How Can Citizens Evaluate Scientific Evidence?

1. How Can Citizens Evaluate Scientific Evidence? With application to the “Global Warming” debate

2. I. What is Science? • This is a major argument in many political disputes • Abortion (viability tests, heartbeats, sonograms, brain waves, etc.) • Environmental Issues • Education (Use of standardized tests, teaching evolution and geology) • National Security (Effects of nuclear weapons esp. nuclear winter, likely casualties from BW use, etc) • My approach: Recount the philosophy of science in order to discover “rules” for • Separating science from pseudo-science • Comparing two scientific theories or explanations

3. A. Pre-Modern Approaches • Plato – Our senses deceive us so observation is worthless and contemplation and deduction bring wisdom • Aristotle – Observe and categorize to find the essence of things, but do not experiment (goal is to understand what is “natural” and changing nature is not “natural”) • Example: Aristotle notes that female animals have fewer teeth  “femaleness.” Extrapolates to humans without examining women (who have same number of teeth as men) • Another example: Since earth is center of universe, objects naturally attempt to return there (i.e. fall). The heavier an object is, the more it desires to be in its natural state (i.e. it falls faster – which is false) • Ptolemy – Use mathematics to “connect the dots” of observation, even though the math may not describe “real” behavior.

4. Example: Planetary Motion According to Ptolemy

5. B. The Enlightenment: Essentialism Rejected • Rediscovery of ancient texts – reveals ancients didn’t know all the answers (example: Ptolemy’s orbits aren’t accurate) • Belief in progress – As economic growth and technology advanced, people came to believe that we would know more in the future (vs. wisdom of the ancients)

6. 3. The Copernican Revolution • Heliocentrism: Copernicus argued that planets revolved around the sun – simpler system than Ptolemy, but not (initially) better at predicting planets’ positions

7. b. Scientists compare models: Cumulative knowledge • Observations undermine idea of “heavenly spheres” – Tycho Brahe observes comet passing through planetary orbits • Galileo observes phases of Venus (predicted by Copernican model but not by Ptolemaic model) and moons of Jupiter (not everything revolves around Earth) • Kepler discovers that geometry (ellipse) describes planetary motion (theory: sun/God animates the universe) • Newton theorizes that simple mathematical laws of gravity might explain Kepler’s model of planetary motion

8. C. The Demarcation Problem: What is Pseudoscience? 1. Newton was a big fan of alchemy

9. C. The Demarcation Problem: What is Pseudoscience? 2. Astrologers use lots of data and math

10. C. The Demarcation Problem: What is Pseudoscience? 3. Phrenologists and other “scientific” racists used precisely measured data

11. 4. Early Lessons • Beware of real scientists operating outside their field of training • Scientific jargon doesn’t make it science • People tend to find examples that support what they already believe (confirmation bias): Astrologers tout successful predictions (ignoring the unsuccessful ones), Phrenologists saw “inferior” people all around them, etc.

12. C. Positivism and its Problems • Positivism: 19th-Century idea that scientific knowledge is the only authentic knowledge and that empirical observation = scientific truth • Induction: Prove statements true through observation, then… • Deduction: combine these statements to make new predictions • Problem = fallacy of induction. Empirical observation cannot prove more than “There are at least…” statements. It cannot prove “all A are B” statements, “A will increase” statements, or even “A is more likely than B” statements

13. Inductive Fallacy: Example 1 • An urn contains a number of colored balls. • When a ball is drawn, it is replaced by an identical one (potentially infinite # observations) • Drawn: 9,000 yellow balls, 1,000 gray balls • What percentage of the balls in the urn are gray?

14. Inductive Fallacy: Example 1 • An urn contains a number of colored balls. • When a ball is drawn, it is replaced by an identical one (potentially infinite # observations) • Drawn: 9,000 yellow balls, 1,000 gray balls • What percentage of the balls in the urn are gray? • ANSWER: 5% -- and 50% are RED. “Top” may be different from previously unobservable “bottom”

15. Inductive Fallacy: Example 2 Will always get fed at 9 AM Thanksgiving at 9 AM Fed at 9 AM everyday for the past few months

16. Inductive Fallacy: Example 3 • How many functions (explanations) will perfectly explain the data? • An infinite number, making dramatically different predictions

17. C. Falsificationism • Karl Popper: Stop trying to confirm theories and try falsifying them instead • Method: Make novel predictions with theory that prove the theory false if they fail to occur (critical experiments) • Result: Scientific theories are never proven true. Science consists of conjectures (theories which haven’t failed yet) and refutations (those which have failed)

