1. 10 IMSC, 20-24 August 2007, Beijing Page 1 Consistency of observed trends in northern Europe with regional climate change projections Jonas Bhend 1 and Hans von Storch 12 1 Institute for Coastal Research, GKSS Research Center, Geesthacht, Germany 2 Meteorological Institute, Hamburg University, Hamburg, Germany

2. 10 IMSC, 20-24 August 2007, Beijing Page 2 Often it is claimed that the recent changes in northern European climate are at least partly anthropogenic even though a human influence has not yet been successfully detected. Hence we investigate whether the recent changes are consistent with regional climate change projections. Therefore, trends in winter (DJF) mean precipitation in northern Europe are compared to human induced changes as predicted by an ensemble of four regional climate model simulations. The patterns of recent trends and predicted changes match reasonably well as indicated by pattern correlation and the similarity is very likely not random. However, the model projections severely underestimate the recent change in winter precipitation by 50 to 80\%. That is, the signal-to-noise ratio of the anthropogenic precipitation change is either rather low or the presently used simulations are significantly flawed in their ability to project changes into the future. European trends contain large NAO-related signals, of which a major unknown part may be unrelated to the anthropogenic signal. Therefore, we also examine the consistency of recent and projected changes after subtracting the NAO signal in both the observations and in the projections. It turns out that even after the removal of the NAO signal, the pattern of trends in the observations are similar to those projected by the models. At the same time, the magnitude of the trends is considerably reduced and closer to the magnitude of the change in the projections.

3. 10 IMSC, 20-24 August 2007, Beijing Page 3 The Baltic Sea Catchment Climate Change Assessment: BACC An effort to establish which knowledge about anthropogenic climate change is available for the Baltic Sea catchment. Working group BACC of GEWEX program BALTEX. Approximately 80 scientist from 10 countries have documented and assessed the published knowledge. Assessment has been accepted by intergovernmental HELCOM Commission as a basis for its future deliberations.

4. 10 IMSC, 20-24 August 2007, Beijing Page 4 Summary of BACC Results Baltic Area Climate Change Assessment Presently a warming is going on in the Baltic Sea region. No formal detection and attribution studies available. BACC considers it plausible that this warming is at least partly related to anthropogenic factors. So far, and in the next few decades, the signal is limited to temperature and directly related variables, such as ice conditions. Later, changes in the water cycle are expected to become obvious. This regional warming will have a variety of effects on terrestrial and marine ecosystems – some predictable such as the changes in the phenology others so far hardly predictable. BACC Group: Assessment of climate change for the Baltic Sea basin, Springer-Verlag, in press The Baltic Sea Catchment Climate Change Assessment: BACC

5. 10 IMSC, 20-24 August 2007, Beijing Page 5 Options Detection: “Is the observed change different from what we expect due to internal variability alone?” – not doable at this time. Trends – are there significant trends? – no useful results. Consistency: “Are the observed changes similar to what we expect from anthropogenic forcing?” Doable: Plausibility argument using an a priori known forcing.

6. 10 IMSC, 20-24 August 2007, Beijing Page 6 „Significant“ trends Often, an anthropogenic influence is assumed to be in operation when trends are found to be „significant“. In many cases, the tests for assessing the significance of a trend are false as they fail to take into account serial correlation. If the null-hypothesis is correctly rejected, then the conclusion to be drawn is – if the data collection exercise would be repeated, then we may expect to see again a similar trend. Example: N European warming trend April – July as part of the seasonal cycle. It does not imply that the trend will continue into the future (beyond the time scale of serial correlation). Example. Usually September is cooler than July.

7. 10 IMSC, 20-24 August 2007, Beijing Page 7 „Significant“ trends Establishing the statistical significance of a trend is a necessary condition for claiming that the trend would represent evidence of anthropogenic influence. Claims of a continuing trend require that the dynamical cause for the present trend is identified, and that the driver causing the trend itself is continuing to operate. Thus, claims for extension of present trends into the future require - empirical evidence for ongoing trend, and - theoretical reasoning for driver-response dynamics, and - forecasts of future driver behavior.

8. 10 IMSC, 20-24 August 2007, Beijing Page 8 The check of consistency of recent and ongoing trends with predictions from dynamical (or other) models represents a kind of „attribution without detection“. This is in particular useful, when time series of insufficient length are available or the signal-to-noise level is too low. The idea is to estimate the driver-related change E from a (series of) model scenarios (or predictions), and to compare this “expected change” E with the recent trend R. If R  E, then we may conclude that the recent change is not due to the suspected driver, at least not completely. Consistency analysis: attribution without detection

9. 10 IMSC, 20-24 August 2007, Beijing Page 9 DJF mean precip in the Baltic Sea catchment Example: Recent 30-year trend R Trend of DJF precip according to different data sources.

10. 10 IMSC, 20-24 August 2007, Beijing Page 10 Consistency analysis Expected signals E six simulations with regional coupled atmosphere-Baltic Sea regional climate model RCAO (Rossby-Center, Sweden) three simulations run with HadCM3 global scenarios, three with ECHAM4 global scenarios; 2071-2100 two simulation exposed to A2 emission scenario, two simulations exposed to B2 scenario; 2071-2100 two simulations with present day GHG-levels; 1961-90 Regional climate change in the four scenarios relatively similar.

11. 10 IMSC, 20-24 August 2007, Beijing Page 11 Consistency analysis R E

12. 10 IMSC, 20-24 August 2007, Beijing Page 12 Δ=0.05% Regional DJF precipitation

13. 10 IMSC, 20-24 August 2007, Beijing Page 13 Consistency analysis Global model scenario Pattern correlations Pattern correlations without NAO HadAM A20.830.75 B20.750.64 ECHAM A20.850.75 B20.840.74 The pattern correlations are all significantly larger than pattern correlations between random combinations of trends. Patterns correlations between “observed” (CRU) trends in DJF seasonal precipitation in the Baltic Sea catchment and “expected” signals derived from scaled RCM changes.

14. 10 IMSC, 20-24 August 2007, Beijing Page 14 Consistency analysis Global model scenario Intensity-ratio  R  /  E  Intensity-ratio without NAO HadAM A22.962.53 B24.503.98 ECHAM A21.941.57 B22.502.07 Ratio of intensities between “observed” (CRU) trends in DJF seasonal precipitation in the Baltic Sea catchment and “expected” signals derived from scaled RCM changes. All model predictions result in too large trends for the past years. When taking out the NAO the situation slightly improves.

15. 10 IMSC, 20-24 August 2007, Beijing Page 15 Regional JJA temperatures

16. 10 IMSC, 20-24 August 2007, Beijing Page 16 All seasons: RCAO-ECHAM B2 scenario Consistency analysis

17. 10 IMSC, 20-24 August 2007, Beijing Page 17 Consistency analysis: Baltic Sea catchment 1.We suggest a consistency analysis to compare model outlooks with recent trends. 2.Consistency of the patterns of model “predictions” and recent trends is found in most seasons. 3.A major exceptation is precip in JJA and SON. 4.Removing the NAO-signal changes improves consistency slightly. 5.The observed trends in precip are stronger than the anthropogenic signal suggested by the models. 6.Possible causes: - scenarios inappropriate (false) - drivers other than CO 2 at work (industrial aerosols?) - natural variability much larger than signal (signal-to-noise ratio  0.2-0.5).