1. CLIMSENS: Constraining total
feedback of the climate system
by observations and models.
Ragnhild Bieltvedt Skeie
Terje Berntsen CICERO and University of Oslo
Gunnar Myhre CICERO
Magne Aldrin Norwegian Computer Center
Marit Holden Norwegian Computer Center
2. Climate sensitivity:
The equilibrium change in the annual mean global surface
temperature following a doubling of the atmospheric CO2
concentration.
IPCC 2007
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3. Constraining the climate
sensitivity
• “Bottom-up” approach. Perturbing the
representations of the climate feedbacks in
GCM models.
• “Observational based” approach. Using
historical observations and simple climate
models
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4. ΔQ = ΔF - λ ΔT
ΔQ: Heat flux in the system
ΔF: Radiative forcing
λ: Climate Feedback Parameter
At equilibrium: ΔQ = 0, λ = ΔF2xCO2 / ΔT2xCO2eq
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8. Statistical model:
The data:
Surface temperature (3 data set, NH and SH averages).
Ocean heat content
Additative bias/correction for baseline
SOI index, Account for El Nino.
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9. Statistical model
Bayesian approach and a MCMC-algorithm:
1. Apriori distributions for parameters and input data.
2. Update your model with observations.
3. Get posteriori uncertainties for your model parameters
and input data. One of them is the climate sensitivity!
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