Utility of Detection and Attribution

1. Utility of Detection and Attribution Hans von Storch Institute for Coastal ResearchGKSS Research Center, Geesthacht, Germanyand CLISAP/KlimaCampus, Hamburg University

2. 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 (cf. Myles‘ „scepticism“)

3. „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.

4. „Significant“ trends Establishing the statistical significance of a trend may be 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 an ongoing trend, and- theoretical reasoning for driver-response dynamics, and- forecasts of future driver behavior.

5. Detection and attribution of non-natural ongoing change • Detection of the presence of non-natural signals: rejection of null hypothesis that recent trends are drawn from the distribution of trends given by the historical record. Statistical proof. • Attribution of cause(s): Non-rejection of the null hypothesis that the observed change is made up of a sum of given signals. Plausibility argument. • 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

6. Global The utility of global d&a is to clarify that the concept of GHG-related anthropogenic climate change is real. Conclusion from a successful d&a:The public is talking about a real effect.

7. Cases of Global Climate Change Detection Studies In the 1990s … weak, not well documented signals. Example: Near-globally distributed air temperature IDAG (2005), Hegerl et al. (1996), Zwiers (1999) In the 2000s … strong, well documented signals Examples: Rybski et al. (2006) Zorita et al. (2009) IDAG, 2005: Detecting and attributing external influences on the climate system. A review of recent advances. J. Climate 18, 1291-1314 Hegerl, G.C., H. von Storch, K. Hasselmann, B.D. Santer, U. Cubasch, P.D. Jones, 1996: Detecting anthropogenic climate change with an optimal fingerprint method. J. Climate 9, 2281-2306 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 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 Zorita, E., T. Stocker and H. von Storch: How unusual is the recent series of warm years? Geophys. Res. Lett.

8. Global mean air temperature Statistics of ΔTL,m, which is the difference of two m-year temperature means separated by L years. Temperature variations are modelled as Gaussian long-memory process, fitted to various reconstructions of historical temperature (Moberg, Mann, McIntyre) The Rybski et al- approach Historical Reconstructions – their significance for “detection”

9. Historical Reconstructions – their significance for “detection” Temporal development of Ti(m,L) = Ti(m) – Ti-L(m) divided by the standard deviation of the m-year mean reconstructed temp record for m=5 and L=20 (top), andfor m=30 and L=100 years. The thresholds R = 2, 2.5 and 3σ are given as dashed lines. Rybski et al., 2006

10. Counting extremely warm years • Among the last 17 years, 1990-2006, there were the 13 warmest years since 1880 (i.e., in 127 samples) – how probable is such an event if the time series were stationary? • Monte-Carlo simulations taking into account serial correlation, either AR(1) (with lag-1 correlation ) or long-term memory process (with Hurst parameter H=0.5+d). • Best guesses •  0.85 d  0.45 (very uncertain) Zorita, et al 2009

11. Regional:Intention: Preparation and design of measures to mitigate expected adverse effects of climate change. Problems: high variability, little knowledge about natural variability; more human-related drivers (e.g. industrial aerosols, urban effects)

12. Log-probability of the event E that the m largest values of 157 values occupy the last17 places in long-term autocorrelation synthetic series Zorita, et al., 2009 Derived from Hadley Center/CRU data for „Giorgi bins“.

13. For regional mean temperatures we have a signal and attribute it to GHGs (see also Jonas‘ talk). What about precip? This information may be relevant for a few sectors, such as agriculture.

14. Regional DJF precipitation Δ=0.05%

15. Regional JJA temperatures

16. Consistency analysis: Baltic Sea catchment • Consistency of the patterns of model “predictions” and recent trends is found in most seasons. • A major exception is precipitation in JJA and SON. • The observed trends in precipitation 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).

17. Local change – another major driver: urban warming Gill et al.,2007

18. Local Station data Jones+Moberg until 2000, afterwards NASA-GISS

19. Conclusions Based on my personal experience in interacting with public, media and policymakers (German bias; all levels): D&A is confronted with requests from different stakeholders, with stakes at different geographical scales, woldviews and perceptions.

20. „Global clients“ want to have proof that the basic concept of man-made global climate change is real. The best answer for this client is an answer which is very robust and not critically dependent on models. – Mostly done. „Regional clients“ want to have best guesses of the foreseeable future, in order to institute adaptive measures – on the scale of medium-size catchment basins not many clear results. „Local clients“ want know how global and local drivers shape the future of the ocal environment, and which measures for mitigation are available, and which levels of adaptation are required. – very little done.

21. Storm surges in Hamburg

22. Sturmfluten in der ElbeVergangenheit Differenz Scheitelhöhen Hamburg - Cuxhaven Sturmfluten in der Elbe deutlich erhöht seit 1962 – aufgrund wasserbaulicher Maßnahmen, vor allem wegen der Verkürzung der Deichlinie