uncertainty quantification (uq) and climate change talking points mark berliner, ohio state issues...

Uncertainty Quantification (UQ) and Climate Change Talking PointsMark Berliner, Ohio State

Issues of continuing interest:

Models, Data, Impacts & Decision Support

1) Disclaimers

a) List is incomplete

b) None are criticisms, just questions and suggestions

c) Please let me know what I missed

2) My Apologies for missing the workshop

Models We know many sources of uncertainty:parameterization; feedbacks; resolution &approximation; nonlinearity; etc. How to help?• Continued model assessment • Claim that models and observations “line up”

needs quantification• Since a key is the response of hydrological cycle,

do real advances require increased resolution?• Multi-model ensembles• Bigger ensembles vrs better models (allocation of

computational resources) • Do more models lead to more confusion?• Lot’s of opportunities for collaboration (both design and

analysis)!!!!

Data1) Paleoclimate: Lot’s of very important work to do: • Assess impacts of forcings without sole reliance on

climate models, though we need to “know” the historical impacts and the forcings.

• Use of proxies is key opportunity for collaboration• Aid in assessment/improvement of the models1) Combining datasets: rebuilding climate variable

global estimated fields w/ uncertainties (eg, temperature fields)

2) Modern climate monitoring: design and use of remote sensing, etc. (“compute” or observe)

3) Operational system for scientists and decision makers to access info on the “state of the planet”

Impacts1) Level I: Sea level; Storms, droughts,

hurricanes; Weather extremes; Fire; Floods; Other environmental systems

2) Level II: Human activities, agriculture, health, economics, national security

3) A viewpoint: hierarchical chains • Global to regional impacts: downscaling

a) Dynamic b) Statistical c) Combinations

Excellent opportunity!!!• Propagation of uncertainty• Collaboration across many disciplines (Level II)

Global vrs

Regional Behavior

Annual mean

precip (cm) 1980-1999: Observed

& simulated based on

multi-model mean.

Figure 8.5

Global vrs

Regional Behavior

Annual mean

precip (cm) 1980-1999: Observed

& simulated based on

multi-model mean.

Figure 8.5

Propagation of uncertainty: (causal) chains

CO2 __> global climate

__> regional climate

__> local weather and hydrological

environment

__> disease patterns

crop yields; etc.

None of these arrows are deterministic or “known”. Rather we need to build probability models for each and then link them. This is where statisticians are expert.

Remark: feedbacks can be very tough.

Decision Support

• Ranges (intervals) vrs probability dist.When are “honest” confidence/prediction intervals too wide to be a value?

• Risk analysis (expected loss) • Explaining/using UQ: “uncertainty is not

ignorance”• Presenting results in useful forms• Sensitivity, robustnessRemark: Some don’t like this area, but it is

being done; I think we should participate!!