Transcript hewitson_regionalcl

Downscaling / Regionalization
Techniques and methodologies
AIACC Workshop, Apr 2002
Bruce Hewitson
CSAG : University of Cape Town
Downscaling: Techniques and methodologies
General concepts and assumptions
Regional Climate Models : Overview of application
Empirical-Statistical downscaling : Overview and
application issues
Decision approach to downscaling choices
AIACC Workshop, Apr 2002
Bruce Hewitson
CSAG : University of Cape Town
Downscaling: a valuable procedure of tremendous potential
facing a minefield of choices
Complex
?
?
?
?
?
Simple
Reliable
Dangerous
Do you need to downscale? What do you NEED rather than WANT
For the scientific question you are asking, can you do with a simple
sensitivity study, use the native GCM data, apply interpolation, add
the GCM anomaly to a baseline data set, or has some else already
generated a suitable product???
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Downscaling: A technique to take GCM atmospheric fields and
derive climate information at a spatial/temporal scale finer than
that of the GCM
“Local” Climate = f (larger scale atmospheric forcing)
R = f (L)
R: predictand - (a set of) regional scale variables
L: predictors - large scale variables from GCM
f: stochastic or quantitative transfer function
conditioned by L, or a dynamical regional climate
model.
Note: the downscaled predictand can only contain variance
that exists in the cross scale relationship captured by f.
Anything else is/must be “made up”
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Two options premised on the same assumptions
Regional Climate Models (RCMs) or Empirical cross scale
functions
Assumptions:
• The GCM is skillful (enough) with regard to the predictors used
in the downscaling -- are they “adequately” simulated by the GCM
“Adequate” requires evaluating the GCM in terms of the predictor
variables at the space and time scales of use!
e.g: For RCMs this could mean the full 3-dimensional fields of
motion, temperature, and humidity, on a 6-12 hour time interval,
over the domain of interest.
Note: Downscaling propagates the GCM error
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Two options premised on the same assumptions
Regional Climate Models or Empirical cross scale functions
Assumptions:
• f is valid under altered climatic conditions - stationarity
ie: the bulk of future synoptic states are
at least represented in present day
records -- the future dominated by
changes in frequency, intensity, and
persistence.
Local Response
Note: This applies to empirical downscaling and and RCMs. If the climate system
is substantially non-stationary, then at the very least empirical downscaling
becomes questionable, possibly much of RCM applications as well.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Two options premised on the same assumptions
Regional Climate Models or Empirical cross scale functions
Assumptions:
• The chosen predictors represent / contain the climate change
signal.
For example (empirical downscaling): if local temperature is well
determined by synoptic scale sea level pressure (SLP), which
shows minimal change into the future.
An effective empirical downscaling may be derived, but, what if
atmospheric moisture content goes up?
The downscaled DT from SLP may be ~0, yet a large DT may
actually exist from the moisture change.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Regional Climate Models
Computationally intensive, physically based (in part), likely the most
viable/valid downscaling in the long term, still somewhat developmental.
Conceptual approach:
Scale an AGCM to a finite domain, calibrate paramterizations for
higher resolution, couple a land surface scheme, force at the
boundaries with atmospheric fields from the GCM - simple?
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Regional Climate Models
Computationally intensive, physically based (in part), likely the most
viable/valid downscaling in the long term, still somewhat developmental.
Conceptual approach:
Scale an AGCM to a finite domain, calibrate paramterizations for
higher resolution, couple a land surface scheme, force at the
boundaries with atmospheric fields from the GCM - simple?
Conceptual issues:
How to interface at boundaries
Inflow versus outflow
Land surface scheme
Spatial resolution
GCM versus RCM physics
AIACC Workshop, Apr 2002
Boundary field updating
Parameterization schemes
Number of levels
Domain sensitivity
1-way versus 2-way nesting
CSAG : University of Cape Town
Regional Climate Models
Practicalities:
• Complex procedure with many implementation decisions that can
determine the result obtained.
• Need to understand why you get the results you see (right answer for
wrong reason problem).
• Selection of domain, physics package, parameterization, and
evaluation of performance is a time-consuming procedure, but essential!
Running a RCM, given suitable IT skills and resources, can be
done in a matter of days.
Achieving understandable and justifiable results can be very
lengthy.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Case example from Africa of implementing an RCMs
Instituting an RCM in an environment where it has not been run
before
16 scientists from around Africa,
two week training workshop,
full IT support,
theory lectures,
all software and scripts configured,
email follow up with participants.
