Downscaling and its limitation on climate change impact assessments
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Transcript of Downscaling and its limitation on climate change impact assessments
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