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Jonas Bhend and Hans von Storch GKSS Research Institute, Geesthacht, Germany

Consistency of observed winter precipitation trends in northern Europe with regional climate change projections. Jonas Bhend and Hans von Storch GKSS Research Institute, Geesthacht, Germany. Motivation. Gap between formal detection and attribution studies and “significant trends” studies

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Jonas Bhend and Hans von Storch GKSS Research Institute, Geesthacht, Germany

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  1. Consistency of observed winter precipitation trends in northern Europe with regional climate change projections Jonas Bhend and Hans von Storch GKSS Research Institute, Geesthacht, Germany

  2. Motivation • Gap between formal detection and attribution studies and “significant trends” studies • Are the recent trends consistent with regional climate change projections? • Plausibility arguments • A priori assumption about the mechanism • Less informative than DnA but no estimate of natural variability needed

  3. Data • Observations: • CRU TS 2.1 monthly precipitation • 0.5° latitude-longitude grid • Climate change scenarios: • RCAO simulations of the SMHI (PRUDENCE) • 0.44° rotated grid • Two different driving GCMs, HadAM3H and ECHAM4/OPYC3 • Two emission scenarios SRES A2 and B2 • Four climate change scenarios defined as the difference between 1961-1990 and 2071-2100 mean

  4. Method • Pattern correlation S: Climate change signal O: Trends in observations • Ratio of Intensities with: and:

  5. Climate change scenarios...

  6. ... and observations

  7. Pattern correlation • Patterns are similar • Better correspondence with ECHAM scenarios • Better correspondence with stronger GHG forcing (A2)

  8. Sensitivity of PCCs • Bootstrap with CRU precip fields • randomly select precip fields • compute trends • correlate trend fields • Autocorrelation: moving blocks bootstrap, 5 years Histogram of PCCs for the Baltic catchment (shaded) and northern Europe (hatched)

  9. Pattern correlation • PCCs for the Baltic catchment significant • PCCs for all of northern Europe are not significant for HadAM B2 • Above findings robust to removal of the NAO

  10. Intensity • Change in the observations much stronger than in scenarios • RCM simulations are wrong • additional forcings • natural variability

  11. Mean change • Change in the observations much stronger than in scenarios • RCM simulations are wrong • additional forcings • natural variability

  12. Different trend lengths • PCCs decrease with increasing trend length • Significance levels are not affected by choice of trend length • Intensity and mean change decrease with increasing trend length

  13. Conclusions - pattern correlation • Baltic catchment: • Regional climate change scenarios are consistent • Observed and expected patterns are similar and significant • Northern Europe: • Regional climate change scenarios are partly consistent • Pattern similarity with HadAM signal could be random

  14. Conclusions - intensity • Both intensity and mean change suggest that: • Assuming the model response to anthropogenic forcing is correct, a large part (30 to 70 percent) of the observed trends is due to other factors (e.g. natural variability).

  15. Thank you for your attention.

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