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Abstract:

Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Panel on Climate Change Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three Special Report on Emissions Scenarios (SRES) A2, A1B, and B1. We developed a statistical method based on neural networks and Bayesian statistics to evaluate the models' skills in simulating late twentieth century temperature over continental areas. Some criteria [model weight indices (MWIs)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. As the study demonstrates, the use of neural networks, optimized by Bayesian statistics, leads to two major results. First, the MWIs can be interpreted as optimal weights for a linear combination of the climate models. Second, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. Model simulations in the southern tip of South America and along the Chilean and Peruvian coasts or in the northern coasts of South America (Venezuela, Guiana) are particularly poor. Overall, our results present an upper bound of potential temperature warming for each scenario. Spatially, in SRES A2, our major findings are that Tropical South America could warm up by about 4°C, while southern South America (SSA) would also undergo a near 2-3°C average warming. Interestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. In SSA, the amplitude of the seasonal cycle tends to increase, while in northern South America, the amplitude of the seasonal cycle would be reduced leading to much milder winters. We show that all the scenarios have similar patterns and only differ in amplitude. SRES A1B differ from SRES A2 mainly for the late twenty-first century, reaching more or less an 80-90% amplitude compared to SRES A2. SRES B1, however, diverges from the other scenarios as soon as 2025. For the late twenty-first century, SRES B1 displays amplitudes, which are about half those of SRES A2. © Springer-Verlag 2006.

Registro:

Documento: Artículo
Título:Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America
Autor:Boulanger, J.-P.; Martinez, F.; Segura, E.C.
Filiación:Tour 45-55/Etage 4/Case 100 UPMC, LODYC, UMR CNRS/IRD/UPMC, 4 Place Jussieu, 75252 Paris Cedex 05, France
Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina
Palabras clave:artificial neural network; atmosphere-ocean coupling; atmospheric general circulation model; Bayesian analysis; climate change; climate prediction; oceanic general circulation model; simulation; South America
Año:2006
Volumen:27
Número:2-3
Página de inicio:233
Página de fin:259
DOI: http://dx.doi.org/10.1007/s00382-006-0134-8
Título revista:Climate Dynamics
Título revista abreviado:Clim. Dyn.
ISSN:09307575
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09307575_v27_n2-3_p233_Boulanger

Referencias:

