1. 1 Hans von Storch: Recent climate change in the Baltic Sea region - manifestation, detection and attribution Based upon: Work done with Klaus Hasselmann, Eduardo Zorita, Armin Bunde, Armineh Barkhordarian, and Jonas Bhend Nordenskjöld Lecture, Göteborgs Universitet, 16 November 2015

2. Hans von Storch 1.Climate researcher (in the field since 1971) 2.Coastal science; statistical analysis 3.Cooperation with social and cultural scientists since 1992 4.Active in IPCC as Lead Author in AR3 (2001) and AR5 (2013). 5.Co-Chair of the BACC-Assessment of Climate Change in the Baltic Sea region, together with Anders Omstedt. 6.Retired Director of the Institute of Coastal Research of the Helmholtz Zentrum Geesthacht, Germany 7.Professor at Universität Hamburg, Meteorologcal Institute and Faculty of Social and Economic Sciences 8.Guest-Professor at the Oean University of China, Qingdao 9.Honorary doctorate of U Göteborg

3. 3 BALTEX Assessment of Climate Change for the Baltic Sea basin - BACC An effort to establish which scientifically legitimized knowledge about climate change and its impacts is available for the Baltic Sea catchment. Approximately 80 scientists from 12 countries have documented and assessed the published knowledge in 2008 in BACC 1; In May 2015, BACC-2 came out, with 141 contributing authors. The assessment has been accepted by the intergovernmental HELCOM commission as a basis for its judgment and recommendations. BACC as „regional IPCC“

4. 4 For the societal debate, at least in the west, there are several questions, which need scientific answers, of significance: a)Is there a change ? What are the dominant causes for such a chance, and what are the expectations for the future? b)Which consequences does this change have for people, society and ecosystems? In this lecture, I am dealing only with (a). We have three tasks Manifestation: The found change is real and not an artifact of the data and data collection process (inhomogeneity) Detection: The found change is beyond what may be expected due to natural (not externally caused) variations. Attribution: A change, which was found to be beyond the range of natural variations, may plausibly and consistently be explained by a certain (mix of) external cause(s). Change – a scientific challenge with societal significance

5. Klaus Hasselmann, the inventor of D&A History: Hasselmann, K., 1979: On the signal-to- noise problem in atmospheric response studies. Meteorology over the tropical oceans (B.D.Shaw ed.), pp 251-259, Royal Met. Soc., Bracknell, Berkshire, England. Hasselmann, K., 1993: Optimal fingerprints for the detection of time dependent climate change. J. Climate 6, 1957 - 1971 Hasselmann, K., 1998: Conventional and Bayesian approach to climate change detection and attribution. Quart. J. R. Meteor. Soc. 124: 2541-2565 5

6. 6 Methodical issues Randomness Significant trends?

7. 7 The 300 hPa geopotential height fields in the Northern Hemisphere: the mean 1967-81 January field, the January 1971 field, which is closer to the mean field than most others, and the January 1981 field, which deviates significantly from the mean field. Units: 10 m Noise as nuisance: masking the signal

8. Where does the stochasticity come from? Stochasticity is a mathematical construct to allow an efficient description of the (simulated and observed) climate variability. Simulation data: internally generated by a very large number of chaotic processes. Dynamical “cause” for real world’s natural unforced variability best explained as in simulation models.

9. 9 Noise or deterministic chaos? Mathematical construct of randomness – an adequate concept for description of features resulting from the presence of many chaotic processes.

10. „Significant“ trends Often, an anthropogenic influence is assumed to be in operation when trends are found to be „significant“. 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 to 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.

11. 11

12. Losses from Atlantic Hurricanes

13. Storm surges in Hamburg

14. Quelle: http://www.dmi.dk/nyheder/arkiv/nyheder-2015/01/2014-er-klodens-varmeste-aar Estimates of global mean temperature increase

15. Temperature increase in the Baltic Sea Region Baltic Sea region (1982-2011) Data: CRU & EOBS

16. 16

17. 17 Estimation of damage if presence of people and values along the coast would have been constant – the change is attributable to socio-economic development Is the massive increase in damages attributable to extreme weather conditions? Losses from Atlantic Hurricanes Pielke, Jr., R.A., Gratz, J., Landsea, C.W., Collins, D., Saunders, M., and Musulin, R., 2008. Normalized Hurricane Damages in the United States: 1900-2005. Natural Hazards Review

18. 18 Storm surges in Hamburg

19. Temporal development of  T i (m,L) = T i (m) – T i-L (m) divided by the standard deviation of the m-year mean reconstructed temp record for m=5 and L=20 (top), and for m=30 and L=100 years. The thresholds R = 2, 2.5 and 3σ are given as dashed lines; they are derived from temperature variations modelled as Gaussian long-memory processes fitted to various reconstructions of historical temperature. The Rybski et al-approach Rybski, D., A. Bunde, S. Havlin,and H. von Storch, 2006: Long-term persistence in climate and the detection problem. Geophys. Res. Lett. 33, L06718, doi:10.1029/2005GL025591

20. Clustering of warmest years 20 Zorita, E., T. Stocker and H. von Storch, 2008: How unusual is the recent series of warm years? Geophys. Res. Lett. 35, L24706, doi:10.1029/2008GL036228,

21. … there is something to be explained 21 IPCC AR5, SPM Thus, there is something going on in the global mean air temperature record, which needs to be explained by external factors.

