Detection of climate change and attribution to causes 26. September 2018 - Polish – German workshops for early career scientists „Advances in Marine and Quaternary Geosciences”, Sczcecin Detection of climate change and attribution to causes 30 min Based upon: Work done with Klaus Hasselmann, Eduardo Zorita, Armineh Barkhordarian, Armin Bunde, and Jonas Bhend Drawings by Michael Schenk © Hans von Storch

VON STORCH, Hans Climate researcher (in the field since 1971) Coastal climate (storms, storm surges, waves; North and Baltic Sea, North Atlantic, Yellow Sea); statistical analysis Director emeritus of the Institute of Coastal Research of the Helmholtz Zentrum Geesthacht, Germany Professor at Universität Hamburg External member of

deconstructing a given record 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

Detection and attribution 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

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

Change – a scientific challenge with societal significance For the societal debate, at least in the west, there are several questions, which need scientific answers, of significance: Is there a change ? What are the dominant causes for such a chance, and what are the expectations fo the future? 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).

Methodical issues Randomness Significant trends?

Noise as nuisance: masking the signal 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

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.

„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. 45 min‘s

Storm surges in Hamburg

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

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

The Rybski et al-approach 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), 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. 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

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

Observed temperature trends in the Baltic Sea region (1982-2011) Observed CRU, EOBS (1982-2011) 95th-%tile of „non-GS“ variability, derived from 2,000-year palaeo-simulations 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. 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) 31

Consistency of recent local change: Storm surges in Hamburg 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

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

Observed and projected temperature trends (1982-2011) 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). 40

Dimension of D&A 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).

Change – a scientific challenge with societal significance For the societal debate, at least in the west, there are several questions, which need scientific answers, of significance: Is there a change ? What are the dominant causes for such a chance, and what are the expectations fo the future? 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).