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Uncertainty in Emissions Projections for Climate Models. J. Reilly, M. Mayer, M. Webster, C. Wang, M. Babiker, R. Hyman, M. Sarofim MIT Joint Program on the Science and Policy of Global Change American Geophysical Union San Francisco , 14-19 December 2000.

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Uncertainty in emissions projections for climate models
Uncertainty in Emissions Projections for Climate Models

J. Reilly, M. Mayer, M. Webster, C. Wang, M. Babiker, R. Hyman, M. Sarofim

MIT Joint Program on the Science and Policy of Global Change

American Geophysical Union

San Francisco, 14-19 December 2000


Motivation

Many climatically important substances (CIS’s) released from many different human activities.

IPCC Special Report on Emissions Scenarios (SRES) was a high profile attempt to develop scenarios but had some important limitations:

Inconsistency—different models for different gases.

No quantification of uncertainty.

May not have covered the full range of possibilities.

Confusion of policy cases with no policy cases.

Motivation


Eppa an economic emissions model

Model of the world economy with all human activities and all CIS’s.

GHGs: CO2, CH4, N2O, SF6, PFC, HFC

Other air pollutants: NOX, SOX, CO, NMVOC, NH3 and carbonaceous particulates

Activities: Energy combustion and production, agriculture and land use, industrial processes, waste disposal (sewage & landfills)

EPPA: An Economic/Emissions Model



Uncertainty analysis approach

Distributions for 8 key parameters: CIS’s.

Labor Productivity Growth (1)

Energy Efficiency Improvement Rate (1)

GHG and Other Pollutant Emissions Factors (6)

Deterministic Equivalent Modeling Method (DEMM)

~1300 model runs to fit 4th order polynomial

10,000 Monte Carlo simulations of polynomial fit to construct distributions.

Construct scenarios with known probability characteristics.

Simulate these scenarios through the MIT IGSM.

Uncertainty Analysis Approach



Global co 2 emissions
Global CO CIS’s. 2 Emissions


Global ch 4 emissions
Global CH CIS’s. 4 Emissions


Global n 2 o emissions
Global N CIS’s. 2O Emissions


Global so 2 emissions
Global SO CIS’s. 2 Emissions


Global no x emissions
Global NO CIS’s. x Emissions


Global co 2 emissions in 2100
Global CO CIS’s. 2 Emissions in 2100


Global ch 4 emissions in 2100
Global CH CIS’s. 4 Emissions in 2100


Global n 2 o emissions in 2100
Global N CIS’s. 2O Emissions in 2100




Global sf 6 emissions in 2100
Global SF CIS’s. 6 Emissions in 2100


Co 2 concentration
CO CIS’s. 2 Concentration


Aerosol forcing
Aerosol Forcing CIS’s.


Ch 4 forcing
CH CIS’s. 4 Forcing


N 2 o forcing
N CIS’s. 2O Forcing


Co 2 forcing
CO CIS’s. 2 Forcing


Total forcing
Total Forcing CIS’s.



Conclusions

SRES CO CIS’s. 2 scenarios cover much of the 95% confidence range but..

Biased somewhat toward the low end of emissions: 4 of 6 scenarios are well below 50% level in 2100

No scenario is particularly close to mean/median

SRES scenarios for other GHGs are narrow.

Fail to consider uncertainty in current emissions when we know current emissions levels very poorly.

High bias for some, Low bias for others—evidence of inconsistency

SOx in particular are all very low—all SRES scenarios optimistic about control.

SRES scenarios are biased somewhat toward high temperatures

MIT emissions scenarios will be available at http://web.mit.edu/globalchange

Conclusions


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