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Detection of anthropogenic climate change

Detection of anthropogenic climate change. Gabi Hegerl, Nicholas School for the Environment and Earth Sciences, Duke University. Temperature trend 1901-2000. Fingerprint methods: lin. regression. Estimate amplitude of model-derived climate change signals X=(x i ) ,i=1..n

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Detection of anthropogenic climate change

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

  2. Temperature trend 1901-2000

  3. Fingerprint methods: lin. regression Estimate amplitude of model-derived climate change signals X=(xi),i=1..n from observation y Best Linear Unbiased Estimator u: noise residual (Hasselmann, 79 etc, Allen + Tett, 99) Vector: eg Temperature(space,time), scalar product: Inverse noise covariance Signal pattern from model, amplitude from observation!

  4. June-July-August Greenhouse gas + sulfate aerosol

  5. uncertainty range • Estimated from coupled model internal variability • Safety checks: • Use model with strong variability • test consistency with observed noise residual u

  6. Contribution of greenhouse gas and sulfate aerosols to to trend 1949-98 o: Greenhouse gas + sulfate aerosol simulation +: Greenhouse gas only o/+inconsistent with observation Ellipse: 90% uncertainty range in obs. Signal estimate from: Hegerl and Allen, 2002

  7. The longer perspective reconstruction of NH warm season temperature Forced component Fat: best fit to paleo Thin: 5-95% range *: significant

  8. 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 gases” • Significant and consistent climate signals in long temperature records

  9. 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 => no • 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

  10. Changes in precipitation extremes stronger

  11. Change in rainfall wettest day/yr NAmerica Consensus Observations show overall increase, too

  12. Annual mean precip changes consistent between two models Wettest day/yr Wettest 5 consecutive days

  13. Results: Anthropogenic vs natural signals, time-space Bars show 5-95% uncertainty limits Allen et al, 2002

  14. Annual mean rainfall change NAmerica consensus

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