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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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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!
uxay ii
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uncertainty range Estimated from coupled model internal
variability
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 observation
Ellipse: 90% uncertainty range in obs. Signal estimate
from: Hegerl and Allen, 2002
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reconstruction of NH warm season temperature
Forced componentFat: best fit to paleoThin: 5-95% range*: significant
The longer perspective
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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 gases”
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 => 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
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Change in rainfall wettest day/yr NAmerica
Consensus
Observations show overall increase, too
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Annual mean precip changes consistent between two models
Wettest day/yr
Wettest 5 consecutive days
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Results: Anthropogenic vs natural
signals, time-space
Bars show 5-95% uncertainty limitsAllen et al, 2002