18 months later, 7 active participants, not all of whom achieved
successful simulations at their home institution.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Running a RCM -- A brief exposure to typical activities
Select a model: preferably one that will run on available
computational infrastructure, with an established user base, and
make a (friendly) contact with an experienced user.
Develop appropriate skills: Unix literate, Fortran/C capable, data
handling and visualization skills.
Implement appropriate infrastructure: Single PC can handle months
to 1 year type simulations. Longer climate simulations require PC
clusters or multiple-CPU workstations.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Running a RCM -- A brief exposure to typical activities
Select a model: preferably one that will run on available
computational infrastructure, with an established user base, and
make a (friendly) contact with an experienced user.
Develop appropriate skills: Unix literate, Fortran/C capable, data
handling and visualization skills.
Implement appropriate infrastructure: Single PC can handle months
to 1 year type simulations. Longer climate simulations require PC
clusters or multiple-CPU workstations.
MM5 v3, land surface model, 110x100 grid points, 23 levels, 60km
resolution
Simulation setup
Results
Hardware
Intel P4
Cost
Speed # PCs Days Hours Min/day ~Hrs/day (1 PC)
$8,000 1500 MHz
6
120
35
17.5
1.75
AMD XP2000+ $12,000 1600 MHz
8
120
18
9
1.2
"4"
120
22
11
N/A
DEC ES40
AIACC Workshop, Apr 2002
$80,000 667 MHz
CSAG : University of Cape Town
Running a RCM -- A brief exposure to typical activities
Domain and resolution: If no one else has done it, establish domain
sensitivity for region of interest. Select horizontal resolution, vertical
levels, physics options. Undertake appropriate sensitivity studies.
Prepare boundary conditions: Establish a means of ingesting
boundary field data into the RCM (and getting it out).
Develop reference climatology: Undertake a 10+ year simulation with
reanalysis boundary conditions.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Running a RCM -- A brief exposure to typical activities
Domain and resolution: If no one else has done it, establish domain
sensitivity for region of interest. Select horizontal resolution, vertical
levels, physics options. Undertake appropriate sensitivity studies.
Prepare boundary conditions: Establish a means of ingesting
boundary field data into the RCM (and getting it out).
Develop reference climatology: Undertake a 10+ year simulation with
reanalysis boundary conditions.
Evaluate reference climatology: This is critical …. if the RCM is not
appropriately simulating key processes, generating a future climate
anomaly pattern has little meaning.
Note: “point and click” solutions are coming (and very welcome),
BUT be wary of running an RCM over a new region without careful
evaluation.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Running a RCM -- A brief exposure to typical activities
Apply GCM control simulation fields, run 10+ year nested simulation.
This provides the reference climatology to which the future climate
simulation will be compared. Evaluate the climatology, does the
GCM/RCM combination generate an appropriate regional climate.
Apply GCM future climate simulation, run 10+ year simulation:
Finally, the regionalized future climate!
“Signal to noise”: Ideally, repeat control and future climate nested
simulations with other ensemble members from the GCM runs.
Then repeat with another GCM!
Analyze your future climate, and the climate anomaly: Can you
understand and explain why the future climate anomaly it the way it
is.
Use the regionalized climate data: Either as direct results, or
possibly by adding the regional anomaly to your baseline
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Regional feedbacks
RCMs are powerful in allowing investigation of process response to
feedbacks and forcings other than from GHG.
Example for southern Africa: Vegetation is almost certain to change
from climate change forcing. What is the feedback to the
atmosphere, and the consequent exacerbation or mitigation of
climate change?
Average NPP 1901-1995
8.5
8
7.5
7
NPP for 20% increase
and decrease on the
1900-1999 record of T,
RH, and ppt simulated
by SDGVM.
Control
Dcrsd PPT
Incrsd RH
Dcrs RH
Dcrs TMP
Incrs PPT
Incrs TMP
6.5
6
5.5
5
4.5
4
1901 1906 1911 1916 1921 1926 1931 1936 1941 1946 1951 1956 1961 1966 1971 1976 1981 1986 1991
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Plant Functional Types
Bare Ground
C3 grasses
C3 grasses
Evergreen Broadleaf Forest
Deciduous Needleleaf
Change in plant functional types modelled by the SDGVM for a 20%
increase in precipitation. The cross-hatching shows areas of change
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Regional feedbacks
Experiment design: MM5v3 RCM,
domain over sub-equatorial Africa,
albedo perturbed by 20% (within
range of natural variability).
3 ensemble simulations for summer
with and without perturbation.
