1. making certain the uncertainties
Climate change
making certain the uncertainties
Group 3: Bok Wen Xuan, Anabella Fong, Goh Jie Sheng

2. outline: What is climate Climate projections:
Energy balance model (EBM)
General circulation model (GCM)
Quantifying uncertainties
Uncertainties neglected
Conclusion

3. what is climate? Average weather experienced there
Interested in changes in variability, and in particular in extremes
“The distribution of weather”

4. climate projections
How are climate projections made?

5. climate projections how are they made?
Need certainty
Climate system is very complex - need simplification of reality
Two examples of models:
Energy balance model (EBM)
General circulation model (GCM)

6. Energy balance Model (EBM)
Change in stored energy = input energy - output energy
Have minimal resolutions
Simple enough - implemented using a calculator or spreadsheet
Crucial role in our understanding of the gross features of climate

7. Energy balance Model Limitations
Largely phenomenological
Can give only limited guidance about future climate
Eg:
Affects temperature
Warmer climate
Reflect visible light back into space
Clouds
Subtleties
Absorbing and re- radiating infrared light from the surface of the Earth
Atmospheric water vapour

8. General circulation model (GCM)
Represents the physics of climate processes
Can determine the net outcome of competing effects
Potential to provide climate information at regional scales

9. General circulation model Limitations

10. General circulation model (GCM)
Dynamic of the climate system defined by: dy/dt = f(y,x)
y(t) : values of all prognostic variables at time t
x(t) : a collection of forcing variables
GCM attempts to solve this equation for a given initial configuration y(t0) and forcing scenario {x(t): t > t0).
t0 : time in the past at which the climate system was relatively stable, such as 1850

11. General circulation model Limitations
Many approximations are involved
GCMs work with a subset of elements of y(t) - impossible to enumerate completely the state of the system at any time
Not possible to represent all of the interrelationships between these elements
Cannot be solved analytically, must use numerical methods - usually involve time and spatial discretion

12. General circulation model Spatial discretion
HadCM3:
Composed of 2 components: the atmospheric model HadAM3 and the ocean model HadOM3. Simulations use a 30 day calendar, where each month is 30 days.
Produce simulations for periods of over a thousand years, showing little drift in its surface climate

13. General circulation model Spatial discretion
HadGEM1:
Developed in 2006
A significant scientific advancement from HadCM3
Provides a basis for further development of models, particularly involving enhanced resolution and full Earth System modelling

14. General circulation model Spatial discretion
HadCM3 has coarse spatial resolution of the grid
HadGEM1 has enhanced resolution
Output on a grid can only approximate the spatial variation in y(t)
Provide credible simulations; much of our detailed understanding of the climate system derives from this type of simulator

15. Quantifying uncertainty
Structural uncertainty:
simplification and approximation rule out a perfect prediction of prognostic variables
source of uncertainty in future projections

16. Quantifying uncertainty
Earth system quantities:
Describe what the Earth is like at a selected moment
Involved in inferring the prognostic variables
Treated as simulator inputs
Come in 3 ways:
Values for Earth system components are fixed
Historical values of forcing variables
Future values of forcing variable

17. Quantifying uncertainty
Correct values of parameters
Do not have an analog in the climate system
Not easy to operationalise in the context of an imperfect simulator

18. Quantifying uncertainty
Input uncertainty - uncertainty about simulator inputs
Parameter uncertainty - uncertainty about parameters
Input
Prognostic variables
Observations
Parameters

19. Uncertainties neglected
Structural uncertainty:
Often treated as negligible
Historical input values are replaced by point estimates
Parameter values were derived from a combination of physical reasoning and a very limited amount of tuning
Incorporate as much physics as possible, to obtain what they consider the best possible point estimate of future climate - at the expense of quantifying uncertainties

20. Uncertainties neglected
2 improvements:
#1. Have multiple simulator evaluations over different values for both the historical inputs and the simulator parameters
#2. Use the distribution from a collection of different simulators, termed a multi-model ensemble

21. Uncertainties neglected
Ensemble of simulations from HadCM3
Observed climate data
UKCP09
Climate projections
Marine and coastal
projections
Key parameters varied within ranges of uncertainty

22. Uncertainties neglected
International Panel on Climate Change Fourth Assessment Report
23 GCMs
Different structural assumptions
Different evaluations

23. Uncertainties neglected #2 limitations
Easy to treat the spread of values as uncertainty - happens when evaluations are displayed together
Cone underestimates uncertainty
Multi-model ensembles cannot be interpreted probabilistically in any conventional sense

24. conclusion
Statisticians have an important role to play in representing and quantifying uncertainty about future climate
Progress requires multidisciplinary collaboration

25. References
Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom. (2016, April 15). Met Office climate prediction model: HadGEM1. Retrieved February 26, 2017, from
HadCM3 GCM Model Information. (2011, May 16). Retrieved February 26, 2017, from
Using Climate Projections. (2015, August 21). Retrieved February 26, 2017, from