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Global climate models Examples of other physical processes that must be parameterized are the transfer of long wave and shortwave radiation in the atmosphere, surface energy exchanges, atmospheric boundary layer process, vertical and horizontal diffusion processes, etc. Certain parameterized process can be treated completely independently from all other nonresolvable processes. Other processes tend to be more tightly coupled with each other (e.g., planetary boundary layer, convection, cloud formation, and radiative transfer). 11.3. AGCMs uncertainties and their use for climate change studies The confidence in future scenarios obtained in model results relies on the ability of the models in representing the present climate. Results of the present climate simulation by several models were discussed in Gates et al. (1999) for the Atmospheric Model Intercomparison Project (AMIP). The largest errors of the ensemble mean were found in the cloudiness and 200 hPa temperature and the smallest errors were identified in the surface air temperature. Global hydrological processes for the AMIP results were discussed in Lau et al. (1996) comparing results from 29 models. Although there was large variability among models, the ensemble mean results of all models were very close to the observations. The ensemble of the models overestimated the rainfall in the tropics and underestimated it in the extratropics. The MCGs have been increasing their complexity to represent in an increasingly suitable manner the physical processes of the climatic system. Though they cannot still represent all of the processes and certain difficulties specially related to the interaction between the radiation and the aerosols exist, their capacity to represent the current climate has been improving, offering an increasing reliability on future projections. The results of these models developed by institutes with high scientific and computer capacity are available through the web page of the Distribution Data Centre (DDC) of the IPCC (www.dkrz.de/ipcc/ddc/html/SRES/ SRES_all.html). Another set of information comes from a simulation of 50 years made by the CPTEC used for the analysis of the current climatology. Table 11.2 lists the models and organizations responsible for each of them as well as the period with available information. In Fact, the confidence in the ability of models such as AGCMs or AOGCMs to project future climates is increased by the ability of several models to reproduce warming trends in the 20th century surface air temperature when driven by the observed increase of greenhouse gases and sulphate aerosols. This is illustrated in figure 11.3. This figure shows three independent simulations with the same greenhouse gas plus aerosol forcing and slightly different initial conditions and a control run with initial conditions of the levels of atmospheric CO2 referred to as pre-industrial era. 145
Global climate models Table 11.2. MCG with available information through DDC. Model Institution Current Period Future Period HADCM3 Hadley Centre for Climate Prediction and Research (Reino Unido) 146 1950-1999 Australia's Commonwealth Scientific CSIRO-mk2 and Industrial Research Organization 1961-1999 1961-2100 (Australia) ECHAM4/ Max Planck Institute OPYC3 für Meteorologie (Alemania) GFDL-R30 Geophysical Fluid Dynamics Laboratory (Estados Unidos) NCAR-PCM National Centre for Atmospheric Research (Estados Unidos) CCCma Canadian Center for Climate Modeling and Analysis (Canadá) CPTEC/COLA Centro de Previsão de Tempo e Estudos Climáticos 1990-1999 1990-2100 1961-1999 1961-2100 1981-1999 1981-2100 1950-1999 1950-2100 1950-1999 In development Fig. 11.3. Observed and modelled global annual mean temperature anomalies (°C) relative to the average of the observations over the period 1900 to 1930. The control and three independent simulations with the same greenhouse gas plus aerosol forcing and slightly different initial conditions are shown from an AOGCM. The three greenhouse gas plus aerosol simulations are labelled 'run 1', 'run 2', and 'run 3' respectively. [Adapted from IPCC 2001]
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CLIMATE CHANGE IN THE LA PLATA BASI
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INDEX FOREWORD . . . . . . . . . .
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7.3. Methodology . . . . . . . . .
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13.3. Regional validation of GCM fo
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1.1. Dropping the hypothesis of sta
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Introduction as they pour water ove
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A better understanding of the obser
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References Introduction Barros, V.
