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How do we know that human influence is changing (regional) climate? Hans von Storch12 and Jonas Bhend1 1Institute for Coastal Research, GKSS Research Center, Geesthacht 2Meteorological Page 1 Institute, Hamburg University 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Questions about ongoing non-natural change • Global anthropogenic change An argument for efforts to mitigate climate change by diminishing global drivers (“political asset”) • Regional anthropogenic change– need to discriminate between global and regional drivers. An argument for efforts to mitigate regional change by diminishing regional drivers (“political asset”), AND an argument to implement adaptive measures to deal with changing risks and opportunities (“information for stakeholders”) Page 2 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Detection and attribution of ongoing change Page 3 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 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: 2541-2565 IDAG, 2005: Detecting and attributing external influences on the climate system. A review of recent advances. J. Climate 18, 1291-1314 Page 4 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Global Page 5 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 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:10.1029/2005GL025591 IDAG, 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 Page 6 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 The Rybski-et al. study Page 7 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 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 Page 8 - 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 5th Study Conference on BALTEX the various reconstructions. Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Among the last 16 years, 19912006, 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 (??) Page 9 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Joint unpublished work by Zorita, Stocker and von Storch, 2007 Counting extremely warm years Regional analysis by Eduardo Zorita. Giorgi-regions Top: AR(1)-memory Bottom: Number N of years in 1991-2006 with annual temperature T larger than maximum prior to 1991 (different time series lengths in different regions!) Page 10 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Zorita, pers. comm 5%-significant Success of detection a blending of strength of signal + length of record + strength of memory Page 11 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 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 quasirealistic climate models Page 12 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Trend in air temperature 1965-1994 1916-1945 Page 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 Signal or noise? 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 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. Page 14 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 “Guess patterns” The reduction of degrees of freedom is done by projecting the full signal S on 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. Example: guess pattern supposedly representative of increased CO2 levels When Gk orthonormal then k = STGk. Page 15 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Page 16 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 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 The attribution problem Attribution is considered to be obtained, when 1) the suspected link between forcing and response is theoretically established, and 2) 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. Page 17 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Attribution 2-patterns problem 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 • (Hegerl et al., 1997) Page 18 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 163-209, ISBN 3-540-65033-4 Zwiers, F.W., 1999: The detection of climate change. In: H. von Storch and G. Flöser (Eds.): Anthropogenic Climate Change. Springer Verlag, Example: Attribution Page 19 Attribution diagram for observed 50year trends in JJA mean temperature. 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. 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Attribution - plausibility From: Hadley Center, IPCC TAR, 2001 Page 20 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Regional: the Baltic Sea catchment Page 21 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 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. Page 22 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 The Baltic Sea Catchment Assessment: BACC Summary of BACC Results Baltic 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 Page 23 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 „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. Page 24 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 „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. Page 25 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007 Overall summary How do we know that human influence is changing (regional) climate? -Statistical comparison of ongoing change with distribution of “naturally” occurring changes – Detection: statistical proof. Attribution: statistical plausibility - ok für global and continental scale temp (IDAG, 2005). 2/3 of warming is very likely man-made. - Consistency of continental temp change with change in regions such as Baltic Sea catchment (temp and related variables; see next presentation by Jonas Bhend), but only part of regional change can be explained consistently by GHG effects. Page 26 5th Study Conference on BALTEX Kuressaare, Saaremaa, Estonia, 4-8 June 2007