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- W2895727685 abstract "I present a computationally efficient stochastic climate model for large spatiotemporalscales (example, for the context of glacial cycle modelling). In analogy with a WeatherGenerator (WG), the model can be thought of as a Climate Generator (CG). TheCG produces a synthetic climatology conditioned on various inputs. Inputs for theCG include the monthly mean sea surface temperature field from a simplified EnergyBalance Model (EBM), surface elevation, surface ice, carbon dioxide, methane, orbitalforcing, latitude and longitude. The CG outputs mean monthly surface temperatureand precipitation using Bayesian Artificial Neural Networks (BANN) for non-linearregression. The CG is trained against the results of GCMs (FAMOUS and CCSM)over the last deglacial (22 ka to present). For validation, CG predictions are compareddirectly against the 120 ka to 22.05 ka interval of FAMOUS results that were notused for CG training. The stochastic noise is added to each prediction by generatingthe random normal distribution with mean from the ensemble networks for a singleguess and Standard deviation computed from 10th and 90th percentile of the BANNpredictive distribution for each time step. For the CG trained against FAMOUS, Ishow the predictive errors (relative to FAMOUS) are comparable to the differencebetween FAMOUS and the CCSM." @default.
- W2895727685 created "2018-10-12" @default.
- W2895727685 creator A5039437496 @default.
- W2895727685 date "2016-12-16" @default.
- W2895727685 modified "2023-09-23" @default.
- W2895727685 title "Climate Generator (Stochastic Climate Representation: 120 ka to present year)" @default.
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- W2895727685 hasPublicationYear "2016" @default.
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