Results: indicate mean temperature
change by up to 0.75 degrees.
Response is from a of change in the
dynamics of circulation, moisture
transport, and cloud formation.
Future climates may perturb albedo
by far more than 20%.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Empirical/Statistical
downscaling
A plethora of competing and
diverse algorithms of widely
different strengths and weaknesses
Region
Africa
South Africa
America
USA
USA
USA
USA
USA
USA
USA
USA
Technique
Predictor
Predictand
Time
Author (s)
TF
C
P
D
Hewitson & Crane, 1996
WT
WG
WG, TF
TF
WG, TF
WG, WT
TF
WG
T
C
Tmax, Tmin
P
C, T, VOR
C, Q
C, T, VOR
C
C, T, RH, W
P
TF
TF, WT
TF
C, TH, O
C, TH, Q
C, W
P
P
T, Tmax, Tmin
D
D
D
D
D
D
D
D
D
D
M
Brown & Katz, 1995
Zorita et al., 1995
Wilby & Wigley, 1997
Crane & Hewitson, 1998
Wilby et al., 1998a, b
Mearns et al., 1999
Sailor & Li, 1999
Bellone et al., 1999
Cavazos, 1997
Cavazos, 1999
Solman & Nuñez, 1999
TF
TF
C
Sea level
Sea level
variability
M
M
Cui et al., 1995, 1996
Cui and Zorita, 1998
New Zealand
New Zealand
WT
TF
Tmax, Tmin, P
T, P
D
D
Kidson & Watterson, 1995
Kidson & Thompson, 1998
Australia
TF
C
C, TH,
VOR, W
C
Tmax, Tmin
D
Schubert &Henderson-Sellers,
1997
Schubert, 1998
Timbal & McAvaney, 1999
Schnur & Lettenmaier, 1999
Mexico and USA
Mexico and USA
Central Argentina
P
T, P
T, P
T
P
Asia
Japanese coast
Chinese coast
Oceania
Australia
Australia
Australia
TF
WT
WT
C
C, T
Europe
Europe
WG
WG, TF
Europe
TF
Europe
TF
VOR, W
C, P, Tmax,
Tmin, O
C, W, VOR,
T, Q, O
C
Germany
Germany
Germany
TF
TF
TF
Tmax, Tmin
P
D
Europe
T
C
AIACC Workshop, Apr 2002
T, P
D
Conoway et al., 1996
Semenov & Barrow, 1996
T, P
M
Murphy, 1998a, b
T, P, vapour
pressure
Phenological event
Storm surge
Salinity
D
Weichert & Bürger, 1998
M
Maak &van Storch, 1997
Von Storch & Reichardt, 1997
Heyen & Dippner, 1998
Germany
Germany
WT
TF
Iberian Peninsula
Iberian Peninsula
Iberian Peninsula
Iberian Peninsula
Spain (and USA)
Spain (and USA)
Spain
Portugal
Portugal
The Netherlands
Norway
Norway (glaciers)
Romania
Romania
Switzerland
Switzerland
Switzerland
Switzerland
Switzerland
Poland
WG
TF
TF
TF
TF
TF
WT
TF
WT
WT
TF
TF
TF
TF
TF
TF
TF
WG
TF
TF
Alps
Alps
Alps
Alps
Alps
Alps,
Alps
Alps
Alps
Alps
Alps
WT
TF
WT
WT
TF
TF
WT
WT
WT
TF, WG
TF
Mediterranean
Mediterranean
North Atlantic
TF
TF
TF
C, P
C
C
North Atlantic
North Sea
TF
TF
C
TF
TF
C
SLP
North Sea coast
Baltic Sea
Region not specified
WT
WT
WT
TF
TF
C
C
C
C
Thunderstorms
Ecological
variables
P
Tmax, Tmin
P, NST
P, NST
Tmax, Tmin
Tmax, Tmin
C
C
C, VOR, W
C, O
C, O
C
C
P
C
C
Local Weather
P
T, sea level, wave
height, salinity,
wind, run-off
C, T
C
T, P
Snow
C, T
C, T
C, T
T, P,
Snow cover
Landslide activity
T, P
P
Weather statistics
P
C, T
C
T, P
T, P and others
Local weather
P
P
P
P
D
Sept, 1998
Krönke et al., 1998
D
D
Cubash et al., 1996
Trigo & Palutikof, 1998
Boren et al., 1999
Ribalaygua et al., 1999
Palutikof et al., 1997
Winkler et al., 1997
Goodess & Palutikof, 1998
Corte-Real et al., 1995
Corte-Real et al., 1999
Buishand & Brandsma, 1997
Benestad, 1999a, b
Reichert et al., 1999
Busuioc & von Storch, 1996
Busuioc et al, 1999
Buishand & Klein Tank, 1996
Brandsma & Buishand, 1997
Widmann & Schär, 1997
Gyalistras et al., 1997
Buishand & Brandsma, 1999
Mietus, 1999
D
D
D
M
D
D,M
M
D
M
M
D
H
D,M
D
D
M
M
Fuentes & Heimann, 1996
Fischlin & Gylistras, 1997
Martin et al., 1997
Fuentes et al., 1998
Gyalistras et al., 1998
Hantel et al., 1998
Dehn, 1999a, b
Heimann and Sept, 1999
Fuentes & Heimann, 1999
Riedo et al., 1999
Burkhardt, 1999
S
M
Palutikof & Wigley, 1995
Jacobeit, 1996
Kaas et al., 1996
M
T
P
Pressure
tendencies
Wave height
Ecological
variables
Sea level
Sea level
C
C, VOR, W
Ecological
variables
M
WASA, 1998