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ABSTRACT The hydrologic cycle of th
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La Plata basin climatology Within t
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in this last description, although
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warm season (October-April), in the
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La Plata basin climatology b. Upper
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La Plata basin climatology 2.3.3. R
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La Plata basin climatology Fig. 2.8
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2.3.7. West and South borders La Pl
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References La Plata basin climatolo
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La Plata basin climatology Robertso
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3.1. Introduction Interannual low f
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Interannual low frequency variabili
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1989). This signal varies along eac
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Interannual low frequency variabili
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The main physiographic and hydrolog
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SUB-BASIN COUNTRY Argentina Bolivia
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the most important stretches of the
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Physiography and Hydrology The Para
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Physiography and Hydrology The Pant
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Physiography and Hydrology forcings
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Physiography and Hydrology It is no
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Physiography and Hydrology tion of
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Physiography and Hydrology INTERNAV
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5.1. Introduction Precipitation and
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From 1956 to 1991, in most of the A
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-15 -20 -25 -30 -35 -40 -15 -20 -25
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Regional precipitation trends 5.3.2
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Regional precipitation trends and L
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trast, other regions suffer floodin
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ABSTRACT The main hydrological tren
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Following are the observed changes:
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In the case of the Paraná River th
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Hydrological trends In order to com
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Hydrological trends In table 6.1 it
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Hydrological trends On the other ha
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the central area of the high basin
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7.1. Introduction Several authors,
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Real evaporation trends a) PET > Pi
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a) b) c) Mean Temperature Real evap
Page 95 and 96: a) b) c) Mean Temperature Real evap
Page 97 and 98: The positive trends of the mean tem
Page 99 and 100: d) e) f) Mean Temperature Real evap
Page 101 and 102: a) b) c) Mean Temperature Real evap
Page 103 and 104: 7.5. Discussion and final comments
Page 105 and 106: ABSTRACT The water, as resource, is
Page 107 and 108: puts in risk large number of person
Page 109 and 110: The main services and problems of w
Page 111 and 112: There are similar problems in diffe
Page 113 and 114: 8.3.3. Energy a. Hydroelectric depe
Page 115 and 116: (chapters 12 and 13), because of th
Page 117 and 118: The main services and problems of w
Page 119 and 120: 9.1. Introduction The emissions of
Page 121 and 122: 9.4. Greenhouse effect The atmosphe
Page 123 and 124: Temperature anomalies (°C) 0.8 0.6
Page 125 and 126: Global climatic change at a global
Page 127 and 128: In view of the unavoidability of th
Page 129 and 130: Background on other regional aspect
Page 135 and 136: Considering the non-linear interact
Page 137 and 138: SST anomalies also have influence o
Page 141 and 142: ABSTRACT The purpose of this chapte
Page 143 and 144: Global climate models Mid-1970’s
Page 145: Global climate models Table 11.1. B
Page 149 and 150: Although changes in weather and cli
Page 151 and 152: Socio-economic variables and also s
Page 153 and 154: Applicability in impact assessments
Page 155 and 156: of the global climate system to the
Page 157 and 158: Climate scenarios B2: Assumes a wor
Page 159 and 160: Climate scenarios The reliability o
Page 161 and 162: Climate scenarios From Table 12.2 i
Page 163 and 164: Climate scenarios Fig. 12.5. The mu
Page 165 and 166: Climate scenarios this would be rel
Page 167 and 168: Results of global circulation model
Page 169 and 170: 13.2.3. Statistical downscaling The
Page 171 and 172: La Plata basin are also considerabl
Page 173 and 174: 10 N EQ 10 S 20 S 30 S 40 S 50 S 10
Page 175 and 176: Regional climatic scenarios Fig. 13
Page 177 and 178: a. Pre-industrial experiment: Initi
Page 183 and 184: Regional climatic scenarios Kalnay,
Page 185 and 186: Low-frequency variability 14.1. Bac
Page 187 and 188: egime occurred during the periods o
Page 189 and 190: Fig. 14.2. As Fig. 14.1, except for
Page 191 and 192: 14.4. SST composites Low-frequency
Page 193 and 194: Low-frequency variability during th
Page 195 and 196: Fig. 14.10. As Fig. 14.9, except fo
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Low-frequency variability In genera
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Low-frequency variability Mantua, N
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15.1. Introduction Statistical anal
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Statistical analysis of extreme eve
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CURRÍCULA OF THE AUTHORS Vicente B
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Curricula of the autors Rubén Mari
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Proyect: SGP II 057: “Trends in t
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