# Report on Statistical Downscaling Scenario generation task of

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```					Report on Statistical Downscaling
The Threat of Dengue Fever - Assessment
of Impacts and Adaptation to Climate
Change in Human Health in the
Caribbean

Student: Lawrence Brown
Supervisors: Anthony Chen, Albert Owino
1
The purpose of climate scenarios
studies
To act as an awareness-raising device
To aid strategic planning and or policy formation
To scope the range of plausible futures
To structure our knowledge (or ignorance) of the
future
To explore the implications of decisions
To function as learning machines, bridging analyses
and encouraging participation
2
DownScaling
   Basically, downscaling is any process where large (coarse)
scale output of models is reduced or made finer.

   The first attempt to DownScale the global models was by
the production of regional models. These regional models
resolutions of 70 to 50 km square.
   However there is still a need to go even further by
DownScaling.
   This is what is require by impact researchers.

3
What we require from downscalin
General Circulate Models supply...
300km

Impact models require ...
10km                        1m
50km

Regional models supply
Point

4
Objectives of SIS06 Downscaling:

To develop future climate scenarios so
that the climate health linkages being
developed and the impacts being studied
can be used to develop adaptaton
strategies.
To contribute to the National
Communications under the UNFCCC
5
DownScaling Methods
   Stochastic DownScaling - Probabalistic
   Dynamical DownScaling - running a higher resolution
RCM within a coarser resolution GCM.
    Weather Typing approaches - grouping of countries with
similar weather
   Regression-based DownScaling - empirical relationships
between local scale predictand and regional scale
predictors.

6
Statistical DownScaling Model (SDSM) developed
by Dr. Rob Wilby, Dr. Christian Dawson and Dr. Elaine Barrow
   Downscales GCM output from course grid to island or
local level

   SDSM is a hybrid of the multiple Regression and
Stochastic DownScaling models.

   The model assumes Stationarity - statistical properties of
the variable being downscaled will not change over time.

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   Cheap - not computational demanding and is
   Flexible - can be tailored to target a variety of
variable e.g. storm surge.
   Generates ensemble of climate scenarios - allows
risk (uncertainty) analysis.
   Rapid application to multiple GCM’s.
   Complementary to regional modelling
   Fairly simple to use and thus accessible beyond
research community

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   Dependent on the realism of the GCM boundary forcing.
   DownScaling in general propagates the GCM error.
   Require high quality (daily, in our case) data for
calibration of the model.
   Relationship between predictor and predictand are often
non stationary.
   Predictor variables used explain only a portion of the
variability.
   Low frequency climatic variables are problematic to
downscale.

9
Steps in downscaling
The process involves basically four steps
   Gathering of the predictor (model output) and predictand
variables (observed).
   Screening: to decide which GCM to use, which variables
are most relevant, the best predictor predictand
relationships, the various locations in the different
countries to downscale, the best transfer scheme to use.
   Calibrating the model and Developing results for the
different time slices.
   Interpretation and Presentation of findings.
10
Overview of the SDSM:
Key functions of SDSM - Menu driven

   ‘Quality Control’ and ‘Transform’ data
   ‘Screen Variables’ (Selection of predictors)
   ‘Calibrate Model’ (Regression equations)
   ‘Weather Generator’ (Synthetic daily weather);
   ‘Analyse Data & Model Output’ (Statistics)
   ‘Compare Results’ (Graphs)
   ‘Generate Scenario’ (Synthetic daily weather
using model predictors)                          11
Domain of interest - map

12
Domain of interest
The coordinates for the area we will be looking at is latitude
10 to 22 degrees north and longitude 55 to 90 degrees
west.
 This area is made up of 27 countries and also sections of
five other countries.
 At first we will be looking at a subgroup which include
 The land area represent nearly 10% of the area of
interest.

13
GCM outputs used and to be used:
   Preliminary _ 1st generation Couple General
Circulation Model developed by the
output formatted for use in SDSM
   Primary model - HadCM3, simulates
Caribbean climate best (Santer, 2001) -
predictors being prepared in an SDSM
friendly format
   Others                                        14
Emission Scenario being used - IS92a
project.

IS92 was proposed in the 1992 Supplement (IPCC,1992) to the
IPCC First Assessment Report of 1990. Basically the IS92
scenarios ranged from a-f (IS92a-f) and considered various
factors that would affect emissions up to the year 2100.

IS92a - a middle range scenario: population reaches 11.3 billion,
convention and renewable energy sources are used. Only
emission scenarios internationally agreed on and national
policies enacted into law are included, e.g., London Amendments
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and the Montreal Protocol.
SRES emission scenarios to be used
SRES emission developed after IS92 was evaluated in
1995: New knowledge relating to: e.g. carbon intensity of
energy supply, income gap between developed and
developing countries and sulphur emissions.