Dippner, 1997a, b
M
M
Langenberg et al., 1999
Heyen et al., 1996
Frey-Buness et al., 1995
Matyasovszky & Bogardi, 1996
Enke & Spekat, 1997
Kilsby et al., 1998
Heyen et al., 1998
CSAG : University of Cape Town
Advantages:
• Computational efficiency
• Rapid application to multiple GCMs
• Tailoring to target variables (eg: storm surge)
• Applicability to broad range of temporal and spatial resolutions
• Accessibility beyond the modeling community
• Complementary to regional modeling
Significant lack of systematic evaluation ….
“More co-ordinated efforts are thus necessary to evaluate the
different methodologies, inter-compare methods and models”
IPCC, TAR 2001
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Two extremes to categories of downscaling:
• Transfer functions relating atmospheric forcing to target
variable
• Stochastic functions and pure weather generators
For both: variance explained as a function of the large scale flow, residual
variance can only be stochastically generated.
Variance explained
For future climate, only change due to the signal contained in the GCM
scale forcing can be accounted for …..
Synoptic scale
AIACC Workshop, Apr 2002
Local scale
CSAG : University of Cape Town
For climate change:
… what proportion of response will be due to sub-GCM grid
scale structure -- independent of the large scale forcing?
…how stationary is the downscaling function -- applicable to
both transfer functions and stochastic functions.
Q: for a given location, which is dominant: local or synoptic
forcing?
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Example of synoptic
dominance: (From a RCM
experiment)
Experiment: Precipitation as a
function of three different SST
fields with identical NCEP
boundary forcing.
Precipitation (primarily convective)
is temporally consistent
independent of the SST fields.
Implies dominance by synoptic
state.
High-res SST
1° SST
Zonal SST
ie: The variance at sub-GCM grid
cells is still conditioned by large
scale flow, empirical downscaling
of future change is strongly viable.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
General categories of methodologies
Transfer
Function
Weather
Typing
Stochastic conditioned
on weather type
Stochastic
Typical downscaling modes:
Downscale from atmospheric instantaneous state to the local climate
response (eg: daily precipitation)
Downscale secondary variable (eg: stream flow)
Relate atmospheric indices (eg: SOI, NAO) to climate statistics
Time downscaling -- downscale the diurunal cycle
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
General categories of methodologies
Transfer
Function
Weather
Typing
Stochastic conditioned
on weather type
Stochastic
Derives a quantitative relationship between predictor(s) and
predictand(s)
eg: Station daily temperature = f (Sea Level Pressure & 500hPa
gpm)
• f typically a regression style function, can / should be non-linear.
• Requires training data of adequate duration to span the range of events
found in future climate.
• If predictands are patterns (eg: EOF) or indices (eg: NAO), one assumes
stationarity of the pattern or index into the future.
• Derived function used with GCM field to downscale control and future
climate.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
General categories of methodologies
Transfer
Function
Weather
Typing
Stochastic conditioned
on weather type
Stochastic
- Method under-predicts peaks, over predicts minimum -- characteristic of a
generalization function
- Residuals represent variance not captured, either from inadequate
predictors, or due to local forcing not represented in GCM fields
Example: ANN-based downscaling of daily rainfall
Effective at capturing temporal evolution consistent with
atmosphere.
