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Long-term monitoring and the perspective of detection and attribution of long-term change

Biologische Anstalt Helgoland 18. September 2012. Long-term monitoring and the perspective of detection and attribution of long-term change. Hans von Storch and Kai Wirtz Institute of Coastal Research Helmholtz-Zentrum Geesthacht Germany. Change !. Change is all over the place,

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Long-term monitoring and the perspective of detection and attribution of long-term change

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  1. Biologische Anstalt Helgoland 18. September 2012 Long-term monitoring and the perspective of detection and attribution of long-term change Hans von Storch and Kai Wirtz Institute ofCoastal Research Helmholtz-Zentrum Geesthacht Germany

  2. 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.

  3. 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: is a stochastic system, which exhibits variations on time scales without an external and identifiable “cause”. (Hasselmann’s “Stochastic Climate Model”)

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

  5. First task: Describing change Observed values depend on the immediate environment of the location where the observation is made. This environment is subject to gradual and abrupt changes. Therefore such data often do not only reflect changes of the statistics but also other factors, such as observation method, practice, location, analysis method … Inhomogeneity is a key constraint, which is usually overseen by non-experts. Improved instruments and analysis introduces into data records such inhomogeneities (and thus, false signals); therefore satellite data as well as re-analyses are in most cases unsuitable for the assessment multi-decadal change

  6. Example of inhomogeneous data • Wind speed measurements • SYNOP Measuring net (DWD) • Coastal stations at the German Bight • Observation period: 1953-2005 This and the next 3 transparencies: Janna Lindenberg, HZG

  7. Inhomogeneity of wind data 1.25 m/s

  8. Inomogeneity of wind data

  9. The increase in damages related to extreme weather conditions is massive – but is it because the weather is getting worse? Losses from Atlantic Hurricanes “Great Miami”, 1926, Florida, Alamaba – damages of 2005 usage - in 2005 money: 139 b$ Katrina, 2005: 81 b$ 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

  10. First task: Describing change • For doing so, we need long time series of (ideally) unchanging quality (no improvement across time!) • This property is named “homogeneity”, the lack is “inhomogeneity”. • Thus, time series such as that one collected on Helgoland Roads are of utmost importance. • These data are successfully used to describe change, but formal detection and attribution studies are needed to assess the present change of North Sea hydro- and ecodynamics.

  11. Using Helgoland Roads data for determining change Schlüter et al. use Helgoland Roads and data 1975-2004. They find a regime shift in 1987/88 and conclude that this shift is driven by hydro-meteorological forcing Mostly Helgoland Roads data First EOF of a series of hydroand ecodynamial parameters Schlüter M.H., A. Merico, K.H. Wiltshire, W. Greve and H. von Storch, 2009: A statistical analysis of climate variability and ecosystem response in the German Bight, Ocean Dyn

  12. Using Helgoland Roads data for determining change • Schlüter’s result raises a number of additional questions: • Was this just an event in an unending sequence of naturally occuring “regime shifts”? (What “change character” has “regime shift”?) • What about the spatial representativity? (Merely a signal for Helgoland, for Deutsche Bucht, or for the North Sea, the world ocean?) • How is the signal related to other change in the region? • The results underline not only the importance of maintaining long-term monitoring programs but also the need of homogeneous (model-based) analysis of the changing regional conditions.

  13. Reconstruction of marine „weather“ in the past decades of years in in possible futures („scenarios“)

  14. Extreme wind events simulated compared to local observations simuliert

  15. Sea surface temperature Helgoland Roads Elke Meyer, pers. comm.

  16. The CoastDat-effort at the Institute for Coastal Research@hzg • Long-term, high-resolution reconstuctions (60 years) of present and recent developments of weather related phenomena in coastal regions as well as scenarios of future developments (100 years) • Northeast Atlantic and northern Europe • Assessment of changes in storms, ocean waves, storm surges, currents and regional transport of anthropogenic substances. • Extension to other regions and to ecological parameters. Applications • many authorities with responsibilities for different aspects of the German coasts • economic applications by engineering companies (off-shore wind potentials and risks) and shipbuilding company • Public information www.coastdat.de

  17. CoastDat allows for a regional contextualization of local Data (such as helgoland Roads – see Scharfe‘s talk) Variance 17% Variance 70%

  18. Climate Change of Scenarios 2030: temperature +1 ±0.4 degree; strong wind +2%±1% (winter); Precipitation –10% summer, +10% winter (±5%); 2085: temperatures +3 ±1.2 degree; strong wind +8%±4%; (winter) precipitation –30% summer, +30% winter (±10%);

  19. External users • More than 60 usersMostly in Europe (Germany)but also China and USA • Diverse users50% commerical (e.g., Swiss Re, Deltares, Flensburger Shipbuilding),25% authorities25% scientific users • Various applicationsOptimal ship designPlanning and design of offshore wind energyMarine energyAssessment of pollution risks and changes (chronic oil pollution, dispersion of lead)Security of shipping

  20. Using CoastDart wind for planning ofOff-shore wind parks

  21. Long-term ecosystem trends observed at Helgoland Algal Biomass (Chlorophyll-a)German Bight March 2003 Model constructed for CoastDateco, a reconstruction of ecological states for 1962-2011 MERIS data Model result (Chla)

  22. First building blocks for CoastDateco Variability in planktondynamicsat Helgoland Roads Model validationusing AWI/Senckenberg data Algal community structure adaptivecellsize Diatom biomass model data

  23. Change is mostly described • - Hydrodynamic/ meteorological change: 1948-today in space-time detail, model based analysis • Ecodynamic change: local time series, first building blocks of model based analysis • Ongoing monitoring in place (BSH, BAW, COSYNA etc.) • Is the change beyond normal variations? (Detection) • If so, what are the most plausible causes? (Attribution).

  24. Regional detection • Long records available in the German Bight, but mostly contaminated by non-climatic signals in ports and estuaries. • Increase of about 20 cm/100 a • No robust recent acceleration identified so far Sea level: Rise, intermittendly accelerated SST: warming, accelerated increase Storms: decadal variability Species: additional warm-water species, Loss of cold-water species No dedicated dection studies known, but more results may become available from the Mini-IPCC assessment NOSCCA due in 2014 (or so). Estimated regional sea level in the German Bight

  25. Regional attribution 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) For global and regional temperatures, this has been done, and the ongoing warming can not be explaining without considering elevated levels of GHG concentrations as a key cause.

  26. Regional attribution • Not systematically pursued on regional and local scales (see Mini-IPCC Report on Baltic Sea Climate Change knowledge, BACC). • Regional manifestations of climate change • enhanced concentrations of greenhouse gases • reduction of industrial aerosols • effect of changing land-use (e.g., urban effects) • So far no tools available for described of possible regional signals of change in recent decades related to land use change and diminished aerosol loads. • Local changes • - Import of alien species; pollution, fishing and other uses • - Changing morphodynamic conditions (storm surges in Hamburg, Bremen etc.)

  27. State of assessing regional change Describing change: Great scientific and operational progress Discrimination between normal and externally forced changes: Some scientific progress, but also claims-making without proper statistical background Attributing external causes : Very little scientific efforts, but lots of interest- and value-lead speculations reflecting the politicization of the environmental debate. (Some of which may be overcome, some is intrinsically political.) Maybe better results in theforthcoming NOSCCA report

  28. There is lots of change in the German Bight and in Northern Germany. It raises concern among public and stakeholders, generates excitement and premature finger-pointing to causes and causers in the public discourse. Thus, cool scientific analysis is needed. The latter needs more focus.

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