18. 4. Falsification and the Demarcation Problem Allows us to reject astrology, etc as pseudo-science: Astrologers rarely make testable predictions, and don’t give up astrology when they fail (I must have missed a star – there it is!). Se also Marxism (“false consciousness” explains failure of people to conform to theory and Freudianism (repression explains failure to observe expected symptoms)

19. 5. Problems of Falsificationism • The ceteris paribus Clause – Theories are tested “all else being equal” but it never is. Popper called abandoning a theory after one bad experiment “naïve falsificationism.” • Virtually all useful scientific theories had “anomalies” when first stated (Copernicus, plate tectonics, etc) – strict falsificationism is a recipe for ignorance • Popper’s solution: require a replacement theory that explains everything the old one did, plus something else, before abandoning old theory (may mean we retain pseudoscience…)

20. D. Comparing Research Programs (Lakatos) • Goal: Retain idea of falsification while acknowledging that scientists do not actually reject theories when anomalies are found • Theories should be stated in a way that permits falsification (i.e. generates testable predictions about unobserved-but-observable data)

21. c. The Methodology of Scientific Research Programs • Research programs rely on multiple theories to identify problems and solve puzzles • Each scientific research program has a “hard core” of unquestioned assumptions and a “protective belt” of auxiliary hypotheses (i.e. attempts to “save” the program from falsification) • Evaluation: Look for “progressive” research programs (making new predictions and discoveries) and reject “degenerative” ones (simply adding to the protective belt without offering new knowledge)

22. Example: Neptune • Astronomers discovered that the orbit of Uranus didn’t match Newton’s predictions • They did NOT give up Newtonian physics • They DID add a new item to the protective belt: something else must be “perturbing” the orbit of Uranus • This turned out to be Neptune: Progressive change to research program • What if…no Neptune? Could hypothesize that some unobservable force acts only on Uranus  no new predictions = degenerative shift

23. d. The Demarcation Problem According to Lakatos • How do we know pseudoscience? • It critiques science without offering an alternative set of testable predictions • It continually invents new hypotheses that explain its previous failures but do NOT make new, falsifiable predictions

24. E. Conclusion: Standards for Evaluating Science • Every model must be tested against another model • Simplest model = random chance (systematic studies of astrology usually show it fails this test) • It takes a model to beat a model – Where an existing theory outperforms chance, critics are obligated to suggest a better explanation for the facts • Non-scientists are bad at this – but so are scientists outside their own field (don’t understand existing models and reasons for rejection of earlier models)

25. 2. What makes one explanation better than another? • Progressive vs. degenerative research programs – A theory or set of theories that keeps making novel, falsifiable predictions beats one that keeps adding new assumptions just to explain what we already know or generates untestablehypotheses (pseudoscience)  informed nonscientists CAN see whether revisions to theories make many or few novel predictions • Utility – Since we cannot be sure theories are True or False (ceteris paribus problem) they need to be useful. Preference for parsimonious theories using observable variables.

26. II. Anthropogenic Global Warming Theory (AGW) as a Research Program • Hypothesized in early 20th century: Relationship between CO2 and temperature understood in 1920s • Largely ignored until 1970s. Why? • Research largely retrospective – focused on climate reconstruction rather than prediction • Research emphasized natural rhythms – changes in earth’s orbit, solar radiation, etc – and expected cooling • Anthropogenic effects assumed to be trivial until environmental movement of 1970s

27. C. Setting Forth the Problem • Question: How do anthropogenic emissions affect climate? • Competing Hypotheses: • They don’t (null hypothesis) • Pollution causes cooling: Aerosols • Pollution causes warming: CO2 and other “warming gases” • Initial Research Activity: Develop long time-series of temperature, warming gases, aerosol levels.

28. Broeker, “Climatic Change: Are We on the Brink of a Pronounced Global Warming?” Science, August 8, 1975, pp. 460-463

29. 1975 Prediction: 388 .70*

30. Recent global average temperature deviations

31. Instrumental Record (NASA)

32. Pre-Instrumental Reconstructions

33. Evolution of temperature records: Patterns in noisy data (Blue = older, red = newer)

34. Confidence intervals: Limited knowledge

35. Why a hockey stick? Program assumes underlying variable (temperature changes) drives tree rings, borehole temperatures, coral growth, thickness of annual ice growth, thickness of annual lake bed sediments, glacier length, etc. It uses principal components analysis to find the trend. Program then raises or lowers the entire graph to match the instrumental data (which forms the “blade” Problem: Averaging noisy (or even random) data will generate a flat line. Splicing with the instrumental record will then produce a blade Note that it is the “stick” which cannot distinguish between stable temperatures and random noise, not the “blade.”

