Detection of an anthropogenic climate change in Northern Europe Jonas Bhend 1 and Hans von Storch 2,3 1 Institute for Atmospheric and Climate Science, ETH Zürich, Switzerland 2 Institute for Coastal Research, GKSS Research Centre, Geesthacht, Germany 3 Meteorological Institute, University of Hamburg, Germany June 15, 2010, 6th Study Conference on BALTEX, Międzyzdroje

2 Observed temperature anomalies

3 Anthropogenic Natural Internal variability Detection and attribution Attribution Anthropogenic Natural Observations External forcings Climate system Detection Internal variability

4 Research questions Is the observed change different from internal variability? Is anthropogenic forcing a plausible explanation? Is anthropogenic forcing a necessary explanation? TemperaturePrecipitation

5 The detection and attribution approach Observed ChangeDeterministic Signals Internal Variability =+ + finite ensemble + forcing uncertainty + model errors + additional forcings + linearity and additivity + model errors+ observation error Physics-based modelsMeasurements

6 Uncertainty assessment i)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 Observations and 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

7 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

8 Detection using optimal fingerprinting Model response is too weak Model response is consistent with observed change No detection

9 Detection with different models, Temperature scaling Model response is too weak No detection Consistency

10 Precipitation scaling Model response is too weak Detection with different models, No detection Consistency

11 Attribution with area-average temperature Natural signal consistentNo detectable natural signal Anthropogenic signal consistent No detectable anthropogenic signal

12 Summary Is the observed change different from internal variability? Is anthropogenic forcing a plausible explanation? Is anthropogenic forcing a necessary explanation? TemperaturePrecipitation ( )

13 Constraining regional projections

14 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.

15 Additional slides

16 Bias in Temperature and Precipitation

17 Observed and simulated variability

18 Influence of downscaling

19 Model data used

20 Detection in dependance of pattern used

21 Detection in dependance of time period analyzed

22 Detection in dependance of pattern used