Urban climate change – the story of several drivers. Change! Detection and attribution Issues No systematic results for urban conglomerates known to me Discussion May 27, URC 2014 Urban Regions under Change: towards social-ecological resilience, Hamburg Hans von Storch

Which „signals“ make up these records? Seasoonal precipiitation (mm) in HH-Fuhlsbüttel (Data: Deutscher Wetterdienst, 2008; Source: Schlünzen et al., 2010) y=36 mm/century y=28 mm/century y=-10 mm/century y=8 mm/century

Change ! Change is all over the place, Change is ubiquitous. What does it mean? Anxiety; things become more extreme, more dangerous; our environment is no longer predictable, no longer reliable. Change is bad; change is a response to evil doings by egoistic social forces. In these days, in particular: climate change caused by people and greedy companies.

Change ! Change is all over the place, Change is ubiquitous. What does it mean? There are other perceptions of change: it provides opportunities; it is natural and integral part of the environmental system we live in. The environmental system is a system with enormous many degrees of freedom, many non-linearities – is short: a stochastic system, which exhibits variations on all time scales without an external and identifiable “cause”. (Hasselmann’s “Stochastic Climate Model”)

„Significant“ trends Often, an anthropogenic influence is claimed 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.

„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.

Wind speed measurements  SYNOP Measuring net (DWD)  Coastal stations at the German Bight  Observation period: First task: Describing change This and the next 3 transparencies: Janna Lindenberg, HZG

1.25 m/s Example: coastal wind data

First task: Inhomogeneity of data

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)

“Detection” - Assessing change if consistent with natural variability (does the explanation need invoking external causes?) “Attribution” – If the presence of a cause is “detected”, determining which mix of causes describes the present change best 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. Detection and Attribution

12 Anthropogenic Natural Internal variability Detection and attribution Attribution Anthropogenic Natural Observations External forcings Climate system Detection Internal variability

Test of the nullhypothesis: „considered climate signal is consistent with natural climate variability“ S t ~ P[µ o, ∑ o ] with S t representing the signal to be examined, whether it is consistent with undisturbed statistics P[µ o, ∑ o ]. The of the distribution of the present climate is given by parameters µ o and ∑ o. Problem is to determine S t and its distribution P. Detection

After we have found a signal to lie outside the range of natural variations, the question arises whether this signal can be causally related to an external factor. Usually, there are many factors, but climatological theory reduces the candidates to just a few (e.g., urban effects, greenhouse gases, volcanic aerosols, solar effects). Then, that mix of processes is attributed to the signal, which fits best to the a-priori assumed link between cause and effect. This may take the form of a best-fit or as the result of a non- rejection of a null hypothesis. Attribution

Detection and attribution 15

Storm surges in Hamburg

Difference in storm surge height between Cuxhaven and Hamburg Height massively increased since 1962 – after the 1962 event, the shipping channel was deepened and retention areas reduced. Storm surges in the Elbe estuary

Observed seasonal and annual area mean changes of 2m temperature over the period in of 2m temperature over the period in comparison with GS signals Observed trends of 2m temperature ( ) Projected GS signal patterns (time slice experiment) 23 AOGCMs, A1B scenario derived from the CMIP3 The spread of trends of 23 climate change projections 90% uncertainty range of observed trends, derived from 10,000-year control simulations  Less than 5% probability that observed warming can be attributed to natural internal variability alone  Externally forced changes are detectable in all seasons except in winter 2m Temperature in the Med Sea Region Barkhordarian, 2013

90% uncertainty range, 9000-year control runs Spread of trends of 22 GS signals Spread of trend of 18 GS signal Spread of trend of CRU3 and GPCC5 observed trends  There is less than 5% probability that observed trends in DJF, JFM, FMA, ASO, SON are due to natural (internal) variability alone.  Externally forced changes are significantly detectable in winter and autumn intervals (at 5% level) Med Sea region: Precipitation over land Barkhordarian, 2013

Climate Change in urban conglomerates A manifestation of three anthropogenic factors 1.Global warming (related to elevated greenhouse gas concentrations) 2.Regional change (related to changing anthropogenic aerosol loads) 3.Local change (related to changing urban size and structure) 20 Bechtel and Schnmidt, 2011

21 Rostock Seasonal cycle of urban heat differences in Rostock: Rostock-Holbeinplatz (Ho) vs. Rostock-Stuthof (St), Rostock-Warnemünde (War) and Gülzow (Gü) Richter et al., 2011

Stockholm 22 Diurnal cycle of the heat island effect in different seasons Differences between Stockholm-Bromma and Tullinge-Air-port. Richter et al., 2011

Increase of mean temperature 23 Mean temperatures in Rostock-Warnemünde and Stockholm Richter et al., 2011 Warming due to urban effects or global warming?

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

Discussion 1.Climate is changing. 2.In cities there are at least three drivers – the local manifestation of global (GH) and regional (aerosols) change, and the changing land use in cities. 3.Many studies on the global effect exist, some on the urban effect, no studies on the regional effect of reduced emissions of aerosols (in Northern Europe) 4.Global and local effects seem to simply add. 5.No efforts are known to me to disentangle the effects on given temperature records of cities. 6.In Hamburg the hat island effect is up to 1K and more, in Rostock up to 0.5K and more and in Stockholm up to 1K and more. 25

Strategic issues 26 1.Since several factors affect urban climate, a combination of mitigation measure may be available to reduce the impact of global warming. Namely - reducing of global emissions - retracting previously formed urban heat islands 2.However, global growth together with global warming may exacerbate the situation, when managing growth fails 3.What is needed of a scientific policy advice - monitoring of urban climate change - separation of effects, due to global, regional and local effects - construction of realistic scenarios, which describe the effect of possible future urban planning.

Falsification Which observations in the coming 5/10 (?) years would lead to reject present attributions? Suggestion: Formulate and freeze NOW falsifiable hypotheses, and test in 5/10 (?) years time – using the independent data of the additional years. Outlook: Urban change, detection and attribution

Which „signals“ make up these records? Seasonal precipiitation (mm) in HH-Fuhlsbüttel (Data: Deutscher Wetterdienst, 2008; Source: Schlünzen et al., 2010) y=36 mm/century y=28 mm/century y=-10 mm/century y=8 mm/century Which „signals“ make up these records? I don‘t know