Special Report on Emission Scenarios (SRES) has 4
groups and 6 scenarios A1 (which has 3 sections A1FI,
A1T and A1B) and A2, B1, B2. - Only A2 and B2 readily
available for use in SDSM, others will be considered.

http://www.cics.uvic.ca/scenarios/index/cgi?More_inf
o-Emissions                                          16
SRES Family

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A2 SRES scenario:
A very heterogeneous world with an underlying theme of self
reliance and self preservation of local identities.
Fertility across regions converges very slowly and this results in
continuous increase in population.
The A2 world “consolidates” into a series of roughly continental
economic regions, emphasizing local culture and roots, e.g.,
some social and political structure moving towards stronger
welfare systems and reduced income inequity while others move
towards lean government.
Environmental concerns relatively weak. Economic growth and
technological change are more fragmented and slower than
other storylines.                                             18
B2 SRES scenario:
A world in which emphasis is placed on local solutions to
economic, social and environmental sustainability.
Education and welfare are widely pursued, reductions in mortality
and to a lesser extent fertility, population reaching about 10 billion
people by 2100.
Income per capita grows at an intermediary rate to reach about
US\$12,000 by 2050.

This generally high educational level promotes both development
and environmental protection.

Technological are pushed less than in A1 and B1 but higher than
in the A2 scenario.                                          19
Work done – training and learning
   Training under auspices of Caribbean/Canadian CC
initiative
   Contact made with various agencies and organisations in
an effort to fill the gaps in the CSGM database.
   Predictors for the HadCM3 (monthly) and CGCM1
gathered.
   The transformation of predictors and predictands to use
in CGCM1.
   The generation of test/preliminary scenarios for Piarco
Agronomy St. Kitts and Guantanimo Bay Cuba.

20
Analysis to date –Piarco airport TNT

Predictants- Rainfall and Temperature

Predictors (and predictor code) used
Rainfall –
Surface vorticity               - ncepp__zna.dat
850 hpa vorticity               - ncepp8_zna.dat
Specific humidity at 500 hpa    - nceps500na.dat
Specific humidity at 850 hpa    - nceps850na.dat
Mean temperature at 2 metres    - nceptempna.dat

21
Analysis to date –Piarco airport (TNT) temp
Predictors used and their code
 Mean seas level pressure         ncepmslpna
 Surface air flow strength        ncepp__fna
 Surface zonal velocity           ncepp__una
 Surface meridonal velocity       ncepp__vna
 Surface vorticity                ncepp__zna
 Surface divergence               ncepp_zhna
 500 hpa air flow strength        ncepp5_fna
 500 hpa vorticity                ncepp5_zna
 500 hpa geopotential height      ncepp500na
 850 hpa zonal velocity           ncepp8_una
 850 hpa specific humidity        nceps850na
 Near surface specific humidity    spncepsphuna   22

 Mean temperature at 2 metre      nceptempna
SDSM Piarco rainfall validation

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Sample result - Rainfall

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Rainfall assessment
   No significant difference in the annual
mean over 90 years

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SDSM Piarco temp validation
Obspiarco   Piarco819
8            0
1            T
9            n
0            W
T            G
m            st
in           s

Jan     21.3096     20.4857

Feb     21.2146     20.6114

Mar     21.6342     21.1696

Apr     22.7023     22.2768

May     23.5374     23.4493

Jun     23.6557     23.4903

Jul     23.2332     23.1077

Aug     23.1371     23.2561

Sep     23.1903     22.9814

Oct     23.1481     22.9169

Nov     22.8687     22.5199
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Dec     22.1423     21.5914
Sample result - temp
Obspiarco8   Piarco2081
190           90T
Tmi           nS
nSt           G2
s.:           sts:
Me            Me
an            an

Jan      21.3096       20.7753

Feb      21.2146       20.7504

Mar      21.6342       21.3123

Apr      22.7023       22.2377

May      23.5374       23.1525

Jun      23.6557       23.3185

Jul      23.2332       22.9726

Aug      23.1371       22.8925

Sep      23.1903       22.8695

Oct      23.1481       22.8291

Nov      22.8687        22.475

Dec      22.1423        21.573
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Assessment of Results to date
These exercises are a learning experience
No importance is placed on these results
More detailed results required to know if the
length of dry or wet spells will increase,
time of year that increases occur, etc.

28
Future Work
Continue collection of data
Look at other GCM outputs – consider
ensembles
Look at SST as possible predictor – need
AOGCM SST output
Compare SDSM results with RCM results
29
AREAS OF CONCERN
The SDSM uses daily climate data - Do we have
enough, high quality daily data (especially
temperature )?

Will we have station data at locations to
develop correlations with areas of Dengue risk?

SST plays important role in Caribbean weather
but is not on the list of predictors for HadCM3.
Only 10% of area of domain is land - GCM see
many area as ocean, may lead to damping of     30

results.
Steps being taken to reduce problem
   Agencies and met offices contacted for data by both
the climate and Epidemiology group data
   In the case of Jamaica where there was a fire some
years ago, steps taken to access data on tape and
interaction with the met office has resulted in
significant data being gathered.
   We are looking at using monthly time scale in case
the daily time scale is not feasible as the seem to be a
plethora of monthly data around.
   Checks will be made to ascertain the level of
dampening that might occur.
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The End

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