  • Allen, M.R., Stott, P.A., Mitchell, J.F.B., Schnur, R., Delworth, T.L., Quantifying the uncertainty in forecasts of anthropogenic climate change (2000) Nature, 407, pp. 617-620
  • Boulanger, J.-P., Leloup, J., Penalba, O., Rusticucci, M., Lafon, F., Vargas, W., Low-frequency modes of observed precipitation variability over the La Plata basin (2005) Clim Dyn, 24, pp. 393-413. , DOI 10.1007/s00382-004-0514-x
  • Coelho, C.A.S., Pezzulli, S., Balmaseda, M., Oblas-Reyes, F.J.D., Stephenson, D.B., Forecast calibration and combination: A simple Bayesian approach for ENSO (2004) J Clim, 17, pp. 1504-1516
  • Collins, W.D., The community climate system model, version 3 (2005) J Clim, , (in press)
  • Degallier, N., Favier, C., Boulanger, J.-P., Menkes, C., Oliveira, C., Rubens Costa Lima, J., Mondet, B., (2005) Early Determination of the Reproductive Number for Vector-borne Diseases: The Case of Dengue in Brazil, , (in press)
  • Delworth, GFDL's CM2 global coupled climate models - Part 1: Formulation and simulation characteristics (2005) J Clim, , (in press)
  • Forest, C.E., Stone, P.H., Sokolov, A.P., Allen, M.R., Webster, M.D., Quantifying uncertainties in climate system properties with the use of recent climate observations (2002) Science, 295, pp. 113-117
  • Giorgi, F., Mearns, L.O., Calculation of average, uncertainty range and reliability of regional climate changes from AOGCM simulations via the "reliability ensemble averaging" (REA) method (2002) J Clim, 15 (10), pp. 1141-1158
  • Giorgi, F., Regional climate information: Evaluation and projections (2001) Climate Change 2001: The Scientific Basis, pp. 583-638. , Houghton JT et al (eds) Contribution of working group I to the 3rd assessment report of the intergovenmental panel on climate change, Chap 10. Cambridge University Press, Cambridge
  • Gnanadesikan, (2005) GFDL's CM2 Global Coupled Climate Models - Part 2: The Baseline Ocean Simulation, , (in press)
  • Gordon, C., Cooper, C., Senior, C.A., Banks, H.T., Gregory, J.M., Johns, T.C., Mitchell, J.F.B., Wood, R.A., The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments (2000) Clim Dyn, 16, pp. 147-168
  • Haak, H., Formation and propagation of great salinity anomalies (2003) Geophys Res Lett, 30, p. 1473. , DOI 10.1029/2003GL17065
  • Johns, T.C., Carnell, R.E., Crossley, J.F., Gregory, J.M., Mitchell, J.F.B., Senior, C.A., Tett, S.F.B., Wood, R.A., The second hadley centre coupled ocean-atmosphere GCM: Model description, spinup and validation (1997) Clim Dyn, 13, pp. 103-134
  • Jones, P.D., Moberg, A., Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001 (2003) J Clim, 16, pp. 206-223
  • MacKay, D.J.C., Bayesian interpolation (1992) Neural Comput, 4, pp. 415-447
  • Marsland, The Max-Planck-Institute global ocean/sea ice modelwith orthogonal curvelinear coordinates (2003) Ocean Model, 5, pp. 91-127
  • Nabney, I.T., Netlab. Algorithms for pattern recognition. Advances in Pattern Recognition (2002), p. 420. , Springer, Berlin Heidelberg New York; Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., Gaffin, S., Gregory, K., Dadi, Z., (2000) IPCC Special Report on Emissions Scenarios, p. 599. , Cambridge University Press, Cambridge
  • New, M.G., Hulme, M., Jones, P.D., Representing twentieth-century space-time climate variability. Part II: Development of 1901-1996 monthly grids of terrestrial surface climate (2000) J Clim, 13, pp. 2217-2238
  • Reilly, J., Stone, P.H., Forest, C.E., Webster, M.D., Jacoby, H.D., Prinn, R.G., Uncertainty in climate change assessments (2001) Science, 293 (5529), pp. 430-433
  • Roeckner, The atmospheric general circulation model ECHAM5 Report No. 349OM (2003); Ruosteenoja, K., Carter, T.R., Jylhä, K., Tuomenvirta, H., Future climate in world regions: An intercomparison of model-based projections for the new IPCC emissions scenarios (2003), p. 83. , The Finnish Environment 644. Finnish Environment Institute; Salas-Mélia, D., Chauvin, F., Déqué, M., Douville, H., Gueremy, J.F., Marquet, P., Planton, S., Tyteca, S., XXth century warming simulated by ARPEGE-Climat-OPA coupled system (2004); Stouffer, (2005) GFDL's CM2 Global Coupled Climate Models - Part 4: Idealized Climate Response, , (in press)
  • Tebaldi, C., Smith, R.L., Nychka, D., Mearns, L.O., (2005) Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multi-model Ensembles, , (in press)
  • Wigley, T.M.L., Raper, S.C.B., Interpretation of high projections for global-mean warming (2001) Science, 293, pp. 451-454
  • Wittenberg, (2005) GFDL's CM2 Global Coupled Climate Models - Part 3: Tropical Pacific Climate and ENSO, , (in press)

Citas:

---------- APA ----------
Boulanger, J.-P., Martinez, F. & Segura, E.C. (2006) . Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America. Climate Dynamics, 27(2-3), 233-259.
http://dx.doi.org/10.1007/s00382-006-0134-8
---------- CHICAGO ----------
Boulanger, J.-P., Martinez, F., Segura, E.C. "Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America" . Climate Dynamics 27, no. 2-3 (2006) : 233-259.
http://dx.doi.org/10.1007/s00382-006-0134-8
---------- MLA ----------
Boulanger, J.-P., Martinez, F., Segura, E.C. "Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America" . Climate Dynamics, vol. 27, no. 2-3, 2006, pp. 233-259.
http://dx.doi.org/10.1007/s00382-006-0134-8
---------- VANCOUVER ----------
Boulanger, J.-P., Martinez, F., Segura, E.C. Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America. Clim. Dyn. 2006;27(2-3):233-259.
http://dx.doi.org/10.1007/s00382-006-0134-8