A method to encapsulate model structural uncertainty in ensemble projections of future climate: EPIC v1.0

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.

[1]  John F. B. Mitchell,et al.  Towards the Construction of Climate Change Scenarios , 1999 .

[2]  Tom M. L. Wigley,et al.  Emulating atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 2: Applications , 2011 .

[3]  Matthew D. Collins,et al.  UK Climate Projections Science Report: Climate Change Projections , 2009 .

[4]  Peter M. Cox,et al.  An analogue model to derive additional climate change scenarios from existing GCM simulations , 2000 .

[5]  A. Tait,et al.  Generating Multiyear Gridded Daily Rainfall over New Zealand , 2005 .

[6]  Greg E. Bodeker,et al.  Methodological aspects of a pattern-scaling approach to produce global fields of monthly means of daily maximum and minimum temperature , 2013 .

[7]  I. Watterson,et al.  Calculation of probability density functions for temperature and precipitation change under global warming , 2008 .

[8]  Tait.  Future projections of growing degree days and frost in New Zealand and some implications for grape growing , 2008 .

[9]  H. J. Arnold.  Introduction to the Practice of Statistics , 1990 .

[10]  Monica C. Jackson,et al.  Introduction to the Practice of Statistics , 2001 .

[11]  D. Ackerley,et al.  Regional climate modelling in New Zealand: comparison to gridded and satellite observations , 2012 .

[12]  Kristen Averyt,et al.  Climate change 2007: Synthesis Report. Contribution of Working Group I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Summary for Policymakers. , 2007 .

[13]  J. Murphy,et al.  A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[14]  V. Pope,et al.  The processes governing horizontal resolution sensitivity in a climate model , 2002 .

[15]  J. Murphy,et al.  Natural Hazards and Earth System Sciences Probabilistic projections for 21 st century European climate , 2010 .

[16]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[17]  T. D. Mitchell,et al.  Pattern Scaling: An Examination of the Accuracy of the Technique for Describing Future Climates , 2003 .

[18]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[19]  G. Bodeker,et al.  Techniques for analyses of trends in GRUAN data , 2014 .

[20]  R. Smith,et al.  CANOPY, SURFACE AND SOIL HYDROLOGY , 1996 .

[21]  Ackerley,et al.  Regional climate modelling in New Zealand , 2012 .

[22]  Malte Meinshausen,et al.  Uncertainties of global warming metrics: CO2 and CH4 , 2010 .

[23]  Reto Knutti,et al.  Climate model genealogy: Generation CMIP5 and how we got there , 2013 .

[24]  Ruth Lorenz,et al.  A climate model projection weighting scheme accounting for performance and interdependence , 2017 .

[25]  John F. B. Mitchell,et al.  The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments , 2000 .

[26]  John F. B. Mitchell,et al.  Workbook on generating high resolution climate change scenarios using PRECIS , 2003 .

[27]  B. Bhaskaran,et al.  Simulation of New Zealand's climate using a high‐resolution nested regional climate model , 2007 .

[28]  M. Webb,et al.  Multivariate probabilistic projections using imperfect climate models part I: outline of methodology , 2012, Climate Dynamics.

[29]  R. Schnur,et al.  Climate-carbon cycle feedback analysis: Results from the C , 2006 .

[30]  V. Pope,et al.  The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3 , 2000 .

[31]  Bhaskaran,et al.  On the application of the Unified Model to produce finer scale climate information for New Zealand , 2002 .

[32]  R. Knutti,et al.  Climate model genealogy , 2011 .

[33]  T. Wigley,et al.  Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration , 2011 .