Downscaling and its limitation on climate change

impact assessments

Sepo HachigontaUniversity of Cape Town

South Africa

“Building Food Security in the Face of Climate

change”4 the May 2010 , ICRAF,

Nairobi

(1 − a)Sπr2 = 4πr2εσT4

GCMsPrimary source of information on climate change projections

Horizontal resolution of about 300km and 10 to 30 vertical

layers

Incoming and outgoing radiation Wind, Temperature, humidity etc.. Clouds formation Precipitation falls How ice sheets grow or shrink, etc. Feedback processes

GCMs

DownscalingProcess of generating higher resolution data or climate change information from relatively coarse resolution GCMs relevant for

adaptation and policy

Two main stream methodologies

Dynamic downscaling / Regional Climate Models (RCMs) (e.g. RegCM, high resolution PRECIS)

Statistical /Empirical downscaling• Weather typing (SOMD – University of Cape Town)• Linear (and nonlinear) regression (SDSM – Rob Wilby)• Artificial Neural Networks• Weather generators

RCMs

• Essentially a model like a GCM but at higher resolution and over a smaller finite domain

• Uses a GCM to establish the boundary fields of the RCM

• The RCM derives a dynamic solution at higher resolution, which is physically consistent with the larger scale circulation of the forcing GCM

Image courtesy of the UK Met. Office

(htp://www.metoffice.gov.uk).

Pros:• Accounts for sub-GCM grid scale forcing (e.g. topography)• Information is derived from physically based models• Better representation of some weather extremes as compared

to GCMs

Cons:• Expensive to run RCMs as compared to statistical

downscaling over a large region• Its dependence on GCM predictors• It is a spatially smoothed product compared to station scale

RCMs

Statistical downscaling

• Involves the development of quantitative relationships between large scale atmospheric variables (predictors) and local surface variables (predictands)

Example: SOMD• A Self Organising Map (SOM) is used to recognise commonly

occurring patterns within multi-dimensional data sets

• Identify modes of circulation over a particular region with each circulation mode being associated with an observed precipitation probability density function (PDF)

Calculate probability of rainfall for each synoptic

pattern

Statistical downscaling

Pros:• Efficient and cheap computation requirements • Its ability to provide point resolution climatic variables

from GCM outputs• Its ability to directly incorporate observations

Cons:• High dependence on the predictors • Vulnerability to non-stationarity of the cross scale

relationships

Climate data and impact assessment

Downscaling does NOT seek to reproduce the real world observation , but rather generate a realistic time evolution that:

• At seasonal and inter-annual scales should match relative magnitude of the temporal evolution of the forcing

• At daily time scales should match the statistics of the daily

events (e.g. frequency of events, etc)

FACT : There will only be one time evolution into the future, but many possible evolutions

Limitations include:• Imperfect ability to model our knowledge into accurate

mathematical equations: e.g. physics, knowledge gaps etc…

• Data formatting techniques (e.g. different spatial resolution of systems)

Climate data and impact assessment

Climate data and impact assessment

• Imperfect observation data

Data typeProvincial, Catchment, Station, Gridded

Station

Spatially average station yields over each region and then compare to observed data.

Data estimation and uncertainty Penman Montieth

FACT: Society cannot wait for perfect models (GCMs and

impact) and methods• We need to make choices today based on the best current

scientific information

• We need to characterize baseline observational climate as best as possible

• We need to use as many models as possible

• We need to downscale or upscale where possible

Thank you