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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, Germany and 2Meteorological Page 1 Institute, Hamburg University, Hamburg, Germany UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Person: Hans von Storch Hans von Storch • director of Institute for Coastal Research @ GKSS • professor for Meteorology at the Meteorological Institute @ U Hamburg • author of „Statistical Analysis in Climate Research“ @ Cambridge U Press (with Francis Zwiers) Page 2 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA The Institute for Coastal Research@gkss An institute of the GKSS Research Center near Hamburg. • Home of international project offices of LOICZ and BALTEX • Shareholder of German Climate Computer Center (DKRZ) Joint programs with • Alfred Wegener-Institute for Polar and Marine Research (Bremerhaven etc.) • Center for Marine and Atmospheric Sciences (ZMAW), which encompasses Max-Planck Institute for Meteorology and geoscience institutes of the University of Hamburg Major focus: Analysis of past, ongoing and possible future regional climate conditions and climate impact, specifically in Northern Europe and marine environments. Mainly: WIND and related variables (storm surges, waves) Page 3 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Questions about ongoing non-natural change • Global anthropogenic change An argument for efforts to mitigate climate change by diminishing 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”), or an argument to implement adaptive measures to deal with changing risks and opportunities (“information for stakeholders”) Page 4 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Detection and attribution of ongoing change Page 5 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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 Page 6 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Global Page 7 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Cases of Global Climate Change Detection Studies … of strong, well documented signals Examples: Rybski et al. (2006) 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 8 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA The Rybski-et al. study Page 9 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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. 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 11 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Joint unpublished work by Zorita, Stocker and von Storch, 2007 Counting extremely warm years 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 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Trend in air temperature 1965-1994 1916-1945 Page 13 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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? 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 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA “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. Example: guess pattern supposedly representative of increased CO2 levels When Gk orthonormal then k = STGk. Page 15 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Page 16 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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 Optimization of the expected signal to noise ratio: ^ 1 NN Gk Gk with the inverse covariance matrix of the internal climate variability. Page 17 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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 Optimizing s/n ratio 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. 3) A contradiction prevails if the null hypothesis “k=1” is rejected. 4) Thus, a non-contradiction is a plausibility-argument. It may be due to a too small data base. Page 18 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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. Page 19 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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 20 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. UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Attribution - plausibility From: Hadley Center, IPCC TAR, 2001 Page 21 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Regional: the Baltic Sea catchment Page 22 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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 23 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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 24 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA „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 25 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA „Significant“ trends Establishing the statistical significance of a trend is a 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 ingoing trend, - theoretical reasoning for driver-response dynamics, - forecasts of future driver behavior. Page 26 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Consistency analysis:attribution without detection The check of consistency of recent and ongoing trends with predictions from dynamical (or other) models represents a kind of „attribution without detection“. This is in particular useful, when time series of insufficient length are available or the signal-to-noise level is too low. The idea is to estimate the driver-related change E from a (series of) model scenarios (or predictions), and to compare this “expected change” E with the recent trend R. If R E, then we may conclude that the recent change is not due to the changing driver, at least not completely. Page 27 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Measures of similarity Page 28 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Consistency analysis: Seasonal precip in the Baltic Sea catchment Example: Changing DJFmean precipitation in the Baltic Sea catchment Trend of precip according to different data sources. Page 29 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Consistency analysis Expected signals E • six simulations with regional coupled atmosphere-Baltic Sea model RCAO (Rossby-Center, Sweden) • three simulations run with HadCM3 global scenarios, three with ECHAM4 global scenarios; 2071-2100 • two simulation exposed to A2 emission scenario, two simulations exposed to B2 scenario; 2071-2100 • two simulations with present day GHG-levels; 1961-90 • Climate change in the four scenarios relatively similar. Page 