Capture low frequency variability well (seasonal and interannual)
Residuals (missing variance), can easily be added stochastically.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
% variance of residuals is proportional to information in predictors
or
Skill of f is proportional to information in predictors
precip (mm*10)
70
60
Observed
50
Downscaled
40
30
20
10
79
76
73
70
67
64
61
58
55
52
49
46
43
40
37
34
31
28
25
22
19
16
13
10
7
4
1
0
Downscaled station precipitation from 1° MRF assimilation data
Possible role in downscaling nested models to point resolution?
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
General categories of methodologies
Transfer
Function
Weather
Typing
Stochastic conditioned
on weather type
Stochastic
Derives a quantized relationship between predictor(s) and
predictand(s)
eg: Station temperature = f (type of Sea Level Pressure pattern)
• Comes from the “synoptic climatology” discipline
• Weather patterns classified into N-different types
• Each type associated with a local climate response
• GCM weather patters matched to types, and assigned a local climate
response
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
General categories of methodologies
Transfer
Function
Weather
Typing
Stochastic conditioned
on weather type
Stochastic
Stochastic / weather generators calibrated on observed data,
conditioned on atmospheric state.
• Very effective at capturing high frequency variance, peaks and extremes
• Requires long term data sets to effectively define stochastic characteristics
• Question of stationarity
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Note the underlying commonality:
All methods are, in effect, algorithms to implement an analog.
ie: each method simply draws a climate response from the
historical record based on some atmopsheric state(s) from the
same historical period
Thus: why not implement a true analog? Simply match a given GCM
field to all possible comparable fields in the historical and take the
closest match?
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Evaluation of issues for effective empirical or statistical downscaling.
1) Choice of predictor variables
Most commonly used are circulation related variables
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Evaluation of issues for effective empirical or statistical downscaling.
1) Choice of predictor variables
2) Predictor spatial representation
• Indices, EOFs, Synoptic classifications, Raw grid data
• Local versus remote (teleconnections)
• Surface versus upper air fields
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Evaluation of issues for effective empirical or statistical downscaling.
1) Choice of predictor variables
2) Predictor spatial representation
3) Antecedent conditions
Local forcing as a function of antecedent events
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Evaluation of issues for effective empirical or statistical downscaling.
1) Choice of predictor variables
2) Predictor spatial representation
3) Antecedent conditions
4) Target (predictand) resolutions
Station scale, impacts scale (scale of user community), RCM scale?
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Evaluation of issues for effective empirical or statistical downscaling.
1) Choice of predictor variables
2) Predictor spatial representation
3) Antecedent conditions
4) Target (predictand) resolutions
5) Training data periods
Observational data that sufficiently spans the relationship for training downscaling
function
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Evaluation of issues for effective empirical or statistical downscaling.
1) Choice of predictor variables
2) Predictor spatial representation
3) Antecedent conditions
4) Target (predictand) resolutions
5) Training data periods
6) Representing the climate change signal
Predictors explaining significant variance may not be predictors sensitive to the
climate change signal
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Evaluation of issues for effective empirical or statistical downscaling.
1) Choice of predictor variables
2) Predictor spatial representation
3) Antecedent conditions
4) Target (predictand) resolutions
5) Training data periods
6) Representing the climate change signal
7) Stationarity of function / predictors
Is climate change primarily characterized by changes in frequency of existing
events?
Are changes in local sub-grid-scale forcing small with respect to synoptic forcing?
Are residuals in downscaling from GCM-resolution due to low predictor resolution,
or sub-grid scale forcing?
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Evaluation of issues for effective empirical or statistical downscaling.
1) Choice of predictor variables
2) Predictor spatial representation
3) Antecedent conditions
4) Target (predictand) resolutions
5) Training data periods
6) Representing the climate change signal
7) Stationarity of function / predictors
A given downscaling implementation needs to take cognizance of, and
evaluate, the dependencies
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Exploring the dependencies.
• Transfer function based methodology: gives dominance to synoptic forcing
• Challenging case: continental summer convective daily precipitation
• NCEP reanalysis 2.5 degree atmospheric predictors
• Station derived precipitation
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Exploring the dependencies.
Transfer function based methodology (Neural nets): gives dominance to synoptic
forcing
• Problematic case: continental summer convective daily precipitation
• NCEP derived predictors
• Station derived precipitation
Topography
Regional context
• steep topography
• elevated inversions
• strong interannual variability
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Dominance by semi-permanent
high pressure systems with
surface thermal trough
1980-86 January mean SLP
AIACC Workshop, Apr 2002
Strong spatial gradients of
precipitation strongly dependant
on moisture transport
1970-98 January mean precip
CSAG : University of Cape Town
Characteristic 7-day back trajectories into test region for downscaling
(shading by specific humidity).
