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Norwegian Study Finds Limited Warming from Doubled Greenhouse Gases

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    CLI MSENS: Con st r a in in g t o t a lf eedback o f t he cl im a te sys temby ob ser va t i ons and m odels.

    Ragnh i ld B iel t ved t Sk e ie

    Terje Berntsen CICERO and University of OsloGunnar Myhre CICEROMagne Aldrin Norwegian Computer CenterMarit Holden Norwegian Computer Center

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    Cl im a t e sen si t i v i t y :The equ i l i b r i um change i n t he annua l m ean g loba l su r facet e m p e ra t u re f o l l o w i n g a d o u b l i n g o f t h e at m o sp h e r i c CO2concen t ra t i on .

    IPCC 2007

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    Const r a i n i ng t he cl im at e

    sens i t i v i t y

    Bottom-up approach. Perturbing the

    representations of the climate feedbacks inGCM models.

    Observational based approach. Using

    historical observations and simple climatemodels

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    Q = F - T

    Q: Heat flux in the system

    F: Radiative forcing

    : Climate Feedback Parameter

    At equilibrium: Q = 0, = F2xCO2/ T2xCO2eq

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    Det a i led RF ca lcu la t ion s:Tr oposph er ic ozon e and aer oso ls

    Emissions

    Oslo CTM2 model

    RF-calculations

    IPCC 2007

    F

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    A sim p le cl im at e m odel :Energy balance model/Upwelling Diffusion Ocean

    StructureofthemodelParametre:

    Schlesinger et al. (1992)

    Name Unit Value

    Mixed layer depth m 60Vertical heat diffusivity cm /sec 0.634Polar parameter - 0.4Vertical velocity, upwelling rate m/yr 4.0Air-sea heat exchange parameter W/(m

    2K) 16.0

    Oceanic interhemispheric heat exchange coeff. W/(m K) 3.5Atmospheric interhemispheric heat exchange coeff. W/(m K) 0.0Climate sensitivity K/(Wm ) 0.8

    Q

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    Observa t i ons

    Surface temperature

    Ocean heat content

    Levitus, GRL 2009

    T

    IPCC 2007

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    St a t i st i ca l m ode l :

    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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    St a t i st i ca l m ode l

    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 parametersand input data. One of them is the climate sensitivity!

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    IPCC 2007

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    W h at s n ew ?

    Improved representation of the radiative

    forcing history

    Longer time period with observations


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