30 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Consistency analysis Page 31 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Consistency analysis Patterns correlations between “observed” (CRU) trends in DJF seasonal precipitation in the Baltic Sea catchment and “expected” signals derived from scaled RCM changes. Global model scenario Pattern correlations Pattern correlations without NAO A2 0.83 0.75 B2 0.75 0.64 A2 0.85 0.75 B2 0.84 0.74 HadAM ECHAM The pattern correlations are all significantly larger than pattern correlations between random combinations of trends. Page 32 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Consistency analysis Ratio of intensities between “observed” (CRU) trends in DJF seasonal precipitation in the Baltic Sea catchment and “expected” signals derived from scaled RCM changes. All model predictions result in too large trends for the past years. When taking out the NAO the situation slightly improves. Global model scenario Intensity-ratio R/E Intensity-ratio without NAO A2 2.96 2.53 B2 4.50 3.98 A2 1.94 1.57 B2 2.50 2.07 HadAM ECHAM Page 33 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Consistency analysis: Baltic Sea catchment 1. In DJF: patterns of trends in “observed” CRU data consistent with patterns of trends predicted by RCAO-scenario. 2. Intensity of trends in 1976-2005 considerably larger than predicted by RCAO scenarios. 3. When talking out the NAO, the dominant pattern of natural variability, the over-prediction of the intensities is somewhat smaller. 4. Possible causes: - scenarios inappropriate (false) - drivers other than CO2 at work (industrial aerosols?) - natural variability much larger than signal (signal-to-noise ratio 0.2-0.5). 5. Similar situation in spring. 6. No consistency in summer and fall. Page 34 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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) Page 35 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA 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. Page 36 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop questions Page 37 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: How do you define change (e.g. time scale, magnitude, direction, significance of change, what signal to noise ratios are you used to working with)? „Change“ is any change of the statistics of any climate and climate impact variable in the course of time. We distinguish between natural change and anthropogenic change; often, however, “change” is tacitly assumed to be anthropogenic – but the IPCC is careful in its wording in this respect. A risk of 5% is usually considered sufficiently small to allow for a rejection of a null hypothesis. Page 38 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: What are the most important changes being investigated in your field and why - what is the motivation? This depends on the view point. Scientists interested in climate dynamics focus on variables which may be considered as proxies for the global climate state. Stakeholders interested in pushing for emission policies ask for changes of global distributions of local weather phenomena (extremes). Scientists interested in specific climate impact questions, such as storm surge statistics, deal often with the infrequent but impactintense regional and local events. Ditto stakeholders responsible for reduction of vulnerability and adaptation in general. Page 39 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: Does your science deal with changes in extremes or changes in average behavior? Both. In general, large scale dynamics are more important than smaller scale dynamics (downscaling paradigm). Since extremes take place on smaller scales, they are usually less important for climate dynamics. For climate impact, for course, smaller scales and thus extremes are very important. Page 40 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: What are the most important positive and negative feedback loops and what methods do you use to identify feedbacks? Many. They are explicitly modeled in GCMs. Models of strongly reduced complexity are interesting and exciting but fail to acknowledge the presence of ubiquitous noise. Page 41 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: How do you treat non-linearities? Even though the climate system is highly non-linear, “interesting” nonlinearities, such as jumps, regime changes, and hysterises, are strongly masked by the stochastic character (born from the presence of ubiquitous smaller non-linearities). In case of changes such as the presence or absence of sea ice or ice-caps such non-linearities may become dominant. The non-linearities are usually considered in the framework of quasi-realistic models (GCMs) – model as may details as you can afford. Page 42 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: When does the delta change approach (or incremental change approach) fail? For instance, when zero/positive variables are involved, such as sea ice cover. Page 43 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: In approaching the problem - do you typically collect more data, pool data, use simulation approaches, or some combination thereof? Pool existing data, simulation approaches with quasi-realistic models; Combining theoretical knowledge (models) with empirical evidence (data)- data assimilation. Page 44 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: When you get a final estimate of change, how do you describe it? With what confidence? … with the caveat that the estimate of natural variability may be incomplete. Page 45 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: How do you establish design criteria (e.g. high-tides with a 100 year return period, wind loads on structures, etc.) in the midst of change, if applicable? Depends on life-time of investment. Provide a range of perspectives for the relevant future, in the form of scenarios. Page 46 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Workshop-questions Question: How do you deal with model outputs from other fields, if they are used (i.e. GCMs)? We use the output of economic wisdom in the form of economic scenarios. Usually, we just believe them as reasonable. Page 47 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA Page 48 UNESCO.IHP workshop "Methods of detection and analysis of change and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA