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Pa#ern scaling using ClimGen: User needs Changing precipita0on variability Interac0on between global & regional responses Tim Osborn & Craig Wallace Clima&c Research Unit, University of East Anglia April 2014 Pa:ern scaling, climate model emulators & their applica&on to the new scenario process NCAR, Boulder, Colorado Work supported by TOPDAD & HELIX EU projects

Pattern scaling using ClimGen

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Pa#ern  scaling  using  ClimGen:  User  needs  

Changing  precipita0on  variability  Interac0on  between  global  &  regional  responses  

Tim  Osborn    &    Craig  Wallace  Clima&c  Research  Unit,  University  of  East  Anglia  

April  2014    

Pa:ern  scaling,  climate  model  emulators  &  their  applica&on  to  the  new  scenario  process  

NCAR,  Boulder,  Colorado    

Work  supported  by  TOPDAD  &  HELIX  EU  projects  

Pattern scaling: meeting user needs

Key  requirements:  •  Explore  spread  (uncertainty?)  of  climate  projec0ons  

•  Pre-­‐CMIP3,  CMIP3,  CMIP5  mul0-­‐model,  QUMP  perturbed  parameters  

•  Generate  projec0ons  for  un-­‐simulated  scenarios  User  needs:  •  Iden0cal  formats  for  all  scenarios  (&  observa0ons)  •  Flexible  temporal,  seasonal  and  geographic  windowing/averaging    

Pattern scaling: meeting user needs

Example  na0onal  average  summer  T  &  P  changes  Pink  =  CMIP3  distribu:on  Open  symbols  =  CMIP3  models  

Key  requirements:  •  Explore  spread  (uncertainty?)  of  climate  projec0ons  

•  Pre-­‐CMIP3,  CMIP3,  CMIP5  mul0-­‐model,  QUMP  perturbed  parameters  

•  Generate  projec0ons  for  un-­‐simulated  scenarios    

Natural variability

ΔT = 0.5, 1.5, 3

For global warming ΔT = 3 K (left panel) or 0.5, 1.5 and 3 K (right panel)

Based on Osborn et al. (under review) Climatic Change

Pattern scaling: meeting user needs

Example  na0onal  average  summer  T  &  P  changes  Pink  =  CMIP3  distribu:on  Open  symbols  =  CMIP3  models      Brown  =  CMIP5  distribu:on  Solid  symbols  =  CMIP5  models  

Key  requirements:  •  Explore  spread  (uncertainty?)  of  climate  projec0ons  

•  Pre-­‐CMIP3,  CMIP3,  CMIP5  mul0-­‐model,  QUMP  perturbed  parameters  

•  Generate  projec0ons  for  un-­‐simulated  scenarios  

Natural variability

ΔT = 0.5, 1.5, 3

For global warming ΔT = 0.5, 1.5 and 3 K (right panel)

Based on Osborn et al. (under review) Climatic Change

Pattern scaling: meeting user needs

Key  requirements:  •  Explore  spread  (uncertainty?)  of  climate  projec0ons  

•  Pre-­‐CMIP3,  CMIP3,  CMIP5  mul0-­‐model,  QUMP  perturbed  parameters  

•  Generate  projec0ons  for  un-­‐simulated  scenarios  

Natural variability

ΔT = 0.5, 1.5, 3

Example  na0onal  average  summer  T  &  P  changes  Pink  =  CMIP3  distribu:on  Open  symbols  =  CMIP3  models      Brown  =  CMIP5  distribu:on  Solid  symbols  =  CMIP5  models    Blue  =  QUMP  distribu:on  Black  le#ers  =  QUMP  models  

For global warming ΔT = 0.5, 1.5 and 3 K (right panel)

Based on Osborn et al. (under review) Climatic Change

Pattern scaling: meeting user needs

Mul0ple  climate  variables  (all  monthly  means,  mostly  land-­‐only):  •  Near-­‐surface  temperature  (mean,  min,  max,  DTR)  •  Precipita0on  &  wet-­‐day  frequency  •  Cloud-­‐cover  (can  es0mate  sunshine  hours  or  radia0on  variables)  •  Vapour  pressure  (can  es0mate  other  humidity  variables)  •  SST  is  currently  the  only  variable  provided  over  the  oceans  

User  needs:  more  derived  variables,  extreme  events  &  variability  •  Hea0ng  &  cooling  degree  days  (HDD  &  CDD)  •  Poten0al  evapotranspira0on  (PET,  e.g.  from  Penman-­‐Mon0eth)  •  Drought  indicators  (e.g.  Standardised  Precipita0on-­‐Evapotranspira0on  

Index,  SPEI)    

How  to  deal  with  climate  (and  weather)  variability?  

Climate variability in pattern scaling: (1) use observations

Sample  from  observed  variability:  •  Realis0c  for  present-­‐day  •  But  doesn’t  change  when  the  mean  climate  changes  

Design  sampling  to  allow  the  separa0on  of  climate  change  and  natural  variability  effects  

•  Use  mul0ple  0me-­‐shided  sequences  instead  of  single  observed  sequence    

Climate variability in pattern scaling: (1) use observations

Sample  from  observed  variability:  •  Realis0c  for  present-­‐day  •  But  doesn’t  change  when  the  mean  climate  changes  

Design  sampling  to  allow  the  separa0on  of  climate  change  and  natural  variability  effects  

•  Use  mul0ple  0me-­‐shided  sequences  instead  of  single  observed  sequence    

Climate variability in pattern scaling: (1) use observations

•  Or  generate  slices  represen0ng  climate+variability  for  specific  amounts  of  ΔT  

