Tim Osborn, talk at HELIX 2nd annual conference, Liege, Belgium, September 2015 For more about HELIX, go to: http://helixclimate.eu/home

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. WHAT IS PATTERN SCALING?

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. 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. 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. 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. 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. • 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. NO

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. PATTERN SCALING PERFORMANCE

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. GCM
RCP2.6
RCP4.5
RCP6
RCP8.5
CMIP5
GCM1
CMIP5
GCM2
…
…
…
CMIP5GCM21

14. Fig. 2 of Osborn et al. (in press) Climatic Change

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. PATTERN SCALING PERFORMANCE
•
LAND AIR TEMPERATURE

21. RCPall
RCP85
RCP26
RCP264560

22. PATTERN SCALING PERFORMANCE
•
LAND PRECIPITATION

24. mmmm
RCPall
RCP85
RCP26
RCP264560

25. FORCED CHANGES IN VARIABILITY
•
PATTERN-SCALING METRICS OF VARIABILITY

26. 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

27. 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

28. 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

29. 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

32. MPI-ESM-MR GCM for RCP8.5, single run
Fig. 3 of Osborn et al. (in press) Climatic Change
1951-2000 reference

38. 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