Detection of anthropogenic climate change

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					                 Detection of anthropogenic
                      climate change
                  Gabi Hegerl,
                  Nicholas School for the
                  Environment and Earth Sciences,
                  Duke University

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                 Temperature trend 1901-2000

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             Fingerprint methods: lin. regression

             Estimate amplitude of model-derived climate
               change signals X=(xi),i=1..n
             from observation y

             Best Linear Unbiased Estimator

                       y a   
             u: noise residual i xi  u
             (Hasselmann, 79 etc, Allen + Tett, 99)
             Vector: eg Temperature(space,time), scalar product: Inverse
               noise covariance
             Signal pattern from model, amplitude from observation!

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             June-July-August Greenhouse gas + sulfate aerosol

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                       uncertainty range
                Estimated from coupled model internal

                Safety checks:
                 – Use model with strong variability
                 – test consistency with observed noise
                   residual u

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             Contribution of greenhouse gas and
             sulfate aerosols to to trend 1949-98
                                o: Greenhouse gas +
                                sulfate aerosol simulation
                                +: Greenhouse gas only
                                o/+ inconsistent with
                                Ellipse: 90% uncertainty
                                range in obs. Signal
                                from: Hegerl and Allen,
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             The longer perspective
 reconstruction of
NH warm season

Forced component
Fat: best fit to paleo
Thin: 5-95% range
*: significant

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                 Conclusions global/NH SAT

                Significant climate change observed
                Uncertainty in distinction between
                 forcings, but:
                “Most of the recent (last 50 yrs) global
                 warming is likely due to greenhouse
                Significant and consistent climate
                 signals in long temperature records
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         Towards detection of anthropogenic
            changes in climate extremes
                How to compare course-grid model with
                 station data?
                Can daily data be substituted by
                 monthly/annual and shift in distribution =>
                Which index to use for early detection
                 (avoid baseball statistics!) that is
                 moderately robust between models?
                 Change in once/few times/yr events robust
                 and strong

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              Changes in precipitation extremes

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                  Change in rainfall wettest day/yr NAmerica

                                             Observations show
                                             overall increase, too

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                  Annual mean precip
                  changes consistent
                  between two models

                  Wettest day/yr

                  Wettest 5 consecutive

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                  Results: Anthropogenic vs natural
                         signals, time-space

    Bars show 5-95% uncertainty limits
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                                         Allen et al, 2002
                  Annual mean rainfall change NAmerica


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