Post-processing and Bias Correction of
Climate Models: Rationale, Assumptions,
and a New Multivariate Approach
Adaptation Canada 2016
Ottawa, Ontario
Alex J. Cannon
Climate Research Division
April 12, 2016
Overview
• What is post-processing? What is bias correction?
– definitions
• Why do we need to post-process climate models?
– fitness for purpose
• What kinds of errors can be corrected?
– Eden et al. (2012) three types
• How model outputs are bias corrected matters…
• Case studies
– GCM  annual maximum precipitation
– RCM  Canadian Fire Weather Index
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What is post-processing? Bias-correction?
• Adjusting model-simulated quantities to make them
correspond more closely to reference data
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bias-correction (systematic errors)
statistical downscaling (differences in spatial scale)
temporal disaggregation (differences in temporal scale)
calibration (reliable estimates of conditional probability)
estimation of variables that aren’t directly simulated
• Adjusting model-simulated quantities to remove
systematic errors relative to reference data
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Correcting biases in…
– single variable: mean, variance, shape of distribution…
– multiple variables: dependence structure
Quantile mapping
Other
characteristics…
Obs.
- autocorrelation
- seasonal cycle
- etc.
Climate model
shift
and scale
Model
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Observed
Observations and reference data
• Impacts and adaptation researchers typically have a historical
reference dataset – in situ observations, observationallyconstrained gridded fields, model outputs, etc. – that is being used
as “truth”, irrespective of its errors and inaccuracies
historical
observations
- assume stationarity
historical
simulations
future
projections
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Why do we need bias correction?
• fitness for purpose
• simple example  threshold exceedances
No. days > 1-mm precip.
Climate model
No. days > 25⁰C Tmax
Historical
observations
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What kinds of errors can be corrected?
• Eden et al. (2012) classify errors in climate
model outputs into three categories:
1. unrealistic large-scale variability or response to
climate forcings;
2. unpredictable internal variability that differs from
observations;
3. parameterizations and unresolved subgrid-scale
orography, etc.
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How model outputs are bias corrected
matters…
Knobs, trade-offs, and unintended consequences
Climate
model
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temporal sequencing
Inter-variable dependence
marginal distributions
Observations
…
Case study #1: daily precipitation
Unintended consequences…
GCM
hist
Quantile
mapping
QDM
Obs
GCM
future
corrected
hist
corrected
future
x3
Cannon et al. (2015, J. Climate)
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Quantile
delta
mapping
Case study #2: Fire Weather Index (FWI)
Handling multiple variables…
Canadian Forest Fire Weather
Index system is a complicated
set of linked indices featuring
nonlinear dependence on
current and past values of

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

24-hr precipitation
2-m relative humidity
10-m wind speed
2-m temperature
at 12:00 LST
Developed at NRCan CFS
- global standard
- future projections of FWI?
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Alexander & De Groot (1998, NRCan CFS)
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Canadian Regional Climate Model
• NAM-44i North American domain (0.5 x 0.5-deg)
• 3-hr outputs or pr, tas, huss, sfcWind, ps, rsds, rlds
1. raw CanRCM4 outputs
2. univariate correction of each variable separately
3. multivariate bias correction of all variables simultaneously
Cannon (submitted, J. Climate)
 Calculate and compare FWI and its sub-indices
 CanRCM4 ERA-Interim evaluation run
 two-fold cross-validation (1989-1999; 2000-2009)
 WFDEI meteorological forcing data set
 observational target
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CanRCM4 bias correction
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Conclusions
• Be aware that bias correction algorithm matters
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What exactly is being corrected and what is not?
Where is the climate model providing information?
Does dependence between variables matter?
Is the climate change signal being corrupted artificially?
Be sure the method is fit for the purpose intended
Be aware of assumptions (and if they are being violated)
Be explicit about how the “knobs” are set
Beware of unintended consequences
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Thank you…
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