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Downscaling methodology:
Transfer function methodology
- derives local response as function of synoptic forcing, excludes sub-grid
scale local forcing (useful for evaluation of dependencies).
- Artificial Neural Nets (analogous to non-linear multiple regression)
- derives non-linear transfer functions between NCEP (2.5°) atmospheric
variables and precipitation (0.25°)
20 years of training data (1980 - 1999)*
Focus not on optimizing results, but a sensitivity study
* Pre-1980 reanalysis data problematic for southern hemisphere
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
1: Evaluation of predictor variables (20 examples)
Surface
700hPa
500hPa
temperature
divergence
divergence
temperature
divergence
temperature
geopotential height
vertical velocity
specific humidity
u wind
v wind
geopotential height
vertical velocity
specific humidity
u wind
v wind
vertical velocity
relative humidity
u wind
v wind
Each predictor used independently to derive a transfer function to
precipitation at 0.25°.
Predictor temporal resolution:
Predictor spatial resolution:
centered on
12 hourly
9 grid cells (7.5° by 7.5°)
target location
48 hour antecedent predictor state included
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Results suggest:
Dominant relationship is with mid
and upper troposphere humidity
and predictors related to vertical
motion.
AIACC Workshop, Apr 2002
Predictor variable
R
Specific Humidity (500hPa)
Vertical Velocity (500hPa)
v wind (700hPa)
Relative Humidity (Surface)
Specific Humidity (700hPa)
Divergence (700hPa)
Temperature (Surface)
Geopotential height (700hPa)
v wind (500hPa)
Divergence (Surface)
Vertical Velocity (700hPa)
Divergence (500hPa)
u wind (Surface)
u wind (500hPa)
Vertical velocity (Surface)
u wind (700hPa)
v wind (Surface)
Temperature (500hPa)
Temperature (700hPa)
Geopotential height (500hPa)
0.56
0.55
0.53
0.53
0.49
0.
0.45
0.44
0.44
0.
0.40
0.
0.34
0.34
0.34
0.34
0.34
0.30
0.27
0.19
CSAG : University of Cape Town
Similar examination of other locations supports the above
results.
Suggests predictors should include mid-troposphere
indicators of humidity and circulation dynamics.
Place Arg Aus Bot Zam Bra Nin3 Cri Ban Mex Chi
Latitude -36 -34 -24 -16 -2
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AIACC Workshop, Apr 2002
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CSAG : University of Cape Town
Based on the above, a set of predictors may be chosen.
eg:
Surface temperature, u and v winds
700hPa specific humidity and geopotential heights
500hPa specific humidity and geopotential heights
Trained function results:
r = 0.7
predicted mean precipitation: 4.2mm/day
observed mean precipitation: 3.8mm/day
300
mm * 10
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Observed
150
Downscaled
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AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Residuals
• From either:
Lack of information in predictors (choice or predictor or resolution)
Local sub-grid scale forcing unrelated to synoptic state
• May be stochastically modeled (stationarity issues)
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mm * 10
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Downscaled
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AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Residuals
• Effect of stochastic addition of
residuals to recover the higher
frequency source of variance
independent of the predictors
• Wmean: mean wet spell duration
(number of days)
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
2: Predictor Spatial Resolution
Test relationship of target variable to atmospheric predictors progressively
further away from region of interest.
ANN downscaling using mid-troposphere (700hPa) specific humidity and
geopotential height
Predictors drawn from progressively larger regions:
a) single NCEP grid cell co-located with target
b) 7.5° by 7.5° window centered on target
c) 15° by 15° window centered in target
d) 22.5° by 22.5° window centered on target
e) 30° by 30° window centered on target
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
2: Predictor Spatial Resolution
Test relationship of target variable to atmospheric predictors progressively further
away from region of interest.
ANN downscaling using mid-troposphere (700hPa) specific humidity and
geopotential height
Predictors drawn from progressively larger regions:
a) single NCEP grid cell co-located with target
b) 7.5° by 7.5° window centered on target
c) 15° by 15° window centered in target
d) 22.5° by 22.5° window centered on target
e) 30° by 30° window centered on target
Spatial resolution
Single cell
7.5 x 7.5
15 x 15
22.5x22.5
30x30
AIACC Workshop, Apr 2002
r
0.39
0.56
0.57
0.54
0.55
Increase in predictor window size,
once large enough to represent
spatial gradient, has minimal
improvement.