Fig. S3 of Osborn et al. (under review) Climatic Change

Climate variability in pattern scaling: (2) perturb observations

Pahern-­‐scale  higher  moments  (e.g.  standard  devia0on,  skew)  •  We  divide  GCM  monthly  precipita0on  0meseries  by  low-­‐pass  filter  •  Represent  the  high-­‐frequency  devia0ons  with  a  gamma  distribu0on  •  Scale  changes  in  gamma  shape  parameter  with  ΔT  

Fig. 1 of Osborn et al. (under review) Climatic Change

Rel

ativ

e ch

ange

in

Climate variability in pattern scaling: (2) perturb observations

Example  applica0on  •  SE  England  grid  cell,  HadCM3  GCM,  July  precipita0on  •  For  ΔT  =  3°C,  pahern-­‐scaling  gives  45%  reduc0on  in  mean  precipita0on  •  But  also  62%  reduc0on  in  gamma  shape  param.  of  monthly  precipita0on  

Fig. 1 of Osborn et al. (under review) Climatic Change

Observed sequence

Sequence x 0.55 Sequence x 0.55

Sequence x 0.55 & perturbed to have 62% lower shape

Is there agreement in GCM-simulated changes of variability?

•  Mul0-­‐model  mean  of  22  CMIP3  GCMs  •  Normalised  change  in  gamma  shape  of  July  precipita0on  

Units: % change / K

Fig. 1 of Osborn et al. (under review) Climatic Change

Is there agreement in GCM-simulated changes of variability?

•  Mul0-­‐model  mean  of  20  CMIP5  GCMs  •  Normalised  change  in  gamma  shape  of  July  precipita0on  

Units: % change / K

Based on Osborn et al. (under review) Climatic Change

Is there agreement in GCM-simulated changes of variability?

•  Mul0-­‐model  agreement  of  22  CMIP3  GCMs  •  Frac0on  of  models  showing  increased  gamma  shape  of  July  precipita0on  

Units: fraction

Based on Osborn et al. (under review) Climatic Change

Is there agreement in GCM-simulated changes of variability?

•  Mul0-­‐model  agreement  of  20  CMIP5  GCMs  •  Frac0on  of  models  showing  increased  gamma  shape  of  July  precipita0on  

Units: fraction

Based on Osborn et al. (under review) Climatic Change

Transform observed rainfall series by factors given by range of ΔT from 0 to 6K Count frequency of short droughts in each transformed series Estimate uncertainty

UK drought frequency vs.

global ΔT

Does pattern-scaling emulate GCM/RCM behaviour?

HadCM3  GCM  HadRM3  RCM  

Can we treat global and regional changes independently? •  Separa0on  into  global  ΔT  &  regional  paherns  is  convenient  •  Especially  for  the  treatment  of  uncertain0es  

Can we treat global and regional changes independently? •  Separa0on  into  global  ΔT  &  regional  paherns  is  convenient  •  Especially  for  the  treatment  of  uncertain0es  

Simple example: Estimating conditional PDFs of UK drought frequency, using HadRM3 RCM pattern-scaling results and the Wigley & Raper (2001) PDFs of ΔT

Simple example: Estimating conditional PDFs of UK drought frequency, using HadRM3 RCM pattern-scaling results and the Wigley & Raper (2001) PDFs of ΔT

Can we treat global and regional changes independently? •  Separa0on  into  global  ΔT  &  regional  paherns  is  convenient  •  Especially  for  the  treatment  of  uncertain0es  

Estimating conditional PDFs of UK drought frequency

Can we treat global and regional changes independently? •  Separa0on  into  global  ΔT  &  regional  paherns  is  convenient  •  Especially  for  the  treatment  of  uncertain0es  

Can we treat global and regional changes independently? •  Separa0on  into  global  ΔT  &  regional  paherns  is  convenient  •  Especially  for  the  treatment  of  uncertain0es  

•  But  can  I  combine  ΔT  derived  from  a  par0cular  climate  sensi0vity  with  any  of  the  GCM  paherns?  

•  Or  are  the  normalised  change  paherns  of  high  sensi0vity  GCMs  systema0cally  different  from  those  of  low  sensi0vity  GCMs?  

Rank  correla0on  between  temperature  and  ECS  for  CMIP3  

Are the normalised change patterns of high sensitivity GCMs systematically different from those of low sensitivity GCMs?

Osborn et al. (in preparation) Rank correlation for 22 GCMs

>80% significant correlations shown

Rank  correla0on  between  temperature  and  ECS  for  QUMP  

Are the normalised change patterns of high sensitivity GCMs systematically different from those of low sensitivity GCMs?

Osborn et al. (in preparation) Rank correlation for 17 GCMs

>80% significant correlations shown

Rank  correla0on  between  temperature  and  ECS  for  CMIP3,  CMIP5  &  QUMP  

Are the normalised change patterns of high sensitivity GCMs systematically different from those of low sensitivity GCMs?

Osborn et al. (in preparation) Rank correlation for 52 GCMs

>80% significant correlations shown

Conclusions: meeting user needs with pattern scaling

Exploring  the  uncertainty  of  climate  projec0ons:  •  Given  wide  mul0-­‐model  ensemble  ranges,  sufficient  to  approximately  

emulate  plume  of  future  regional  changes  Increasing  demand  for  emula0on  to  include  variability  &  represent  extremes:  •  Need  to  treat  variability  with  care,  sufficient  sampling  etc.  •  Can  pahern-­‐scale  higher  order  parameters  (e.g.  standard  devia0on,  

skew)  and  perturb  observed  variability  accordingly  •  More  complicated  changes  (e.g.  shid  in  ENSO  behaviour)  cannot,  

however,  be  captured  Systema0c  differences  between  normalised  paherns  from  low  and  high  sensi0vity  models  complicates  the  separate  treatment  of  uncertainty  in  global  ΔT  and  regional  climate  change