"Pattern scaling using ClimGen: users needs, changing precipitation variability, and interaction between global/regional responses" presentation by Tim Osborn and Craig Wallace, NCAR, April 2014

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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?

7. 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

8. 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

9. 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

10. 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
Relativechangein

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

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

18. 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

19. 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

20. 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

21. 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?

22. 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

23. 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

24. 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

25. 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