1. 1 Detection and Attribution of Climate Change Ben Santer Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory, Livermore, CA 94550 Email: santer1@llnl.gov American Statistical Association: A Statistical Consensus on Global Warming Boulder, Colorado. Oct. 26 th, 2007

2. 2 Why is detection and attribution work important? l It is another form of model evaluation l Successful simulation of historical changes in climate enhances confidence in projections of future climate change l In an environment where there is still political debate regarding the reality of a human effect on global climate, it is imperative to have “sound science” on the nature and causes of climate change

3. 3 Structure of talk l Introduction l Charge to discussion leaders  Where do you believe a consensus has formed?  Where can consensus be expected in the near future?  Where can statistical science provide further assistance to future research? l Conclusions

4. 4 Introduction: Definition of “consensus” l Con sen susNoun. 1. “A view or stance reached by a group as a whole or by majority will.” 2. “General agreement.” (American Heritage College Dictionary) From the Latin “consentire”, to agree.

5. 5 Introduction: Do the IPCC AR4 findings constitute a “consensus” of climate science experts? l 152 Coordinating Lead Authors and Lead Authors l Authors were from over 30 countries l Drafts of Working Group I Report were subjected to two rounds of review and revision l Report was reviewed by over 650 individual experts, as well as by governments and international organizations l In total, over 30,000 written comments were received l Summary for Policymakers was approved (line-by-line) by officials from 113 governments l Report outline in Nov. 2003. Acceptance of SPM and underlying chapters in Feb. 2007

6. 6 Structure of talk

7. 7 Where do you believe a consensus has formed?

8. 8 “Unequivocal” warming of the climate system l The oceans and land surface have warmed l The troposphere has warmed l Atmospheric water vapor has increased l Sea level has risen l Glaciers have retreated over most of the globe l Snow and sea-ice extent have decreased in the Northern Hemisphere l Individually, these changes are consistent with our scientific understanding of how the climate system should be responding to human influences

9. 9 Where do you believe a consensus has formed?

10. 10 The scientific evidence for a human “fingerprint” on global climate has strengthened over time “The balance of evidence suggests a discernible human influence on global climate” “There is new and stronger evidence that most of the warming observed over the last 50 years is attributable to human activities” “Most of the observed increase in globally averaged temperatures since the mid-20 th century is very likely* due to the observed increase in anthropogenic greenhouse gas concentrations”

11. 11 Observations Red: Model “All forcing” results Blue: Model “Solar+volcanic” results Natural factors alone cannot explain the recent warming of the Earth’s surface Black: Observed surface temperature changes

12. 12 l Strategy: Search for a computer model-predicted pattern of climate change (the “fingerprint”) in observed climate records l Assumption:Each factor that influences climate has a different characteristic signature in climate records l Method:Standard signal processing techniques l Advantage:Fingerprinting allows researchers to make rigorous tests of competing hypotheses regarding the causes of recent climate change What is “climate fingerprinting”?

13. 13 Human-caused fingerprints have been identified in many different aspects of the climate system Tropospheric temperatures Tropopause height Stratospheric temperaturesSurface specific humidity Ocean temperatures Zonal-mean rainfall Near-surface temperature Sea-level pressure Water vapor over oceans Continental runoff Atmospheric temperature

14. 14 We have made considerable progress in defining the “fingerprints” of different forcings 1. Solar 3. Well-mixed greenhouse gases 5. Sulfate aerosol particles 2. Volcanoes 4. Ozone 6. 1 st five factors combined Height (km) Pressure (hPa) °C/century Santer et al., Climate Change Science and Policy, 2007

15. 15 Fingerprint detection explained pictorially…. Time-varying observed patternsTime-varying control run patterns t=1 t=2 t=3 t=4 t=n t=1 t=2 t=3 t=4 t=n Projection onto model fingerprint Signal and noise time series Signal-to-noise ratios Projection onto model fingerprint Model fingerprint

16. 16 l Model-based estimates of natural internal variability are an integral component of D&A research l Why do we rely on models for these estimates?  They can be used to perform the control experiments that we can’t conduct in the real world l Why is it difficult to estimate natural internal variability from observations?  We want to estimate noise on multi-decadal to century timescales  Most observational records are too short for this purpose  Signal and noise are convolved – difficult to achieve unambiguous partitioning Estimating the “noise” of natural internal variability

17. 17 Optimal fingerprinting: A brief example A) MODELS: WATER VAPOR “FINGERPRINT”B) MODELS: LEADING PATTERN OF “CLIMATE NOISE” C) OBSERVATIONS: CHANGE IN WATER VAPOR (1988-2006) EOF loading (A-B) or total linear change in W o in kg/m 2 (C)

18. 18 D&A in a “multi-model” framework: Use of multiple models to estimate fingerprints and noise

19. 19 Estimating signal-to-noise ratios and “detection times”

20. 20 Estimating signal-to-noise ratios and “detection times”

21. 21 For water vapor, there is no evidence that “climate noise” is systematically underestimated in IPCC AR4 models Average model water vapor variability is slightly larger than in observations

