Empirical-statistical downscaling What, why and how?
Rasmus Benestad & Abdelkader Mezghani
Global Climate Models
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Best tool to estimate future climate statistics
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Global and large-scales
Why worry about global warming?
Why worry about global warming?
Local statistics ← large-scale situation.
Downscaling: scale dependency.
Different scales are linked
What is downscaling?
Local clouds
Regional situation
Local
vegetation
Local surface characteristics
What is downscaling?
Local clouds
Regional situation
X = f(large-scale, geography, local processes)
X = f(large-scale,geography) + g(local processes)
Downscaling: X=f(Y,z) predictable
Local
vegetation
Local
noise
g(.) - not dependent on the large-scale?
Traditionally → f(.)
Local surface characteristics
Different types of downscaling
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Regional climate models and empiricalstatistical downscaling (ESD)
Different philosophies
− Information source: 'Empirical' vs 'theoretical'
Zooming ≠ downscaling
Local details not predictable in terms of large
scales.
End-users want 'zooming' – what does the local
climate look like?
Traditional methods downscale – aspects
related to the large scales
Downscaling – really...
Small-scale noise
Stochastic & stationary
Known PDF & temporal structure.
Statistical time series models
Stationary?
Why?
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Local climate
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Bridge: virtual and real worlds
Added value
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Independent to RCMs
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Different strengths and weaknesses
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Maximize the amount of information
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Unconventional information
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Validation!
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Not computer intensive
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Large GCM ensembles and long time series
Diagnostics
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Also apply to (regional) climate model results.
What do you need?
Local data!
Compared to RCMs
RCM not just downscaling
= boundary conditions + internally produced variability
Comparison with ESD
– same metrics?
What is large scale?
What is the models' minimum skillful scale?
Large-scale (predictor patterns)
Challenge – choosing proper predictors
Concern: non-stationarity
Capture the 'signal'
Well-simulated
Good representation of reality
Similarity between reanalyses
Which global climate model is best?
Validation
Poona
Wet-day mean µ
Tele-connection pattern
How?
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What's the question?
– Tailor to the problem
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Local knowledge
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climatology, meteorology, physics
Statistics
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Aggregated
Parameters of distributions
Temperature statistics
stdv
Future mean temperature
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Connection to large-scales
Future temperature range
Affected by large-scales?
Probability
pdf=f(large-scale)?
pdf=f(small scale)?
The exponential distribution
The mean µ is key!
qp = -ln(1-p) µ
Number of wet events: X > 10 mm/day
EU-SPECS
FP-7
Future wet-day mean precipitation
µ → extremes
Future wet-day frequency
Affected by large-scales.
Temporal structure
Spell length in days.
Auto-correlation
function?
Dry
Wet
Downscaling dry spells
Predictors: TAS + SLP; annual
aggregate
EU-SPECS
FP-7
The physical picture
The planetary heat loss
Outgoing longwave radiation (OLR)
Tools esd
Data
Processing
Analysis
Validation
Downscaling
Visualisation
Mean precipitation amount (mm/day)
CV mean precipitation amounts
Mean wet-day frequency
Standard deviation: precipitation
Standard deviation: precipitation
Statistics
Extract relevant information
Unconventional?
Possibilities
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Regridding
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Anomaly/climatology
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aggregation
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EOF, PCA, CCA
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Downscaling & regression
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Synchronise & date stamp
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Conversion methods
Philosophy
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User-friendly & intuitive
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Quick & efficient
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Versatile
Save time spent with data
handling and processing
Inspired by
Lego
R S3-methods and classes
Implications
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Common/standard data structure
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Meta-data
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Data format
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Controlled vocabulary & definitions
Summary...
ESD is not just about getting some numbers
Answer to the question and increased insight
Making sense of available information
Validation
Global change, historical climate, phenomena, and
downscaled results
ESD – a tool for analysing climate models