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Report on Statistical Downscaling Scenario generation task of SIS06: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

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The purpose of climate scenariosTo provide data for impact/adaptation/assessment

studiesTo act as an awareness-raising device To aid strategic planning and or policy formationTo scope the range of plausible futuresTo structure our knowledge (or ignorance) of the

futureTo explore the implications of decisionsTo function as learning machines, bridging analyses

and encouraging participation

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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.

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What we require from downscalin

300k

m

50km

Poin

t

General Circulate Models supply...

Impact models require ...

Regional models supply

1m10km

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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

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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.

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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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Advantages of SDSM Cheap - not computational demanding and is

readily transportable. 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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Disadvantages of SDSM 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.

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Steps in downscalingThe 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.

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‘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)

Overview of the SDSM: Key functions of SDSM - Menu driven

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Domain of interest - map

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Domain of interestThe 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 Trinidad & Tobago, Jamaica, St Kitts and Barbados

The land area represent nearly 10% of the area of interest.

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GCM outputs used and to be used: Preliminary _ 1st generation Couple General

Circulation Model developed by the Canadians (CGCM1)- readily available, output formatted for use in SDSM

Primary model - HadCM3, simulates Caribbean climate best (Santer, 2001) - predictors being prepared in an SDSM friendly format

Others

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Emission Scenario being used - IS92a

IS92 readily available, later SRES not readily available at start of 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 and the Montreal Protocol.

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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_info-Emissions

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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.

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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.

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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

Airport in TNT, Grantley Adam Airport Barbados, Agronomy St. Kitts and Guantanimo Bay Cuba.

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Analysis to date –Piarco airport TNT

Predictants- Rainfall and Temperature

Predictors (and predictor code) usedRainfall –Surface vorticity - ncepp__zna.dat850 hpa vorticity - ncepp8_zna.datSpecific humidity at 500 hpa - nceps500na.datSpecific humidity at 850 hpa - nceps850na.datMean temperature at 2 metres - nceptempna.dat

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Analysis to date –Piarco airport (TNT) tempPredictors 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 Mean temperature at 2 metre nceptempna

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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 validationObspiarco

8190Tmin

Piarco8190TnWGsts

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

Dec 22.1423 21.5914

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Sample result - tempObspiarco8

190TminSts.: Mean

Piarco208190TnSG2sts: Mean

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.

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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

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AREAS OF CONCERNThe 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 results.

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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