Emulating GCM projections by pattern scaling: performance and unforced climate variability
-
Upload
tim-osborn -
Category
Education
-
view
245 -
download
2
Transcript of Emulating GCM projections by pattern scaling: performance and unforced climate variability
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
• 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
CMIP3 x 22 CMIP5 x 23 QUMP x 17
Normalised change pa=erns
ClimGen • Pa=ern scaling
• Changes in precipita.on variability are included
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
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
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
• 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?
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
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
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
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
Rel
ativ
e ch
ange
in
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
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
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
MPI-ESM-MR GCM for RCP8.5, single run
Fig. 3 of Osborn et al. (in press) Climatic Change
1951-2000 reference
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