36. Measuring Past CO2 • Recent measurements: Direct atmospheric sampling, esp. from Mauna Loa (high) • Long-term measurements: Ice cores. Note that bubbles aren’t trapped in deep cores until many years pass (4000 to 6000) but new ice (Law Dome site) has gas from as recent as 1978 and correlates with isolated 19th century readings.

37. D. Early Predictions • Invention of GCMs (computerized climate models) allows predictions BUT depends on many unknown parameters • Early models ignore ocean circulation, focus entirely on atmosphere (usually without clouds)  usually predict massive warming, but fail to “postdict” past temperature changes • Problem of Feedback Loops: The environment is interconnected, not strictly linear

38. E. Climate Feedbacks • Three of the most important direct climatic feedbacks to greenhouse forcing are: • water vapor feedback, • cloud cover feedback and the • ice-albedo feedback.

39. 1. Water Vapor Feedback • The concentration of water vapor in the atmosphere increases rapidly with rising temperature (about 6%/°C). • This is the basis for the strong positive water vapor feedback in current climate models • increases in temperature produce increases in atmospheric water vapor which in turn enhance the greenhouse forcing leading to further warming).

40. 2. Ice-Albedo Feedback • A warmer Earth will have less snow and ice cover, resulting in a lower global albedo and consequent absorption of more solar radiation. This, in turn causes a further warming of the climate. • Most GCMs have simulated this positive surface albedo feedback, but • significant uncertainties exist over the size of the effect, particularly for sea-ice

41. 3. Cloud Feedbacks • The effects of changes in cloud on a change of climate have been identified as a major source of uncertainty in climate models • clouds contribute to the greenhouse warming of the climate system by absorbing more outgoing infrared radiation (positive feedback), • they also produce a cooling through the reflection and reduction in absorption of solar radiation (negative feedback)  generally thought to be stronger

42. 4. Why feedbacks matter: The SO2 Surprise • Environmentalism says that belching out clouds of black smoke is probably a bad idea • One component of “dirty” exhaust (coal, diesel, etc.) is sulfur dioxide (SO2) – and associates sulfates • SO2 is a warming gas BUT • Sulfate aerosols cool AND SO2 helps form clouds, generating a negative feedback loop that reduces warming • Aerosols are removed form the atmosphere much more quickly than gases • Result: Limiting some types of emissions may accelerate warming (by removing a cooling loop) rather than slowing it

43. F. Findings • Warming Gases (GHGs) • Most of Earth’s atmosphere is climatically neutral: GHGs present in trace amounts only • Implication: Relatively small releases (on a global scale) may significantly increase concentrations

44. c. Trends in GHGs • Since the mid-1800s, • atmospheric levels of carbon dioxide have increased 30 percent (from 280 parts per million to 360 parts per million), • the concentration of methane has more than doubled (to about 1.72 parts per million), and • nitrous oxide levels have increased by a more modest 10-15%. • CFCs have appeared in the atmosphere

45. The historical data

46. 2. The “Carbon Cycle” – Nature vs. Human Contributions a. The Cycle: The earth's natural processes continually exchange massive quantities of carbon. • Oceans release about 90 billion tonnes of CO2 into the atmosphere each year. • Decaying vegetation adds another 30 billion tonnes annually, while • another 30 billion tonnes each year is released from the natural respiration of living creatures and plants.

47. b. Human Sources of CO2 • Human activities add an extra 3% to this natural cycle, or about 7 billion tons per year. Why worry about a 3% increase? The majority of human CO2 comes from the burning of fossil fuels. The residence time of CO2 in the atmosphere is ~100 to 200 years

48. c. Carbon Sinks • Absorption by the oceans and plants removes the natural -- and much of the human -- CO2 from the atmosphere. • BUT: The result of this "carbon cycle" is a net increase of about 3.1 to 3.5 billion tons of CO2 annually to the atmosphere. • Implication: Natural cycle dwarfs human contributions in any one year, but many years of cumulative “extra” emissions that just slightly overload the cycle start to add up

49. d. Humans reduce carbon sinks

50. d. Humans reduce carbon sinks