CSAG : University of Cape Town
2: Predictor Spatial Resolution
Downscaling a function of information content in predictors -- a function of
resolution.
eg: Station daily rainfall downscaled from MRF (1°) atmospheric fields:
Average error: 0.5 mm/day
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Observed
Suggests variance from sub-grid scale forcing is minimal in this case
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
3: Predictor Antecedent State
Test relationship of target variable to inclusion of the antecedent state of
atmospheric predictors.
Predictors used as:
a) time coincident with target
b) time coincident with target and increasing lag in 12 increments to 96
hours lag.
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
3: Predictor Antecedent State
Test relationship of target variable to inclusion of the antecedent state of
atmospheric predictors.
Lags of at least 24
hours are very
beneficial
Correlation
Predictors used as:
a) time coincident with target
b) time coincident with target and increasing lag in 12 increments to 90
hours lag.
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AIACC Workshop, Apr 2002
CSAG : University of Cape Town
5: Training data period
Test sensitivity of downscaled function to data used in training.
Case 1 Train on 1980s -- test with 1990s
Case 2 Train on 1990s -- test with 1980s
Case 3 Train on 1982/83 -- test with 1980s and 1990s
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
5: Training data period
Test sensitivity of downscaled function to data used in training.
Case 1 Train on 1980s
Case 2 Train on 1990s
Case 3 Train on 1982/83
For each case, test function on independent decades.
Case 1:
Trained on 1980s, predicted 1980s:
Trained on 1980s, predicted 1990s:
r = 0.66
r = 0.59
mean ppt: +7%
mean ppt: -9%
Case 2:
Trained on 1990s, predicted 1990s:
Trained on 1990s, predicted 1980s:
r = 0.78
r = 0.51
mean ppt: +6%
mean ppt: +18%
Case 3:
Trained on 82/82, predicted 1980s:
Trained on 82/83, predicted 1990s:
r = 0.33
r = 0.11
mean ppt: -34%
mean ppt: -28%
Where training data spans the variability, performance good
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
6: Representing the climate change signal
Predictors that explain the most variance may not be the predictors that capture
the climate change signal.
Test: for each predictor, determine the climate change signal
Train on the predictors, and predict from GCM control and future climate
simulations
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
6: Representing the climate change signal
Predictors that explain the most variance may not be the predictors that capture
the climate change signal.
Test: for each predictor, determine the climate change signal
• Train on the predictors, and predict from GCM control and future
climate simulations
Predictor variable
Future - present
downscaled % change
Specific humidity (500hPa) 4.49
Specific humidity (700hPa) 4.71
Surface Temperature
2.43
Surface u-wind
-5.47
Surface v-wind
1.06
500hPa geopotential heights
0.26
700hPa geopotential heights
-1.63
Note: Choice of predictor may change sign of downscaled
response
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
6: Representing the climate change signal
Downscaling using:
Specific humidity (500hPa)
Specific humidity (700hPa)
Surface u-wind
Surface v-wind
500hPa geopotential heights
700hPa geopotential heights
Or excluding humidity:
Surface u-wind
Surface v-wind
500hPa geopotential heights
700hPa geopotential heights
AIACC Workshop, Apr 2002
Future - control: +2.1%
Future - control: -3.5%
CSAG : University of Cape Town
Spatial consequences
Downscaled summer precipitation
anomaly (future - present)
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
7: Stationarity:
Predictors: Do future synoptic events have present day
representation
Transfer function: Stability of relationship
Sub-grid scale forcing: % contribution to local variance, feedbacks to
atmosphere
At a minimum, evaluate predictors ...
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
7: Stationarity
Consider distribution of 700hPa geopotential height fields in GCM control simulation
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
7: Stationarity
Frequency of occurrence of each mode may be determined, and change
under future climate calculated
% change in frequency of occurrence from future-control simulations:
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AIACC Workshop, Apr 2002
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48
CSAG : University of Cape Town
7: Stationarity
% change in frequency of occurrence from CSM future-control simulations:
-53
-16
-24
-39
-62
-13
22
52
-32
-28
5
35
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150
-45
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Similarity of future patterns to present day may be determined, and a measure of change
in pattern calculated.
% change in pattern from CSM future-control simulations:
5
-17
8
3
31
9
12
12
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4
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-6
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5
-11
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Where significant increases in frequency have occurred, variance of pattern modes has
generally decreased.
