1. 1 Detection and attribution of climate change for the Baltic Sea Region 16-19 June 2015, Baltic Sea Science Conference, Riga Hans von Storch, Institute of Coastal Research, Geesthacht and Armineh Barkhordarian, UCLA

2. 2 16-19 June 2015, Baltic Sea Science Conference, Riga Detection and attribution of climate change for the Baltic Sea Region Hans von Storch and Armineh Barkhordarian The climate in the Baltic Sea Region (BSR) has seen changes in terms of air temperature and precipitation amounts, in recent decades. We have examined if these changes are within the range off natural variations, as given my multi-millennial “control” simulations with conventional climate models. It turns out that temperature has seen an increase in all seasons, as well as annually, which is beyond this range, so that we may conclude that we “detect” a change, which needs explanation by anthropogenic factors. Similarly for precipitation amounts, even if the pattern in different seasons and for the year is variable. When we compare these changes “which need explanation” with what climate models suggest as responses to elevated greenhouse gas concentrations (GHG), we find that the induced temperature change fits the sign of the observed change, but is too weak. In terms of precipitation, we find sometimes inconsistency, i.e., opposite sign, and different magnitudes. Thus, the change may be in part related to elevated GHGs, but not entirely so. To shed further light on the attribution issue, we fitted a regression model, which describes BSR annual temperature and precipitation amounts as a response to Northern Hemisphere temperature and BSR aerosol emissions. The predictor “Northern Hemisphere temperature” is supposed to describe mostly the GHG related change, but it includes also global aerosol effects, global volcanic effects as well as cosmic effects such as solar activity. The regression models fit the observed records rather well, even if the year-to-year variability is underestimated, as was to be expected. Then, we modified the BSR aerosol emissions in the regression model – being constant at low levels since 1920, and being constant at high levels since 1980. It turns out that constant emissions lead to a reduced temperature increase in recent decades and to a positive trend in precipitation amounts. Thus, regional aerosol emissions together with global GHG atmospheric accumulation together may be “attributed” as causes of the recent trends in BSR climate change, at least qualitatively.

3. 3 Detection and attribution of change Based upon: Work done with Klaus Hasselmann, Eduardo Zorita, Armin Bunde, Armineh Barkhordarian, and Jonas Bhend

4. 4 The issue is deconstructing a given record with the intention to identify „predictable“ components. „Predictable“ -- either natural processes, which are known of having limited life times, -- or man-made processes, which are subject to decisions (e.g., GHG, urban effect) Differently understood in different social and scientific quarters. The issue is also to help to discriminate between culturally supported claims and scientifically warranted claims

5. 5 What is behind this time serie? Omstedt, 2005

6. Detection and attribution 6 Detection:Determination if observed variations are within the limits of variability of a given climate regime. If this regime is the undisturbed, this is internal variability (of which ENSO, NAO etc. are part) If not, then there must be an external (mix of) cause(s) foreign to the considered regime. Attribution:In case of a positive detection: Determination of a mix of plausible external forcing mechanisms that best “explains” the detected deviations Issues: Uniqueness, exclusiveness, completeness of possible causes

7. 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 7

8. 8 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? 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

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15. 15 Methodical issues Randomness Significant trends?

16. 16 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

17. 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.

18. 18 Noise or deterministic chaos? Mathematical construct of randomness – an adequate concept for description of features resulting from the presence of many chaotic processes.

19. „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.

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21. Losses from Atlantic Hurricanes

22. 22 Storm surges in Hamburg

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

24. Temperature increase in the Baltic Sea Region 1982-2011, Data: CRU & EOBS

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26. 26 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

27. 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. Consistency of recent local change: Storm surges in Hamburg von Storch, H. and K. Woth, 2008: Storm surges, perspectives and options. Sustainability Science 3, 33-44

28. 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

29. Clustering of warmest years 29 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,

30. … there is something to be explained 30 IPCC AR5, SPM Thus, there is something going on in the global mean air temperature record, which needs to be explained by external factors.

31. Observed temperature trends in the Baltic Sea region (1982-2011) 31 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

34. “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

35. 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.

36. 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.

37. Observed and projected temperature trends (1982-2011) 37 Projected GS signal patterns (RCMs) Observed trend patterns (CRU) 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).

38. Solar surface irradiance in the Baltic Sea Region 38  A possible candidate to explain the observed deviations of the trends in summer and autumn, which are not captured by 10 RCMs, is the effect of changing regional aerosol loads 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

40. Discussion: Attribution 1.Attribution needs guess patterns describing the expected effect of different drivers. 2.Non-attribution may be attained by detecting deviations from a given climate regime (the case of the stagnation) “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 (the case of the Baltic Sea Region) 4.Impact studies need guess patterns of other drivers, mostly socio-economic drivers (the case of Hamburg storm surges and hurricane damages) 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.

41. 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

42. 42 Precipitation (1979-2008) Observed (CRU3, GPCC6, GPCP) Projected GS signal (ENSEMBLES) In winter (DJF) non of the 59 segments derived from 2,000 year paleo-simulations yield a positive trend of precipitation as strong as that observed. There is less than 5% probability that observed positive trends in winter be due to natural (internal + external) variability alone (with less than 5% risk). In spring (MAM), summer (JJA) and Annual trends externally forced changes are not detectable. However observed trends lie within the range of changes described by 10 climate change scenarios, indicating that also in the scenarios a systematic trend reflecting external forcing is not detectable (< 5% risk). In autumn (SON) the observed negative trends of precipitation contradicts the upward trends suggested by 10 climate change scenarios, irrespective of the observed dataset used.

43. 43 (Barkhordarian et al, Climate Dynamics 2013) Precipitation (over land of Mediterranean Sea) 1966-2005, CMIP3

44. 44 Changes in Large-scale circulation (SON)  Observed trend pattern shows areas of decrease in SLP over the Med. Sea and areas of increase in SLP over the northern Europe. Observed trend pattern of SLP in SON contradicts regional climate projections.  The mismatch between projected and observed precipitation in autumn is already present in the atmospheric circulation. Mean Sea-level pressure Projected GS signal pattern (RCMs) Observed trend pattern (1978-2009)

45. 45 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? 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