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Emulating GCM projections by pattern scaling: performance and unforced climate variability

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Tim Osborn, talk at HELIX 2nd annual conference, Liege, Belgium, September 2015

For more about HELIX, go to: http://helixclimate.eu/home

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Emulating GCM projections by pattern scaling: performance and unforced climate variability

  1. 1. EMULATING GCM PROJECTIONS BY PATTERN SCALING • PERFORMANCE • UNFORCED CLIMATE VARIABILITY Liege, September 2015 Tim Osborn, Craig Wallace Climatic Research Unit, School of Environmental Sciences, UEA, UK • With contributions from Jason Lowe, Dan Bernie Meteorological Office Hadley Centre, UK
  2. 2. WHAT IS PATTERN SCALING?
  3. 3. • Pattern scaling assumes a linear relationship between local climate change & global temperature change • A GCM-simulated “pattern of climate change” is scaled to represent any scenario of global temperature change ΔVx,t ≈ ΔTt . αx
  4. 4. CMIP3  x  22   CMIP5  x  23  QUMP  x  17   Normalised  change  pa=erns   ClimGen   •  Pa=ern  scaling   •  Changes  in   precipita.on   variability  are   included  
  5. 5. CMIP3  x  22   CMIP5  x  23  QUMP  x  17   Global  temperatures   Normalised  change  pa=erns   ClimGen   •  Pa=ern  scaling   •  Changes  in   precipita.on   variability  are   included  
  6. 6. CMIP3  x  22   CMIP5  x  23  QUMP  x  17   Pa=ern  scaling   Global  temperatures   Normalised  change  pa=erns   ClimGen   •  Pa=ern  scaling   •  Changes  in   precipita.on   variability  are   included  
  7. 7. CMIP3  x  22   CMIP5  x  23  QUMP  x  17   Pa=ern  scaling   Global  temperatures   Normalised  change  pa=erns   ClimGen   •  Pa=ern  scaling   •  Changes  in   precipita.on   variability  are   included  
  8. 8. • Pattern scaling assumes a linear relationship between local climate change & global temperature change • A GCM-simulated “pattern of climate change” is scaled to represent any scenario of global temperature change ΔVx,t ≈ ΔTt . αx • If the linear assumption is correct, the pattern-scaled climate projection should match (emulate) what the GCM would have simulated for that scenario • But, is this assumption valid?
  9. 9. NO
  10. 10. In general, NO • But, although it is not perfect, the linear relationship works quite well in many cases • The errors are real, but are often small in comparison to the many other uncertainties
  11. 11. PATTERN SCALING PERFORMANCE
  12. 12. Climate timeseries (observed or GCM-simulated) are climate response to forcings plus a realisation of unforced (internally- generated) climate variability We’re interested in both but prefer to deal with them separately, not least because you cannot generate a sequence of unforced variability by pattern-scaling For ClimGen, we try to obtain patterns that represent the forced climate response: •  Use initial condition ensembles (where available) •  Pool simulations across multiple forcing scenarios (all RCPs) •  Regress change against global ΔT using all 1951-2100 data Forced climate response & unforced climate variability
  13. 13. GCM   RCP2.6   RCP4.5   RCP6   RCP8.5   CMIP5  GCM1   CMIP5  GCM2   …   …   …   CMIP5GCM21  
  14. 14. Fig. 2 of Osborn et al. (in press) Climatic Change
  15. 15. Global temperature projection HELIX specific warming levels HadGEM2-ES (RCP8.5) 2°C 4°C 6°C A more specific evaluation of performance: One GCM (HadGEM2-ES) for specific warming levels
  16. 16. PATTERN SCALING PERFORMANCE • LAND AIR TEMPERATURE
  17. 17. RCPall RCP85 RCP26 RCP264560
  18. 18. PATTERN SCALING PERFORMANCE • LAND PRECIPITATION
  19. 19. mmmm RCPall RCP85 RCP26 RCP264560
  20. 20. FORCED CHANGES IN VARIABILITY • PATTERN-SCALING METRICS OF VARIABILITY
  21. 21. Pattern scaling: unforced climate variability changes? Pa=ern-­‐scale  higher  moments  (e.g.  standard  deviaGon,  skew)   •  We  divide  GCM  monthly  precipitaGon  Gmeseries  by  low-­‐pass  filter   •  Represent  the  high-­‐frequency  deviaGons  with  a  gamma  distribuGon   •  Scale  changes  in  gamma  shape  parameter  with  ΔT   Fig. 1 of Osborn et al. (in press) Climatic Change Relativechangein
  22. 22. How to utilise projected changes in distribution shape? Perturb the observations Example  applicaGon   •  SE  England  grid  cell,  HadCM3  GCM,  July  precipitaGon   •  For  ΔT  =  3°C,  pa=ern-­‐scaling  gives  45%  reducGon  in  mean  precipitaGon   •  But  also  62%  reducGon  in  gamma  shape  param.  of  monthly  precipitaGon   Fig. 1 of Osborn et al. (in press) Climatic Change Observed sequence Sequence x 0.55 Sequence x 0.55 Sequence x 0.55 & perturbed to have 62% lower shape
  23. 23. Is there agreement in GCM-simulated changes of variability? •  MulG-­‐model  agreement  of  22  CMIP3  GCMs   •  FracGon  of  models  showing  increased  gamma  shape  of  July  precipitaGon   Units: fraction Based on Osborn et al. (in press) Climatic Change
  24. 24. MPI-ESM-MR GCM for RCP8.5, single run Future frequency > 0.08 means the 8%ile is more frequent than during the 1951-2000 reference period See paper for equivalent results for 4, 6, 12, 20%iles Fig. 3 of Osborn et al. (in press) Climatic Change Projected changes in frequency of very dry summer months
  25. 25. MPI-ESM-MR GCM for RCP8.5, single run Fig. 3 of Osborn et al. (in press) Climatic Change 1951-2000 reference
  26. 26. CLOSING REMARKS •  GCMs can be approximately emulated by pattern- scaling •  Better for temperature than for precipitation •  Precipitation is fine if patterns are diagnosed from suitable runs •  Don’t diagnose patterns from RCP2.6 & extrapolate to large warming •  Don’t falsely penalise pattern-scaling performance by evaluating against a single GCM run •  Pattern-scaling has been extended to project changes in unforced climate variability •  For precipitation in ClimGen, but could be extended to temperature variability •  Perturb the observed monthly climate record by pattern-scaled changes in both mean & variability

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