Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America
Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of...
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2007
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| LEADER | 09537caa a22006737a 4500 | ||
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| 001 | PAPER-6733 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518203626.0 | ||
| 008 | 190411s2007 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-33845452718 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Boulanger, J.-P. | |
| 245 | 1 | 0 | |a Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America |
| 260 | |c 2007 | ||
| 270 | 1 | 0 | |m Boulanger, J.-P.; UPMC, LODYC, UMR CNRS/IRD/UPMC, 4 Place Jussieu, 75252 Paris Cedex 05, France; email: jpb@lodyc.jussieu.fr |
| 506 | |2 openaire |e Política editorial | ||
| 504 | |a 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 | ||
| 504 | |a 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 (2006) Clim Dyn, 27, pp. 233-259 | ||
| 504 | |a Collins, W.D., Bitz, C.M., Blackmon, M.L., Bonan, G.B., Bretherton, C.S., Carton, J.A., Chang, P., Smith, R.D., The Community Climate System Model: CCSM3 (2006) J Clim, 19, pp. 2122-2143 | ||
| 504 | |a Delworth, T.L., GFDL's CM2 global coupled climate models. Part 1: Formulation and simulation characteristics (2006) J Clim, 19 (5), pp. 643-674 | ||
| 504 | |a Gnanadesikan, A., GFDL 's CM2 global coupled climate models. Part 2: The baseline ocean simulation (2006) J Clim, 19 (5), pp. 675-697 | ||
| 504 | |a 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 | ||
| 504 | |a Haak, H., Formation and propagation of great salinity anomalies (2003) Geophys Res Lett, 30, p. 1473. , DOI 10.1029/2003GL17065 | ||
| 504 | |a 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 | ||
| 504 | |a Marsland, S., The Max-Planck-Institute global ocean/sea ice model with orthogonal curvelinear coordinates (2003) Ocean Model, 5, pp. 91-127 | ||
| 504 | |a Minetti, J.L., Vargas, W.M., Trends and jumps in the annual precipitation in South America, south of 15°S (1997) Atmófera, 11, pp. 205-221 | ||
| 504 | |a Minetti, J.L., Vargas, W.M., Pressure behaviour of the subtropical Atlantic anticyclone and its influenced region over South America (1999) Aust Met Mag, 48, pp. 69-77 | ||
| 504 | |a Nabney, I., Netlab, T., Algorithms for pattern recognition (2002) Advances in Pattern Recognition, p. 420. , Springer, Berlin Heidelberg New York | ||
| 504 | |a 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 | ||
| 504 | |a Roeckner, E., The atmospheric general circulation model ECHAM5 (2003), Report No. 349OM; 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) The Finnish Environment 644, p. 83. , p Finnish Environment Institute | ||
| 504 | |a 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, R., GFDL's CM2 global coupled climate models. Part 4: Idealized climate response (2006) J Clim, 19 (5), pp. 723-740 | ||
| 504 | |a Tebaldi, C., Smith, R.L., Nychka, D., Mearns, L.O., Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles (2005) J Clim, 8, pp. 1524-1540 | ||
| 504 | |a Wittenberg, A.T., GFDL's CM2 global coupled climate models. Part 3: Tropical Pacific climate and ENSO (2006) J Clim, 19 (5), pp. 698-722 | ||
| 504 | |a Zhou, J., Lau, L.-M., Does a monsoon climate exist over South America? (1998), 11, pp. 1020-1040 | ||
| 520 | 3 | |a Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere-Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the "state-of-the-art" of present climate modeling. First, various criteria were computed making it possible to evaluate the models: skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes. © Springer-Verlag 2006. |l eng | |
| 593 | |a UPMC, LODYC, UMR CNRS/IRD/UPMC, 4 Place Jussieu, 75252 Paris Cedex 05, France | ||
| 593 | |a Departamento de Computación, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina | ||
| 593 | |a Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a BAYESIAN ANALYSIS |
| 690 | 1 | 0 | |a CLIMATE CHANGE |
| 690 | 1 | 0 | |a CLIMATE CONDITIONS |
| 690 | 1 | 0 | |a CLIMATE FORCING |
| 690 | 1 | 0 | |a CLIMATE MODELING |
| 690 | 1 | 0 | |a GREENHOUSE GAS |
| 690 | 1 | 0 | |a PRECIPITATION (CLIMATOLOGY) |
| 690 | 1 | 0 | |a SIMULATION |
| 690 | 1 | 0 | |a TRANSFER FUNCTION |
| 690 | 1 | 0 | |a TWENTY FIRST CENTURY |
| 700 | 1 | |a Martinez, F. | |
| 700 | 1 | |a Segura, E.C. | |
| 773 | 0 | |d 2007 |g v. 28 |h pp. 255-271 |k n. 2-3 |p Clim. Dyn. |x 09307575 |w (AR-BaUEN)CENRE-567 |t Climate Dynamics | |
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| 856 | 4 | 0 | |u https://doi.org/10.1007/s00382-006-0182-0 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_09307575_v28_n2-3_p255_Boulanger |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09307575_v28_n2-3_p255_Boulanger |y Registro en la Biblioteca Digital |
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| 963 | |a VARI | ||
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