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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and technical paper
02 Jan 2018
Review status
This discussion paper is a preprint. A revision of the manuscript for further review has not been submitted.
The Climate Generator: Stochastic climate representation for glacial cycle integration
Mohammad Hizbul Bahar Arif1, Lev Tarasov2, and Tristan Hauser3 1Faculty of Engineering and Applied Science, Memorial University, Canada
2Department of Physics and Physical Oceanography, Memorial University, Canada
3Department of Environmental and Geographical Science, University of Cape Town, South Africa
Abstract. This paper presents a computationally efficient stochastic approach to simulate atmospheric fields (specifically monthly mean temperature and precipitation) on large spatial-temporal scales. In analogy with Weather Generators (WG), the modelling approach can be considered a Climate Generator (CG). The CG can also be understood as a field-specific General Circulation climate Model (GCM) emulator. It invokes aspects of spatio-temporal downscaling, in this case mapping the output of an Energy Balance climate Model (EBM) to that of a higher resolution GCM. The CG produces a synthetic climatology conditioned on various inputs. These inputs include sea level temperature from a fast low-resolution EBM, surface elevation, ice mask, atmospheric concentrations of carbon dioxide, methane, orbital forcing, latitude and longitude. Bayesian Artificial Neural Networks (BANN) are used for nonlinear regression against GCM output over North America, Antarctica and Eurasia.

Herein we detail and validate the methodology. To impose natural variability in the CG (to make the CG indistinguishable from a GCM) stochastic noise is added to each prediction. This noise is generated from a normal distribution with standard deviation computed from the 10 % and 90 % quantiles of the predictive distribution values from the BANNs for each time step. This derives from a key working assumption/approximation that the self-inferred predictive uncertainty of the BANNs is in good part due to the internal variability of the GCM climate. Our CG is trained against GCM (FAMOUS and CCSM) output for the last deglacial interval (22 ka to present year). For predictive testing, we compare the CG predictions against GCM (FAMOUS) output for the disjoint remainder of the last glacial interval (120 ka to 22.05 ka). The CG passes a climate Turing test, an indistinguishability test in analogy with the original Turing test for artificial intelligence. This initial validation of the Climate Generator approach justifies further development and testing for long time integration contexts such as coupled ice-sheet climate modelling over glacial cycle time-scales.

Citation: Arif, M. H. B., Tarasov, L., and Hauser, T.: The Climate Generator: Stochastic climate representation for glacial cycle integration, Geosci. Model Dev. Discuss.,, in review, 2018.
Mohammad Hizbul Bahar Arif et al.
Mohammad Hizbul Bahar Arif et al.
Mohammad Hizbul Bahar Arif et al.


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Publications Copernicus
Short summary
This study is a first step answer to the following question: Can you use emulators (machine learning techniques) to make the output of fast simple climate models (a 2-D energy balance model in this test case) indistinguishable from that of a much more computationally expensive General Circulation climate model (GCM) within the uncertainties of GCMs? Our preliminary test of this concept for large spatio-temporal contexts gives a positive answer.
This study is a first step answer to the following question: Can you use emulators (machine...