WFM 6311: Climate Change Risk Management

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WFM 6311 Climate Change Risk Management
Lecture-9 Statistical Downscaling Techniques

• Akm Saiful Islam

Institute of Water and Flood Management
(IWFM) Bangladesh University of Engineering and
Technology (BUET)
March, 2013
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Topics

• Approach of downscaling
• Techniques of downscaling
• Strength and weakness
• Statistical downscaling using SDSM

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General Approach to Downscaling

• Applicable to
• Sub-grid scales (small islands, point processes)
  
• Complex/ heterogeneous environments
• Extreme events
• Exotic predictands
• Transient change/ ensembles

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Types of downscaling

• Dynamical climate modelling
• Synoptic weather typing
• Stochastic weather generation
• Transfer-function approaches

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Dynamic downscaling

• Dynamical downscaling involves the nesting of a
  higher resolution Regional Climate Model (RCM)
  within a coarser resolution GCM.
• The RCM uses the GCM to define timevarying
  atmospheric boundary conditions around a finite
  domain, within which the physical dynamics of the
  atmosphere are modelled using horizontal grid
  spacings of 2050 km.

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Limitations of RCM

• The main limitation of RCMs is that they are as
  computationally demanding as GCMs (placing
  constraints on the feasible domain size, number
  of experiments and duration of simulations).
• The scenarios produced by RCMs are also sensitive
  to the choice of boundary conditions (such as
  soil moisture) used to initiate experiments

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Advantages of RCM

• The main advantage of RCMs is that they can
  resolve smallerscale atmospheric features such
  as orographic precipitation or lowlevel jets
  better than the host GCM.
• Furthermore, RCMs can be used to explore the
  relative significance of different external
  forcings such as terrestrialecosystem or
  atmospheric chemistry changes.

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Regional Climate Model
Limited area regional models require
meteorological information at their edges
(lateral boundaries) These data provide the
interface between the regional models domain and
the rest of the world The climate of a region is
always strongly influenced by the global
situation These data are necessarily provided by
global general circulation models (GCMs) or from
observed datasets with global coverage
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Weather classification LWT scheme to condition
daily rainfall
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Weather typing

• Weather typing approaches involve grouping local,
  meteorological data in relation to prevailing
  patterns of atmospheric circulation. Climate
  change scenarios are constructed, either by
  resampling from the observed data distributions
  (conditional on the circulation patterns produced
  by a GCM), or by generating synthetic sequences
  of weather patterns and then resampling from
  observed data.

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• Weather pattern downscaling is founded on
  sensible linkages between climate on the large
  scale and weather at the local scale.
• The technique is also valid for a wide variety of
  environmental variables as well as multisite
  applications. However, weather typing schemes ca
  be parochial, a poor basis for downscaling rare
  events, and entirely dependent on stationary
  circulationtosurface climate relationships.

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Limitation of Weather typing

• Potentially, the most serious limitation is that
  precipitation changes produced by changes in the
  frequency of weather patterns are seldom
  consistent with the changes produced by the host
  GCM (unless additional predictors such as
  atmospheric humidity are employed)

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Stochastic weather generators

• Stochastic downscaling approaches typically
  involve modifying the parameters of conventional
  weather generators such as WGEN, LARSWG or
  EARWIG.
• The WGEN model simulates precipitation
  occurrence.
• Furthermore, stochastic weather generators enable
  the efficient production of large ensembles of
  scenarios for risk analysis.

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Weather generator

• WGEN model simulates precipitation occurrence
  using twostate, first order Markov chains
  precipitation amounts on wet days using a gamma
  distribution
• temperature and radiation components using
  firstorder trivariate autoregression that is
  conditional on precipitation occurrence.

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Advantages of weather generator

• Climate change scenarios are generated
  stochastically using revised parameter sets
  scaled in line with the outputs from a host GCM.
• The main advantage of the technique is that it
  can exactly reproduce many observed climate
  statistics and has been widely used, particularly
  for agricultural impact assessment.

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Limitations weather generator

• The key disadvantages relate to the low skill at
  reproducing inter-annual to decadal climate
  variability, and to the unanticipated effects
  that changes to precipitation occurrence may have
  on secondary variables such as temperature.

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Transfer functions

• Transfer-function downscaling methods rely on
  empirical relationships between local scale
  predictands and regional scale predictor(s).
  Individual downscaling schemes differ according
  to the choice of mathematical transfer function,
  predictor variables or statistical fitting
  procedure.

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Types of transfer functions

• To date, linear and nonlinear regression,
  artificial neural networks, canonical correlation
  and principal components analyses have all been
  used to derive predictorpredictand
  relationships.

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Strength and weakness of transfer function

• The main strength of transfer function
  downscaling is the relative ease of application,
  coupled with their use of observable transscale
  relationships.
• The main weakness is that the models often
  explain only a fraction of the observed climate
  variability (especially in precipitation series).

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SDSM

• Developed by Loughborogh university, UK
• www.sdsm.org.uk
• Data can be downloaded from Canadian Climate
  Change Scenario network (CCSN)
• http//www.cccsn.ca/?pagedst-sdi
• SDSM is best described as a hybrid of the
  stochastic weather generator and transfer
  function methods.

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SDSM- Statistical Downscaling Model
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SDSM Algorithm

• Optimisation Algorithm SDSM 4.2 provides two
  means of optimising the model Dual Simplex (as
  in earlier versions of SDSM) and Ordinary Least
  Squares. Although both approaches give comparable
  results, ordinary Least Squares is much faster.

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• The User can also select a Stepwise Regression
  model by ticking the appropriate box.
• Stepwise regression works by progressively
  adding all parameters into the model and
  selecting the model which models the predictand
  most strongly according to one of two criteria
  either AIC(Akaike information criterion) or
  BIC(Bayesian information criterion).

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• The Akaike information criterion is a measure of
  the relative goodness of fit of a statistical
  model.
• In statistics, the Bayesian information criterion
  (BIC) or Schwarz criterion (also SBC, SBIC) is a
  criterion for model selection among a finite set
  of models.
• When fitting models, it is possible to increase
  the likelihood by adding parameters, but doing so
  may result in overfitting. The BIC resolves this
  problem by introducing a penalty term for the
  number of parameters in the model. The penalty
  term is larger in BIC than in AIC.

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Patuakhali Tmin(1961-2001)
Predictor variable Partial r
Ncepmslpas (mean sea level pressure) 0.763
ncepp500as (500 hpa geopotential height) 0.308
ncepp850as (850 hpa geopotential height) 0.61
ncepr850as (relative humidity at 850 hpa) 0.383
Mean E 34.2
Mean SE 1.461
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