Transcript Downscaling: An Introduction (Regionalisation) Why do we need to downscale? The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action.

Downscaling: An Introduction
(Regionalisation)
Why do we need to downscale?
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Because there is a mismatch of scales between what
climate models can supply and what environmental
impact models require.
Point
Global Climate Models supply...
1m
10km
50km
300km
Impact models require ...
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Downscaling Using GCMs
GCM output is generally the starting point of
any regionalisation technique, so:
• GCMs should perform well in simulating
circulation and climatic features affecting
regional climates, e.g., jet streams, storm
tracks
• it is better to use variables where sub-grid
scale variations are weak, e.g., mean sea
level pressure
Main advantage of using GCMs is that:
• internal physical consistency is maintained
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
A variety of methods and techniques have been
developed to address this scale problem:
1. High resolution and variable resolution
AGCM time-slice experiments - numerical
modelling
2. Regional Climate Models (RCMs) - dynamic
downscaling
3. Empirical/statistical and statistical/dynamical
models - statistical downscaling
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
But the very simplest approach is
the interpolation of grid box
outputs
• Overcomes problems of discontinuities in
change between adjacent sites in different
grid boxes
But
• introduces a false geographical precision
to the estimates
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Interpolation
CGCM1 GHG only,
Winter, Maximum
temperature change
(°C), 2020s
Interpolated to 0.5°
lat/long resolution
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
A
Main downscaling
approaches:
D
D
• higher resolution
experiments
I
or
G
• empirical/statistical or
statistical/dynamical
downscaling
processes
N
V
A
L
U
E
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
High Resolution Models
Numerical models at high resolution
over region of interest
• GCM time-slice experiments
• variable resolution GCMs
• high resolution limited area models
(regional climate models - RCMs)
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
REGIONAL CLIMATE MODELS
1. Driven by initial conditions,
time-dependent lateral
meteorological conditions and
surface boundary conditions
which are derived from GCMs
(or analyses of observations)
2. Account for sub-grid scale
forcings (e.g. complex
topographical features and
land cover inhomogeneity) in a
physically-based way
3. Enhance the simulation of
atmospheric circulations and
climatic variables at finer
spatial scales
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Comparison of detail in precipitation patterns over
western Canada as simulated by CGCM1 and CRCM.
CGCM1
CRCM
[Source: G. Flato, in Climate Change Digest: Projections for Canada’s Climate Future, H.G. Hengeveld.]
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
The Canadian RCM - CRCM
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
CRCM/NCEP
Screen Temperature (ºC)
5-year mean: Winter
CRCM-CRU2
CRU2
Validation = work in progress
Runs are underway
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
CRCM/NCEP
Precipitation rate (mm/day)
5-year mean: Winter
CRCM-CRU2
CRU2
Validation = work in progress
Runs are underway
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
High Resolution Models
ADVANTAGES
• are able to account for important local forcing
factors, e.g., surface type & elevation
•
•
•
•
DISADVANTAGES
dependent on a GCM to drive models
computationally demanding
few experiments
may be ‘locked’ into a single scenario,
therefore difficult to explore scenario
uncertainty, risk analyses
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Spatial Scale of Scenarios
Effect of scenario resolution on impact outcome
[Source: IPCC, WGI, Chapter 13]
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Empirical/Statistical,
Statistical/Dynamical Methods
PREDICTAND
PREDICTORS
Sub-grid scale climate  = f(larger-scale climate)
• Transfer functions - calculated between large-area and/or
large-scale upper air data and local surface climates
• Weather typing - relationships calculated between
atmospheric circulation types and local weather
• Weather generator parameters can be conditioned upon
the large-scale state
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Main Assumptions
• Predictors are variables of relevance to
the local climate variable being derived
(the predictand) and are realistically
modelled by the GCM
• The transfer function is valid under
altered climatic conditions
• The predictors fully represent the
climate change signal
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Transfer Functions
Grid Box
Area
Select
predictor
variables
Predictor variables
e.g., MSLP, 500, 700 hPa geopotential heights,
zonal/meridional components of flow, areal T&P
Calibrate
and verify
model
Transfer function
e.g., Multiple linear regression, principal
components analysis, canonical correlation
analysis, artificial neural networks
Observed station data for
predictand
Site variables for
future, e.g., 2050
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Extract
predictor
variables
from GCM
output
Drive
model
Transfer Functions
Fundamental Assumption
the observed statistical relationships will continue to be
valid under future radiative forcing
ADVANTAGES
• much less computationally demanding than physical
downscaling using numerical models
• ensembles of high resolution climate scenarios may be
produced relatively easily
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Transfer Functions
•
•
•
•
•
DISADVANTAGES
large amounts of observational data may be required to
establish statistical relationships for the current climate
specialist knowledge required to apply the techniques
correctly
relationships only valid within the range of the data used
for calibration - projections for some variables may lie
outside this range
may not be possible to derive significant relationships
for some variables
a predictor which may not appear as the most significant
when developing the transfer functions under present
climate may be critical for determining climate change
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Weather Typing
Pressure fields
from GCM
Identify
weather
types
Derive
Calculate
weather
types
Select
classification
scheme
Relationships between
weather type and local
weather variables
Observed
weather
variables
Drive
model
Local weather
variables for,
say, 2050
Statistically relate
observed station or
area-average
meteorological data to
a weather
classification scheme.
