Abstract
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 indicative 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) and 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 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 largely 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.
Highlights
While future changes in climate will follow a single trajectory, it is highly unlikely that any single climate model projection will correctly simulate that trajectory
The Ensemble Projections Incorporating Climate model uncertainty (EPIC)-generated time series show a long-term evolution consistent with expectations from regional climate models (RCMs) simulations, including the effects of the spread in those simulations. While it cannot be directly seen from the time series plotted in Fig. 8, the EPIC-generated time series exhibit changes in weather variability consistent with RCM projections of expected changes in the first four modes of weather variability
The Tglobal time series were generated by a simple climate model (SCM) tuned to 19 different atmosphere–ocean GCM (AOGCM) and 10 different carbon cycle models and used as a predictor for the long-term change in Tmax and Tmin
Summary
While future changes in climate will follow a single trajectory, it is highly unlikely that any single climate model projection will correctly simulate that trajectory. The use of a single model projection is insufficient for assessing the potential future state of the climate. Probabilistic simulations of future climate, presented as probability density functions (PDFs), give decision makers a much clearer picture of likelihoods of future climate states compared to Published by Copernicus Publications on behalf of the European Geosciences Union. PDFs of future climate that consider a greater number of sources of uncertainty, including uncertainty resulting from structural differences in the underlying models, provide more robust information needed for quantitative risk assessments, since the likelihood of any particular trajectory can be better estimated
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