Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2017-36
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Model description paper
24 Feb 2017
Review status
This discussion paper is under review for the journal Geoscientific Model Development (GMD).
A method to encapsulate model structural uncertainty in ensemble projections of future climate
Jared Lewis1, Greg E. Bodeker1, Andrew Tait2, and Stefanie Kremser1 1Bodeker Scientific, 42 Russell Street, Alexandra, 9320, New Zealand
2National Institute of Water and Atmospheric Research, Wellington, New Zealand
Abstract. A method, based on climate pattern-scaling, has been developed to expand a small number of projections of fields of a selected climate variable (X) into an ensemble that encapsulates a wide range of model structural uncertainties. The method described in this paper is referred to as the Ensemble Projections Incorporating Climate model uncertainty (EPIC) method. Each ensemble member is constructed by adding contributions from (1) a climatology derived from observations that represents the time invariant part of the signal, (2) a contribution from forced changes in X where those changes can be statistically related to changes in global mean surface temperature (Tglobal), and (3) a contribution from unforced variability that is generated by a stochastic weather generator. The patterns of unforced variability are also allowed to respond to changes in Tglobal. The statistical relationships between changes in X (and its patterns of variability) with Tglobal are obtained in a "training" phase. Then, in an "implementation" phase, 190 simulations of Tglobal are generated using a simple climate model tuned to emulate 19 different Global Climate Models (GCMs) and 10 different carbon cycle models. Using the generated Tglobal time series and the correlation between the forced changes in X and Tglobal, obtained in the "training" phase, the forced change in the X field can be generated many times using Monte Carlo analysis. A stochastic weather generator model is used to generate realistic representations of weather which include spatial coherence. Because GCMs and Regional Climate Models (RCMs) are less likely to correctly represent unforced variability compared to observations, the stochastic weather generator takes as input measures of variability derived from observations, but also responds to forced changes in climate in a way that is consistent with the RCM projections. This approach to generating a large ensemble of projections is many orders of magnitude more computationally efficient than running multiple GCM or RCM simulations. Such a large ensemble of projections permits a description of a Probability Density Function (PDF) of future climate states rather than a small number of individual story lines within that PDF which may not be representative of the PDF as a whole; the EPIC method corrects for such potential sampling biases. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts and implications of climate change in a probabilistic way. A web-based tool, using the EPIC method to provide probabilistic projections of changes in daily maximum and minimum temperatures for New Zealand, has been developed and is described in this paper.

Citation: Lewis, J., Bodeker, G. E., Tait, A., and Kremser, S.: A method to encapsulate model structural uncertainty in ensemble projections of future climate, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2017-36, in review, 2017.
Jared Lewis et al.
Jared Lewis et al.
Jared Lewis et al.

Viewed

Total article views: 254 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
202 39 13 254 4 14

Views and downloads (calculated since 24 Feb 2017)

Cumulative views and downloads (calculated since 24 Feb 2017)

Viewed (geographical distribution)

Total article views: 254 (including HTML, PDF, and XML)

Thereof 254 with geography defined and 0 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 27 Apr 2017
Publications Copernicus
Download
Short summary
The Ensemble Projections Incorporating Climate model uncertainty (EPIC) method uses climate pattern-scaling to expand a small number of daily maximum and minimum surface temperature projections into an ensemble that captures the structural uncertainty between climate models. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts of climate change in a probabilistic and computationally efficient way.
The Ensemble Projections Incorporating Climate model uncertainty (EPIC) method uses climate...
Share