Hence: 700hPa geopotential height fields under a future climate are spanned by events
in present day simulation
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Some conclusions:
Empirical/statistical downscaling has pragmatic attractions.
Appropriate implementation can produce downscaled results
consistent to changes in synoptic forcing.
Care is needed!!!
AIACC Workshop, Apr 2002
CSAG : University of Cape Town
Decision process to implement downscaling
Preparation
Are you looking for a sensitivity study, projection, or probabalistic prediction :
What is needed versus wanted versus realistic?
Temporal: Resolution and duration. ie: hourly, daily, monthly seasonal, etc.,
and 1 year through to decadal etc.
Spatial: Resolution and domain. Ie: point or station scale, through regional
scale / areal average.
Variable: Direct or derived (eg: temperature versus storm surge)?
If multivariate, is phase matching between variables important?
Do you need statistics or time series?
Source: Has/is an appropriate solution available elsewhere? Has it been
evaluated?
Baseline data: What is available (and when)? Does it match all above
requirements?
Decision process to implement downscaling
GCM data
Which GCM(s), from where, and how/why are you selecting them? When will
they be available?
What SRES or other forcing scenario(s) are used?
Are native temporal and spatial resolutions appropriate to the task?
(Recognize skill level is typically > 7-9 grid cells)
Validation (Evaluation): (Essential for understanding what you get in the end!)
Has the GCM been evaluated at the spatial/temporal resolution of intended
use?
If not, how will you evaluate it?
To what degree is the GCM future climate statistically stationary?
What skill level/margin of error is acceptable?
Decision process to implement downscaling
Resources
What computational hardware resources are available?
What are your own/team IT skills (programming, script writing, only point and
click, system administration, data handling, etc)?
Decision process to implement downscaling
Choice
Do you need to downscale, is direct GCM output ok, is applying a GCM
anomaly field to a baseline climatology ok, is interpolation ok? If so, do it!
Choose appropriate downscaling method based on answers above
RCMs or Empirical/statistical
Decision process to implement downscaling
RCM Regionalization
• Which model and why? How fast will it run
under available resources? Will it even run
on available resources?
• Will areal averages (!) be what you need?
• What spatial resolution and number of
levels.
• What map projection.
• Domain selection and domain sensitivity.
• Has a baseline climatology been run?
What boundary conditions were used?
What duration used to derive the
climatology?
• Land surface scheme -- what choice?
• Model tuning, has it bben tuned, how and
why?
• Is stationarity of parameterization
important?
Validation (evaluation): (Essential -especially w.r.t feedbacks!)
•How was/will baseline evaluation done
(see under GCM)?
•Are the errors acceptable -- do they induce
larger problems?
•Nested control run climatology -- how long
(long enough?)
•What are the errors, are they acceptable?
Be very careful here, paying attention to
feedback processes.
•What is the domain topography like -- is
the model hydrostatic, does it need to be
non-hydrostatic?
•What is the driving : nested resolution
ratio?
•Is domain large enough to recapture subGCM grid-scale variance over domain of
interest?
•Feedbacks: are they recognized? What
degree of consequence will ignoring them
have on results? (eg: changing veg)
•What is the synoptically forced versus
locally forced variance ratio.
•What is the signal to noise ration of the
Decision process to implement downscaling
Empirical downscaling
Note: Can ONLY generate predictand
varinace that is inherent in the cross scale
relationship with GCM-scale data. The rest is
"made up"!!!
• Predictor-predictand relationship: how strong
is it?
• Are the required predictors available from
the GCM?
• Does the training/validation data adequately
span the variance structure of the climate
system?
• Is the "synoptic" forced predictand variance
enough for the application, do you need to
recover locally forced variance?
• Do the predictors carry the climate change
signal?
• Is the relationship strongly non-linear?
• What domain size and temporal
resolution/duration of the predictors?
• Are the GCM predictors stationary, can the
degree of non-stationarity be accepted?
• Is multivariate phasing important?
• Method: Weather generator, transfer
function, weather typing, true analogue, some
combination?
• Do computational and IT resources meet the
methods requirements.
• Predictor pre-processing -- yes, no, how,
why? (eg, EOFs etc).
• Are the pre-processed predictor forms stable
-- eg: are the EOFs or climate indices of the
training data valid under future climate?
• Validation: how will you validate the training
procedure?
• Independent test versus training data -where do they fall within data space?
• What are the residuals, and biases after
training?
• Should/do the GCM predictor field need bias
correction?
• Downscaling function stationarity: can it be
tested or evaluated?