22. 22 Structure of talk

23. 23 Where can consensus be expected in the near future? 1. We will have some form of “operational attribution” capability 2. D&A studies will routinely use information from large, multi-model ensembles (and will make more intelligent use of this information) 3. Structural uncertainties in observations will become an integral part of D&A research 4. We will have formally identified anthropogenic fingerprints  At sub-continental spatial scales  In variables more relevant to climate impacts  In plant and animal distributions and abundances 5. Fingerprinting will be feasible with increasingly shorter (< 30-year) observational records

24. 24 Structure of talk

25. 25 Where can statistical science provide further assistance to future D&A research? 1. In assessing sensitivity of D&A results to “model quality” 2. By contributing state-of-the-art space-time modeling approaches to “fill in the gaps” in observational datasets with sparse, space- and time-varying coverage 3. By helping to provide a better assessment of the “trade-offs” between ensemble size (for any individual model) and the number of models contributing to a multi- model average 4. By contributing improved methods for assessing whether human influences have modulated the statistical behavior of existing modes of natural variability 5. By bringing statistical rigor to regression-based predictions of hurricane activity 6. Better constraining the Transient Climate Response obtained from D&A methods

26. 26 Future research I: Sensitivity of D&A results to “model quality” l A number of recent studies have attempted to weight model projections of future climate change (generally by model performance in simulating present-day climatological means) l Thus far, no attempt to use any form of weighting in multi-model D&A work l All multi-model D&A studies to date are “one model, one vote” l Are results from current multi-model D&A studies biased by inclusion of information from models with noticeable deficiencies in simulation of variability?

27. 27 Future research I: Sensitivity of D&A results to “model quality”

28. 28 l Concerns have been expressed about the reliability of model-based estimates of the natural variability of ocean temperatures (e.g., Lyman et al., 2006) l Casts doubt on reliability of D&A results obtained with ocean temperatures l How do we address these concerns?  Better quantification of uncertainties in observed variability estimates (e.g., AchutaRao et al., 2007). Involves use of both physical models (ocean data assimilation products) and statistical models  Identify and adjust for the effects of instrumental biases in different ocean observing systems (Church et al., in preparation)  Revisit ocean D&A studies with improved, bias-corrected observational data  Use proxy data to obtain better constraints on model estimates of natural internal variability (e.g., multi-century SST reconstructions from corals) Future research II: Improvement of observational datasets with sparse, space- and time-varying coverage

29. 29 19551965 19751985 Number of Observations at the 100m Level Future research II: The ocean observing network has changed dramatically over time Source: AchutaRao et al., JGR (2006)

30. 30 Future research II: Do models systematically underestimate “observed” ocean temperature variability? Time-variability of “complete” ocean temperature data (°C) Time-variability of “sub-sampled” ocean temperature data (°C) Source: AchutaRao et al., PNAS (2007) Models with volcanoes Models without volcanoes

31. 31 Future research II: Sampling model data at locations of ocean observations improves model-data agreement Time-variability of “complete” ocean temperature data (°C) Time-variability of “sub-sampled” ocean temperature data (°C) Source: AchutaRao et al., PNAS (2007) Models with volcanoes Models without volcanoes

32. 32 Future research II: Implications of observational uncertainty for D&A research From Church et al. (in preparation)

33. 33 Conclusions l We have identified human “fingerprints” in a number of different aspects of the climate system:  Temperature (land and ocean surface; stratosphere and troposphere; zonal-mean profiles through the atmosphere; upper 700 meters of the ocean; ocean heat content; height of thermal tropopause)  Atmospheric circulation (mean sea-level pressure)  Moisture-related variables (zonal-mean rainfall; surface specific humidity; total water vapor over oceans; continental runoff) l The climate system is telling us a physically- and internally-consistent story l From my own biased personal perspective, the collaboration between statisticians and climate scientists in the area of D&A research has been very successful. These interactions have been facilitated by:  IDAG (International Detection and Attribution Group)  IMSC (International Meetings on Statistical Climatology)

34. 34 A brief history of D&A research: Some important milestones 19791980198119821983198419851986 1987198819891990199119921993 1994 19951996199719981999200020012002 Publication of IPCC SAR; Fingerprinting with atmospheric temperature and SAT Publication of first paper on the theory of optimal detection Publication of IPCC First Assessment Report Publication of IPCC TAR; Fingerprinting with ocean heat content 200320042005200620072008 Publication of IPCC FAR; Fingerprinting with zonal- mean rainfall, water vapor, and surface specific humidity Fingerprinting with tropopause height, sea-level pressure, MSU T4 and T2 temperatures Fingerprinting with continental runoff; CCSP Report 1.1 resolves MSU problem First use of Bayesian methods in D&A studies Application of pattern correlations and multi- variable methods to D&A problem Introduction of space-frequency D&A approach; Detection of GS fingerprint in SAT First work on S/N ratios in climate model data First application of optimal detection method to problem of detecting human influences on climate Recognition that “Optimal detection is regression”; First use of space-time D&A methods First assessment of “fractional attributable risk” for an extreme event Introduction of “multi-pattern” fingerprinting All sins of omission or commission are unambiguously attributable to Ben Santer