Detection of an anthropogenic climate change in Northern Europe

Jonas Bhend1 and Hans von Storch2,3

1 Institute for Atmospheric and Climate Science, ETH Zürich, Switzerland2 Institute for Coastal Research, GKSS Research Centre, Geesthacht, Germany3 Meteorological Institute, University of Hamburg, Germany

June 15, 2010, 6th Study Conference on BALTEX, Międzyzdroje

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Observed temperature anomalies

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Anthropogenic

Natural

Internalvariability

Detection and attribution

Attribution

Anthropogenic

Natural

Observations

External forcings

Climate system

Detection

Internalvariability

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Research questions

Is the observed change different from internal variability?

Is anthropogenic forcing a plausible explanation?

Is anthropogenic forcing a necessary explanation?

Temperature Precipitation

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The detection and attribution approach

Observed Change Deterministic SignalsInternal

Variability= ++ finite ensemble+ forcing uncertainty+ model errors+ additional forcings+ linearity and additivity

+ model errors+ observation error

Physics-based modelsMeasurements

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Uncertainty assessmenti) Stable over wide range of truncations?

ii) Residuals from fit = internal variability?

iii) ...

Total least squares

Method

Transformation (and truncation)Internal variability is translated to ‘white noise’

Signal-to-noise optimization

Observationsand

Simulations

Hasselmann, 1979: On the signal-to-noise problem in atmospheric response studies. Meteorology of Tropical Oceans

Allen and Stott, 2003: Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Climate Dynamics

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Observations and simulations used

Observations

Interpolated land station data

Temperature: CRUTEM 3v

Precipitation: GPCC v4

Simulations

Global model data from CMIP3

ALL:anthropogenic and natural forcing

ANT: anthropogenic forcing only

Jones and Moberg, 2003: Hemispheric and large-scale surface air temperature variations. Journal of Climate

Schneider et al. 2008: Global precipitation analysis products of the GPCC. Technical report, DWD

Meehl et al. 2007: The WCRP CMIP3 multimodel dataset - a new era in climate change research. BAMS

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Detection using optimal fingerprinting

Model response is too weak

Model response is consistent with observed change

No detection

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Detection with different models, 1943-1997

Temperature scaling

Model response is too weak

No detection

Consistency

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Precipitation scaling

Model response is too weak

Detection with different models, 1943-1997

No detection

Consistency

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Attribution with area-average temperature

Natural signal consistentNo detectable natural signal

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Summary

Is the observed change different from internal variability?

Is anthropogenic forcing a plausible explanation?

Is anthropogenic forcing a necessary explanation?

Temperature Precipitation

( )

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Constraining regional projections

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Outlook

- Further develop method for detection and attribution

- Systematic model biases (e.g. Huntingford et al. 2006)

- Detection and attribution results from the global scale?

- Model improvement

- Include locally important forcing mechanisms

- Wait for more change / stronger signals

- Thank you for your attention.

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Additional slides

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Bias in Temperature and Precipitation

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Observed and simulated variability

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Influence of downscaling

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Model data used

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Detection in dependance of pattern used

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Detection in dependance of time period analyzed

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Detection in dependance of pattern used