Consistency of observed trends in northern Europe with regional climate change projections

1. Consistency of observed trends in northern Europe with regional climate change projections Jonas Bhend1 and Hans von Storch12 1Institute for Coastal Research, GKSS Research Center, Geesthacht, Germany 2Meteorological Institute, Hamburg University, Hamburg, Germany

2. 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.

3. The Baltic Sea Catchment Climate Change Assessment: BACC • Summary of BACC ResultsBaltic 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

4. 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. Options

5. „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.

6. „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.

7. Consistency analysis: attribution without detection 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.

8. DJF mean precip in the Baltic Sea catchment Example: Recent 30-year trend R Trend of DJF precip according to different data sources.

9. 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.

10. Consistency analysis R E

11. Regional DJF precipitation Δ=0.05%

12. Consistency analysis Patterns correlations between “observed” (CRU) trends in DJF seasonal precipitation in the Baltic Sea catchment and “expected” signals derived from scaled RCM changes. The pattern correlations are all significantly larger than pattern correlations between random combinations of trends.

13. Consistency analysis 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 predictionsresult in too largetrends for the past years.When taking out theNAO the situationslightly improves.

14. Regional JJA temperatures

15. Consistency analysis All seasons: RCAO-ECHAM B2 scenario

16. Consistency analysis: Baltic Sea catchment • We suggest a consistency analysis to compare model outlooks with recent trends. • Consistency of the patterns of model “predictions” and recent trends is found in most seasons. • A major exceptation is precip in JJA and SON. • Removing the NAO-signal changes improves consistency slightly. • The observed trends in precip are stronger than the anthropogenic signal suggested by the models. • Possible causes:- scenarios inappropriate (false)- drivers other than CO2 at work (industrial aerosols?)- natural variability much larger than signal (signal-to-noise ratio  0.2-0.5).