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How do we know that human influence is changing (regional) climate?

How do we know that human influence is changing (regional) climate?. Hans von Storch Institute for Coastal Research, GKSS Research Center, Geesthachtand Meteorological Institute, Hamburg University. Detection and attribution of ongoing change. Omstedt, 2005.

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How do we know that human influence is changing (regional) climate?

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  1. How do we know that human influence is changing (regional) climate? Hans von Storch Institute for Coastal Research, GKSS Research Center, Geesthachtand Meteorological Institute, Hamburg University Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  2. Detection and attribution of ongoing change Omstedt, 2005 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  3. 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. • Different definition: „Detection is the process of demonstrating than an observed change is significantly different (in a statistical sense) than can be explained by natural internal variability“ (IPCC, TAR, 2001; see also IDAG, 2005) • 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: 2541-2565IDAG, 2005: Detecting and attributing external influences on the climate system. A review of recent advances. J. Climate 18, 1291-1314 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  4. Global Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  5. Cases of Global Climate Change Detection Studies … of strong, well documented signals Examples: 1) Rybski et al. (2006) 2) Counting recent extremes … of weak, not well documented signals. Example: Near-globally distributed air temperature IDAG (2005), Hegerl et al. (1996), Zwiers (1999) 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:10.1029/2005GL025591IDAG, 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 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  6. The Rybski-et al. study 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 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  7. 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 - Statistics of ΔT(m,L) which is the difference of two m-year NH temperature means, separated by L years. - Temperature variations are modeled as Gaussian long-memory process, fitted to the various reconstructions. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  8. Counting extremely warm years • Among the last 16 years, 1991-2006, there were the 12 warmest years since 1881 (i.e., in 126 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 •  0.8 H = 0.5 + d  0.5+0.3 (??) Joint unpublished work by Zorita, Stocker and von Storch, 2007 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  9. How do we determine the control climate? In general, the data base for the “control”/undisturbed climate is not good: • With the help of the limited empirical evidence from instrumental observations, possibly after suitable extraction of the suspected „non-natural“ signal. • By projection of the signal on a proxy data space, and by determining the stats of the latter from geoscience indirect evidence (e.g., tree rings). • By accessing long „control runs“ done with quasi-realistic climate models Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  10. Signal or noise? Trend in air temperature 1965-1994 1916-1945 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 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  11. Reducing the degrees of freededom Specific problem in climate applications: usually very many (>103) degrees of freedom, but the signal of change resides in a few of these degrees of freedom. Example: Signal = (2, 0, 0, ...0) with all components independent. Power of detecting the signal, depends on degrees of freedom. Thus, the dimension of the problem must be reduced before doing anything further. Usually, only very few components are selected, such as 1 or 2. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  12. “Guess patterns” The reduction of degrees of freedom is done by projecting the full signal S one or a few several “guess patterns” Gk, which are assumed to describe the effect of a driver. S = k k Gk + n with n = undescribed part. When Gk orthonormal then k = STGk. Example: guess pattern supposedly representative of increased CO2 levels Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  13. 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 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  14. Optimizing s/n ratio Optimization of the expected signal to noise ratio: with the inverse covariance matrix of the internal climate variability. 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 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  15. The attribution problem • Attribution is considered to be obtained, when • the suspected link between forcing and response is theoretically established, and • the data do not contradict that k=1 in the assumed representation S = k k Gk + n. • A contradiction prevails if the null hypothesis “k=1” is rejected. • Thus, a non-contradiction is a plausibility-argument. It may be due to a too small data base. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  16. Attribution • 2-patterns problem (Hegerl et al. 1997) • guess patterns for climate change mechanisms taken as first EOFs of a climate change simulation on that mechanism. • only CO2 increase • increase of CO2 and industrial aerosols as well. • orthogonalisation of the two patterns • estimation of natural variability through GCM control simulations done at MPI in Hamburg, GFDL in Princeton and HC in Bracknell. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  17. Example: Attribution Attribution diagram for observed 50-year trends in JJA mean temperature. 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 The ellipsoids enclose non-rejection regions for testing the null hypothesis that the 2-dimensional vector of signal amplitudes estimated from observations has the same distribution as the corresponding signal amplitudes estimated from the simulated 1946-95 trends in the greenhouse gas, greenhouse gas plus aerosol and solar forcing experiments. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  18. Attribution - plausibility From: Hadley Center, IPCC TAR, 2001 Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  19. Regional: the Baltic Sea catchment Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  20. The Baltic Sea Catchment Assessment: BACC An effort to establish which knowledge about anthropogenic climate change is available for the Baltic Sea catchment. Working group BACC of GEWEX program BALTEX. Approximately 80 scientist from 10 countries have documented and assessed the published knowledge. Assessment has been accepted by intergovernmental HELCOM commission as a basis for its future deliberations. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  21. The Baltic Sea Catchment Assessment: BACC • Summary of BACC ResultsBaltic Area Climate Change Assessment • Presently a warming is going on in the Baltic Sea region. • No formal detection and attribution studies available. • BACC considers it plausible that this warming is at least partly related to anthropogenic factors. • So far, and in the next few decades, the signal is limited to temperature and directly related variables, such as ice conditions. • Later, changes in the water cycle are expected to become obvious. • This regional warming will have a variety of effects on terrestrial and marine ecosystems – some predictable such as the changes in the phenology others so far hardly predictable. BACC Group: Assessment of climate change for the Baltic Sea basin, Springer-Verlag, in press Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  22. „Significant“ trends • Often,an anthropogenic influence is assumed to be found when trends are found to be „significant“. • In many cases, the tests for assessing the significance of a trend are false as they fail to take into account serial correlation. • 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 – 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. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  23. „Significant“ trends Establishing the statistical significance of a trend is 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 change. Thus, claims for extension of present trends into the future require- empirical evidence for ongoing trend, and- theoretical reasoning for driver-response dynamics, and- forecasts of future driver behavior. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  24. Overall summary • How do we know that human influence is changing (regional) climate? • Statistical analysis of ongoing change with distribution of “naturally” occurring changes – detection, statistical proof.- ok für global and continental scale temp. • Consistency of continental temp change with change in regions such as Baltic Sea catchment (temp and related variables; see Jonas’ presentation) Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  25. Overall summary • How do we know that human influence is changing (regional) climate? • Attribution (of causal drivers) is a plausibility argument: determine consistency of ongoing change with expected changes. • Done for global and continental scale temp (and related) variables (see IDAG). • First efforts on regional scales (see JonasÄ’ presentation). Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

  26. Workshop on added vales of regional climate models and detection and attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg

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