final_program_abstracts[1]

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11 IMSC Session Program Kaplan EL, Meier P. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53: 457-481. Kim T, Valdés, J B, Yoo C. 2003. Nonparametric approach for estimating return periods of droughts in arid regions, Journal of Hydrologic Engineering, ASCE 8(5):237–246. Li Q, J S Racine. 2008. Nonparametric estimation of conditional CDF and quantile functions with mixed categorical and continuous data, Journal of Business and Economic Statistics 26(4): 423-434. Nicholls N. 1984. A system for predicting the onset of the North Australian wetseason. Journal of Climatology 4: 425-435. Maia A H; Meinke, H. 2009. Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models. International Journal of Climatology.Available on line: http://www3.interscience.wiley.com/journal/122677357/abstract Power S, Haylock M, Colman R, Wang X. 2006. The predictability of interdecadal changes in ENSO activity and ENSO teleconnections. Journal of Climate 19: 4755- 4771. Schemper, M.1992. Cox analysis of survival data with non-proportional hazard functions. The Statistician 41, 455-465. Abstracts 201
11 IMSC Session Program No title provided Wednesday - Poster Session 5 Ruth Doherty We explore the impact of climate model uncertainty upon predictions of future vegetation carbon stocks, for the period up to 2100, generated by a dynamic global vegetation model (DGVM) under a particular emissions scenario (SRES A2). Deterministic simulations are generated from the Lund-Potsdam-Jena (LPJ) model using climatic inputs derived from nine general circulation models (GCMs). Simulated changes between 1961-1990 and 2070-2099 range from +26 to +133 gtC, and are in broadly good agreement with those obtained in a recent previous study using the LPJ model. Simulated values for the 20th century are also obtained by running LPJ with observed climate data, and this provides a baseline against which the other runs can be compared. Time series regression models are used to analyse the discrepancies between each of the GCM-based simulations and the baseline simulation, and a form of model averaging - in which we average across GCM-based simulations and across models for each discrepancy - is then used to combine these into a probabilistic projection for global stocks of vegetation carbon. Abstracts 202
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