Downscaling and its limitation on climate change impact assessments

1. Downscaling and its limitation on climate change impact assessments Sepo Hachigonta University of Cape Town South Africa “Building Food Security in the Face of Climate change” 4 theMay 2010 , ICRAF, Nairobi

2. (1 − a)Sπr2 = 4πr2εσT4

3. GCMs Primary source of information on climate change projections • Incoming and outgoing radiation • Wind, Temperature, humidity etc.. • Clouds formation • Precipitation falls • How ice sheets grow or shrink, etc. • Feedback processes Horizontal resolution of about 300km and 10 to 30 vertical layers

5. GCMs

6. Downscaling Process of generating higher resolution data or climate change information from relatively coarse resolution GCMs relevant for adaptation and policy

8. 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

9. 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).

10. RCMs 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

11. 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)

12. Calculate probability of rainfall for each synoptic pattern

13. 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

14. 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)

15. Climate data and impact assessment 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)

16. Climate data and impact assessment • Imperfect observation data

17. Data type Provincial, Catchment, Station, Gridded

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

19. Data estimation and uncertainty Penman Montieth

20. 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

21. Thank you