5 December 2016 - "Expert Forums on Atmospheric Chemistry" of VDI, DECHEMA and GDCh on "New and emerging technologies: Impact on air quality and climate", Frankfurt Hans von Storch: Deconstruction of anthropogenic climate change: Manifestation, detection, attribution Based upon work done with Klaus Hasselmann, Eduardo Zorita, Armineh Barkhordarian, and Jonas Bhend

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 for the future? Which consequences does this change have for people, society and ecosystems? In this talk, 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.

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

„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

Michael Schrenk, © von Storch, HZG

Losses from Atlantic Hurricanes

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

Michael Schrenk, © von Storch, HZG

Losses from Atlantic Hurricanes Is the massive increase in damages attributable to extreme weather conditions? Losses from Atlantic Hurricanes Estimation of damage if presence of people and values along the coast would have been constant – the change is attributable to socio-economic development 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

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

… 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

Clustering of warmest years Counting of warmest years in the record of thermometer-based estimates of global mean surface air temperature: In 2007, it was found that among the last 17 years (since 1990) there were the 13 warmest years of all years since 1880 (127 years). For both a short-memory world (𝛼=0.85) and for a long-memory world (d = 0.45) the probability for such an event would be less than 10-3. Thus, the data contradict the null hypothesis of variations of internal stationary variability 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,

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

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) 18

Michael Schrenk, © von Storch, HZG

Michael Schrenk, © von Storch, HZG

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

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. 22

patterns (RCMs) patterns (CRU) Projected GS signal patterns (RCMs) Observed trend patterns (CRU) 23

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).

Michael Schrenk, © von Storch, HZG

Solar surface irradiance in the Baltic Sea Region 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) 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

Michael Schrenk, © von Storch, HZG

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).

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).

Discussion: Attribution Attribution needs guess patterns describing the expected effect of different drivers. 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. 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 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.

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? 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).