 15�S, and theopposite trend to the north of this latitude. This pattern is consistent with a maximum warming in the lowerAmazon basin and northern coast of South America. It is also coherent with the intensification of thelongitudinal pressure difference between the equatorial Pacific and Atlantic oceans during summer. Thispattern could be explained as a faster and more southerly penetration of the ITCZ into the continent duringthis season.

(iv) Enhanced convective activity in the ITCZ along the Pacific coast of South America north of the Equator.(v) Precipitation increase in the subpolar regime, slightly higher during winter, in correspondence with greater

warming rates at higher latitudes.

As pointed out by Burgos (1991), any significant modification in the current limits of the main agriculturalsystems in South America due to a climate change would produce a severe economic impact in the whole region.There is also a great concern about any climate impact over the extensive rainforest that covers the continentalequatorial belt, with possible implications at regional and global scales (Burgoset al., 1991). The Andean forestin the southern part of the continent could be seriously threatened if eventually a southward shift in the semi-aridconditions materializes according to model results.

Adaptive or preventive stategies for a warmer climate, including forest fire protection, forest management andagricultural policies, require the design of climate change scenarios based on accurate climate modelling. Morereliable and detailed modelling of critical variables such as precipitation are still necessary. Some improvementsare awaited from high-resolution GCM, or nested climate modelling. In the short term, great expectation is placedon results from coupled atmosphere–ocean GCMs. Modifications in the ocean–atmosphere interaction inresponse to an enhanced greenhouse effect, could be of significant importance in regional climate modifications.

ACKNOWLEDGEMENTS

This work was performed partly as a research visitor in the Division of Atmospheric Research (DAR), CSIRO,Australia. I want to express my gratitude to B. Hunt, Leader of the Water Resources and Climate ChangeProgram of DAR, for the useful discussions and continuous assistance. This work was supported with fundsprovided by the Global Change Regional Research Program of Argentina (PROINGLO), I want to thank J.


Burgos, leader of the program, for his permanent encouragement. I specially want to thank P. Whetton for hiscomments about the manuscript and valuable help. Most of the graphics were processed with the efficientassistance of E. Davies at the Centro Nacional Patago´nico, Argentina. I would like to thank also B. Pittock, A.Rivas, and M. Dentoni for reading the manuscript, to Andrea Salvatore (Servicio Meteorologico Nacional), and toJ. Y. Li and L. D. Rotstayn (DAR) for their programming assistance.

REFERENCES

Boer, G. J., Arpe, K., Blackburn, M., De´que, M., Gates, W. L., Hart, T. L., le Treut, H., Roeckner E., Sheinin D. A., Simmonds, I., Smith, R.N. B., Tokioka, T., Wetherald, R. T. and Williamson, D. 1991. ‘An intercomparison of the climates simulated by 14 atmospheric generalcirculation models’,CAS/JSC Working Group on Numerical Experimentation, Report No. 15, WMO/TD, No. 425, World MeteorologicalOrganization, Geneva.

Boer, G. J., McFarlane, N. A. and Lazare M. 1992. ‘Greenhouse gas-induced climate change simulated with the CCC second-generationgeneral circulation model’,J. Climate, 5, 1045–1077.

Burgos, J. J. 1991. ‘Escenarios del impacto econo´mico social del cambio global del clima en la Argentina’,Acad. Nac. Agron. Vet. BuenosAires, XLV , (9)

Burgos, J. J., Fuenzalida Ponce, H. and Molion, L. C. B. 1991. ‘Climate change predictions for South America’,Climatic Change, 18, 223–239.

Colman, R. A., MacAvaney, B. J. and Wetherald, R. T. 1994. ‘Sensitivity of the Australian surface hydrology and energy budgets to adoubling of CO2’, Aust. Meteorol. Mag., 43, 105–116.

ECMWF 1993.The Description of the ECMWF/WCRP Level III-A Global Atmospheric Data Archive, Technical Attachment, EuropeanCentre for Medium-range Weather Forecasts, Reading, 49 pp.

Hart, T. L., Bourke, W., McAvaney, B. J., McGregor, J. L. and Forgan, B. W. 1990. ‘Atmospheric general circulation simulations with theBMRC global spectral model: the impact of revised physical parameterizations’,J. Climate, 3, 436–459.

Henderson-Sellers, A. and Giambelluca, T. W. 1995.Climate Change: Developing Southern Hemnisphere Perspectives, Wiley, Chichester.Houghton, J. T., Jenkins, G. J. and Ephraums, J. J. 1990.Climate Change: The IPCC Scientific Assessment, Cambridge University Press,

Cambridge.Houghton, J. T., Callander, B. A. and Varney, S. K. (eds) 1992.Climate Change 1992: the Supplementary Report to the IPCC Scientific

Assessment, Working Group 1, Bracknell, Cambridge University Press, Cambridge.Legates, D. R. and Willmott, C. J. 1990. ‘Mean seasonal and spatial variability in gauge-corrected, global precipitation’,Int. J. Climatol., 10,

111–127.Manabe, S., Wetherald, R. T. and Stouffer, R. J. 1981. ‘Summer dryness due to an increase of atmospheric CO2 concentration’,Climatic

Change, 3, 347–385.McFarlane, N. A., Boer, G. J., Blanchet, J. P. and Lazare, M. 1992. ‘The Canadian Climate Centre second-generation general circulation

model and its equilibrium climate’,J. Climate, 5,1013–1044.McGregor, J. L., Gordon, H. B., Watterson, I. G., Dix, M. R. and Rotstayn, L. D. 1993.The CSIRO 9-level Atmospheric General Circulation

Model, Division of Atmospheric Research, CSIRO, Melbourne, Technical Paper No. 26, 89 pp.Mitchell, J. F. B. 1989. ‘The "greenhouse" effect and climate change’,Rev. Geophys., 27, 115–139.Mullan, A. B. and Renwick, J. A. 1990.Climate Change in New Zealand Region Inferred from General Circulation Models, New Zealand

Meteorological Service, 142 pp.Pittock, A. B. and Salinger, M. J. 1982. ‘Towards regional scenarios for a CO2-warmed Earth’,Climatic Change, 4, 23–40.Schwerdrfeger, W. 1976.Climates of Central and South America, World Survey of Climatology, Vol. 12, Elsevier, Amsterdam, 532 pp.van Loon, H., Taljaard, J. J., Sasamori, T., London, J., Hoyt, D. V., Labittzke, K. and Newton, C. W. 1972.Meteorology of the Southern

Hemisphere, Meteorological Monographs, Vol.13, No. 35, American Meteorological Society, 263 pp.Whetton, P. H. and Pittock, A. B. 1991.Australian Region Intercomparison of the Results of some General Circulation Models used in

Enhanced Greenhouse Experiments, CSIRO Division of Atmospheric Research, Technical Paper No. 21, 73 pp.Whetton, P. H. and Pittock, A. B. 1993. ‘The growing consensus in simulated regional climate for enhanced greenhouse conditions: is there a

palaeoclimatic analogue?’,Proceedings of the Conference Quaternary Palaeoclimatic Mapping: A Protocol for Australia, MonashUniversity, December 1992,Quaternary Australasia, 11 (1), 81–87.

Whetton, P. H., Fowler, A. M., Haylock, M. R. and Pittock, A. B. 1993. ‘Implications of climate change due to the enhanced greenhouse effecton floods and droughts in Australia’,Climate Change, 25, 289–317.

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