22. Zorita, et al., 2009 Regional clustering of warmest years

23. Observed temperature trends in the Baltic Sea region (1982-2011) 23 Observed CRU, EOBS (1982-2011) 95th-%tile of „non-GS“ variability, derived from 2,000-year palaeo-simulations  An external cause is needed for explaining the recently observed annual and seasonal warming over the Baltic Sea area, except for winter (with < 2.5% risk of error) Estimating natural variability: 2,000-year high-resolution regional climate palaeo-simulation (Gómez-Navarro et al, 2013) is used to estimate natural (internal + external) variability. Baltic Sea region

26. Difference betwenn peak heights of storm surges in Cuxhaven and Hamburg Main cause for recently elevated storm surges in Hamburg is the modification of the river Elbe – (coastal defense and shipping channel deepening) and less so because of changing storms or sea level. von Storch, H. and K. Woth, 2008: Storm surges, perspectives and options. Sustainability Science 3, 33-44 Consistency of recent local change: Storm surges in Hamburg

27. “Guess patterns” When doing attribution, often “guess patterns” are used, which supposedly describe the fingerprint of the effect of a possible cause. The reduction of degrees of freedom is done by projecting the full signal S on one or a few several “guess patterns” G k, which are assumed to describe the effect of a given driver. S =  k  k G k + n with n = undescribed part. Example: guess pattern supposedly representative of the impact of increased CO 2 levels Hegerl et al., 1996

28. The ellipsoids enclose non-rejection regions for testing the null hypothesis that the 2-dimensional vector of signal amplitudes estimated from observations has the same distribution as the corresponding signal amplitudes estimated from the simulated 1946-95 trends in the greenhouse gas, greenhouse gas plus aerosol and solar forcing experiments. Zwiers, F.W., 1999: The detection of climate change. In: H. von Storch and G. Flöser (Eds.): Anthropogenic Climate Change. Springer Verlag, 163-209, ISBN 3-540-65033-4 Attribution diagram for observed 50- year trends in JJA mean temperature.

29. IPCC 2007 Additional ly man- made factors Only natural factors „observations“ Attribution: Can we describe the development of air temperature by imposing realistic increasing greenhouse gas and aerosol loads on climate models? Yes, we can.

30. 30 Projected GS signal patterns (RCMs) Observed trend patterns (CRU)

31. Temperature change in the Baltic Sea Region Guess patterns: 10 simulations of RCMs from ENSEMBLES project. Forcing  Boundary forcing of RCMs by global scenarios exposed to GS (greenhouse gases and Sulfate aerosols) forcing  RCMs are forced only by elevated GHG levels; the regional response to changing aerosol presence is unaccounted for. “Signal” (2071-2100) minus (1961-1990); scaled to change per decade. 31

32. Observed and projected temperature trends (1982-2011) Observed CRU, EOBS (1982-2011) Projected GS signal, A1B scenario 10 simulations (ENSEMBLES)  DJF and MAM changes can be explained by dominantly GHG driven scenarios  None of the 10 RCM climate projections capture the observed annual and seasonal warming in summer (JJA) and autumn (SON).

34. Solar surface irradiance in the Baltic Sea Region 34  A possible candidate to explain the observed deviations of the trends in summer and autumn, which are not captured by 10 RCMs, could be the effect of changing regional aerosol emissions Observed 1984-2005 (MFG Satellites) Projected GS signal (ENSEMBLES) 1880-2004 development of sulphur dioxide emissions in Europe (Unit: Tg SO2). (after Vestreng et al., 2007 in BACC-2 report, Sec 6.3 by HC Hansson

35. 35 Michael Schrenk, © von Storch, HZG

36. Climate Change in the Baltic Sea Region Temperature is rising since some decades. This increase is beyond the range of our estimate of natural variations. We need an explanation by external (man-made) drivers. We can explain this increase in temperature in winter and spring by considering elevated CO2 levels as sole external forcing. In summer and fall, however, the effect of elevated greenhouse gases is insufficient to alone explain the warming. Thus, other drivers must be at work. A candidate would be the steady reduction of anthropogenic aerosol-generation in Northern Europe since about 1980. Since aerosols tend to cool the atmosphere in the warm season, a reduction of the aerosol load would go with an additional warming. More work needed. A similar discrepancy between observed change and expected change is also found for continental circulation and, consequently, precipitation amounts in summer and fall (not shown). 36

37. Strength of the argument Statistical rigor (D) and plausibility (A). D depends on assumptions about “internal variability” A depends on model-based concepts. Thus, remaining doubts exist beyond the specified. How do we determine the „natural variability“? With the help of the limited empirical evidence from instrumental observations or analyses, possibly after suitable extraction of the suspected „non-natural“ signal. By accessing long „control simulations“ done with quasi-realistic models. By projection of the signal on a proxy data space, and by determining the statistics of the latter from geoscience indirect evidence (e.g., tree rings). Dimension of D&A

38. Discussion: Attribution 1.Attribution needs guess patterns describing the expected effect of different drivers. 2.Non-attribution may be attained by detecting deviation from a given climate regime. “Non-attribution” means only: considered factor is not sufficient to explain change exclusively. 3.Regional and local climate studies need guess patterns (in space and time) of more drivers, such as regional aerosol loads, land-use change including urban effects 4.Impact studies need guess patterns of other drivers, mostly socio-economic drivers General: Consistency of change with a set of expected responses is a demonstration of possibility and plausibility; but insufficient to claim exclusiveness. Different sets of hypotheses need to be discussed before arriving at an attribution.

39. 39 For the societal debate, at least in the west, there are several questions, which need scientific answers, of significance: a)Is there a change ? What are the dominant causes for such a chance, and what are the expectations fo the future? b)Which consequences does this change have for people, society and ecosystems? We have three tasks Manifestation: The found change is real and not an artifact of the data and data collection process (inhomogeneity) Detection: The found change is beyond what may be expected due to natural (not externally caused) variations. Attribution: A change, which was found to be beyond the range of natural variations, may plausibly and consistently be explained by a certain (mix of) external cause(s). Change – a scientific challenge with societal significance