Preprints
https://doi.org/10.5194/gmd-2017-276
https://doi.org/10.5194/gmd-2017-276
Submitted as: development and technical paper
 | 
02 Jan 2018
Submitted as: development and technical paper |  | 02 Jan 2018
Status: this preprint was under review for the journal GMD. A revision for further review has not been submitted.

The Climate Generator: Stochastic climate representation for glacial cycle integration

Mohammad Hizbul Bahar Arif, Lev Tarasov, and Tristan Hauser

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Mohammad Hizbul Bahar Arif, Lev Tarasov, and Tristan Hauser
 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Mohammad Hizbul Bahar Arif, Lev Tarasov, and Tristan Hauser
Mohammad Hizbul Bahar Arif, Lev Tarasov, and Tristan Hauser

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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.