Weather classes may
be defined objectively
(e.g. by PCA, neural
networks) or
subjectively derived
(e.g., Lamb weather
types [UK], European
Grosswetterlagen)
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Weather Typing
Fundamental Assumption
the relationships between weather type and local
climate variables will continue to be valid under
future radiative forcing
ADVANTAGES
• founded on sensible physical linkages between
climate on the large scale and weather on the local
scale
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Weather Typing
DISADVANTAGES
• the fundamental assumption may not hold differences in relationships between weather type
and local climate have occurred at some sites during
the observed record
• scenarios produced are relatively insensitive to future
climate forcing - using GCM pressure fields alone to
derive types, and thence local climate, does not
account for the GCM projected changes in, e.g.,
temperature and precipitation, so necessary to
include additional variables such as large-scale
temperature and atmospheric humidity
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Downscaled vs. original GCM
Ex. Animas River Basin (US) with Hydrologic Model
Delta Change = HadCM2 results (raw data)
Grey area = 20 ensembles with downscaled climate scenario
Simulated = with observed data
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
[Source
Hay et al.
(1999)]
Weather Generators
Precipitation Process
Occurrence
Amount
Non-precipitation variables
Maximum temperature
Minimum temperature
Solar radiation
Model calibration
Synthetic data generation
Climate scenarios
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
LARS-WG:
wet and
dry spell
length
Weather Generators
Spatial Downscaling
Spatial Downscaling
Calibrate weather
generator using
area-average
weather
Calibrate weather
generator for each
individual station
within area
Calculate changes
in parameters from
grid box data
Area
Area parameter set
Station parameter set
Apply changes in
parameters derived from
difference between area
and grid box parameter
sets to individual station
parameter files; generate
synthetic data for
scenario
Grid Box
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Weather Generators
Temporal Downscaling
Observed station data
WG
Parameter file containing
statistical characteristics
of observed station data
Monthly scenario
information
Generate daily weather
data corresponding to
scenario
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Weather Generators
Fundamental Assumption
The statistical correlations between climatic variables
derived from observed data are assumed to be valid under a
changed climate.
ADVANTAGES
• the ability to generate time series of unlimited
length
• opportunity to obtain representative weather time
series in regions of data sparsity, by interpolating
observed data
• ability to alter the WG’s parameters in accordance
with scenarios of future climate change - changes
in variability as well mean changes
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Weather Generators
DISADVANTAGES
• seldom able to describe all aspects of
climate accurately, especially persistent
events, rare events and decadal- or centuryscale variations
• designed for use, independently, at
individual locations and few account for the
spatial correlation of climate
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project
Further Reading
• IPCC TAR(2001) - Chapter 10 & 13 (www.ipcc.ch)
• Wilby & Wigley (1997): Downscaling general
circulation model output: a comparison of
methods. Progress in Physical Geography 21,
530-548
• Hewitson & Crane (1996): Climate downscaling:
techniques and application. Climate Research 7,
85-95
• Goodess et al. (2003) : The identification & evaulation of
suitable scenario development methods for the estimation of future
probabilities of extreme events,Tyndall Centre, Rep. 4. report
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the
Climate Change Action Fund and provides climate change scenarios and related
information to the VIA community in Canada
Prepared by Elaine Barrow, CCIS Project